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An Evaluation of Crowdsourcing asa Tool for Marketing Activities
Terrence Edison Brown
Industrial Marketing
Department of Business Administration, Technology and Social SciencesDivision of Administration and Industrial Engineering
ISSN 1402-1544ISBN 978-91-7790-274-4 (print)ISBN 978-91-7790-275-1 (pdf)
Luleå University of Technology 2018
DOCTORA L T H E S I S
Terrence Edison B
rown A
n Evaluation of C
rowdsourcing as a Tool for M
arketing Activities
An Evaluation of Crowdsourcing as a Tool for Marketing Activities
Terrence Edison Brown [email protected]
Submitted in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Industrial Marketing, Business Administration and Industrial Engineering Department of Business Administration, Technology and Social Sciences
Luleå University of Technology (LTU) University Campus, SE-971 87 Luleå, Sweden
Printed by Luleå University of Technology, Graphic Production 2018
ISSN 1402-1544 ISBN 978-91-7790-274-4 (print)ISBN 978-91-7790-275-1 (pdf)
Luleå 2018
www.ltu.se
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Dedication
To James Edison Brown, M.D., F.A.C.S.
For teaching me the importance of education
And Theresa H. Brown
For teaching me how to read and
for too many other things to list
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Abstract
Advances in technology and social media have facilitated the rapid development of crowdsourcing as an innovative tool within the field of marketing. This has driven researchers to investigate more deeply the phenomenon of crowdsourcing as a marketing innovation. The overall purpose of this thesis is expressed as: To explore and describe the use of crowdsourcing within the field of marketing. More specifically, the primary purpose of the thesis is to understand better - How crowdsourcing can be used as a marketing tool. This thesis aims to illuminate the gap in the extant marketing literature by reviewing current academic knowledge surrounding crowdsourcing and marketing. The use of the crowd as a marketing tool is growing primarily because of the advent of the Internet; however, as technology continues to advance the possibilities, challenges and side effects of crowdsourcing also change. These also need to be investigated continually. More specifically as digital marketing moves from a Web 2.0 environment to a Web 3.0 environment there will be new opportunities as well as pitfalls. As a result, new and relevant marketing problems exist at the nexus of crowdsourcing and marketing.
The research problem is sub-divided into the following four research questions: • RQ1: To what extent are crowdfunding platforms accessible to organizations as a marketing
channel and, if so, what role can these platforms play? • RQ2: How will the shift from Web 2.0 (and active-user input) to Web 3.0
(and passive-data/sensor–based input) impact the new opportunities/product development process?
• RQ3: How can user-generated content help firms make strategic decisions about new business opportunities?
• RQ4: How is the evolution of crowdsourcing impacting information externalities and consumer privacy and how is this impacting marketing?
This research is further divided into two sections. The first part investigates marketing activities specifically new opportunities/product development, advertising and promotion, and marketing research. The second section focuses on one of the possible repercussions of crowdsourcing in the marketing process. Most research on crowdsourcing focuses on the first section (i.e., the marketing activities) and how crowdsourcing is a positive marketing tool. Much less research aims its attention on the consequences and/or potential negative aspects of crowdsourcing.
This thesis consists of two published papers and two studies. Each project handles one of research questions. The first two papers and the first study focus on three marketing activities (i.e., advertising, promotion and sales, new product and service development, and market research). The second study focuses on one of the possible consequences of the growth of crowdsourcing as a tool in the marketing process.
While each paper and study has its own individual contributions, the overall contribution of this study is multi-fold. First, it develops a definition of crowdsourcing as: a tool or process by which the firm can increase or expand the resources to which it has access to by using the collective effort of a group of individuals or organizations. Second, as a result of these four research projects, crowdsourcing can further be seen as a situational, contextual and flexible tool that can be used in many different organizational contexts. The specific context for this thesis is marketing and as a result, crowdsourcing can play a wide variety of marketing roles. Keywords: Crowdsourcing, Marketing activities, Crowdfunding, User-generated content, Demand-side strategy, Predictive marketing, Consumer privacy
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Sammanfattning
Framsteg inom teknik och sociala medier har underlättat för den snabba utvecklingen av crowdsourcingen som ett innovativt verktyg inom marknadsföringen. Detta har drivit forskare att undersöka fenomenet crowdsourcing som marknadsföringsinnovation djupare. Det övergripande syftet med avhandlingen uttrycks som: Att utforska och beskriva användandet av crowdsourcing inom marknadsföringen. Mer specifikt är det primära syftet med avhandlingen att få en bättre förståelse – Hur crowdsourcing kan användas som ett marknadsföringsverktyg. Denna avhandling syftar till att belysa klyftan i den existerande marknadsföringslitteraturen genom att granska den nuvarande akademiska kunskapen kring crowdsourcing och marknadsföring. Att använda allmänheten som ett marknadsföringsverktyg växer främst till följd av Internets intåg; men eftersom att tekniken fortsätter att utvecklas så ändras även möjligheterna med, utmaningarna för och biverkningarna av crowdsourcing. Dessa behöver även utredas kontinuerligt. Närmare bestämt, att digital marknadsföring går från en Web 2.0-miljö till en Web 3.0-miljö innebär såväl nya möjligheter, som fallgropar. Följaktligen förekommer nya och relevanta marknadsföringsproblem i samband med crowdsourcing och marknadsföring.
Studiens problem är uppdelat i följande fyra studiefrågor: • RQ1: I vilken utsträckning är plattformar för crowdfunding tillgängliga för organisationer
som marknadsföringskanal och, i så fall, vilken roll kan dessa plattformar spela? • RQ2: Hur kommer övergången från Web 2.0 (och input från aktiva användare) till Web 3.0
(och input från passiv data/sensorbaserad) påverka de nya möjligheterna/produktutvecklingsprocessen?
• RQ3: Hur kan användar-genererat innehåll hjälpa företag att fatta strategiska beslut om nya affärsmöjligheter?
• RQ4: Hur påverkar crowdsourcingens utveckling information externt och konsumenternas integritet, och hur påverkar detta marknadsföring?
Denna studie är fortsatt indelad i två områden. Den första delen utreder marknadsföringsaktiviteter; specifikt nya möjligheter/produktutveckling, annonsering och promotion, och marknadsföringsresearch. Det andra området fokuserar på en av de möjliga effekterna av crowdsourcing i marknadsföringsprocessen. Mest forskning om crowdsourcing fokuserar på det första området (dvs., marknadsföringsaktiviteterna) och hur crowdsourcing är ett positivt verktyg i marknadsföringen. Mycket mindre forskning riktar sitt fokus på konsekvenserna och/eller de potentiella negativa aspekterna av crowdsourcing.
Avhandlingen består av två publicerade artiklar och två studier. Varje projekt hanterar en av forskningsfrågorna. De första två artiklarna och den första studien fokuserar på tre marknadsföringsaktiviteter (dvs., annonsering, promotion och försäljning, ny produkt- och serviceutveckling, och marknadsföringsresearch). Den andra studien fokuserar på en av de möjliga följderna av tillväxten av crowdsourcingen som ett verktyg i marknadsföringsprocessen.
Samtidigt som varje artikel och studie har sina egna individuella bidrag är det övergripande bidraget till denna studie flersidig. Till att börja med utvecklar den en definition av crowdsourcing som: ett verktyg eller process genom vilken företaget kan öka eller expandera resurserna som det har tillgång till genom att använda den kollektiva insatsen från en grupp individer eller organisationer. Vidare, som ett resultat av dessa fyra forskningsprojekt, kan crowdsourcing ses som ett situationsanpassat, kontextuellt och flexibelt verktyg som kan användas i många olika organisatoriska sammanhang. Den specifika kontexten för denna avhandling är marknadsföring och som ett resultat kan crowdsourcing spela ett spektrum av roller i marknadsföringen. Nyckelord: Crowdsourcing, Marknadsföringsaktiviteter, Crowdfunding, Användar-genererat innehåll, Demand-sidestrategi, Prediktiv marknadsföring, Konsumentens integritet
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Acknowledgements
These efforts are not solo efforts. I would to thanks many people. Some I will name and others I will not. That’s not to mean the ones I don’t mention are unimportant. I would like to thank my supervisors Professor Åsa Wallström, Associate Professor Jan Kietzmann, and Associated Professor Tim Foster and Luleå University of Technology for this opportunity. I would warmly thank Professor Albert Caruana for his critique and suggestions as my Pie seminar opponent. I would also like to thank my co-authors Edward Boon and Professor Leyland Pitt. I would like to thank the professors and instructors in this Industrial Marketing program. I would like to especially thank Mana Farshid for her extra special support and assistance during these few years. I would like to thank the group members in my various courses as well as all my program cohort members. We have shared an experience and an adventure. Most of all I would like to thank my close family and friends for sacrificing with me whether they knew it at the time or not, including but not limited to, Linn, Mia, Jane, Carol, Sherman, Charles, Trinita, and the Sjöbergs. With that in mind I would like to thanks Messi, Zlatan, Signe, Greta, and Annica for allowing me to take time from them at home. Finally, I would like to thank The Chair with which I have a love/hate relationship.
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TableofContentsDedication...................................................................................................................................................iii
Abstract........................................................................................................................................................v
Sammanfattning.........................................................................................................................................vi
Acknowledgements..................................................................................................................................vii
List of Tables and Figures........................................................................................................................xi
1 OVERVIEW OF THE RESEARCH........................................................................................................11.1 Introduction....................................................................................................................................1
1.1.1 The research area..........................................................................................................................11.1.2 Why focus on crowdsourcing and marketing?...........................................................................11.1.3 Gap identification............................................................................................................................21.1.4 Research problem..........................................................................................................................31.1.5 Development of research questions............................................................................................41.1.6 Delimitation.....................................................................................................................................71.1.7 Research disposition.....................................................................................................................8
1.2 A brief literature summary.........................................................................................................101.2.1 The Crowd.....................................................................................................................................101.2.2 What is crowdsourcing?..............................................................................................................121.2.3 The Crowd in Marketing..............................................................................................................151.2.4 Consumer Privacy........................................................................................................................161.2.5 Predictive Analytics......................................................................................................................171.2.6 Summary.......................................................................................................................................17
1.3 Methodology................................................................................................................................181.3.1 Introduction...................................................................................................................................181.3.2 Potential research approaches..................................................................................................191.3.3 Selected research approaches..................................................................................................211.3.4 Research strategy and design....................................................................................................221.3.5 Quality criteria...............................................................................................................................24
1.4 Summary of individual papers and studies............................................................................271.4.1 Paper 1: Seeking funding in order to sell: Crowdfunding as a marketing tool....................281.4.2 Paper 2: Sensor-based entrepreneurship: A framework for developing new products and services.......................................................................................................................................................301.4.3 Study 1: Leveraging user-generate content for demand-side strategy.................................321.4.4 Study 2: An evolution of crowdsourcing: Implications for marketing....................................34
2 THE INDIVIDUAL PAPERS AND STUDIES......................................................................................362.1 Paper 1 - Seeking funding in order to sell: Crowdfunding as a marketing tool................372.2 Paper 2 - Sensor-based entrepreneurship: A framework for developing new products and services..........................................................................................................................................452.3 Study 1 - Leveraging user-generated content for demand-side strategy..........................582.4 Study 2 - An evolution of crowdsourcing: Implications for marketing..............................83
3 SUMMARY - CONTRIBUTIONS AND CONCLUSIONS.................................................................1013.1 Answers to the Research Questions.....................................................................................1013.2 Theoretical Contributions........................................................................................................1043.3 Managerial and Practitioner Contributions...........................................................................1063.4 Avenues for Future Research.................................................................................................108
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3.5 Limitations..................................................................................................................................1093.6 Conclusion.................................................................................................................................110
4 REFERENCES...................................................................................................................................113
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List of Tables and Figures Table1:ResearchApproachesusedinthisdissertation ____________________________________22Table2:DataCollectionMethodsusedinthisdissertation__________________________________24Table3:ConceptualResearchQualityFactorsfortheThreeConceptualProjects________________25Table4:Criteriatocriticallyappraisefindingsfromqualitativeresearch_______________________26 Figure1:Dissertationstructureandresearchquestions_____________________________________9
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1 OVERVIEW OF THE RESEARCH 1.1 Introduction
1.1.1 The research area
Marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large. (American Marketing Association, 2013)
The ultimate purpose of a business is to create a customer (Drucker, 1954). Creating that customer
consumes firm resources (Drucker, 1958; Faulkner, 2017; Kelley, 2017). A lack of firm resources drives a need for creativity and innovation (Stevenson & Jarillo, 1990; Stevenson, 1983; Stevenson & Gumpert, 1985). Marketing innovations coupled with technological innovations (e.g., ICT, the Internet, digitalization, etc.) have led to customers leaving the role of “just” consumers and getting involved more directly and actively in the marketing process (Bowes & Hippel, 2008; Heinonen et al., 2010; von Hippel, 2005; Zwass, 2010) and others have pushed the business environment to become more dynamic (Aaker & Adler, 1984). This “new” business environment calls for more nontraditional marketing activities including entrepreneurial marketing (Morris, Schindehutte, & LaForge, 2002; Morrish, Miles, & Deacon, 2010; Morrish & Morrish, 2011). Ultimately, leading to growing use of innovative marketing strategies, techniques, and tools.
One of these “new” techniques, crowdsourcing, is having an increasingly significant effect on marketing (cf., Bal, Weidner, Hanna, & Mills, 2016; Bayus, 2013; Castronovo & Huang, 2012; Corte, 2013; Djelassi & Decoopman, 2013; Pihl, 2013; Simula, Töllinen, & Karjaluoto, 2012; Whitla, 2009 and others). Although the modern concept of crowdsourcing, especially its current name, has been attributed to Howe (2006), crowdsourcing as a practice can be traced back at least three hundred years (Spencer, 2012). So, the idea of using the crowd to solve problems is not new. However, there is no doubt that the rapid advances in information technology and the growth of Web 2.0 has dramatically increased the use of crowdsourcing (Howe, 2006). Examples of crowdsourcing can be seen in far-ranging and diverse services and activities such as citizen science, the military, mapping and location, human intelligent tasks, journalism, medical diagnosis, medical research, translation/proofreading/transcription, surveillance, distributed computing, simulation, gaming, genealogy, support, music, mining, data prediction, legal advice, business solutions, and education. Crowdsourcing can also be seen in firm activities such as business solutions, product development, support, venture capital, and research.
Future technology advances can allow crowdsourcing to have a greater impact on firm activities, especially marketing (cf., Andriole, 2010; Fleisch, Weinberger, & Wortmann, 2014; Garrigos-Simon, Alcamí, & Ribera, 2012; Garrigos-Simon & Narangajavana, 2015; Jelonek & Wyslocka, 2015; Lipiäinen, 2014; Tiago & Veríssimo, 2014). As a result, the effects and consequences of crowdsourcing on marketing will be more significant in the future. Therefore, broadening and deepening the understanding in this area is an objective of this research.
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1.1.2 Why focus on crowdsourcing and marketing?
The idea for this research originates from my previous research agenda and teaching in the field of entrepreneurship. My earlier research on business models led me to crowdsourcing. As I researched business models, I discovered that creative entrepreneurs and marketers were using crowdsourcing not only as a business model but also as a marketing tool.
It is important to focus on crowdsourcing and marketing for at least three primary reasons: 1) crowdsourcing is part of the digitalization of business (Tiago & Veríssimo, 2014); 2) crowdsourcing is an example of entrepreneurial marketing (Schindehutte & Morris, 2009); and 3) while most of the discussion and research around crowdsourcing has a positive bias, not all direct and side effects are positive (cf., Harris, 2011; Simula, 2013).
As (primarily) a digital technique, crowdsourcing is part of the movement toward the digitalization of marketing, which is part of the rapidly growing digitalization of business in general.Competitive pressure drives digital marketing efforts (Tiago & Veríssimo, 2014), hence along with it the use of innovative digital techniques like crowdsourcing in marketing efforts. The importance of digital marketing is that it should lead to higher customer engagement and better relationships with customers.
Secondly, Morris, Schindehutte, & LaForge (2001, p. 31) pointed to an ongoing problem with marketing in that there is a disconnect between marketing research and marketing practices. Ironically, while some firms are using new and innovative marketing techniques and tactics, many other more traditional, larger and staid firms have stuck to old rules of thumb, “formula-based thinking” and imitative marketing rather than innovative marketing practices, which includes crowdsourcing activities.
If Khandwalla (1977) is correct and marketing activities are essential for dealing with uncertainties in turbulent environments, and entrepreneurship is also a way in which firms can even thrive in turbulent environments, then entrepreneurial marketing (EM) is one of the very best methods to thrive in an increasingly turbulent and complex environment. This is especially true if marketing is the organizational home for entrepreneurial process and activities (Morris & Paul, 1987; Murray, 1981). Further Zeithaml & Zeithaml (1984) even suggest that firms can also gain some control over their environments by using innovative marketing strategies.
Firm success in the 21st century requires very different skills and competencies than what were previously needed. Not only is the world more turbulent, the speed of change demands that the firm be flexible, willing to experiment, and even reinvent their business models. Moreover, once the firm makes these changes, it must be willing to change them again and again. This pushes firms towards a more entrepreneurial mindset and more entrepreneurial practices (Schindehutte & Morris, 2009).
Finally, the changing role of the consumer is having some wide-ranging and unexpected (possibly negative) effects (cf., Gebauer, Fuller, & Pezzei, 2013; Harris, 2011; Simula, 2013). Crowdsourcing and co-creation are enabling the creation of a large number of new and innovative new products and services, which are expanding the kinds of experiences consumers can experience. However, the creation of these new services often requires firms to have access to large amounts of higher quality and more intimate personal information of the user by using crowdsourcing and co-creation applications and platforms. Thus, the age of crowdsourcing is having an impact on the demand and supply of personal and private information, because of countless new apps and services that produce data. This is having a direct effect on consumer privacy. Further, all of this is happening just at a time as the right to privacy is put at greater risk, because of the Internet of Things (IoT’s), Big Data and artificial intelligence (AI).
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1.1.3 Gap identification
As the digitalization of business has become increasingly important (Fleisch et al., 2014; Hull, Hung, Hair, Perotti, & DeMartino, 2007; Rashid, 2017; Uzunoglu, 2011) the use of crowdsourcing has also become more relevant for marketers and researchers (e.g., Bakić, Kostić, & Nešković, 2014; Brabham, 2009; Brabham, 2008; Dawson & Bynghall, 2012; Djelassi & Decoopman, 2013; Howe, 2008; Moisseyev, 2013; Roth & Kimani, 2014; Whitla, 2009; Zadeh & Sharda, 2014). As a result, research that broadens and extends crowdsourcing into the area of marketing, particularly after Whitla (2009), has become increasingly relevant (see Section 1.2 for additional details).
Moreover, Tiago & Veríssimo (2014) stated changes in consumer behavior due to changes in the digital landscape require firms to rethink their digital marketing efforts, especially concerning engagement and calls for additional research in this area. Furthermore, they stated that much of the research has focused on digital marketing from the consumers’ perspective rather than from the firm’s perspective.
Vukovic, Das, & Kumara (2013) provide a bridge from Web 2.0 crowdsourcing to Web 3.0 crowdsourcing as they looked at the crowd as a sensor rather than the technology-based sensors described herein. However, these scholars called for further crowdsourcing research in this area as system complexity grows and more advanced applications are deployed. In other words, they pointed out the need for more research in crowdsourcing as the IoT’s, AI and other human-computer interaction devices are deployed. This is the environment business faces today.
Garrigos-Simon et al. (2012) define Web 3.0 as semantic web technologies, which include data gathered in all sorts of ways including IoT’s (i.e., sensor-based). “The importance of the participation of people, not only customers or employees, in the whole business process is shown to its greatest extent in ‘crowdsourcing,' an important business model in the Web 3.0 era.” (Garrigos-Simon et al., 2012, p. 1886). They go further by saying that crowdsourcing can be important for marketing (e.g., promotion, design, and development of products and processes). The article ends by saying that it was “just a first step” and call for further research into these Web 3.0 technologies, crowdsourcing, the value chain, and especially marketing (Garrigos-Simon et al., 2012, p. 1888). Despite its popularity especially in the popular press and literature, there is still comparatively little well-founded knowledge on crowdsourcing, marketing and the new advanced technologies (Zogaj, Bretschneider, & Leimeister, 2014).
1.1.4 Research problem
The advances in technology and social media have facilitated the rapid development of crowdsourcing as an innovative technique and an innovative tactic within the field of marketing. Therefore according to several researchers (cf., Agafonovas & Alonderiene, 2013; Della Corte et al., 2013; Kleemann, Voß, & Rieder, 2008; Marjanovic, Fry, & Chataway, 2012; Stanke & Drogosch, 2015; Tiu Wright, Pires, Stanton, & Rita, 2006), additionally investigate into the phenomenon of crowdsourcing as a marketing innovation is needed. Based on the research discussed so far, the purpose of this thesis could be expressed as: To explore and describe the use of crowdsourcing within the field of marketing. More specifically, the primary purpose of the thesis is to understand better - How crowdsourcing can be used as a marketing tool. This thesis aims to illuminate the gap in the extant marketing literature by reviewing current academic knowledge surrounding the marketing strategies, activities, and crowdsourcing. There is a growing importance of the use of the crowd as a marketing tool, primarily because of the advent of the Internet; however, as technology continues to advance the possibilities, challenges and side effects for crowdsourcing also change and as a result need to be
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investigated continually. More specifically as digital marketing moves from a Web 2.0 environment to a Web 3.0 environment there will be new opportunities as well as pitfalls (cf., Andriole, 2010; Fleisch, Weinberger, & Wortmann, 2014; Garrigos-Simon, Alcamí, & Ribera, 2012; Garrigos-Simon & Narangajavana, 2015; Jelonek & Wyslocka, 2015; Lipiäinen, 2014; Tiago & Veríssimo, 2014). As a result, new and relevant marketing problems exist at the nexus of crowdsourcing and marketing. Thus, the overarching research problem can be presented as -
How can crowdsourcing be used as a marketing tool1?
This research question is further divided into two sections. The first part investigates marketing activities specifically new opportunities/product development, advertising and promotion, and marketing research (e.g., Dawson & Bynghall, 2012; Whitla, 2009). The second section focuses on one of the possible repercussions of crowdsourcing used in the marketing process (e.g., Gebauer et al., 2013; Morphy, 2009). Most research on crowdsourcing (cf., Harris, 2011; Simula, 2013) focuses on the first section (i.e., the marketing activities) and how crowdsourcing is a positive marketing technique. Much less research aims its attention on the consequences and/or potential negative aspects of crowdsourcing (Heidenreich, Wittkowski, Handrich, & Falk, 2015). For more details see Study 2.
The research problem is sub-divided into the following four research questions: • RQ1: To what extent are crowdfunding platforms accessible to organizations as a marketing channel and, if so,
what role can these platforms play? • RQ2: How will the shift from Web 2.0 (and active-user input) to Web 3.0
(and passive-data/sensor–based input) impact the new opportunities/product development process? • RQ3: How can user-generated content help firms make strategic decisions about new business opportunities? • RQ4: How is the evolution of crowdsourcing impacting information externalities and consumer privacy and how
is this impacting marketing?
This thesis consists of two published papers and two studies. The first two papers and the first study focus on three examples of marketing activities (i.e., advertising, promotion and sales, new product and service development, and market research). The second study focuses on one of the possible consequences2 of the growth of crowdsourcing as a tool in the marketing process (e.g., Baccarella, Wagner, Kietzmann, & McCarthy, 2018; Chowdhury, Gruber, & Zolkiewski, 2016; Gebauer et al., 2013; Gylling, Elliott, & Toivonen, 2012; Healy & McDonagh, 2013; Heidenreich et al., 2015).
1 The dissertation is written primarily from the marketer’s or firm’s perspective. However, in Study 2 the examination of consumer privacy is examined from the consumers’ perspective as well as the firms. 2 Study 2 focuses on the loss of privacy as one of the negative outcomes or consequences resulting from the growth of crowdsourcing as a marketing strategy for two major reasons. First, privacy is undergoing a fundamental change due to new technology and innovation (Martin & Murphy, 2017). Second, privacy is such a significant issue for marketing research (Smith et al., 2011). However, there are other possible negative consequences including, but not limited to, role conflicts (Gebauer et al., 2013), a lack of fairness (Gylling et al., 2012), the absence of shared understanding (Grayson & Ambler, 1999), cyberbullying, addictive use, and fake news (Baccarella et al., 2018).
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1.1.5 Development of research questions Formulation of research question 1
One of the most visible forms of crowdsourcing, especially among start-ups is crowdfunding3 (Richter, Kraus, Brem, Durst, & Giselbrecht, 2017). Websites such as FundedByMe, Indiegogo, and Kickstarter have attracted much attention for their ability to enable organizations and individuals to raise funds from ordinary people, who contribute or invest for various reasons. This phenomenon is called crowdfunding and permits organizations and individuals to obtain investments that they might otherwise have difficulty getting from sources such as banks, angel investors, and stock markets. In simple terms, crowdfunding is raising external capital for a project or a venture by a group of individuals (i.e., the crowd) rather than a group of professionals (Belleflamme, Lambert, & Schwienbacher, 2014; Schwienbacher & Larralde, 2010). Many well-known startups had their origins in crowdfunding including Pebble E-Paper Watch and Oculus Rift. However, some creative marketers have begun to look at using crowdfunding in an innovative, and perhaps unintended way. Startups, as well as established organizations, have begun to use these crowdfunding websites not only as a source of financing but also as a promotional platform (Golić, 2014). RQ1 - To what extent are crowdfunding platforms accessible to organizations as a marketing channel and, if so, what role can these platforms play? Formulation of research question 2
Despite the growth of crowdsourcing applications and services, we are just at the beginning of the possibilities of crowdsourcing technologies. This is because almost all of the current and traditional crowdsourcing applications have been active, Web 2.0 – based, where the user adds value by intentionally contributing data (Zhao & Zhu, 2014). Whether the crowdsourcing application is designed for idea generation/new product development, crowd voting, crowd micro-tasks or crowd solution generation (Prpíc, Shukla, Kietzmann, & McCarthy, 2015), individuals actively input data usually through their desktop/laptop or maybe increasingly smartphone. With the number of connected devices, sensors and actuators expected to grow to over 75 billion in the next few years, most of the new opportunities likely lie in passive-input crowdsourcing (Columbus, 2016).
In Vukovic et al.'s, (2013) research on ubiquitous crowdsourcing they briefly discuss sensing crowdsourcing and refer to product design and marketing; their focus was on the crowd as sensors rather than on the underlying technology. Recognizing this, they called for further research in this area. Accordingly, up to now most of the development, discussion, and research around crowdsourcing has focused on active-input crowdsourcing (Prpić, 2016). However, the real transformative pressure will come from passive sources of data generated primarily by the development and growth of sensor technology. Imagine what the shift to Web 3.0, passive data and sensor-based opportunity development could bring (Andriole, 2010; Fleisch, Weinberger, & Wortmann, 2014; Garrigos-Simon, Alcamí, & Ribera, 2012; Garrigos-Simon & Narangajavana, 2015; Jelonek & Wyslocka, 2015; Lipiäinen, 2014; Tiago & Veríssimo, 2014)? Put in another way, the torrent of passively, sensed data by IoT’s sensors combined with Big Data technology is creating an environment that will allow the creation of countless new products and services that will create value for consumers and industrial customers (Lohr, 2012).
3 Crowdfunding can be defined as the process of using the crowd to raise financial capital. In other words, it is crowdsourcing the fundraising process. Crowdfunding is a subset of overall marketing innovation of crowdsourcing (Belle et al., 2013; Gerber & Hui, 2013; Paschen, 2016).
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RQ2 - How will the shift from Web 2.0 (and active-user input) to Web 3.0 (and passive-data/sensor–based input) impact the new opportunities/product development process? Formulation of research question 3
“From a marketing research4 perspective crowdsourcing gives opportunity to reach large potential consumer groups…. (and) in many cases crowdsourcing offers cheaper and quicker opportunities for gathering market information” (Gatautis & Vitkauskaite, 2014, p. 1247). Therefore, crowdsourcing approaches and applications are being used in market research. Using the tools available today it is possible to collect and analyze the text on the websites, blogs, forums, communities and other places people and customers gather and use content analysis techniques (Krippendorff, 2013). The use of advanced, computer-aided methods like artificial neural networks (ANNs) and genetic algorithms (GAs) is beginning to appear in research (Chen, 2009; Dirsehan, 2015; Law & Au, 1999; Meehan, Lunney, Curran, & Mccaughey, 2013; Olmeda & Sheldon, 2002). However, partly because this type of analysis is still in its infancy (Chen, 2009), there is a gap or opportunity to explore usage of tools that can examine natural language text data. Natural language analysis is important as the text created by users and consumers (i.e., user-generated content (UGC)) is natural language. Further, while some of these techniques have been used to look at firm issues such as electronic word of mouth (eWOM) (Dirsehan, 2015), complaints (Chen, 2009), and expert systems/decision support (Cheng, White, & Chaplin, 2012), customer relationship management (Liao, Chen, & Deng, 2010), and new product development (Liao et al., 2010), few have used these tools and unstructured UGC to investigate or help with strategic decisions.
The following problem is that most research discussions on business strategy take a supply-side or a top-down approach (cf., Adner & Zemsky, 2006; Stokes, 2000) for two primary reasons. Firstly, that is where the lion share of the research takes place. Next, until recently there has not been a simple, efficient, and effective method to access, collect and analyze vast amounts of end-user data. However, now vast amounts of crowd-produced data are readily accessible. Further, the amount will only grow as IoT’s devices come online. Fortunately, the tools to analyze this Big Data are beginning to be developed as well.
Unsurprisingly, a focus on bottom-up or demand-side strategy is appropriate especially in marketing strategy, where the customer plays such a crucial role (Stokes, 2000). This view is important because according to (Priem, 2007, p. 233), “The consumer is the ultimate arbiter of a strategist’s success.” RQ3 - How can user-generated content help firms make strategic decisions about new business opportunities?
Taken together research questions 1, 2, and 3 form the first subset of the research that deals with how crowdsourcing impacts three essential marketing activities (e.g., product development, advertising and promotion, and marketing research). The next research question deals with the consequence of using crowdsourcing.
4 The original text says “market researches perspective . . .”, which is probably a typesetting error.
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Formulation of research question 4
Crowdsourcing and co-creation are closely related concepts in that both are a process in which individuals or groups create value for the firm (Zwass, 2010). Both concepts describe collaborative processes involving individuals, groups, the organization or firm, and stakeholders, usually customers or users.
In this new world of crowdsourcing and co-creation, many new products and services have expanded, improved and enhanced our customer experience. There are countless new services to come, but at what cost? There is a dark side to crowdsourcing and co-creation. According to Morphy (2009), Gebauer, Fuller, and Pezzei (2013) and others (cf., Harris, 2011; Simula, 2013) most of the research on crowdsourcing and co-creation has focused on the compelling and positive benefits of using co-creation rather than the potential negative consequences and outcomes. Heidenreich, Wittkowski, Handrich, & Falk (2015) contended that research highlights the positive aspects of collaborative activities, so as a result, there is a lack of research that investigates the dark side.
One of these increasingly important, negative aspects of crowdsourcing is the (loss of) privacy. A substantial amount of information about the average citizen-consumer is currently being “collected, analyze and acted upon in complete secret” by private enterprise, governments, and others (Brodeur & Leman-Langlois, 2006; Leman-Langlois, 2008, p. 119). As consumers exchange in transaction with firm personal and private information is released as by-product. As crowdsourcing techniques and technology evolve and improve, this information is being collected, merged, and crowdsourced. Paradoxically, while this can hurt the consumer privacy, it will also be an opportunity for marketers as they will gain more in-depth understanding into the thinking and behavior of their customers. RQ4 - How is the evolution of crowdsourcing impacting information externalities and consumer privacy and how is this impacting marketing?
In this study three conceptual models5 are built that help explain Research Question 4. While the first three research questions examine three different ways in which crowdsourcing can be beneficially used as a marketing tool, Research Question 4 investigates one of the effect or consequence of the use of crowdsourcing in marketing activities, specifically the loss of privacy.
1.1.6 Delimitation
In this section, we delimit the scope and focus of this dissertation. Marketing is a broad concept and practice (American Marketing Association, 2013). Additionally, crowdsourcing has also become a broad and widely applied technique, even if we concentrate our attention only on the nexus of marketing and crowdsourcing. For example, in Dawson & Bynghall's (2012) book for marketers, they discussed seven areas where crowdsourcing could assist the marketing activities - content creation, idea generation, product development, customer insights, customer engagement, customer advocacy, and pricing. In Zogaj et al.'s (2014) research pointed to three market activities in which crowdsourcing could be useful - product design/development, promotion and sales, and support. While Whitla (2009) discussed three broad marketing activities in which crowdsourcing had begun to have an impact - product development, advertising and promotion, and marketing research.
5 Conceptual models are generalizations and abstraction of real world social activities and events (Powell-Morse, 2017). Conceptual models can help researchers recognize, comprehend, or mimic the underlying subject the model exemplifies (Merriam-Webster.com, 2018). In this case the subject is the market for privacy and its ecosystem.
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After, an extensive evaluation of the pertinent research and research gaps (e.g., Alberts, Campbell, & Louw, 2010; Andriole, 2010; Bakić, Kostić, & Nešković, 2014; Beard, 2013; Fleisch et al., 2014; Fuchs, Prandelli, Schreier, & Dahl, 2013; Garrigos-Simon et al., 2012; Garrigos-Simon & Narangajavana, 2015; Gatautis & Vitkauskaite, 2014; Jelonek & Wyslocka, 2015; Lipiäinen, 2014; Marsden, 2009; Sigala, 2015; Stanke & Drogosch, 2015; Tiago & Veríssimo, 2014; Vukovic et al., 2013 and others), this dissertation research is limited to 1) crowdsourcing and these three marketing activities - product development, advertising, promotion, and sales and marketing research and 2) a consequence of crowdsourcing and these marketing activities, it impact on consumer privacy. These three marketing activities are most mentioned in the literature, while privacy is a growing concern in this time of apps, smart devices and IoT’s (Martin & Murphy, 2017).
1.1.7 Research disposition
In sum, this thesis has as its overarching research question how crowdsourcing as a tool or technique can impact marketing activities. This research can be placed into two sub-sections: 1) crowdsourcing and marketing strategies and 2) the consequences of using crowdsourcing in marketing activities. Together the two sub-sections and the four research questions join together to tell a story of how crowdsourcing is affecting marketing. In the end, this thesis research examines how crowdsourcing powered by new technologies can play a role in marketing activities and what are potential negative aspects of that role.
The complete thesis structure and research questions are offered in Figure 1. Paper 1 examines how crowdsourcing can be used as a marketing channel and promotional tactic. This paper speaks to Research Question 1. Paper 2 explores how crowdsourcing can be used as in the new opportunities/product development process. This paper tackles Research Question 2. Study 1 proposes that crowdsourcing can be used as a marketing research tool to support strategic decision-making. This empirical study focuses on user-created content in the hospitality industry, and answers Research Question 3. Study 2 looks at the consequences crowdsourcing and consumer privacy. Furthermore, Study 2 addresses Research Question 4.
In the next section, there is a brief summary of the existing literature on the crowd, consumer privacy and predictive analytics to help provide theoretical justification and place the thesis in a conceptual context. It is not within the scope of this introductory chapter to fully develop the appropriate literature as each of the papers and studies reviews directly the specific and appropriate literature for their corresponding research questions. Following the literature summary is a methodology section and a summary of the two papers and two studies. This is followed by the complete versions of the papers and studies. The thesis ends with a section that summarizes the contributions, limitations and finally presents a brief conclusion.
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9
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1.2 A brief literature summary
1.2.1 The Crowd
While there are many entrepreneurial and innovative techniques and methods, one of the oldest is by using the crowd, collective intelligence, and collective action. Crowdsourcing has conceptual and theoretical links to a number of domains including open innovation as pioneered by Chesbrough (2003), outsourcing (Surowiecki, 2004b), user innovation (von Hippel, 2005; Von Hippel, 2005), user generated content (UGC), (Daugherty, Eastin, & Bright, 2008), co-creation (Zwass, 2010), open source (Howe, 2008), collective intelligence (Saxton, Oh, & Kishore, 2013), and the social web (Kietzmann, Hermkens, McCarthy, & Silvestre, 2011). Open Innovation
Crowdsourcing can be seen from the perspective of innovation, especially open innovation as pioneered by Hayek (1945). No one person, company, or organization has a monopoly on knowledge. As a result, knowledge must be seen as distributed throughout society (Hayek, 1945). Those firms that can open their research and development processes and incorporate some of this knowledge can create value for their firm. Firms can also capture value by opening up their innovation processes (Schenk & Guittard, 2009). More specifically they should identify and absorb knowledge that exists outside their traditional organizational boundaries and incorporate it into their innovation processes. In other words, they can and should make money by using knowledge, data and other resources that were created and existed outside the legal bounds of the firm. Additionally, the firms can also create value by monetizing their internally developed knowledge (e.g., selling and licensing their patents, etc.).
As crowdsourcing relies on data, knowledge, activities, and resources from "others," especially those outside the formal bounds of the organization, it is easy to see the connection to open innovation. One difference between open innovation and crowdsourcing is that open innovation can be seen as a two-way process involving the firm exchanging resources while outsourcing can be seen as primarily one-way (Schenk & Guittard, 2009). Outsourcing
It is hard not to notice how fundamental the concept of outsourcing is to crowdsourcing. In fact, many researchers define crowdsourcing as a form of outsourcing (e.g., Schenk & Guittard, 2009) including Howe himself (Lacity & Hirschheim, 2012). At the root of outsourcing is contracting someone outside the organization to provide goods or services that the organization employees or agents commonly provide (Saxton et al., 2013). It is relatively clear that certain aspects of crowdsourcing and outsourcing overlap as the businesses using these practices both seek to source resources from outside the organization to help them achieve their business objectives (Schenk & Guittard, 2009, 2011). User Innovation
User innovation as presented by (von Hippel, 2005; Von Hippel, 2005) is a process where there is a shift from firm-based innovation to more user-based innovation. More specifically, the users are active in the innovation process. Crowdsourcing has some similar characteristics to user innovation; it differs in at least five significant respects. The first four were detailed by (Schenk & Guittard, 2009, 2011). One, crowdsourcing is a firm-driven process, while not surprisingly user innovation is a user-driven
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one. Two, crowdsourcing has many uses, while user innovation is limited to innovation. Three, through crowdsourcing many different types of people can contribute to the process, while in user innovation it is limited to the users of the end product. Four, while crowdsourcing users, may be involved in the firm's innovation process, but that does not mean the user provides feedback within the innovation process. Five, with user innovation there is the concept of the lead user, who is known to the firm. In crowdsourcing members of the crowd are generally unknown and often anonymous, especially up and until the contribution is directly recognized by the firm. User-generated content
The area of user-generated content (UGC), co-creation, co-production is often seen as closely related to user innovation. The advances and widespread growth of Web 2.0 technologies have led to an explosion in the creation and distribution of UGC. UGC can be defined as “media content created or produced by the general public rather than by paid professionals and primarily distributed on the Internet” (Daugherty, Eastin, & Bright, 2008, p. 19). Content produced by the crowd is UGC by that definition, but of course, crowdsourcing can be much broader than "just" content creation. Co-creation
Co-creation has been defined broadly as value created by consumers; sponsored co-creation defined as co-creation activities carried out by the consumer, but at the request of the firm or producer; autonomous co-creation defined as co-creation activities carried out by individual or communities voluntarily and independently of organization benefiting from the value created (Pitt, Watson, Berthon, Wynn, & Zinkman, 2006). It is clear how crowdsourcing and co-creation, especially sponsored co-creation, are related in that both are a process in which individuals or groups create value for the firm. The major difference is that co-creation usually results in a product or service, while crowdsourcing has many additional dimensions and outcomes. Open Source
While open source does apply to products, services, and ideas (Pitt, Watson, Berthon, Wynn, & Zinkman, 2006), it has become synonymous with the open source software (OSS) movement. OSS is an approach to software development and distribution whereby individuals, organizations and even for-profit businesses contribute freely, and the resulting software is released in a nonproprietary manner. Prime examples of the success of OSS can be seen through the operating system Linux, the web server Apache, the database MySQL, and the web software Wordpress, which together form the basic infrastructure of the entire Internet. Howe (2008) defines crowdsourcing as “an application of open source principles to other industries (Rouse, 2010).
While Howe is not entirely correct, there are similarities between open source and crowdsourcing. For example, open sourcing can be seen as a way to obtain firm resources from outside the organization, which touches both on the outsourcing aspects of crowdsourcing as well as the open innovation aspects of it. Further, as OSS is most typically a crowd and social activity, there is another link to crowdsourcing. Although there are some connections, there are also some apparent differences. These differences center around the idea of who directs leads or guides the process and who owns the results. For example, crowdsourcing is typically firm-directed and open source is a collective or community-driven activity. Additionally, the results of crowdsourcing are proprietary and not open. Whether the crowdsourcing results in new ideas, new solutions, new products, etc., these are owned by
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the firm initiating/owning the crowdsourcing project. In open source, the results are the exact opposite, nonproprietary. Crowd and collective intelligence
Crowdsourcing is a neologism that combines crowd and outsourcing (Rouse, 2010). We mentioned briefly above crowdsourcing's link to outsourcing; we now turn our attention to the crowd itself. What is so important about the crowd? Prpic and Shukla (2013) used Saxton et al. (2013) to explain how and why the concept of the crowd is important because knowledge is dispersed and distributed. It is not owned or controlled centrally. This is increasingly true as information technology and communication advances push this distribution to the extreme. Ironically it is these same advances that make possible the partial re-consolidation of this knowledge with the crowdsourcing framework.
It is important to note when thinking about crowds there is an underlying bias towards the benefits of the crowd regarding the "wisdom of crowds" and collective intelligence (Surowiecki, 2005). This is the idea that crowds produce decision superior to individuals. However, while crowds and the wisdom of crowds overlap partially, it is not the same thing (Saxton et al., 2013). Crowds can produce superior decisions under specific conditions. Best practice in crowdsourcing attempts to create these particular conditions. Social web
Contrary to common belief, crowdsourcing is not new. Above we have provided just a few successful examples that are several hundred years old. However, recent advances in advanced Internet technologies, and perhaps more importantly, social changes brought on by these technical advances have led to an explosion of the social web or Web 2.0 (Kietzmann et al., 2011) and social media (Saxton et al., 2013). The social web can be defined as media rich web applications that are easy to use, decentralized and highly interactive, which facilitate the creation of massive amounts of UGC (Saxton et al., 2013). The applications include but are indeed not limited to Facebook, YouTube, Wikipedia, Instagram, and TripAdvisor.
One of the critical aspects of the social web is its ability to access, tap and aggregate tacit knowledge that lay latent in the heads of hundreds, thousands and potentially millions of users and organize it into some usable format (Saxton et al., 2013). Crowdsourcing is one of those social applications that can facilitate this process and create value for the firm.
Ultimately, while crowdsourcing is linked to concepts including open innovation, outsourcing, user innovation, open source, collective intelligence and the social web, crowdsourcing is about using the crowd to create value for the initiating organization. Although organizations have had success in using the crowd in the past, its usage was limited due to technical and logistical challenges. However, recently because of considerable advances in technology accompanied by socio-technical changes in large part brought on by this technological innovation, crowdsourcing has grown and become more mainstream. Appreciating the consequences of these developments, researchers (e.g., Lusch & Vargo, 2006) have argued that advances in co-creation (crowdsourcing’s close cousin) will ultimately persuade firms to join with customers to co-create the entire marketing program.
1.2.2 What is crowdsourcing?
Given the above, it is still not clear what crowdsourcing is. Many researchers have attempted to define crowdsourcing, but most of their definitions are limited and incomplete (Estelles-Arolas &
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Gonzalez-Ladron-de-Guevara, 2012). Crowdsourcing can touch on so many activities, include crowdfunding (cf., Belleflamme, Lambert, & Schwienbacher, 2014), decision support (cf., Chiu, Liang, & Turban, 2014), idea creation, crowd-voting, micro-tasks, crowd solutions (cf., Prpić, Shukla, Kietzmann, & McCarthy, 2015), and user-generated content (cf., Daugherty, Eastin, & Bright, 2008) just to name a few. Therefore, defining it comprehensively has proved not to be easy. To start, it may be best to raise the level of analysis. Is it a theory; a concept; a strategy; a business model; a tool; a process or what?
A theory?
In order to determine whether or not crowdsourcing is a theory, we must examine what is required for a theory. According to Wacker's (1998, p. 361) definition, “theory must have four basic criteria: conceptual definitions, domain limitations, relationship-building, and predictions.” First, there are countless definitions of crowdsourcing. For example, researchers Estelles-Arolas & Gonzalez-Ladron-de-Guevara (2012) exhaustively researched the definitions in an attempt to create a comprehensive description. They found 40 different definitions from 32 distinct articles. The definitions ranged from “a process of outsourcing of activities by a firm to an online community or crowd in the form of an ‘open call’“ (Whitla, 2009, p. 15) to a “new innovation business model through the internet” (Peng & Zhang, 2010, p. 1) to a “tool for addressing problems in organizations and business” (La Vecchia & Cisternino, 2010, p. 425) to “just a rubric for a wide range of activities (Howe, 2008, p. 17).” The comprehensive definition that (Estelles-Arolas & Gonzalez-Ladron-de-Guevara, 2012) finally arrived at required 121 words.6 Therefore, crowdsourcing does not yet have an agreed upon definition.
The second criterion is that a theory must have a limited domain. The domain of a theory can be described as a specific setting of context in which the theory can be applied. The limitation answers questions like when and where can the theory be applied (Wacker, 1998). As the range and diversity in definitions demonstrate (Estelles-Arolas & Gonzalez-Ladron-de-Guevara, 2012), there is no single specific context in which crowdsourcing can be limited. The blurring of boundaries shows that crowdsourcing fails this second criterion. Furthermore, in some cases (cf., Berdou, 2012; Gaudenzi, 2014) crowdsourcing is taken to mean almost any collaborative effort or technology.
The third criterion is that a theory should describe some relationship between and among variables. Another way to look at this relationship idea, is ‘‘The primary goal of a theory is to answer the questions of how, when or where, and why . . . unlike the goal of description, which is to answer the question of what or who ’’ (Bacharach, 1989, p. 489). What is the relationship building aspects of crowdsourcing? Crowdsourcing does not have or accomplish this either.
The fourth and final criterion of a theory is its predictive ability. Typically related to the theory’s relationship between and among the variables, theory should be able to use those relationships to predict whether, if, or when certain events could or should occur. Given crowdsourcing does not describe a set of relationships among variables, it is not strictly possible for it to make predictions. In sum, crowdsourcing fails all four criteria, thus cannot be seen as a theory.
6 “Crowdsourcing is a type of participative online activity in which an individual, an institution, a non-profit organization, or company proposes to a group of individuals of varying knowledge, heterogeneity, and number, via a flexible open call, the voluntary undertaking of a task. The undertaking of the task, of variable complexity and modularity, and in which the crowd should participate bringing their work, money, knowledge and/or experience, always entails mutual benefit. The user will receive the satisfaction of a given type of need, be it economic, social recognition, self-esteem, or the development of individual skills, while the crowdsourcer will obtain and utilize to their advantage what the user has brought to the venture, whose form will depend on the type of activity undertaken” (Estelles-Arolas & Gonzalez-Ladron-de-Guevara, 2012, p.197).
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A concept?
While the definition of the term concept, can be difficult to precisely explain (cf., Goguen, 2005; Jackendoff, 1989; Mammen, 2008) a working definition is “the notion of a concept designates an abstract idea or model that corresponds to something concrete in reality or in language.” (Samset, 2010, p. 90). Samset (2010, p. 90) goes further to explain “a concept is a mental construction intended to support the solution of a problem or the satisfaction of a need.” So, while crowdsourcing itself may not be a single concept, there may be a multi-pronged conceptual foundation behind crowdsourcing. For example, the idea of collective wisdom and collective intelligence (Saxton et al., 2013; Surowiecki, 2004a), open innovation Chesbrough (2003), outsourcing (Schenk & Guittard, 2009, 2011), open source (Howe, 2008), social web or Web 2.0 (Kietzmann et al., 2011), social media (Zwass, 2010), user-generated content (Daugherty, Eastin, & Bright, 2008) and co-creation (Zwass, 2010) all may have a underlying relation to crowdsourcing. Despite this, it is difficult to claim crowdsourcing is a single concept. A strategy?
On the other hand, many researchers have called crowdsourcing a strategy. It has been called a sourcing strategy for organizations (Soliman, 2013). It is been called an innovation strategy (Pisano, 2015). Crowdsourcing has been described a method of democratizing strategy development within a firm (Stieger, Matzler, Chatterjee, & Ladstaetter-Fussenegger, 2012). It has also been called a “digital business strategy” (Tuli, Kohli, & Bharadwaj, 2007, p. 472). While it seems that crowdsourcing can be used strategically, that is not the same thing as defining crowdsourcing as a strategy. Surprisingly, strategy is one of those terms that everyone knows what it is until you ask them to define it (Magretta, 2002).
A business model?
Similarly to the term strategy, business model is one of the most overused terms especially without a clear understanding for what exactly it is (Magretta, 2002). Therefore, ironically one of the more popular ways researchers define crowdsourcing is by calling it a business model. For example, Kohler (2015) calls crowdsourcing a business model to create and capture value. Others claim that crowdsourcing ‘‘describes a new web-based business model’’ or a ‘‘strategic model’’ (Brabham, 2008, p. 79). Unfortunately, because crowdsourcing seems to cover a wide variety of organizational activities in a wide variety of contexts, calling it a business model is not specific enough. Some have attempted to broaden it by calling crowdsourcing a business model innovation (Walter & Back, 2010). Others have tried to narrow the definition to describe it as a production development business model (Djelassi & Decoopman, 2013). Even Saxton et al. (2013) made a laudable attempt to clarify things by first calling crowdsourcing a sourcing model, then conceptualizing it as a production model, and finally in their definition calling it a sourcing model again. Perhaps it is just an example of how difficult it is to define crowdsourcing.
Tool or Process?
Researchers have also called crowdsourcing other things such as a method (cf., Llorente & Morant, 2015) or a technique (cf., Demartini, Difallah, & Cudré-Mauroux, 2012; Mitry et al., 2013; Reid, 2013). While (Soliman, 2013) called crowdsourcing a strategy, a closer examination reveals he was
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really calling crowdsourcing a tool to be used in the production process. Similarly, Gast & Zanini (2012) refer to crowdsourcing not as a strategy, but rather a tool to use to create strategy.
More recently in a special issue of the journal Business Horizons dedicated to crowdsourcing, editor Kietzmann proposed a newly refined and updated definition to revise the one initially proposed by Howe. He defined crowdsourcing as "The use of IT to outsource any organizational function to a strategically defined population of human and non-human actors in the form of an open call (Kietzmann, 2017, p. 152). While this definition is a significant improvement over Howe's, it still has some issues. Firstly, while crowdsourcing today is being driven in large part by the advances in information communication technology (ICT), ICT is not required for crowdsourcing. Secondly, the term open call is not well defined. Certainly, there has to be a process to "get the word out," but it is unclear how wide the net needs to be cast. Does it mean that we need a full-blown advertising and promotional campaign or can we inform a group of customers in our existing brand community?
Given the above literature, the findings of this dissertation’s papers and studies, and for the purpose of this research, crowdsourcing is defines as - a tool (or process) by which the firm can increase or expand the resources to which it has access by using the collective effort of a group of individuals or organizations. This definition used in this study is close to (Garrigos-Simon, Narangajavana, & Galdón-Salvador, 2014) who describe crowdsourcing as a process that can comprise of various organizational tasks traditionally handled within the organization that are opened to stakeholders’ of all types inside and outside the organization including customers, employees, partners, suppliers or the general public who are interested in taking part in one of those diverse organization activities.
Using the crowd is an innovative way in which firms can solve certain business problems by tapping the resources that exist often outside the organization. While there are many entrepreneurial and innovative tools and processes, one of the oldest is by using the crowd, collective intelligence, and collective action. In other words, by using the collective effort of a group of individuals, the firm can increase or expand the resources to which it has access. These resources can be of all types including financial, human, technical, physical, managerial, informational, social, etc.
Utilizing the crowd provides an innovative way for firms to solve particular business problems by tapping the resources that often exist outside the organization. Crowdsourcing is a situational, contextual and flexible tool that can be used in many different organizational contexts. This multifunctional tool’s precise role depends on the context. The specific context throughout for this thesis is marketing and the use of crowdsourcing as a marketing tool.
1.2.3 The Crowd in Marketing
The use of crowdsourcing as a marketing tool has also become more relevant for marketers and researchers as the digitalization of business has become increasingly important (Fleisch et al., 2014; Hull, Hung, Hair, Perotti, & DeMartino, 2007; Rashid, 2017; Uzunoglu, 2011).
As a result, research that broadens and extends crowdsourcing into the area of marketing, particularly after Whitla (2009), has become increasingly relevant. He divided the marketing activities in which crowdsourcing could play a role in three broad areas – product development, advertising and promotion and finally market research. Subsequently in their practitioner-oriented book Dawson & Bynghall (2012) more finely divided the areas into content creation, idea generation, product development, customer insights, customer engagement, customer advocacy and pricing (Bakić et al., 2014).
There has been significant research looking at crowdsourcing and ideation (i.e., new idea/production creation). More specifically researchers have looked at the users’ role as an innovator in the new idea arena (e.g., Bogers et al., 2010; Howe, 2008), while other focused on new product, idea
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generation competitions (e.g., Leimeister et al., 2009). Paper 2 in this dissertation extends this literature.
While others have examined crowdsourcing and new product and solution search (e.g., Afuah & Tucci, 2012) and others have researched crowdsourcing and new product design (e.g., Tran, ul Hasan, & Park, 2012).
Still others have begun to look at crowdsourcing’s role in promotion and distribution (e.g., Howe, 2008), advertising (e.g., Brabham, 2009; Brabham, 2008; Roth & Kimani, 2014) and brand development (e.g., Djelassi & Decoopman, 2013; Moisseyev, 2013; Zadeh & Sharda, 2014). Paper 1 in this dissertation extends this literature.
Several researchers have looked at crowdsourcing and market research. As there is a close link to new idea generation and market research some focused there (e.g., Poetz & Schreier, 2012). Behrend, Sharek, Meade, & Wiebe (2011) looked at the viability of crowdsourcing for survey research. Others discussed how crowdsourced market research could complement traditional market research, especially in the areas of where timeliness is vital like trend analysis and new market development (Kleemann et al., 2008). Further, much of the source material for the trend analysis and market development comes from crowdsourced user-generated content (UGC). Even further, Poynter (2013) discussed how crowdsourcing would continue to disrupt the practice of market research. For example, research has begun to explore how the gamification of crowdsourcing affects data quality in market research (Cechanowicz, Gutwin, Brownell, & Goodfellow, 2013). Study 1 in this dissertation extends this market research and user-generated content research.
Moreover, some crowdsourcing researchers have narrowed their focus to look at how the rise of crowdsourcing business financing (i.e., crowdfunding) has unexpectedly impacted marketing. For example, (Cowley, 2016; Freeman, 2015) argued that crowdfunding was performing three essential marketing functions – promotion, research, and a marketing channel. Others observed that crowdfunding was a useful technique for market testing/market research (Cowley, 2016; Freeman, 2015). Finally, Gerber & Hui (2013) asserted that the marketing function of crowdfunding was just as important as the fundraising. Paper 1 of this dissertation extends this crowdsourcing/crowdfunding research.
1.2.4 Consumer Privacy
Privacy is a significant issue within marketing research (e.g., Smith, Dinev, & Heng, 2011), particularly since the arrival of the Internet. Privacy can be “described as ‘the right to be let alone,’ and is related to solitude, secrecy, and autonomy.” (Wang, Lee, & Wang, 1998, p. 64). It is also important from the consumers’ perspective as Kiyotaki & Wright (1989) found in their extensive multi-disciplinary review of privacy that privacy was one of the US consumers’ most important concerns with 72% of those polled concerned about online tracking and profiling.
These results are partially contradictory. Citizens assert privacy is essential to them; nevertheless, they permit companies to extract vast quantities of data from them and about them 24 hours a day using apps, smart devices and soon to be millions of IoT's sensors (BIS Research, 2017). However, are consumers just donating their personal and private information? In most cases, they are not. In fact, in most cases the personal information is a by-product of a market transaction between the consumer and firm.
The volume of personal and private information has recently exploded with the introduction of new hardware (e.g., smartphones, smart devices, etc.), new software (e.g., apps, AI, etc.) and new marketing techniques and tools. It has been estimated that the growth of personal data is 50% annually (Blundell, 2014). As the amount of personal data grows, more of it is available for marketers to use in an attempt
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to acquire a competitive advantage by gaining deeper insight to their customers and prospective customers.
1.2.5 Predictive Analytics
Predictive analytics (PA) is the practice of constructing and evaluating data-driven forecasting models (Verbraken, Lessmann, & Baesens, 2012). Unlike explanatory models and analytics, PA is built to make operationally accurate projections. Marketing is the foremost user of PA in areas such as customer acquisition, budgeting, and forecasting among others (Eckerson, 2007). “Predictive marketing is the practice of extracting information from existing customer datasets to determine a pattern and predict future outcomes and trends” (Everstring, 2015, p. 2). Predictive marketing is a young research field (cf., Artun & Levin, 2015), although PA as a practice is a few decades old.
Until recently process was slow, challenging and not so accurate (Karr, 2018). However according to The 2015 State of Predictive Marketing Survey Report, predictive marketing has been increasing rapidly (Everstring, 2015). This growth can be attributed to converging factors including the increased number and availability of tools, increasingly more accurate and sophisticated algorithms, more customer data, centralize and accessible data, and increased computing power to handle all the data and new algorithms (Everstring, 2015). According to Demirkan & Delen (2013), these advances are coming at the correct time as the old methods and systems are not able to handle the increasing demand for real-time, accurate business insights. Though PA is a system or technology that learns from customer experience (data) to predict the future behavior of customers, customers are typically taken in aggregate rather than as individual customers (Siegel, 2013). Although this will change as the crowdsourcing individual consumer information evolves.
1.2.6 Summary
In sum, the research shows firms and marketers need to act more entrepreneurially (e.g., Miller & Friesen, 1983; Zahra, 1986) and more resourcefully (e.g., Morris et al., 2002; Stevenson & Jarillo, 2007) to succeed in today's more dynamic and turbulent business environment. Further, that these actors have to expand their operations outside the normal boundaries of the organization to create relationships with external stakeholders, but also to gain access to resources outside the bounds of the organization. As a result, crowdsourcing has become a useful marketing tool. Therefore, this dissertation focuses on crowdsourcing’s impact on three specific marketing strategies and one consequence of this interaction. Although there has been some research on these topics, this dissertation’s purpose is to examine crowdsourcing and marketing within the context of advancing technologies.
The objective of this literature summary was not to be an exhaustive one, but instead, it is an attempt to do three things. First, to provide theoretical lenses through which we can study crowdsourcing as a marketing tool. Second, to provide some language that will allow us to clearly and succinctly present our arguments. Thirdly, to provide some background literature that will give some scope and boundaries to the four research projects presented. In the next section, the methodology used in this thesis is presented.
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1.3 Methodology
1.3.1 Introduction
Research methodology is a systematic way to solve a research question. Each researcher has many choices to make in undertaking the research project. These choices have at their root the researcher's philosophical and theoretical beliefs, the details of the research project and the researcher’s experience and skill. As every research procedure has strengths and weaknesses, one of the researcher's most powerful research tools is his or her choice in these decisions. Selecting proper methods to use is vital as it creates a guide for answering the specified research questions.
This section is structured in the following manner: Firstly, we discuss the two major types of scientific inquiry – conceptual and empirical. Next, we briefly review the two predominant paradigms within the empirical context. Thirdly, we present some potential research approaches available to investigators and the approaches selected for this dissertation with justification. Finally, we end the section with a discussion of research quality criteria for both conceptual research and qualitative research.
There are two major types of scientific inquiry conceptual and empirical. Conceptual research is a mode of scientific investigation that necessitates “abstract thinking —to conceptualize, delimit, and solve real-world problems” (Fawcett et al., 2014, p. 2). Examples of conceptual research include theory development, theoretical reviews, theory application, methodological analysis, reviews of empirical literature and commentaries, book reviews and the like (Robey & Baskerville, 2012).
Conceptual research permits us to perceive phenomena through different theoretical lenses (Collis & Hussey, 2013). Conceptual research is often viewed as the theory-building part of the scientific process, but also conceptual research opens the way for empirical research to push researchers further along the research cycle towards models, frameworks, and theories (Meredith, 1993).
Ultimately, conceptual research plays a vital role in any field’s research environment and can stimulate demand for empirical research (Fawcett et al., 2014). Ironically, MacInnis (2011) in her thorough piece on conceptual contributions to marketing points out that despite the American Marketing Association’s (AMA) call in 1988 for increasing conceptual research in marketing and many prominent researchers calling for the same (e.g., Kerin, 1996; Stewart & Zinkhan, 2006; Webster Jr, 1992; Zaltman, LeMasters, & Heffring, 1982), the amount of conceptual research and the number of conceptual marketing articles continue to decline to the detriment of the advancement of marketing thought, knowledge, and progress (Yadav, 2010a). However, pure conceptual research is the domain of philosopher and abstract thinkers, but conceptual frameworks can drive empirical research. Empirical research based on observation or experience or collected data. So rarely in applied fields like marketing and management are their purely conceptual research. Most social science research is at least partially empirical.
Empiricism is knowledge gained by experience derived from an individual’s senses (Creath, 2017). While empiricism is a philosophical doctrine, empirical is a research practice that is evidence-based (Jary & Jary, 2000). Within the empirical context, there are two main research paradigms. Evidence or data can be in the form of numbers or in the form of a narrative, depending on the form of research - quantitative or qualitative. The positivism paradigm is typically associated with the quantitative methods, while the interpretivist paradigm is typically associated with the qualitative data. The positivist paradigm is characterized by concepts such as quantitative, objective, scientific, and traditionalist, while the interpretivist paradigm is characterized by concepts such as qualitative, subjective, humanist, and phenomenological (Blomkvist & Hallin, 2015; Collis & Hussey, 2013).
Although these two research paradigms are quite different and the debate between the true believers on both sides quite heated, there are some critical commonalities (Fawcett et al., 2014). For example
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and according to Collis & Hussey (2013, p. 50) both paradigms “use research questions to drive the research;�use various methods to collect quantitative and/or qualitative research data; use various methods to summarize or otherwise reduce the research data; apply techniques to analyze the data; discuss the results or findings; and draw conclusions.”
1.3.2 Potential research approaches
According to Collis and Hussey (2013) research, specifically empirical research, can be classified according to 1) purpose of the research, 2) the process of the research, 3) the logic of the research, and 4) the outcome of the research. The purpose of the research describes the reason why the research is done. The process of the research describes in what manner the data was collected and analyzed. The logic of the research describes whether the research reasoning moves from general to specific or from specific to general. Finally, the outcome of the research describes whether the research results apply specifically to the case examined or contributes more generally.
The purpose of the research can also be broken down into four categories. First is exploratory research. Exploratory research investigates issues, topics problems where there are few previous studies. The objective of exploratory research is to gain insight that can provide a foundation for future research. Exploratory research can often be flexible and unstructured and can include observation, cases, and historical analysis. Second is descriptive research. Descriptive research describes the phenomenon being researched or the research problem. Descriptive research paints a picture of the current situation. Descriptive research tends to go further than exploratory research and uses methods such as surveys, comparative and correlational techniques (C. Kothari, 2004). The third purpose is analytical or explanatory research. In this type of research, the researcher analyzes how or explains why the phenomenon occurs. This type of research often uses statistical analysis to examine the relationship among research variables. Variables can be defined “as anything that has a quantity or quality that varies” (Kowalczyk, 2018) and is measurable. The fourth and final type of research is predictive research. The objective of predictive research is to generalize the analysis so that it can be extended and use for similar research problems. Typically, this is accomplished by developing hypothesize relationships. Therefore, predictive research attempts to answer the “how," “why” and “where” questions the current situation, but also for future similar situations.
The second way research is classified by process. The two primary categories are quantitative and qualitative7.
Qualitative data collection methods should be of particular interest to those researchers designing research under the interpretivist paradigm. Qualitative data or non-numerical data can be collected in many ways including but not limited to semi-structured interviews, participating observations, contextual understanding, case study, content analysis, observation, conversations, interviews, focus groups, action research, email, reflective journals, logs, textual and visual analysis of all types, ethnography, grounded theory, hermeneutics and other soft, rich data approaches (Gioia, Corley, & Hamilton, 2013). Qualitative data is often unstructured and collected from multiple sources. Words associated with qualitative include explore, generate, develop, create, meaning and personal experience (Creswell, 2013b).
7 Before we get too far, we must make an important note, when we speak about quantitative and qualitative here, we are speaking about data and how it is collected. We are ascribing to Collis and Hussey's (2013) view of separating the paradigmatic discussion above from this research process issue here. Positivist researchers can collect and use qualitative data. Interpretivist researchers can collect quantitative data to provide a more richness and completeness.
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Quantitative data implies the use of numbers and structured empirical gathering. This type of data is collected through, polls, questionnaires, surveys, experiments, trials or pre-existing statistical data. Quantitative data also tends to be structured and systematically collected. Words associated with quantitative include factors, determinants, relationship, causes and effect (Creswell, 2013b).
The third classification is the logic of the research that can be divided into two primary types – deductive or inductive. Deductive research is seen as moving from the more general to the specific. In other words, a deductive researcher "works from the ‘top down,' from a theory to hypotheses to data to add to or contradict the theory" (Creswell and Clark 2017, p. 23). A theoretical or conceptual framework is created and then tested empirically. Quantitative data is generally associated with the deductive approach and implies a focus on reasoning and testing (Eisenhardt, 1989; Glaser, Strauss, & Strutzel, 1968; Kozinets, 2002; Krippendorff, 1989; Thompson, 1997; Yin, 1994). As a result, deductive logic is an appropriate classification for empirical research.
Inductive research is seen as moving from the specific to the more general. In other words, an inductive researcher works from “bottom-up, using the participants’ views to build broader themes and generate a theory interconnecting the themes” (Creswell and Clark 2017, p. 23). In this process theory is developed or induced from observation or other empirical data. According to Bryman (2015), qualitative data is more often associated with inductive reasoning and generating theory (Gioia, Corley, & Hamilton, 2013). As a result, inductive logic is also an appropriate classification for empirical research.
The fourth and final way research is classified is in terms of outcome. This classification can be divided into two categories – basic and applied. Basic research, also called fundamental or pure research, refers to –
“. . . research that is undertaken for its own sake – to advance knowledge; to develop theory; to solve an interesting theoretical puzzle; to address a curiosity of the researcher – without any immediate concern for whether doing so will produce anything ‘useful’ or ‘practical’ or ‘generalizable’” (Palys, 2008, p. 57).
On the other hand, applied research refers to research that deals with existing, specific and practical problems (Collis & Hussey, 2013). Learning how to improve a firm’s marketing effectiveness is an example of an applied research problem.
It is important to note, many researchers or perhaps most researchers use the terms qualitative and quantitative to denote the research methods or style rather than the nature of the data collected and use. With this, they imply that the research is either purely quantitative or purely qualitative, black or white. Quantitative purist claim that social science research must be objective, while qualitative purist maintain that there is no objectivity but rather are multiple realities (Johnson & Onwuegbuzie, 2004). Also, while there is an ongoing debate on which empirical approach is superior, these approaches, in reality, are not mutually exclusive, and further can be used to answer the same questions in different ways (Soiferman, 2010). Increasingly researchers are realizing, that like most things, gray may be the color of choice. In other words, using mixed methods is gaining popularity (cf., Creswell, 2013b, 2014a; Creswell & Clark, 2017; Johnson & Onwuegbuzie, 2004; Onwuegbuzie & Leech, 2005; Onwuegbuzie & Teddlie, 2003). The mixed method can be defined merely as combining qualitative and quantitative data to get a more thorough understanding of the research problem and questions (Creswell, 2013b). This mixed method is becoming popular in social, behavioral and health sciences. Further, it can be especially useful method to explore new fields of scientific inquiry.
While mixing the two types of data and styles is one way to handle this conflict between researchers, another way is to reframe the discussion “by de-emphasizing the terms quantitative and
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qualitative research and, instead, sub-dividing research into exploratory and confirmatory methods” (Onwuegbuzie & Leech, 2005, p. 268).
1.3.3 Selected research approaches
In this compilation thesis, there are four individual research projects – two published papers and two, yet to be published, studies. In this section, we describe more specifically the research approaches chosen for the each of these four research projects.
Paper 1 is conceptual research of the theory development type as it focuses on how crowdfunding websites can be used as marketing platforms to enhance brand image or to sell products directly. Additionally, it explains why and how crowdfunding is a useful marketing tool. Although this paper is not predictive in the strict sense, it is normative as it develops and presents a decision-tree to guide managers in their decision process as they determine how best to use crowdfunding sites in their marketing strategy. Although predominately a conceptual paper, it does use illustrative examples to help make its case.
Paper 2 is also conceptual research of the theory development type as it develops the idea of crowdsourcing as a technique for new product developing in an environment shifting from Web 2.0 to Web 3.0 and explains how sensors will change how new opportunities, products and services are developed. Although this paper is not predictive in the strict sense either, a framework is developed that can be used by marketers, entrepreneurs and managers in their new opportunity/product development process. As predominately a conceptual paper it only uses illustrative examples to help make its case.
The purpose of Study 1 is primarily explanatory. It is an empirical study focusing on how prospects, customers, the brand community and the crowd can be used for market research. Further, this analytical study further demonstrates how this crowdsourced data can help the firm make strategic decisions taking a demand-side strategy perspective. The process of this research is a massive qualitative study that uses content analysis and data mining to extract valuable information from the crowd through their user-generated content. The logic of this study is primarily inductive. Finally, the research outcome of Study 1 is also applied.
Finally, Study 2 is conceptual research of the theory development type. This study extends the ideas of crowdsourcing, informational externalities, and consumer privacy. It also develops three different models that help explain how the evolution of crowdsourcing of information externalities in impacting consumer privacy. As a predominately a conceptual paper it only uses illustrative examples to help make its case.
While Paper 1 and 2 as well as Study 1 are conceptual pieces of the theory development type, this does not mean that they resulted in producing grand theories or even a theory; however, they do contribute to theory.
As this dissertation is a compilation of two papers and two studies looking at the topic of crowdsourcing and marketing across four different central research questions, one would expect that the research approaches of each would differ. Why? Because research methods should be driven by the research questions themselves (Punch, 2013). There are many different philosophical perspectives and methodological approaches a researcher can take (cf., Collis & Hussey, 2013; Creswell, 2007; Kothari, 2004). However, unlike many doctoral researchers we do not think it necessary to interject myself into the metaphysical, epistemological, axiological and methodological debate, but just to humbly place this thesis research in some context. Given this, the overall approach of this thesis is neither positivist nor interpretivist, but rather pragmatic (Collis & Hussey, 2013; Creswell, 2007; Johnson & Onwuegbuzie, 2004; Onwuegbuzie & Leech, 2005). The guiding principle of this perspective is to focus on what
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works rather than a single philosophy, method, or type and source of data. In the next section, we briefly present the research strategy and design.
A summary of the methodologies employed in this dissertation is presented in Table 1.
Table 1: Research Approaches used in this dissertation
Paper Subject Type of conceptual research
Purpose Process Logic Outcome
Paper 1 Crowdfunding as promotional, advertising and sales tool
Theory Development
Not Applicable
Not Applicable
Not Applicable
Not Applicable
Paper 2 Crowdsourcing as a new product and service development tool
Theory Development
Not Applicable
Not Applicable
Not Applicable
Not Applicable
Study 1 Crowdsourcing as a market research tool
Not Applicable
Explanatory Qualitative Inductive Applied
Study 2 The effects of crowdsourcing on consumer privacy
Theory Development
Not Applicable
Not Applicable
Not Applicable
Not Applicable
1.3.4 Research strategy and design
To get a sense of this dissertation, it is essential to understand the research strategy and the research design. The overall research strategy for this thesis is a unique set of choices of research methods linked to each specific problem. The research design refers to the decisions a researcher makes concerning the methodology and methods he or she uses to answer the research questions and achieve the research purpose (Bryman, 2015; Collis & Hussey, 2013; De Vaus & De Vaus, 2001). However, in practice, the idea of research design is generally limited to the choice of one of three empirical methods – quantitative, qualitative or mixed (Creswell, 2013a). In this section, I will briefly discuss the research strategy and then the research design of this thesis.
As stated previously, there are two major types of scientific inquiry - conceptual and empirical. Three out of the four research projects in this thesis are predominately conceptual. So when is conceptual research appropriate? Fawcett et al. (2014) point to three major conditions that describe when conceptual research is particularly helpful. Firstly, when the research problem is not conducive to data-driven investigation. Secondly, when phenomena are new and emerging, there is likely to be a lack of empirical data. Thirdly, new paradigms require new, big original thoughts and ultimately new conceptualizations and theories (Kuhn, 1970). Further,
“by allowing us to explore emerging phenomena, conceptual research enables us to: (1) improve the timeliness, and managerial applicability of our work and (2) move beyond reporting new phenomena to play an active role in shaping the conversation via ‘sensegiving’—the process of
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shaping other academics’ and practitioners’ understanding of the phenomena” (Fawcett et al., 2014, p. 3)
Additionally, marketing researchers (e.g., Kerin, 1996; Stewart & Zinkhan, 2006; Webster Jr, 1992; Yadav, 2010b; Zaltman et al., 1982) have called for more conceptual research in marketing.
A conceptual approach was appropriate in Paper 1 because 1) web-enhanced, crowdfunding is a relatively new practice, especially when used as a marketing innovation rather than a fundraising tool; 2) it allowed the research to be timely and to take advantage of the managerial usefulness; and 3) it gave the research an opportunity to help shape other researchers understanding of the new phenomenon (Fawcett et al., 2014).
A conceptual approach was appropriate for Paper 2 as well because 1) the idea of the convergence of IoT’s, sensor-based crowdsourcing and new product development is new; 2) it was not a data-driven investigation; and 3) it also gave the paper an opportunity to help shape other researchers understanding of the new phenomenon (Fawcett et al., 2014).
A conceptual approach was appropriate for Study 2 because 1) the idea that the evolution crowdsourcing is having substantial impact on consumer privacy through the crowdsourcing of information by-products is a new concept; 2) it was not a data-driven investigation; and 3) it also gave the research an opportunity to help shape other researchers understanding of the timely and fast-moving phenomenon (Fawcett et al., 2014).
When is qualitative research appropriate? Bartunek, Rynes, & Ireland (2006) found that qualitative research was “overrepresented in Academy of Management Journal’s survey regarding the most interesting management-related articles published in the past 100 years” (Pratt, 2009, p. 856). Qualitative research is appropriate when examining contemporary and real-life business issues where the focus is on exploration and asking the what, how and why questions (Fawcett et al., 2014).
We will take the common understanding of the concept research design to discuss the research in Study 1. This extensive, qualitative study uses the qualitative technique of content analysis and data mining to extract valuable information from user comments (cf., Krippendorff, 1989, 2013). Using an advanced tool (i.e., Leximancer 4.5) semantic mapping of natural language was conducted. The empirical context for this study is the largest service sector industries in the world, travel, tourism, and hospitality (Leiper, 2008a). It is important to note and perhaps a bit ironic, that while this interpretivist analysis uses qualitative data, “the algorithmic basis of Leximancer is strongly suggestive of a quantitative and positivistic approach to the analysis of data” (Crofts & Bisman, 2010, p. 188), it hints to a possible mixed methods feeling.
The qualitative approach was appropriate in Study 1 because: 1) it examines ongoing and real-life management concerns where the focus was on exploration and asking the what, how and why questions (Fawcett et al., 2014) and 2) the study is data-rich research that involves the subjective attitudes, opinions and behavior of a large group of customers (Kothari, 2004). According to Fawcett et al. (2014, p. 6) “Qualitative research as scientific inquiry relies on storytelling to make sense of real-world dilemmas. Qualitative research allows informants to tell their stories and derives meaning from patterns that emerge within and across stories.” Additionally, Gephart, (2004, p. 455) stated, “Qualitative research starts from and returns to words, talk, and texts as meaningful representations of concepts." In this study using the qualitative techniques of content analysis and data mining were particularly appropriate and effective to extract the users' stories from their comments.
A summary of the data collection methods employed in this dissertation is presented in Table 2.
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Table 2: Data Collection Methods used in this dissertation
Paper Subject Method of Inquiry Data Collection Method
Paper 1 Crowdfunding as promotional,
advertising and sales tool
Conceptual using examples for illustration
Conceptual study Data not collected
Paper 2 Crowdsourcing as a new product
and service development tool
Conceptual using examples for illustration
Conceptual study Data not collected
Study 1 Crowdsourcing as a market
research tool Empirical
Qualitative
Study 2 The effects of crowdsourcing on
consumer privacy
Conceptual using examples for illustration
Conceptual study Data not collected
1.3.5 Quality criteria
It is important that research is done with scientific rigor to ensure research quality. For conceptual research, there are a few ways by which you can judge the quality or contribution of the research (Fawcett et al., 2014; Whetten, 1989). For empirical research, quality commonly refers to aspects of the decisions the researcher made with particular attention to the study design, data selection, and collection, measurement, and protection against systematic and unsystematic bias. For quantitative research, this typically focuses on two common assessments of research quality - reliability and validity (cf., Collis & Hussey, 2013; C. R. Kothari, 2004). However, qualitative research designs, are generally assessed regarding their credibility, transferability, dependability, and confirmability, amongst other factors (cf., Creswell & Miller, 2000b; Hannes, 2011). In this section, we will first describe the quality aspects of conceptual research. Second, we will briefly review the quality criteria for qualitative research. As this dissertation does not contain quantitative data or analysis, a discussion of reliability and validity will be largely excluded8.
Whetten (1989) presented seven factors which researchers can determine the quality of conceptual management research –
• What's new - Does the research contribute something? • So what – Will the results change thinking or practice? • Why so – Are the arguments convincing? • Well done – Are all the elements handled properly? • Done well – Is the paper well written? • Why now – Is this research timely? • Who cares - Are academic interested in this topic? Managers? Others?
Accordingly, Paper 1, Paper 2 and Study 1 do contribute to the field, will change thinking on a relevant topic, are timely and are of interest to academics, managers, and others. Thus, these conceptual pieces
8 The concepts of reliability and validity are important for empirical research both quantitative and qualitative. However, the specific terms are most often used in describing quantitative research. While the underlying ideas are the same, in qualitative research other terms such as dependability and credibility may be more often used (Hannes, 2011). See Table 4 for more details.
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of research appear to meet the quality standard on criteria. However, concerning the criteria – Why so, Well done and Done well – it is best left up to the readers to decide.
Table 3 provides a summary of the quality factors for Paper 1, Paper 2 and Study 4.
Table 3: Conceptual Research Quality Factors for the Three Conceptual Projects
Research What’s new? So what? Why so?
Well done
Done well
Why now? Who cares?
Paper 1 1) Crowdfunding as a marketing and sales channel
2) Decision-tree
Yes, will help in a small by providing an alternative view on the crowdfunding platform
Growing use of crowdfunding platforms
Researchers and marketers
Paper 2 1) Sensor-based Crowdsourcing
2) Typology for developing new products and services
Yes, in a small way will researchers and marketers
IoT’s is coming
Researchers and marketers
Study 2 1) The evolution of crowdsourcing impacting consumer privacy
2) Models describing and explaining the evolution of crowdsourcing information externalities
Yes, in a small way the thinking of researchers, consumers and perhaps policymakers
Growth in personal information externalities and the growth in apps, sensors, and AI
Researchers, consumers and perhaps policymakers
Source – Adapted from (Whetten, 1989)
The quality criteria for research using qualitative data are a bit different (cf., Creswell & Miller,
2000b; Hannes, 2011). The first criterion is credibility. Credibility evaluates whether the data is representative of the opinions of the group being studied. In other words, how well does the data fit the sample studied. The second criteria are transferability, which gauges whether research findings apply to different, but similar situations. The third criteria are dependability, which assesses whether the process of research is done in a manner that can be replicated. The fourth criteria are confirmability. Confirmability gauges the degree to which the results are qualitatively verifiable through the evaluation being based on the data and thorough scrutiny of the research process.
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Table 4: Criteria to critically appraise findings from qualitative research
Aspect Qualitative Term Quantitative Term Truth value Credibility Internal Validity Applicability Transferability External Validity or
generalizability Consistency Dependability Reliability Neutrality Confirmability Objectivity
Source - (Hannes, 2011)
According to these criteria, the qualitative Study 2 does seem to meet the quality standard. Firstly,
the research target was New York customers of steakhouses, Italian restaurants and seafood restaurants who commented on their experiences. The actual sample was almost 300,000 comments posted on the industry's largest platform, TripAdvisor. In other words, the data is representative and therefore credible. Secondly, this study explored how marketers could use crowdsourced content to develop demand-side strategic insights and opportunities. While the data was limited regarding geography and industry, there is no indication that the same process could not be used in a wide variety of situations. As a result, the study meets the transferability criteria. Thirdly, one of the primary advantages of using a computer-assisted content analysis tool like Leximancer is its reliability. It helps to eliminate many of the human biases that can surface using more manual techniques (Smith & Humphreys, 2006). Consequently, the study meets the dependability criteria as well. Finally, with objective resulting from the use of Leximancer combined with a straightforward research process, this study does seem to satisfy the confirmability criteria.
In summary, Papers 1, Paper 2 and Study 2 are conceptual and seem to have met or exceeded the quality criteria. Additionally, as Paper 1 and Paper 2 have been published. Therefore, they have also been subjected to a rigorous review process. Study 2, the lone empirical study, does seem to meet the quality criteria laid out by experts (cf., Creswell & Miller, 2000b; Hannes, 2011).
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1.4 Summary of individual papers and studies
In this section, we give a preview of the four research projects in this thesis. The first three research projects deal with specific marketing strategies. Firms can engage in one two or all three of them; therefore, the order of the first three pieces present is not essential. Each project speaks to an individual research question that comes from the overarching research problem as follows:
Research Problem: How crowdsourcing can be used as a marketing tool?
From this research problem, four research questions were expressed as follows:
• RQ1: To what extent are crowdfunding platforms accessible to organizations as a marketing channel and, if so, what role can these platforms play?
• RQ2: How will the shift from Web 2.0 (and active-user input) to Web 3.0 (and passive-data/sensor–based input) impact the new opportunities/product development process?
• RQ3 - How can user-generated content help firms make strategic decisions about new business opportunities?
• RQ4: How is the evolution of crowdsourcing impacting information externalities and consumer privacy and how is this impacting marketing?
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1.4.1 Paper 1: Seeking funding in order to sell: Crowdfunding as a marketing tool9 Introduction As competition grows and as each firm strives to find an edge or a competitive advantage, crowdfunding sites can be used as a secret weapon that allow firms to leverage their campaign success many times over. In fact, so much so that the money raised can pale in comparison to the value of the media exposure gained.
Websites such as Kickstarter and Indiegogo have attracted much attention for their ability to enable organizations and individuals to raise funds from ordinary people who contribute for many reasons. This phenomenon is called crowdfunding (Belleflamme et al., 2014; Schwienbacher & Larralde, 2010). Crowdfunding is a subset of the tool crowdsourcing (Belle, Lambert, & Schwienbacher, 2013; Gerber & Hui, 2013; Paschen, 2016). Crowdfunding is crowdsourcing the fundraising process. Crowdfunding can be defined as the process of using the crowd to raise financial capital. More recently, established organizations have begun to use crowdfunding websites not only as a source of financing but also as marketing platforms. In this way, they have been able to ensure a ready market for their new offerings, with full sales pipelines, and to use the platforms as vehicles to boost brand image and gain support for brand-related causes.
Purpose
The purpose of this article is three-fold. First, it examines the extent to which crowdfunding
websites are accessible to organizations as a marketing channel, and if so, what role they can play. Second, it considers a few illustrative cases and by determining what motivated them to engage with a crowdfunding community, and how they structured their campaign. Third, the paper further evaluates the objectives that firms can have when they start a campaign, and the constraints they can encounter that limit their choice of approach. This research intends to answer the question -
RQ1: To what extent are crowdfunding platforms accessible to organizations as a marketing
channel and, if so, what role can these platforms play?
Findings The paper does find that crowdfunding web platforms can be used as marketing channels. By
matching a firm's objectives in its crowdfunding project and by selecting a crowdfunding approach based on objectives and constraints, the firm can identify which type of crowdsourcing approach, it should choose (i.e., reward-based crowdfunding, crowdsourcing and funding, equity-based crowdfunding, or branding).
9 Published: Brown, T., Boon, E., & Pitt, L. (2017). Seeking funding in order to sell: Crowdfunding as a marketing tool. Business Horizons, 60(2), 189-195.
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Contribution Researchers have looked at issues at the nexus of crowdsourcing and market activities including
promotion and distribution (e.g., Howe, 2008), advertising (e.g., Brabham, 2009; Brabham, 2008; Roth & Kimani, 2014) and brand development (e.g., Djelassi & Decoopman, 2013; Moisseyev, 2013; Zadeh & Sharda, 2014). Paper 1 contributes to and extends this literature.
Secondly, some crowdsourcing researchers (Cowley, 2016; Freeman, 2015; H. J. Smith et al., 2011) have begun focusing on how crowdfunding has unexpectedly impacted marketing. Paper 1 also joins, contributes and extends this crowdsourcing/crowdfunding research.
The research additionally contributes by developing a decision tree to help marketing managers make the correct decisions when working with crowdfunding websites and to make the findings more applicable to them. The paper also provides a warning. Specifically, this adaptation of crowdfunding for marketing purposes is not without its problems. Organizations would be well advised to consider not only the opportunities these platforms provide but also their limitations and risks.
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1.4.2 Paper 2: Sensor-based entrepreneurship: A framework for developing new products and services10
Introduction
As the Internet of Things (IoT’s) begins to dominate the technology landscape, there will be new
products and services that will become technically and financially feasible. Internet technologies and advancements in social interaction tools have led to an increase in the use of the crowd as a provider of business solutions (Fleisch et al., 2014). Yet, we have seen a mere fraction of the possibilities of crowdsourcing technologies (Andriole, 2010; Fleisch, Weinberger, & Wortmann, 2014; Garrigos-Simon, Alcamí, & Ribera, 2012; Garrigos-Simon & Narangajavana, 2015; Jelonek & Wyslocka, 2015; Lipiäinen, 2014; Tiago & Veríssimo, 2014). This is because most of the development, discussion, and research on crowdsourcing has focused on active-input crowdsourcing.
However, the real transformative pressure will come from passive sources of data generated primarily by developing and growing sensor technologies. This next generation of crowdsourcing will be a game changer for new product development. As crowdsourcing systems proliferate, more input will be acquired from sensors, artificial intelligence, bots, and other devices. As a result of this explosion, the variety of product and service opportunities will swell as entrepreneurs become more aware of technologies merging–—such as the combination of crowdsourcing, sensors, and big data into a new type of entrepreneurship: sensor-based entrepreneurship. Purpose
This research presents crowdsourcing as one of the likely business models that can bring viability
to these new sensor-based opportunities. Next, advances in IoT’s technologies, Big Data, and sensors have led to the next generation in crowdsourcing techniques and applications built on passively inputted data rather than actively inputted data. This research clarifies four next-generation types of crowdsourcing. Third, by looking at two dimensions of this passive data (i.e., level of aggregation and location/ownership of the sensors), a typology is created to act as an opportunity development tool to assist marketers and entrepreneurs in creating new products and services. This research intends to answer the question -
RQ2: How will the shift from Web 2.0 (and active-user input) to Web 3.0 (and passive-data/sensor–based input) impact the new opportunities/product development process?
Findings
Web 2.0 has resulted in the growth of crowdsourcing tools used in marketing strategies, especially
using active data input techniques. However, as more sensor-based technologies are introduced, the deluge of passive-data from these sensors will have tremendous impact on how marketers perceive, develop, and exploit new business opportunities.
10 Solo Published: Brown, T. (2017). Sensor-based entrepreneurship: A framework for developing new products and services. Business Horizons, 60(6), 819-830.
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Contribution Some researchers have explored crowdsourcing’s impact on ideation (i.e., new idea/production
creation) (e.g., Bogers et al., 2010; Howe, 2008; Leimeister et al., 2009). Paper 2 contributes and extends this literature. More specifically this research contributes in three ways. First, this research examines, analyzes and presents how a shift to Web 3.0 and the convergence of IoT's with big data has created a new source of product possibilities. Sensor-based entrepreneurship is leveraging IoT's to exploit new business opportunities by creating new goods and services. Second, this research highlights how this shift from active data input to passive, sensor-based input has opened the way for crowdsourcing as a potential business tool for many sensor-based services. The final contribution of this research was to develop a framework to aid sensor-based marketers and entrepreneurs in the crafting of new sensor-based products and services.
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1.4.3 Study 1: Leveraging user-generate content for demand-side strategy11 Introduction
The industrial context of this study is the hospitality industry. The hospitality industry has two
essential characteristics that make this study valuable. Firstly, with the related travel and tourism industries, the three businesses make up the world's largest industry in the service sector (Leiper, 2008b). Secondly, consumers in these industries have been at the forefront of creating and using comment and reviews (i.e., UGC) to make their purchasing decisions.
Furthermore, the demand-side view of strategy is under-researched, especially in the areas of tourism, travel, and hospitality (Harrington, Chathoth, Ottenbacher, & Altinay, 2014) when viewed against the predominant perspectives in strategic management (e.g., resource-based view, transaction-cost economics, positioning, and dynamic capabilities) are supply-side views (Priem, 2007).
Using the tools available today it is possible to scrape and mine the websites, blogs, forums, communities and other places customers gather and use content analysis techniques to extract meaning from the content (Krippendorff, 2013). Web 2.0 sources of qualitative data like blogs, forums, feedback (i.e., user-generated content (UGC)) can offer better research material than offline traditional qualitative diaries (Stafford & Gonier, 2007). As a result, using crowdsourced, user-generated content can be a productive marketing research approach to investigate how demand-side strategic decision-making can be supported in the travel, tourism, and hospitality industries.
Purpose
The primary purpose of this study is to examine and describe a technique to help decision-makers
and market researchers in exploring user-generated content to make strategic decisions. The second objective of this research is to explore how crowdsourcing can be used as an innovative tool to do market research. Researchers in the hospitality and tourism industry can take advantage of the tremendous amount of data, much of it created by customers, and available on the Internet, even though gathering data is a continuing challenge in empirical inquiries (Gerdes, Jr & Stringam, 2008). Firms should be able to improve their market research and strategic decision-making by using this user-generated content is a source of customer intelligence. Utilizing several of the sources of intelligence to develop a more intimate and wide-ranging view of the customer is the future of travel, tourism and hospitality marketing. This research endeavors to answer this question -
RQ3: How can user-generated content help firms make strategic decisions about new business
opportunities?
Findings This confirmed found that accessing consumer insight directly can be a valuable approach in
assisting marketers in making decisions, especially demand-side strategic decisions. It further found that crowdsourcing through the use of user-generated content can be a valuable technique in conducting market research.
11 Unpublished: A study conducted for this dissertation.
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Contribution Theoretical contributions of this study extend the demand-side strategy literature (Priem, 2007;
Priem, Li, & Carr, 2012), the crowdsourcing and market research literature (e.g., Behrend et al., 2011; Cechanowicz et al., 2013; Kleemann et al., 2008; Poynter, 2013), and the user-generation/social media literature (Daugherty et al., 2008; Kietzmann et al., 2011; Kleemann et al., 2008; Saxton et al., 2013; Zwass, 2010).
Additionally, there are some expectedmanagerial contributions as a result of the study aswell.Theanalysisofthedatainthisstudyisexpectedtoproducesomeactionablesuggestionsformanagersofsteakhouses,Italianrestaurants,andseafoodrestaurants.
Insightful recommendations can make the differences in building a competitive advantage in the highly competitive hospitality business (Kandampully, 2006). Secondly, the study should show managers that large amounts of UGC could be analyzed by using modern tools accessible to them.
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1.4.4 Study 2: An evolution of crowdsourcing: Implications for marketing12 Introduction
By using the collective effort of individual customers or groups of customers, the firm can expand
the resources to which it has access (Chesbrough & Crowther, 2006; Trompette, Chanal, & Pelissier, 2008). Crowdsourcing allows for capturing higher value than either the company or the consumer could create independently. Typically, market transactions including ones between buyer and seller create information as a by-product of the exchange. This personal and private information has generally in the past been shared between the buyer and seller. However, now as crowdsourcing technologies and techniques have advanced and evolved, this once private information is collected, merged, aggregated and crowdsourced by firms. Thus, this change in the crowdsourcing process is having an impact on consumer privacy, and at the same time, creating marketing opportunities for the firm as it enables them to gain greater customer insight. This study highlights the evolution of crowdsourcing and how this is affecting consumer privacy and marketing. Purpose
The purpose of this study is to first define crowdsourcing, looks at privacy and social exchange
theory; second it proposes three conceptual models of how crowdsourcing of personal information is evolving and how this is affecting the balance of power between the consumer and the firm. This research answers this question -
RQ4: How is the evolution of crowdsourcing impacting information externalities and consumer
privacy and how is this impacting marketing? Findings
This research develops a definition of crowdsourcing. It further develops and proposes three models
that represent the evolution of crowdsourcing in three different stages of development. These three stages are based on the amount and the level of sophistication of the crowdsourcing processing of information externalities that result from market transactions between the consumer and the firm. The stages are also based on how advanced the technology surrounding the processes are as well. Ultimately, through the crowdsourcing of personal and private information are advancing to the most advance stage whereby a digital twin of each consumer is created. This twin can be used in a predictive analytic process to forecast the thoughts, behaviors and future actions of each consumer. Contribution
This study attempts to contribute to theory and practice. Firstly, this study contributes by
developing a definition of crowdsourcing. Secondly, building on theories of social exchange (Emerson, 1976) and microeconomics, specifically the literature on externalities (Dijkstra, 2007; Hutchinson, 2016), the study posits three models of the crowdsourcing of personal information externalities. Next,
12 Unpublished: A study conducted for this dissertation.
35
the study utilizes these conceptualizations to describe and propose a new concept – the digital twin. Finally, this study gives some suggestions on how the crowdsourcing of a digital twin has a negative impact on consumers’ privacy, but at the same time creates marketing opportunities for firms.
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2 THE INDIVIDUAL PAPERS AND STUDIES13 Paper 1 - Seeking funding in order to sell: Crowdfunding as a marketing tool Paper 2 - Sensor-based entrepreneurship: A framework for developing new products and services Study 1 - Leveraging user-generated content for demand-side strategy Study 2 - An evolution of crowdsourcing: Implications for marketing
13 Each paper and each study are independent research projects that contain their own reference sections. The reference section, Chapter 4, contains the references for Chapters 1 and 3.
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2.1 Paper 1 - Seeking funding in order to sell: Crowdfunding as a marketing tool
Advertising, promotion and sales strategies come in all different forms. As rivalries develop and as
each firm endeavors to discover an edge, the utilization of crowdfunding platforms can be utilized as a special advantage.
Websites such as Kickstarter, Indiegogo, and FundedByMe have attracted much attention for their ability to enable startups and individuals to raise funds from ordinary people who contribute for many reasons. This phenomenon is called crowdfunding. More recently, established organizations have begun to use crowdfunding websites not only as a source of financing but also as marketing platforms. In this way, they have been able to ensure a ready market for their new offerings, with full sales pipelines, and to use the platforms as techniques to boost brand image and gain support for brand-related causes.
Paper 1 connects to the overarching research question as it is a conceptual paper exploring crowdfunding as an advertising, sales, promotion, and marketing channel.
Seeking funding in order to sell: Crowdfundingas a marketing tool
Terrence E. Brown a, Edward Boon b, Leyland F. Pitt c,*
a Lulea University of Technology, Lulea, Sweden & The Royal Institute of Technology, Stockholm, Swedenb School of Business and Technology, Webster University Geneva, Bellevue, SwitzerlandcBeedie School of Business, Simon Fraser University, Vancouver, Canada
1. Crowdfunding: From creativeprojects to a commercial launchplatform
Eric Migicovsky created the Pebble smartwatch,which perpetually remained on because it was madeof e-paper and had a battery life of up to 10 days.
While he was able to raise some funds for its devel-opment through a business incubator, he failed toraise sufficient venture capital. Migicovsky thenturned his hopes to the crowdfunding website Kick-starter (Netburn, 2012). The Pebble campaignlaunched in April 2012 with an initial fundraisingtarget of $100,000. Backers could pay $115 to pre-order a Pebble watch, which would later retail for$150. Pebble reached its funding goal within 2 hoursand went on to raise $10.3 million with almost70,000 individual pledges (Newman, 2012). Froma venture capital finance perspective, the Pebble’s
Business Horizons (2017) 60 , 189—195
Available online at www.sciencedirect.com
ScienceDirectwww.elsevier.com/locate/bushor
KEYWORDSCrowdfunding;Crowdsourcing;Branding strategy;Relationshipmarketing;Socialentrepreneurship
Abstract Websites such as Indiegogo and Kickstarter have attracted much attentionfor their ability to enable organizations and individuals to raise funds from ordinarypeople who contribute for a number of reasons. This phenomenon is called crowd-funding. Crowdfunding permits organizations and individuals to obtain investmentsthey otherwise might not receive from more traditional sources such as banks, angelinvestors, and stock markets. A number of now well-known startups had their originsin crowdfunding. More recently, established organizations have begun to use crowd-funding websites not only as a source of finance, but also as marketing platforms. Inthis way, they have been able to ensure a ready market for their new offerings, withfull sales pipelines, and to use the platforms as vehicles to boost brand image and gainsupport for brand-related causes. This adaptation of crowdfunding for marketingpurposes is not without its problems, however, and organizations would be welladvised to consider not only the opportunities these platforms provide, but also theirlimitations and risks.# 2016 Kelley School of Business, Indiana University. Published by Elsevier Inc. Allrights reserved.
* Corresponding authorE-mail addresses: [email protected] (T.E. Brown),
[email protected] (E. Boon), [email protected] (L.F. Pitt)
0007-6813/$ — see front matter # 2016 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.bushor.2016.11.004
38
appeal to funders on Kickstarter made it one of themost successful crowdfunding campaigns ever. Peb-ble was also a massive marketing triumph as it wenton to become a major consumer brand and sold overa million smartwatches by the end of 2014 (Seifert,2015).
The Pebble Kickstarter campaign often is used toillustrate the power of crowdfunding as a source offinancing for entrepreneurs who do not have accessto other sources. Crowdfunding allows founders toseek financing by attracting relatively small contri-butions from a large number of individuals using theinternet (Mollick, 2014). There are many crowd-funding websites, ranging from large sites such asKickstarter, Indiegogo, and Gofundme to niche web-sites such as Teespring (t-shirt crowdfunding), Do-norChoose.org (charity fundraising), and Patreon(fundraising for online content creators). In 2015,an industry report by Massolution predicted that theannual amount of crowdfunding would soon reach$34 billion, surpassing venture capital (Barnett,2015). The crowdfunding phenomenon also starteda wave of academic research on various phenomena,including its dynamics and economics (Agrawal,Catalini, & Goldfarb, 2014; Mollick, 2014), key suc-cess factors for entrepreneurs (Belleflamme, Lam-bert, & Schweinbacher, 2014; Etter, Grossglauser, &Thiran, 2013), and the motivations of project back-ers (Gerber & Hui, 2013).
If the original Pebble campaign showcased thepotential of crowdfunding, it was success of thesecond campaign that indicated how crowdfundingmight develop in the future. In February 2015,Pebble launched an initiative for its new model,the Pebble Time. This second campaign reachedits fundraising goal of $500,000 in only 17 minutes.By the end of the campaign, Pebble had raised $20.3million from more than 75,000 backers (Lapowsky,2015). Although Pebble CEO Migicovsky averred thatthe company’s return to Kickstarter was driven bythe desire to ‘‘work directly with you, the commu-nity that got us here’’ (Lapowsky, 2015), whetherthe firm really needed crowdfunding the secondtime around is questionable. By the end of 2014,Pebble had sold over a million smartwatches (Sei-fert, 2015), which were then readily available atmajor retailers such as Best Buy and Amazon. Thecompany and its brand had been well established.Even if Pebble Technology Corporation lacked read-ily available capital to develop a new smartwatchmodel, it is unlikely that it would have had problemsobtaining funding elsewhere. Venture capital andpublic stock issues would likely have been attractedto the fact that Pebble was now a leading brand inthe smartwatch space, with a successful offeringand a large fan base.
Wired Magazine suggested that Pebble’s secondcampaign was about marketing: Kickstarter gavePebble access to its community to promote anddistribute its product and guarantee a sales pipeline(Lapowsky, 2015). Advertising Age claimed thatPebble’s primary purpose entailed distinguishingitself from main smartwatch competitors Appleand Samsung by selling its watches through a differ-ent channel, and that Kickstarter played the samerole Direct Response Television (DRTV) had in thepast. In other words, Kickstarter was changing froma place to fund creative projects into a commerciallaunch platform for established firms (Knox, 2015).
The success of this second crowdfunding cam-paign raised the question: Was Pebble uniquelypositioned to launch its new smartphone throughKickstarter because of its history and standing with-in the community, or can other firms also use crowd-funding websites as a marketing channel? Thecrowdfunding sites themselves seem to believe so;in reaction to the success of the Pebble Time,Kickstarter’s CEO stated that ‘‘the real power andutility is not in money; it is in community anddistribution’’ (Lapowsky, 2015). Kickstarter’s com-petitor, Indiegogo, has also begun courting largeorganizations and has started a new activity calledenterprise crowdfunding (Kastrekas, 2016). Howev-er, given that the crowdfunding industry is coming ofage and has become more competitive, it is notsurprising that crowdfunding websites welcome thisnew revenue stream (Lang, 2016).
In this article, we examine the extent to whichcrowdfunding websites are accessible to organiza-tions as a marketing channel and, if so, what rolethey can play. We do this by (1) considering a numberof campaigns that have already been carried out byestablished firms, (2) determining what motivatedthese firms to engage with a crowdfunding communi-ty, and (3) how they structured their campaigns.Based on these examples, we evaluate the objectivesfirms may have when starting a campaign and theconstraints they may encounter limiting their choiceof approach. To make our findings applicable to mar-keting managers, we present a decision tree to helpthem make the right choices when working with acrowdfunding website. Finally, we discuss risks asso-ciated with creating a crowdfunding campaign andlook at how the involvement of larger organizationsmay affect the crowdfunding industry over time.
2. Crowdfunding as a form ofmarketing
Gerber and Hui (2013) identified a number of rea-sons why company founders might launch a project
190 T.E. Brown et al.
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on a crowdfunding website. Naturally, the mostsignificant motivation is fundraising. Crowdfund-ing gives entree to financial support for peopleand organizations that do not have easy access tobanks, angel investors, and venture capitalists.The process of founding and launching a crowd-funding campaign is also less time intensive thanother options, as no legal applications or approvalprocedures are involved. However, Gerber and Huiidentified a number of founders who consideredthe marketing aspect of crowdsourcing just asimportant as–—and in some cases, more importantthan–—raising funds. Starting a crowdfunding proj-ect introduces the product or cause to a highnumber of people and allows founders to formrelationships with their backers that could be usedto obtain feedback or to generate returning cus-tomers. Other motivations that were identifiedinclude the need to maintain control (e.g., byself-publishing a book) and the desire to learnfundraising skills.
Moisseyev (2013) suggested that for small firms,crowdfunding can be considered a marketing tooland that it can be used in three ways. First, a projectcan be used as a research tool to assess the quality ofcreative ideas. By tracking the number of backersand the feedback from social media, organizationscan compare their product ideas with those ofcompetitors. Second, crowdfunding can be usedto promote a new product, not only reaching peoplewho back the project but also the entire crowdfund-ing community. Finally, crowdfunding can be used asa direct sales channel by rewarding backers with thefirst samples or versions of offerings and ensuring areadily available sales pipeline.
A well-known example of a successful crowd-funding project run by an established company isthat of FirstBuild, a subsidiary of General Electric.In July 2015, FirstBuild launched a campaign onIndiegogo for a countertop nugget ice maker, theOpal. The project was very successful. While First-Build’s funding goal was only $150,000, within amonth the firm had raised nearly $2.8 million fromover 6,000 backers (Cowley, 2016). In an interview,FirstBuild Director Naturajan Venkatakrishnanhighlighted the importance of the crowdfundingeffort: ‘‘The benefits of launching a new productlike Opal using the Indiegogo crowdfunding plat-form allows us immediate feedback on market ac-ceptance’’ (Freeman, 2015). Following its successon Indiegogo, FirstBuild soon made the Opal avail-able for purchase in regular stores (Cowley, 2016).In other words, General Electric used its Indiegogocrowdfunding project as a test market to evaluatethe potential demand for its product before com-mitting to wide-scale distribution.
In contrast, a different approach was taken by toyand game manufacturer Hasbro. Hasbro workedwith Indiegogo to organize a contest wherein thecrowdfunding community was asked to submit gameideas. The competition received 500 submissionsand the game Irresponsibility was selected as thewinner. Hasbro then started a crowdfunding cam-paign to sell the game, which raised $10,487 from249 backers (Kastrekas, 2016). Although this ex-ceeded the fundraising goal of $3,500 by 300%, itis possible that the low number of backers is thereason why the game does not yet appear to beavailable through regular retail channels at the timeof this writing. Essentially, Hasbro used Indiegogo asa form of crowdsourcing (Afuah & Tucci, 2012, 2013;Prpic, Shukla, Kietzmann, & McCarthy, 2015) ratherthan crowdfunding. The company’s objectives wereto generate ideas from a crowd of enthusiasts andthen to use that crowd as a means of judging productviability.
A variant of regular (i.e., donation-based or re-ward-based) crowdfunding is called equity crowd-funding, in which project backers are compensatedwith financial returns such as equity, equity-likeshares, or dividends (Bretschneider, Knaub, &Wieck, 2014). One of many examples is CamdenTown Brewery, a London-based beer company thatraised over £2.75 million through the equity crowd-funding website Crowdcube, allowing the businessto expand its production capacity and to export itsCamden Lager beer outside of the U.K. (Lang, 2016).In December 2015, global drinks manufacturerAnheuser-Busch InBev purchased Camden TownBrewery. Although Anheuser-Busch InBev did notdisclose how much it paid for Camden, it wasestimated that shareholders received a return oninvestment of about 70% (Davies, 2015).
A final example showcasing another approachinvolves the beer brand Shock Top, owned byAnheuser-Busch InBev. Shock Top ran a contestnamed Shock the Drought on Indiegogo, in which itasked the crowdfunding community to come up withinnovative ideas to address the drought in Californiain the summer of 2015. From the ideas submitted,Anheuser-Busch InBev selected three projects:Drop-a-Brick 2.0, a brick that can be placed intotoilet tanks to reduce water consumption; EvaDrop,a shower head with a sensor and timer to ensure nowater is wasted; and the Droppler Water Monitor, avisual and interactive water gauge. Each of theseprojects was supported financially and raised aware-ness (Kastrekas, 2016). Since the Indiegogo projectby Shock Top was not linked to the launch of anew product, the campaign can be considered aform of branding, as it associated the brand withan important cause.
Seeking funding in order to sell: Crowdfunding as a marketing tool 191
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3. Match company objective withcrowdfunding approach
It is evident from the aforementioned examples thatcrowdfunding websites can serve as valuable mar-keting channels, and that there are various ap-proaches firms can take to engage with them. Thestarting point for selecting the right approach en-tails determining what the company wishes toachieve. Table 1 presents the primary objectivesof each of the crowdfunding projects that werediscussed in the previous section. However, it shouldbe clear that although these firms may have hadcertain objectives, they may have gained otherbenefits as well. The table therefore shows boththe primary objective (marked with X) and theadditional benefits (marked with +).
The objective of the first Pebble campaign was toraise capital after its founder was unable to attractfunding elsewhere. This led to some additional,unexpected benefits in the form of product promo-tion and direct sales. Since the product had alreadybeen fully designed, the company was not lookingfor feedback or ideas. In contrast, the second Pebblecampaign deliberately entailed the purposes of mar-keting, registering direct sales, and promoting thenew smartwatch.
As stated by Naturajan Venkatakrishnan of First-Build, the primary objective of the Opal ice machinecampaign was to obtain feedback from the marketbefore mass production. As the project appeared tobe successful, it also helped to promote the productand generate sales.
Hasbro’s initiative was entirely different thanFirstBuild’s, in that Hasbro did not have a productwhen it started engaging with the community ofIndiegogo. The project essentially consisted of twoparts: the company first organized a crowdsourcingcontest to generate game ideas (Afuah & Tucci,2012, 2013) and then crowdfunded the winninggame to evaluate its market potential.
Although the Shock Top beer brand also created acontest, it was not related to the company product,
but rather to a cause: finding solutions to the Cal-ifornia drought. It is quite common for brands tobuild their identities around a certain cause orvalue. For example, Unilever’s Dove brand was re-invigorated in the early 2000s through its Campaignfor Real Beauty, which challenged media-drivendefinitions of beauty as seen on runways in favorof individuals’ confidence and well-being as mea-sures of ‘real beauty’ (Deighton, 2007). The waythat Shock Top set up its contest with Indiegogoillustrates how crowdfunding websites can be usedto strengthen a brand’s identity. Finally, CamdenTown Brewery created an equity-based crowdfund-ing campaign motivated by the sole purpose ofraising capital, which it then used to expand itsproduction facilities.
4. Select a crowdfunding approachbased on objectives and constraints
While firms have a range of options regarding how toengage with crowdfunding websites, the examinedcampaigns show that not all of these options areavailable to all firms. The selected approach there-fore depends not only on the objective of the crowd-funding campaign, but also on certain constraints.First, if the firm wants to organize a reward-basedcrowdfunding campaign, it needs to offer a physicalproduct that is a good investment opportunity andfor which potential backers are willing to prepay.Certain product categories such as consumer tech-nology and design do well on crowdfunding sites,which is why both Pebble watches and the FirstBuildOpal ice maker were successful.
In contrast, both Camden Town Brewery andShock Top sell beer, and neither company was look-ing for capital to develop a new product; therefore,reward-based campaigns were not a possibility forthese firms. Since the objective of Camden Townwas to attract funding to increase its productioncapacity, the company’s only option was to offerequity in exchange for funding. The objective of
Table 1. Firm objectives of crowdfunding campaigns
Campaign Raisecapital
Promoteproduct
Get marketfeedback
Directsales
Crowdsourcingideas
Branding
Pebble 1 x + +
Pebble 2 + + x
FirstBuild Opal + x +
Hasbro + x
Shock Top x
Camden Town xx = primary objective; + = additional benefits
192 T.E. Brown et al.
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Shock Top was to promote its brand and associate itwith solutions to the California drought; therefore,the company chose to back projects that would helpreduce water consumption.
Besides the availability of an attractive physicalproduct, another constraint relates to readiness ofthe product. Both Pebble and FirstBuild had finisheddesigns and did not look for input from the crowd-funding community. In fact, FirstBuild director Ven-katakrishnan was surprised to learn that backers ofthe Opal crowdfunding campaign not only wanted toreceive regular updates about the delivery date, butalso wanted to be involved in the ice maker’s design(Cowley, 2016). Conversely, Hasbro did not have afinished product and was simply looking for ideas.Therefore, Hasbro first organized a contest and thencreated a campaign to find backers for the winninggame.
Figure 1 shows a decision tree that summarizesthe discussion in this section and the approachesfirms can take to engage with crowdfunding web-sites as based on their objectives and constraints. Ifthe company has a physical product that is fullydeveloped, it can create a reward-based crowd-funding campaign, offering the product as a rewardto backers once it is ready. This is the approach thatwas chosen by Pebble (both campaigns) and First-Build. It should be noted that although these cam-paigns may have had different objectives (raisefunds vs. generate direct sales vs. obtain marketfeedback), the objectives were satisfied with similarapproaches. If the company offers a physical prod-uct that is appealing to the crowdfunding commu-nity but does not yet have its product ready, thecompany can undertake a two-step crowdsourcingand funding approach; here, the firm first engageswith the community to generate ideas and then usesthe interest generated to attract financial backersand to sell products. This combo approach wasselected by Hasbro when it organized a contest tocome up with game ideas.
If the company does not have a physical productbut wants to raise capital through a crowdfundingwebsite, it may organize an equity-based crowd-funding campaign; this was the approach chosen
by Camden Town Brewery. Finally, if a company doesnot have a physical product but wants to engage withthe crowdfunding community to promote its brand,that company may opt for a branding approach byassociating itself with other crowdfunding projects(e.g., to support a certain cause or value).
5. Consider crowdfunding strategicallyand realistically
As this article demonstrates, crowdfunding websitescan be used as marketing tools not just by compa-nies with a history of crowdfunding (e.g., Pebble)but also other established firms. Campaigns can beorganized to cover a range of different objectives,including raising funds, registering direct sales, andgenerating product ideas.
However, it is clear that not all possible ap-proaches are available to all firms. Two constraints,as discussed in the previous section, play a role.First, some firms do not have a product for whichconsumers are willing to prepay before delivery;these firms have fewer options, but can still gener-ate capital via equity-based fundraising. The ShockTop campaign illustrates another approach that suchfirms can take: by associating the brand with othercrowdfunding campaigns to support a particularcause. The second identified constraint relates tofirms that do not have a fully developed productavailable. These firms can first use the crowdfundingcommunity to create product ideas and then devel-op a campaign to generate financing and presales forthe community’s best ideas.
Firms that plan to interact with crowdfundingplatforms must be willing to dedicate a lot of effort,not just toward creating a project that appeals topotential backers but also to providing these backerswith product fundraising and development updates.In addition, there are reputational risks related tocrowdfunding. For example, a project could fail toreach its crowdfunding goals, or the final productcould be delayed and/or not meet backers’ expec-tations. The latter occurred with the video gameModus, which was delivered two years late andlacked many of its promised features. This led toserious reputational damage for its creator, gamedesigner Peter Molyneux, and widespread criticismof Kickstarter for allowing firms to get away with thissans financial or legal repercussions (Orland, 2015).
Established firms’ increasing interest in usingcrowdfunding websites may have a profound im-pact on the crowdfunding industry. While thisdevelopment is beneficial for crowdfunding web-sites in that it provides them with an additionalsource of revenue and possible media publicity, it
Figure 1. Decision tree for crowdfunding approaches
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may pose a problem for the many individuals andsmall organizations that rely on them. Crowdfund-ing might be the only way these constituents areable to raise sufficient funds to start or grow theiractivities. Part of the problem is that crowdfund-ing websites do not exactly offer level playingfields. Past research has shown that attracting alarge number of backers in the first hours of acrowdfunding project is vital for its success (Etteret al., 2013). This means that large organizationscould use their advertising budgets to attractearly backers and gain momentum, thereby over-shadowing smaller project creators. Large orga-nizations may also be able to build up experiencethrough multiple crowdfunding projects, whichwill increase their effectiveness over time. Thisoption might not be available to others.
This begs the question: How does the increasinginvolvement of established organizations in crowd-funding affect potential investors? Although someindividuals who invest in crowdfunding projects doso merely to collect rewards, others participate be-cause they want to support a small business owner oran individual with an interesting product idea (Gerber& Hui, 2013). It is questionable whether this lattergroup will be eager to support large organizationsin the same way. This could explain why Anheuser-Busch InBev chose the relatively small brand ShockTop rather than a larger brand like Budweiser to workwith Indiegogo, and why the General Electric subsidi-ary that produced the Opal ice maker was namedFirstBuild rather than a more recognizable affiliatetitle like GE Home Cooling. These examples supportthe notion that potential backers are not as eager tosupport a large organization, even if they show aninterest in the offered product.
Nevertheless, it is likely that the pressure oncrowdfunding websites to find new revenue sourcesand the eagerness of firms to enter this new channelwill change the crowdfunding industry over time. Itis vital that marketing managers be aware crowd-funding still largely consists of early adopters oftechnology and design, activists who support causesand local projects, and enthusiasts who are willingto help budding artists. These are not the kind ofconsumers who will buy anything that is offered tothem; they will support the projects and offeringsthey like, irrespective of the firm and crowdfundingwebsite that offers them.
References
Afuah, A., & Tucci, C. L. (2012). Crowdsourcing as a solutionto distant search. Academy of Management Review, 37(3),355—375.
Afuah, A., & Tucci, C. L. (2013). Value capture and crowdsourcing.Academy of Management Review, 38(3), 457—460.
Agrawal, A. K., Catalini, C., & Goldfarb, A. (2014). Some simpleeconomics of crowdfunding. NBER Innovation Policy and theEconomy, 14(1), 63—97.
Barnett, C. (2015, June 9). Trends show crowdfunding to surpassVC in 2016. Forbes. Retrieved March 31, 2016, from http://www.forbes.com/sites/chancebarnett/2015/06/09/trends-show-crowdfunding-to-surpass-vc-in-2016/#5ee0b9c8444b
Belleflamme, P., Lambert, T., & Schwienbacher, A. (2014). Crowd-funding: Tapping the right crowd. Journal of Business Ventur-ing, 29(5), 585—609.
Bretschneider, U., Knaub, K., & Wieck, E. (2014, June 9—11).Motivations for crowdfunding: What drives the crowd to investin start-ups? In Proceedings of the European Conference onInformation Systems (pp. 1—11). Tel Aviv: ECIS.
Cowley, S. (2016, January 6). Global brands, taking cue fromtinkerers, explore crowdfunding. The New YorkTimes. Retrieved March 31, 2016, from http://www.nytimes.com/2016/01/07/business/global-brands-taking-cue-from-tinkerers-explore-crowdfunding.html?_r=0
Davies, R. (2015, December 21). Camden Town Brewery sold toworld’s biggest drinks company. The Guardian. RetrievedMarch 31, 2016, from http://www.theguardian.com/business/2015/dec/21/camden-town-brewery-sold-inbev-worlds-biggest-drinks-company
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Gerber, E. M., & Hui, J. (2013). Crowdfunding: Motivations anddeterrents for participation. ACM Transactions on Computer-Human Interaction, 20(6) 34:1—34:32.
Kastrekas, J. (2016, January 6). Indiegogo wants huge companiesto crowdfund their next big projects. The Verge. RetrievedMarch 31, 2016, from http://www.theverge.com/2016/1/6/10691100/indiegogo-enterprise-crowdfunding-announced-ces-2016
Knox, D. (2015, March 5). What marketers can learn from PebbleTime’s Kickstarter launch. Advertising Age. Retrieved March31, 2016, from http://adage.com/article/digitalnext/brands-learn-pebble-time-s-kickstarter-launch/297415/
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Lapowsky, I. (2015, February 24). Pebble’s insane success provesthat Kickstarter is now a marketing tool. Wired. RetrievedMarch 31, 2016, from http://www.wired.com/2015/02/pebble-time-kickstarter/
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2.2 Paper 2 - Sensor-based entrepreneurship: A framework for developing new products and services
As the Internet of Things (IoT’s) begins to dominate the technology landscape, there will be new
products and services that will become technically and financially feasible. Internet technologies and advancements in social interaction tools have led to an increase in the use of the crowd as a provider of business solutions. Yet, we have seen a mere fraction of the possibilities of crowdsourcing technologies. This is because most of the development, discussion, and research on crowdsourcing has focused on active-input crowdsourcing.
However, the real transformative pressure will come from passive sources of data generated primarily by developing and growing sensor technologies. This next generation of crowdsourcing will be a game changer for new product development. As a result of this explosion, the variety of new opportunities/products will swell as entrepreneurs become more aware of technologies merging - such as the combination of crowdsourcing, sensors, and big data into a new type of entrepreneurship: sensor-based entrepreneurship.
Paper 2 connects to the overarching research question as it is conceptual paper investigating how crowdsourcing can be used in the new product development process.
Sensor-based entrepreneurship: A frameworkfor developing new products and services
Terrence E. Brown a,b
aThe Royal Institute of Technology, 100 40 Stockholm, Swedenb Luleå University of Technology, 971 87 Luleå, Sweden
1. Sensors lead to new types ofproducts
Imagine a scenario in which you have signed up forcar insurance. When the paperwork is completed,your agent places a small box half the size of a pack
of cigarettes under your dashboard. You think noth-ing of it and frankly forget about it soon after youleave his office. Six months later, you get a letterfrom your agent. In the envelope is a refund checkfor several hundred dollars. It seems that you havebeen rewarded because you have been driving safe-ly according to the insurance company’s standards.But how did they know how safe you have been? Itturns out that the small box is a telematics devicefull of sensors that capture and transmit data
Business Horizons (2017) 60, 819—830
Available online at www.sciencedirect.com
ScienceDirectwww.elsevier.com/locate/bushor
KEYWORDSInternet of Things;Crowdsourcing;Crowdsourcinginnovation;Big data;Big data strategy;Passive data collection
Abstract As the Internet of Things (IoT) begins to dominate the technologylandscape, there will be new products and services that will become technicallyand financially feasible. Internet technologies and advancements in social interac-tion tools have led to an increase in the use of the crowd as a provider of businesssolutions. Yet, we have seen a mere fraction of the possibilities of crowdsourcingtechnologies. This is because most of the development, discussion, and researcharound crowdsourcing has focused on active-input crowdsourcing. However, the realtransformative pressure will come from passive sources of data generated primarilyby developing and growing sensor technologies. This next generation of crowdsour-cing will be a game changer for entrepreneurial opportunities. As crowdsourcingsystems proliferate, more input will be acquired from sensors, artificial intelligence,bots, and other devices. As a result of this explosion, the variety of product andservice opportunities will swell as entrepreneurs become more aware of technologiesmerging–—such as the combination of crowdsourcing, sensors, and big data into a newtype of entrepreneurship: sensor-based entrepreneurship. The purpose of thisresearch is to contribute by (1) clarifying the next generation of crowdsourcingand (2) developing and presenting a framework to help sensor-based entrepreneursplan, develop, and map their new products and services.# 2017 Kelley School of Business, Indiana University. Published by Elsevier Inc. Allrights reserved.
E-mail address: [email protected]
0007-6813/$ — see front matter # 2017 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.bushor.2017.07.008
46
deemed relevant to whether you have been drivingsafely. Your insurer has created a new, valuableservice by collecting your data and customizing aservice for you. Progressive Insurance, Allstate, andmany other insurers are offering similar servicesalready.
Six months later, you get another letter from yourcar insurance company. Knowing what you received6 months ago, you quickly open the envelope.However, to your surprise, instead of another re-fund check, you read that your insurance rates arebeing increased. You are perplexed because youthink you have continued to be a safe driver. Itseems that you have been tagged for higher ratesbecause the insurance company has combined thedata from all its clients in the area and deemed yourneighborhood an unsafe place to own a car. Yourcreative insurance company has again created anew service, but in this case it has aggregatedthe data at a higher level and crowdsourced anew service.
Now, think about the smart home. For many, thismay not be so challenging since the smart home hasbeen a reality (in part) since the introduction ofthermostats. However, as the number of sensors inthe home and in-home appliances balloon, the pos-sibilities for new tailored and customized serviceswill also increase. For example, even today we cancontrol our lighting, heating, cooling, security, lawnmaintenance, cleaning, and music digitally andremotely. Most recently, cooking appliances havebeen brought online and connected. Smart refrig-erators are joining smart TVs in the home as well. Allof this exists, and we are still at an early stage ofproducts and services that use our own data to makeour lives more convenient in some ways.
What if your mobile device could fight diseaseand illness? The TrackYourTinnitus project does justthat (Pryss, Reichert, Langguth, & Schlee, 2015).Tinnitus is a ringing in the ear condition that mayaffect up to one in five people. Currently, there areno effective therapies. The TrackYourTinnitus proj-ect uses a mobile phone app and sensors to helpassess ambient background noise. By combining thedata, the app creates longitudinal data sets thataggregate the individual’s demographic and clinicalcharacteristics together with the user’s response tospecific therapeutic interventions. As a result, theapp facilitates evidence-based treatment sugges-tions for individual patients. In other words, thistechnology allows for clinical trials (i.e., evidence-based medicine) at a low cost that leads to individ-ual treatments.
Think of the possible healthcare and medicalsolutions that could be created using this type ofsensor technology to acquire and analyze huge
datasets and conduct clinical trials. What possibleillnesses or diseases could be treated using thesecutting-edge diagnostic and therapeutic manage-ment techniques? Advancements in technology–—including sensor technology, big data, and band-width–—accompanied by creative business models,are enabling entrepreneurs to sense, collect, ana-lyze, and aggregate data in previously unimaginedways to produce value-adding services for consum-ers and industrial customers. Let the revolutionbegin!
As the Internet of Things (IoT) begins to dominatethe technology landscape, there will be many newproducts and services that will become technicallyand financially feasible. Recently, internet technol-ogies and advances in social interaction toolshave led to a sharp increase in the use of the crowdas a provider of business solutions. For example,the use of the crowd and crowdsourcing in servicessuch as Wikipedia, Challenge.gov, reCAPTCHA,InnoCentive, Waze, Threadless, Kickstarter, eYeka,TopCoder, CrowdFlower, MTurk, and Kaggle haveblossomed. Yet, we have seen only the tip of theiceberg in terms of possibilities of crowdsourcingtechnologies. This is because most of the develop-ment, discussion, and research around crowdsourc-ing have focused on active-input crowdsourcing.However, the real transformative pressure willcome from passive sources of data generatedprimarily by the development and growth of sensortechnology.
1.1. Internet of Things
According to McKinsey (Bauer, Patel, & Veira, 2014),IoT:
Refers to the networking of physical objectsthrough the use of embedded sensors, actua-tors, and other devices that can collect ortransmit information about the objects. Thedata amassed from these devices can then beanalyzed to optimize products, services, andoperations.
Put another way, IoT is the interworking of devices(i.e., things that are connected remotely for dataexchange and analysis). When I refer to sensors,I am speaking broadly about GPS, Bluetooth, RFID,and other related devices that can provide accessto real-time information. The expected and de-sired result is more efficient problem solving andvalue creation. As the phenomenon of IoT growsquickly, and subsequently is being referred to asthe third wave in internet development, it is im-possible to predict its eventual size and impactaccurately. However, given this large but uncertain
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impact, it seems vital that entrepreneurs andmanagers understand the IoT as well as the poten-tial pitfalls and opportunities these developmentsmay bring.
1.2. Big data
IoT, including its sensors and actuators and otherdata-collecting devices, will produce a tremendousamount of data that needs to be stored and ana-lyzed. For example, calculations indicate that theflight of a single Boeing 787 produces 40 terabytesof data an hour, while certain mining operationswill produce 2.4 terabytes of data per minute.The entire IoT system is predicted to generate403 zettabytes of data a year by 2018, up from113.4 zettabytes in 2013 (Lima, 2015). To putthat into perspective, 1 zettabyte is equivalent to1 billion terabytes or 1 trillion gigabytes. Real-timeprocessing has dramatically changed the way enter-prises operate and significantly reduced the timeit takes for decision makers to get access to opera-tional data, analyze it, and make more astutedecisions in real time. The entire IoT ecosystemwill hinge on big data’s ability to store and analyzethis information.
While it may be obvious that IoTwill likely changeadvertising as marketers and publishers have newchannels to deliver personalized messages andcontent, it may be less obvious that these sametechnological advances will change the way newproducts and services are conceived, initiated, de-veloped, and produced. Entrepreneurs and market-ers will produce new products and services usingdata from passive sources. As a result of this explo-sion, the variety and volume of product and serviceopportunities will swell as managers and entrepre-neurs become more aware of the merging of tech-nologies like that of crowdsourcing, sensors, and bigdata. As advancements continue and once firms andentrepreneurs figure out how to pool and amalgam-ate the data, a whole new set of products andservices will be developed.
Crowdsourcing as a concept can be many things,including a business strategy, an operational tech-nique, or a business model. Whitla (2009) discussedthe use of crowdsourcing in the product develop-ment process not only as a source of crowd-generated ideas and feedback, but also as abusiness model and source of user-generated con-tent (UGC) that could be used for further productdevelopment or directly for sale. To be sustainablein the long run, IoT development must be driven byprofitable business models. Therefore, it could beimportant to look at crowdsourcing as a possibleway to produce real business opportunities and
services in this new era of sensor-based entre-preneurship.
1.3. Motivation for the research
The market research group BIS Research (2017)estimates that by 2025 there will be over 100 billionconnected devices, sensors, and actuators thatmake up IoT. According to IT and business profes-sionals surveyed by Gartner, 43% of enterprisesplanned to use IoT by the end of 2016 alone(Kawamoto, 2016). Also according to Gartner’sresearch, the primary reason the number is noteven higher is that firms have not yet found theright business-related reasons to justify largeinvestments. In other words, businesses have notyet found the right new products and servicesto develop. Secondly, they have not yet found theright business models to make the finances work.Thus, as digital technology becomes increasinglyimportant to reach business objectives, businessmodels become increasingly important for contin-ued digital technology progress. The combinationof digital technology change and business modelinnovation is causing entire industries to be created,destroyed, and restructured (Nylén & Holmström,2015). It is for this reason we must understand betterthe processes underlying this digital technologytransformation and the business opportunities driv-ing it and emerging from it.
1.4. Purpose of this research
The purpose of this research is fourfold. First, as wemove from Web 2.0 to Web 3.0 there will be manynew possible products and services created. Like-wise, there will be many possible business oppor-tunities for entrepreneurs in this new area ofsensor-based entrepreneurship. Second, with thehuge number of new opportunities, there will bemassive financial investments required. Accordingto Forbes, IDC predicts the worldwide market forIoT solutions is expected to grow from $2.71 trillionin 2015 to a mindboggling $7.06 trillion in 2020(Columbus, 2016) and $10 trillion by 2025 (BISResearch, 2017). Viable business models will haveto accompany these opportunities. My researchpresents crowdsourcing as one of the likely businessmodels that can bring viability to these new sensor-based opportunities. Third, advances in IoT tech-nologies, big data, and sensors led to the currentgeneration in crowdsourcing techniques and appli-cations built on passive data rather than activedata; I aim to clarify four next-generation typesof crowdsourcing. Fourth, by looking at two dimen-sions of this sensor-sourced and passive data (level
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of aggregation and location/ownership of the sen-sors), a framework can be created to act as anopportunity development tool to assist entrepre-neurs in creating new products and services. Thisarticle describes this framework and tool.
2. The internet and business models
According to Teece (2010, p. 183), “every newproduct development effort should be coupled withthe development of a business model which definesits ‘go to market’ and ‘capturing value’ strategies.”The development of new products and servicesshould be intimately related to the business modelextracting or capturing the value of that newproduct or service. As technology progresses, eachnew internet phase leads to new digital businessmodel patterns, which is appropriate becausehaving sound (i.e., profitable) business logics iskey for sustained technological development.
If you plot new business model patterns andchanges in web development over time, the follow-ing image emerges as shown in Figure 1 (Fleisch,Weinberger, & Wortmann, 2014). Currently, itappears that we are at a transition point betweenWeb 2.0 and Web 3.0. We are moving from an era inwhich users create value to an era in which sensorscreate value.
Business opportunities occur during times oftransition and change. I expect this transition willbe no different; there will be significant businessopportunities at the nexus of user-added value andsensor-added value. Additionally, there is a second,related transition occurring. With the multitude ofsensors being deployed during the move to IoT, we
will also be moving from an era of active inputcrowdsourcing to one of passive input crowdsourc-ing. The expected deluge of passive data sensed,collected, analyzed, and stored will create thou-sands of opportunities for new products and servicesfor attentive entrepreneurs and entrepreneurialfirms. So far in the Web 2.0 environment, crowd-sourcing services and applications have been creat-ed and successfully used in wide and diverse areas.1
Imagine what the shift to Web 3.0, passive data, andsensor-based entrepreneurship could bring.
3. Leveraging the crowd
Although the modern concept of crowdsourcing–—especially its current name–—has been attributed toHowe (2006), crowdsourcing as a practice can betraced back at least a few hundred years. In 1714,the British government crowdsourced a solution toThe Longitude Problem, which caused the deaths ofthousands of sailors each year. Parliament passedthe Longitude Act of 1714, which offered £20,000(approximately £5 million today) for a method todetermine longitude within 30 miles. It was subse-quently solved by an uneducated, 21-year-old,
Figure 1. Adapted internet waves and the digital business model patterns to which they gave rise
1 These areas include: citizen science, the military, architec-ture, mapping and location, film production, marketing, humanintelligent tasks, journalism, medical diagnosis, medical re-search, medical testing, translation/proofreading/transcrip-tion, surveillance, distributed computing, skills assessment,business development, healthcare, creative services, simula-tion, gaming, genealogy, feedback, music, mining, governmentoversight, data prediction, legal advice, business solutions, ed-ucation, and venture capital.
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Scottish carpenter and clockmaker named JohnHarrison (Spencer, 2012). A little more than150 years later, the Oxford English Dictionary useda crowd of 800 readers to catalog words in 1884(Blog Blowfish, 2015). In 1936, Toyota used crowd-sourcing in developing its logo (Pratap, 2014). Theidea of using the crowd to solve problems is not new.
3.1. Concepts related to crowdsourcing
Although crowdsourcing as a research topic is rela-tively new, it is related and connected to a numberof longstanding concepts and research streams.While there are many entrepreneurial and innova-tive techniques and methods, one of the oldest isthat of using the crowd, collective intelligence, andcollective action. In other words, by using thecollective effort of a group of individuals, the firmcan increase or expand the resources to whichit has access. Crowdsourcing has conceptual andtheoretical links to a number of domains includingopen innovation as pioneered by Chesbrough(2003), outsourcing (Schenk & Guittard, 2011),open source (Howe, 2006), collective intelligence(Surowiecki, 2004), social web or Web 2.0 (Saxton,Oh, & Kishore, 2013), social media (Kietzmann,Hermkens, McCarthy, & Silvestre, 2011), andco-creation (Zwass, 2010).
There is no doubt that rapid advances in infor-mation technology and the growth of Web 2.0 havedramatically increased the use of open and morecollaborative innovation processes, both at the in-dividual and organizational levels. This has led tonew and different styles of new product creationand development. As we move to Web 3.0, there arenew sets of entrepreneurial opportunities created.However, these phenomena combine with big datapouring out of the millions of sensors soon to bedeployed and create what research has namedsensor-based entrepreneurship. Sensor-based en-trepreneurship can be defined as a sub-categoryof digital entrepreneurship.
3.2. Traditional crowdsourcing
As stated above, recent advances in both technolo-gy and social behavior have increased the use of thecrowd in business activities. The use of the crowdand crowdsourcing in services have blossomed dur-ing the Web 2.0 era. Standalone firms have inde-pendently created crowdsourcing applications, ashave corporate giants such as Ford, Dell, Pepsi, IBM,and Unilever. Different types of crowdsourcing ac-tivities have been described and categorized inalternative ways. One of the more widely usedcategorizations is the typology devised by Prpi!c,
Shukla, Kietzmann, and McCarthy (2015), who pro-posed a typology that divided crowdsourcing appli-cations into four major categories: crowd voting,micro-task crowdsourcing, idea crowdsourcing, andsolution crowdsourcing. In all of these cases, indi-viduals actively input data into the crowdsourcingplatform. This is not the case in some of the newcrowdsourcing techniques.
4. New generation crowdsourcing
In this section, I present the four next-generationcrowdsourcing techniques: situated crowdsourcing,spatial crowdsourcing, crowdsensing, and crowd-sourcing through wearable devices. I then introducea table that systematically categorizes these newcrowdsourcing techniques by types of data input(active versus passive) and task subject (individualversus environment) (Prpi!c, 2016; Prpi!c, Brown, &Kietzmann, 2016).
4.1. Situated crowdsourcing
Situated crowdsourcing, using situated technolo-gies, received little attention in crowdsourcing re-sources until recently (Goncalves, 2015; Goncalveset al., 2013; Hosio, Goncalves, Lehdonvirta,Ferreira, & Kostakos, 2014). Goncalves (2015,p. 21) defined situated technologies as “computa-tional elements in everyday environments” likepublic displays and embedded tablets. One of theprimary benefits of situated devices is that they canbe targeted precisely at certain groups of people toleverage their local knowledge. Placing stationarypublic screens and consoles directly in a locationwhere input can be received can be a simple andaffordable tactic to capture specific types of data.This can be a significant advantage to certain typesof crowdsourcing. In sum, this technique requiresactive, individual, human input with the task sub-ject also at the individual level.
4.2. Spatial crowdsourcing
The second type of active techniques are centeredaround spatial crowdsourcing (Cheng et al., 2014;Cheng, Lian, Chen, Han, & Zhao, 2016). As spatialcrowdsourcing can take into account space as wellas time dimensions, it increases the possibilities andvariety of data that can be collected and putthrough the crowdsourcing system. This temporalspatial dynamic may be able to increase the level ofreal-world authenticity. However, this increase inrealism and complexity brings challenges. Spatialcrowdsourcing has a number of sources of potential
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variability, including the ever-changing environ-ment as well as the ever-changing human element.For example, consider the spatial task for the crowdto take pictures of a certain monument. The time ofday the picture is taken is considered, as well as theskill of the photographer–—and these are only two ofthe possible sources of variation in the task. Thisexample harkens back to the idea that active andhuman-derived input may be flexible but less reli-able, while sensor-derived input may be highlyreliable but limited. In sum, this crowdsourcingtechnique combines active individual input withthe task subject of the environment.
4.3. Crowdsensing
Crowdsensing allows one to leverage standalone,environmentally placed sensors or sensors embed-ded in hardware devices to crowdsource data as aresult of mobility, smartphone and tablet portabili-ty, and wifi/mobile networking (e.g., Ganti, Ye, &Hei, 2011; Malatras & Beslay, 2015). As these tech-niques are sensor-based, data inputs from a crowdemploying said devices do not require human inter-vention to create or supply the data content, butoften rely on human mobility to move the portabledevice throughout the environment, thus coveringspatial-temporal ground with the sensors in thedevice. Data may even be collected without theexpress knowledge and/or permission of the partic-ipating human. When combined with stationary andfixed-location sensors, crowdsensing broadens therange of the environment that can be detected bysensors, thereby covering a greater portion of theenvironment or community. Some examples of ap-plications include air and water quality, measuringtraffic congestion and road conditions, parkingavailability, outages of public works (e.g., brokenfire hydrants, damaged traffic lights), and real-timetransit tracking. In sum, this crowdsourcing tech-nique involves primarily passive data input withthe task subject being the environment. However,active human data input is also possible with thistechnique.
4.4. Wearables crowdsourcing
Wearables allow for the leverage of built-in sensorsin hardware devices attached to the humanbody and/or through apparel or accessories. Theboundary of the wearable technology market isfragmented and not formally defined. It includesdevices such as smart glasses, smart watches,activity/fitness monitors, and glucose and heartrate monitors, as well as wearable cameras andvirtual reality headsets. With this next-generation
crowdsourcing, wearable sensors sense, collect,and transmit data about the specific wearer ofthe device and supply such individually focuseddata to a crowdsourcing application incorporatingthe data. Wearable crowdsourcing is the most wellknown of these applications. In sum, wearablecrowdsourcing involves primarily passive data inputand the individual as the task subject.
5. Passive versus active data input
Most of the current and traditional crowdsourcingapplications are/have been active and Web 2.0-based, wherein the user adds value. Whether thecrowdsourcing application is designed for idea gen-eration and newproduct development, crowd voting,crowd micro-tasks, or crowd solution generation,individuals actively input data usually through theirdesktops, laptops, or even smartphones.
With the number of connected sensors and ac-tuators expected to grow to nearly 50 billion in thenext 3 years, most of the new opportunities likelylie in the passive half of the typology. For thisreason, most of this article focuses on the passiveportion of the typology. It is quite clear that IoT,sensors, and big data will lead to a tremendous floodof data from individuals and the crowd. This flood ofdata will lead to currently unimagined new prod-ucts, new services, and new opportunities for thoseable to take advantage of these opportunities.
If we start at the tophalf of thetypology (Figure 2),we are looking at passive data quadrants highlight-ing both crowdsensing and wearable crowdsourcingtechniques. This passive input is sensor-based, IT-derived, and generally not human-derived. Othershave called this method of input opportunisticinput (Chatzimilioudis, Konstantinidis, Laoudias,& Zeinalipour-Yazti, 2012). As discussed, this inputcomes from sensors (fixed or mobile) and artificialintelligence or bots. Today, cars have close to100 sensors, smart homes can have hundreds,and even smartphones have six sensors at a mini-mum (Bryzek, 2012). The basic consumer smart-phone can have an accelerometer, gyroscope,compass, pressure sensor, audio sensor, image sen-sors, microphone, GPS, thermometer, light sensor,altimeter, humidity sensor, proximity sensors, andhealth sensors and actuators. In the next fewyears, these numbers are expected to skyrocketto several hundred sensors per person. As a resultof this explosion, there are implications that needto be considered.
Passive data input can occur in a number of ways.The sensor may collect and transmit input to thesystem without any human interaction or direct
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knowledge. Or, the sensor might collect and trans-mit the input without any human interaction, butthe human user is aware that this input is beingloaded into the system. For example, the averageperson is aware that there are sensors in his or hermobile phone without knowing the exact number ortheir precise functions. Additionally, that sameperson is vaguely aware that the phone serviceprovider and also perhaps the phone manufacturertracks his or her movements. However, he or shedoes not know the extent to which this trackingoccurs. This difference is not inconsequential as thisnon-voluntary participation can lead to businesssolutions (i.e., new products and services) andother value-creating activities. In other words, pas-sive data input has significant privacy and owner-ship issues that need deeper investigation, althoughthat is not undertaken in this article.
As we are in the midst of a revolution in mobilecommunications (Kietzmann et al., 2013), it shouldnot be surprising that the explosion in mobile deviceuse–—especially smartphones and tablets–—has im-pacted crowdsourcing. The idea of mobile technol-ogy usually evokes images of smartphones–—asafe association, given the projected 6.1 billionsmartphone subscriptions that will exist by 2020
(Ericsson, 2015). However, tablets–—as well as agrowing number of mobile RFID-type devices(Samans, Blanke, Corrigan, & Drzeniek, 2015)–—alsofactor into the pool of mobile devices, as well asthe individual sensors in both smartphones andtablets. Given the growing number of sensorsin these devices, it appears likely that the sheervolume of possible passive submissions emanatingfrom these sensors into crowdsourcing platformswill quickly overtake the volume of active, human-derived input.
The sensors in mobile vehicles like cars andtrucks, however, are often more forgettable thanthose in handheld devices. As stated above, theaverage car currently has approximately 100 sen-sors. While not all of these sensors may be appro-priate for passive input into crowdsourcing systems,it is not hard to see the possibilities and potentialof massive crowdsourcing applications worldwidewith huge numbers of participants.
As a result, the window of opportunity lies in thepassive data coming by means of IoT sensors andrelated technologies. The torrent of passivelysensed data by IoT sensors, combined with big datatechnology, is creating an environment that willallow the creation of countless new products and
Figure 2. Next generation crowdsourcing
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services that will generate value for consumer andindustrial customers. It is also spawning a new typeof entrepreneurship.
6. Sensor-based entrepreneurship
Given rapid technical advances, many of the busi-ness opportunities are digital rather than tradition-al. This increase in digital activities or digitalizationcuts across most industries. As a result, digitalentrepreneurship will become more commonplace.Digital entrepreneurship can be called a subcatego-ry of entrepreneurship “in which some or all ofwhat would be physical in a traditional organisationhas been digitised” (Hull, Hung, Hair, Perotti, &DeMartino, 2007, p. 293). These activities includedigital production, digital marketing, digital selling,and digital distribution.
As IoT becomes more widespread, sensor-relatedactivities will also increase across most industries.As a result, sensor-based entrepreneurship will be-come more commonplace, too. As previously stat-ed, sensor-based entrepreneurship can be seen as asubcategory of digital entrepreneurship in whichsome or all products and services are derived fromdata collected from free standing or embeddedsensors and related devices. The entrepreneurial
opportunities exploited by sensor-based entre-preneurship typically reside at the nexus of IoT,passive data from sensors, and big data.
7. Framework for sensor-basedentrepreneurial opportunities
In this section, I build upon my review up to thispoint to introduce and briefly discuss a frameworkthat organizes sensor-based entrepreneurship op-portunities by two salient characteristics: level ofdata aggregation and sensor location. In Figure 3, Iillustrate the four product and service developmenttechniques separated into a four-quadrant matrixdetermined by these two characteristics.
7.1. Level of data aggregation
When developing or planning a new sensor-basedproduct or service, it must be determined how tocombine the sensor-generated data. An entrepre-neur must ask: “Should the data be used just for theindividual from which it is sourced, or should thatdata be combined with others' (i.e., the crowd's)data as we develop our product?” The level ofaggregation is not binary, but rather should bemeasured along a continuum.
Figure 3. Sensor-based entrepreneurship opportunity development framework
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7.2. Sensor location
When developing or planning a new sensor-basedproduct or service, it must also be determinedwhere to locate the data-providing sensors. Anentrepreneur must ask: “Are the sensors locatedor owned at the individual level, or are they locatedin the environment/community and owned byothers?” Sensor location is along a continuum, notbinary.
8. Implementing the framework as anopportunity development mappingtool
As stated, viable business models are key to thefurther development of IoTand sensor-based entre-preneurship. Whitla (2009) suggested that crowd-sourcing techniques could be used for new productand service development. This has been shown to betrue by the emergence of many idea generation andrelated crowdsourcing applications (e.g., InnoCen-tive, Unilever Foundry IDEAS). However, most ofthese applications have relied on active data inputfrom individuals. On the other hand, as the IoTtransitions from its initial phases to more function-al stages of implementation, it will unleash a floodof passive data that makes it perfect for using thecrowd and crowdsourcing techniques for productdevelopment and creation. It is important to notethat this discussion entails two different uses forthe crowd. First is the use of the crowd as a sourcefor idea generation, relevant to services like In-noCentive (although in a passive rather than activesense). Second is the use of the crowd and crowd-sourcing as a business model and/or strategy with-in the new product/service development process.This latter perspective is the primary objective ofthis article.
This mapping tool provides a framework for look-ing at potential sensor-based opportunities andpatterns. It further helps entrepreneurs to planand develop new products, new services, andnew business opportunities, as well as portfoliosof their existing products and services.
8.1. Quadrant 1
Entrepreneurs who have access to individually lo-cated sensor data may want to think about creatingservices in this quadrant. A service in which data iskept at this individual level and not aggregated withthe data of others would be mapped to this box.For example, an entrepreneur’s service that em-ploys data from consumers using wearable devices,
processes that data, and relays it back to the indi-vidual wearing it would map to this quadrant. Thiscould, for example, be a case of a simple wearabledevice like a smart watch that senses and collectsbasic human vital signs and presents it back to thewearer/owner in an easy-to-view format.
8.2. Quadrant 2
The location of the service in the map can varybased on which opportunity the entrepreneur istrying to exploit. Using the example of the sameentrepreneur from the previous section, consider ifshe now wants to explore the possibility of poolingthe data from individuals (i.e., the crowd). This newpotential service would be mapped to Quadrant2. As a result of the aggregation of the data, thisnew service is a crowdsourced service. The entre-preneur may want to provide both types of servicesand use the framework to begin to map her portfolioof offerings.
8.3. Quadrant 3
If the entrepreneur has access to sensor data fromsensors that exist out in the environment, the prod-uct or service designed may start in this quadrant. Ifthe entrepreneur wants to develop a service that insome way relays the data collected back to theindividual, the service would be mapped here.Consider an example from the food services indus-try. Restaurants could recommend specific dishes topotential customers based on their levels of activityand weight-loss goals. In the banking industry, thenew service could be a contactless payment systemor a cardless ATM. This service and level of custom-ization assumes that the user has a device thatcan interact with the environmental sensors. Newservices in this quadrant may be challenging toconceive given the powerful potential opportunitiesthat become apparent when data is aggregated,such as in Quadrants 2 and 4.
8.4. Quadrant 4
If the entrepreneur has access to data from sensorsin the environment or community (i.e., not ownedby the end user) and also wants to aggregate thedata across the crowd, the service would be plottedto this quadrant. For example, assume that therestaurant from the previous example is a nation-wide chain and now has access to the data ofmillions of customers. As a result, the recommen-dations, recipes, and meal plans can be based onthe experiences of millions of people rather thanjust the single consumer. Aggregation of crowd data
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can open the floodgate to create a greater variety ofservices.
9. Discussion
Today, it is easier to see potential sensor-basedbusiness opportunities in Quadrants 1 and 2. Thisis in large part due to the fact that wearables havetaken the lead in sensor-based products and ser-vices. With users benefiting from healthier living,increased safety, and the ease of use of the initialwearable products, it is predicted that there will beover 250 million wearables in use by 2018 (Power,2016).
One of the fastest growing areas for sensor-basedbusiness opportunities, especially wearables, is inagriculture. Sensing networks and farm manage-ment information systems supported by sophisticat-ed data and visual data analytics tools are booming.The market for wearable technology for animals isexpected to grow from around $1 billion currentlyto $2.5 billion by 2025 (Sensors, 2014). Animalwearables include body temperature monitors,GPS location monitors, e-pills to track health, iden-tification earrings, and more.
For Robson, Pitt, and Kietzmann (2016, p. 179),wearables “refer to the technological enhancementof products that can be worn on almost any part of theanatomy (e.g., watches, glasses, shoes)”; however,for the purpose of sensor-based entrepreneurship, weneed to use the slightly broader term ‘personables.’This is in keeping with Ganti et al.’s (2011) categori-zation of personal- versus community-sensingactivities. For example, a phone is not considered awearable, but is a personal item that you carry. Yourcar is not a wearable either, but it is a personal itemthat can function in a way similar to a wearable. Asensor that measures your individual carbon footprintis personal, but not wearable. High-tech pills youswallow are not really wearables, either. With thisdefined, there is a tremendous future in developingservices for the personables market.
Quadrants 2, 3, and 4 are expected to havesignificant big data implications. To collect somuch data from the crowd and dispersed sensorswill require tremendous storage, high-speed band-width, and data-crunching power.
Quadrants 1 and 3 will lead to a large number ofnew products, services, and business opportunities;however, the number may be ultimately limited.There are only so many ways even the most creativeentrepreneur can return a user’s own data. There-fore, it is expected that the bulk of the opportu-nities will be created with aggregated data andcrowdsourcing.
Finally, by plotting the products and services indevelopment the entrepreneur can see his or herproduct or service portfolio take shape. By viewingthe patterns or groupings of offerings, the entre-preneur may be able to see clusters of relatedtechnologies, related manufacturing techniques,or related target markets and make use of thatinformation.
10. Summary and implications
IoT will change how we interact with objects andhow we interact with each other. All of this newdata will also change how firms interact with theircustomers as this data can be used to help thembuild stronger and more personal relationships. Theimplications will not just be with end-user consum-ers. In fact, the greater impact may very well be inthe industrial sector; industries such as mining,manufacturing, agriculture, and oil and gas produc-tion are already feeling the impact of sensor-basedentrepreneurship and new product development.
11. Aim and future research
The aim of this research has been to investigate howa shift to Web 3.0 and the convergence of IoT withbig data has created sensor-based entrepreneur-ship. Sensor-based entrepreneurship is leveragingIoT to exploit new business opportunities by creat-ing new products and services. I have attemptedfurther to highlight how this shift from active datainput to passive, sensor-based input has opened theway for crowdsourcing as a potential business modelfor many sensor-based services. The final objectiveof this research was to develop a framework to aidin the crafting of new sensor-based products andservices.
This framework can be used as a tool to helpentrepreneurs design, develop, and plot their initialsensor-based business opportunities. While the va-riety of possible new products and services is large,the crowdsourcing space offers particularly strong(potential) entrepreneurial opportunities as thedata gathered and aggregated by the sensors opensa myriad of possible new services with a viablebusiness model.
For a more complete mapping of potentialbusiness opportunities, the target market for whomthe products or services are created must becontemporaneously taken into account as well.For example, should the service be sold back tothe individual, should it be sold to industrial orbusiness customers, or is there a market in the
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public sectors? More research should be done toimprove the framework by including matters suchas the target market.
This research has focused on the positive aspectsof IoT and sensor-based entrepreneurship and pas-sive input crowdsourcing, but as with any newdevelopment there are pros as well as cons. Addi-tional research is needed to explore more of thenegative aspects in order to present a completepicture.
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2.3 Study 1 - Leveraging user-generated content for demand-side strategy The industrial context of this study is the hospitality industry, specifically the restaurant business.
The hospitality industry has two essential characteristics that make this study valuable. Firstly, with the related travel and tourism industries, the three businesses make up the world's largest industry in the service sector. Secondly, consumers in these industries have been at the forefront of creating and using comment and reviews (i.e., user-generated content (UGC)) to make their purchasing decisions.
Additionally, the demand-side view of strategy is under-researched, especially in the areas of tourism, travel, and hospitality when viewed against the predominant perspectives in strategic management (e.g., resource-based view, transaction-cost economics, positioning, and dynamic capabilities) are supply-side views.
Using the tools available today it is possible to scrape and mine the websites, blogs, forums, communities and other places customers gather and use content analysis techniques to extract meaning from the content. Web 2.0 sources of qualitative data like blogs, forums, feedback (i.e., UGC) can offer better research material than offline traditional qualitative diaries. As a result, using crowdsourced, user-generated content may be a productive marketing research approach to investigate how demand-side strategic decision-making can be supported in the travel, tourism, and hospitality industries.
Study 1 connects to the overarching research question as it is an empirical study that explores how crowdsourcing (especially in the form of user-generated content) can be used in market research to explore strategic questions.
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STUDY 1
Leveraging user-generated content for demand-side strategy
“The consumer is the ultimate arbiter of a strategist’s success” (Priem, 2007, p. 233).
1 Introduction This section provides the overall background for this study. It discusses how crowdsourced, user-generated content can be helpful in for in developing demand-side strategies especially in the travel, tourism, and hospitality industries. In doing so, it also presents the study’s research purpose and research question.
According to Fernandes & Fernandes (2017) the amount of user-generated content (UGC) in the hospitality industry1 has exploded. While UGC can take many forms, much of the growth has been in the form of online reviews (OR) with a growth rate of 800% during the last few years (De Ascaniis, Borrè, Marchiori, & Cantoni, 2015). More importantly, it has been reported that service ratings and online reviews are the second and third most important factors in making hospitality booking decisions, with the only price ranking higher (Fernandes & Fernandes, 2017).
According to Tyson (1986, p. 9), business intelligence (BI) is an umbrella concept for the six following types of intelligence: customer intelligence, competitor intelligence, market intelligence, technological intelligence, product intelligence, and environmental intelligence.
BI has a role to play in strategic decision making in the hospitality industry (Lau, Lee, & Ho, 2005). Much of the customer intelligence in marketing centers around CRM, but not all. Plenty of customer intelligence exists outside the boundary of the firm. Much of it exists in crowdsourced social media, forums, blogs and review sites. Schuckert, Liu, & Law (2015) did an extensive review on the role of online customer reviews in the hospitality industry and found that reviews can be a strategic tool and have a crucial role in hospitality and tourism management.
Poynter (2013) posited that crowdsourcing was disrupting traditional methods of market research. If this is true then travel, tourism and hospitality are ideally positioned to take advantage of these advances. The hospitality business is more competitive than most industries (Singal, 2015). Because of this intense competition access to consumer-generated content should help restaurateurs to know their customers and potential customers better and consequently acquire a competitive advantage.
In the future travel, tourism and hospitality managers will be able to take more significant advantage of the information provided by the consumers and the community to make better tactical, operational and strategic decisions about how to run their businesses. Furthermore, they will have better and easier to use tools.
According to the Statistica Digital Advertising Report, in 2017 firms spent a total of US $43.78 billion in 2017 (Fipp.com, 2018) on social media advertising. As social media continues to mature, its role continues to evolve as well. Kietzmann, Hermkens, McCarthy, & Silvestre (2011) claimed that social media is no longer just a way for users to share content among themselves, but rather a multiple dimensional platform that can have a substantial impact on firms’ reputations, performance and survival. As a result, firms have begun to focus on how this sharing of content and conversation among the users can be tapped and used in their favor. One way in which they can do this is by using social media and its content to conduct market research. By investigating user-generated material more thoroughly, the firms can better align their social media messages to the different and 1 According to Brotherton's (2003) extensive study on the hospitality industry, hospitality can be defined as hotels, restaurants, and contract food services. This study focuses on restaurants as a subset of the overall hospitality business.
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unique needs of their social media users (Zhu & Chen, 2015). In doing this, they can better leverage the increasingly important social media.
While the predominant strategic perspectives including the resource-based view (RBV), transaction cost economics (Williamson, 1971) and positioning (Porter, 1980) tend to ignore the ultimate objects of strategy, the customer, the advent of social media may lead to a change. With the growth of social media and other UGC, there is a significant opportunity to use the views, thoughts, ideas, attitudes, etc. from the actual consumer to help build strategy from the bottom up, rather than just top down.
Using the tools available today it is possible to collect and analyze the text on the websites, blogs, forums, communities and other places people and customers gather and use content analysis techniques (Krippendorff, 2013). Advanced, computer-aided methods like artificial neural networks and genetic algorithms are beginning to be used more frequently (Chen, 2009; Dirsehan, 2015; Law & Au, 1999; Meehan et al., 2013; Olmeda & Sheldon, 2002b); however, partly because this type of analysis is still in its infancy (Chen, 2009), there seems to be a gap or opportunity to explore the use of tools that can examine natural language text data. Natural language analysis is important as the text created by users and consumers (i.e., user-generated content (UGC)) is natural language. Further, while some of these techniques have been used to look at firm issues such as electronic word of mouth (eWOM) (Dirsehan, 2015), complaints (Chen, 2009), and expert systems/decision support (Cheng, White, & Chaplin, 2012), customer relationship management (Liao, Chen, & Deng, 2010), and new product development (Liao et al., 2010), few have used these tools and unstructured UGC to investigate or help with strategic decisions.
Rathi and Given (2010) pointed to online market research’s relevance in a Web 2.0 environment as it allows researchers opportunities to explore both qualitative and quantitative questions. New tools not only improve data collection but also will enable consumers and users to participate in the research process.
“From a marketing research2 perspective crowdsourcing gives opportunity to reach large potential consumer groups…. (and) in many cases crowdsourcing offers cheaper and quicker opportunities for gathering market information” (Gatautis & Vitkauskaite, 2014, p. 1247). For this and other reasons, crowdsourcing approaches are being used in market research.
Content created by the crowd including online reviews play a dual role in marketing, particularly in the travel, tourism and hospitality businesses. On the one hand, online reviews can be a source of powerful online or electronic word-of-mouth (eWOM) (Chen & Xie, 2008). On the other hand, online reviews can be a source of rich information perfect for market research as it contains the unfiltered opinions, needs, wishes, desires, and, complaints of customers. “One possible solution is to conduct qualitative research that provides very little structure that would influence people’s comments” (Dolnicar & Ring, 2014, p.42).
Despite all of the benefits of using crowdsourcing in the marketing research process, there are some challenges. While Whitla (2009) is typically cited as the source of the idea of crowdsourcing as a market research tool, he had some reservations as well. First, he believed that data from the closed end questions used in most surveys and analyzed quantitatively was not as good as qualitative data. Secondly, he thought that there were problems with the responses from paid respondents. Here he seemed to be explicitly referring to crowdsourcing sites like MTurk (cf., Casler, Bickel, & Hackett, 2013). Thirdly, he stressed the potential sampling problems by suggesting the process could lead to non-representative samples. Beard (2013) added a fourth challenge when she indicated that the process of organizing crowdsourcing projects might be time-consuming and complex for those not used to managing them. Can crowdsourcing be used as a market research tool, while avoiding some of these challenges? Yes, in fact, by using the crowd without the crowd knowing it researchers can get targeted, sensitive, and unstructured data in a natural setting. In other words, by collecting and analyzing reviews, comments, blog posts, support
2 The original text says ”market researches perspective . . .”, which is probably a typesetting error.
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requests and any other form of user-generated text content, researchers can gain great insight into the customer.
One of the major problems with this rapidly growing pool of consumer information has been the text-mining technology applicable to the travel and tourism business has not been readily available at a reasonable price or any price. As a result, the potential of this data for customer intelligence was seen as something for the future (Lau et al., 2005). However, fortunately, the technology has caught up with the demand. These technological advances are timely as they open up the pathway for researchers to explore strategy from an alternative perspective.
The second problem is that most research discourse on business strategy has taken a supply-side or a top-down approach (cf., Adner & Zemsky, 2006; Stokes, 2000) for two primary reasons. One, that is where the vast majority of the research has taken place. Two, methodologically there has not been an easy way to access, collect and analyze vast amounts of end-user data until recently. The enormous amounts of crowd-produced data are accessible now and will only grow especially as The Internet of Things (IoT's) devices come online. Concurrently, the tools to analyze this Big Data are beginning to be developed as well. Unsurprisingly, a focus on bottom-up or demand-side strategy is appropriate especially in marketing strategy, where the customer plays such a crucial role (Stokes, 2000).
1.1 Research Purpose
To help and support the firm in making critical strategic decisions customer and market intelligence is needed. To capture that intelligence, market research is required. Increasingly, that research is taken place online. Although advanced text analyzing tools like Leximancer have begun to make serious inroads into tourism, travel and hospitality research, it is just at the beginning stages (M. Cheng & Edwards, 2017).
The primary purpose of this study is to examine and describe a method to support decision-makers and market researchers in analyzing user-generated content to make strategic decisions. The secondary goal of this research is to investigate how crowdsourcing can be used as an innovative tool to do market research. Collecting data is a continuing challenge in empirical investigations; however, researchers in the hospitality and tourism industry can take advantage of the tremendous amount of data, much of it created by customers, and available on the Internet (Gerdes, Jr & Stringam, 2008). These challenges have pushed market research online. As this UGC is a source of customer intelligence, firms should be able to improve their market research resulting in better strategic decision-making. The future of travel and tourism marketing lies in using multiple sources of intelligence to get a more intimate and complete view of the customer. Resulting in the question -
How can user-generated content help firms make strategic decisions about new business
opportunities? Serendipitously, these three things (i.e., 1. the large pool of crowdsourced, consumer-generated
content, 2. new tools available to gather and analyze the content, and 3. a dearth of demand-side strategy research) come together now, to create a research gap that this research attempts to fill. In this study, we take a demand-side strategy approach (cf., Adner & Zemsky, 2006; Priem, 2007a; Priem, Li, & Carr, 2012; Stokes, 2000) to investigate and demonstrate how crowd-created, user-generated content (cf., Kietzmann et al., 2011; Zwass, 2010) can be used to make important firm decisions.
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2 Theoretical Foundation This section provides some theoretical context for the study. It briefly gives some background on online research, user-generated content, crowdsourcing, demand-side strategy, and the use of content analysis. 2.1 Why Online Market Research?
There are multiple reasons why a good portion of market research has moved online. Some researchers have pointed to the declining response rate of offline surveys (Fawcett et al., 2014; Stafford & Gonier, 2007). Others have claimed that online responses are of a higher quality regarding higher degrees of honesty and more spontaneity (Aaker, Kumar, Day, & P, 2011). When combined with being cheaper than self-documentations and paper and pen diaries, online research seems to have clear benefits (Stafford & Gonier, 2007). Further, more and more consumers are using online reviews (Stafford & Gonier, 2007) making them more comfortable giving their opinions online.
Additionally, the ability to develop and connect to an online community enables continuous market monitoring which can lead to better customer relationships as well (Comley, 2008; Mathwick, 2002). Further, advances in social media tools and technologies have increased the likelihood of interactivity in market research (Austin, Jennings, Schlack, & Lerman, 2007). Ultimately, the use of these tools has helped change the market research environment by facilitating the growth of consumer empowerment and engagement (Mathwick, 2002). 2.2 User-Generated Content and Crowdsourcing
The concepts of crowdsourcing, user-generated content (UGC), and co-creation is often seen as closely related to user innovation. UGC can be defined as “media content created or produced by the general public rather than by paid professionals and primarily distributed on the Internet” (Daugherty, Eastin, & Bright, 2008, p. 19). In other words, it is content produced by the crowd. The improvements and extensive growth of Web 2.0 technologies have led to an explosion in the creation and distribution of UGC. 2.3 Crowdsourcing in Market Research
Whitla's (2009) early research on how crowdsourcing can be used as a marketing innovation pointed to three specific areas: advertising and promotion, product development and market research. Concerning market research he highlighted two significant reasons why crowdsourcing market research was a good thing: 1) low-cost access to large numbers of people and consumers (also see Gatautis & Vitkauskaite, 2014) and 2) possible access to quality information through access to experts. Unwittingly, a good portion of market research already uses crowdsourcing. Ironically, the most common form of crowdsourcing in market research is the simple collection of data using surveys (Zadeh & Sharda, 2014).
Rathi and Given (2010) suggested that we were moving to a new phase in research called Research 2.0. In this new era, researchers can use the collective power of the crowd through crowdsourcing techniques not only for data collection but also for data analysis and writing. In fact, we can even say that the use of crowdsourcing, “Collapse(s) the market research process into an instance of firm-consumer co-creation.” (Brabham, 2012, p.10). Further by utilizing the crowd and co-creation, researchers can achieve even greater insight (Rathi & Given, 2010) directly without the need for marketing or research agencies.
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2.4 Data and Text Mining in the Travel, Tourism and Hospitality Business
According to Krippendorff (2013, p. 24), “Content analysis is a research technique for making replicable and valid inferences from texts (or other meaningful matter) to the context of their use.” The rise of customer-generated Web 2.0 content (e.g., blogs, forums, newsgroups, social media platforms, review sites, and crowdsourcing systems) offers a chance for researchers and managers gather intelligence from a vast number of customers and other stakeholders (Chen, Chiang, & Storey, 2012).
Text mining user-generated content is a partial solution to some of the possible challenges surrounding crowdsourcing market research especially regarding the extraction and interpretation of UGC. Firstly, text mining is a qualitative technique that avoids concern with quantitative methods (Collis & Hussey, 2013). Secondly, by scraping the data from The Internet, the researcher also avoids using paid respondents. Thirdly, with proper planning, the researcher can target specific groups by demographics and psychographics and scrape particular content. In doing this, the researcher can in many cases solved the non-representative sample problem. Fourthly, while creating unique crowdsourcing platforms to can be complex, expensive and time-consuming (Peppard, Edwards, & Lambert, 2011), collecting data from The Internet can be less complicated, less expensive, and less time-consuming.
There are a few additional benefits that content analysis of crowdsourced data can bring. Tradition market research approaches tend to create artificial environments. As the research participants are aware they are involved in a research project, their answers are often biased (Carson, 2008). So, by collecting online text, the research creates a less artificial and less biased environment. Secondly, this method is less intrusive, and as a result, it has fewer complications associated with direct interaction with human subjects (Lu & Stepchenkova, 2015).
Although there are many marketing research advantages in using text analysis and UGC, it is not without issues and challenges (Akehurst, 2009; Carson, 2008). It may be challenging to analyze text manually. Unfortunately, as the number of reviews explodes, it has become increasingly difficult to use the traditional and manual methods of text analysis, especially with the unstructured nature of most UGC. Therefore, the utility of UGC in research may be limited. Fortunately, there has recently been rapid advances in technology both software and hardware that has opened up the possibilities (Olmeda & Sheldon, 2002). For example, there has been the development of automated computer-aided systems (e.g., Leximancer) that use machine learning to analyze natural language processing and lexical and semantic resources (Biroscak, Scott, Lindenberger, & Bryant, 2017). 2.5 Demand Side Strategy
The dominant strategic views including the resource-based view (RBV), transaction cost economics (Williamson, 1971), positioning (Porter, 1980), and the dynamic capability view (DCV) (Eisenhardt & Martin, 2000; Teece, Pisano, & Shuen, 1997), focus on the firm or the supply-side, while fundamentally ignoring the ultimate objects of strategy, the customer. As entrepreneurial opportunities arise out of an interaction of the supply side and the demand side (Venkataraman & Sarasvathy, 2001), some entrepreneurship and marketing researchers (cf., Stokes, 2000) are beginning to look at the demand-side. However, in comparison very little strategy research has focused on the demand-side (Brief & Bazerman, 2003; Harrington, Chathoth, Ottenbacher, & Altinay, 2014a). In fact, some researchers feel that understanding the customer is “largely superfluous to the overall goal of the strategy field” (Madadok & Coff, 2002, p. 12).
Alternatively, Priem and his colleagues believe that consumers and their preference can play a central role in strategic issues such as diversification, forward and backwards integration, industry evolution, consumer and firm heterogeneity, systems for judging value, co-creation and opportunity development (Priem, 2007a; Priem et al., 2012; Ye, Priem, Alshwer, & Ye, 2018). Demand-side research can be defined as a “look downstream from the focal firm, toward product markets and
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consumers, rather than upstream, toward factor markets and producers, to explain and predict those managerial decisions” (Priem, Li, & Carr, 2012, p. 347).
Interestingly, the research fields of tourism and hospitality seem to be a fruitful area for the demand-side approach to strategic management. According to Harrington et al. (2014) the area of hospitality and tourism research, specifically, the concept of revenue or demand management is particularly suited to a demand-side strategic approach. This study aims to demonstrate that there are other research areas that the demand-side applies to as well. For example, a demand-side approach seems especially relevant to help restaurants develop their marketing positioning strategy (Brooksbank, 1994). Brooksbank (1994, p. 10) quoted Doyle’s definition of position strategy as such -
“Positioningstrategyreferstothechoiceoftargetmarketsegmentwhichdescribesthecustomersabusinesswillseektoserveandthechoiceofdifferentialadvantagewhichdefineshowitwillcompetewithrivalsinthesegment”(Doyle, 1983).
In sum, in this research, we argue that the crowd through its production of online content can aid firms in their demand-side marketing research, particularly concerning strategic decision-making. Furthermore, as the amount of user-generated content continues to grow, new tools and techniques allow firms and managers to explore consumers more profoundly and to create value, new products and service and new business opportunities (Kim & Mauborgne, 2005a, 2005b).
2.6 Common Data Collection Technique Used for Demand-Side Strategy
Although demand-side strategic analysis is less well researched, particularly in marketing and management, it is more often used in public services like utilities (cf., Arteconi et al., 2017; Rosa, Machlis, & Keating, 1988), natural resources (cf., Zulu, 2010), hospitals (cf., Kreipl & Lingenfelder, 2002; Mark & Elliott, 1997), infrastructure (cf., Shyu & Chiu, 2002), for example. In these cases, data is collected in two primary ways – large government surveys or electronic and digital metering systems in the case of energy consumption. In marketing and management, demand-side research methods are more varied and are also more geared toward smaller sample sizes. Examples of these traditional data collection techniques can include survey, experimental, observational, and panel. However, these commonly used methods do not take into account the growing techniques of online market research. Fortunately, these techniques have been adapting to the Internet, advanced ICT technologies and social media (Comley, Beaumont, Comley, & Beaumont, 2011). The growth of these techniques have led to new observational methods that utilize the crowd including, but not limited to, netnography (Kozinets, 2002; Kozinets, Hemetsberger, & Schau, 2008; Mata, Quesada, Rica, & Mata, 2014), research communities (Comley & Beaumont, 2011), and use of UGC (Stafford & Gonier, 2007). This study aims to demonstrate how managers can use large crowds and user-generated content (i.e., consumer reviews) to assist in making demand-side strategic decisions.
3 Methods This methods section describes the data used in this study regarding from where it was collected and how it was collected. It also describes tools and techniques used in the data analysis.
This study uses qualitative data and computer-assisted content analysis. Qualitative data was
selected because user-generated content is typically in the form of non-numerical, unstructured, and text rich data (Gioia, Corley, & Hamilton, 2013). Words associated with qualitative data include exploring, generating, developing, and creating meaning and personal experience (Creswell, 2013). Therefore, qualitative data matched the objective of this research project.
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Computer-assisted analysis was selected because it helps remove some researcher bias (Penn-Edwards, 2010), improves quality and consistency of the analysis (Penn-Edwards, 2010; A. E. Smith & Humphreys, 2006) and significant increases the size of the datasets that can be used (Lau et al., 2005; Penn-Edwards, 2010). As a result, computer-assisted analysis has a significantly positive impact on the quality, reliability, and validity of the analysis.
3.1 Data
According to Fernandes & Fernandes (2017), the amount of user-generated content (UGC) in the hospitality industry has grown tremendously. While UGC in the hospitality business can take many forms, including websites, blogs, chat, and other forms of content, much of the growth has been in the form of online reviews. Online reviews have grown over eight-fold in recent years (De Ascaniis et al., 2015). Furthermore, researchers have reported that online reviews are the most important criteria consumers use in making hospitality booking decisions, with the only price ranking higher (Fernandes & Fernandes, 2017). Therefore, collecting data from these online review sites seems crucial.
The specific source of the data was TripAdvisor, the most extensive travel and hospitality review site containing more than 535 million reviews and opinions covering the world's most extensive selection of travel listings worldwide including 4.4 million restaurants (TripAdvisor, 2017). TripAdvisor has been used in countless academic studies as a quality source of customer reviews and comments (cf., Berezina, Bilgihan, Cobanoglu, & Okumus, 2016; Fernandes & Fernandes, 2017; Garrigos-Simon & Narangajavana, 2015; Lee, 2014 and many others). 3.1.1 Data Collection
From this overall sampling frame of user comments using a customized application, we collected customer reviews and comments from three restaurant segments in New York State – steakhouses, Italian restaurants, and seafood restaurants. This collection included all of their comments from the introduction of TripAdvisor in 2000 until January 8, 2018. Therefore, our data sample was 52,281 steakhouse comments 182,569 Italian restaurant comments and 47,197 seafood restaurant comments for an overall total of 282,087 comments. 3.2 Analysis
Today using the modern content analysis techniques it is feasible to gather and examine the text on the websites, blogs, social media platforms and other places customers congregate (Krippendorff, 2013). The use of advanced, computer-aided techniques like artificial neural networks and genetic algorithms are becoming more common (Chen, 2009; Dirsehan, 2015; Law & Au, 1999; Meehan, Lunney, Curran, & Mccaughey, 2013; Olmeda & Sheldon, 2002); however, partly because these type of analyses are still in its infancy (K. C. Chen, 2009), there seems to be an opportunity to explore the usage of specialized tools like Leximancer, that can examine natural language text data. 3.2.1 Leximancer
Leximancer was chosen for this analysis. It is a more powerful and flexible tool than its near competitors. Further, Leximancer is a highly advanced data mining software package for visualizing the structure of concepts and themes from the text and can analyze natural language. Its machine learning techniques border on artificial intelligence (AI) (Marr, 2016).
Leximancer utilizes a machine-learning system and inductive techniques, to uncover the core ideas in the text, and how they interrelate (for greater detail, see Smith & Humphreys, 2006). The
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machine learning procedure recognizes constellations of words and phrases that move together throughout the text (i.e., co-occurrence) and counts them (i.e., frequency). These clusters of words are then sorted together as a concept, which is grouped into broad themes shown as tinted circles on the map, which are heat-mapped to signify significance. (Brochado, Troilo, & Shah, 2017). In addition to the color of the themes, they are differentiated by size as well as proximity to each other (Robson, Farshid, Bredican, & Humphrey, 2013). To highlight additional levels of meaning in the text, Leximancer does both a conceptual (thematic) analysis and a relational (semantic) analysis (Campbell, Pitt, Parent, & Berthon, 2011). Concepts plotting close to each other describe a tighter semantic association. Additionally, Leximancer is particularly useful because of its ability to process natural language (cf., Biroscak et al., 2017; Penn-Edwards, 2010; Rodrigues, Brochado, Troilo, & Rodrigues, 2016).
The analysis process consists of four primary steps (see Table 1 for details). A Leximancer multi-colored conceptual or theme maps were created for each type of restaurant (i.e., Italian, Steakhouse and Seafood) as well as a map for the three types combined in Section 4.
Table 1 - The Four Steps Analysis Process for Leximancer
Steps Reasons Step 1 Understand the nature of the data All of the data was in the form of consumer
comments from the same web platform (i.e., TripAdvisor). As a result, understanding the data was straightforward.
Step 2 Prepare the data As the data was scraped using a propriety method, it was clean. There was no extraneous data or formatting they could create problems for the Leximancer analysis.
Step 3 Adjust concepts/seeds/text classifications
Like terms were combined. For example, restaurant and restaurants.
Step 4 Interpret the concepts • Examine the higher level of ideas to gain conceptual and relational insights
• Comparing the frequency of concepts and positioning of concepts in and across themes graphic depictions of the results
• In the final analysis, went back to the original comments to review related concepts to build the story or narrative
Adapted from (Aaker et al., 2011; Austin et al., 2007; Comley, 2008; Fawcett et al., 2014; Mathwick, 2002; Stafford & Gonier, 2007)
4 Results and Narrative Analysis This section presents the results and the narrative analysis for the steak restaurants, the Italian restaurants, the seafood restaurants, as well as restaurants taken as a whole. Through examining these narratives, demand-side stories begin to emerge.
These findings consist of two different, but related types of analysis – conceptual analysis and relational analysis (Biroscak et al., 2017). Conceptual analysis is a result of Leximancer ranking the underlying concepts. Recall, concepts are groups of words that move through the text together. The relational analysis is a result of Leximancer defining the conceptual boundaries of the concepts by
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grouping or clustering related concepts into higher-level themes (i.e., spheres) that are mapped and plotted different colors based on importance.
After the initial Leximancer analysis a further check of the underlying comments was done for the three restaurant segments. The following narratives or stories were derived by combining both types of analyses. In this section, the steakhouse, Italian, seafood, and overall user results will be discussed.
4.1 Steakhouse
The analysis shows the steakhouse customers want to be served. Let's dig a bit deeper here. Eight themes were identified including Time, Service, Place, Food, Wine, Best, Salad, and Reservation. The first theme Time was grouped and linked with the concepts time, happy, enjoyed, visit, recommend, and future among a few others. The second theme was Service and was grouped and linked with service, atmosphere, staff, friendly, special, attentive, and food among a few others. The third theme was Place, which was grouped and linked to place, people decided, table, arrived, bar, business, night, and dinner among a few others. For additional and greater detail see Figure 1, p. 68.
Initially, one might expect that meat would be the most critical element to the success of a steakhouse; however, it appears to be not that simple. These customers want to have a good time, which is a combination of the atmosphere, a friendly and knowledgeable staff, a good and easy to find location and the food. Once the customer gets to the food, there is no doubt it is essential, especially the quality of meat and how it is prepared. Delivering a well-seasoned, well-cooked steak is required and necessary. However, for the customers to feel like they have been well served, it is the combination of the steak, and the wine served. As a result, it is essential for each customer to have the proper information about the wine provided by the menu or a waiter. The steakhouse diner looks to a knowledgeable waiter to help co-create the dining experience for them (Zwass, 2010).
A steakhouse must have a good wine selection or inventory. Further, it is vital that the proper recommendations be provided by the menu or the waiter again. So ultimately, the best situation is a combination of service, food, steak and location that determine the quality of the restaurant. Although that combination gets you in the door, to close the deal, one must be able to accompany the quality beef with the appropriate wine recommended by knowledgeable information provided by a menu or waiter. Steakhouse customers are not price sensitive. They are more concerned with the price/quality trade-off. Customers will pay for quality. If anything, ironically, they are more concerned about the price of desserts. 4.2 Italian
In the Italian restaurant data, eleven themes were identified including Food, Restaurant, Wine, Pasta, Time, Sauce, Pizza, Ordered, Lunch, Try, and Reservation. The first theme Food was grouped and linked with concepts food, service, friendly, atmosphere, staff, warm, nice, Italian, wonderful, quality and prices. The second theme Restaurant was grouped and linked to concepts restaurant, dining, experience, recommend, friends, evening, looking, night, bar, dinner, family, room, and area. The third theme Wine was grouped and linked to concepts wine, enjoyed, fantastic, meal, excellent, glass, menu, wonder and top chef. For more and greater detail see Figure 2, p. 70.
The analysis further shows the Italian restaurant customers want a full restaurant experience after a good food experience. This desire is similar to the steakhouse situation but different enough to be meaningful. Firstly, success in the Italian starts with good food. However, food in this sense includes the service and a friendly atmosphere working together. The Italian restaurant is a package
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Figure 1- Steakhouse Restaurant Customers’ Theme and Concept Map
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of experiences where dining takes place with friends or family. The importance and expectation of wine are high. Wine is more crucial than in a steakhouse because of increased expectations. There seems to be an Italian food "brand promise" (Smith, 2003) that accompanies the wine and food. Accordingly, you cannot have Italian food without good pasta and pizza. These customers are very picky about their pasta and pizza. It is a critical item but is not easy to do well as there are many different styles, ingredients, processes, crusts, etc. Closely related to this is the importance of sauce, whether it be red sauce, white sauce, meat sauce, meatless sauce, cheese sauce, etc. Steak, on the other hand, is quite different, as you "just" need quality meat cooked well.
Italian dishes are more complex versus the simpler dishes of a steakhouse. A significant part of this full restaurant experience is the ability of the customer to order and try different food and menu items, especially at lunch. Therefore, an extensive and varied menu is important. It is interesting to note that Italian restaurants seem more lunch-oriented, while the steakhouse is more dinner-oriented.
Overall for the Italian restaurant diner, experiencing the food and wine is key. Enjoying the meal with others (i.e., family and friends) is part of that experience. Having dining reservations are not critical. In this segment of the restaurant business, more people make recommendations, and more people follow those recommendations. This finding fits with the research. With services and experiences, consumers are more likely to seek recommendations (Glynn Mangold, Miller, & Brockway, 1999). Ultimately, the Italian restaurant business seems more complicated than the steakhouse business.
4.3 Seafood
In the seafood restaurant data, eleven themes were identified including Food, Restaurant, Time, Delicious, Ordered, Table, Menu, Lunch, Dinner, Roll, and Cooked. The first theme Food was grouped and linked with concepts food, worth, quality, service, nice, excellent, price, and wine. The second theme Restaurant was grouped and linked to the concepts restaurant, feel, place, friendly, staff, attentive atmosphere, and service. The third theme Time was grouped and linked to concepts time, experience, dining, visit, enjoyed and thank. For additional and greater detail see Figure 3, p.71.
The analysis further shows the seafood restaurant customers want fresh and delicious seafood in a friendly atmosphere with excellent service. The concept of service in a seafood establishment is again different than the concept of being served in a steakhouse.
Firstly, food is seen as a combination of quality food at a reasonable price in a friendly place. While service in a steakhouse is mainly about quality servers, in a seafood restaurant it is about an attentive, friendly staff in general. It is a place where the customer enjoys their time, which is similar to the Italian restaurant experience. As one might expect the taste of the food, especially the freshness and quality of the seafood, is critical and must taste great.
The story of the dining experience is a priority for the seafood diner. These diners like to describe the story of their experience as in who ordered what and why. They like to share their experiences and the experiences of others in their dining party. By this, they can experience the food and atmosphere vicariously. In part as of result of this, the lowest level of analysis for the seafood restaurant is not necessarily the individual diner, but rather the table or family (or community). In other words, the restaurant needs to focus on satisfying the entire dining party as a whole rather than a single person at the table.
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Figure 2 - Italian Restaurant Customers’ Theme and Concept Map
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Figure 3 – Seafood Restaurant Customers’ Theme and Concept Map
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Further, for the diners, getting and waiting for a table is important, especially at night. However, as long as the restaurant has a good bar or something for the customer to do while they wait, waiting is accepted. Finally, the menu's information and the information supplied by the waiters are essential. The co-creation of the dining experience is in large part dependent on the waiter and the other staff. Waiters are more than just the order taker. Waiters join the chef as a co-creator of the experience (Zwass, 2010), similar to the steakhouse and the wine server. The knowledge of the waiter is less critical, but instead how he or she manages the experience is crucial.
4.4 Combined
Ultimately, in the combined restaurant data, ten themes were identified including Food, Restaurant, Delicious, Wine, Ordered, Recommend, Table, Salad, Waiter, and Review. The first theme Food was grouped and linked to concepts food, Italian, service, attentive, friendly, staff, wonderful, nice lunch, quality, price, excellent, and atmosphere. The second theme Restaurant was grouped and linked to concepts restaurant, friends, family, experience enjoyed, dinner, dining, time, night, wait, and evening. The third theme Delicious was grouped and linked to concepts delicious, fresh, tasty, dishes, pizza, best, amazing, perfect and pasta. For more additional and greater detail see Figure 4, p.73 and Table 2 which shows the number of time concepts were mentioned in the top ten themes.
Table 2– Combined Overall Themes and Concepts Hits
Theme Concept Hits 1 Food food, service, excellent, nice, wine
51394 2 Restaurant restaurant, place, staff, friendly, atmosphere
45808 3 Time time, experience, visit, enjoyed, thank, dining
31546 4 Delicious delicious, fresh, fish, seafood, best
29407 5 Ordered ordered, dishes, sushi, course
22733 6 Table table, bar, wait, night
22635 7 Menu menu, meal, waiter
20914 8 Roll roll, lobster, salad, shrimp
15802 9 Lunch lunch, recommend
11169 10 Dinner dinner
8484 11 Cooked cooked
4099 The combined analysis shows in the New York restaurants (i.e., steakhouse, Italian, and seafood) customers want good food and service with a friendly and attentive staff delivered at a reasonable price in a place with a pleasant atmosphere. While this may seem obvious, it can be some of the details that make the difference. Food is critical, but what makes a quality restaurant experience or visits is service, staff, and atmosphere. Additionally, to make delicious food it needs to be tasty, fresh, but perhaps more importantly paired with the correct wine. If customers have to wait before being seated, the restaurant should have something for them to do or a nice bar for them. Further, restaurants should not underestimate the importance of waiters. They are the face of the restaurant and drive the diners experience. The attitude and knowledge of the wait staff should be given attention. Finally, reviews and recommendation are important and can help reinforce each other.
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Figure 4 – Overall Restaurant Customers’ Theme and Concept Map
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5 Discussion This next section describes the significant findings of the demand-side analysis for each of the restaurant segments and overall. The section further points to several opportunities revealed from the analysis.
If a firm wanted to enter the New York restaurant business or expand their presence, there are some subtle and some obvious things it should take into account. Firms wanting to create competitive advantages can use the theme maps to develop innovative ideas. For example, the steakhouse story revealed that providing techniques to get quality information about wine to the customer could lead to a competitive advantage. Therefore, they may be an opportunity to enhance the wine information system. This improvement could be handled by developing a better IT system or by providing extra education to the wait staff.
The Italian restaurant case revealed the giving and receiving of restaurant and food recommendations play a prominent role. As a result, this may reveal an opportunity in how to promote, advertise and market an Italian restaurant given recommendations play such a pivotal role. The use of social media (Kietzmann et al., 2011), eWOM (Y. Chen & Xie, 2008; Litvin, Goldsmith, & Pan, 2008), online customer reviews (Jeong & Mindy Jeon, 2008; Melián-González, Bulchand-Gidumal, & González López-Valcárcel, 2013; Shoemaker & Lewis, 1999) and other techniques may be especially useful if properly designed.
This demand-side analysis revealed some potential opportunities for seafood restaurants as well. Firstly, a food supply chain in the seafood business is different than in a steakhouse or with Italian food. Acquiring high-quality meat is straightforward and most top steakhouse age much of their meat in-house. Italian food is made with basic ingredients that can be supplied from any local grocer. However, given that fresh and high-quality seafood is so vital for success in the seafood restaurant business, developing a new, specialized, innovative supply chain may be a key. Some customers even gave credit to establishments with direct links to fish markets. Secondly, insights from the reviews reveal the lowest level of analysis should perhaps be the table rather than the individual diner. As a result, there may be some valuable opportunities to create new services or experiences at the table level. Thirdly, there seems to be an opportunity for seafood restaurants to use waiters and staff as more active participants in the co-creation of the dining experience.
While this analysis of the New York restaurant scene points to some potential opportunities, it also raises a few questions. For example, it has recently been said that information communication technology (ICT) is one of the most important driving forces in the hospitality industry today and in the future, primarily related to customer-centric approaches (DiPietro & Wang, 2010). However, in the three narratives, comments related to ICT were noticeably absent. Why?
In summary, demand-side strategy can be described as strategy that is formulated from the bottom up (cf., Adner & Zemsky, 2006; Stokes, 2000). In other words, it is strategy that has the end-users or customers at its root or foundation (Priem, 2007b). This study demonstrates that online marketing research including the crowd, UGC, and content analysis can be used to gather customer insights, which can serve as feedstock for strategic decision-making. This research demonstrates that by using data mining and text analysis, the typical firm can draw on the insights from hundreds of thousands (or more) customers to quickly and efficiently develop demand-side strategies and potential business opportunities.
6 Implications This next section describes the theoretical and managerial implications of this research as well as its contributions.
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6.1 Theoretical Implications
There are three theoretical implications of this study. First, the results of this study support the idea that crowdsourcing has a role to play in market research (e.g., Brabham, 2012b; Gatautis & Vitkauskaite, 2014; Rathi & Given, 2010; Whitla, 2009; Zadeh & Sharda, 2014). Additionally, the study supports the broader claim that online market research is a good place to do market research (e.g., Aaker et al., 2011; Austin et al., 2007; Comley, 2008; Fawcett et al., 2014; Mathwick, 2002; Stafford & Gonier, 2007). Second, this study supports the notion that data, text mining, and content analysis are appropriate and valuable qualitative techniques when the researcher needs to extract meaning and inferences from crowdsourced text (Krippendorff, 1989, 2013). Further, given the amount of social and user-generated content produced by the travel, tourism, and hospitality industry, these techniques, especially computer-assisted methods like Leximancer, can be especially helpful in gathering insight for decision-making (Carson, 2008; Lu & Stepchenkova, 2015; Olmeda & Sheldon, 2002). Third, the dominant perspectives in strategic management (e.g., the resource-based view, transaction-cost economics, positioning, dynamic capabilities, etc.) are supply-side views (Priem, 2007a). Accordingly, the demand-side view of strategy is under-researched, especially in the areas of tourism, travel, and hospitality (Harrington et al., 2014a). This study shows that techniques like computer-assisted text mining and content analysis can be used to analyze large data sets from customers quickly and efficiently.
This study’s primary research question is - How can user-generated content help firms make strategic decisions about new business opportunities. This research has demonstrated that by using user-generated content from customers in the form of online reviews (i.e., the demand-side), managers can gain specialized knowledge to help them make more insightful strategic decisions and uncover potential business opportunities. 6.2 Managerial Implications
This study’s data and analysis revealed:
1. Steakhouse customers want to be served. 2. Italian restaurant customers want a full restaurant experience assuming a
threshold of good food has been attained. 3. Seafood restaurant customers want fresh and delicious seafood in a friendly
atmosphere with good service.
Restaurants must have a clear vision of who they are, who their customers are, where and how they want to compete. In other words, they need to have a marketing position strategy (Brooksbank, 1994). A demand-side strategy approach where strategy is derived bottom-up from the customers seems to be an extremely appropriate method for managers and marketers to use to help determine the restaurant's positioning strategy, as an example. Restaurant customers expect different things from different restaurant segments. These subtleties can make all the difference in building a competitive advantage in the highly competitive hospitality business (Kandampully, 2006). Looking ahead, taking a demand-side strategic approach seems highly relevant for firms in the travel, tourism and hospitality industries, especially as more customer-center business models show promise (Kandampully, 2006).
In addition to the potential opportunities discussed above, this study has some additional implications for managers. Using Leximancer and other related systems can help managers develop new ideas and discover new potential business opportunities. What is special and different with this technique is that firms can explore the demand-side more easily and with larger sample sizes which had been a limiting factor in the past (Olmeda & Sheldon, 2002). In other words, they can gain new insight from the crowd, community, and customers. Managers can explore the vast amount of
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consumer-generated content swiftly and efficiently. Further, the ease of use of these tools makes them more accessible to managers. It enables managers themselves to extract useful information and act on it directly rather than outsourcing their market research and by default some of their decision-making authority and power to so-called experts.
7 Conclusion This section briefly presents the major conclusions of this study. It also describes the study’s limitations. The section ends with several suggestions for future research.
In the highly competitive industries of tourism, travel, and hospitality, it is critical for managers to make not only the correct operational decisions but the correct strategic decisions as well. This study investigates and demonstrates how to leverage customer intelligence in the form of nearly 300,000 customer comments to aid in strategic decision-making. We illustrated how an automated computer-assisted text-mining method could help to transform online consumer-generated textual content into meaningful and actionable customer intelligence for strategic decision-making (Lau et al., 2005). While most strategy is developed top-down, demand-side strategy is developed bottom up with the customer at the center. User-generated content can be extremely valuable for uncovering what the elements that your customers are asking for or about which they are complaining. In other words, what are your customers’ hot buttons?
While many companies are having success with crowdsourcing as a market research technique, the process is not without its challenges (Stafford & Gonier, 2007). One of the solutions is - Using the crowd without the crowd knowing it. Using the tools available today it is possible to scrape and mine the websites, blogs, forums, communities and other places people and customers gather. Web 2.0 sources of qualitative data like blogs, forums, feedback (i.e., user-generated content (UGC)) can offer better research material than offline traditional qualitative diaries (Aaker et al., 2011).
This study uses the advanced, lexical and semantic-based application Leximancer. However, as we move into even more futuristic technology, it is important to know that there are tools available to most hospitality firms at an accessible price. As IoT’s devices are rolled out, managers will be flooded with customer data (Tepeci, 1999). It will be important to quickly determine what data is valuable and what data is trash. Big Data analytics will be needed, and AI technologies have great promise for the future of data mining. 7.1 Limitations
As in all research (Cheng & Edwards, 2017), this study has a few limitations. Firstly, the data collected were from customers and restaurants in New York, United States. Secondly, the comments were retrieved from a single web platform, TripAdvisor. While TripAdvisor is the largest site of its kind in the world, it is just one site. Secondly, it was assumed that the user-generated content is truthful and accurate. While many studies have used and continue to use UGC, we do need to be aware that not all the comments are real. Finally, this large dataset was taken and analyzed as a whole. It is possible that if the dataset was subdivided into smaller groups, more precise results could have been obtained. For example, comments from the higher ranked restaurants could have been examined and compared to the lower ranked ones. 7.2 Future Research
There are some broad avenues for future research. Firstly, there are research opportunities to investigate further how crowdsourcing can be used in market research. Technologies like artificial intelligence improve almost daily, which points to questions about how AI combined with customer
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intelligence can assist managers to gain even deeper insight and make better decisions. As social media and user-generated content continue to grow and IoT’s deploys, will marketers be able to use AI to develop demand-side strategies that were not imaginable previously.
Secondly, there are research opportunities specifically aimed at the restaurant industry at the strategic and operational level. For example, at the more strategic level, there are research questions related to ICT investment. Some researchers (e.g., DiPietro & Wang, 2010) have pointed to ICT as an influential force in the restaurant business. If it is so essential and substantial investments are taking place, why wasn't mentioned by the customers? Are restaurants not making investments or at least the right investments? Are they not using the technologies correctly? Alternatively, is it something else entirely? Whatever it is the answer to these questions may lead to new opportunities, new products and service, and competitive advantage.
Similarly, there are also more operational questions to be investigated. For example, future research could focus on the gender difference within restaurant segments as well as across segments. How are male customers of steakhouse restaurants differ than female customers in these same restaurants? Or how are female steakhouse customers similar to female seafood restaurant customers? Additionally, the analysis of each restaurant segment generated from eight to eleven higher-level themes. This study scratches the surface of themes and underlying concepts. For example, research could investigate how lunch customers differ than dinner customers within segments as well as across segments. Other research could focus on how the reviews and recommendations vary across restaurant segments.
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2.4 Study 2 - An evolution of crowdsourcing: Implications for marketing By using the collective effort of individual customers or groups of customers (i.e., crowdsourcing),
the firm can expand the resources to which it has access. One of the primary reasons for the growth of crowdsourcing is the advances and widespread growth of information and communications technologies (ICT), especially Web technologies, have led to an explosion of possible crowdsourcing and co-creation opportunities.
This research examines how this age of crowdsourcing has impacted privacy and the market for privacy. To crowdsource services at a high level often requires an exchange of personal data from the customer to the firm. This study builds three conceptual models that help describe the evolution of crowdsourcing personal information across three different timeframes. These three stages are based on the amount and the level of sophistication of the crowdsourcing processing of information externalities that result from market transactions between the consumer and the firm. Ultimately, through the crowdsourcing of personal and private information are advancing to the most advance stage whereby a digital twin of each consumer is created. This twin can be used in a predictive analytic process to forecast the thoughts, behaviors and future actions of each consumer.
Most research on crowdsourcing focuses on the first section (i.e., the marketing activities) and how crowdsourcing is a positive marketing technique. Much less research aims its attention on the consequences and/or potential negative aspects of crowdsourcing. This second study connects to the overarching research question as it is a conceptual study that explores the consequences of crowdsourcing for market for privacy.
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STUDY 2
An evolution of crowdsourcing: Implications for marketing
1 Introduction
The modern idea of crowdsourcing has been attributed to Howe (2006). However, crowdsourcing as a practice can be traced back at least three hundred years (Surowiecki, 2004). The rapid advances in information technology and the growth of Web 2.0 has dramatically increased the use of crowdsourcing in recent years. Examples of its use include companies like McDonald’s, Starbucks, Lego, Samsung, Lay’s, as well as service firms like Waze, Wikipedia and SETI@Home.
Crowdsourcing usually includes a crowd, the initiator, and an organizational task (Estelles-Arolas & Gonzalez-Ladron-de-Guevara, 2012). Many researchers have attempted to define crowdsourcing, but most of their definitions are limited and incomplete (Estelles-Arolas & Gonzalez-Ladron-de-Guevara, 2012). Crowdsourcing can incorporate diverse activities, so that defining it comprehensively has not proved to be an easy task. These activities can include: crowdfunding (cf., Belleflamme, Lambert, & Schwienbacher, 2014), decision support (cf., Chiu, Liang, & Turban, 2014), idea creation, crowd-voting, micro-tasks, crowd solutions (cf., Prpić, Shukla, Kietzmann, & McCarthy, 2015), and user-generated content (cf., Daugherty, Eastin, & Bright, 2008).
While most research emphasizes the positive aspects of crowdsourcing, a few researchers have also explored its more negative aspects (cf., Baccarella, Wagner, Kietzmann, & McCarthy, 2018; Gebauer, Fuller, & Pezzei, 2013; Heidenreich, Wittkowski, Handrich, & Falk, 2015). While most of the discussion in the popular press extol its success, crowdsourcing is just like any business technique and frequently fails to meet expectations (Chowdhury, Gruber, & Zolkiewski, 2016). The dysfunctional, value-destroying or negative aspects include role conflicts (Gebauer et al., 2013), a lack of fairness (Gylling, Elliott, & Toivonen, 2012), the absence of shared understanding (Grayson & Ambler, 1999), while the “dark side of social media include cyberbullying, addictive use, trolling, online witch hunts, fake news”, etc. (Baccarella et al., 2018, p. 431). More precisely, there seems to be a gap in the literature around the potential negative consequences of crowdsourcing. One of the more negative consequences of crowdsourcing is its effect on consumer privacy (Baccarella et al., 2018).
Crowdsourcing is changing the nature of the exchange relationship between the consumer and the firm and the use the firm makes of private information. While the media is periodically obsessed with identity theft, corporate hacking and data theft (e.g., Conger, 2016; Roof, 2017), billions of people using apps and other devices are willing to hand over significant and intimate data on a real-time basis twenty-four hours a day. Not surprisingly, crowdsourcing has led to some unexpected and powerful consequences. In a typical commercial transaction, the customer exchanges money for a good, service and or experience from a firm. However, as a result of the exchange, additional spillover information is also produced (Choi, Jeon, & Kim, 2018; Dijkstra, 2007, 2009; Dijkstra & Van Assen, 2008; Hutchinson, 2016). Microeconomics theory calls this spillover an externality (Hutchinson, 2016). A commercial transaction allows the firm to capture information about the buyer (such as: name, address, type of credit card, credit status, etc.) and transaction details (such as: price, time, location of the purchase, etc.). This information by-product represents a resource that can potentially improve the performance of the firm but the possible loss of the buyer’s privacy represents a negative by-product of the transaction. Crowdsourcing is enabling organizations to aggregate and synthesize customer’s personal and private information externality resulting in information asymmetry between the firm and the consumer. As a result, the balance of power in the traditional market exchange relationship has become skewed. Advances in artificial intelligence (AI) are likely to attenuate this. The convergence of the Internet of Things (IoT’s), sensor-based
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crowdsourcing (Fleisch, Weinberger, & Wortmann, 2014; Spiekermann, 2005) Big Data and AI will have an unprecedented impact on consumer behavior, privacy and marketing.
This study highlights the evolution of crowdsourcing and how this is affecting consumer privacy and marketing. The research asks - how is the evolution of crowdsourcing impacting information externalities and consumer privacy and how is this impacting marketing? The paper proceeds as follows: first it defines crowdsourcing, looks at privacy and social exchange theory; second it proposes three conceptual models of how crowdsourcing of personal information is evolving and how this is affecting the balance of power between the consumer and the firm. It then identifies and highlights the rise of the “digital twin” with its benefits and concerns. This is followed by a discussion that draws implications and conclusions.
2 Crowdsourcing
We start by looking at what constitutes crowdsourcing. Is it a theory; a concept; a strategy; a business model; a tool; a process or what? According to the definition by Wacker's (1998, p 361) “a theory must have four basic criteria: conceptual definitions, domain limitations, relationship-building, and predictions." An examination of the literature shows crowdsourcing fails all four criteria, thus it cannot be said to be a theory. If it is not a theory, is it a concept? While the definition of the term concept, can be difficult to precisely explain (cf., Goguen, 2005; Jackendoff, 1989; Mammen, 2008) a strong working definition is that “the notion of a concept designates an abstract idea or model that corresponds to something concrete in reality or in language.” (Samset, 2010, p. 90). The diversity of what constitutes crowdsourcing indicates that it is not a single concept, and there is a multi-pronged conceptual foundation behind crowdsourcing. These include, the idea of collective wisdom and collective intelligence (Saxton, Oh, & Kishore, 2013), open innovation (Chesbrough, 2003), outsourcing (Schenk & Guittard, 2009, 2011), open source (Howe, 2008), social media (Zwass, 2010), user-generated content (Daugherty, Eastin, & Bright, 2008) and co-creation (Zwass, 2010). All may have an underlying relation to crowdsourcing suggesting that it is difficult to claim the existence of a single concept.
So, is crowdsourcing a strategy? It has been called a sourcing strategy for organizations (Soliman, 2013) and an innovation strategy (Pisano, 2015). It has also been described as a method of democratizing strategy development within a firm (Stieger, Matzler, Chatterjee, & Ladstaetter-Fussenegger, 2012) and a digital business strategy (Tuli, Kohli, & Bharadwaj, 2007). While it seems that crowdsourcing can be used strategically, that is not the same thing as defining crowdsourcing as a strategy. If it cannot be said to be a strategy, is it a business model? Indeed, one of the more popular ways researchers define crowdsourcing is by calling it a business model. For example, Kohler (2015) calls crowdsourcing a business model to create and capture value (cf., Brabham, 2008; Djelassi & Decoopman, 2013; Saxton et al., 2013; Walter & Back, 2010). Unfortunately, as with strategy, the notion of business model is one of those overused terms that suffers from a lack of clarity as to what exactly it is (Magretta, 2002).
Finally, is crowdsourcing a tool or a process? While Soliman (2013) called crowdsourcing a strategy, a closer examination reveals he was actually referring to it as a tool to be used in the production process. Similarly, Gast & Zanini (2012) refer to crowdsourcing, not as a strategy, but rather a tool used to create strategy. More recently, (Kietzmann, 2017, p. 152) defined crowdsourcing as "The use of IT to outsource any organizational function to a strategically defined population of human and non-human actors in the form of an open call.” While this definition is a significant improvement over that by Howe, it still has some issues. First, while crowdsourcing today is being driven in large part by the advances in information communication technology (ICT), it is not necessarily required for crowdsourcing. Secondly, the term open call is not well defined. Certainly, it is necessary to "get the word out," but it is unclear how wide the net needs to be cast. Does it mean that we need a mass advertising and promotional campaign or can we simply inform a group of customers in our existing brand community? An alternative definition of crowdsourcing is
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provided by Garrigos-Simon, Narangajavana, & Galdón-Salvador (2014) who describes it as a process that can comprise of various organizational tasks traditionally handled within the organization that are opened to stakeholders’ of all types inside and outside the organization including customers, employees, partners, suppliers or the general public who are interesting in taking part in one of those diverse organization activities. Given the above, for the purpose of this study, crowdsourcing is defined as: a tool or process by which the firm can increase or expand the resources to which it has access to by using the collective effort of a group of individuals or organizations.
Utilizing the crowd provides an innovative way for firms to solve particular business problems by tapping the resources that often exist outside the organization. While there are many entrepreneurial and innovative tools available, by using the collective effort of a group of individuals, the firm can increase or expand the resources to which it has access. Crowdsourcing is a situational, contextual and flexible tool that can be used in many different organizational contexts. The specific context for this article is marketing.
3 Consumer Privacy
Crowd sourcing raises privacy issues. It has been noted by Ohlhausen & Okuliar, (2015) that the issue of privacy owes its origin to a paper in Harvard Business Law review by Warren and Brandeis, (1890, p. 193) who express their alarm that the then new technology of “instantaneous photographs and newspaper enterprise have invaded the sacred precincts of private and domestic life; and numerous mechanical devices threaten to make good the prediction that ‘what is whispered in the closet shall be proclaimed from the house-tops,” Ironically, almost 130 years later, society now faces the same issue, with technology and a level of intrusion that they could not have dreamt off. While the legal definition of privacy may have its roots in the “right to be left alone” (cf., Warren & Brandeis, 1890) in more recent times it has been evolved to the mean the right to own and disclose one’s personal information (e.g., Westin, 1967).
The amount of available and accessible private information has exploded with the advent of new hardware (e.g., smartphones and smart devices), software (e.g., apps and AI) and marketing tools (e.g., crowdsourcing). “When we communicate, interact, or even just go shopping - both online and offline - we leave data trails and digital footprints behind us, generating information about our lives and activities as we go” (Buchanan et al., 2007, p. 157). The demand and value of this data has increased for firms as they now can use the data across many products to gain greater customer insight.
While the media is periodically obsessed with identity theft, corporate hacking and data theft (e.g., Conger, 2016; Roof, 2017), billions of people are willingly, through apps and other means, to hand over data. Herein lies a paradox. Consumers claim privacy is important to them, but at the same time, they are giving away or allow firms to take vast amounts of data from them and about them literately 24 hours a day using apps, smart devices and a widening prevalence of the IoT’s.
4 Social exchange theory
While different approaches can be adopted to explain crowdsourcing and the privacy phenomena (cf., Martin & Murphy, 2017), an exchange perspective (Blau, 1964; Emerson, 1976; Homans, 1958) can provide insight on the changes in power dynamics brought about by the evolution that has occurred in crowdsourcing. Social exchange theory (SET) is primarily a rational approach, built on the personal belief of self-interest (e.g., Blau, 1964; Homans, 1958), where parties to the transaction or exchange decide on the basis of costs, benefits and competing alternatives. The origin of SET spans diverse disciplines including anthropology (e.g., Firth, 1967; Sahlins, 1972), social psychology (e.g., Gouldner, 1960; Kelley & Thibault, 1978), sociology (e.g.,
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Blau, 1964), management (e.g., Wayne, Shore, & Liden, 1997), and marketing (e.g., Cropanzano & Mitchell, 2005; Sheth & Uslay, 2007) where exchange remains an essential aspect of the overall marketing concept. Of course "Exchange, the act of giving or taking one thing in return for another is not unique to marketing and, by unspoken acclamation, is a central concept in virtually all human sciences" (Sheth & Uslay, 2007, p. 302). Researchers generally agree that social exchange comprises a sequence of interdependent interactions that create obligations and are contingent on the behavior of other parties (Cropanzano & Mitchell, 2005). Consumers are willing to engage in an exchange or transaction when they feel benefits outweigh perceived costs (American Marketing Association, 2008, 2013). Perfectly competitive markets require symmetry of information. Therefore, for this deal to work correctly, there needs to be enough transparency so that both sides can calculate the costs and there exists enough trust between the parties for a transaction to occur (Awad & Krishnan, 2006; Benassi, 1999; Luo, 2002; Zucker, 1986). Asymmetry distorts the market as it gives a significant advantage to one side (Stigler, 1957). In the past, the amount of private information an individual possessed was relatively small and it was easy to keep personal details private. However, as technology progressed, and more details became accessible online, the amount of private information has grown.
The exchange relationship may or may not create externalities. These are defined as “direct consequences (positive or negative) of exchanges, for the well-being of actors that are not themselves involved in the exchange” (Dijkstra, 2007, p. 4). Externalities, also called spillover (Hutchinson, 2016), can take many forms. Examples of negative externalities include firms releasing pollution from production into the air or water, or the run-off of farmers’ fertilizer into a local river. Examples of positive externalities include people raising bees for honey, but the bees also pollinate plants and flowers.
Today, crowdsourcing employs product exchanges that continually need to be fed personal information. Some of this information is added willfully and voluntarily, but new technologies can allow firms to capture vast amounts of personal information that is less willingly and knowingly conceded. Moreover, big data analytics “have made it possible to draw more accurate inference about those consumers who had not shared their personal data based on the data gleaned from those who had shared.”(Choi, Jeon, & Kim, 2018, p. 1). These information externalities are increasingly playing a significant role as crowdsourcing evolves.
5 Models of crowdsourcing
This research focuses on personal and private information as externalities of a market transaction involving crowdsourcing. Three models of crowdsourcing are considered. Model I describes the age of pre-crowdsourcing characterized by an environment that did not make extensive use of ICT. Model II describes the age of crowdsourcing. This is the present situation, a period of transition to Model III that describes the situation in the near future - the age of IoT's.
5.1 Model I – The age of pre-crowdsourcing
The pre-crowdsourcing age can be defined as the time before Web 2.0, smartphones, and smart devices. With less ICT around, crowdsourcing was slower, on a smaller scale and less pervasive. Typically, there are just two actors involved in a simple market transaction - the consumer (Jane) and the firm directly involved in the exchange - See Figure 1. The consumer and the purchase transaction are the only sources of personal information. As a result the personal information created was limited and stayed between the firm and the consumer. This is not to say that occasionally data was not sold in the pre-crowdsourcing age, but it was more limited.
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5.2 Model II – The Age of Crowdsourcing
Currently, we are in “The Crowdsourcing Age” (cf., Hitlin, 2016; Hune-Brown, 2015; Hurt, 2006; Moon, 2017; van Tilburg, 2016), where the introduction of new personal digital technologies and the app ecosystem surrounding them has considerably augmented the amount of personal information available (cf., Kugler, 2018; Syrjanen, 2018). In addition, technology advances have made it possible for data to be seamlessly exchanged and crowdsourced as it moves around the network. Indeed, the evidence demonstrating a growing exchange of personal data is compelling. According to Statista, as of March 2017, the App Store for Apple products contained 2.2 million applications and the android app store, Google Play, contained 2.8 million applications (Statista, 2017). It is also estimated that there will be 2.87 billion smartphone users by the year 2020 (BIS Research, 2017). Today's 2 billion-plus smartphone users make use of an average of 9 different apps every day and 30 different apps each month (BIS Research, 2017; Statista, 2017). Clearly, this potentially represents considerable personal information externalities. The previously simple bilateral exchange in Model I has evolved into a more complex network exchange shown in Model II - see Figure 2. The entities or units making up a network’s membership can be termed objects, actors, nodes, and the network concept relates to the fact that these nodes have relationships, links or ties between and among each other (Contractor, Monge, & Leonardi, 2011; Granovetter, 1983; Wasserman & Faust, 1994). A list of nodes and actors in the market for personal information as depicted in Model II are listed in Table 1. In essence, Model II shows a much more complicated scenario than that in Model I. The primary difference between the two is three-fold. First, technology has increased the number of nodes or actors involved in a single transaction. Second, technology has increased the volume of personal information that is created as a by-product of each transaction. Third, technology has enabled firms to crowdsource vast amounts of diverse personal information. This has growing implications for consumers and firms.
Table 1: Nodes/Actors in Model II
Actors When and how involved Consumer The consumer’s personal and private information is
released as a by-product of the transaction. Direct Firms (F1) Directly provide goods, services, or experiences in
exchange and as direct access to the private information
Indirect Firms (F2) Receive personal information from direct firms in an exchange or purchase
Friends of consumers (FC) Allow access to other friends’ personal information usually inadvertently
Government (G) Can play the role of supplier personal information or a user of personal information
Data Brokers (DB) Secretive organizations that buy and sell personal information
Credit Agencies (CA) Firms that collect financial data and score consumers’ financial health
Web browser, cookies, etc. Web browser, data, cookies, etc. MegaSites (e.g., Facebook, Google, Amazon, etc.) MegaSites (e.g., Facebook, Google, Amazon, etc.) Sensors (e.g., phones, smart devices, and other IoT’s devices
Sensors (e.g., phones, smart devices, and other IoT’s devices
Criminals/Bad guys Typically use the private and personal information with criminal intent
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It is suggested that the evolution in crowdsourcing recorded in Model II whereby a consumers’ personal and private information is accumulated and aggregated by the network of market actors to produce a virtual image of the consumer results in what can be termed a digital doppelganger. A doppelganger can be defined as someone who nearly resembles another, yet is not a twin (“Doppelganger,” 2003). Although the digital doppelganger is not a twin, marketers can use it to make accurate behavioural predictions of the underlying physical person. Despite being at the early stages, there are examples of how marketers are developing digital doppelgangers. For example, earlier it was mentioned that while the information dossier being created by data brokers and other may contain thousands of pieces of data, they are incomplete. The results can be intensely personal and private. Marketers and others are hungry to “know a person’s ethnicity, spending habits, sexual orientation, and specific illnesses such as HIV, diabetes, depression or substance abuse. This information may be found directly in data broker’s databases, or, increasingly, it may be predicted from other data” (Boutin, 2016). The brokers use the data they do have and predictive analytics to fill in the blanks.
The notion of digital doppelganger is intimately connected to predictive analytics (PA) and predictive marketing. Predictive analytics deals with the processes employed to construct and evaluate data-driven forecasting models (Verbraken, Lessmann, & Baesens, 2012). Marketing is the leading user of PA in areas such as customer acquisition, budgeting, and forecasting among others (Eckerson, 2007). “Predictive marketing is the practice of extracting information from existing customer datasets to determine a pattern and predict future outcomes and trends” (Everstring, 2015, p. 2).While predictive marketing as a practice is a few decades old, the process has been slow, challenging and not so accurate (Karr, 2018). However the recent growth in predictive marketing can be attributed to a number of converging factors (The 2015 State of Predictive Marketing Survey Report). First, the increased number and availability of tools; second the increasingly more accurate and sophisticated algorithms; third, the availability of more customer data; fourth, centralized and accessible data, and; finally, increased computing power able to handle the data and algorithms (Everstring, 2015).
PA is intended to make operationally accurate forecasts. PA took a significant stride forward, especially at the individual consumer level, with Google’s introduction of Google Adsense®. This was built on the technology created by a firm Google purchased and is behind Google Network of Content Sites (Cutts, 2003; Tyler & Mayzel, 2003). It allows for predicting surfers’ interests so that they are shown ads that are relevant and related to their interests. Google went a step further when they added Adsense® to Gmail, their free email service founded in 2004 (McCracken, 2014). With this combination, Google reads and analyzes every email and then serves ads based on its contents. In 2010, Google went even further when they began to combine a users’ search history with the content of the pages they viewed to serve even more precise and customized ads (Pepitone, 2010). Therefore, in sum, the evolution of crowdsourcing is increasing the opportunities for marketers to know, understand and predict their customers better. The downside is that it also opens up the consumer to potential violations of privacy and possible abuse. That is the present. It is but a transition point as new even more advanced technology waits on the horizon.
5.3 Model III – The Age of IoT’s
The age of IOT’s is a post-crowdsourcing age. It is projected that by 2025 there will be over 100 billion connected devices, sensors, actuators, and cameras to make up the IoT’s (Columbus, 2016). The entire IoT’s system is predicted to generate 403 zettabytes of data a year by 2018, up from 113.4 zettabytes in 2013 (marketsandmarkets.com, 2017). To put that into perspective, one zettabyte is equivalent to 1 billion terabytes or 1 trillion gigabytes. This is not to say that the entire IoT’s system is about personal information but the system is consuming, analyzing and storing vast amounts of data and some of that is detailed personal information. Many of these sensors will be
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adding rich personal details including real-time personal location and behavioral information with aspects in video and audio (Buntz, 2016; Newman, 2016).
In the context of Model III the creation of the digital doppelganger or digital twin of the consumer by the networked firms will become increasingly complete and provide a near identical digital version of the consumer. Practically all personal data would have emigrated online and the crowdsourcing of personal information from multiples transactions and sources within the network will create the digital twin - a digital representation of individual consumers. Model III in Figure 3 outlines the emergence of the digital twin out of the interaction of network members. In the original conceptualization, the term ‘digital twin’ represented a specific concept used in the industrial manufacturing sector, especially in the developing, manufacturing and management of aerial vehicles (O’Connor, 2017). Indeed, Glaessgen & Stargel, (2012, p. 7) defined it as “an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin.” We adapt this concept to the circumstances of customers so that just as in the case of jets, space, and other flying vehicles initially contemplated, the digital twin of the consumer can be used by firms to monitor, diagnose and make increasingly more accurate predictions of both twins purchasing, desires, thinking, voting, general behavior, etc. (Everstring, 2015). Therefore, more apps, more sensors, more smart devices, more nodes in the network, machine learning and further deployment of AI, results in a higher volume and quality of personal information and market transactions resulting in the creation of a near perfect digital twin. The Age of IoT’s sees Model II transform into Model III. At face value, differences between Model II and Model III do not seem significant; rather it is just more of everything. However, adding sensors (S) and smart devices to the network means more nodes, more information, and a higher degree of accuracy in predictions resulting in superior digital twins. These advances are significant and will move predictive analysis from the traditional statistical data analysis to the modern and futuristic, neural networks, machine learning, Big Data and AI (Dasgupta, Dispensa, & Ghose, 1994).
Big Data is driving the development of AI (Ryan, 2016). Therefore, accompanying this flood of new information will be the ability to analyze it through the further development and deployment of AI. Therefore, as Big Data grows and the amount of personal information grows accordingly, there is a corresponding growth in consumers’ personal information to be used in marketing. Increased marketing data has both benefits and costs (Payne, Bettman, & Johnson, 1993). Higher levels of data will have a positive impact on the decision-making, predictive power, and the predictive accuracy of the network (Glazer, Steckel, & Winer, 1992). Unfortunately, this development is not without its concerns. The increase in predictive power, provides opportunities for dark side activities and abuse. Unscrupulous persons or organizations could kidnap the twin, the virtual representation of a consumer, and use it for wide-ranging, illicit activities. For example, this could enable criminals to impersonate another person to commit fraud to a much greater extent that “ordinary” identity theft. While such risks are clear, the crowdsourcing of personal and private information can cause situations where the average firm may be faced with grey areas involving consumer privacy.
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6 Conclusions and Implications
This study contributes to the understanding of crowdsourcing in various ways. First it provides a conceptual definition of crowdsourcing. Crowdsourcing can be seen as a tool that can play many different organizational and extra-organization roles depending on context. As technology continues to advance the roles, together with the intended and unintended consequences of crowdsourcing also change and evolve. The study also contributes to the field of marketing by providing three conceptual models outlining the evolution of crowdsourcing techniques and the impact of private information created through market transactions.
Second, the study extends the microeconomic concept of information externalities to crowdsourcing. It outlines how informational externalities produced as a by-product of market transactions have evolved as crowdsourcing has developed leading to unintended consequences (Choi et al., 2018). It further demonstrates that the same information can create a spillover that can concurrently represent a positive externality for the firm and a negative externality for the consumer. The firm can aggregate and crowdsource personal and private information to better gain insight on the attitude and behavior of customers while customers suffer a loss in privacy and a potential loss of self.
Third, this study contributes to the social exchange theory discourse. The information asymmetry caused by the crowdsourcing of informational externalities causes a shift in balance of power in the buyer-seller exchange relationship as the firm increasingly gains in “power” to the detriment of the customer (cf., Edvardsson, Tronvoll, & Gruber, 2011; Nayyar, 1990) particularly because of the information externality, specifically in terms of loss of privacy (Stigler, 1957). Such an imbalance is likely to witness higher calls for state intervention to protect customers and redress the balance. There is already evidence of this as some governments and the EU have shown a willingness to intervene to rebalance the exchange equation.
The study identifies a number of consequences for customers. One positive implication for consumers is that the Age of IoT’s will continue to create new crowdsourced and co-created goods, services and experiences. Secondly, in-depth knowledge created by the crowdsourcing information will make it possible for firms to increasingly tailor-make products to meet individual customer requirements. This can be true if the insight that the digital twin can provide is used correctly. Unfortunately, that is probably the extent of the good news for the consumer. The evolution of crowdsourcing involving personal information is likely to be mostly negative for consumers. For example, insurance companies are already using sensor-based information to help determine car insurance rates by mapping and recording customers’ driving location and history (Singh, 2017). Imagine the impact on insurance rates, mortgage rates and ability to get a job, etc., once your digital twin incorporates all your fitness, health, eating habits, medical activities or even your genetic background and “determines” or predicts your current and future medical condition. The only consolation may be that this will be so for all.
Another concern is that it is likely to be very challenging for consumers to remove even small errors in their credit report (White, 2012). So, what happens when there are errors in personal information used to create the digital twin? This question is not a hypothetical question. Pam Dixon, executive director of the World Privacy Forum, an advocacy organization, reported a 50% error rate from the data broker, they tested (Boutin, 2016). Further, given the amount and speed at which the information is being combined and the lack of ownership or responsibility for that information, it is likely that digital twins with bad data will not be changed. Furthermore, bad data may be introduced unintentionally or possibly intentionally by someone with an agenda. Either would result in the "birth" of an incorrect digital twin which could have unfortunate consequences including faulty predictive power.
Although there are many potential implications for marketers and firms, this section focuses on some of the practical implications for organizations with access to a consumer's digital twin. The
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existence of a digital twin and predictive analytics will increasingly allow marketers to gain a fine-grained understanding of who each customer is but it may also be used to develop and test new products and services. While marketers may want to use digital twins at the individual level as well as the macro level, governments and politicians may be more interested in an aggregation of twins. Instead of guessing what the public would think about any proposed piece of legislation they could ask a sample aggregation of twins. This might mean the death of the polling industry or at least a substantial change. Unfortunately, criminals may also be able to take advantage of digital twins. They could use the twins to determine not only who is most susceptible to fraud and schemes, but to which schemes they are most susceptible.
A final thought - Crystal ball gazing such as this is never easy. As the unattributed humorous saying goes – It is difficult to make predictions, especially about the future.
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3 Summary - Contributions and Conclusions
This dissertation has stated earlier that the ultimate purpose of a business is to create a customer (Drucker, 1954). Creating that customer consumes firm resources (Drucker, 1958; Faulkner, 2017; Kelley, 2017). A lack of firm resources drives a need for creativity and innovation (Stevenson & Jarillo, 1990; Stevenson, 1983; Stevenson & Gumpert, 1985). Marketing innovations coupled with technological innovations (e.g., ICT, the Internet, digitalization, etc.) have led to customers leaving the role of “just” consumers and getting involved more directly and actively in the marketing process (Bowes & Hippel, 2008; Heinonen et al., 2010; von Hippel, 2005a; Zwass, 2010) and others have pushed the business environment to become more dynamic (Aaker & Adler, 1984). This “new” business environment calls for more nontraditional marketing techniques and practices.
One of these “new” techniques, crowdsourcing, is having an increasingly significant effect on marketing (cf., Bal, Weidner, Hanna, & Mills, 2016; Bayus, 2013; Castronovo & Huang, 2012; Corte, 2013; Djelassi & Decoopman, 2013; Pihl, 2013; Simula, Töllinen, & Karjaluoto, 2012; Whitla, 2009 and others). The idea of using the crowd to solve problems is not new. However, there is no doubt that the rapid advances in information technology and the growth of Web 2.0 has dramatically increased the use of crowdsourcing (Howe, 2006).
Future technology advances can allow crowdsourcing to have a greater impact on firm activities, especially marketing (cf., Andriole, 2010; Fleisch, Weinberger, & Wortmann, 2014; Garrigos-Simon, Alcamí, & Ribera, 2012; Garrigos-Simon & Narangajavana, 2015; Jelonek & Wyslocka, 2015; Lipiäinen, 2014; Tiago & Veríssimo, 2014). As a result, the effects and consequences of crowdsourcing on marketing will be more significant in the future. Therefore, broadening and deepening the understanding in this area has been a critical objective of this dissertation’s research.
This final chapter discusses the research findings for each included paper and study. It further summarizes the theoretical and managerial/practitioner contributions of the research. Finally, the chapter suggests some possible future research directions and then concludes.
3.1 Answers to the Research Questions
Paper 1 successfully answers Research Question 1.
RQ1 - To what extent are crowdfunding platforms accessible to organizations as a marketing channel and, if so, what role can these platforms play? More specifically this paper shows how crowdfunding platforms, created to help firms and other organizations raise capital, can be used as promotion, sales, and idea generating platforms as well. As a result, firms can use crowdfunding sites strategically rather than just operationally. Furthermore, depending on the aims of the firms and their constraints, varying strategies, tactics and techniques may be appropriate.
Paper 2 also answers its research question. RQ2 - How will the shift from Web 2.0 (and active-user input) to Web 3.0 (and passive-data/sensor–based input) impact the new opportunities/product development process?
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The Internet of Things will change how we interact with "things" and how we connect. The majority of this new information will likewise change how firms communicate with their customers as this information can be utilized to enable them to build more profound and more personal relationships. These changes will not only be in the consumer sectors. The more prominent effects might be in the industrial sectors. For example, industries including manufacturing, mining, farming, and oil and gas are currently feeling the effect of sensor-based business and new product development.
Study 1’s investigation is successful in answering its primary research question.
RQ3 - How can user-generated content help firms make strategic decisions about new business opportunities? This study has shown that by utilizing customer-created content online reviews (i.e., the demand-side), managers can acquire specialized knowledge to enable them to make keener strategic choices and reveal potential business opportunities. In the intensely competitive businesses of tourism, travel, and hospitality, it is vital for managers to make the right strategic choices. This examination shows how to leverage customer insight (i.e., UGC), from approximately 300,000 customer comments to assist in making possible strategic decisions. The study described how an automated computer-assisted text-mining method could help convert online text content into meaningful and actionable customer intelligence for strategic decision-making (Lau et al., 2005). While most strategy is created top-down, demand-side strategy is developed bottom-up with the customer at the epicenter (Brief & Bazerman, 2003; Harrington, Chathoth, Ottenbacher, & Altinay, 2014). UGC can be extremely valuable for revealing what customers are grumbling about or demanding.
Study 2 has shown that the changes in crowdsourcing information externalities are influencing consumers’ privacy and in so answers the following question.
RQ4 - How is the evolution of crowdsourcing impacting information externalities and consumer privacy and how is this impacting marketing? Study 2 answers RQ4 by examining how technological advances are changing crowdsourcing. In this process, the study proposes three models that represent the evolution of crowdsourcing during three different stages of development. Three stages are based on the amount and the level sophistication of the crowdsourcing processing of information externalities that result from market transactions between the consumer and the firm. Overall
The overall research question for this thesis is –
How can crowdsourcing be used as a marketing tool?
This thesis has demonstrated how crowdsourcing as a tool can impact marketing activities. Explicitly, it has shown how crowdsourcing can be used for promotion, sales as well as fundraising. It has further shown how crowdsourcing can play a role in new product and opportunity development. Additionally, it has shown how crowdsourcing’s role is likely to increase as we move into the era of IoT’s and Big Data. Next, this thesis has demonstrated how crowdsourcing can be used in market research. While much of the discourse around crowdsourcing and marketing is overwhelmingly positive (cf., Gebauer, Fuller, & Pezzei, 2013; Harris, 2011; Morphy, 2009; Simula, 2013), not all of the direct effects or indirect effects are positive. Some consequences can be detrimental. Therefore, the final study examines how crowdsourcing is having a primarily
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negative impact on the consumer privacy of the while simultaneously creates opportunities for marketers.
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3.2 Theoretical Contributions
Firstly, researchers have looked at issues at the nexus of crowdsourcing and market activities including promotion and distribution (e.g., Howe, 2008), advertising (e.g., Brabham, 2009; Brabham, 2008; Roth & Kimani, 2014) and brand development (e.g., Djelassi & Decoopman, 2013; Moisseyev, 2013; Zadeh & Sharda, 2014). Paper 1 in this dissertation contributes to and extends this literature by developing the idea that crowdfunding, in particular. can be used as a tool to accomplish all three.
Secondly, a few crowdsourcing researchers (cf., Cowley, 2016; Freeman, 2015; Smith, Dinev, & Heng, 2011) have begun focus on how crowdfunding has unexpectedly impacted marketing. Paper 1 also joins, contributes and extends this crowdsourcing/crowdfunding research by showing how the approaches firms can take in using with crowdfunding websites as based on their objectives and constraints.
The research additionally contributes by proposing a decision-tree to aid in the decision process.1 The paper also provides a warning. Specifically, this adaptation of crowdfunding for marketing and sales purposes is not without its challenges. Organizations would be well advised to consider not only the opportunities these platforms provide but also their limitations and risks.
Paper 2 investigates, analyzes and demonstrates how an evolution to Web 3.0 and the merging of IoT’s with Big Data has generated a new source of product, service and experience possibilities (Andriole, 2010; Fleisch, Weinberger, & Wortmann, 2014; Garrigos-Simon, Alcamí, & Ribera, 2012; Garrigos-Simon & Narangajavana, 2015; Jelonek & Wyslocka, 2015; Lipiäinen, 2014; Tiago & Veríssimo, 2014). Other researchers have explored crowdsourcing’s impact on ideation (i.e., new idea/production creation) (e.g., Bogers et al., 2010; Howe, 2008; Leimeister et al., 2009). Paper 2 contributes and extends this literature in three ways. Firstly, the research in Paper 2 examines, analyzes and presents how a shift to Web 3.0 and the convergence of IoT's with big data has created a new source of product possibilities. Sensor-based entrepreneurship is leveraging IoT's to exploit new business opportunities by creating new goods and services. Secondly, this research further highlights how this shift from passive data input to active data input (Prpić, 2016), has paved the way for crowdsourcing as a potential business model for many sensor-based services (Fleisch et al., 2014). The final theoretical contribution of this research was to develop a framework to aid researchers in thinking about the development of sensor-based products and services.
There are five primary theoretical implications of Study 1. The prevailing views in strategic management (e.g., resource-based view, transaction-cost economics, positioning, and dynamic capabilities) are supply-side views (Priem, 2007). Accordingly, the demand-side view of strategy is under-researched, especially in the areas of tourism, travel, and hospitality (Harrington et al., 2014). Furthermore, using a demand-side strategic approach seems highly relevant for firms in the travel, tourism and hospitality industries, especially as more customer-center business models show promise (Kandampully, 2006). Study 1 contributes to theory giving empirical support for the idea by using user-generated content from customers in the form of online reviews (i.e., the demand-side), managers can gain specialized knowledge to help them make more insightful strategic decisions and uncover potential business opportunities (Priem, 2007; Priem, Li, & Carr, 2012), specifically in these industries.
Secondly, there are a group of researchers that that have started to explore how crowdsourcing is affecting market research (e.g., Behrend et al., 2011; Cechanowicz et al., 2013; Kleemann et al., 2008; Poynter, 2013). Study 1 gives support to the notion that crowdsourcing can be used in market research (e.g., Brabham, 2012b; Gatautis & Vitkauskaite, 2014; Rathi & Given, 2010; Whitla, 2009; Zadeh & Sharda, 2014). Also, the study supports the broader assertion that online market
1 This decision tree serves a dual purpose. First, it is a conceptual tool that researchers can build and extend. Secondly, it is a tool that managers and other practitioners can use.
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research is a reliable alternative to offline market research (e.g., Aaker et al., 2011; Austin et al., 2007; Comley, 2008; Fawcett et al., 2014; Mathwick, 2002; Stafford & Gonier, 2007). Study 1 contributes and extends this market research and user-generated content research.
Fourthly, this study supports the idea that data, text mining, and content analysis are suitable and valuable qualitative methods to use when the researcher is required to draw out significance and subtleties from crowdsourced text (Krippendorff, 1989, 2013). Further, given the amount of UGC in industries like travel, tourism, and hospitality, these methods, particularly computer-assisted methods like Leximancer, can be particularly valuable in analyzing customer intelligence for decision-making (Carson, 2008; Lu & Stepchenkova, 2015; Olmeda & Sheldon, 2002).
Finally, this study contributes by demonstrating and supporting the idea that social and user-generated content (i.e., crowdsourced content) produced by the travel, tourism, and hospitality industry, can be especially helpful in developing insight for decision-making (Carson, 2008; Lu & Stepchenkova, 2015; Olmeda & Sheldon, 2002).
Study 2 is a conceptual study that contributes in three primary ways. While many researchers continue to struggle to define the term crowdsourcing (cf., Estelles-Arolas & Gonzalez-Ladron-de-Guevara, 2012), this study contributes by making an attempt to develop a definition of crowdsourcing as well. Secondly, building on theories of social exchange (Emerson, 1976) and microeconomics, specifically the literature on externalities (Dijkstra, 2007; Hutchinson, 2016), the study posits three models of the crowdsourcing of personal information externalities. Next, the study utilizes these conceptualizations to describe and propose a new concept – the digital twin. This twin is an ultra-realistic virtual representation of the customer that can be utilized for an assortment of decision-making scenarios and behavioral simulations but can strip the individual consumer of much of his or her privacy, but at the same time create marketing opportunities for firms. Overall
Each of the four research projects described above has made an attempt to contribute and extended the research. Taken together, these four projects have strived to add our understanding of how utilizing the crowd provides an innovative way for firms to solve particular marketing problems by tapping the resources that often exist outside the organization. While there are many entrepreneurial and innovative tools available, by using the collective effort of a group of individuals, the firm can increase or expand the resources to which it has access and further its marketing objectives.
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3.3 Managerial and Practitioner Contributions
Paper 1 demonstrates how entrepreneurs of startups, as well as managers of larger firms, can use crowdfunding strategically rather than "just" a way to raise small amounts of capital. One of the primary ways in which this paper accomplishes this is by the development and presentation of a decision tree. By using the decision tree firms can determine how to engage with crowdsourcing platforms given their objectives and limitations. For example, depending on whether the firm as a product ready to sell and its goals, there are four different choices the firm could make.
The primary managerial contribution of Paper 2 was to develop a framework to assist sensor-based marketers and entrepreneurs in the crafting of new sensor-based products and services. More specially, the sensor-based entrepreneurship opportunity development framework looks at the level of data aggregation and the location of the sensor (e.g., IoT’s device) in the environment. Then based on these two axes marketers can utilize the framework as an opportunity development mapping tool. In order words, marketers can examine possible sensor-based opportunities and patterns. The framework additionally assists marketers and managers to design and grow new business opportunities, as well as administer collections of their current products and services.
Study 1 provided direct suggestions for management in the steak, Italian, and seafood restaurant industries, especially in New York. This study’s data and analysis revealed three specific recommendations concerning positioning: 1) steakhouse customers want to be served; 2) Italian restaurant customers want a complete restaurant experience assuming a good food experience; and 3) seafood restaurant customers want fresh and tasty seafood in a friendly atmosphere with excellent service.
One lesson that the data revealed is that eatery customers expect different things from distinctive restaurant sectors. These subtle differences can have a significant effect in constructing a competitive advantage in the highly competitive hospitality business (Kandampully, 2006).
A second lesson is the use of Leximancer, and other computer-assisted systems can help managers develop new ideas and discover potential business opportunities. What is distinctive about this method is that firms can examine the demand-side more efficiently and with larger sample sizes which had been a constraining factor in the past (Olmeda & Sheldon, 2002). Managers can investigate vast amounts of consumer-generated content quickly and effectively. Further, the usability of these tools makes them more accessible to managers. It empowers managers themselves to identify useful information and act on it directly rather than outsourcing their market research and by default some of their decision-making authority to supposed experts.
In Study 2 while there are many implications for marketers and firms, the emphasis here will be on some of the practical implications of having access to a consumer's digital twin. With the digital twin and predictive analytics, marketers cannot only have a fine-grained understanding of who each customer is but may also be able to use the twin to develop and test new products and services, understand the implications of some of their decisions before they are implemented, and gain deeper insight at the individual consumer level.
Also, not all firms will have direct access to all digital twins, but one can envisage marketing firms providing limited access to the twins by renting or leasing them. Put another way, a firm with direct access could rent the twin to answer the specific marketing questions of the those without access. This service could take the form similar to how IBM provides varying degrees of access to its Watson AI system (Eaton, 2013). This scenario seems even more conceivable when one considers the power of combining of hundreds, thousands or millions of digital twins.
Governments and politicians may only require aggregation of many twins. Rather than speculating on what the citizenry would think about a planned piece of legislation, they could ask the aggregation. This development might mean a substantial change to the polling industry. However, marketers may want to use digital twins at the individual level as well as the macro level.
Regrettably, criminals may also be capable of exploiting the twins. For example, they could use the twins to establish not only who is most susceptible to fraud and schemes, but to which schemes they are most predisposed.
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Overall
Each paper and each study in this thesis has offered suggestions, recommendations, and contributions for marketers, managers, entrepreneurs and other practitioners. In sum, the main contribution is to inform these practitioners that crowdsourcing does have a role to play in many facets of marketing depending on the context.
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3.4 Avenues for Future Research
In Paper 1 several open questions could serve as exciting areas for research. Will more firms see crowdfunding sites as viable and cost-effective platforms for promotion and sales? As established and larger firms begin to use crowdfunding platforms, how will that possibly impact investors? How will this "encroachment" change the attitudes and behaviors of those users who "invest" in firms because they want to help small, new and innovative firms?
In Paper 2 one of the tangible results in this article was the development of a framework to assist marketers and entrepreneurs design, develop, and plot their initial sensor-based business opportunities. More research is needed to improve this tool. For example, the marketer should incorporate the details about the target customer for these new products. Therefore, there should be perhaps a way to integrate the target market into the framework.
Study 1 also leads to some interesting questions. As social media and UGC continue to expand and IoT’s devices are rolled out how will technologies like artificial intelligence aid managers in developing more precise insights and make better decisions? Additionally, will marketers be able to use AI to create demand-side strategies and opportunities previously unimaginable?
Further, there are potential, restaurant-specific research questions. For example, DiPietro & Wang (2010) suggested that information and communications technology (ICT) is having a significant effect on the restaurant business. So much so that substantial investments are being made. If this is true, why didn't the customers mention it in this study? Are restaurants not making investments? Alternatively, are they not making the correct one? Alternatively, perhaps the restaurants are not implementing the technologies correctly?
Finally, in there is an opportunity to extend the research in Study 2 by comparing how the comments in high ranked restaurants differ from the comments in low ranked restaurants and how the following strategic suggestion would vary, if at all.
Study 2 has resulted in potential research questions as well. For example, how close are marketers to producing digital twins? What is needed to move digital doppelgangers to digital twins? How will firms use digital twins? How much privacy are consumers will to give up? How will consumers’ need for privacy increase, decrease or fade away entirely? Overall
This thesis examined three specific marketing activities and how crowdsourcing is affecting them. One of the potentially fruitful directions for future research is to examine if and how crowdsourcing can impact other marketing activities. For example, Dawson & Bynghall (2012) pointed out content creation, idea generation, customer insights, customer engagement, customer advocacy and pricing as additional marketing strategies where crowdsourcing can play a role. Further, as researchers and consumers move into the near future, it will be interesting to see how powerful new tools (e.g., AI) combined with Big Data will produce and affect marketing activities directly. Additionally, there may be a fertile research area in investigating the spillover effects both positive and negative for both the consumer and the marketer as a consequence of the growth of crowdsourcing in other marketing activities.
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3.5 Limitations
As in research generally (Cheng & Edwards, 2017), this research has a few limitations. “The research process can be viewed as a series of interlocking choices, in which we try simultaneously to maximize several conflicting desiderata” (McGrath, 1981, p. 179). In other words, conducting research is mainly about making choices. The researcher makes good choices and less good choices in attempting to answer the research questions. This research followed the same pattern. Firstly, three of the four research projects are conceptual pieces. The decision trees, frameworks, and models presented are all theoretical. Although I believe they are conceptually strong, they have not been empirically tested.
Secondly, Study 1 was an empirical study. While there are restaurants and restaurant customers from all over the world, a choice was made to limit the geographical location. The data collected were from customers and restaurants in New York, United States. Furthermore, the comments were obtained from a specific web platform, TripAdvisor, the largest site of its kind in the world. Additionally, it was presumed that the user-generated content was truthful and accurate. While countless studies have used and continue to utilize UGC, we do need to be cognizant that not all the comments are genuine, unfortunately. Finally, the dataset was analyzed as a whole; the results may have improved if the data had been sub-divided. For example, the analysis could have examined the difference between higher ranked restaurants versus the lower ranked ones.
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3.6 Conclusion
This dissertation is a compilation of four individual research projects. However, there is a red thread that runs through all four, and it lies at the nexus of crowdsourcing and marketing. The primary red thread in the thesis is the overarching research question - How crowdsourcing can be used as a marketing tool? While each paper and study lends a different angle or perspective to this question, they each contribution by showing how crowdsourcing can impact marketing activities. Furthermore, these projects combined with the reviewed literature, lead to a definition of crowdsourcing as - a tool (or process) by which the firm can increase or expand the resources to which it has access by using the collective effort of a group of individuals or organizations.
Having a better understanding of crowdsourcing is essential because while marketing is a resource generating activity (Ballantyne & Varey, 2006), marketing is a resource consuming activity as well (Drucker, 1958; Faulkner, 2017; Kelley, 2017). However, it does not necessarily mean the firm must have ownership of the resources, but rather just access to them. Given few organizations have all the resources they need or want, most organizations are resource constrained (Penrose, 2009). Some are constrained because they are small and some because they are young (cf., Senyard, Baker, Steffens, & Davidsson, 2014; Wolff & Pett, 2006). Others are large and old but are resource constrained because they have been less successful in the past. However, the problem is not that there are too few resources, the problem is that they are too few resources that the organization owns or has control over. These resources exist mostly outside the organization. Here is where crowdsourcing fits.
While most effects are positive like the benefits in promotion, new opportunity development, and market research; however, there are negative aspects less frequently discussed. There is a dark side of crowdsourcing, and one of this is the effect on consumer privacy. As advancing technology, IoT's, AI and Big Data drive the future to unimagined possibilities; there may be a role for public policymakers.
As this study is being written (Fall 2018) new sweeping EU-wide rules have recently been implemented. Europe seems to be taking the lead in the privacy issue. The EU has enacted a wide-ranging set of rules and regulations called the General Data Protection Regulation (GDPR) (de Hert & Papakonstantinou, 2016; Mantelero, 2013). These comprehensive rules cover personal privacy, control and notification, transparency, and IT/training under the EU's Digital Single Market Strategy. An initial overview of these new regulations reveals that they will be quite onerous and expensive for companies and there are substantial penalties for non-compliance. These laws will have an impact, but it is unclear what that effect will be. For example, firms can still collect data just as they always have so long as they get specific consent from the user. Further the language of the agreement must be in “plain language”, plain language rules already exist in many locations and that hasn’t change behavior, in part, because people don’t read the many page EULA’s2 now (Kottasová, 2018a). The EU has yet to figure out how to make people read and understand legalese, although I am sure they are working on it.
However, one impact we can be sure of is smart firms will find innovative ways to circumvent some of these rules because crowdsourcing and co-creation can help firms create competitive advantages (Vargo & Lusch, 2004b; Woodruff, 1997). For example, some firms have raised the age of their users as the rules for children are more strict (Kottasová, 2018b). Others have “moved” their data out reach of the EU regulators like Facebook (Hern, 2018), while others have “moved” a portion of their data into the EU like Dropbox (Smolaks, 2016). As private information has become more abundant and more valuable for the firm, it has become a higher priority. Now as privacy has
2 In 2016 the Norwegian Consumer Council produced a video where they read the entire EULA’s for an average iPhone and its apps. It took over 32 hours (Flesland & Myrstad, 2016).
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come to the fore, policymakers may have an increasingly larger role to play, especially in the area of oversight. “The balancing of beneficial uses of data sources with the privacy rights of individuals is truly one of the most challenging public policy issues of the information age.” (Martin & Murphy, 2017). While it is too early to know how it will impact things, it doesn’t directly impact activities outside of Europe. The full impact will be measured in years to come.
Ultimately, utilizing the crowd provides an innovative way for firms to solve particular business problems by tapping the resources that often exist outside the organization. While there are many marketing, entrepreneurial, and innovative tools available, by using the collective effort of a group of individuals, the firm can increase or expand the resources to which it has access.
Crowdsourcing can be seen in far-ranging and diverse services and activities such as citizen science, the military, mapping and location, human intelligent tasks, journalism, medical diagnosis, medical research, translation/proofreading/transcription, surveillance, distributed computing, simulation, gaming, genealogy, support, music, mining, data prediction, legal advice, and education. Crowdsourcing is a situational, contextual and flexible tool that can be used in many different organizational contexts. This dissertation demonstrates that crowdsourcing can also be used as a tool in firm marketing activities such as advertising/promotion, product development, and marketing research.
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