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ISBN 978-952-62-2377-3 (Paperback)ISBN 978-952-62-2378-0 (PDF)ISSN 0355-3191 (Print)ISSN 1796-220X (Online)

U N I V E R S I TAT I S O U L U E N S I SACTAA

SCIENTIAE RERUM NATURALIUM



OULU 2019

A 732

Nataliya Shevchuk

APPLICATION OF PERSUASIVE SYSTEMS DESIGN FOR ADOPTING GREEN INFORMATION SYSTEMS AND TECHNOLOGIES

UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING

A 732

AC

TAN

ataliya Shevchuk

ACTA UNIVERS ITAT I S OULUENS I SA S c i e n t i a e R e r u m N a t u r a l i u m 7 3 2

NATALIYA SHEVCHUK


Academic dissertation to be presented with the assent ofthe Doctoral Training Committee of InformationTechnology and Electrical Engineering of the University ofOulu for publ ic defence in Pohjanmaansal i (L1) ,Linnanmaa, on 14 October 2019, at 12 noon

UNIVERSITY OF OULU, OULU 2019

Copyright © 2019Acta Univ. Oul. A 732, 2019

Supervised byProfessor Harri Oinas-Kukkonen

Reviewed byAssociate Professor Jacqueline CorbettProfessor Juho Hamari

ISBN 978-952-62-2377-3 (Paperback)ISBN 978-952-62-2378-0 (PDF)

ISSN 0355-3191 (Printed)ISSN 1796-220X (Online)

Cover DesignRaimo Ahonen

JUVENES PRINTTAMPERE 2019

OpponentProfessor Stefan Seidel

Shevchuk, Nataliya, Application of persuasive systems design for adopting GreenInformation Systems and Technologies. University of Oulu Graduate School; University of Oulu, Faculty of Information Technologyand Electrical EngineeringActa Univ. Oul. A 732, 2019University of Oulu, P.O. Box 8000, FI-90014 University of Oulu, Finland

Abstract

Critical environmental situation requires academics and practitioners of various disciplines joinefforts at planning sustainable development of the society. In information systems research, GreenInformation Systems and Technologies domain is the one that directly contributes to strengtheningenvironmental values of individuals, communities, and organizations.

Abundance of technology influences people most of the time. Although this influence is notalways positive, research on persuasive technologies and behavior change support systemssearches for the beneficial ways to utilize the impact of information systems on the daily activitiesof individuals. Ability to assist with encouraging certain behaviors is a defining characteristic ofmany contemporary systems and devices. Building on the knowledge obtained from the healthdomain, this dissertation investigates using persuasive technologies for fostering pro-environmental behavior change.

Theoretical frameworks of the articles included in the dissertation are based primarily on thePersuasive Systems Design (PSD) model, accompanied by the other prominent socio-psychological theories commonly utilized in information systems research. This dissertationfocuses on finding out how to encourage people to adopt and continue using mobile applicationsdesigned to assist with acquiring sustainable habits and behaviors.

The dissertation is composed of the historical analysis, systematic literature review, and fourexperimental studies that explore different aspects of persuasive Green IS/IT. Besides analyzingexisting research using PSD model as a tool for evaluation, new empirical studies provide freshinsights on Green IS/IT designed for behavior change. Addressed topics include technologyadoption and continuance intention, perceived persuasiveness, self-disclosure and risk,endogenous motivations, gamification, and cognitive absorption. Conducted statistical analysesinvestigate relationships among PSD constructs and other related concepts to discover factors thatcan convince people initiate and continue using Green IS/IT to develop or maintainenvironmentally sound practices.

Overall, the results reveal a high potential of the PSD model to become a vehicle forenhancement of the existing Green IS/IT. The dissertation provides implications for bothacademics and practitioners as well as suggestions for the future evolvement of persuasive GreenIS/IT research.

Keywords: behavior change, Green IS/IT, persuasive systems design model, persuasivetechnology, sustainable behavior, technology adoption

Shevchuk, Nataliya, Persuasive Systems Design -mallin soveltaminen kestävääkehitystä tukevien tietojärjestelmien ja informaatioteknologioiden käyttöönotontukena. Oulun yliopiston tutkijakoulu; Oulun yliopisto, Tieto- ja sähkötekniikan tiedekuntaActa Univ. Oul. A 732, 2019Oulun yliopisto, PL 8000, 90014 Oulun yliopisto

Tiivistelmä

Ympäristön ja ilmaston vakava tilanne vaatii kaikilta osapuolilta kestävää kehitystä tukevaa toi-mintaa. Niin sanotut ‘vihreät’ tietojärjestelmät ja informaatioteknologiat pyrkivät vaikuttamaanihmisten käyttäytymiseen vahvistamalla yksilöiden, yhteisöjen ja organisaatioiden ympäristöar-voja ja niiden mukaista käyttäytymistä.

Yltäkylläinen teknologiatarjonta vaikuttaa käyttäytymiseemme koko ajan. Vaikka tämä vai-kutus ei aina olekaan positiivista, vakuuttavien ja suostuttelevien teknologioiden sekä käyttäyty-misen muutosta tukevien järjestelmien tutkimus pyrkii etsimään niitä hyödyllisiä keinoja, joillatietojärjestelmien avulla voidaan vaikuttaa myönteisesti jokapäiväiseen elämäämme. Moniennykyaikaisten järjestelmien ja laitteiden perusominaisuutena voidaankin nykyään pitää niidenkykyä rohkaista ja kannustaa käyttäjiä myönteiseen kohdekäyttäytymiseen. Tämä väitöskirjarakentuu erityisesti terveystoimialalta omaksuttuun suostuttelevia teknologioita koskevaan tutki-mustietoon ja pyrkii soveltamaan tätä ympäristöystävällisen käyttäytymisen vahvistamiseen.

Tämän väitöstutkimuksen johtavana teoreettisena viitekehityksenä toimii Persuasive Sys-tems Design -malli (PSD). Sen lisäksi hyödynnetään joitakin muita tietojärjestelmätutkimukses-sa hyödynnettyjä sosiaalipsykologian teorioita. Väitöskirja keskittyy tarkastelemaan, miten voi-daan kannustaa ottamaan käyttöön mobiilisovelluksia, jotka on jo alun perin suunniteltu tuke-maan kestävän kehityksen kannalta myönteistä käyttäytymistä ja käyttäytymisen muutosta, sekäjatkamaan niiden käyttöä.

Väitöskirja koostuu aihealueen historiallisesta analyysistä ja systemaattisesta kirjallisuuskat-sauksesta sekä neljästä kokeellisesta tutkimuksesta, jotka tutkivat ’vihreiden’ tietojärjestelmienja informaatioteknologioiden vakuuttavia ja suostuttelevia ohjelmisto-ominaisuuksia. Sen lisäk-si että olemassa olevaa kirjallisuutta on analysoitu PSD-mallia käyttäen, samaan malliin pohjau-tuvat empiiriset tutkimukset tuottavat tuoreita oivalluksia käyttäytymisen muutosta tukevaansuunnitteluun liittyen. Tutkimuksessa tarkastellaan teknologian käyttöönottoa, aikomuksia jat-kaa teknologian käyttöä, koettua vakuuttavuutta ja suostuttelevuutta, sensitiivisten tietojen jaka-mista ja siihen liittyviä riskejä, sisäsyntyistä motivaatiota, pelillistämistä sekä kognitiivistaabsorptiota. Tilastollisten analyysien avulla tarkastellaan PSD-mallin ydinkäsitteiden ja muidenkonstruktien välisiä suhteita, jotta tunnistettaisiin niitä tekijöitä, joiden avulla voidaan vaikuttaahyödyllisten ‘vihreiden’ tietojärjestelmien ja informaatioteknologioiden käyttöönottoon ja käy-tön jatkamiseen, ja yleisemmin kannustamaan ympäristöystävällisiin käytänteisiin.

Tutkimustulokset osoittavat PSD-mallin merkityksen kestävää kehitystä tukeville tietojärjes-telmille ja informaatioteknologioille. Suoritetun tutkimuksen perusteella ehdotetaan teemoja jat-kotutkimusta varten. Väitöstutkimuksen tuloksilla on merkitystä sekä tieteenharjoittajille ettäkäytännön soveltajille.

Asiasanat: green IS, green IT, kestävä kehitys, käyttäytymisen muutos, persuasivesystems design, PSD, teknologian omaksuminen, vakuuttavat ja suostuttelevatteknologiat

Dear Mom and Dad, thanks for sharing your DNA

9

Acknowledgements

The OASIS journey would have been impossible to endure without the focus and

alertness provided by coffee even at the toughest times of the Finnish winters.

I appreciate my supervisor’s, Harri Oinas-Kukkonen’s, decision to accept an

Oulu Business School graduate in the Oulu Advanced Research on Service and

Information Systems team, his mentorship, considerateness, and

accommodativeness. I am grateful to all my former and current coworkers for

creating a pleasant work environment. Thanks to Michael Oduor and Kirsi Halttu

for being my first OASIS office mates, and to Xiuyan Shao and Liisa Kuonanoja

for the exceptionally amiable atmosphere in the “girls room”. Thanks to Pasi

Karppinen for collaboration on multiple mutual tasks. Thanks to Markku Kekkonen

for the pre-publication “language translation services”, and I owe Piiastiina Tikka

a big time for the last minute language review. Thanks to the follow-up group, Petri

Pulli and Li Zhao, for keeping the doctoral training on track. I am glad to have

worked with Aryan Firouzian, Iikka Paajala, Babar Chaudary, and Salman Mian.

I also appreciate assistance of the university staff, in particular Marja-Liisa

Liedes’s efforts at making encounters with the bureaucratic issues less stressful.

I am grateful to the instructors of the Fab Academy course: Jani Ylioja, Antti

Mäntyniemi, Juha-Pekka Mäkelä, and Dorina Rajanen, as well as the “fabmates”:

Jari Pakarinen, Iván Sánchez Milara, Mikko Toivonen, Eino Antikainen, and Yrjö

Louhisalmi for their patience with helping me keep up with the learning pace.

I am grateful to my coauthors, Kenan Degirmenci, Henry Oinas-Kukkonen,

and Vladlena Benson, for their dedication, meticulousness, and persistence. I am

thankful to the pre-examiners of the dissertation, Jacqueline Corbett and Juho

Hamari, for their insightful and valuable suggestions for improving the dissertation.

I appreciate the feedback of all anonymous reviewers, who have commented on my

articles, including constructive criticism of the Reviewer 2.

I am grateful to my all friends from around the globe. I do reserve special

thanks to the friends in Oulu for making the time here memorable. I am very

thankful to my family and especially to my mom who has always been by my side

despite being far away. And I thank Charlie for his endless unconditional support

24/7/365 in a common year and 24/7/366 on a leap year.

September 2019 Nataliya Shevchuk

11

Abbreviations

BCSS Behavior change support system

e.g. exempli gratia

etc. et cetera

i.e. id est

IS Information systems

IT Information technologies

OIT Organismic integration theory

PLS Partial least squares

PSD Persuasive Systems Design

SEM Structural equation modeling

SLR Systematic literature review

TAM Technology acceptance model

TPB Theory of planned behavior

TR Technology readiness

TRA Theory of reasoned action

TTF Task-technology fit

UTAUT Unified theory of acceptance and use of technology

13

Original publications

This dissertation is based on the following publications:

I Shevchuk, N., & Oinas-Kukkonen, H. 2019. “Role of Persuasive Systems Design in Increasing Intention to Adopt Green IS: Case JouleBug Mobile Application,” In Proceedings of the International Conference on Information Systems Development (ISD2019), pp. 1-12.

II Shevchuk, N. & Oinas-Kukkonen, H. 2019. “Influence on Intention to Adopt Green IS: Boosting Endogenous Motivations with Persuasive Systems Design,” In Proceedings of the Hawaii International Conference on System Science (HICSS), pp. 2055-2064.

III Shevchuk, N., Degirmenci, K., & Oinas-Kukkonen, H. 2019 (Forthcoming). “Adoption of Gamified Persuasive Systems to Encourage Sustainable Behaviors: Interplay between Perceived Persuasiveness and Cognitive Absorption,” In Proceedings of the International Conference on Information Systems (ICIS), p. 1-17.

IV Shevchuk, N., Benson, V., & Oinas-Kukkonen, H. 2019. “Risk and Social Influence in Sustainable Smart Home Technologies: A Persuasive System Design Study,” In Benson, V. & McAlaney, J. (Eds.), Cyber Influence and Cognitive Threats (pp. 185-216). Academic Press.

V Shevchuk, N., Oinas-Kukkonen, H., & Oinas-Kukkonen, H. 2019. “History of Green Information Systems and Technologies: Origin, Development, and Contemporary Trends,” Faravid. Pohjois-Suomen Historiallisen Yhdistyksen Vuosikirja, 47/2019, pp. 93-128. http://pro.tsv.fi/pshy/julkaisut/Faravid_47-2019.html.

VI Shevchuk, N., & Oinas-Kukkonen, H. 2016. “Exploring Green Information Systems and Technologies as Persuasive Systems: A Systematic Review of Applications in Published Research,” In Proceedings of the International Conference on Information Systems (ICIS), pp. 1-11. https://aisel.aisnet.org/icis2016/Sustainability/Presentations/11/.

15

Contents

Abstract

Tiivistelmä

Acknowledgements 9

Abbreviations 11

Original publications 13

Contents 15

1 Introduction 17

1.1 Research question ................................................................................... 18

1.2 Research approach .................................................................................. 19

1.3 Dissertation structure .............................................................................. 21

2 Theoretical background 25

2.1 Persuasive technology ............................................................................. 25

2.2 Persuasive Systems Design (PSD) model ............................................... 27

2.2.1 Primary task support ..................................................................... 30

2.2.2 Dialogue support .......................................................................... 30

2.2.3 Credibility support ........................................................................ 31

2.2.4 Social support ............................................................................... 32

2.3 Encouraging sustainable behavior ........................................................... 33

2.4 Green IS/IT ............................................................................................. 37

2.5 IS adoption .............................................................................................. 40

3 Research methodology 47

3.1 Historical method .................................................................................... 49

3.2 Systematic literature review .................................................................... 50

3.3 Statistical analysis with partial least squares structural equation

modeling (PLS-SEM) ............................................................................. 51

3.3.1 Measurement validation ............................................................... 52

3.3.2 Common method variance ............................................................ 55

3.4 Ethical issues ........................................................................................... 56

4 Research contributions 57

4.1 Historical overview of Green IS/IT domain ............................................ 57

4.2 Analyzing literature on Green IS/IT with persuasive features ................ 59

4.3 Investigating Green IS systems with empirical studies: data

collection and data analysis results ......................................................... 61

4.4 Major themes in empirical studies .......................................................... 68

4.4.1 Perceived persuasiveness and Green IS ........................................ 68

16

4.4.2 Impact of motivations on Green IS adoption ................................ 72

4.4.3 Users’ perceptions of risk and self-disclosure in persuasive

Green IS ........................................................................................ 74

4.4.4 Gamification and cognitive absorption in persuasive

Green IS ........................................................................................ 77

4.4.5 Green IS/IT adoption and continuance intention .......................... 80

4.5 Summarizing hypotheses investigated in empirical studies .................... 81

5 Discussion 85

5.1 Implications ............................................................................................. 88

5.1.1 Implications for research .............................................................. 88

5.1.2 Implications for practice ............................................................... 91

5.2 Limitations and future research ............................................................... 92

6 Conclusions 95

List of references 97

Appendices 115

Original publications 121

17

1 Introduction

Over the course of the past decade, sustainability has become a topic for avid

discussions. As Russo (2003) pointed out, the term sustainability “acquired [..]

many overlapping definitions”. Sustainability is the capacity to endure and can be

defined as “the long-term maintenance of well-being, which has environmental,

economic, and social dimensions, and encompasses the concept of stewardship and

responsible resource management” (Vachon, 2012, p. 1). Enforcing sustainable

development presumes “meeting the needs of the present without compromising

the ability of future generations to meet their own needs” (United Nations, 1987).

Resource overconsumption, global climatic disruption, waste production, species

extinction, population growth (Tomlinson, 2010, p. 29) are examples of serious

modern concerns that endanger sustainable future. As the environmental issues

remain critical (Dedrick, 2010), the society desperately needs to incorporate

sustainable practices in daily routines. Fundamental, long-lasting changes in

consumption patterns and practices lie at the heart of sustainability.

Emerging Green Information Systems and Technologies (Green IS/IT) present

a myriad of internal and external opportunities in fostering environmentally

friendly lifestyle (Tomlinson, 2010, p. iii). For instance, Green IS/IT can have

multiple effects on economics, ecological monitoring, as well as on cultivating

people’s everyday sustainable consumption choices including reducing the impact

of e-waste, reinforcing sustainable interaction design, and decreasing energy usage

by computational systems (Tomlinson, 2010, p. iii). Thus, a potential of the Green

IS/IT solutions and applications in contributing to sustainability should not be

neglected.

Modern advanced technologies, analytics, and informatics are omnipresent and

powerful enough to alter behaviors and break habits. Continually improving

pervasive mobile technologies, such as smartphones, tablets, and wearables offer

plenty of opportunities to persuade and influence individuals’ behaviors (Woodruff

& Mankoff, 2009). Particularly, persuasive technologies in web and mobile

applications promoting physical activity have become increasingly prevalent with

the increased access to and usage of smartphones. These applications incorporate

numerous persuasive design features for modification of the users’ behavior.

Context plays an important role in influencing behavior by persuasion; e.g.,

individuals are being motivated and inspired to pursue fitness goals and improve

their wellbeing (Matthews, Win, Oinas-Kukkonen, & Freeman, 2016).

18

Environmental quality strongly depends on human behavior patterns (Koger &

Winter, 2011; Gardner & Stern, 2002; Vlek & Steg, 2007), and thus can be managed

by changing relevant behaviors to reduce negative environmental impact.

Designing practices and policies for maintaining sustainable future requires

understanding pro-environmental behavior psychology as well as the ability to use

behavior-altering techniques to ensure enduring behavioral modifications.

Hence, knowledge and practices previously gained in Persuasive Systems

Design (PSD) and Behavior Change Support Systems (BCSSs) research (Oinas-

Kukkonen, 2013; Oinas-Kukkonen & Harjumaa, 2009) can build a foundation for

a systematic approach to creating innovative solutions that promote sustainable

lifestyles. An overview of sustainable behavior psychology and encouraging pro-

environmental behavior emphasizes the need of persuasion as a tool for inducing

behavior change. Understanding background of PSD and BCSSs as well as

domains in which these concepts have been investigated highlights potential of

these theoretical approaches to be applied in emerging contexts, such as Green

IS/IT.

Considering that the same underlying factors are required to trigger behavior

changes in any context (e.g. health, well-being, sustainability etc.), the established

frameworks previously applied in health and well-being related domains could also

be used as a roadmap for paving a path towards sustainable future. Correspondingly,

sustainability constitutes a novel context for employing behavior changing and

persuasive systems. Nevertheless, unlike in the case of improving personal health

or well-being, pro-environmental behavior change is different and more

challenging because the benefits of the latter are rather indirect and accrue not only

to the individual whose behavior is being changed but also to the society at large.

Thus, it is important to outline perspectives of merging theoretical understanding

of the PSD model foundations and pro-environmental behavior facilitation. These

theories will provide a valuable essence for the Information Systems (IS)

researchers and practitioners who directs their efforts towards a more sustainable

future.

1.1 Research question

Although information systems and technologies are never neutral and always

impact the users (Oinas-Kukkonen & Harjumaa, 2009), they are not always focused

on changing attitudes and behaviors of the users. Therefore, implementation of

additional techniques is necessary in order to steer the influence of the information

19

systems and technologies in the desired direction. Considering the viewpoint of the

users, the individuals are more likely to be convinced by the system to change

attitudes or behavior if persuasive principles (Oinas-Kukkonen & Harjumaa, 2009)

are incorporated in this system. Persuasive features possess significant potential in

enhancing information systems and technologies, because these features have been

recognized to enrich intervention strategies by encouraging engagement with the

systems (Kelders, Kok, Ossebaard, & Van Gemert-Pijnen, 2012). Considering this,

persuasive principles are hypothesized to assist the users to initiate engagement

with the individual level Green IS/IT aimed to promote sustainable behavior as well

as to eliminate attrition of these systems and technologies. Hence, the main research

question of the dissertation focuses on exploring the following:

How do persuasive design principles encourage adoption and continuous use

of Green IS/IT?

Although there is an abundance of research on both Green IS/IT and on

persuasive systems and technologies, there is a rather limited number of studies

which consider the overlap of these two areas. Only a handful of studies analyze in

depth how the persuasive properties contribute to advancing Green IS/IT and to

facilitating users’ attitude and behaviour change (e.g. Corbett, 2013; Brauer,

Ebermann, & Kolbe, 2016). Thus, the dissertation aims to fill the existing research

gap with the series of studies which merge the knowledge of the persuasive systems

domain with the Green IS/IT one while targeting the core research question.

1.2 Research approach

Studying the ability of persuasive technologies to engage users is an essential

research path (Chatterjee & Price, 2009). Therefore, this dissertation explores how

persuasive design principles affect perceived persuasiveness (Oinas-Kukkonen,

2010) of the Green IS/IT users as well as behavioral intention (Fishbein & Ajzen,

1975) of the users to engage with Green IS/IT in the future. Additionally, the

dissertation examines several other correlations among the PSD principles and

related relevant constructs.

To build a foundation for the quantitative research, qualitative studies are

carried out. Historical analysis takes a broad perspective of the Green IS/IT domain

and examined its history. The analysis explores development of the field as well as

the theoretical and practical Green IS/IT solutions applicable to organizations,

individuals, and the society. The paper focuses on the following research question:

20

How has the Green IS/IT domain been developing throughout different

historical periods?

The goal of conducting a systematic literature review is to find IS articles

which discuss persuasive Green IS/IT systems and applications. Thus, the paper

not only contributes to research by providing analysis of the discovered systems

and applications but also sheds light on the overall state of the IS field on persuasive

Green IS/IT. The focal research question is:

Which persuasive features are present in Green IS/IT systems that are featured

in top IS publications?

Since adoption and continuous use of Green IS/IT appear to be crucial in the

Green IS literature, Studies I–IV take a quantitative approach to investigate how

behavioral intention to adopt or continue using Green IS/IT is affected by

persuasive features through attitude and/or perceived persuasiveness (Lehto,

Oinas-Kukkonen, & Drozd, 2012; Oinas-Kukkonen, 2010; Oinas-Kukkonen,

2013). In Study I, both perceived persuasiveness and attitude are considered as

predictors of adoption intention. While persuasive features are used as variables

forecasting perceived persuasiveness, constructs of social affirmation, perceived

effort, and perceived effectiveness were considered to affect attitude.

Correspondingly, the research question of Study I is phrased as follows:

How does perceived persuasiveness combined with the attitude influence

intention to adopt Green IS?

To explore a more diverse range of factors relevant for Green IS adoption, and

to enrich the insights obtained in Study I, Study II brings in Organismic Integration

Theory (OIT) (Deci & Ryan, 2002) to proceed with investigation of Green IS/IT.

Therefore, Study II also considers the interplay of different types of endogenous

motivations (Malhotra, Galletta, & Kirsch, 2008) and the persuasive features

(Oinas-Kukkonen & Harjumaa, 2009). The research question of the study is

formulated as follows:

How do endogenous motivations, influenced by persuasive systems design,

shape intention to adopt Green IS?

Because gamification in one of the trending topics in Green IS/IT literature as

discovered in the historical analysis and in the literature review, Study III, similarly

to Studies I and II, is motivated by the need to continue exploring adoption intention

(Davis, Bagozzi, & Warshaw, 1989; Venkatesh, Thong, & Xu, 2012; Lehto &

Oinas-Kukkonen, 2015) in the context of a gamified persuasive Green IS. Study III

incorporates theories related to gamification that call for utilizing new constructs,

21

such as cognitive absorption (Agarwal & Karahanna, 2000; Lowry et al., 2012).

The research question of the study is:

How do perceived persuasiveness and cognitive absorption affect the intention

Bhattacherjee to adopt a gamified persuasive system?

Unlike Studies I-III which consider adoption intention, Study IV focuses on

intention to continue (Bhattacherjee, 2001) using a Green IS, since it is another

measure of success of an IS (Anda & Temmen, 2014). Moreover, Study IV

considers how possible negative aspects of IS impact the user-system interaction.

Thus, Study IV targets the following questions:

How does persuasive systems design influence intention to continue using a

smart metering system?

How do risk and self-disclosure alter application of persuasive systems design

in a smart metering system?

To answer the questions, relationship of constructs representing the four

persuasive categories (Oinas-Kukkonen & Harjumaa, 2009), one persuasive

postulate (Lehto, Oinas-Kukkonen, & Drozd, 2012), perceived persuasiveness

(Lehto, Oinas-Kukkonen, & Drozd, 2012; Oinas-Kukkonen 2010; Oinas-

Kukkonen 2013), perceived risk (Benson, Saridakis, & Tennakoon, 2015; Malhotra,

Kim, & Agarwal, 2004), and self-disclosure (Posey, Lowry, Roberts, & Ellis, 2010;

Benson, Saridakis, & Tennakoon, 2015) are investigated.

1.3 Dissertation structure

The dissertation continues with the presentation of the theoretical background

relevant to the investigated subject (Section 2), defines research methodology in

(Section 3), describes research contributions of the papers (Section 4), discusses

findings, implications, and limitations (Section 5), and draws conclusions (Section

6). Figure 1 presents the summary of the articles included in the dissertation. Figure

2 presents empirical Studies I–IV, research questions of the studies, as well as

relationships among them based on the researched constructs.

22

Research approach Research context Articles

Fig. 1. Research approaches, research context, and related articles.

CONCEPTUAL STUDIES

History research of Green IS/IT

History of Green IS/IT: origin, development and

contemporary trends

Literature review of examples of persuasive Green IS/IT and their

PSD analysis

Exploring Green IS/IT as persuasive systems: a systematic review of

applications in published research

EMPIRICAL STUDIES

Impact of perceived persuasiveness and

attitude on intention to adopt a persuasive Green

IS app (JouleBug)

Role of persuasive systems design in

increasing intention to adopt Green IS

Influence of PSD on endogenous motivations

and their affect on attitude in Green IS

Influence on intention to adopt Green IS: boosting endogenous motivations with persuasive systems

design

Impact of PSD on perceived persuasiveness and cognitive absorption in a gamified persuasive Green IS (GreenApes)

Adoption of sustainable behavior through

gamified persuasive systems

Role of perceived risk and self-disclosure on the users' interaction with the persuasive Green IS app

(Hive thermostat)

Risk and self-disclosure in sustainable persuasive smart home technologies

What factors influence adoption and continuance intention of Green IS/IT?

23

Fig. 2. Empirical Studies I–IV: research questions and research constructs.

25

2 Theoretical background

2.1 Persuasive technology

Unlike humans, computers have a capacity to maintain a high level of interactivity

as well as to adjust influence tactics depending on a situation, and thus, are capable

of more effective persuasion (Fogg, 2003). Additionally, what helps computers to

be more persistent is their ability to display information in manifold ways, process

massive volumes of data, scale according to demand, offer a higher degree of

confidentiality and anonymity, as well as be accessible from all over the world.

Although, technologies by themselves do not have an intention to influence users,

the services implemented though them enable technologies to facilitate behavior

change and streamline the behavior change process (Lockton, 2012). Persuasive

technologies influence users’ perceptions and behaviors by applying diverse tactics

to support different outcomes and behavior change strategies (Berkovsky, Freyne,

& Oinas-Kukkonen, 2012; Oinas-Kukkonen, 2013).

Despite the fact that research on persuasive technologies has been introduced

relatively recently (Fogg, 2003), the concept of persuasion has been known in the

history for centuries. In the broad sense of the word, ‘persuasion’ is influence. It

refers to influencing a person’s beliefs, attitudes, intentions, motivations, or

behaviors (Fiske, 2018). In Ancient Greece, rhetoric and elocution were hailed as

the highest standards for effective communication. An Ancient Greek philosopher

Aristotle stated the following: “Let rhetoric [be defined as] an ability, in each

[particular] case, to see the available means of persuasion. This is the function of

no other art; for each of the others is instructive and persuasive about its own

subject” (Dow, 2015, p. 49). He outlined the three basic techniques of persuasion:

Ethos (“character”: ethical appeal, convincing by credibility or character), Pathos

(“experience”: emotional appeal, persuading by appealing to emotions), and Logos

(“word”: appeal to logic, prompting by using logic or reason). Despite the ancient

origin, modern-day behavioral experiments on persuasion had not been initiated

until the 20th century (Petty & Briñol, 2008). In a more contemporary interpretation,

persuasion is “the type of communication that brings about change in people”

(Bostrom, 1983, p. 8) or “human communication designed to influence the

autonomous judgments and actions of others” (Simons, Morreale, & Gronbeck,

2001, p. 7). Briñol and Petty (2009, p. 71) summarized the circumstances for

persuasion process as follows: “In the typical situation in which persuasion is

26

possible, a person or a group of people (i.e., the recipient) receives an intervention

(e.g., a persuasive message) from another individual or group (i.e., the source) in a

particular setting (i.e., the context).” Briñol and Petty (2009) concluded that

successful persuasion takes place when the target of change (e.g., attitudes, beliefs)

is modified in the desired manner.

Studying persuasion in the context of computers, Fogg (2003) defined

persuasive technologies as technologies designed to bring a required change in

people’s behaviors or attitudes. By addressing both attitudes and behaviors, Fogg’s

definition added a new dimension to the research of behavior change technologies.

Moreover, Fogg (2003) noted that persuasive technologies can be classified as

social actors, that are able to sustain their social influence potential even in the

absence of other people. Rapid evolution of the web and development of ambient

technologies have created prospects of effective persuasive technologies and digital

interventions (Verbeek, 2009). To define systems that are essentially interactive and

designed to influence users’ behaviors or attitudes, Oinas-Kukkonen and Harjumaa

(2009) coined a term “persuasive systems” defined as “computerized software or

information systems designed to reinforce, change or shape attitudes or behaviors

or both without using coercion or deception” (p. 486).

Already now, numerous studies have confirmed that information technologies

could alter people’s behaviors and/or attitudes by motivating them to perform

desired behaviors. Nowadays, persuasive technologies and systems have been

employed in multiple domains, such as e-commerce (Adaji & Vassileva, 2017),

education (Mintz & Aagaard, 2012; Orji, Vassileva, & Greer, 2018), health and

fitness (Matthews, Win, Oinas-Kukkonen, & Freeman, 2016; Coombes & Jones,

2016; Chen, Cade, & Allman-Farinelli, 2015), encouraging smoking cessation (Van

Agteren, Lawn, Bonevski, & Smith, 2018), promoting healthy aging (Mohadis,

Mohamad Ali, & Smeaton, 2016), safety (Chittaro, 2012; De Vries, Galetzka, &

Gutteling, 2014; Alvarez, Dal Sasso, & Iyengar, 2018; Rebelo, Noriega, & Veronesi,

2019), tourism (Vičič & Šukljan 2016; Tussyadiah, Wang, Jung, & tom Dieck,

2018), security (Lindgren & Wuropulos, 2017), business (Lindgren, 2017),

environment (Zachrisson & Boks, 2012; Yang, Kong, Sun, & Zhang, 2018;

Sullivan et al., 2016), ecology (Ganglbauer, Reitberger, & Fitzpatrick 2013;

Mohadis & Mohamad Ali, 2015), energy efficiency (Anda & Temmen, 2014;

Casado-Mansilla et al., 2018; Hopf, Sodenkamp, & Kozlovskiy, 2016), water

conservation (Albertarelli et al., 2018; Vu et al., 2011), and transport and mobility

(Anagnostopoulou et al., 2016; Sunio & Schmöcker, 2017; Klecha & Gianni, 2017).

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2.2 Persuasive Systems Design (PSD) model

Continuing Fogg’s (2003) work, Oinas-Kukkonen and Harjumaa (2009) developed

the Persuasive System Design model (PSD model), a systematic framework that

describes key issues, a process model, and design principles for developing and

evaluating persuasive information systems (Figure 3). The PSD model has been

accepted as a practical framework for researchers to facilitate a detailed assessment

of the persuasion context and persuasive features as well as for designers and

practitioners for a better understanding and estimation of the needs and

expectations of the target audiences. The model has already been applied in the

several areas, such as ubiquitous applications for well-being (Yap et al., 2018),

personal wearable devices for physical training (Bartlett, Webb, & Hawley, 2017),

and design for information systems that promote healthy behavior (Karppinen et

al., 2018). The model is theoretical and it guides thorough the evaluation of the

persuasion context (the Intent, the Event, and the Strategy) and provides a selection

of the persuasive techniques.

Fig. 3. PSD Model (adapted from Oinas-Kukkonen & Harjumaa, 2009).

Oinas-Kukkonen (2013) advises to investigate persuasive postulates before

analyzing the context or considering persuasive features of the system. The seven

postulates should be considered as a foundation of the system design rather than

detailed instructions for its implementation (Oinas-Kukkonen, 2013). Two

postulates relate to the persuasion strategies, two to the perception of the users, and

three to the actual system features (Oinas-Kukkonen & Harjumaa, 2009).

The first postulate states that “Information technology is never neutral”; on the

contrary, it is constantly influencing people’s attitudes and behaviors, whether or

not this impact is intentional (Oinas-Kukkonen & Harjumaa, 2009; Oinas-

Kukkonen, 2013). Therefore, persuasion should not be considered as a single act

but rather as a process (Harjumaa, 2014). The second postulate informs that

PersuasivePostulates

Persuasive Context

• Intent• Event

• Strategy

PersuasiveFeatures

• Primary Task Support

• Dialogue Support• Credibility

Support• Social Support

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“People like their views about the world to be organized and consistent,” and,

according to Harjumaa (2014), it originates from the Cognitive Consistency Theory

(Festinger, 1957; Fraser, 2001) as well as Cialdini’s (2009) principle of

commitment and cognitive consistency. The third postulate highlights that

“Persuasion is often incremental” meaning that instead of a one-time consolidated

suggestion, it is easier to influence people with the series of incremental

suggestions (Mathew, 2005; Harjumaa, 2014). The next postulate, derived from

Petty and Cacioppo’s (1986) Elaboration Likelihood Model (ELM), emphasizes

that “Direct and indirect routes are key persuasion strategies”, and it claims that

using an indirect route in persuasion is one of the main persuasion strategies. The

fifth postulate, “Persuasive systems should aim at being both useful and easy to

use”, stems from the determinants of the Technology Acceptance Model (TAM)

(Davis, 1989). If a system is completely unusable or difficult to use, it is highly

unlikely to be persuasive. The sixth postulate, which also originates from the ELM,

suggests that “Persuasive systems should aim for unobtrusiveness” implying that

persuasive systems should avoid disturbing users while they are performing the

primary tasks. Additionally, the principle of unobtrusiveness means that the

appropriate (or inappropriate) moments to persuade in a given situation should be

carefully considered. The final postulate is related to the first one and states that

“Persuasion through persuasive systems should always be open” (Oinas-Kukkonen,

2013). Fogg (2003), Cassell, Jackson, & Cheuvront (1998), and Miller (2002)

claimed that persuasion should enable voluntary participation in the persuasion

process (Harjumaa, 2014).

The analysis of the persuasion context is the next key component of the PSD

model, which consists of recognizing the intent of the persuasion, understanding

the persuasion event, and defining the persuasion strategy (Oinas-Kukkonen, 2013).

A tool for initiating persuasive context investigation and recognizing the intent of

a persuasive system is the Outcome/Change matrix (O/C matrix) (Oinas-Kukkonen,

2013). The main purpose of the O/C matrix is evaluating the intent and the outcome

of a persuasive system. Successful outcomes in the matrix are formation, alteration,

and reinforcement of attitudes, behaviors, or compliance. A forming outcome (F-

Outcome) represents a development of a behavior that did not exist previously.

Discontinuing a behavior is also considered as a development of a new behavior

(F-Outcome). An altering outcome (A-Outcome) denotes the changes in an

individual’s involvement in a behavior, e.g. increasing or decreasing involvement

in this behavior. A reinforcing outcome (R-Outcome) means the reinforcement of

current behaviors or attitudes in order to make them more resistant to change.

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Reinforcement is crucial for providing the user with support to continue

engagement in the new behavior.

Furthermore, in the O/C matrix, the changes are classified into three categories:

a change in the act of complying (C-Change), a behavior change (B-Change), and

an attitude change (A-Change) (Oinas-Kukkonen, 2013). The C-Change only aims

to ensure that the person complies with the requests of the system. The goal of the

system supporting a B-Change is to stimulate a deeper behavior change instead of

merely maintaining compliance. Logically, a one-time compliance is much easier

to accomplish compared to achieving a long-term behavior change. The aim of the

most persistent A-Change is to modify not only an individual’s behavior, but also

an individual’s attitude.

Analyzing the persuasive event consists of understanding three types of

contexts: the use context, the user context, and the technology context (Oinas-

Kukkonen & Harjumaa, 2009). The use context depends on the problem domain,

which dictates the necessary features. Usually, it is beneficial to involve the

professionals of the field in the system design process and in creation of the content

for it (Harjumaa, 2014). The user context comprises user-dependent features, such

as the user’s goals, motivation, lifestyle, and others, while the technology context

includes features that depend on technology. Understanding of strategy corresponds

to the third postulate: selecting between the direct and indirect routes that are the

key persuasion strategies (Harjumaa, 2014). When the use context is challenging

and the user cannot carefully evaluate the content of the message, it is important to

choose the indirect route.

The PSD model provides a range of design features and software

functionalities for developing effective BCSSs. The model comprises four distinct

software categories aimed at improving persuasiveness of information systems.

The categories include primary task support, user-system dialogue support,

credibility support, and social support.

Webb, Joseph, Yardley, and Michie (2010) state that behavior change

interventions which use more behavioral change techniques produce better results.

However, this argument is subject to criticism. Therefore, the PSD model does not

encourage implementation of all possible features in order to increase effectiveness

of a persuasive system. It means that the persuasiveness of a system does not

depend on a quantity of the utilized features. Thus, a successful development of a

persuasive system depends on the quality of carefully selected functionalities and

features.

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2.2.1 Primary task support

Primary task support enables reflection on own behavior, setting personal goals,

and monitoring own progress to achieving these goals (Oinas-Kukkonen &

Harjumaa, 2009). Additionally, it strives to adjust the persuasive system to match

the individual’s personal tasks as well as to make the user-system interaction

simpler by reducing user disorientation, intellectual burden and cognitive load of

the system use. Primary task support also helps users in decision-making (Lehto,

2013). For example, Eysenbach (2000, p. 1715) elaborates: “Decision aids differ

from information aids mainly in that they contain explicit components to help users

clarify their values: the patient’s personal values and the utility or importance of

the risks and benefits of each alternative are elicited”. Persuasive features included

in the primary task support category are reduction, tunneling, tailoring,

personalization, self-monitoring, simulation, and virtual rehearsal (Oinas-

Kukkonen & Harjumaa, 2009). Harjumaa (2014) states that all design principles in

this category are grounded on the tool category presented in Fogg’s (2003) work.

2.2.2 Dialogue support

The dialogue support category consists of the persuasive principles that facilitate

human–computer interaction (Oinas-Kukkonen & Harjumaa, 2009). The category

outlines features assisting with keeping the users active and motivated in using the

system with the ultimate aim to help the users reach their intended behavior. Users

often consider their interactions with IT artifacts to be interpersonal in nature

because IT artifacts can be perceived as social actors (Fogg & Nass, 1997; Nass &

Moon, 2000). Hence, individuals tend to engage with the IT artifacts in a manner

similar to interacting in social settings. This highlights an importance of

maintaining the dialogue between the IT artifact and the individual as well as

augmenting the user-system interaction which is beneficial for guiding the users to

achieving anticipated behavioral changes. In dialogue support, the crucial elements

are system-to-user prompts, triggers, subtle reminders, suggestions, and positive

feedback. Fogg and Nass (1997, p. 559) noted: “…[A]dding either sincere praise

or flattery to training and tutorial software may well increase user enjoyment, task

persistence and self-efficacy.” Furthermore, dialogue support can be enriched with

the virtual rewards granted to the users for a successful achievement of certain

milestones. Consequently, dialogue support stimulates the users’ positive affect and

feelings, which in turn are capable of influencing the users’ confidence in the source,

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i. e. the users’ perception of credibility of the system. Encouraging the enhanced

dialogue between the users and the information systems, Consolvo et al. (2009)

highlighted that a system-to-user dialogue must use exclusively positive

reinforcement for facilitating behavior changes. The user should be rewarded for

engaging in the target behavior and for a successful pursuit of own objectives but

should not be punished in case of not being able to perform the desired behavior

(Consolvo et al., 2009). The dialogue support category is comprised of the

following features: praise, rewards, reminders, suggestion, similarity, liking, and

social roles (Oinas-Kukkonen & Harjumaa, 2009). Harjumaa (2014) notes that

these design principles are partly grounded on the social actor category

(attractiveness, similarity, and praise) and media category (virtual rewards)

featured by Fogg (2003) with reminders and social role being the newly introduced

design principles.

2.2.3 Credibility support

The credibility support category describes features that introduce reliability,

dependability and aid in designing more credible systems (Oinas-Kukkonen &

Harjumaa, 2009). The higher the credibility of the source is, the more persuasive it

is (Pornpitakpan, 2004). The credibility support category ensures that a user relies

on the system’s advice to proceed to the desired results. Credibility and trust have

been recognized as related constructs. Everard and Galletta (2005, p. 60) pinpoint

that the apparent difference between trust and credibility is that “trust is an attribute

of an observer (to have trust), whereas credibility is an attribute of another person

or an object of interest (to be credible).” Computer credibility requires the users to

accept the system’s guidance, trust the given information, and believe the output

(Everard & Galletta, 2005). Thus, trust can be considered as an indicator of

credibility. Online trust has been previously classified into several types, such as

knowledge-based trust, institution-based trust, and cognition-based trust. Sillence,

Briggs, Harris, and Fishwick (2006) suggest different aspects that are likely to

control how much trust individuals are comfortable to put into an online system:

(1) credible and aesthetic visual design, (2) branding of the system including

presence of familiar images or trusted logos; (3) perceived expertise expressed

through the quality of information; and (4) personalization. Wathen and Burkell

(2002) posit that the overall credibility of the system is determined by the source

of the information (e.g., expertise, trustworthiness), the receiver (e.g., cultural

factors, prior beliefs), and characteristics of the message (e.g., plausibility, quality).

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The PSD model contains the following principles for supporting system credibility:

trustworthiness, expertise, surface credibility, real-world feel, authority, third party

endorsements, and verifiability (Oinas-Kukkonen & Harjumaa, 2009). Harjumaa

(2014) notes that the design principles in this category have been adopted and

modified from the Fogg’s (2003) work.

2.2.4 Social support

In contemporary life, people’s social relationships are increasingly developed and

maintained via technology-mediated communication (Oinas-Kukkonen, 2013).

Burrows et al. (2000, p. 101) state: “Whether or not the large number of social

actors who currently engage in online self-help and social support constitute

themselves into virtual communities is a key area for debate. But whatever

conceptualization one favors, there is no doubt that growing numbers of people

across the globe are using e-mail, the World Wide Web, mailing and discussion lists,

news groups, MUDs, IRC, and other forms of computer mediated communication

(CMC) to offer and receive information, advice and support across a massive range

of health and social issues.” These technology-mediated interactions are able to

facilitate online relationships that constitute social support (Lehto, 2013).

Although there are several opinions on defining social support, it is commonly

acknowledged that social support has multiple layers: emotional, instrumental,

appraisal, and informational (Lehto, 2013). According to Uchino (2006), social

support may be connected to the aspects of the social network (groups, familial

ties), specific behaviors (e.g., emotional or informational support), or the

individuals’ perceived availability of support resources. Shumaker and Brownell

(1984, p. 11) describe social support as “an exchange of resources between two

individuals perceived by the provider or the recipient to be intended to enhance the

well-being of the recipient”. Thus, online social support promotes inspiration,

distribution of knowledge and experiences (Shumaker & Brownell, 1984). Barak,

Klein, and Proudfoot (2009) state that there is a variety of online groups represented

in both synchronous and asynchronous set-ups, such as web-based discussion

forums or bulletin boards, live chat rooms, e-mail lists. Unlike traditional support

groups, online ones provide more liberty and flexibility as with people are able to

join or leave at their will at any time (Barak, Klein, & Proudfoot, 2009). Important

advantages of the online social networks are anonymity, privacy and

nonjudgmental interactions where the exchanged social support is expressed via

encouragement and motivation, information, and shared experiences (Hwang et al.,

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2010). Additionally, knowledge sharing in virtual communities can be sparked by

community-related and personal outcome expectations and is governed by social

connections, trust, norm of reciprocity, identification, common views, and language

(Chiu, Hsu, & Wang, 2006). Thus, persuasive principles in the social support

category attempt to motivate users through social influence. In the PSD model, the

following persuasion techniques are in this category: social learning, social

comparison, normative influence, social facilitation, cooperation, competition, and

recognition (Oinas-Kukkonen & Harjumaa, 2009). Harjumaa (2014) notes that

these features are based on the Fogg’s (2003) principles on mobility and

connectivity.

2.3 Encouraging sustainable behavior

Based on the definition of pro-environmental behavior (Steg & Vlek, 2009),

sustainable behavior is a series of actions that harm the ecosystem as little as

possible, or even benefit it. There are several steps to for encouraging sustainable

behavior. Firstly, it is required to examine factors that promote environmental

quality, then apply necessary interventions, and systematically evaluate the effects

of the interventions (Geller, 2002). Steg and Vlek (2009) outline the steps (see

Figure 4) required for transforming behavior from unsustainable to pro-

environmental. Below a more detailed explanation of these steps is presented, since

understanding psychology of encouraging sustainable behavior should not be

neglected when designing and implementing Green IS/IT.

Fig. 4. Supporting Sustainable Behavior Change Framework (Steg & Vlek, 2009).

Identifying behaviors to be changed

Examining behavior-

influencing factors

Designing and applying

interventions

Evaluating effects of

interventions

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Identifying behaviors to be changed

Driving the overall increase in pro-environmental mindset and supporting behavior

change begins with selection and assessment of specific behaviors. According to

Steg and Vlek (2009), environmental psychologists need to prioritize targeting

behaviors that can considerably contribute to environmental change. Several

analyses, such as input-output or lifecycle, are currently available to access

environmental impact. After establishing which behaviors require immediate

modifications due to their harmful impact on sustainability of the environment, it

is necessary to determine whether such changes are feasible and if these alterations

will be acceptable. Feasibility and acceptability highly depend on behavioral

factors and intervention strategies that are taken into account in the further stages

of the framework. Furthermore, valid measures must be established to ensure

proper assessment of the desired behavior change. While some researchers consider

self-reports to be reliable enough to reflect actual behavior, the others have found

discrepancies between observed and self-reported behaviors (Steg & Vlek, 2009).

Therefore, it is crucial to find feasible measurements of the actual behavior change.

Because of the variety of underlying behavioral factors and feasible strategies,

selecting target group(s) of individuals is also necessary as well as deciding

whether the groups can be treated homogenously or whether specific interventions

for each group will be required.

Furthermore, instead of studying sustainable behaviors in isolation,

environmental psychologists favor identifying behavioral patterns, including

behavioral antecedents (e.g. attitude, contextual factors, etc.). It has been

determined that in addition to common environmental considerations, factors, such

as status, comfort, effort, and behavioral opportunities, determine pro-

environmental behavior (Steg & Vlek, 2009).

Examining behavior-influencing factors

Once behaviors that need to be modified are discovered, the causes of these

behaviors should be examined. Determining factors that shape a behavior may be

more complicated than expected since these factors stem from motivations which

are rarely homogeneous. For example, the Goal-Framing Theory (Lindenberg,

2006) suggests that behaviors are triggered by multiple motivations resulting in one

focal goal which has the strongest influence on the individual’s information

35

processing while the other existing goals increase or decrease the strength of the

focal goal.

The individual’s approach to weighing costs and benefits constitutes one

behavior-forming factor (Steg & Vlek, 2009). This notion stems from the Theory

of Planned Behavior (Ajzen, 1991) which suggests that people make reasoned

choices and choose alternatives with highest benefits and lowest costs (e.g., in

terms of money, effort and/or social approval). Additionally, the theory on the

meaning of material possessions (Carrier & Dittmar, 2006) suggests that the use of

material goods fulfils three functions (instrumental, symbolic, and affective), and

thus provides insights in individuals’ choices to use a commodity. Hence, Steg

(2005) examined the role of affect in explaining environmental behavior.

Furthermore, previous research determined that various moral and normative

concerns heavily influence the individuals’ behaviors. Studies on the value-basis of

environmental beliefs and behavior revealed that the more strongly individuals

relate to values beyond their immediate own interests (self-transcendent, prosocial,

altruistic or biospheric values), the more likely they are to acquire pro-

environmental behavior. Similarly, research focused on environmental concerns

showed that higher environmental concern is associated with acting more pro-

environmentally (Steg & Vlek, 2009). One of the frequently used tools to measure

environmental concern is the New Environmental Paradigm scale (Dunlap & Van

Liere, 1978; Dunlap, Van Liere, Mertig, & Jones, 2000). Moral obligations to act

pro-environmentally have also been researched and related to sustainable behavior

based on the norm-activation model (NAM) (Schwartz, 1977) and on the value-

belief-norm theory of environmentalism (VBN theory) (Stern, 2000). The influence

of social norms on any behavior (including sustainable) is supported by the theory

of normative conduct (Cialdini, Kallgren, & Reno, 1991) and distinguishes two

types of influential social norms. Injunctive norms describe the extent to which

behavior is supposed to be commonly approved or disapproved, while the

descriptive norms reflect the extent to which behavior is perceived as common.

In addition to motivation, a range of contextual factors facilitate or constrain

sustainable behavior change (Steg & Vlek, 2009), for instance, availability and

quality of recycling centers, public transport, cost of sustainable alternatives, etc.

Context affects behavior in several ways, such as directly or via relationships with

the motivational factors (e.g. attitudes, affect, or personal norms). Adverse

contextual factors are able to outweigh the positive influence of personal

motivations and attitudes towards sustainable behavior. Finally, apart from the

factors that drive reasoned behavioral choices, habits, guided by automated

36

cognitive processes rather than by elaborate reasoning, also highly impact

sustainable behavior. Computer simulation is one way to study the formation and

reinforcement of habits (Jager & Mosler, 2007) to design effective interventions.

Designing and applying interventions

The next step in promoting sustainable behavior is choosing appropriate

interventions for changing existing behaviors. Among various classifications of

possible strategies, the ones most frequently referred to are (1) antecedent and

consequence, and (2) informational and structural (Steg & Vlek, 2009).

Determining the most effective choice of strategy depends on the specific barriers

that inhibit individuals to act pro-environmentally. Ideally, several intervention

types should be used simultaneously since generally there are usually multiple

obstacles to sustainable behavior. Furthermore, successful interventions must be

tailored to different target groups, and thus different strategies are required.

Antecedent strategies (information and education, prompting, modelling,

behavioral commitments, and environmental design) are meant to change factors

that precede behavior raising problem awareness, informing about choice options,

and announcing the likelihood of positive or negative consequences. Meanwhile,

the consequence strategies (feedback, rewards, and penalties) are targeted at

altering consequences that follow behavior.

Informational strategies aim to change prevalent motivations, perceptions,

cognitions and norms. Education and information dissemination increase

individuals’ awareness of environmental problems, environmental impacts of

behavior, and introduce behavioral alternatives. Persuasion can be focused on

influencing attitudes, strengthening altruistic and ecological values, and increasing

commitment to act pro-environmentally. Additionally, using social support and role

models has been successful in supporting sustainable behavior through intensifying

social norms and informing individuals about the perceptions, efficacy and

behavior of others (Abrahamse, Steg, Vlek, & Rothengatter, 2005; Lehman &

Geller, 2004; Schultz et al., 2007). Informational strategies can be vital for

implementing structural strategies, i.e. informing people of the need and

consequences of the structural strategies will increase successful outcomes of the

latter.

Structural strategies increase individuals’ opportunities to act sustainably and

make pro-environmental behavior choices more attractive, and thus are targeted at

changing the circumstances not controlled by the individuals themselves, such as

37

contextual factors (e.g. availability and the actual costs and benefits of behavioral

alternatives). For instance, the availability and quality of products and services may

be altered, environmentally harmful behavioral options can be limited, and new

improved sustainable behavior options may be provided. Often, implementation of

structural strategies takes the form of either rewarding proper behavior or punish

unwanted behavior (Geller, 2002). For instance, regulations can be implemented

enforcing legal measures and provisioning punishment of violations. Similarly,

pricing policies can decrease prices of pro-environmental choices or increasing

prices of unsustainable alternatives.

Evaluating effects of interventions

The final step in the process of supporting sustainable behavior is evaluation of

effects of interventions. It should focus not only merely on changes in

environmental behaviors, but also on other aspects. For instance, it is important to

record changes in behavioral determinants that can provide an insight into why

intervention programs were successful or not. Additionally, monitoring changes in

environmental impact reveals whether the ultimate goal of behavioral interventions

was reached. Moreover, observing changes in the overall quality of life is a crucial

part of the evaluation process since it directly relates to the more general notion of

sustainable development. With continuous monitoring of behavior, one can assess

whether the success of interventions is able to withstand the test of time.

2.4 Green IS/IT

Molla, Cooper and Pittayachawan (2009) recognized that “environmental

sustainability should be extended to IT”. The internal and external opportunities

presented by the Green Information Systems and Technologies (Green IS/IT) can

have multiple effects on economics, ecological monitoring, and people’s everyday

lifestyles as well as on reducing impact of e-waste, enabling sustainable interaction

design, and decreasing energy consumption by computational systems (Tomlinson,

2010).

Historically, the Green IS and IT concepts have had various definitions and

their interrelation has had different interpretations. Numerous approaches to

treating these concepts emerged, because in essence Green IS is closely related to

Green IT, as well as the Green ICT, and Green Computing. Nevertheless, so far,

Green IT and Green IS notions have been dominating in research (Recker, 2016).

38

The IS literature contains several outlooks on how these concepts have been treated,

for example:

– Green IS and Green IT are considered as completely separate phenomena

(Brooks, Wang, & Sarker, 2012; Erek, Löser, & Zarnekow, 2012; Hedman,

Henningsso, & Selander, 2012; Löser, 2012),

– Green IT is viewed as a part of Green IS (Brooks, Wang, & Sarker, 2012;

Melville, 2010),

– Green IS and Green IT terms are used interchangeably, synonymously and/or

without differentiation them at all (Huang 2008; Malhotra, Melville, & Watson,

2013; Osch & Avital, 2010; Tushi, Sedera, & Recker, 2014).

Apparently, these different perceptions stem from the debate on distinction between

IS and IT (Brooks, Wang, & Sarker, 2012), because they are the core components

of Green IS and Green IT. According to Jenkin, Webster, and McShane (2011)

whenever Green IS and Green IT are viewed as separate concepts in academic

literature, the distinction is made based on the type of the research focus and the

impact created on the environment. Classically, Green IT addresses energy

consumption and waste associated with the use of hardware and software, which

tends to have a direct and positive impact, such as improving the energy efficiency

of hardware and data centers, consolidating servers using virtualization software,

and reducing amount of the leftover material associated with obsolete equipment

(McLaren, Manatsa, & Babin, 2010; Watson, Boudreau, Chen, & Huber, 2008).

Since Green IT has been noted as a compound noun of Green (referring to

environment) and IT, it deals with a myriad of environmental issues, such as

environmental pollution, energy consumption, disposal and recycling of material

resources, and so on (Trimi & Park, 2013). A notion close in nature to Green IT,

Green Computing, is conceptualized as the practice of implementing policies and

procedures that improve efficiency of computing resources to reduce energy

consumption and environmental impact of their use (Baliga, Ayre, Hinton, &

Tucker, 2011; Walke, Joghee, & Kabiraj, 2010). Recker (2016) outlines the three

main Green IT considerations as follows:

– Resource requirements of manufacturing IT equipment,

– Power consumption of IT devices,

– Electronic waste generated by disposing outdated IT equipment.

Based on these considerations, Green IT practices distinguished so far aim to

measure the decrease in negative environmental effects. Yet, Green IT on its own

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is not able to initiate an all-encompassing change in enhancing information

processing solutions to better serve for creation of sustainable environment.

Therefore, limited only to the IT function without leveraging the potential of IS to

decrease enterprise-wide environmental impacts, Green IT practices alone cannot

solve the environmental challenge. Conversely, Green IS has been noted for a

potential to change practices, decisions and business processes as well as

recognized for its wide-reaching environmental impact (Recker, 2016).

Researchers who isolate Green IT from Green IS generally agree that Green IS

can be developed with or without Green IT to support environmental sustainability

initiatives (Dedrick, 2010; Fradley, Troshani, Rampersad, & De Ionno, 2012;

Jenkin, Webster, & McShane, 2011; Seidel & Recker, 2012; Vom Brocke et al.,

2013; Watson, Boudreau, Chen, & Huber, 2008). Therefore, Green IS is broadly

defined as the composition of structures that assist individuals and organizations in

making environmentally sustainable decisions conveniently and effectively,

enabling and effectuating environmentally sustainable work practices (Recker,

2016; Watson, Boudreau, Chen, & Huber, 2008). Green IS is distinguished as the

study and practice of IS-enabled organizational process reengineering with green

objectives that improve environmental and economic performance, advance

cooperative knowledge management (Watson, Boudreau, Chen, & Huber, 2008),

and create positive impacts indirectly. Green IS initiatives include investment in IS,

its deployment, use, and management to minimize the negative environmental

impacts of IT, business operations, and end-user products and services (Watson,

Boudreau, Chen, & Huber, 2008). Since Green IS practices are the measures that

increase positive environmental impacts by decreasing the environmental effects of

business operations and advancing corporate sustainability through IS, the main

categories of the Green IS affordances are organizational sense-making,

information democratization, work virtualization, and output management (Seidel,

Recker, & Vom Brocke, 2013). Thus, overall, Green IS is considered to be a more

impactful force than Green IT alone.

Proceeding with contextualizing Green IS/IT in this dissertation, Green IT will

be treated as part of the Green IS. Similarly to Löser’s (2013) perspective, Green

IT here is being viewed as an integral component of the encompassing concept of

Green IS. Thus, this Green IS/IT history quest interprets the IT as a subset of IS

(Ansari, Ashraf, Malik, & Grunfeld, 2010), and comprises definitions of the Green

IT, Green IS, Green ICT, and Green Computing.

40

Behavior Changing Green IS

Creating behavior changing Green IS/IT requires insights from multiple disciplines

such as psychology, information systems, and environmental science. In order to

bridge knowledge acquired from persuasive systems and sustainable behavior

change research, the steps of encouraging sustainable behavior need to be

combined with the persuasive systems design into one comprehensive theoretical

model as shown in Figure 5.

Fig. 5. Composition of Behavior Changing Green IS/IT.

2.5 IS adoption

Eysenbach (2000) states that increasing knowledge on how individuals interact

with technology and how they process and act on information will be helpful for

development a comprehensive IS for the end users. Indeed, understanding user

acceptance of IS has long been a relevant research question in the IS studies. In

research related to IS, topics of acceptance and/or adoption establish a wide-

ranging sphere dedicated to analyzing the causes, influences, and determining

factors that improve the acceptance of information systems and technologies in

different application areas (Venkatesh, Morris, Davis, & Davis, 2003). The terms

adoption and acceptance in IS research are frequently used synonymously (Bagozzi,

2007), referring to an individual’s willingness to use a certain technology. Despite

that, it is important to recognize that acceptance does not automatically translate

into adoption. For example, an individual could accept a certain system or a

technology because of the positive emotions bought about by this system or

technology, but adoption might not necessarily happen because of different reasons.

Therefore, formation of an intention–behavior gap (Maier, Laumer, Eckhardt, &

Weitzel, 2012) constitutes a crucial topic to be considered in the adoption and

acceptance research.

Persuasive Systems Design

SupportingSustainable Behavior Change

Framework

Behavior Changing

Green IS/IT

41

Numerous theories have been crafted to assist understanding the phenomenon

of adoption and acceptance of systems and technologies. For instance, the theory

of Diffusion of Innovation (Rogers, 1995) established the foundation for

conducting research on acceptance and adoption of innovative technological

solutions. The theory holds that diffusion is “the process by which an innovation is

communicated through certain channels over time among the members of a social

system” (Rogers, 1995, p. 5). The Diffusion of Innovation theory proposed that

innovation and its adoption are preceded by several stages, such as understanding,

persuasion, decision, implementation, and confirmation. When mapping these

stages on a graph, Rogers developed an S-shaped adoption curve that positioned

several groups of users depending on their openness to innovation, such as

innovators, early adopters, early majority, late majority, and laggards.

Parasuraman and Colby (2001) introduced a similar classification of the users

called Technology Readiness (TR). It refers to the user’s inclination to embrace and

use new technologies for achieving goals in different spheres of life ranging from

work to home. Thus, based on the individual’s technology readiness score and the

technology readiness, the users were separated into five technology readiness

segments, namely, explorers, pioneers, skeptics, paranoids, and laggards

(Parasuraman & Colby, 2001). This classification is similar to the S-shaped

adoption curve (Rogers, 1995). Both concepts of Diffusion of Innovation and

Technology Readiness have a market focus and thus are vital to be considered by

organization for achieving a successful introduction of novel systems and

technologies.

Another related notion that emphasizes importance of the technology in

individual performance is Task-Technology Fit (TTF) (Goodhue & Thompson,

1995). The better fit between an individual’s task and technology, the more likely

the user is to utilize the technology. This leads to an increase in the individuals’

performance since the technology meets the task requirements and expectations of

the user. Thus, the task-technology affects efficiency, effectiveness, and the quality

of the work the individual produces. The best applications of the model are

evaluating the actual usage of the technology and receiving feedback regarding the

new technology. Furthermore, the task-technology fit is suitable for testing the

technology applications that have already been introduced to the marketplace.

Theory of Reasonable Action (TRA) (Fishbein & Ajzen, 1975), one of the most

frequently used theories in IS literature, determines behavior based on behavioral

intention influenced by attitudes. Fishbein and Ajzen (1975) defined “attitude” as

the individual’s evaluation of an object, and “belief” as a link between an object

42

and some attribute, while defining “behavior” as a result or intention. Attitudes are

reactions based on series of beliefs about the object of behavior. According to the

TRA, another factor that is able to influence behavioral intention is the individual’s

subjective norms that determine what the attitude of the individual’s close

community’s to a certain behavior is.

Another popular theory utilized in IS adoption and acceptance research is

Theory of Planned Behavior (Ajzen, 1991) which explains factors that determine

the individual’s behavioral intention which in turn influences the individual’s the

actual use of the system or technology. The first two factors are the same as in the

TRA (Fishbein & Ajzen, 1975): attitude and subjective norms, while the third factor

is the perceived behavioral control – the control that the users perceive as a limiting

aspect of their behavior. Later, an extension of this theory was introduced – the

Decomposed Theory of Planned Behavior (Decomposed TPB) (Taylor & Todd,

1995). Just like in the TPB, in the Decomposed TPB consists of three main factors

influence behavior intention and actual behavior adoption: attitude, subjective

norms and perceived behavior control. However, Taylor and Todd (1995) suggested

that decomposing the belief structures into multi-dimensional constructs improves

understanding of these relationships within the model.

A strong stance in the area of IS adoption and acceptance research was

established by the Technology Acceptance Model (TAM) (Davis, 1989). The reason

for developing TAM was the absence of the valid measurement scales for predicting

user acceptance of technology while the available subjective measures were not

validated and their relationship to the system usage was ambiguous. Therefore, the

two-fold purpose of creating TAM was to outline general factors that affect

technology and systems’ acceptance and to explain the users’ behavior in relation

to the end-user IS and IT. The TAM model introduces perceived usefulness and

perceived ease of use. Perceived usefulness is defined the degree to which the user

views the use of a particular system or technology as an enhancement of the job

performance while the perceived ease of use refers to the extent to which the

potential user expects that familiarizing with the system or technology will be

effortless (Davis, 1989). The users’ beliefs regarding the system are likely to be

influenced by other factors, i.e. external variables. Later, Venkatesh and Davis

(1996) discovered that both perceived usefulness and perceived ease of use have a

direct influence on behavior intention, thus potentially reducing the need for the

attitude construct. In spite of being a significant theory, TAM has been subject to

criticism and revision. For example, among the drawbacks of TAM the following

have been pointed out: (1) an exclusive focus on inspecting a specific technology

43

in a detailed context unavoidably leading to a belief that the users get only benefits

from their interactions with a system or a technology; (2) a strong belief in a rational

user; and (3) the model’s deterministic nature that excludes investigation of the

users’ real-life choices in more diverse use contexts (Al-Natour & Benbasat, 2009;

Benbasat & Barki, 2007; Blechar Constantiou, & Damsgaard, 2006; Constantiou,

Lehrer, & Hess, 2014).

Developing the theory further, Venkatesh and Davis (2000) proposed its

extension – TAM2. This version provides a more extended explanation of the

reasons the users find a given system useful at different points in time: pre-

implementation, one month post-implementation and three months post-

implementation. Venkatesh and Davis (2000) found that TAM2 is relevant for both

mandatory and voluntary environments. TAM2 posits that the user’s mental

assessment of the fit between objectives at work and the results of performing job

tasks achieved by using the system is responsible for shaping perception of the

usefulness of the system (Venkatesh & Davis, 2000). In the further extension of the

theory, Venkatesh and Bala (2008) combined TAM2 (Venkatesh & Davis, 2000)

with the model of the determinants of perceived ease of use (Venkatesh, 2000), and

developed an integrated model of technology acceptance known as TAM3. TAM3

includes four different types of determinants of perceived usefulness and perceived

ease of use, such as individual differences, system characteristics, social influence,

and facilitating conditions. In TAM3, relationships between the perceived ease of

use and perceived usefulness, between computer anxiety and perceived ease of use,

and between perceived ease of use and behavioral intention were moderated by

experiences. Besides TAM itself, similar research on technology acceptance

considered instrumental beliefs as drivers of individual intention to use a system or

technology (Agarwal & Karahanna, 2000). It has been theorized that the holistic

experiences with technology can also depend on factors like enjoyment (Van der

Heijden, 2004) and flow (Agarwal & Karahanna, 2000).

Most of the further attempts to extend TAM were based on the following

strategies (Wixom & Todd, 2005): (1) presenting factors from the related models;

(2) suggesting supplementary or alternative belief factors; and (3) examining

antecedents and moderators of the constructs of perceived usefulness and perceived

ease of use. Modifications to TAM have incorporated a range of constructs, such as

trust, cognitive absorption, self-efficacy, job relevance, image, result

demonstrability, disconfirmation, information satisfaction, top management

commitment, personal innovativeness, information quality, system quality,

44

computer anxiety, computer playfulness, and perceptions of external control

(Benbasat & Barki, 2007).

Another crucial milestone in the IS adoption and acceptance research is the

Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh,

Morris, Davis, & Davis, 2003) built on the previous technology acceptance models

and theories with the aim to increase explanation of technology acceptance

behavior. UTAUT can explain over 70% of all technology acceptance behavior, and

thus it is able to clarify factors which influence the acceptance of novel

technologies. The UTAUT model comprises constructs of performance expectancy,

effort expectancy, social influence and facilitating conditions as predictors of

behavioral intention of the system use and actual system use behavior. Performance

expectancy describes the level of usefulness of the given system or technology for

the user, and hence it corresponds to the degree of suitability of the system for

supporting the tasks performed by the user (Venkatesh, Thong, & Xu, 2012).

Meanwhile, effort expectancy considers the efforts (e.g. stress, cognitive capacity)

that the user needs to put in for the system to operate (Venkatesh, Thong, & Xu,

2012). Social influence refers to the manipulative effect of social peers on the user’s

decision to use the system. This construct is important because in a social context

individuals have a tendency to adjust their behavior to conform to the opinions and

views of those who are around (Venkatesh, Thong, & Xu, 2012). Finally, the factor

of facilitating conditions defines the perceived availability of the arrangements

required for the usage of the system. For example, the factors can be technological,

such as the availability of essential hardware to use a specific software or service,

or personal, such as resources (e.g. time, money) to acquire or use the system

(Venkatesh, Thong, & Xu, 2012).

Since the original UTAUT model received criticism for being another model

with the sole business focus (Venkatesh, Thong, & Xu, 2012), an extension of the

model was created. In UTAUT2, the constructs of hedonic motivation, price value,

and habit were added to improve the fit of the model for the consumer use context

(Venkatesh, Thong, & Xu, 2012). The hedonic motivation construct corresponds to

the fun and pleasure perceived by the user of a certain technology or systems. Price

value was defined as the tradeoff between perceived benefits for the user and the

costs accrued from the use of the technology. In organizational context (UTAUT),

there was no need for a strict distinction between the value of technologies and

usage-cost because usually employees do not pay for the system use. However, in

a consumer use context, the user has to consider the cost-benefit tradeoff for using

the technology and/or services it provides. Thus, in this context, the price value is

45

much more influential on the behavioral intention of technology use compared to

organizational setting (Venkatesh, Thong, & Xu, 2012). Another new construct

called habit refers to the experiences of people in technology use. It can influence

the behavioral intention of technology use based on the previous encounters with

the similar technologies as well as promote automatic behavior formed with the

existing experience resulting into encouraging the user to use a given technology

(Venkatesh, Thong, & Xu, 2012).

Apart from TAM, UTAUT and their extensions, more contemporary

technology acceptance and adoption models and techniques have been introduced

in the IS research. For example, Al-Natour and Benbasat (2009) proposed an

interaction-centric model for adoption and use of IT artefacts. Walden and Browne

(2009) introduced a Sequential Adoption Theory, which explains herding behavior

in early adoption of novel technologies. Beaudry and Pinsonneault (2010) argued

that emotions are essential drivers of acceptance behavior.

Furthermore, some specific observations regarding technology and system

acceptance and adoption have been made. For instance, Beaudin, Intille, and Morris

(2006) suggested that consumers’ adoption of pervasive monitoring systems is

likely to increase, if these systems are designed to support personalized,

longitudinal self-investigation which assists the users in recognizing attributes and

circumstances that impact their social, cognitive, and physical health. Jimison et al.

(2008) observed that oftentimes lack of a perceived benefit, lack of convenience,

lack of trust of the provided information, and burdensome data entry, as well as

other technical issues can inhibit the users from adopting an information system or

technology. Moreover, people are reluctant to use systems that do not fit seamlessly

into the users’ regular daily routines. Clearly, technologies are not able to facilitate

self-monitoring and self-management or change behaviors when the users do not

adopt the technology (Chatterjee & Price, 2009, Kim & Chang, 2007; Or & Karsh,

2009; Rahimpour, Lovell, Celler, & McCormick, 2008).

Although there are several technology acceptance and adoption models and

frameworks, they do not consider the importance of emotions and habits in

continuing IS/IT use, nor so they reflect the change of the users’ interactions with

IT artefacts over time as the user-system relationship develops (Al-Natour &

Benbasat, 2009; De Guinea & Markus, 2009; Li & Ku, 2011). This criticism

sparked increased attention to IT postadoption/IT continuance (Bhattacherjee,

2001). Continuance intention stands for the intention to continue using a system

after initial acceptance and aimed to explain the success of the system taking into

account the psychological motivations that affect users’ continuance intention

46

decisions that emerge only after the initial use of the system (Bhattacherjee, 2001;

Bhattacherjee & Barfar, 2011). The main purpose of research on IS/IT continuance

is to predict actual behaviors rather than intentions. Moreover, researchers have

suggested to focus on operationalizing and measuring the IS/IT usage behavior

instead of the mere intention (Bhattacherjee & Barfar, 2011). The IS Continuance

model (Constantiou, Lehrer, & Hess, 2014) investigates ¨the key factors for

continued use linking satisfaction and perceived usefulness (a construct borrowed

from TAM) to the user’s intention to continue using the system.

Existing Green IS/IT adoption literature is focused on organizational

perspectives, for instance, understanding Green IS initiatives (Wang, Chen, &

Benitez-Amado, 2015), organizational motivation to adopt Green IS (Molla, 2009;

Molla & Abareshi, 2012), enablers and barriers to Green IS adoption by

organizations (Seidel, Recker, Pimmer, & Vom Brocke, 2010), corporate social

responsibility (Chou & Chen, 2016), etc. In the scarce works on the individual

adoption of Green IS/IT, the majority studies also focus is on the work-related

contexts, for example, managerial decisions (Seidel, Recker, & Vom Brocke, 2013),

employees perceptions (Molla, Abareshi, & Cooper, 2014), leadership capabilities

(Tan, Pan, & Zuo, 2015), etc. Only several studies considered individual

perspectives on adoption of Green IS for personal use (Wunderlich, Kranz, & Veit,

2013; Kranz & Picot, 2011). While oftentimes pro-environmental behavior for a

business entity is not only a competitive advantage but also a legal requirement,

many of the individual non-work-related sustainable choices are more difficult to

trigger because they are not influenced by the regulatory compliance. Therefore,

findings on Green IS/IT adoption research in work-related circumstances do not

translate directly to the individual user cases in which adoption of Green IS/IT is

different, much more challenging, and needs further research.

Furthermore, in spite of the popularity of technology acceptance and adoption

studies, a very scarce body of knowledge considers acceptance and adoption of

persuasive systems. Relying on the existing IS adoption theories, primarily TRA,

TPB, TAM and UTAUT, and IS Continuance, this dissertation attempts to craft a

novel approach for investigating individual users’ intentions to adopt and to

continue using persuasive Green IS/IT.

47

3 Research methodology

A research approach is a way of conducting research that embodies a specific style

and utilizes different methods or techniques (Galliers, 1990). The dissertation

employs two distinct methodological approaches. Historical analysis and

systematic literature review are qualitative descriptive (positivist) studies, and the

empirical studies are quantitative positivist ones. According to Sarker, Xiao, &

Beaulieu (2012, p. 8), “unlike in quantitative studies, researchers in qualitative

studies are instruments of observations and analysis.” All papers employ the PSD

model (Oinas-Kukkonen & Harjumaa, 2009) which functions as an observational

lens and a pattern for systematically codifying the observations.

Gregor (2006) states that the adopted research approach is likely to vary

depending on a type of the IS theory used. The main identified types are based on

the goals of the theory, including following: (1) analysis and description of the

phenomena of interest, (2) explanation for the occurrence of events, (3) prediction

of the future events provided that specific preconditions are met, and (4)

prescription for the construction of an artefact (Gregor, 2006, p. 619). Additionally,

Gregor (2006) provides the following classification of theories: (I) theory for

analyzing, (II) theory for explaining, (III) theory for predicting, (IV) theory for

explaining and predicting, (V) theory for design and action. Although there are five

different types of theory identified, they are not mutually exclusive but rather

interrelated, thus some studies can include aspects from several types of theory

(Gregor, 2006). Determining the theory type used in the dissertation, theories for

explaining and predicting describe best the content presented in the dissertation.

All four empirical studies utilize quantitative positivist methods which have

been recognized as the most common in the IS research (Avgerou, 2000; Chen &

Hirschheim, 2004; Myers & Liu, 2009; Orlikowski & Baroudi, 1991). Bryman

(1984) posits that quantitative methodology is a tactic for conducting social

research which applies natural science to social phenomena. Furthermore, Bryman

(1984, p. 77) describes the positivist approach as “exhibiting a preoccupation with

operational definitions, objectivity, replicability, causality, and the like”. Similarly,

Iivari (1991, p. 257) maintains that “Positivism seeks to explain and predict what

happens in the social world by searching for regularities and causal relationships

between its constituent elements'” and “Positivism regards scientific knowledge as

consisting of regularities, causal laws and explanations”. Describing quantitative

positivist research, Straub, Gefen, and Boudreau (2005), pinpointed two

requirements: an emphasis on quantitative data, and an emphasis on positivist

48

philosophy. Straub, Gefen, and Boudreau (2005, p. 222) stated that the data

represents “values and levels of theoretical constructs and concepts and the

interpretation of the numbers is viewed as strong scientific evidence of how a

phenomenon works”. Additionally, Straub, Gefen, and Boudreau (2005, p. 222)

noted that “sources of data are of less concern” compared to the fact that

empirically gathered data constitutes the scientific evidence. Considering the

emphasis on positivist philosophy, data used in a study should provide meaning and

evidence that can be used to confirm or to reject the theoretical assumptions (Straub,

Gefen, & Boudreau, 2005).

Positivism favors the hypothetico-deductive scientific method, according to

which the scientific inquiry is based on formulating hypotheses that can be either

confirmed or rejected using analysis of the collected data (Chen & Hirschheim,

2004; Orlikowski & Baroudi, 1991). Thus, the positivist approach is grounded on

the occurrence of a priori relationships among variables relevant to the observable

phenomena. The positivist studies are mainly theory-testing since they focus on

growing the predictive understanding of phenomena (Orlikowski & Baroudi, 1991,

p. 5). The following points describe features of the positivist studies: (1)

formulating hypotheses, models, or causal relationships among constructs; (2)

using quantitative methods to test theories or hypotheses (most often but not

always); and (3) objective, value-free interpretation (Chen & Hirschheim, 2004, p.

201). Järvinen (2000, p. 3) also notes that in theory testing research, “the theory,

model or framework is either taken from the literature, or developed or refined for

that study”. Therefore, the empirical studies can be considered as the theory-testing

ones.

Järvinen (2000) points out that in theory-testing research the most commonly

utilized methods are surveys, field studies, and experiments. In the empirical

studies, the instruments of objective observation are the surveys. Bryman (1984, p.

77) states that a survey is the preferred tool within quantitative practice: “Through

questionnaire items concepts can be operationalized; objectivity is maintained by

the distance between observer and observed along with the possibility of external

checks upon one’s questionnaire; replication can be carried out by employing the

same research instrument in another context; and the problem of causality has been

eased by the emergence of path analysis and related regression techniques to which

surveys are well suited”.

49

3.1 Historical method

Historical methods are comprised of a collection of techniques and approaches that

come from both traditional history and social research. Neuman (2014) states that

the methodology was initially developed in the XIX century by social thinkers such

as Marx, Durkheim, and Weber. According to Toland and Yoong (2013), historical

methods in information systems were initially utilized by Mason, McKenney and

Copeland (1997) in their studies of Bank of America, Lyons Electronic Office (LEO)

and American Airlines. Toland and Yoong (2013) also found that a similar approach

was used in a study of the use of IT in Texaco within a forty-year period.

Historical methods provide understanding of the present by studying the past,

and gaining an awareness of the long-term economic, social and political forces

that shape events. Historical methods are highly beneficial since wide insights of

the researched domain are obtained (Toland & Yoong, 2013). The historical method

raised interest among social scientists because of its potential to expand their

research horizons (Porra, Hirschheim, & Parks, 2014). Specifically, information

systems researchers can gain new observations by considering the long-term

context in which their research is situated. Porra, Hirscheim, and Parks (2014) point

out that there are several reasons to encourage the use of the historical methods,

one of them is building an identity of the discipline. The underlying value that

historical research delivers is hidden in its unique potential for understanding

complex phenomena measured in terms of their scope and duration.

Historical analysis considers Information Systems History (ISH) as the point

where IS meets history (Oinas-Kukkonen & Oinas-Kukkonen, 2014); thus the

paper views Green IS/IT history as a chronological account of sustainable ideas

combined with practical and theoretical technological developments. To

contextualize the present of Green IS/IT, the paper refers to contextualizing

relevant trends and tendencies in the past. Hence, the paper conducts periodization

and generalization of the history of sustainability and technology, which are the

components of the Green IS/IT history. By mapping these components to sources

in the past, the paper employs historical research approach that helps retrieving an

encompassing spectrum of what enabled appearance and growth of the present-day

Green IS/IT domain.

50

3.2 Systematic literature review

The evidence-based paradigm advocates objective evaluation and synthesis of the

relevant empirical results to a particular research question through a process of

systematic literature review and integration of that evidence into professional

practice (Sackett et al., 2000). Kitchenham (2004, p.1) defines a systematic

literature review (SLR) as “a means of identifying, evaluating and interpreting all

available research relevant to a particular research question, or topic area, or

phenomenon of interest”. While the individual studies contributing to a SLR are

referred to as primary studies, a SLR itself is a form of a secondary study.

Among numerous reasons for conducting a SLR, Kitchenham (2004) identified the

most common ones:

– Summarizing the existing evidence about a treatment or technology (e.g. to

summarize the empirical evidence of the benefits and limitations of a specific

treatment or technology),

– Identifying gaps in current research in order to suggest areas for further

scientific inquiry,

– Providing a framework/background for appropriately locate new research

directions.

Besides the abovementioned reasons, a SLR can also be initiated to examine the

extent to which empirical evidence supports or contradicts theoretical hypotheses,

or even to assist with crafting new hypotheses.

As noted by Kitchenham (2004), a SLR significantly differs from a

conventional literature review. Firstly, a SLR starts with defining a review protocol

that identifies the addressed research question and the methods to be used to

perform the review. Secondly, a SLR is conducted using a defined search strategy

that aims to find as much of the relevant literature as possible. Thirdly, a SLR

documents the search strategy so that readers can access its rigor and completeness.

Fourthly, a SLR requires explicit inclusion and exclusion criteria to assess each

potential primary study. Fifthly, a SLR outlines the information to be collected from

each primary study including quality criteria for evaluating each primary study.

Lastly, a SLR is a prerequisite for quantitative meta-analysis.

A SLR consists of several pre-defined stages with several substages, such as:

(I) the review (i. identification of the need for a review, ii. development of a review

protocol), (II) conducting the review (i. identification of research, ii. selection of

51

primary studies, iii. study quality assessment, iv. data extraction and monitoring, v.

data synthesis), (III) reporting the review.

3.3 Statistical analysis with partial least squares structural

equation modeling (PLS-SEM)

The research models in empirical studies were analyzed statistically using partial

least squares (PLS), a structural equation modeling (SEM) approach, which is a

technique for simultaneous estimation of relations among multiple constructs. Its

primary objective is prediction and explanation of target constructs (Hair, Hult,

Ringle, & Sarstedt, 2013). PLS-SEM is used to develop theories in exploratory

research (Gefen, Rigdon, & Straub, 2011), which is appropriate when the purpose

of the model is to predict rather than to test an established theory (Hair, Hult, Ringle,

& Sarstedt, 2013). Moreover, PLS is reasonably robust to deviations from a

multivariate distribution. The above-mentioned features of PLS-SEM provide the

rationale for employing it in the studies. SmartPLS software (Ringle, Wende, &

Becker, 2015) was used for the data analyses in Studies I–IV.

The statistical objectives of PLS-SEM is similar to the linear regression: to

demonstrate explained variance in the latent variable, to indicate the strength of the

relationship between latent variables in terms of R2 values, and test the significance

of the relationship between latent variables by estimating t-values and reporting

their corresponding p-value (Gefen, Straub, & Boudreau, 2000; Hair, Hult, Ringle,

& Sarstedt, 2013). Effect sizes determine whether the effects indicated by

relationship coefficients are small (0.02), medium (0.15), or large (0.35) (Cohen,

1988).

PLS-SEM analysis is carried out in two steps. The first step, the measurement

model, analyzes the relation of each indicator in a research model to its

corresponding constructs. The measurement model represents the relation between

constructs and their corresponding measures. The properties of the constructs are

assessed in terms of their validity and reliability, and measurement items that are

not at acceptable levels are removed. Evaluating the measurement model addresses

internal consistency (the composite reliability), indicator reliability, convergent

validity, and discriminant validity. Reliability and consistency are measured using

Cronbach’s alpha and composite reliability with values ranging between 0 and 1.

High values indicate higher levels of reliability (values higher than 0.7 are

considered to be acceptable) (Fornell & Larcker, 1981). For composite reliability,

values from 0.6 to 0.7 are acceptable in exploratory research, while in more

52

advanced stages of research, values between 0.7 and 0.9 are considered satisfactory.

A value of 0.95, however, indicates unnecessary redundancy in the construct items

(Hair, Hult, Ringle, & Sarstedt, 2013). The inter-construct correlations, the square

root of the average variance extracted (AVE), and the indicators’ factor loadings,

are good measures of convergent validity – the extent to which two or more items

measure the same construct (Bagozzi & Phillips, 1982). High loadings indicate that

the measurement items correctly measure the same phenomenon and share a high

proportion of variance (Hair, Hult, Ringle, & Sarstedt, 2013). The second step, the

structural model, evaluates the hypothesized relations among the constructs. The

evaluation of the predictive capabilities of the structural model is based on the

significance of the path coefficients and relations between the constructs (Hair, Hult,

Ringle, & Sarstedt, 2013). Assessing the structural model determines how well the

empirical data support the theory, showing the relations between different

constructs and specifying how the constructs are related (Hair, Hult, Ringle, &

Sarstedt, 2013). The key results are the path coefficients and the R2 values, which

represent the hypothesized relations among the constructs and the percentage of the

total variance of the dependent variable explained by the independent variables.

The complete bootstrapping method with 5000 resamples and parallel processing

with no sign changes was used in all Studies I–IV. Two-tailed bias-corrected and

accelerated (Bca) bootstrap was the confidence interval method used in Studies I–

IV.

According to Hair, Hult, Ringle, and Sarstedt (2013) PLS-SEM minimum

sample size should be equal to the larger of ten times the largest number of

structural paths directed at a particular latent construct in the structural model. The

sample sizes exceeded this requirement in all studies.

3.3.1 Measurement validation

Developing improved measures for key theoretical constructs has been a priority

for the research in the IS field. Better measures for predicting and explaining

system use have both theoretical and practical value for researchers and the broader

community (Davis, 1989). Although, the IS field has had many changes, the

problems of rigor have persisted. To maintain a high quality of IS research, a

scrupulous validation of instruments used to gather data is required, since it

constitutes a platform for findings and interpretations and thus is important in the

positivist quantitative field. Moreover, in positivist research, there is a need to

ensure that data gathering is as objective as possible and an accurate representation

53

of the underlying phenomena is provided (Boudreau, Gefen, & Straub, 2001;

Straub, Boudreau & Gefen, 2004).

In the empirical studies, mainly previously validated instruments were used

because they allow for accumulating knowledge, maintaining comparability

between studies, and saving time. However, it is important not to make significant

alterations in the validated instruments in order not to reduce their efficiency.

Additionally, the need for their explanation despite the space limitations in articles

is an apparent drawback (Boudreau, Gefen, & Straub, 2001).

Boudreau, Gefen, and Straub (2001) argue that the use of previously validated

instruments is efficient but does not automatically result in improved constructs.

There are theoretical and practical reasons for employing previously validated

instruments. A major theoretical reason is that doing so allows researchers to

accumulate knowledge and maintain comparability between studies (Boudreau,

Gefen, & Straub, 2001). The fast pace of technological change often restrains

researchers from investing time in novel instrument development. In terms of

instrument validation, studies using “first generation” statistical techniques (e.g.

regression and ANOVA) seem to substantially differ from studies using “second

generation” techniques (e.g. SEM) (Boudreau, Gefen, & Straub, 2001). Boudreau,

Gefen, and Straub (2001) argue that a plausible explanation for this difference is

that SEM analyzes both the structural model (the presumed causation) and the

measurement model (the loadings of observed items). Thus, validity assessment is

built into SEM: the validity statistics are explicit in the output, and the degree of

statistical validity directly affects the overall model fit (Boudreau, Gefen, & Straub,

2001). In contrast, in first generation techniques, validity and reliability analyses

are not related to the actual hypothesis testing and do not determine the overall fit

indices. Each of the quantitative field studies utilizes a slightly different

measurement instrument. According to Straub, Boudreau, and Gefen (2004),

theories are more robust when variant forms of measurement produce similar

outcomes. Since the aim of this dissertation is theory exploration, the variant forms

of measurement can be actually seen as a strength as opposed to a weakness. From

these variant forms of measurement, we can synthesize perhaps a more robust

measurement instrument compared to the one where exactly the same forms of

measurement are utilized in all of the studies.

The indicators of the measurement instrument employed in Studies I–IV

(Appendices 1–4) were derived from a number of sources to operationalize the

constructs (see Table 1). Before the studies, the survey items were checked by

54

several researchers from the same field to ensure that the items demonstrated

sufficient face and expert validity.

Table 1. Summary of the constructs used in Studies I–IV.

Construct Definition Source

Primary task

support

The system's ability (1) to adapt to individual user's tasks,

reducing complex tasks into smaller subtasks, (2) to reflect on

the user's behavior, personal goal setting, and track progress

toward them.

Oinas-Kukkonen &

Harjumaa, 2009

Dialogue support The system's ability to provide relevant feedback to the users

via words, images, sounds, and other forms of media.

Oinas-Kukkonen &

Harjumaa, 2009

Credibility support The system's ability to provide the users trust, believability,

reliability, and credibility

Oinas-Kukkonen &

Harjumaa, 2009

Social support The system's ability to motivate the users by leveraging social

influence and performing a role of a social actor.

Oinas-Kukkonen &

Harjumaa, 2009

Unobtrusiveness A measure of implementation and continued use that (1)

depends on whether users can use the system as a seamless

part of their daily routines, (2) reflects whether the system fits

with user’s environment in which the system is used.

Lehto, Oinas-Kukkonen, &

Drozd, 2012

Perceived

persuasiveness

An individual’s favorable impressions of the system. Lehto, Oinas-Kukkonen, &

Drozd, 2012; Oinas-

Kukkonen 2013

Perceived risk An individual’s impression of the uncertainty, probability of

loss, and adverse consequences associated with using the

system.

Benson, Saridakis, &

Tennakoon, 2015;

Malhotra, Kim, & Agarwal,

2004

Self-disclosure The act of revealing personal information about oneself to the

system

Posey, Lowry, Roberts, &

Ellis, 2010; Benson,

Saridakis, & Tennakoon,

2015

Continuance

intention

An Individual’s intention to continue using a system after initial

acceptance

Bhattacherjee, 2001

Cognitive

absorption

A state of deep involvement with the system characterized by

perceived enjoyment, temporal dissociation, focused

immersion, control, and curiosity

Agarwal & Karahanna,

2000; Lowry et al., 2012

Intention to adopt

(Intention to use)

An individual’s behavioral intention to start using the system in

the future.

Davis, Bagozzi, &

Warshaw, 1989;

Venkatesh, Thong, & Xu,

2012; Lehto & Oinas-

Kukkonen, 2015

55

Construct Definition Source

External PLOC An individual’s perceived reasons for behavior performed to

satisfy an external demand or compliance to authority

Ryan & Connell, 1989;

Wunderlich, Kranz, & Veit,

2013

Internal PLOC An individual’s feelings of volition where the self is perceived a

cause of behavior



2013

Introjected PLOC An individual’s feelings of an apparent conflict caused by

misalignment of the perceived external behavioral (social)

influences and personal norms and values



2013

Attitude (towards

Green IS)

An individual’s evaluative integration of cognitions and affects

(positive or negative feelings ) experienced in relation to the

system

Davis, Bagozzi, &

Warshaw, 1989; Lehto &

Oinas-Kukkonen, 2015

Perceived

effectiveness

An individual’s impression of the extent to which using the

system will provide benefits (effectiveness) in performing

certain activities.

Venkatesh, Morris, Davis,

& Davis, 2003;

Venkatesh, Thong, & Xu,

2012

Perceived effort An individual’s impression of the expected degree of ease

associated with the use of the system

Venkatesh, Morris, Davis,

& Davis, 2003; Lehto &

Oinas-Kukkonen, 2015

Social affirmation Social influence and subjective norms that reflect an

individual's perception of how the others evaluate the system

followed by the individual's motivation to comply with the

expectations expressed by the others and receive the

affirmation from them

Ajzen, 1991; Venkatesh,

Thong, & Xu, 2012;


2013

3.3.2 Common method variance

Given the volume of the technology acceptance and adoption studies, it is crucial

to maintain the research rigor and not to misuse the concepts or report false

positives (Goodhue, Lewis, & Thompson, 2011). Although, earlier, limited

attention was paid to the common method variance (CMV) within the IS discipline,

now more critiques considering it have emerged since the field has expanded

substantially, and more researchers have recognized the issues occurring in these

types of studies.

In Studies I–IV, most of the variables were measured using the same instrument,

thus CMV poses a potential threat to the validity of the results. To minimize CMV

ex ante, the respondents were assured of the anonymity and confidentiality of the

studies, and they were encouraged to answer as honestly as possible. Several

measures were also taken ex post to test and possibly control for CMV. Very high

56

correlations (>0.90) between latent variables serve as a “red flag” for common

method bias (Pavlou, Liang, & Xue, 2007). Upon inspection of the data analyses,

none of the correlations were found to be near the 0.90 level. Additionally,

SmartPLS software calculates Full Collinearity VIFs (Variance Inflation Factors).

Kock and Lynn (2012) suggest that for variance-based SEM (such as PLS) the VIFs

are lower than 3.3. All VIFs measures indicated no detrimental effect of the CMV

on the obtained results (all VIFs <4).

3.4 Ethical issues

Ethical guidelines of the Finnish Advisory Board on Research Integrity and of the

University of Oulu were carefully followed in all studies involving human

participants (Studies I–IV). These studies constituted neither physical, nor

psychological risk to the participant, who voluntarily chose whether to participate

or not. Only adults were involved in the studies. Prior to being involved in a study,

participants read and signed informed consent forms that provided details about the

purpose of the research, the utilized procedures, and confirmed the participants’

confidentiality, privacy, and anonymity. The collected responses and the results of

the studies were used exclusively for scholarly purposes. Historical analysis and

systematic literature review had no ethical concerns.

57

4 Research contributions

4.1 Historical overview of Green IS/IT domain

The historical overview of the Green IS/IT domain was conducted with the aim to

answer the following research question:

How has the Green IS/IT domain been developing throughout different

historical periods?

The paper aimed to achieve the specific goals: (1) trace the origin of the Green

IS/IT concept, (2) apply the Information Systems History (ISH) research methods,

such as periodization, contextualization and generalization (Oinas-Kukkonen &

Oinas-Kukkonen, 2014), (3) highlight the Green IS/IT significance in IS and other

disciplines, and (4) summarize the current state of the Green IS/IT research.

Combination of computer science, management and organization theory,

operations research, accounting (Davis & Olson, 1984), and development of

technology initiated appearance of Information Systems (IS) research field in 1960s,

initially referred to as “Management Information Systems” (Hirschheim & Klein,

2012). Since then, the IS field has been growing and changing together with the

disciplines it was initially composed of. To contextualize evolution of the IS field,

the paper employs the Four Era periodization by Hirschheim and Klein (2012)

mapping the evolution of the Green IS/IT to these periods:

– The First Era (from mid-1960s to mid-1970s) began when the first IS

departments appeared in organizations. Although the term “sustainability” had

been coined by this time, the significance of sustainable development of

humankind had been recognized and the overall environmental consciousness

had increased, there was still no significant Green IS/IT practices implemented.

– The Second Era (from mid-1970 to 1980s) was marked by introduction of

personal computers, attempts to develop massive parallel CPUs and artificial

intelligence, and competition for the leading position in IS hardware (Butler,

1997). The highlight of the period was the establishment of the World

Commission on Environment and Development (WCED) (the Brundtland

Commission, United Nations) in 1987 by the UN to craft long-term

environmental strategies for the international community, including Our

Common Future report (1987).

– The Third Era (from mid-1980s to late 1990s) was the period of emerging

departmental computing and decentralization. For the first time in history,

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Green IS/IT programs, standards and initiatives started appearing (Raza, Patle,

& Arya, 2012).

– The Fourth and most eventful Era in both IS and Green IS/IT began in late

1990s with commercialization of the Internet, enhancement of organizational

methods of communication, business strategies, and promotion of

environmentally conscious trends. More industrial standards for environmental

care supported by institutions and individuals were established. Businesses and

other organizations have started modifying their current processes and

strategies related to Green IS/IT. Academic research gained a critical role in

addressing environmental issues, investigation of theoretical and practical

frameworks for organizational, societal and individual needs.

Having paralleled the IS eras and Green IS/IT development, the paper observes that

during the First and Second IS Eras Green IS/IT existed only in the form of

conceptual, ethical, social, and political notion turning into more practical concept

in the Third and Fourth Eras. Overall, the continuity in the IS history due the

evolution from MIS to pervasive IS corresponds to the continuous development of

Green IS/IT.

The paper also analyzed how Green IS/IT is approached by practitioners and

academics. Partly the Third Era (1992–1993) and the Fourth Era (since 1999) are

marked with implementation of numerous Green IS/IT initiatives. Governmental

and industrial programs, benchmarks and rating tools were created to support and

rate success of organizational Green IS/IT practices. Having appeared in late 2000s

during the most recent part of the Fourth Era of IS in academic investigation (Tushi,

Sedera, & Recker, 2014), the Green IS/IT research still requires more directions,

explanations of relevant theoretical foundations, studies, conceptualizations and

proposals.

To form a richer understanding of the dominant types of studies in the Green

IS/IT research, the paper classified previously identified by Tushi, Sedera, and

Recker (2014) prevalent theories and frameworks into several general IS theory

types outlined by Oinas-Kukkonen (2015). This generalization distinguishes

theories and frameworks that refer to the (1) individual user behaviors, (2) social

behaviors (of both individual users and groups/networks of users), and (3)

organizational behaviors. The classification disclosed that most of existing theories

and frameworks (23 examples) assist organizations in implementing, utilizing and

evaluating Green IS/IT initiatives. Meanwhile, theoretical approaches for

individuals (3 examples) and social groups (7 examples) are scarce. Thus, historical

59

analysis suggests that the Green IS/IT (both in academia and in practice) should

devote more attention to creating Green IS/IT theoretical and practical solutions

beyond the scope of the organizational and job-related contexts. Additionally, the

paper highlighted research trends that have recently appeared and are likely to lead

the progress the domain at least in the near future.

4.2 Analyzing literature on Green IS/IT with persuasive features

To exemplify how Persuasive Systems Design (PSD) principles (Oinas-Kukkonen

& Harjumaa, 2009) are used in Green IS/IT applications, a systematic literature

review of articles which describe systems aimed at decreasing humans’ harmful

impact on environment was conducted. The main research question of the paper:

Which persuasive features are present in Green IS/IT systems featured in top

IS publications?

Apart from the focal research question, this paper had several other objectives,

namely: (1) to reveal importance of behavior change in adopting Green IS/IT and

(2) encourage considering a wider range of persuasive principles for facilitating

acceptance and strengthening impact of the Green IS and applications.

To identify articles suitable for the review, databases Scopus, ProQuest,

EBSCOHost Web, and Web of Science were used to search for words “green”,

“sustainab*”, and “ecolog*” in abstracts, titles and keywords of the articles

published between years 2005 and 2016 in 8 core IS journals (Management

Information Systems Quarterly, Journal of the Association for Information Systems,

The Journal of Strategic Information Systems, Journal of Management Information

Systems, Journal of Information Technology, Information Systems Journal,

European Journal of Information Systems, Information Systems Research) as well

as Information Systems Frontiers, IEEE Software, IEEE Pervasive Computing, and

Business & Information Systems Engineering. Scopus database search returned 181

results, ProQuest – 216, EBSCOHost Web IS – 94, and Web of Science – 138. To

be included in the final review, the articles were required to meet certain criteria.

The included articles (1) contained empirical evidence, (2) contained a description

of an application or a system, as well as (3) discussed behavior change. Articles

that did not meet all these requirements were excluded while the selected articles

were reviewed. The article selection process is shown in Figure 6. The PSD model

was used as a framework for analysis of the systems described in the chosen articles.

60

Fig. 6. Article selection process (Shevchuk & Oinas-Kukkonen, 2016).

When the selection process was completed, the following systems were analyzed:

personal carbon management systems in organizations (Corbett, 2013); smart

meters for sustainable homes (Fitzpatrick & Smith, 2009); carbon tracker (Hilpert,

Kranz, & Schumann, 2013); Velix – web-based energy feedback system for

electricity customers (Loock, Staake, & Thiesse, 2013); environmentally

munificent bypass systems in long-haul trucking (Marett, Otondo, & Taylor, 2013);

several mobile applications: greenMeter, FindGreen, Bike Your Drive, Seafood

Watch, Ilincpro (Pitt et al., 2011).

The review revealed that all of the systems incorporate certain persuasive

principles, yet some principles are much less distinct than the others or even absent

at all (for the full list of the persuasive principles identified in systems discussed in

analyzed articles see Table 2 in SLR article, p. 7). This suggests that the analyzed

systems have similar areas of improvement. Overall, the primary task support

category was the most represented category in all analyzed articles and systems.

This observation can be explained by the designers’ focus on carrying out the

primary task of the system. Yet without strengthening the other design principles,

persuasiveness of the systems loses its full potential and becomes less effective

which ultimately undermine achieving the objectives of the primary task.

Thus, SLR concludes that the designers of the systems should place more

emphasis on (1) dialogue support principles to increase interaction between the

users and the system to lead the users towards their goal(s); (2) credibility support

principles to prove reliability of the system, and thus increase the users perception

of the persuasiveness of the system; and on (3) ensuring engagement of the users

with social support principles, since social influence contributes to augmenting

overall persuasiveness of the system.

629 databases search results:

Scopus – 181, ProQuest –

216, EBSCOHost Web – 94,

Web of Science – 138

222 unique articles found

(duplicates eliminated)

189 articles excluded based on abstract

33 full text articles assessed

and categorized

6 articles reviewed

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4.3 Investigating Green IS systems with empirical studies: data

collection and data analysis results

Empirical Study I

Investigation of the Green IS/IT research domain conducted in historical analysis

and SLR revealed that mobile apps are the most abundant and easily available to

the users types of Green IS. Thus, Study I aimed to examine factors that affect

perceived persuasiveness and attitude towards a specific mobile app in order to

answer the following question:

How does perceived persuasiveness combined with attitude influence intention

to adopt a Green IS?

Research model created for Study I predicted that constructs related to the PSD

model are likely to influence perceived persuasiveness that in turn would have an

impact on intent to adopt a Green IS. It was also predicted that intention to adopt

would be influenced by attitude, which is influenced by social affiliation, perceived

effort, and perceived effectiveness. In sustainable behavior context, primary task

support features can help overcome psychological barriers and reduce the perceived

effort of engaging in environmental sustainable behavior that some people view as

a burden.

JouleBug, a free mobile application available for iOS and Android, was chosen

as an example of a Green IS for testing the hypotheses of Study I. The app was

created to assist its users with making their everyday habits more sustainable at

home, work, and play (https://joulebug.com/). The app encourages its users to

discover how they can use resources in an environmentally friendly manner. The

app organizes sustainability tips into “Actions” that the users can explore and

complete in order to “go green”. When the users finish an action in real-time, they

“buzz” (i.e. post a text message with or without an image) within the app to notify

the other users of the app of the own achievement. For each “buzz” the users are

rewarded with points; additionally, the users receive extra recognitions called

“Badges” when they become experts in a certain field of sustainability. The app

allows the users to track own impact on environment using the stats and the

“Trophy Case”. The users are encouraged to follow friends and neighbors to

observe through the feed how they are making a difference in the community and

extend the network of people with whom sustainable achievements are shared. The

app also occasionally offers different local and national challenges that take place

62

over a certain period and allow the users to compete at being “the greenest” either

individually or in teams depending on a challenge.

In Study I, functionality of the app was analyzed using the PSD model as a

method for assessment. In the course of several days of trialing the app, the

following categories and features were found: (a) primary task support: self-

monitoring, reduction, simulation; (b) dialogue support: praise, rewards,

suggestions, reminders, liking; (c) credibility support: surface credibility; (d) social

support: social learning, social facilitation, recognition, competition, social

comparison). Based on the analysis, the study focused on categories that were

represented by more than one persuasive feature. Therefore, credibility support was

omitted, and the study was carried out focusing on the items from the three PSD

model categories (constructs):

1. Primary task support: reduction, self-monitoring, and simulation.

2. Dialogue support: praise, reward, and liking.

3. Social support: social learning, social facilitation, and social comparison.

To assess the respondents’ comprehension of functionality of the app, one of the

items in each PSD related construct was reversed and to each construct an item that

represents a persuasive feature not present in the examined application was added,

such as tunneling in primary task support, social role in dialogue support, and

normative influence in social support.

The survey was implemented in an online software tool Webropol 2.0 and was

distributed to the respondents online via multiple social media platforms.

Participants were asked to watch a video showing functionality of the JouleBug

mobile app. Questions related to the PSD, perceived persuasiveness, perceived

effort, perceived effectiveness, social affirmation, attitude, and intention to adopt,

as well as demographic questions about age, gender, education, and employment

were asked. In total, 81 complete answers were obtained, which contained no

missing responses since all of the questions were set as mandatory. Despite that, 20

responses had to be eliminated due to the uniform responses to all questions or due

to inadequately short time taken to complete the survey, leaving 61 responses for

the further analysis. Gender distribution of the analyzed sample was 82% female

and 18% male. The largest age groups in the sample were 18–24 year old (43%)

and 25–34 year old (41%), followed by 7% of 45–54 year old and 5% of 35–44

year old and 55–64 year old.

In the structural model, primary task support, dialogue support, and social

support explained 52% of the variance in perceived persuasiveness. Dialogue

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support alone explained almost 41% of the variance in primary task support and

20% of the variance in social support. Social affirmation, perceived effort, and

perceived effectiveness explained 67% of the variance in attitude. Together attitude

and perceived persuasiveness explained 76% of the variance in intention to adopt.

The study also investigated impact of gender on intention to adopt. When

controlling for the effect of gender on intention to adopt, a statistically significant

negative impact was encountered (β = -0.181, p = .005) which increased the

percentage of the overall variance explained to 79%. Finally, when exploring

influence of the other sample-related variables, such as age, education, and

employment, on intention to adopt, it was found that none of them has a statistically

significant relationship with the dependent variable.

Empirical Study II

Although oftentimes in the IS research, motivation is considered to be primarily

exogenous (i.e. behavior is a result of external stimuli) (Wunderlich, Kranz, & Veit,

2013), the subjective psychological meanings of the external stimuli and the type

of motivation (i.e. autonomous versus controlled) have proven to be more

important than the mere amount of motivation (Deci & Ryan, 2002). Based on this

knowledge, Study II considered various types of motivations, focusing on the

following research question:

How do endogenous motivations, influenced by PSD, shape intention to adopt

Green IS?

Study II predicted that all constructs related to the PSD model are likely to

influence all types of PLOCs (Ryan & Connell, 1989) because the persuasive

support categories (Oinas-Kukkonen & Harjumaa, 2009) are likely to trigger

different types of motivations and either cause alignment or misalignment with the

personal norms. Additionally, prior research (Malhotra, Galletta, & Kirsch, 2008;

Wunderlich, Kranz, Totzek, Veit, & Picot, 2013; Wunderlich, Kranz, & Veit, 2013)

showed that all types of PLOCs have influence on the individuals’ attitudes, and

therefore, it was assumed that these relationships would hold up in Study II as well.

Moreover, based on the theory of reasoned action (TRA) (Fishbein & Ajzen, 1975)

and the theory of planned behavior (TPB) (Ajzen, 1991), strong connections

between attitude towards Green IS and intention to adopt Green IS were expected

to be observed.

In Study II, there was no concrete Green IS system or app. This approach was

taken in order not to bias the respondents with a certain example, but rather to

64

explore their expectations as to how they envision a perfect Green IS app. The

survey was implemented via an online software tool Webropol 2.0 and was

distributed to the potential respondents (students of University of Oulu) via email

containing an invitation to participate and the link to the questionnaire. Prior to

answering the questions, participants were asked to imagine an ideal (in their

opinion) mobile application that could help them acquire pro-environmental

behavior (i.e. Green IS). In addition to questions related to the PSD categories,

perceived loci of control (PLOCs), attitude and intent to adopt, demographic related

questions were asked. In total, 78 complete answers were obtained, which

contained no missing responses since all of the questions were set as mandatory.

Among the participants of the study, gender distribution was 64% female and 36%

male. Respectively, age distribution was 47% of 25–34 year old, followed by 45%

of 18–24 year old, 4% of 3 –44, and 4% of 45–55 year old.

To understand which features the respondents perceived as the most important

in a mobile app for assisting with leading pro-environmental lifestyle, mean values

(μ) of each design principle were calculated (Appendix 2). The results showed that

the three highest-rated features in each category were the following: reduction,

simulation, self-monitoring in primary task support, suggestion, praise, liking in

dialogue support, trustworthiness, expertise, surface credibility in credibility

support, and competition, normative influence, and social comparison in social

support. Comparing means among categories, the respondents found system

credibility to be the most important (μ = 3.901), followed by primary task support

(μ = 3.533), dialogue support (μ = 3.207), and, finally, social support (μ = 2.491).

Based on the PLS-SEM analysis, in the structural model, dialogue support

explained 16% of the variance in primary task support, 8% of the variance in

credibility support and 10% of the variance in social support. Together primary task

support, dialogue support, and social support explained 19% of the variance in

external perceived locus of control (PLOC), almost 25% in introjected PLOC, and

2% in internal PLOC. Internal PLOC, external PLOC, and introjected PLOC

explained 40% of the variance in attitude which alone explained 50.5% of the

variance in intention to adopt a Green IS app.

Empirical Study III

Gamification is known for its ability to influence the users’ psychological states,

decision-making, and behavior. Because both gamification and PSD prove to be

effective at engaging people in certain activities, Study III aimed to analyze how a

65

gamified persuasive mobile application influences performance of sustainable

behavior. The core research question of Study III was:

How do perceived persuasiveness and cognitive absorption in a gamified

persuasive Green IS facilitate sustainable behavior?

Research model developed for the study anticipated that all PSD related

constructs (Oinas-Kukkonen & Harjumaa, 2009) would influence both perceived

persuasiveness (Oinas-Kukkonen & Harjumaa, 2009) and cognitive absorption

(Agarwal & Karahanna, 2000; Lowry et al., 2012). Additionally, it was expected

that dialogue support would influence the other PSD constructs. The model

proposed that cognitive absorption would impact both perceived persuasiveness

and intention to adopt the app.

To test the hypotheses of Study III, GreenApes app available for iOS and

Android was used (https://www.Greenapes.com). The app was developed as a part

of the European Union’s Horizon 2020 research and innovation program. It

provides a digital platform in the form of a social network for users to engage in

sustainable behaviors, such as using renewable energy, recycling, or choosing

sustainable modes of transportation, which include walking, cycling, public

transport, or car sharing. The app includes gamification elements, such as points

and rewards. Points (called BankoNuts) are collected by connecting the app with

third party apps for validation, for example, car2go for carsharing, or Apple

Health/Google Fit for walking and cycling. Partners of the app offer rewards, e.g.

grocery stores offer discounts on a purchase, or cafes give free smoothies for a

certain amount of collected BankoNuts. Additional gamification elements include

badges (e.g., “JungleSurfer”) and leaderboards (e.g. “most inspiring apes”).

Each of the survey constructs contained three respective items chosen based on

the features present in the gamified application, namely;

1. Primary task support: reduction, self-monitoring, personalization;

2. Dialogue support: praise, rewards, social role;

3. Credibility support: trustworthiness, surface credibility, expertise;

4. Social support: social learning, social comparison, recognition.

Additionally, open-ended questions about examples of sustainable behaviors the

respondents engaged in, and about the characteristics of the app the respondents

liked or disliked were asked. Finally, demographic-related questions about gender,

age, education, and employment were included in the survey.

The data was collected with an online survey distributed via social media

platforms and with a paper-based survey distributed to the university students. The

66

same procedure was used in both cases: each participant freely explored the app for

about 15–20 minutes and afterwards completed the survey. In total, 93 complete

responses suitable for analysis were received (59 collected with the online survey

and 34 with the paper-based one). The gender distribution of the participants was

the following: 46% female, 52% male, and 2% preferred not to identify their gender.

The majority of participants were 25–34 years old (53%), with the other large

groups being 35–44 year old (19%), followed by 18–24 (18%) year old, 45–54 year

old (5%), 55–46 year old (3%), and 65–74 year old (1%). Additionally, the

environmental awareness measure on a 7-point Likert scale had a mean of 5.65 (SD

= 1.04), suggesting that the participants were highly environmentally aware.

Results of the PLS-SEM analysis showed that dialogue support related

significantly to primary task support, to credibility support, and to social support

explaining 55%, 35%, and 18% of the variances respectively. Primary task support,

credibility support, and social support showed no significant relationship to

perceived persuasiveness, while dialogue support and cognitive absorption did.

Overall, almost 71% of the variance in perceived persuasiveness was explained.

Cognitive absorption (higher-order component) showed strong relationships with

its lower-order components (perceived enjoyment, temporal dissociation, focused

immersion, control and curiosity). Primary task support and social support showed

no statistically significant relationship to cognitive absorption, while dialogue

support and credibility support did, resulting in explaining 72% of the variance in

cognitive absorption. Perceived persuasiveness and cognitive absorption explained

almost 74% of the variance in intention to use gamified application.

Empirical Study IV

In the energy domain, IS are used to monitor household energy consumption and

provide feedback to the user (Graml, Loock, Baeriswyl, & Staake, 2011; Loock,

Staake, & Thiesse, 2013; Oppong-Tawiah et al., 2014; Weiss, Staake, Mattern, &

Fleisch, 2012). User interaction with the smart metering has become a frequently

addressed area in Green IS research (Brauer, Ebermann, & Kolbe, 2016).

Nevertheless, since the research had not yet addressed encouraging

environmentally conscious behavior with the use of smart thermostats at homes,

Study IV addressed this topic. The study aimed to answer two research questions:

1. How does persuasive systems design influence intention to continue using a

smart metering system?

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2. How do risk and self-disclosure alter implementation of persuasive systems

design in a smart metering system?

Research model developed for the study predicted negative relationships

between risk (Benson, Saridakis, & Tennakoon, 2015; Malhotra, Kim, & Agarwal,

2004) and unobtrusiveness (Lehto, Oinas-Kukkonen, & Drozd, 2012), as well as

between self-disclosure (Posey, Lowry, Roberts, & Ellis, 2010; Benson, Saridakis,

& Tennakoon, 2015) and perceived persuasiveness (Lehto, Oinas-Kukkonen, &

Drozd, 2012; Oinas-Kukkonen 2010; Oinas-Kukkonen 2013). Positive impact of

unobtrusiveness on dialogue support and on credibility support was hypothesized.

Dialogue support was predicted to influence all other persuasive categories.

Primary task support and credibility support were anticipated to influence

perceived persuasiveness, while perceived persuasiveness and social support were

predicted to influence intention to continue using the Green IS.

To collect the data for Study IV, Hive Active Heating 2

(https://www.hivehome.com/products/hive-active-heating) was used as an example

of Green IS. Hive Active Heating 2 is a schedule-based heating system used for

illustrating a case of persuasive features in smart thermostats. Hive, produced by

British Gas, is the system widely known in the UK, where data was collected. The

smart thermostat provides control of heating and water as well as lets users set

schedules, enable holiday mode, and adjust the zone temperature. Although the

thermostat can be installed in a house as the sole control system, most people also

install the Hive app on their smartphones or use the Hive website from a PC or

laptop to tweak the heating settings. Hive uses the phone’s GPS to monitor the

user’s location and alert the user to turn off and on the heating when leaving and

returning home. The user can set up app or text message notifications that alert

when the temperature reaches a specified level. The user can set the dates of leaving

and coming back to the ‘holiday mode’, which automatically lowers the

temperature in the house for the time the user is away, and increases it back upon

arrival. Thus, with the comfort of controlling the climate and energy use, the smart

thermostat requires users to experience self-disclosure and perceived risk among

the other perceptions when using the system. Because the persuasive features are

currently not implemented in Hive, the enhanced graphical interfaces that

incorporate all four categories of features into the system were created specifically

for Study IV.

A paper-based pilot survey session was, to validate and improve the

questionnaire conducted before collecting responses for the final data analysis.

Participants of the session watched an animated PowerPoint presentation showing

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implementation of the new features in Hive (enhanced interfaces were displayed)

and were asked to fill out the survey afterwards. Based on this session, the survey

was improved by changing order of the questions to keep the participants focused

as well as by modifying the introduction description of the study, in which the

participants were informed about seeing new features added to Hive.

In the actual survey, implemented in an online software tool Webropol 2.0 and

distributed online via email, the users were shown images of the enhanced interface

and were asked questions regarding the researched constructs and the

demographics. The items were measured using a 7-point Likert scale ranging from

‘Strongly disagree’ to ‘Strongly agree’ to determine to which extent participants

agree with the statements. In total, 50 complete answers were obtained, one of

which was omitted from the data analyses due to uniform responses to all items.

There were no missing responses since all of the questions were set as mandatory.

Demographically, 50% of respondents were female, while 45% were male;

majority were 25–34 years old (33%), followed by the smaller age groups: 18% of

45–54 year old, 14% of 35–44 year old, 12% of 18–24 year old, 8% of 65–74 year

old, and 2% of 75 year old or older.

The results of the PLS-SEM analysis provided substantial support for the

proposed research model since all the hypotheses were supported. In the structural

model, perceived risk explained 16% of the variance in system unobtrusiveness,

which in turn explained 34% of the variance in dialogue support. Together,

unobtrusiveness and dialogue support explain half of the variance in system

credibility. Additionally, dialogue support explained 35% of the variance in social

support and almost 71% of the variance in primary task support. Consequentially,

credibility support, primary task support, and self-disclosure explained 75% of the

variance in perceived persuasiveness. Finally, social support together with

perceived persuasiveness explained 58% of the variance in continuance intention.

4.4 Major themes in empirical studies

4.4.1 Perceived persuasiveness and Green IS

Researching perceived persuasiveness of Green IS is one of the major themes in

empirical Studies I, III, and IV. Specifically, these studies examined perceived

persuasiveness of mobile applications, since the apps are the most often

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encountered, easier to use instances of Green IS readily available to the wide

audience.

Attempts to convince a user to change a behavior define intended

persuasiveness, i.e. an extent to which the design of the system incorporates

persuasive potential. Subsequently, perceived persuasiveness is an extent to which

this potential is realized, i.e. how much the user (the recipient of the persuasive

message) perceives the message as being convincing. In the existing models of

attitude change, messages are presented, received, processed, and if successful,

recipients’ attitudes shift towards the advocated position (Crano & Prislin, 2011).

The altered attitude may lead to subsequent behavior change under appropriate

conditions. Thus, effective persuasion happens when the target of change (e.g.,

attitudes, beliefs) is modified in the desired direction. It is believed that “an attitude

represents an evaluative integration of cognitions and affects experienced in

relation to an object” (Crano & Prislin, 2011, p. 347). In line with the previous

studies (Lehto, Oinas-Kukkonen, & Drozd, 2012; Lehto, Oinas-Kukkonen, Pätiälä,

& Saarelma, 2012), in the empirical studies II–IV, perceived persuasiveness was

defined an individual’s favorable impression of the system.

Study I hypothesized that perceived persuasiveness is influenced by primary

task support, dialogue support and social support. Credibility support was omitted

as a predictor of perceived persuasiveness due to the lack of the persuasive features

from the credibility support category in the app investigated in Study I. Study I

theorized that if a system supports fulfillment of the primary task, the users are

likely to view it as more convincing, i.e. primary task support has an influence on

perceived persuasiveness. Furthermore, dialogue support was hypothesized to

impact perceived persuasiveness due to the evidence found in the previous studies

(Lehto, Oinas-Kukkonen, & Drozd, 2012; Lehto, Oinas-Kukkonen, Pätiälä, &

Saarelma, 2012). Social support was hypothesized to influence perceived

persuasiveness because opinions of friends, family and peers are highly likely to

change one’s view on adoption of sustainable behavior (Gifford, 2011).

Furthermore, when feeling a necessity to join a community, some people tend to be

open to adjusting own behavior established by the current members of that

community (Petkov, Köbler, Foth, & Krcmar, 2011). Perceived persuasiveness in

Study I was hypothesized to be one of the predictors (another one was attitude) of

intention to adopt the app, because previous studies have shown that perceived

persuasiveness has a moderate but significant impact on intention to adopt the

system (Lehto, Oinas-Kukkonen, & Drozd 2012; Lehto, Oinas-Kukkonen, Pätiälä,

& Saarelma, 2012).

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Study III posited that perceived persuasiveness is influenced by the four

persuasive categories as well as by cognitive absorption. Influence of the credibility

support on perceived persuasiveness was hypothesized based on the premise that a

highly credible source is usually perceived as more persuasive than a low-

credibility one (Pornpitakpan, 2004). Relationship between cognitive absorption

and perceived persuasiveness was assumed because smartphone users tend to

exhibit immersive behavior when engaging with mobile apps; thus, cognitive

absorption is essential in the context of application usage. Moreover, flow (a

concept related to cognitive absorption) has been known to influence consumer

experience and attitude formation (Chen, Wigand, & Nilan, 1999). Similarly,

Studies I and II suggested that perceived persuasiveness influences intention to use

the app.

Study IV suggested that perceived persuasiveness is influenced by primary task

support, credibility support, and self-disclosure, while being one of the

determinants (together with social support) of the intention to continue using the

app. The influence of self-disclosure on perceived persuasiveness was assumed to

be negative because the requirement to reveal personal information and potential

negative consequences associated with self-disclosure are likely to diminish

perceived persuasiveness of the system. Additionally, since perceived

persuasiveness had been known to impact intention to adopt IS, Study IV postulated

that it will also be a predictor of intention to continue using a Green IS app.

Contrary to what was anticipated, among the three persuasive categories

included in the Study I, only primary task support showed a significant impact on

perceived persuasiveness. One explanation of this outcome (different from Studies

II, IV) can be the experimental nature of the study and the context of the app. It is

possible that in order to perceive the app investigated in Study I as persuasive and

to have an overall favorable impression of it, the participants of the survey needed

only primary task support features of the app. Additionally, the overwhelming

presence of technologies and social media might have impacted the respondents in

such a way that they do not associate communication with the app (dialogue support

features) and with the other users of the app (social support features) with the

overall favorable perception of the system (perceived persuasiveness). This finding

exemplifies the premise that not all possible persuasive features have to be present

in a system, because implementation of additional persuasive features does not

guarantee increase of the overall persuasiveness, and in some cases can even

contribute to the decrease in the overall persuasiveness (Oinas-Kukkonen, 2013).

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In Study III, out of all persuasive design principles only dialogue support had

a statistically significant impact on perceived persuasiveness proving that the

computer-human dialogue facilitated by the system creates an overall favorable

impression of the system. Regarding primary task support, credibility support, and

social support not showing a sufficient impact on preserved persuasiveness,

(similarly to Study I): such an outcome could be attributed to the specific

application used in the study. However, other reasons are possible. For example,

considering primary task support, the high level of the respondents’ awareness of

what sustainable behavior is and which actions contribute to it could have made the

primary task support category less influential for these individuals, since these

users did not need to rely much on the app to support carrying out the primary task.

Statistical insignificance of the credibility support category could be explained by

the lack of the users’ interest in scrutinizing to the credibility of the app, i.e. the

users might be content with the credibility of the app as long as the app is visually

similar to other ones the users had used before. In such scenario, it is likely that

credibility support system neither contributes to nor undermines perceived

persuasiveness of the system. The reason for the social support being statistically

irrelevant to perceived persuasiveness might be the same as in Study I, i.e. an

excessive abundance of technologies and social media might have undermined the

respondents’ overall favorable perception of the system.

Findings of Study IV supported a positive influence of primary task support

and credibility support on perceived persuasiveness, as well as a negative influence

of self-disclosure on perceived persuasiveness. Thus, indeed, the need to disclose

information to the Green IS app contributed to the decrease in the overall favorable

impression of the system. The positive influence of the primary task on perceived

persuasiveness (which was not observed in Studies III and V) could be explained

by the higher importance of the role of the primary task in the app investigated in

Study VI. Compared to the game-like apps, that are likely to be used mostly for fun,

researched in Studies III and V, the app in Study VI is an app for controlling the

thermostat of the house, and thus, the primary function of temperature control via

the app is likely to be more important than the primary function of making

sustainable lifestyle more fun. A similar reason might be a valid explanation of the

significant impact of the social support on perceived persuasiveness in Study VI,

i.e. the context of the app determined whether the actions of the peers can sway an

individual’s own view of the system.

Regarding the impacts of perceived persuasiveness on intention to adopt the

app (hypothesized in Studies I and III), and on intention to continue using the app

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(hypothesized in Study IV), both were supported in Studies I, III, and IV. Moreover,

in these studies, perceived persuasiveness outweighed the other predictors of both

adoption and continuance intention. Specifically, in Study I, perceived

persuasiveness outweighed attitude in determining adoption intention; in Study III,

influence of cognitive absorption on adoption intention was lesser compared to

perceived persuasiveness; and in Study IV, perceived persuasiveness had a higher

statistical significance in predicting continuance intention compared to social

support.

4.4.2 Impact of motivations on Green IS adoption

Most of the IS research so far has hypothesized that motivations are mainly

exogenous, suggesting that behavior is largely a result of external stimuli

(Wunderlich, Kranz, & Veit, 2013). Nevertheless, the subjective psychological

meanings of these stimuli and the type of motivation, i.e. autonomous versus

controlled, have shown to be even more essential than solely the amount of

motivation (Deci & Ryan, 2002). Therefore, Study II suggests that PSD is an

approach that is likely to affect people’s motivations and consequentially

encourage desired behavioral patterns.

To analyze several types of motivations in persuasive Green IS, the Organismic

Integration Theory (OIT) (Deci & Ryan, 2002) was considered in Study II, since it

has been used in several scientific areas to explain the perceived degree of self-

determination on behavior. OIT is a tool for explaining what the user experiences

or feels as well as how this affects the user’s intentions and behaviors. The theory

proposes that an individual’s behavior is volitional and that an individual is an

initiator of all behaviors (Deci & Ryan, 2002). Therefore, OIT views stimuli as

affordances and opportunities used by an individual to satisfy own needs, and not

as direct causes of the individual’s behavior. OIT clarifies whether the users of a

system feel autonomy, external pressure, or both by examining the users’

psychological states in terms of perceived locus of causality (PLOC). PLOC,

defined as the degree to which actions are initiated from and endorsed by the

individual (Ryan & Connell, 1989), describes the relative autonomy of behavior. It

is said that the users’ feelings affect behavior regardless of the presence of external

forces, i.e. users can feel compulsion even in absence of environmental pressures

(e.g. behaving from guilt or obligation, rather than by own choice). Therefore,

according to OIT, the user’s perceptions of volition and compulsion depend on

PLOC rather than on external stimuli. OIT recognizes that various feelings range

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from volition to compulsion, and respectively can be characterized as internal and

external PLOCs. In additions to these types of PLOCs, there is another one, the

introjected PLOC, which explains a combined impact of both autonomy and

external pressure on the behavior of a user eager to act in a manner different from

the one forced on the user by the system. Figure 7 represents how different types

of PLOC and motivations match up.

Fig. 7. Endogenous motivations (Malhotra, Galletta, & Kirsch, 2008).

Analyzing the influence of PSD on different types of motivations by interpreting

the results of PLS-SEM conducted in Study II, interesting findings emerged. None

of the persuasive categories appeared to affect internal PLOC, suggesting that this

type of motivation truly stems from the personal disposition of the individual and

is difficult to be altered. Both dialogue and social support systems appeared to

affect introjected PLOC, proposing that the interaction with both a Green IS app

and the other users of this app impacts the person’s extrinsic motivation even when

it is introjected (i.e. does not align with the user’s personal beliefs). External PLOC

was impacted only by credibility support, suggesting that the person’s external

regulation of behavior is strengthened by the perception of trust, believability,

reliability, and credibility of the Green IS, while neither the performance of the

primary task nor the interaction with the Green IS or with its other users

significantly impacted external PLOC. Primary task support category did not show

influence on any of the PLOCs. Meanwhile, dialogue support system, affected not

only the introjected PLOC, but also all other persuasive categories, emphasizing

how important maintaining an effective dialogue between the system and its users

is for the overall success of the Green IS application. Considering the impact of the

PLOCs on attitude towards the Green IS, the findings show that all types of PLOCs

have a statistically significant impact on attitude. Among the three, the internal

PLOC has the strongest influence on attitude, with the introjected PLOC showing

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the weakest impact. Consequentially, attitude showed a strong statistically

significant relationship to intention to adopt the Green IS.

4.4.3 Users’ perceptions of risk and self-disclosure in persuasive

Green IS

Seamless interconnectedness of devices brings convenience and benefits. However,

with the positive aspects of it, numerous disadvantages related to privacy and trust

are often mentioned in IS literature. Despite that, the closely related concepts of

self-disclosure and perceived risk are not often discussed. Yet, with the increased

human-computer interaction, it is important to explain how self-disclosure and

perceived risk impact people’s relationship with technologies, specifically the

persuasive Green IS mobile apps. Hence, one of the key contributions of Study IV

is the introduction of the perceived risk and self-disclosure constructs in the context

of a persuasive smart thermostat system.

Self-disclosure in Study IV was defines as “the act of revealing personal

information about oneself to another” (Collins & Miller, 1994, p. 457) individual,

organization, or group. Personal information handling and self-disclosure online

are highly relevant areas of concern related to the use of mobile apps. Self-

disclosure is intentional and purposeful, and refers to what individuals voluntarily

and deliberately reveal about themselves to others (Pearce & Sharp, 1973). Factors

such as target audience, how well they are known to the person disclosing

information etc. could influence the quantity and quality of information disclosed

(Jourard & Richman, 2015). In addition to studies in IS field, phenomenon of self-

disclosure has been considered in social psychology, sociology, interpersonal

communication, as well as marketing, social media, and e-commerce (Kim, 2015).

Fairness of information exchange, privacy benefits, and privacy apathy affect self-

disclosure in e-commerce (Sharma & Crossler, 2014). Fairness of information

exchange is particularly important in an online setting since the customers lose

control over the information once it has been disclosed. Furthermore, information

disclosed by customers for non-transactional purposes might be easily misused by

the online vendors (e.g. targeted advertising or sale to data aggregators) (Balough,

2011). Although the essence of the self-disclosure concept for the smart meter users

is slightly different, the users face the similar kinds of issues related to sharing

information. Users of smart meter are recognized as the owners of their data, yet

this does not ensure full control over personal data. Assuming that the users are

empowered to authorize or refuse data disclosure to third parties, the data remains

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subject to security breaches, inadvertent disclosure by the smart meter provider,

and unknowingly authorized releases by the consumer to third parties – all of which

would result in personal data reaching possibly unsecured areas on the Internet,

where it may remain permanently (Balough, 2011).

Perceived risk in Study IV was defined in terms of the uncertainty and

consequences associated with the consumer’s actions (Bauer, 1960, p. 390). It is

crucial in decision-making and can be understood as “a combination of uncertainty

plus seriousness of outcome involved”. Logically, perceived risk increases with the

magnitude of consumers’ perceptions of the uncertainty, probability of loss, and

adverse consequences associated with buying a product (or service) (Cunningham,

1967). Perceived risk can encompass several types of risk, including financial,

physical, functional, social, and time-loss risk (Jacoby & Kaplan, 1972; Kaplan,

Szybillo, & Jacoby, 1974; Roselius, 1971); thus, it should be treated as a multi-

dimensional construct. Overall, the two major components of perceived risk are the

probability of a loss and the subjective feeling of unfavorable consequences (Ross,

1975). From a perspective of IS, perceived privacy risk is the perception of a

possible exposure or potential violation of the user’s private information

(Featherman & Pavlou, 2003), e.g. service providers intentionally collecting,

disclosing, transmitting, or selling personal data without a consumer’s knowledge

or permission, hackers intercepting such information etc. (Yang, Liu, Li, & Yu,

2015). Privacy risk beliefs are “expectation[s of] … a high potential for loss …

associated with the release of personal information” to the others users in online

communities (Malhotra, Kim, & Agarwal, 2004, p. 341). Furthermore, perceived

risk is apparent in IS usage decisions when users experience feelings of doubt,

discomfort, anxiety (Dowling & Staelin, 1994) or conflict (Bettman, 1973), which

often occur when personal information is transferred via vulnerable communication

infrastructures (e.g., the Internet). In relation to the Internet use, these concerns are

found to be critical (Hoffman, Novak, & Peralta, 1999), because online transactions

involve more perceived risk than the traditional, face-to-face ones. Choosing to use

online applications under security threats, individuals are becoming increasingly

accustomed to considering the risks associated with online transactions. Since data

gathering with smart meters involves transmission over the Internet and allows

service providers to identify the users’ lifestyle, perceived risk is indeed a relevant

concern. In this context, perceived risk constitutes a presence of uncertainty related

to the use of smart meters and allows for a possibility of suffering a loss while using

the systems.

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To explain relationship of self-disclosure and perceived risk, the framework of

Study IV referred to the Adaptation Level Theory (Helson, 1964) as well as to the

persuasion process, involving the user, the system, and the desired outcomes.

Adaptation Level Theory posits that the individuals perceive new stimuli or

experiences as deviations from the existing cognitions. New cognitions are viewed

as a shift from their prior baseline or reference levels (adaptation levels). Hence,

new cognitions tend to remain in the vicinity of prior cognitions (which allow for

the current optimum level – homeostasis), adjusted appropriately for any new

positive or negative stimuli. Considering this, the later-stage cognitions can be

viewed as an additive function of prior cognitions plus the deviation or discrepancy

from those levels due to the actual experience (Bhattacherjee & Premkuma, 2004).

Based on the Adaptation Level Theory, the user’s persuasion process depends on

pre-system usage beliefs and attitudes which play a role in determining the

‘reference level’ as well as impact the course of persuasion aimed at changing a

certain behavior. In Study IV, perceived risk and self-disclosure were considered as

the factors that shape the users’ pre-system usage disposition towards Green IS.

Since perceived risk is associated with the subjective feeling of unfavorable

consequences, its presence interferes with the capacity of the system to fulfill the

users’ positive expectations. Therefore, it was hypothesized that the presence of

perceived risk negatively impacts unobtrusiveness, i.e. the higher amount of the

perceived risk is linked to the more adverse impact of the system on the users’

routines. In Study IV, self-disclosure together with primary task support and

credibility support were hypothesized to determine perceived persuasiveness. As

expected, negative influence of both perceived risk and self-disclosure were

supported. Thus, the users’ perception of risk tends to affect whether the user finds

the system to be unobtrusive (the higher perception of risk, the more obtrusive the

system appears to be from the users’ perspective). This finding provides an insight

in factors that affect the users’ judgement about unobtrusiveness of the system.

Furthermore, self-disclosure decreases perceived persuasiveness. Therefore, the

higher level of self-disclosure to the app is associated with the individuals’ lower

favorable impression of the system. These findings support the previous research

about self-disclosure: people do not automatically self-disclose important

information about themselves, despite the innate desire for acceptance and

relational formation (Taylor, 1968). Hence, in the context of using a persuasive

smart thermostat system that controls an individual’s home environment, the need

of high self-disclosure might lead to negative consequences in terms of the user-

application interaction.

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4.4.4 Gamification and cognitive absorption in persuasive Green IS

Because both gamification and PSD proved to be effective at engaging people in

certain activities, Study III discussed and analyzed the impact of a gamified

persuasive system (a mobile app) on intention to engage in sustainable behavior.

Motivated by a need to understand the users’ holistic experiences with IS, Study III

focused on viewing perceived persuasiveness and cognitive absorption as

important components required for the desired behavioral outcomes.

A research of existing literature showed that oftentimes pro-environmental

behavior is seen as not enjoyable because it is linked to personal disadvantages like

behavioral constraints or loss of comfort (Midden & Ritsema, 1983). Because

behavior change is not typically fun and sometimes not voluntary (Schoech, Boyas,

Black, & Elias-Lambert, 2013), resistance to sustainable conduct increases when

changes in existing routines are required (Bengtsson & Ågerfalk, 2011).

Gamification strategies that were hailed as effective for attracting users and

improving motivation are described as “simpler, easier, sustainable, and fun ways

to develop healthier habits based on behavior-change psychology, alternative

reality games, and quantified self-methods and techniques” (Schoech, Boyas, Black,

& Elias-Lambert, 2013, p. 199). In addition to the innate power of gamification,

social web and smartphone application use have greatly expanded the ability of

employing multiuser, social game-like techniques that occur in real time (Schoech,

Boyas, Black, & Elias-Lambert, 2013).

Compared to persuasive systems and technologies, gamification is more

focused on invoking users’ intrinsic motivations than on the overall attitude or

behavior change (Huotari & Hamari, 2012). Because gamification techniques are

viewed as a promising approach for evoking different behavioral changes, they

have been applied to facilitating sustainable practices (e.g. waste separation (Lessel,

Altmeyer, & Krüger, 2015) or reduction of energy use (Orland et al., 2014)). For

instance, ranking lists allow users to compare their own performance with the

others, creating a competitive environment for reducing the energy use or pursuing

sustainable modes of transportation (Loock, Staake, & Thiesse, 2013); visual aids

(a growing or dying tree) assist people in realizing the extent to which their mobility

behavior affects the environment (Froehlich et al., 2009). Although gamification

techniques play an important role in persuasive systems, they constitute only a

small part of potential persuasive design principles (Brauer, Eisel, & Kolbe, 2015).

Liu, Santhanam, and Webster (2017) discovered several definitions of

gamification in existing literature: (1) the use of game elements in non-game

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contexts (Deterding, Dixon, Khaled, & Nacke, 2011), (2) a process of providing

game-like experiences to users, commonly with the end-goal of affecting user

behavior (Huotari & Hamari, 2012, p. 19), (3) “adding game elements to an

application to motivate use and enhance the user experience” (Fitz-Walter,

Tjondronegoro, & Wyeth, 2012, p.1), (4) “the use of game design elements (e.g.

points, leaderboards and badges) in non-game contexts … to promote user

engagement” (Mekler, Brühlmann, Opwis, & Tuch, 2013, p. 1138), (5) “the use of

game-based elements such as mechanics, aesthetics, and game thinking in non-

game contexts aimed at engaging people, motivating action, enhancing learning,

and solving problems” (Borges, Durelli, Reis, & Isotani, 2014, p. 216).

Highlighting that gamification is incorporated into real-world systems while their

functionality is preserved, Liu, Santhanam, and Webster (2017) proposed the

following definition: “the incorporation of game elements into a target system

while retaining the target system’s instrumental functions” (p. 1013). Liu,

Santhanam, and Webster (2017) identified experiential and instrumental outcomes

of gamified systems. The duality of gamification results into meaningful outcomes

as the design provides not only enjoyable experiences but also enhances

instrumental task outcomes. Possible experiential outcomes could be flow,

cognitive absorption, enjoyment, joy (Agarwal & Karahanna, 2000), as well as

attention, arousal and other similar. Study III focused on two experiential outcomes,

namely: (1) cognitive absorption considered as a measure for assessment of

involvement with the app, and (2) perceived persuasiveness considered as a

measure of the user’s favorable impression of the system. Instrumental outcomes

refer to the main goal(s) that the gamified system helps the users achieve (Liu,

Santhanam, & Webster, 2017), i.e. intention to engage in sustainable behavior,

influenced by intention to use the app.

Cognitive absorption, chosen in Study III as one of the experimental outcomes,

is based on the following closely inter-related concepts: the personality trait

absorption (Tellegen & Atkinson, 1974), flow (Csikszentmihalyi, 1975), and

cognitive engagement (Webster & Ho, 1997). Absorption is an individual

disposition or trait, i.e., an intrinsic dimension of personality that leads to episodes

of total attention where all individual’s attentional resources are consumed by the

object of attention (Tellegen & Atkinson, 1974). Csikszentmihalyi (1975, p. 36)

described flow as “the holistic experience that people feel when they act with total

involvement” and initially identified six components of it: merging action-

awareness, centering of attention, loss of self-consciousness, feeling of control,

coherent and noncontradictory demands, and autotelic nature. Webster and Ho

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(1997) stated that cognitive engagement relates to playfulness, and that the state of

playfulness is identical to flow, only with dimensions of intrinsic interest, curiosity,

and attention focus, excluding the feeling of control.

The construct of cognitive absorption used in Study III consists of control,

curiosity, focused attention, and temporal dissociation (measures of flow

(Csikszentmihalyi, 1975; Trevino & Webster, 1992)), as well as heightened

enjoyment which represents a synthesis of the intrinsic interest dimension of flow

(Webster, Trevino, & Ryan, 1993) and perceived enjoyment (Davis, Bagozzi, &

Warshaw, 1992). These components of cognitive absorption were adopted from

Agarwal and Karahanna (2000, p. 673), who defined cognitive absorption as “a

state of deep involvement with software” consisting of the five dimensions: (1)

temporal dissociation, or the inability to register the passage of time while engaged

in interaction; (2) focused immersion, or the experience of total engagement where

other attentional demands are ignored; (3) heightened enjoyment, capturing the

pleasurable aspects of the interaction; (4) control, representing the user’s

perception of being in charge of the interaction; and (5) curiosity, tapping into the

extant the experience arouses an individual’s sensory and cognitive curiosity.

Cognitive absorption was found to be one of the factors that influences

perceived persuasiveness. Therefore, when a user is immersed in interaction with

the system, the user is likely to develop a favorable impression of the system.

Among the persuasive categories, dialogue support and credibility support

expressed a statistically significant impact on cognitive absorption. Apparently, the

credible look and feel of the app as well as the dialogue between the system and

the user contribute to the state of deep involvement with the app. Primary task

support did not display a statistically significant relationship with cognitive

absorption, suggesting that assistance with fulfilling the primary task does not

create an immersive involvement with the app. Nevertheless, since this is the first

study that examined impact of the PSD categories on cognitive absorption, more

research (involving different systems and/or apps) is required to understand better

the relationship between PSD and cognitive absorption. It is possible that a short-

term use of the investigated gamified persuasive system was a reason why some of

the PSD categories did not show significant effects on cognitive absorption.

Additionally, Study III discovered that although cognitive absorption influenced

intention to adopt the gamified persuasive app, its impact was smaller than the

impact of perceived persuasiveness. Yet, the findings of Study III imply that that

PSD model is a beneficial tool for gamification design.

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4.4.5 Green IS/IT adoption and continuance intention

Studies I, II, and III researched intention to adopt Green IS systems aiming to

understand what factors increase the users’ willingness to use a mobile app that

encourages a sustainable lifestyle. Moreover, Study III also considered influence

of intention to adopt a Green IS on intention to adopt sustainable behavior. Yet it is

crucial not only to convince people to adopt a certain application, but also to ensure

that the usage of it is continuous. Since after a certain period the users ignore or

discontinue using the applications, until the post-adoption usage of the IT can be

confirmed, it is premature to classify an IT adoption as a success because the

ultimate viability of an IT is dependent on individuals’ continued usage of the IT

(Bhattacherjee, 2001; Karahanna, Straub, & Chervany, 1999). If the enthusiasm

over the initial adoption of an IT diminishes after individuals gain experience from

using it, then the IT will suffer from decreased usage and may even fall into disuse

subsequently (Thong, Hong & Tam, 2006). Thus, Study IV researched continuance

intention of Green IS apps.

In addition to perceived persuasiveness hypothesized to be a predictor of

adoption and continuance intention in Studies I, III, and IV, constructs of attitude

(Studies I and II), cognitive absorption (Study III), and social support (Study IV)

were considered. According to the theory of reasoned action (TRA) (Fishbein &

Ajzen, 1975), the theory of planned behavior (TPB) (Ajzen, 1991), and the

technology acceptance model (TAM) (Davis, Bagozzi, & Warshaw, 1989), attitude

of an individual influences intention, an essential component of performing a

behavior. Cognitive absorption was considered in Study III, because the related

concept of flow was recognized to be useful in predicting behavioral intent of both

experimental and goal-oriented types of behaviors (Hoffman & Novak, 1996).

Social support was theorized to be one of the predictors of continuance intention in

Study IV, because social interaction with people with similar interests or personal

goals can increase willingness to engage in sustainable behavior (Ebermann &

Brauer, 2016). Moreover, social support design principles motivate users by

leveraging social influence that is fundamental for pro-environmental mindset and

behavior (Gifford, 2011; Siero, Bakker, Dekker, & Van Den Burg, 1996; Stern,

Dietz, & Guagnano, 1995).

In Study I, attitude related in a statistically significant manner to intention to

adopt the Green IS app. Attitude in this study was predicted by social affirmation

and perceived effectiveness, while the hypothesized influence of perceived effort

on the users’ attitude was rejected. Social affirmation combines the existing notions

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of social influence and subjective norms. Thus, it refers to an individual’s

perception of how important others regard the target behavior and whether they

expect the individual to perform that behavior (Ajzen, 1991; Fishbein & Ajzen,

1975). Because the users get approval from friends, neighbors, or family members,

social influence affects the users’ own perceptions and acceptance of a Green IS.

In the UTAUT model (Venkatesh, Morris, Davis, & Davis, 2003), performance

expectancy predicts attitude; hence, in Study IV, a similar construct, perceived

effectiveness (Lehto & Oinas-Kukkonen, 2015) was adopted to capture the success

of the mobile app. The construct measures users’ perceptions regarding whether the

system is successful in adopting a pro-environmental behavior. It was deemed

logical that, if the users do not perceive the app to be effective, they are more likely

to have an unfavorable attitude towards using the system. In theoretical models of

technology adoption, perceived ease of use (TAM (Davis, Bagozzi, & Warshaw,

1989)) and effort expectancy (UTAUT (Venkatesh, Morris, Davis, & Davis, 2003))

have been central constructs in explaining intention to use the system. Effort

expectancy is defined as the degree of ease associated with the use of the system.

In Study II, attitude, predicted by different perceived loci of control (external,

internal, and introjected), turned out to influence intention to adopt a Green IS

system in a statistically significant manner (discussed in Section 4.4.2). In Study

III, cognitive absorption was confirmed to be a predictor of intention to adopt the

Green IS app. Cognitive absorption was influenced only by the dialogue support

and credibility support among all persuasive categories (discussed in Section 4.4.4).

In Study IV, the relationship between social support and continuance intention was

verified by the findings of the study. Additionally, it was observed that social

support was influenced by dialogue support. In Study IV, using the app was

considered to be the goal behavior, while engaging in sustainable behavior was

thought to be a means to achieve the goal behavior (implementation intention).

Ultimately, Study IV showed that a goal intention to use the app activated an

intention to engage in sustainable behavior.

4.5 Summarizing hypotheses investigated in empirical studies

Overall, empirical Studies I–IV form the stepping stone for the further theory

development regarding factors contributing to perceived persuasiveness and

adoption of persuasive Green IS/IT. From a practical perspective, it is beneficial to

recognize the most influential constructs leading to perceived persuasiveness, and

in turn, adoption and prolonged use of the system. This type of knowledge will

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assist in guiding the design and development processes of Green IS/IT. Table 2

summarizes the hypothesized relationships between the constructs.

Table 2. Summary of the hypotheses in Studies I–IV.

Hypothesis I II III IV

Attitude -> Intention to Adopt S S N/A N/A

Cognitive Absorption -> Intention to Use N/A N/A S N/A

Cognitive Absorption -> Perceived Persuasiveness N/A N/A S N/A

Credibility Support -> External PLOC N/A S N/A N/A

Credibility Support -> Internal PLOC N/A NS N/A N/A

Credibility Support -> Introjected PLOC N/A NS N/A N/A

Credibility Support -> Cognitive Absorption N/A N/A S N/A

Credibility Support -> Perceived Persuasiveness N/A N/A NS S

Dialogue Support -> Cognitive Absorption N/A N/A S N/A

Dialogue Support -> Credibility Support N/A S S S

Dialogue Support -> External PLOC N/A NS N/A N/A

Dialogue Support -> Internal PLOC N/A NS N/A N/A

Dialogue Support -> Introjected PLOC N/A S N/A N/A

Dialogue Support -> Perceived Persuasiveness NS N/A S N/A

Dialogue Support -> Primary Task Support S S S S

Dialogue Support -> Social Support S S S S

External PLOC -> Attitude N/A S N/A N/A

Internal PLOC -> Attitude N/A S N/A N/A

Introjected PLOC -> Attitude N/A S N/A N/A

Perceived Effectiveness -> Attitude S N/A N/A N/A

Perceived Effort -> Attitude NS N/A N/A N/A

Perceived Persuasiveness -> Continuance Intention N/A N/A N/A S

Perceived Persuasiveness -> Intention to Use N/A N/A S N/A

Perceived Persuasiveness -> Intention to Adopt S N/A N/A N/A

Perceived Risk -> Unobtrusiveness N/A N/A N/A S

Primary Task Support -> Cognitive Absorption N/A N/A NS N/A

Primary Task Support -> External PLOC N/A NS N/A N/A

Primary Task Support -> Internal PLOC N/A NS N/A N/A

Primary Task Support -> Introjected PLOC N/A NS N/A N/A

Primary Task Support -> Perceived Persuasiveness S N/A NS S

Self-Disclosure -> Perceived Persuasiveness N/A N/A N/A S

Social Affirmation -> Attitude S N/A N/A N/A

Social Support -> Cognitive Absorption N/A N/A NS N/A

Social Support -> Continuance Intention N/A N/A N/A S

Social Support -> External PLOC N/A NS N/A N/A

Social Support -> Internal PLOC N/A NS N/A N/A

Social Support -> Introjected PLOC N/A S N/A N/A

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Hypothesis I II III IV

Social Support -> Perceived Persuasiveness NS N/A NS N/A

Unobtrusiveness -> Credibility Support N/A N/A N/A S

Unobtrusiveness -> Dialogue Support N/A N/A N/A S

Note. S = “Supported”, NS = “Not Supported”, N/A = hypothesis not tested

Figure 8 generalizes dependence of constructs used in all empirical studies.

Adoption/Continuance intention is at the bottom of the figure, indicating that this

construct represents the ultimate focus of interest, i.e. the studies aimed to research

factors that influence Green IS adoption/continuance intention. Perceived

persuasiveness and attitude are the two main constructs that showed significant

influence on adoption/continuance intention. Impact of perceived persuasiveness

on adoption/continuance intention was significant in Studies I, III, and IV, while

impact of attitude on adoption/continuance intention was supported in Studies I and

II. Studies I and II showed that endogenous motivations, social affirmation, and

perceived effectiveness influence attitude. Constructs that impacted perceived

persuasiveness are the PSD categories (primary task support, dialogue support, and

credibility support in Studies II–IV), cognitive absorption (Study III), and self-

disclosure (Study IV). More specifically, perceived persuasiveness was statistically

significantly influenced by (1) the primary task support in Studies I and IV; (2) the

dialogue support in Study III, and (3) credibility support in Study IV. Furthermore,

the results of Study IV suggested that perceived risk influences unobtrusiveness

that impacts dialogue and credibility support categories.

Fig. 8. Dependence of constructs in empirical Studies I–IV.

Ado

ptio

n/C

onti

nuan

ce

Inte

ntio

n

Perceived Persuasiveness

PSD Categories Unobtrusiveness Perceived Risk

Cognitive Absorption

Self-Disclosure

Attitude

Endogenous Motivations

Social Affirmation

Perceived Effectiveness

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5 Discussion

This dissertation examines role of PSD (Oinas-Kukkonen & Harjumaa, 2009) in

enhancing Green IS as well as adoption and continuous use of the persuasive

systems for encouraging sustainable behavior. Persuasive system features are

assumed to enhance the participation and engagement to the interventions (Kelders,

Kok, Ossebaard, & Van Gemert-Pijnen, 2012); thus, it is deemed beneficial to

examine the level of use of such features in the Green IS/IT context. Pro-

environmental behavior change is a timely relevant topic because it has a direct

effect on sustainable development of the society. Furthermore, an additional

challenge to achieve pro-environmental behavior change arises due to the nature of

this behavior: the negative impact of individuals’ unsustainable habits is more

difficult to observe compared to the negative impact of individuals’ harmful health-

related habits. Therefore, a better understanding of approaches that encourage

adoption and continuous use of Green IS is strongly required.

In order to learn about the current state of Green IS, historical review and SLR

were carried out. These papers are qualitative and descriptive evaluations. More

specifically, Historical analysis is an overview of the problem domain; it aids in

understanding the area of investigation and the potential of PSD model for the

future development of the Green IS/IT field. Meanwhile, the systematic literature

review (SLR) provides a comprehensive overview of the problem domain.

Utilizing historical research methods in historical analysis paper helped to

contextualize and generalize Green IS/IT concept that up to date has only been

looked at as a novel creation of the recent stage of the technological age. By placing

it in the context of the related sustainable and general IS trends, Study I highlighted

its importance for the society in different historical periods. Additionally, the paper

presented a wide range of Green IS/IT related standards and programs created for

organizations and individuals. The assessment of the findings suggested that while

both types of practical initiatives are crucial and impactful, the ones crafted for

individuals have been scarcer and less advanced. Likewise, in academia, Green IS

has been studied more often on organizational and social levels than on the

individual one. Therefore, the historical analysis calls for generating more practical

and theoretical Green IS solutions and studies targeted specifically for the

individual users. Additionally, the study highlighted examples of the novel research

themes that are likely to dominate in the field in the near future.

The results from SLR suggested that there is room for improvement and further

development in both designing and implementing interventions in Green IS for pro-

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environmental behavior. The evaluation of the existing mobile applications aimed

at fostering sustainable behavior lends support to the notion that the systems may

lack persuasive potential, thus possibly inhibiting both adoption and prolonged

engagement. Among all available persuasive features, only primary task support

ones were relatively strong across the inspected applications. Although by no

means all of the persuasive techniques are required to be present in each application

for increasing persuasiveness of the system, a diversification of the features used

in applications can be valuable for determining which techniques are the most

advantageous for increasing adoption of Green IS/IT and a long-term engagement

of the users. Thus the key assumption based on findings of SLR suggests that

without strengthening a wider range of the design principles, persuasiveness of the

Green IS/IT is at risk of losing its full potential, becoming less effective, and

ultimately undermining achievement of the primary task.

Qualitative studies created the grounds for the quantitative Studies I–IV, which

are classified as the field studies. The findings of these studies provide empirical

evidence for the relevance of the proposed theoretical research models, and

therefore contribute to the existing body of knowledge. Empirical Studies I–IV

explored a range of themes related to using PSD as a tool for facilitating Green IS

adoption and continuous use. Several different instances of Green IS were

considered in these studies, all of which were mobile apps. Specifically, Studies I–

IV investigated social network-like mobile apps, with aim to make behavior change

more entertaining. Contrarily, the app examined in Study IV is connected to the

smart thermostat, and thus more pragmatic in nature, since it is meant to be not only

entertaining but also practical for controlling home environment of the user. Study

II surveyed the users about how they envision an ideal Green IS app aimed at

fostering sustainable behavior, and thus did not offer any concrete examples in

order not to bias the respondents. Studies I–III researched intention to adopt Green

IS apps, while Study IV considered intention to continue using the Green IS app.

The most notable themes reflected in Studies I–IV are (1) intention to adopt

and continue using persuasive Green IS, (2) perceived persuasiveness of Green IS,

(3) gamification and cognitive absorption of persuasive Green IS, (4) interrelation

of endogenous motivations with the PSD in Green IS context, and (5) perceived

risk and self-disclosure associated with persuasive Green IS.

A research on how a combination of perceived persuasiveness and user’s

attitude influence intention to adopt a Green IS was the focus of Study I. In this

study, credibility support category was not investigated because it was not as

distinct as the other categories in the mobile app used as a sample Green IS. Study

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I inspected relationship of dialogue support with the other persuasive categories,

and these hypothesized relationships were supported. However, only primary task

support showed impact on perceived persuasiveness, while neither dialogue

support nor social support did. Researching which constructs influence attitude

towards a Green IS, it was found that social affirmation and perceived effectiveness

do have an impact while perceived effort does not. Additionally, comparing the

impact of attitude and perceived persuasiveness on intention to adopt, it was

observed that perceived persuasiveness has a noticeably stronger influence.

Furthermore, inspection of the control variables showed that only one of them,

namely, gender had a significant impact on intention to adopt the Green IS,

specifically, that women tend to have a more positive attitude compared to men.

The impact of persuasive features on endogenous motivations and how they

consequentially shape intention to adopt Green IS was the main theme of Study II.

The research framework of this study was based on the PSD model and Organismic

Integration Theory (OIT). The study confirmed the connection of dialogue support

to the other categories showing how crucial human-computer dialogue is for

functionality of a persuasive system. Moreover, the findings of Study II revealed

that none of the persuasive categories affected internal PLOC, suggesting that this

type of motivation solely stems from the personal disposition of an individual and

is difficult to be altered even with persuasive techniques. Both dialogue and social

support systems affected introjected PLOC, proposing that interaction with both

the Green IS app and the other users of the app impacts the person’s extrinsic

motivation even when it is introjected (i.e. does not align with the user’s personal

beliefs). External PLOC was impacted only by credibility support, suggesting that

the person’s external regulation of behavior is strengthened by the perception of

trust, believability, reliability, and credibility of the Green IS, while nor

performance of the primary task or interaction with the Green IS or interaction with

its other users significantly impacted external PLOC. As expected, all types of

endogenous motivation showed impact on attitude towards the Green IS and

consequentially contributed to intention to adopt the Green IS.

Since gamification is able to alter the users’ psychological states, decision-

making and behavior, and thus is related to persuasive technology, Study III

focused on how PSD and cognitive absorption concepts influence perceived

persuasiveness and intention to use the gamified app leading to intention to engage

in sustainable behavior. Research model of Study III used elements of Liu,

Santhanam, and Webster’s (2017) gamification framework and the PSD model, and

a system under investigation was a gamified Green IS. The study discovered that

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perceived persuasiveness outweighs cognitive absorption in determining intention

to use a gamified persuasive Green IS.

Perceived risk and self-disclosure constructs in the context of the persuasive

smart thermostat system were introduced in Study IV. The study predicted

perceived persuasiveness and continuance intention of a persuasive smart

thermostat system. The utilized theoretical framework of the persuasion process

was based on the PSD model and Adaptation Level Theory. Findings reveal that

perceived risk contributes to decreasing unobtrusiveness, an essential characteristic

of a persuasive system, as well as that self-disclosure decreases the overall

perceived persuasiveness of the system. The study supported the hypothesized

importance of perceived persuasiveness in continuance intention of the Green IS

under investigation – a persuasive smart thermostat system.

Overall, through empirical investigation, Studies I–IV initiated a multi-angled

approach to extending knowledge on a variety of themes related to persuasive

Green IS. In the future, the existing findings should be confirmed, and more

comprehensive research models should be constructed and tested in different

settings with various combinations of persuasive design principles to understand

better the hypothesized relationships.

5.1 Implications

5.1.1 Implications for research

The dissertation contributes to the body of knowledge by demonstrating the

essential role of PSD categories in persuasive Green IS/IT adoption and continuous

use. The dissertation uses theoretical models that incorporate, explain and predict

key issues related to the persuasive Green IS, such as perceived persuasiveness,

attitude, cognitive absorption, motivations, perceived risk, self-disclosure, and

adoption/continuance intention of persuasive systems were constructed and tested

with exploratory approach (PLS-SEM). The constructs were derived and

operationalized from the existing body of knowledge. The persuasive system

categories presented in the PSD model (Oinas-Kukkonen & Harjumaa, 2009)

showed a significant role in the proposed research models.

Regarding research methodologies, the dissertation proposes that using a

variety of research approaches can provide a more thorough understanding of the

issues related to the persuasive Green IS. A combination of quantitative and

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qualitative techniques appears to be an effective way of investigating the persuasive

Green IS from multiple angles. While the qualitative methods provide a means for

studying the history of the domain as well as the PSD analysis for Green IS, the

quantitative ones give an insight on relationships among constructs related to the

persuasive Green IS. Thus, it is advisable that the future research continues using

manifold approaches for getting new findings in the persuasive Green IS domain.

Moreover, the dissertation has also contributed methodologically to the refinement

and validation of measures related to Green IS/IT domain. Since the same (or very

similar) measures had been used previously, such replication and reuse provides

greater support for the robustness of the measures in different types of research

situations.

There are several opinions on deciding on the optimal number of persuasive

features to be used in the persuasive Green IS. The PSD model does not claim that

there is a relationship between the number of features implemented in the

persuasive system and the effectiveness of that system (Oinas-Kukkonen &

Harjumaa, 2009). However, Webb, Joseph, Yardley, and Michie (2010) claimed that

interventions which employ more behavior change techniques have a larger effect

compared to the interventions with fewer techniques. Conversely, Kelders, Kok,

Ossebaard, and Van Gemert-Pijnen (2012) noted that the effect of the behavior

change interventions is decreased given that the exposure to the intervention is not

optimal. Based on the findings of the historical analysis, it could be beneficial to

incorporate features from multiple persuasive categories given that they will

engage with them without feeling overwhelmed (i.e. experiencing obtrusiveness).

Additionally, empirical Studies I–IV suggest that context influences the importance

and relationships among the PSD categories. Thus, PSD should not be concerned

merely with the number of principles implemented within a system, but also with

discovering persuasive principle configurations optimal for different persuasion

contexts.

Furthermore, empirical Studies I–IV highlighted relevant impact of perceived

persuasiveness on intention to adopt and on intention to continue using persuasive

Green IS. The significance of perceived persuasiveness of IS in other domains had

already been previously recognized in research. For instance, Kelders, Kok,

Ossebaard, and Van Gemert-Pijnen (2012) pointed out that persuasiveness

influences system use adherence. Moreover, Lehto, Oinas-Kukkonen, Pätiälä, and

Saarelma (2012) also recognized that perceived persuasiveness has a statistically

significant impact on intention to adopt a persuasive system. Therefore, researchers

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should continue investigation of the system features that foster perceived

persuasiveness.

Although previous studies have shown beneficial effects of gamification for

systems aimed at fostering sustainable behavior, Study III not only confirmed these

findings but also approved the connection between PSD features of a gamified

persuasive Green IS and both perceived persuasiveness and cognitive absorption.

Even though cognitive absorption has a smaller influence on intention to adopt a

Green IS, it is worth noting that it has both direct and indirect (through perceived

persuasiveness) impact on gamified Green IS adoption intention. Thus, this finding

alerts researchers to acknowledge that cognitive absorption is a crucial factor in

explaining both perceived persuasiveness and intention to adopt the app.

Furthermore, the researchers need to be aware of the impact of the users’ own

beliefs, motivations, and perceptions on adopting and continue using persuasive

Green IS. Study II showed that different types of endogenous motivations are tied

directly to attitude towards the persuasive Green IS. Therefore, researchers should

further investigate interrelation of the persuasive categories, perceived

persuasiveness and motivations, in order to discover more efficient ways of

increasing people’s enthusiasm about the persuasive Green IS. Nevertheless, as

Study IV pinpointed, the users are likely to be cautious about Green IS because of

the threat of perceived risk and self-disclosure associated with using the systems.

Researching further these and other negative factors related to the use of persuasive

Green IS will initiate a search for remedies to mitigate these issues and

consequentially reduce or eliminate the factors that harm intentions to adopt and

continue using the system.

Empirical studies discovered several potentials for creating theoretical

perspectives that have never been discussed before. For example, the research

model of Study II offered an application of a new point of view (i.e. OIT) in

conjunction with the PSD. By the same token, the integration of cognitive

absorption in the research model in Study III, proves openness and capability for

extending existing theory not only in the field of gamification but also in the

adoption literature. Regarding findings related to the PSD principles, strong

impacts of dialogue support on social support (Studies I, II, III, IV), credibility

support (Studies II, III, IV), and primary task support (Studies I, II, III, IV) have

been confirmed in Green IS/IT context. This might indicate that the four PSD

categories are not equal. Rather, at least in certain domains, a hierarchy among the

principles was observed, i.e. dialogue support was found to be antecedent to the

other categories. All empirical studies examined only one-way relationships

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between dialogue support and the other PSD categories. Thus, future studies should

examine bi-directional influences to provide a better understanding of the

relationships among the PSD categories and perhaps discover another dimension

to the PSD model. Another crucial aspect to consider is a possible mediating role

of some persuasive categories, for instance, a mediating effect of primary task

support in the influence of dialogue support on perceived persuasiveness.

5.1.2 Implications for practice

The main purpose of persuasive technologies is influencing people’s attitudes and

inducing behavior change. Using the PSD model, practitioners can design own

systems or modify existing features to develop new approaches. Fundamentally,

persuasive technology research is focused on persuasive systems that have a high

potential for providing benefits to their users. However, in order for the systems to

be of an advantage to the users, they should be engaging enough for the users to

adopt and continue using them.

Since engagement with the persuasive systems is likely to increase chances of

successful behavior change, designing, implementing, and evaluating features and

aspects of the system that the users find captivating needs to be prioritized. As the

empirical Studies I–IV show, it is beneficial for practitioners to understand the

factors that increase likelihood of intention to adopt and continue using Green IS,

the type of persuasive systems investigated in this dissertation. In addition to

perceived persuasiveness, other factors such as attitude and cognitive absorption,

proved to be crucial. Additionally, practitioners should not neglect underlying

aspects that influence these factors, namely, persuasive categories and postulates,

self-disclosure, perceived risk, perceived effectiveness, and social affirmation as

well as endogenous motivation and demographics (in particular, gender). This

information will be an aid for the practitioners in guiding the design and

development processes of persuasive systems aimed at sustainable behavior change.

Unquestionably, the use context (Oinas-Kukkonen & Harjumaa, 2009) matters

in the design and development of persuasive Green IS. Depending whether the

system is meant to serve in a social, organizational or individual context (as

classified in the historical analysis) different strategies and techniques will have to

be used in the systems fostering environmentally friendly mindset. Certainly, some

aspects are likely to be very similar, yet the change mechanisms will differ

depending on which group of the users the system is focused on.

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Workplaces can easily utilize the existing infrastructure, that is, systems and

technologies that are currently being used by the employees to initiate pro-

environmental behaviors at worksites. Presenting Green IS within the

organizational setting can spark employees’ interest in and engagement with the

persuasive systems. Furthermore, introduction of Green IS in actual physical places

provides another opportunity for researchers to test various combinations of

persuasive features and investigate a wider range of users, thereby complementing

the existing body of knowledge.

In addition to advantages presented by the persuasive systems for sustainable

behavior change, there are also numerous challenges associated with IS, such as

“technostress” (Ayyagari, Grover, & Purvis, 2011), fatigue towards social

networking, or even “social overload” (Maier, Laumer, Eckhardt, & Weitzel, 2012)

which was observed in Study I and III. Additionally, enjoyment with the system

has been frequently identified as a desirable for driving adoption and continuous

system use (Turel & Serenko, 2012). As shown in Study III, gamification elements

can contribute to bringing the users enjoyable experience (an aspect of cognitive

absorption) with using Green IS. However, it is worth to note that the positive

reinforcement evoked with cognitive absorption can also be a key element in the

formation of adverse outcomes, such as technology-related addictions (Turel &

Serenko, 2012). Thus, there is a hazard that excessive cognitive absorption can

initiate development of a “bad habit,” a strong psychological dependency (i.e.

technology addiction) on the use of the system artifact (Turel & Serenko, 2012).

Finally, both designers and users of persuasive systems should acknowledge

that technology alone might not be able to become a solution for acquisition of pro-

environmental behavior and sustainable development of the world. Green IS is only

a tiny drop in the sea of all resolutions (e.g. governmental regulations and policies)

required to improve permanently the well-being of the society.

5.2 Limitations and future research

Despite providing insightful findings, the papers contain certain limitations that

need to be acknowledged and eliminated in future research. For example, the

historical analysis can be improved by adding a deeper analysis of the historical

Green IS/IT periods, as well as researching an even broader scope of literature on

Green IS/IT and related concepts to investigate thoroughly the origins and

development of the domain. Findings of SLR might not be generalizable since the

paper reviews a handful of applications described in the articles encountered

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through the systematic literature review. Additionally, discussion of the persuasive

features was subject to interpretation of the reviewed articles. To increase

generalizability, in the future, a wider range of articles should be considered, and

the actual applications should be evaluated in person.

Regarding all empirical Studies I–IV, the responses collected in the studies

were self-reported, which limits generalizability of the findings because of the

possible mismatch with the actual attitudes and behaviors of participants. Thus, the

related future research should focus on examining the effects of the factors studied

in this work on the actual behavior rather than intentions, perceptions, and other

self-reported factors that might interfere with the validity of the results. Increasing

the surveyed sample size and diversifying it will result into a higher generalizability

of the findings. In addition, it would be beneficial to determine the respondents’

environmental awareness in all studies and trace how the results differ before and

after engagement with the persuasive system. So far, only Study III assessed

environmental awareness of the individuals.

In the future studies, research methods in the studies needed to be changed to

capture behaviors of the long-time users of the investigated systems. In Study I, the

respondents were introduced to the application by watching a video which provides

an overview of the app but is not equivalent to the actual usage of it. Study II had

no specific application for the survey participants to refer to so the responses

depended heavily on individual discernment. In Study III, the respondents were

briefly introduced to the app, so their responses are not indicative of the long-term

experience with the system. In Study IV, the respondents viewed screenshots of the

interface of the smart thermostat app. Using a prototype would resemble better the

user interface, and a manipulation check could be added to identify whether the

persuasive features were perceived by the respondents as such. Moreover, in the

future studies, other data collection methods, such as experiments, interviews,

focus groups, could be used to provide a stronger support for the obtained results.

Considering data analysis in Studies I–IV, PLS-SEM techniques were used to

assess the collected responses. Adding other approaches from the larger family of

quantitative methods could provide important new insights. Future research should

also look into construct development and exploring more interaction effects among

variables.

In addition, linking specific persuasive features to objectively measured

behavioral outcomes would improve the obtained findings. To understand the

dynamics and transformation of persuasive mechanisms over time, mixed-method

longitudinal studies would be required. A series of other observations can be made,

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including but not limited to the following: (1) Does the significance of the

persuasive categories change over time in different stages of adoption of a

persuasive system? Which categories are more important in the early/later stages?

(2) Can persuasive features be incorporated in the systems in different ways? (3)

Which persuasive techniques assist with maintaining the users’ motivation in

pursuing their goals? (4) How to sustain commitment and consistency in behavior

while retaining unobtrusiveness over time? (5) How to prolong positive effects of

cognitive absorption to continue motivating the users without causing “technostress”

or addiction to persuasive systems? (6) How to mitigate the impact of perceived

risk and self-disclosure on adoption and continuous use of persuasive systems?

Another drawback of Studies I–IV is that the actual behavior change was not

measured. Also the studies did not use confirmatory theory-testing techniques.

Nevertheless, the research frameworks presented in the studies can provide a base

for the further theory-testing research. The reviewed theoretical concepts, the

created research models, and the proposed extended framework could be applied

and tested in several different settings, such as large and more diverse user groups,

different Green IS, and a longer engagement with persuasive systems. Ideally, it

would be relevant to set up experiments which help to link the PSD categories with

the tangible outcomes. For instance, some of the investigated research questions

could be the following: (1) Are the users of the persuasive systems adopting more

sustainable behavior over the course of using the system? (2) Which persuasive

categories are the most helpful in fostering behavior change? (3) Can the users

achieve permanent behavioral and habitual change in addition to the temporary

modification of attitudes?

95

6 Conclusions

This dissertation examined a role of Persuasive Systems Design (PSD) in

facilitating adoption and continuous use of persuasive systems for encouraging pro-

environmental habits. Sustainability was chosen as the principal domain of interest

because of its growing relevance and importance for the future development of the

society. Combining knowledge on pro-environmental behavior change with the

PSD model, this dissertation explored the effects of persuasive design principles on

users’ behavior with respect to their engagement in sustainable actions through

Green IS/IT, specifically, mobile applications created for fostering sustainable

habits. The papers presented in this dissertation aim to advance the design of the

future IS. Therefore, this dissertation presents insights for both researchers and

practitioners on how persuasive design features could be implemented in IS

targeted at facilitating pro-environmental behavior change.

The dissertation includes several contributions. Firstly, theoretical background

of persuasive systems, IS acceptance and adoption was reviewed. Next, history and

scientific literature of the Green IS/IT domain were analyzed and discussed. In the

empirical studies, several Green IS mobile applications were evaluated, new

research models were designed, and corresponding measurement instruments were

developed. Finally, directions for studying persuasive Green IS/IT in the future

were proposed. These contributions supplement the existing body of knowledge

and can be instrumental for scholars focusing on research related to sustainable

behavior facilitated by IS.

For practitioners working on design and development of persuasive systems,

the dissertation implies that building appropriate persuasive mechanisms into IS

can increase the likelihood of adoption and use continuance of the systems.

Technology may provide the means to aid the individual users in their tasks, but

successful implementation, prolonged use, and frequent engagement may depend

on whether the users have an opportunity to use the system as a seamless part of

their daily routines, i.e. whether the system guarantees unobtrusiveness, absence of

perceived risk, and a low level of self-disclosure. Apart from that, maintaining the

users’ motivation is critical for both continuous engagement with IS and

achievement of the behavior change goals.

97

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Appendix 1: Study I measurement items

Construct Items Loading Source Primary Task Support (PRIM)

Self-monitoring: The app helps me track and monitor my behavior. Tunneling: The app guides me through the process which helps perform the target behavior Simulation: The app allows me to simulate the cause and effect of my behavior on the environment. Reduction: The app increases complexity of sustainable behavior by breaking it unnecessarily into separate tasks.(reverse scaled)

0.904 - 0.890 -

Oinas-Kukkonen & Harjumaa, 2009

Dialogue Support (DIAL)

Praise: The app encourages me based on my behavior. Rewards: The app reveals my failure in performing my target behavior. (reverse scaled) Social role: The app offers a virtual specialist to establish a personal relationship with me. Liking: The app is visually appealing in terms of look and feel.

1.000 - - -


Social Support (SOCI)

Social learning: The app allows me to observe actions and outcomes of other people. Social comparison: The app prevents me from comparing myself with others.(reverse scaled) Social facilitation: The app shows me the other people’s level of engagement in sustainable behavior. Normative Influence: The app shows me what sustainable behavior norms are.

0.952 - 0.852 -


Perceived Persuasiveness (PEPE)

The app has an influence on me. The app is personally relevant for me. The app makes me reconsider my habits. The app persuades me to adopt desirable sustainable behavior.

0.885 0.880 0.936 0.839

Oinas-Kukkonen, 2013; Lehto, Oinas-Kukkonen, & Drozd, 2012

Perceived Effectiveness (EFFE)

My chances of becoming more sustainable would improve by using the app. In my opinion, using the app would have an effect on my sustainable behavior. My chances of becoming more sustainable would decrease by using the app. In my opinion, the app would have no effect on my sustainable behavior. (reverse scaled)

0.920 0.954 - 0.730

Venkatesh, Morris, Davis, & Davis, 2003; Venkatesh, Thong, & Xu, 2012

Perceived Effort (EFFO)

Using the app does not require a lot of effort. Using the app is straightforward. Using the app requires considerable time and effort. (reverse scaled) Using the app is burdensome. (reverse scaled)

0.786 - 0.762 0.892

Venkatesh, Morris, Davis, & Davis, 2003; Lehto & Oinas-Kukkonen, 2015

Social Affirmation (AFFI)

People who influence my attitudes would recommend the app. People who are important to me would think positively of me using the app. People whom I appreciate would encourage me to use the app. My friends would think using the app is a good idea.

0.812 0.901 0.942 0.912

Davis, 1989; Ajzen, 1991; Venkatesh, Thong, & Xu 2012

Attitude (ATTI) All things considered, I find using the app to be a wise thing to do. All things considered, I find using the app to be a bad idea. (reverse scaled) All things considered, I find using the app to be a positive thing. All things considered, I find the app to be favorable to use.

0.899 0.874 0.884 0.936

Wunderlich, Kranz, Totzek, Veit, & Picot 2013; Davis, 1989

Intention to Adopt (ADOP)

I would use the app in the future. I would be willing to try the app in the future. I would consider using the app in the future. I would not use the app in the future. (reverse scaled)

0.946 0.904 0.963 0.950

Davis, 1989; Lehto & Oinas-Kukkonen, 2015

Note. Items in italics were deleted due to values of the outer loadings significantly below 0.7

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Appendix 2: Study II measurement items

Construct Items Mean Loading Source Primary Task Support (PRIM)

(Reduction) The app should decrease the complexity of my target behavior by breaking it into simpler tasks. (Tunneling) The app should guide me towards the target behavior by enabling only tasks that contribute to it. (Tailoring) The app should provide me information targeted to people like me. (Personalization) The app should provide individualized information targeted just for me. (Self-monitoring) The app should help me track and monitor my behavior. (Simulation) The app should allow me to simulate the cause and effect of my behavior on the environment. (Rehearsal) The app should allow me to practice my target behavior.

3.910 3.244 3.436 3.026 3.782 3.859 3.474

- - - - - 0.856 0.796

Oinas-Kukkonen &Harjumaa, 2009


(Praise) The app should give positive feedback based on my behavior. (Rewards) The app should reward me for achieving my goals. (Reminders) The app should remind me of my goals and tasks to achieve. (Suggestion) The app should provide me suggestions to help achieve my goals. (Similarity) The app should imitate my personality. (Liking) The app should appeal to me in terms of its look and feel. (Social role) The app should be based on a virtual character who would establish a personal relationship with me.

3.808 3.654 3.397 3.949 2.141 3.744 1.756

0.873 0.840 0.773 - - - -


Credibility Support (CRED)

(Trustworthiness) The app should be truthful, fair, and unbiased. (Expertise) The app should provide competent and up-to-date information. (Surface credibility) The app should look professional. (Real-world feel) The app should provide information about the service provider. (Authority) The app should contain information provided by a trusted authority. (Third-party endorsements) The app should provide endorsements from external experts. (Verifiability) The app should provide a means to verify the accuracy of its content via outside sources.

4.577 4.564 3.962 3.256 4.013 3.064 3.872

- - - 0.794 - 0.868 -



(Social learning) The app should enable me to observe actions and outcomes of other people. (Social comparison) The app should enable to compare my behavior with the behavior of others. (Normative influence) The app should suggest me what people are normally expected to do. (Social facilitation) The app should provide a means for figuring out who is performing the target behavior along with me. (Cooperation) The app should enable cooperation among the users. (Competition) The app should enable competition among the users. (Recognition) The app should give me public recognition for my behavior.

2.462 2.744 2.795 2.282 2.897 2.410 1.846

0.885 0.794 - 0.842 - - -


External PLOC (EXTR)

I use/would use the app because it is recommended by my energy supplier. because it is recommended by governmental institutions. because using the app offers me financial incentives. because the European Union recommends using similar apps.

3.667 4.154 4.603 4.026

0.860 0.923 - 0.877

Ryan &Connell, 1989; Wunderlich, Kranz, & Veit, 2013

Internal PLOC (INTR)

I use/would use the app because I want to help protecting the environment. because I personally like using the app. because I think it is personally important to myself. because I want to learn how to use the app.

6.182 5.013 5.338 3.532

- 0.872 0.754 -

Ryan &Connell, 1989; Wunderlich,

117

because I enjoy using the app. 4.364 0.871 Kranz, & Veit, 2013

Introjected PLOC (ITRJ)

I use/would use the app because I would feel bad about myself if I didn’t use the app. because my peers think that I should use the app. because of a current trend to do something to help protecting the environment. because my friends think that I should use the app. because I want my colleagues to like me.

2.974 3.038 3.744 3.000 2.282

0.834 0.903 - 0.872 -

Ryan & Connell, 1989; Wunderlich, Kranz & Veit, 2013

Attitude (ATTI)

I assume that it is a good idea to use the app. I think that it is reasonable to use the app. All in all, I think it is a bad idea to use the app. I like the idea to use the app.

5.474 5.385 5.833 5.295

0.894 0.852 0.843 0.876

Davis, Bagozzi, & Warshaw, 1989; Wunderlich, Kranz, & Veit, 2013

Intention to Adopt (INTE)

I would use the app in the future. I would be willing to try the app in the future. I would consider using the app in the future. I can imagine myself using the app in the future.

5.051 5.756 5.731 5.372

0.859 0.890 0.883 0.897

Davis, Bagozzi, & Warshaw, 1989; Lehto & Oinas-Kukkonen, 2015


118

Appendix 3: Study III measurement items


The app decreases complexity of sustainable behavior by breaking it into separate tasks. The app helps me track and monitor my behavior. The app provides me personalized content related to performing target behavior.

0.700 0.853 0.869



The app encourages me based on my behavior. The app rewards me for performing my target behavior. The app offers a virtual specialist to support me in performing my target behavior.

0.916 0.818 0.770



The app is truthful, fair, and unbiased. The app has a professional appearance. The app provides competent up-to-date information.

0.744 0.804 0.876



The app allows me observe actions and outcomes of other people. The app allows me compare myself with others. The app enables receiving public recognition for performing my target behavior.

0.852 0.820 0.801



The app has an influence on me. The app is personally relevant for me. The app makes me reconsider my habits. In my opinion, the app is convincing.

0.919 0.916 0.907 0.874

Oinas-Kukkonen, 2013; Lehto, Oinas-Kukkonen, & Drozd

Cognitive Absorption (COAB): Perceived Enjoyment (PEEN)

I found using the app to be enjoyable. I had fun using the app. Using the app was boring. (reverse scaled) The app really annoyed me. (reverse scaled) The app experience was pleasurable. The app left me unsatisfied. (reverse scaled)

0.887 0.913 0.882 0.806 0.787 0.814

Agarwal & Karahanna, 2000; Lowry et al., 2012

Temporal Dissociation (TEDI)

Time appeared to go by very quickly using the app. I lost track of time when I was using the app. Time “flew” when I used the app.

0.890 0.899 0.909

Focused Immersion (FOIM)

I was able to block out most of distractions when I was using the app. I was absorbed in what I was doing when I used the app. I was immersed in the app. I was distracted by other attentions very easily while using the app. (reverse scaled)

0.748 0.957 0.922 0.471

Control (CTRL)

I had a lot of control in the app. I could choose freely what I wanted to see or do in the app. I was in control in the app. I had no control over my interaction in the app. (reverse scaled) I was allowed to control my interaction in the app.

0.878 0.854 0.891 0.407 0.719

Curiosity (CURI)

This app experience excited my curiosity. This app experience made me curious. This app experience aroused my imagination.

0.960 0.967 0.917

Intention to Use (INT_USE)

I would use the app in the future. I would be willing to try using the app in the future. I would consider using the app in the future. I can imagine myself using the app in the future.

0.962 0.965 0.962 0.960

Davis, Bagozzi, & Warshaw, 1989; Venkatesh, Thong, & Xu, 2012; Lehto & Oinas-Kukkonen, 2015


119

Appendix 4: Study IV measurement items


The SHT app makes it easier for me to reach my sustainability goals. The SHT app helps me in reaching my sustainability goals. The SHT app helps me keep track of my progress. The SHT app should guide me in reaching my goals through a process or experience.

- 0.947 0.951 -



The SHT app encourages me to reach my sustainability goals. The SHT app rewards me for reaching my sustainability goals. The SHT app provides me with appropriate feedback. The SHT app service provides me with reminders for reaching my sustainability goals.

0.884 0.924 0.922 0.921



The SHT app is trustworthy. The SHT app is reliable. The SHT app shows expertise. The SHT app instills confidence in me.

0.935 0.953 - 0.895



The SHT app allows me to observe actions and outcomes of other people’s sustainable behavior. The SHT app allows me to compare my sustainable behavior with the others. The SHT app shows me who and to what extent other people perform sustainable behavior. The SHT app gives me public recognition about my sustainable behavior.

0.907 - 0.954 0.925


Unobtrusiveness (UNOB)

Using the SHT app fits into my daily life. I find that using the SHT app is convenient. I always find time to adjust the SHT app settings. Using the SHT app does not interfere with my daily routine.

0.859 - 0.873 0.907

Lehto, Oinas-Kukkonen, & Drozd, 2012


The SHT app has an influence on me. The SHT app is personally relevant for me. The SHT app makes me reconsider my habits. The SHT app persuades me to adopt desirable sustainable behavior.

- 0.898 0.954 0.924

Oinas-Kukkonen, 2013; Lehto, Oinas-Kukkonen, & Drozd, 2012

Perceived Risk (RISK)

In general, it would be risky to give information to the SHT app. There would be high potential for loss associated with giving information to the SHT app. There would be too much uncertainty associated with giving information to the SHT app. It is safe to provide information to the SHT app. (reverse scaled)

0.854 0.853 - 0.812

Benson, Saridakis, & Tennakoon, 2015; Malhotra et al., 2004

Self-Disclosure (DISC)

It usually bothers me when mobile apps ask me for personal information. When mobile apps ask me for personal information, I sometimes think twice before providing it. It bothers me to give personal information to so many mobile apps. Usually, I feel certain providing personal information to mobile apps. (reverse scaled)

0.831 0.969 0.963 -

Posey, Lowry, Roberts, & Ellis, 2010; Benson, Saridakis, & Tennakoon, 2015

Continuance Intention (CONT)

I will be using the SHT app in the future. I intend to continue using the SHT app. I am considering discontinuing using the SHT app. (reverse scaled) I am not going to use the SHT app from now on.

0.968 0.964 - -

Bhattacherjee, 2001


121

Original publications

I Shevchuk, N., & Oinas-Kukkonen, H. 2019. “Role of Persuasive Systems Design in Increasing Intention to Adopt Green IS: Case JouleBug Mobile Application,” In Proceedings of the International Conference on Information Systems Development (ISD2019), pp. 1-12.

II Shevchuk, N. & Oinas-Kukkonen, H. 2019. “Influence on Intention to Adopt Green IS: Boosting Endogenous Motivations with Persuasive Systems Design,” In Proceedings of the Hawaii International Conference on System Science (HICSS), pp. 2055-2064.

III Shevchuk, N., Degirmenci, K., & Oinas-Kukkonen, H. 2019 (Forthcoming). “Adoption of Gamified Persuasive Systems to Encourage Sustainable Behaviors: Interplay between Perceived Persuasiveness and Cognitive Absorption,” In Proceedings of the International Conference on Information Systems (ICIS), p. 1-17.

IV Shevchuk, N., Benson, V., & Oinas-Kukkonen, H. 2019. “Risk and Social Influence in Sustainable Smart Home Technologies: A Persuasive System Design Study,” In Benson, V. & McAlaney, J. (Eds.), Cyber Influence and Cognitive Threats (pp. 185-216). Academic Press.

V Shevchuk, N., Oinas-Kukkonen, H., & Oinas-Kukkonen, H. 2019. “History of Green Information Systems and Technologies: Origin, Development, and Contemporary Trends,” Faravid. Pohjois-Suomen Historiallisen Yhdistyksen Vuosikirja, 47/2019, pp. 93-128. http://pro.tsv.fi/pshy/julkaisut/Faravid_47-2019.html.

VI Shevchuk, N., & Oinas-Kukkonen, H. 2016. “Exploring Green Information Systems and Technologies as Persuasive Systems: A Systematic Review of Applications in Published Research,” In Proceedings of the International Conference on Information Systems (ICIS), pp. 1-11. https://aisel.aisnet.org/icis2016/Sustainability/Presentations/11/.

Reprinted with permission from Association for Information Systems, Atlanta, GA

(I), (II), (III), and (VI), Elsevier (IV), Faravid (V).

Original publications are not included in the electronic version of the dissertation.


Book orders:Granum: Virtual book storehttp://granum.uta.fi/granum/

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A 732

Nataliya Shevchuk


UNIVERSITY OF OULU GRADUATE SCHOOL;UNIVERSITY OF OULU,FACULTY OF INFORMATION TECHNOLOGY AND ELECTRICAL ENGINEERING

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