towards a measure of omni-channel shopping value in the ... › fb07 › fileadmin › datas ›...

Towards a measure of Omni-Channel Shopping

Value in the context of integrated omni-channel

fashion retail settings

Master Thesis Exposé

Submitted by

Juliette Beck

European Master in Business Studies

Kassel, 31st October 2018

Towards a measure of Omni-Channel Shopping Value in the context of integrated omni-channel fashion retail settings

2

ABSTRACT Title: Towards a measure of Omni-Channel Shopping Value in the context of integrated omni-

channel fashion retail settings.

Background: Change is one of the most certain features to describe contemporary societies,

and distinctly so the fashion retail industry. This statement has been validated by theories and

empirical studies seeking to address the disruptive and on-going digitalization of retailing. It

encompasses upheavals in business models, increase in the variety of channel and store

formats, changes in retail technologies to fundamentally new ideas and concepts. Such

innovations are critical to pursue growth opportunities and establish sustainable advantage in

a market place characterized by escalating customer expectations and increasingly fierce

competition.

Survival in today’s fast-paced fashion retail industry drove retailers to transform their

traditional brick and mortar into adopting multi-channels business models combining

simultaneously e-commerce and established fixed stores. In recent years, it gradually shifted

to a focus on omni-channel retailing taking a broader perspective on consumers and how

shoppers are influenced and transit through channels during one integrated fashion shopping

process creating a seamless customer experience of superior value. In parallel, digitalization

allowed consumers to ubiquitously access the internet rising their expectations in term of

shopping. Indeed, it gave them the ability to consistently, interchangeably and simultaneously

use a multitude of offline, online and mobile channels and touchpoints during their shopping

journey. Regardless of the type of channels chosen, this led consumers to expect consistent and

unequal service resulting in rewarding personalized brand experiences.

Purpose: This study addresses at capturing the value of omni-channel fashion shopping in the

current context of integrated omni-channel fashion retail channels and formats proliferating

since the integration of advanced digital technologies. Specifically, a comprehensive adapted

and extended version of the research model of the Omni-Channel Shopping Value (OC-SV)

developed by Huré, Picot-Coupey and Ackermann (2017) will be applied. It will articulate the

different dimensions of OC-SV and the various channels analysed as multiple integrated

touchpoints including contemporary formats becoming current game changers in fashion retail.


3

Additionally, this study attempts at appraising the value of omni-channel fashion shopping to

allow retailers to monitor the adequacy and relevance of their omni-channel decisions.

Therefore, this study contributes to omni-channel channel management and retailing research

by broadening the conceptualization of OC-SV emphasizing on the contemporary context of

deeper integration of fashion retail channels in an ubiquitous manner and the ever growing

number of touchpoints per channel. It explores the OC-SV that stems from the level of

integration of three most popular fashion shopping channels composed themselves by multiple

contemporary touchpoints. The channels’ shopping value (SV) themselves derive from three

distinctive motivational dimensions of SV: utilitarian, hedonic et social. Up to the knowledge,

it is one of the first studies to empirically investigate the OC-SV in the context of integrated

omni-channel fashion retail settings.

Methodology: The empirical part of this thesis will follow a quantitative approach in the form

of an online survey. The questionnaire will be designed using Sphinx Declic and will target

male and female apparel consumers with an age range from 16 to 35 years old residing in

Switzerland and in France which can designate one brand with which they encountered an

omni-channel experience. Based on previous literature and already existing models, it will aim

at testing the appropriateness of the conceptual model and derived hypotheses, considering

retail channels as multiple integrated touchpoint settings. Data will then be analysed by

conducting Structural Equation Modelling (SEM) using the application software SmartPLS.

Keywords: Omni-channel retailing, Omni-Channel Shopping Value, Omni-channel

integration, Fashion retailing, retail digitalization, seamlessness.


4

TABLE OF CONTENT ABSTRACT ....................................................................................................................................................2 LIST OF FIGURES ........................................................................................................................................5 LIST OF TABLES..........................................................................................................................................5 LIST OF ABBREVIATIONS .........................................................................................................................5 1. INTRODUCTION .................................................................................................................................6

1.1. BACKGROUND ...............................................................................................................................6 1.1. PROBLEM STATEMENT AND RESEARCH QUESTION ........................................................................8 1.2. PURPOSE .......................................................................................................................................8 1.3. CONTRIBUTION .............................................................................................................................9

2. LITERATURE REVIEW ................................................................................................................... 10 3. RESEARCH DESIGN AND HYPOTHESES ..................................................................................... 26

3.1. RESEARCH MODEL ............................................................................................................................ 26 3.2. DEVELOPMENT OF HYPOTHESES ........................................................................................................ 27

3.2.1. Omni-Channel Shopping Value .................................................................................................. 27 3.2.2. Channels’ value dimensions........................................................................................................ 29 3.2.3. Role of omni-channel intensity ................................................................................................... 29

4. RESEARCH METHODOLOGY ........................................................................................................ 30 4.1. QUESTIONNAIRE DESIGN .................................................................................................................... 30 4.2. SAMPLE .............................................................................................................................................. 31 4.3. DATA COLLECTION ............................................................................................................................ 32 4.4. DATA ANALYSIS ................................................................................................................................. 32

CHAPTERS OVERVIEW* ......................................................................................................................... 33 WORK PLAN ............................................................................................................................................... 34 REFERENCES ............................................................................................................................................. 35


5

LIST OF FIGURES Figure 1. Adapted research model based on the Omni-Channel Shopping Value model developed by Huré et al. (2017). .......................................................................................... 26

LIST OF TABLES Table 1. Literature review of existing scales (adapted from Huré et al. (2017) ..................... 31

LIST OF ABBREVIATIONS

• e.g. exempli gratia/for example

• H: Hypothesis

• HSV: Hedonic Shopping Value

• M-HSV: Mobile Hedonic Shopping Value

• M-SSV: Mobile Social Shopping Value

• M-USV: Mobile Utilitarian Shopping Value

• OC-SV: Omni-Channel Shopping Value

• Off-HSV: Offline Hedonic Shopping Value

• Off-SSV: Offline Social Shopping Value

• Off-USV: Offline Utilitarian Shopping Value

• On-HSV: Online Hedonic Shopping Value

• On-SSV: Online Social Shopping Value

• On-USV: Online Utilitarian Shopping Value

• RQ: Research Question

• SSV: Social Shopping Value

• SEM: Structural Equation Modeling

• SV: Shopping Value

• USV: Utilitarian Shopping Value


6

1. INTRODUCTION

This section intends to firstly outline the context of the study, from which will stem a concise

statement of the research problem and consecutively definitions of the research questions,

purposes and contribution. Secondly, it aims to provide an overview of the structure of the present

thesis outlining its core sections.

1.1. Background Digitalization denotes a major on-going and disruptive transformation of contemporary

societies encompassing many components of business and individual’s daily life, and distinctly

so the fashion retail industry. Following the emergence of Internet and new technologies,

fashion retailers shifted their focus from single-channel retail settings with traditional brick and

mortar stores offering face-to-face experiences, to multi-channel retail environments with the

disruptive development of the online channel and e-commerce. It removed the spatiotemporal

barriers to shopping and typically included retail channels such as stores, online websites and

direct marketing (catalogues). The multichannel shopper was visiting the retailer via separate

channels according to different purposes (e.g., search for information online, purchase made

offline, and contact customer service via telephone) with no overlap during the shopping

journey.

Whereas in the past decade multichannel retailing was in vogue, scholars now observed a

transition to omni-channel retailing taking a broader scope on channels and how to enable their

integration for customers to use them simultaneously during one integrated fashion shopping

process (Verhoef, Kannan & Inman, 2015). Additional digital channels and various new store

formats continue to proliferate while boundaries between physical and online fashion retailing

become blurred and begin to vanish (Brynjolfsson et et al., 2013). This omni-channel

integration offers to retailers a wide range of opportunities to create innovative offerings

designed for customers to evolve towards a seamless shopping experience (Huré et al., 2017).

At the same time, digitalization allowed consumers to ubiquitously access the internet rising

their expectations in terms of shopping (Rigby, 2011; Brynjolfsson, Hu & Rahman, 2013).

Indeed, the ability to consistently, interchangeably and simultaneously use a multitude of

offline, online and mobile channels and touchpoints was provided during their shopping

journey (Hagberg, Sundstrom, & Egels-Zandén 2016). Regardless of the type of channels


7

chosen, this led consumers to expect consistent and unequal service that would result in

assuming that retailers need to design utmost rewarding and personalized brand experiences

(Piotrowicz & Cuthbertson, 2014; Verhoef et al., 2015). This enables the latter to enhance SV

for customers, driving satisfaction and loyalty and, in return, to maximize the firm value.

Consequently, retailers are challenged by the necessity to synchronize effectively bricks and

clicks providing a simultaneous combination of physical and digital touchpoints and handle

their retailing mix consistently to ensure an optimized alignment of the fundamentals of the

brand on every touchpoints (Verhoef et al., 2015; Picot-Coupey, Huré, & Piveteau, 2016).

Therefore, measuring channels and associated touchpoints value is necessary to appraise the

suitability of such synchronization efforts.

Although the concept of shopping value is recent but not new among scholars

(Rintamäki, Kanto, Kuusela & Spence, 2006; Hsiao, Yen, & Li, 2012; Leroi-Werelds,

Streukens, Brady & Swinnen, 2013; Kumar & Reinartz, 2016), the focus so far has mainly

been on the derived value from the different fashion retail channels considered as separated

shopping means (Huré et al., 2017), and not on the value stemming from their simultaneous

integration. Thus, the notion of OC-SV has not been yet precisely captured by existing research

on shopping value. Furthermore, previous research does not allow to accurately grasp the

concept of fashion retail channels, as it exclusively considers them as single-channels settings.

Yet, these channels are all composed by multiple contemporary touchpoints which emerged

following the digitalization of fashion retailing.

This study is composed by six main sections. After the above-mentioned introduction, a

thorough literature review will provide a solid theoretical background organized around key

themes to effectively and currently contextualize the area and relevance of research. It will also

formed the basis for this research paper’s investigation and served as guidelines for the

preparation of the research protocol. Moreover, it will allow the reader to grasp the core

constructs of the object of discussion.

The third section will present the adapted research model used to answer the research

questions, from which the research hypotheses will be developed and discussed. Thereafter,

the methodology implemented will be described in the fourth section, composed by the

questionnaire design, sample of respondents, data collection method and adopted data analysis

approach.


8

Results retrieved from the online survey will be presented and analyzed using the

application software SmartPLS in the firth section. It will aim at answering and either accepting

or rejecting the hypotheses developed through this study.

Finally, the sixth section will broaden the results presented in the previous section by

providing an interpretation of the findings in the current contextualization of the existing

literature in the field. It attempts to reflect on how this study has contributed to bring the

theoretical framework forward by providing additional substantial understanding of the topic.

It will also state the theoretical and managerial implications coming along the thesis’ findings.

Furthermore, it will examine the limitations of the study and provide recommendations for

future research on the topic.

1.1. Problem statement and Research Question

As developed previously, effectively integrating and synchronizing channels as well as

associated touchpoints allow retailers to enhance and optimize the omni-channel customer

experience from which thrives value. It results in driving and reinforcing customer satisfaction

and loyalty which leads in return to benefit the firm, maximizing its own value (Huré et al.,

2017). Thereby, it appears relevant to explore and investigate the main channels’ SV in the

current context of integrated omni-channel fashion retail environments, which gathered and

integrated, form the OC-SV. Thus, the research on SV which derived from the concept of value,

already extensively studied by previous scholars (Rintamäki et al., 2006; Hsiao, et al., 2012;

Leroi-Werelds et al., 2013; Kumar et al., 2016; Huré et al., 2017), will take a new turn. Its

focus will be revisited by considering the crossovers effects between the various integrated

channels values and their associated touchpoints. These interaction effects occur when

customers shop in multiple touchpoints simultaneously, as allowed by the digitalization of

fashion retailing.

Hence, the present thesis is a bid to answer the following research questions:

RQ1: How can OC-SV be captured within the context of integrated omni-channel fashion

retail settings and multiple associated touchpoints?

RQ2: How can OC-SV be appraised within the context of integrated omni-channel fashion

retail environments and multiple associated touchpoints?

1.2. Purpose


9

Building of previous research and the above-mentioned problem statement, the purpose of

this study is twofold. Firstly, it aims at capturing the value of an omni-channel fashion shopping

experience in the current context of integrated omni-channel fashion retail channels and

formats proliferating since the on-going digitalization of fashion retailing first occurred. A

comprehensive adapted version of the research model of the Omni-Channel Shopping Value

developed by Huré et al. (2017) will be applied. It will articulate the different dimensions of

OC-SV and the main shopping channels analyzed as multiple touchpoints including

contemporary formats becoming game changers in fashion retail. Thereby, the online channel

will integrate the following touchpoints; social networks and retailer’s website while the offline

channel will consider shop-in-shops and pop-up stores as contemporary retail formats. This

aims at illustrating that OG-SV stems from integrated channels SV composed by multiple

touchpoints. While the channels’ SV themselves derive from three distinctive motivational

dimensions of shopping value: utilitarian, hedonic et social.

Secondly, this study attempts at appraising the value of omni-channel fashion shopping to

allow retailers to monitor the adequacy and relevance of their omni-channel decisions.

1.3. Contribution

This study contributes to omni-channel channel management and retailing research by

broadening the conceptualization of OC-SV emphasizing on the contemporary context of

deeper integration of fashion retail channels in an ubiquitous manner and the ever growing

number of touchpoints per channel. It explores the OC-SV from a new perspective assuming

not only that it stems from the value of the different separated fashion retail channels but

specifically from their level of integration. It also assumes that to grasp the concept of fashion

retail channels, the latter should not be consider as single-channels settings anymore but as

composed by multiple contemporary touchpoints. Up to the knowledge, it is one of the first

studies to empirically investigate the OC-SV in the context of integrated omni-channel fashion

retail settings and multiple touchpoints.

2. LITERATURE REVIEW

Before entering into the core part of the present thesis, this section aims to gather, condense, evaluate, contrast and present the most relevant

existing research on the topic of shopping value within the context of omni-channel retail settings. Therefore, it provides a literature review

organized in the form on a table around key themes that should support the reader in grasping the particular context of the topic and upcoming

hypotheses to comprehend the quantitative approach of this study in a smoother manner.

# Author, year, title, journal

Constructed analyses Scales

Hypotheses or objectives proposed

Methodology Analyses Main Contributions

Further research suggested

1 Arnold, M. J. Reynolds, K. E. (2003). Hedonic shopping Motivations. Journal of Retailing, 79 (2), 77-95.

• Hedonic Shopping Motivations

• No scale used • Objective: investigating the hedonic reasons individuals go shopping and developing a scale to measure them

• Qualitative inquiry: 98 depth interviews • Questionnaire for scale purification with 48 hedonic motivation items • Two-part questionnaire (2 weeks apart): 18 hedonic motivations items in the first part and a variety of variables used for nomological and predictive validity tests, and age,

• Content Analysis and Categorization • Scale purification process: - Item analyses - Exploratory factor analyses - Confirmatory factor analyses - Initial assessment of scale reliability, unidimensionality, and convergent and, discriminant validity

• Six broad categories of hedonic shopping motivations emerged: - Adventure Shopping - Social Shopping - Gratification Shopping - Idea Shopping - Role Shopping - Value Shopping

• Test the hedonic shopping motivations scale in other retail channels, namely, online and catalog


11

income, gender items in the second part

• Scale validation: - Replicate Confirmatory Factor Analysis - Asses Measurement Invariance, Nomological Validity, and predictive Validity. - Cluster analysis

2 Rintamäki, T., Kanto, A., Kuusela, H., & Spence, M. T. (2006). Decomposing the value of department store shopping into utilitarian, hedonic and social dimensions: Evidence from Finland. International Journal of Retail & Distribution Management, 34(1), 6–24.

• Customer Value • Hedonic, utilitarian and social dimensions of shopping value • Consumer behavior

• No scale used • Objective: decompose total customer value as perceived by department stores shoppers into hedonic, utilitarian, and social dimensions to test empirically the conceptualization in a Finnish department store context. It represent possible differentiating factors in the highly competitive and commoditized retail markets

• Questionnaire administrated over three days at a major department store in Finland: 364 respondents

• Three measurement models to establish construct validity: one per value dimension

• Tripartite conceptualization of total customer value that incorporates utilitarian, social and hedonic value dimensions in a department store shopping context • Social value is an independent construct

3 Puccinelli, N. M., Goodstein, R. C., Grewal, D., Price, R., Raghubir, P.,

• Consumer behavior

• No scale used • Theoretical paper summarizing the literature review on consumer behavior

• Propose an organizing framework representing the

• Theoretical paper suggesting ways in which retailers can


12

Stewart, D. (2009) Customer Experience Management in Retailing: Understanding the Buying Process. Journal of Retailing, 85 (1), 15-30.

• Consumer decision process

and suggesting that its numerous elements play important roles during various stages of the consumer decision process.

stages of the consumer decision process according to elements of consumer behavior that affect them

leverage consumer behavior

4 Kang, J., Park-Poaps, H. (2011). Motivational antecedents of social shopping for fashion and its contribution to shopping satisfaction, Clothing and Textiles Research Journal, 29 (4), 331-347.

• Social shopping behavior • Fashion shopping

• Multidimensional scale developed by Kang and Park-Poaps (2011) was used to measure social shopping for fashion: 16 items for 5 dimensions of social shopping for fashion

H1: Social comparison orientation positively influences social shopping for fashion H2: Social shopping for fashion positively influences shopping satisfaction

• Theory-based model tested through three dimensions social comparison, social shopping for fashion, and shopping satisfaction • Online survey: 858 undergraduate students as respondents

• Structural Equation Modeling using AMOS software • Confirmatory factor analyses

• Social comparison orientation has effects on several dimensions of social shopping for fashion • Social shopping dimensions for fashion affect product and/or experience satisfaction • Motivations toward social comparison are significant drivers of social shopping for fashion • Social browsing carries implications for retail performance

• Examine social shopping in terms of online shopping exclusively or compare online and in-store shopping


13

5 Gensler, S., Verhoef, P.C., Böhm, M. (2012). Understanding consumers' multichannel choices across the different stages of the buying process. Marketing Letters, 23 (4), 987-1003.

• Consumer buying process • Multichannel integration • Consumer behavior • Channel experience

• Choice probability scale (ranging from 0 to 100)

• Utility-based model to explain consumers’ channel choices in the different stages of the buying process (where channel utility reflects channel attributes, experience, and spillover)

• Market research conducted among 500 German banking customers from consumer panel. • Face to face interviews • Large-scale pretest with 434 students

• Exploratory factor analysis

• Establishing an integrative approach is important as channel attributes, experience, and spillover effects matter • Convenience and quality as important drivers of consumers’ channel choices. Price does not drive channel choice

• Develop the model in the Fashion retail sector

6 Lazaris, C., Vrechopoulos, A. (2013). From Multichannel to “Omnichannel” Retailing: Review of the Literature and Calls for Research. 2nd International Conference on Contemporary Marketing Issues, (ICCMI), 6.

• Multichannel retailing • Omni-channel retailing

• No scale used • Theoretical paper summarizing the literature review on Omni-channel retailing

• Multidisciplinary approach: topic invested in Marketing, E-commerce and Information Systems research outlets

• Clarifying the main differences observed between multichannel and omni-channel retailing

• Conduct exploratory empirical research to obtain an understanding of consumer behavior patterns and characteristics. Then use conclusive research design

7 Blázquez, M. (2014). Fashion Shopping in Multichannel Retail: The Role of

• Consumer fashion-shopping experience

• Personal Shopping Value scale by Babin et al. to calculate the hedonic and

• H1: Technology changes the role of physical stores

• Quantitative survey considering two channels: brick-and-mortar stores and the Internet.

• Exploratory factor analysis, applying principal component analysis with varimax

• Technology enables integration of channels and gives new relevance to

• Qualitative inquiry to get deeper/richer insights into consumer’s


14

Technology in Enhancing the Customer Experience. International Journal of Electronic Commerce, 18(4), 97-116.

• Multichannel experience (In-store and online experience) • Hedonic and Utilitarian Shopping Values

utilitarian means for the brick-and-mortar and the Internet shopping experiences • Hedonic Shopping Motivations scale developed by Arnolds and Reynolds adapted by To et al. to the online environment.

• H2: Internet and the digital technology are changing the multi-channel shopping experience • H3: The level of online shopping familiarity shapes the multichannel fashion experience and influences consumer's motivations to buy in different channels

Sample: men (31,3%) and women (68,6%) from 16 to 54 years old with fashion shopping experience and familiar with the process of searching/buying clothes, in both channels. Snowball Sampling.

rotation to determine how observed variables were linked to their underlying factors in the multi-item scale applied for both channels • Shopping value perceptions compared through one way analysis of variance (ANOVA). Dependent variables: means of hedonic and utilitarian value perceived in both channels. Factor: experience in buying fashion online

physical stores. It is a medium to enhance high-quality customer experience • Hedonic elements in the fashion shopping experience are of major importance and the use of different technologies creates an enjoyable fashion experience online. • Each retail channel complements the other. The holistic experience begins before a customer’s enters the store and continues after the customer leaves

experiences in fashion shopping (in relation to the influence of e-commerce on in-store experience perceptions) • Field experiments to test specific technologies, most relevant for consumers • Target respondents with high relevance for fashion retailers (young people, older shopper, whose relationship with technology is very different) • Differentiate devices used to buy fashion online (tablets, smartphones). Conduct research about touchscreen devices (interactive


15

functionality implies a totally different shopping experience)

8 Lazaris, C., Vrechopoulos, A., Fraidaki, K., Doukidis, G. (2014). Exploring the “Omnichannel” Shopper Behaviour. AMA SERVSIG, International Service Research Conference, 13-15 June, 1-5.

• Omni-channel retailing • Shopper Behavior

• No scale used • Η1: Shoppers’ omni-channel retailing intensity affects the frequency of their mobile Internet usage • Η2: Shoppers’ omni-channel retailing intensity affects the research online - purchase offline behavior • Η3: Shoppers’ omni-channel retailing intensity affects the importance shoppers attach to the offline retail stores’ assisting technologies • Η4: Shoppers’ omni-channel retailing intensity affects the research offline - purchase online behavior

• Segmentation study of omni-shoppers into groups according to the degree they use omni-channel retail capabilities and practices (i.e. “omni-channel intensity”) • Exploratory quantitative empirical research design: online questionnaire conducted in Greece. 1324 respondents

• Independent-Samples Kruskal-Wallis non-parametric test with pairwise comparisons to observe potential differences between groups

• Behavioral patterns exist related to the degree that the shoppers engage in omni-channel behavior: - Full omni-shoppers use simultaneously mobile Internet for retailing purposes within the physical store - The majority of the consumers use the Internet for information search either they buy online or offline - Omni-shopping avoiders do not use internet for retailing purposes - Omni-shoppers tend to research offline and then purchase online

• Employment of a shoppers’ “panel” logic/mechanism for shoppers that are active both online and offline could contribute to the effective investigation of their behavior in the omni-channel retail environment


16

9 Piotrowicz W.,

Cuthbertson R., (2014) Introduction to the Special Issue Information Technology in Retail: Toward Omnichannel Retailing, International Journal of Electronic Commerce, 18:4, 5-16.

• Channel integration: the omni-channel • Technology in retail

• No scale used • Discussion on the role of information technology in retail, new business models (omni-channel, focusing on the interaction between the consumer and then brand and less on the channel used), and the future role of traditional stores as e-commerce advances

• Focus groups: 2 panels: academics and practitioners focusing on the impact of technology in: 1. Multichannel and in-store retailing and development of new business models. 2. Mobile technologies 3. Customer experience and supplier relationship

• Need for channel integration, impact of mobile technologies, growing role of social media, changing role of physical brick-and-mortar stores, need to respond to growing customer requirements

10 Baxendale, S. Macdonald, E. K. Wilson, H. N. (2015). The Impact of Different Touchpoints on Brand Consideration. Journal of Retailing, 91 (2), 235-253.

• Retail touchpoints

• Likert-scales • Understanding the relative impact of multiple touchpoints on brand consideration

• Real-time experience tracking (RET) method: - Online survey to collect demographics and brand consideration - Text messages sent when encountering one of the brands to capture touchpoints as they occurred • Listwise deletion for handling missing data • 4 Models, with different variables

• First study on the relative impact of brand, retailer, peer and earned touchpoints on the customer’s brand relationship • Assessment of touchpoint impact needs to take into account touchpoint positivity and not just frequency • Assessing the impact of encounters on key outcomes


17

taken into consideration

11 Beck. N., Rygl. D. (2015). Categorization of multiple channel retailing in Multi-, Cross-, and Omni-Channel Retailing for retailers and retailing. Journal of Retailing and Consumer Services, 27, 170-178.

• Omni-channel retailing • Multichannel retailing • Cross channel retailing

• No scale used Theoretical paper summarizing the literature on multiple channel retailing categories for retailers and retailing

• Literature review • Taxonomy of multiple channel retailing • Literature classification table • A Mobile Click and Collect Shop as a way of illustrated a new retailing format

• Clear defined boundaries between the concept Multi-, Cross-, Omni-channel retailing and categorization proposed

• Focus further research on Cross- and Omni- Channel Retailing with high impact factors, since it is currently sparse compared to Multi-channel retailing

12 Herhausen, D., Binder, J., Schoegel, M., Hermann, A. (2015). Integrating Bricks with Clicks: Retailer-Level and Channel-Level outcomes of Online-Offline Channel Integration. Journal of Retailing, 91 (2), 309-325.

• Omni-channel retailing • Channel integration

• No scale used • H1: Online–offline channel integration (OI) (a) increases perceived service quality and (b) decreases perceived risk of the Internet store H4: Customer’s Internet shopping experience (a) decreases the positive effect of online–offline channel integration on perceived service quality of the Internet store and (b) decreases

• Theoretical model (where perceived service quality and perceived risk of the Internet store mediate the impact of OI while the Internet shopping experience of customers moderates the impact of OI). • 3 testing studies

• Regression analysis

• OI leads to a competitive advantage and channel synergies rather than channel cannibalization

• Applying the same theoretical model considering channel integration as simultaneously providing online terminals in physical stores and a physical store locator in mobile channels.


18

the negative effect of online–offline channel integration on perceived risk of the Internet store

13 Verhoef, P. C., Kannan, P. K., & Inman, J. (2015). From Multi-Channel Retailing to Omni-Channel Retailing: Introduction to the Special Issue on Multi-Channel Retailing. Journal of Retailing, 91(2), 174-181.

• Multichannel retailing • Omni-channel retailing

• No scale used • Theoretical paper summarizing the literature review on both multichannel and omni-channel retailing movements

• 11 submissions from contributors • Classification of articles per channel paradigm

• Analysis of secondary performance data and CRM databases with econometric models • Analysis of survey data and experiments

• Multi-channel retailing is moving to Omni-channel retailing • Main stream of research in literatures according channels: 1. Impact of channels on performance 2. Customer behavior across channels 3. Retail mix across channels.

• New research should adopt an omni-channel focus on Shopper Behavior across channels: aiming to model choice behavior of multiple channels and touchpoints simultaneously

14 Hagberg J., Sundstrom M., Egels-Zandén N. (2016). The digitalization of retailing: an exploratory framework. International Journal of Retail & Distribution Management, Vol.

• Digitalization of retailing • Retail consumer interface

• No scale used • Theoretical paper summarizing the literature review on digitalization of retailing transforming the retail consumer interface

• Overall conceptual framework for digitalization in the retail-consumer interface consisting of: 1. Exchanges 2. Actors 3. Offerings 4. Settings

• Significant and on-going transformation of the retailer-consumer interface thanks to digitalization through: - exchanges (through changes in communication, transactions and distribution)

• Conduct in-depth analysis of each elements of the consumer interface


19

44 Issue: 7, pp.694-712.

- actors (through intermixing of humans and digital technologies, blurring of boundaries, new actors roles and relationships) - offerings (through changes of products and services, extensions of offerings, new forms of pricing) - settings (through including traditional and new ones and their intermixing)

15 Picot-Coupey K., Huré E., Piveteau L. (2016). Channel design to enrich customers’ shopping experiences: Synchronizing clicks with bricks in an omni-channel perspective – the Direct Optic case. International Journal of Retail and Distribution

• Transition from Multichannel to Omni-channel strategy • Customer shopping experience

• No scale used • Objectives: investigate the challenges e-tailers are confronted with when synchronizing clicks with bricks into an omni-channel perspective; and second, to shed light on the possible ways to overcome these challenges in order to successfully

• In-depth longitudinal case study conducted within the French on-line eyewear retailer Direct Optic (1,500+ hours of participant observation and 118 interviews) following an ethnographic approach

• Content analysis both manually and with NVivo software

• A transition is necessary to evolve from a multi-channel to an omni-channel strategy to face the challenges in shifting strategies • Challenges evolved gradually in terms of scope and priority, and can be categorized into two main categories:

• Additional work investigating omni-channel implementation and follow up studies to better understand how to synchronize brick with clicks in an omni-channel strategy.


20

Management, 44(3), 336-368.

implement an omni-channel strategy

- strategy-related challenges - development-related challenges (retailing)

16 Ailawadi, K. L. Farris, P. W. (2017). Managing Multi- and Omni-Channel Distribution: Metrics and Research Directions. Journal of Retailing, 93 (1), 120-135.

• Multichannel distribution • Omni-channel distribution

• No scale used • Find the specific metrics that will facilitate reliable analysis of the relationship between distribution and marketing objectives to be incorporated in practitioners and academics’ models

• Framework for measuring and managing distribution

• Comprehensive set of metrics for monitoring multi- and omni-channel distribution (more relevant for marketers than academics) • Consumers buy online for convenience in -search, ordering, and in delivery.

• From an omni-channel distribution perspective, information is needed on the different intermediaries that a consumer visits on the path to purchase to determine what functions are being performed along the path to purchase

17 Hagberg, J., Jonsson, A., Egels-Zandén, N. (2017). Retail digitalization: Implications for physical stores. Journal of Retailing and Consumer Services, 39, 264-269.

• Digitalization • Physical stores

• No scale used • Theoretical paper which aims at identify and analyze emerging trends and transformations that digitalization brings to the retail industry, with special focus on the physical store setting by gathering scientific papers

• Framework based on understandings of implications in the context of digitalization and physical stores to discuss how the six papers of this special issue contribute to the knowledge and understanding

• Three nuances in the context of digitalization and physical stores: - Implications as effect: effects of digitalization becoming increasingly visible for physical stores - Implication as integration: digitalization of in-

• Further research on the unfolding implications of digitalization for physical stores


21

store shopping as a result of opportunities raised by smartphones. Retailers should support smartphone-enabled shopping activities. - Implication as value: physical stores have several different roles

18 Huré E., Picot-Coupey K., Ackermann C. (2017) Understand omni-channel shopping value: a mixed method study. Journal of Retailing and Consumer Services, 39, 314-330.

• Omni-Channel Shopping Value

• Scale developed by Babin et al. (1994) to measure utilitarian and hedonic values from offline, online and mobile shopping • Scale developed by Aurier et al. (2004) to measure the global value derived from shopping from the brand • Scale developed by Coraux and Bergadaà (2004) to measure attitude towards shopping

• Objective: capturing Omni-Channel Shopping Value by considering the interaction effects between each touchpoint’s value and its relative influences (attempt to model and accurately measure Omni-Channel Shopping Value). H1: OC-SV is caused by touchpoints’ SV H2: touchpoints are formed by utilitarian, hedonic

• Mixed method design: - Exploratory Quantitative study (online; 59 from 102 respondents for data analysis residing in France). - Qualitative study based on semi-structured interviews

• Exploratory factor analysis • Component factor analysis with varimax rotation (Kaiser-Meyer Olkin (KMO) value and Bartlertt’s test of sphericity appropriate • partial least squares approach for structural equation modelling (PLS-SEM) to test the model with software PLS 3

• OC-SV is caused by the Shopping Value (composed of three dimensions: utilitarian, hedonic, social) derived from each touch point. • This causal effect is moderated by omni-channel shopping perceived consistency (product availability, price consistency, and service continuity) and seamlessness

• Further research should considerate more touch points in multi-channel settings: such as the contribution of social networks. As for physical stores, more formats should be considered such as: shop-in-shops and pop-up stores


22

• Scale developed by by Rintamäki et al. (2006) to measure social values from offline, online and mobile shopping

and social dimensions H3: Omni-channel intensivity positively moderate the relationships between each touch point SV and the global omni-channel SV.

19 Saghiri, S., Wilding, R., Mena, C., Bourlakis, M. (2017). Toward a three-dimensional framework for omni-channel. Journal of Business Research, 77, 53-67.

• Omni-channel retailing

• No scale used • To theorize the concept of the omni-channel through a conceptual framework by addressing two key questions: - What are the main building blocks of omni-channel systems? - What are the main enablers to operationalize the omni-channel system?

• Exploratory research: - developing a conceptual framework - validating it based on seven cases studies - operationalizing the framework by interviewing industry experts (from two retailers, one wholesaler, two manufacturers) (cf. interview guide)

• Open coding (validity tested by cross comparison and adjustment of coded interviews) and axial coding methods

• Three-dimensional framework for omni-channel systems: - Channel stage - Channel type - channel agent • Integration and visibility considered as main enablers, supporting the framework

• Exploring further the omni-channel retail phenomenon through focusing on omni-channel performance, channel choice and business models


23

20 Boardman, R., McCormick, H. (2018) Shopping channel preference and usage motivations: Exploring differences amongst a 50-year age span. Journal of Fashion Marketing and Management, 22 (2), 270-284.

• Multichannel retailing • Shopper motivations • Consumer behaviour

• No scale used • Having a better understanding of the relationship between channel preferences and motivations for usage when shopping for apparel to identify the preferred shopping channels for different age groups and the reasons behind

• Pilot study prior to the research with 6 participants to assess problems • Qualitative in-depth interviews. Sample: 50 female participants, aged 20-70, customers of a fashion retailer (providing e-commerce and m-commerce platforms, catalogues and stores)

• Multi-channel shopping behaviour increases with age • E-commerce is the most popular shopping channel due to convenience, selection, adventure/exploration and idea shopping • Physical stores preferred channel for the 60s + due to convenience and enjoyment

• Future research could be conducted to see how the type of brand (luxury vs high street) could affect consumers’ multi-channel shopping behavior, or the type of product offering (experience vs search products)

21 Galipoglu, E., Kotzab, H., Teller, C., Yumurtaci Hüseyinoglu, I. Ö., & Pöppelbuß, J. (2018). Omni-channel retailing research – state of the art and intellectual foundation. International Journal of Physical Distribution and Logistics Management, 48 (4), 365-390.


• Kruskal’s stress measure • Multidimensional scaling

• Extended literature review on omni-channel retailing

• Multi-method approach by conducting a content-analysis-based literature review of 70 academic papers

• Co-citation analysis • Cluster analysis and component analysis performed on the correlation matrix

Identify, evaluate and structure the literature of omni-channel retailing in the fields of logistics and supply chain management

Not relevant


24

22 Harris, P., Dall'Olmo Riley, F., Hand, C. (2018).Understanding multichannel shopper journey configuration: An application of goal theory. Journal of Retailing and Consumer Services, 44, 108-117.

• Shopping journey • Multichannel • Shopping motivations

• No scale used • Objective: Apply goal theory to an examination of the ways in which individual consumers’ shopper journeys are configured within and across product categories

• Exploratory qualitative study of 76 shopper journeys from depth interviews with 13 multichannel shoppers

• Close analysis of the text via immersion by repeated reading and listening • Transcript subject to thematic analysis • First stage: combination of descriptive, in vivo, process emotion and value coding • Second stage: open and axial coding

• Shopping behavior, as manifested in multichannel shopper journey configuration, appears to be shaped by multiple drivers (goals) operating at varying levels of abstraction. Higher level goals are relatively stable but lower level goals vary over time, place and context resulting in heterogeneity of journey configuration. Goal theory is proposed as a more suitable lens through which to examine multichannel shopping behavior, overcoming the deficiencies inherent in shopping motivation theory

• Conduct quantitative research to test relationships between shoppers’ goals and shopping behavior

23 Larke, R., Kilgour, M., O’Connor, H. (2018). Build


• No scale used • Objective: extend the understanding of how retailers

• Case study analysis based on in-depth interviews

• Content analysis • Difficult to achieve OCR in terms of unifying

• OCR strategy appears likely to vary across store


25

touchpoints and they will come: transitioning to omni-channel retailing. International Journal of Physical Distribution and Logistics Management, 48 (4), 465-483.

• Multichannel retailing

make the transition to Omni-channel retailing (OCR)

customer experience across multiple channels • Potential for cross-channel integration through multiple, but integrated touchpoints, and the leveraging of existing multichannel retail infrastructure and systems

format and product category. Thus, further research should focus on the importance of leveraging brands to maintain and grow customer trust and support for OCR

3. RESEARCH DESIGN AND HYPOTHESES

The following section presents the research model along with the developed research

hypotheses this thesis aspires to answer.

3.1. Research Model

As argued by Huré, et al. (2017) to orientate further research on OC-SV, analysing

channels considering multiple touchpoints extending the number of formats available would

be valuable and consistent with today’s reality of multitude of existing fashion retail formats.

Therefore, the research model used for the present thesis is an adaptation and extension of the

OC-SV research model recently proposed and tested by these authors. The complete

framework of the model is illustrated in Figure 1.

Figure 1. Adapted research model based on the Omni-Channel Shopping Value model developed by Huré et al. (2017).


27

OC-SV was modelled by the authors according to the nature of SV and the attributes of

the omni-channel shopping experience. The three most popular shopping channels have been

considered as generating value by their employment during the omni-channel shopping

experience: the offline channel, the online channel and the mobile channel. In different words,

the value produced during the shopping experience is the consequence of the positive

correlations with the three channels. From which, three distinctive dimensions of shopping

value have been acknowledged which encountered the most substantial support among

scholars: utilitarian, hedonic et social. Subsequently, these scholars also included a moderating

variable, namely the omni-channel intensity.

The adapted version of the OC-SV research model proposes a different configuration by

positioning OC-SV at the centre of the model, extending the impacts of the various channels

on this focal point. Furthermore, the proposed extension adds four variables to the model,

namely shop-in-shops and pop-up stores as the offline channel touchpoints’ as well as social

networks and retailer’s website as the online channel touchpoint’s. They are considered as

desirable features given the current profusion of touchpoints (Baxendale, Macdonald &

Wilson, 2015; Ailawadi & Farris, 2017; Larke, Kilgour, & O’connor, 2018) shaping and

directly influencing the fashion retail sector.

3.2. Development of Hypotheses

3.2.1. Omni-Channel Shopping Value

The current research is based on the assumption that the omni-channel shopping

experience is a complex structure of influencing variables. The degree of value generated by

the contrasting shopping motivational dimensions and the different touchpoints deriving from

the major retail channels is assumed to be explanatory for enhancing the customer experience

and optimizing the consumer’s overall fashion journey in acquiring new apparel products.

In the field of fashion retailing, research has traditionally considered touchpoints in

single-channel settings (Melero, Sese, & Verhoef, 2016). Yet the development of advanced

technologies which caused the digitalization of retailing led to an ever greater convergence of

retail channels (Verhoef et al., 2015; Hagberg et al., 2016; Galipoglu, Kotzab, Teller,

Yumurtaci Hüseyinoglu, & Pöppelbuß, 2018), namely omni-channel, emphasizing on the

tradeoffs that arise between the different touchpoints during the shopping journey. At the same

time, the fast technology adoption by consumers into their lifestyles further drove channel


28

evolution reinforcing this channel-agnostic approach to shopping (Boardman & McCormick,

2018).

Huré et al. (2017) shed a new perspective by examining channels as multiple, yet separated

enablers, converging to create shopping value. But as argued by previous research, the focus

shifted to omni-channel retailing where the high degree of integration of multiple channels

simultaneously is a key enabler to enhance the omni-channel shopping experience (Lazaris &

Vrechopoulos, 2014; Piotrowicz et al., 2014; Herhausen, Binder, Schoegel, & Hermann, 2015;

Saghiri, Wilding & Mena, 2017; Simone & Sabbadin, 2017; Larke et al., 2018), and in doing

so, its value. In this respect, channels integration also allows retailers to provide customers

what they want during each step of the shopping process (Gensler, Verhoef & Böhm, 2012).

Moreover, the mobile channel has been taken into account next to the more traditional Offline

and Online channels given the growing prevalence of smart mobile devices and applications,

becoming themselves increasingly advanced (Rigby, 2011; Brynjolfsson et al. 2013;

Piotrowicz et al., 2014; Beck & Rygl, 2015; Kaczorowska-Spychalska, 2017; Park & Lee,

2017; Galipoglu et al., 2018). Hence, the following hypothesis is suggested:

H1: Omni-channel SV stems from the integration of Online, Offline and Mobile channels SV.

The scope of the Offline channel is broadened by mobile devices translating the internet

into a component of physical stores (Bodhani, 2012; Lazaris et al., 2014; Hagberg, Jonsson &

Egels-Zandén, 2017) which led to the proliferation of new retail store formats such as pop-up

stores (Kim, Fiore, Niehm & Jeong, 2010; Hagberg, et al., 2016; Picot-Coupey, et al. 2016).

The list of touchpoints forming channels is not exhaustive (Baxendale et al., 2015; Lark et al.,

2018), and some guidance have been given to consider not only channels by themselves, but

analyse them as combinations of touchpoints (Huré et al., 2017) to measure and evaluate their

contribution to the overall omni-channel SV. Therefore, two contemporary store formats have

been considered of main interest to propose the second hypothesis:

H2: Offline SV stems from shop-in-shops and pop-up stores.

The Online fashion retail channel is taking a broadened scope considering the abilities

and influences of advanced technologies in fashion retail. Apart from the e-commerce retailer’s

website, the influence of social networks became game changer in this industry. Indeed, the

mobile revolution (Rigby, 2011; Brynjolfsson et al. 2013; Piotrowicz et al., 2014; Beck et al.,


29

2015; Kaczorowska-Spychalska, 2017; Park et al., 2017) together with the extended role of

social medias (Verhoef et al., 2015) allow consumers to create interactions between visited

stores and their whole social network in the grounds of rating, promoting or asking for advice

(Blázquez, 2014 ; Piotrowicz et al, 2014). The third hypothesis is therefore suggested as

followed:

H3: Online SV stems from social network and retailer’s website.

3.2.2. Channels’ value dimensions

Multiple scholars in the literature considered the significance of utilitarian and hedonic

dimensions to summarize the perceived shopping value (Babin, Darden, & Griffin, 1994;

Arnold & Reynolds, 2003; Michon, Yu, Smith & Chebat, 2007; Hsiao et al., 2012; Blázquez,

2014). Further research recognized a third dimensional factor to SV, namely social value

(Rintamäki et al., 2006; Kang & Park-Poaps, 2011; Huré et al., 2017) generated by the

enhancement of status and self-esteem during the shopping process.

The fourth hypothesis is therefore proposed as follow:

H4: Channels SV are composed by utilitarian, hedonic and social dimensions.

3.2.3. Role of omni-channel intensity

A continuum has been illustrated between multi-, cross-, and omni-channel based on

the intensity of channel integration (Lewis, Whysall & Foster, 2014; Picot-Coupey et al., 2016;

Huré et al., 2017). Therefore, omni-channel intensity is here considered as the level of channel

integration and can be evaluate under the scope of the retailing mix in relation to the degree of

seamlessness and perceived consistency (Huré et. al., 2017). As depicted through Huré et al.

(2017) previous research, the present study will assume that it affects the SV of each channels

to global omni-channel SV as a moderating variable and propose the following hypothesis:

H5: Omni-channel intensity will moderate H1- H3.


30

4. RESEARCH METHODOLOGY

The below section first describes the sample targeted for the study. Thereafter, it defines the

research instrument and the chosen data analysis approach to answer the research questions

of the present thesis.

4.1. Questionnaire design

The first section of the survey will briefly cover the notion of omni-channel fashion

shopping by formulating a short definition, after which respondents will be asked whether they

can designate a brand which provided them an omni-channel fashion shopping experience.

Participants disclosing that they never had such omni-channel fashion shopping journey will

be considered out of scope and withdrawn from the sample.

To appraise with relevance the constructs of this present thesis, literature has been reviewed as

follow: Table 1 depicts previously tested scales for the variables of the above-mentioned

research model used for this study.

Construct Derived constructs/variables Authors Omni-Channel

Shopping Value Aurier, Evrard &

N’Goala (2004) Offline Shopping

Value Shop-in-shops

Offline Shopping Value

Pop-up stores

Online Shopping Value

Social networks


Retailer’s website


Utilitarian Offline Shopping Value Babin et al., (1994)


Hedonic Offline Shopping Value Babin et al. (1994)


Social Offline Shopping Value Rintamäki et al.

(2006) Online Shopping

Value Utilitarian Online Shopping Value Babin et al. (1994)

Online Shopping Value Hedonic Online Shopping Value Babin et al. (1994)


31


Social Online Shopping Value Rintamäki et al.

(2006) Mobile Shopping

Value Utilitarian Online Shopping Value Babin et al. (1994)

Mobile Shopping Value

Hedonic Mobile Shopping Value Babin et al. (1994)

Mobile Shopping Value

Social Mobile Shopping Value Rintamäki et al.

(2006) Omni-channel

intensity Seamlessness Huré et al. (2017)

Omni-channel intensity Perceived Consistency Huré et al. (2017)

Table 1. Literature review of existing scales (adapted from Huré et al. (2017)

Sphinx Declic will be the software used to design and distribute online the survey. A pre-test

of the questionnaire will be run beforehand to prevent from any inquiries that might occur

during the definitive administration of the survey, testing its workability and comprehension.

4.2. Sample

Experiencing omni-channel fashion shopping demonstrates to be a frequent and

widespread component of fashion shopping due to the integration of advanced technologies to

our day-to-day life. Thereby, the study will address apparel consumers regardless of their

gender residing in France and Switzerland, giving the familiarity of the researcher with those

two countries. The two-country sample will be composed by voluntary female and male online

respondents with an age range from 16 to 35 years old considering their fashion consciousness

and how anchored omni-channel usage is to young individuals’ daily lives.

The non-probability snowball sampling procedure will be followed, considering the

accessibility of the respondents to the researcher. The minimum recommended number of

respondents is estimated at 377, as calculated by the online sample size calculator Check

Market (https://www.checkmarket.com/sample-size-calculator/). The following criteria were

applied: a 5% margin error, a 95% confidence level, 50% of estimated response rate and a

population size of 20 000 individuals since the sample size does not vary much for larger

populations.


32

4.3. Data Collection

The administration of the online survey will allow to collect data with the aim of

providing inputs to statistical means to test the appropriateness of the above-mentioned

hypotheses. Data is mainly expected to be collected by means of social media and emails. A

modest incentive will be involved to attract respondents and stimulate their responses.

4.4. Data Analysis

The data will be analyzed by using the software application SmartPLS to conduct SEM.

Firstly, it is assumed that a descriptive analysis of the sample will be conducted, followed by

means, standard deviations and correlation of the constructs. Thereafter, the assumed analysis

will take upon convergent validity. Finally, the hypotheses will be tested using path coefficient

and T values (Hair, Hult, Ringle & Sarsted, 2014). Under the scope of this study fall the

following variables: OC-SV, Offline SV, Online SV, Mobile SV, Shop-in-Shops, Pop-up

Stores, Social Networks, Retailer’s website. Shopping motivational perceived values such as

Offline Utilitarian, Hedonic and Social Values, Online utilitarian, Hedonic and Social Values,

and lastly Mobile Utilitarian, Hedonic and Social values, will also be considered. Thereafter,

omni-channel intensity will be added as moderating variable.


33

CHAPTERS OVERVIEW* 1. INTRODUCTION 1.1. Background

1.2. Problem statement and Research Question 1.3. Purpose 1.4. Contribution

2. LITERATURE REVIEW 2.1. Fashion Retailing 2.2. Omni-Channel Shopping Experience 2.2.1. Shopping Behaviors Intricacy 2.2.2. Brand-focused 2.2.3. Simultaneous Integration of Multiple Channels 2.3. Omni-channel Intensity 2.3.1. Seamlessness 2.3.2. Perceived Consistency 2.4. Shopping Value 2.4.1. Utilitarian Dimension 2.4.2. Hedonic Dimension 2.4.3. Social Dimension 3. RESEARCH DESIGN AND HYPOTHESIS 3.1. Research model 3.2. Development of Hypotheses 3.2.1. Omni-Channel Shopping Value 3.2.2. Channels’ value Dimensions 3.2.3. Role of Omni-channel Intensity 4. RESEARCH METHODOLOGY 4.1. Questionnaire design 4.2. Sample 4.3. Data Collection 4.4. Data Analysis 5. ANALYSIS OF EMPIRICAL FINDINGS 5.1. Descriptive Analysis of the Sample 5.2. Measurement Model Assessment

5.3. Structural Equation Model Assessment 5.4. Discussion and Interpretation of Results

6. CONCLUSION 6.1. Findings

6.2. Practical and Theoretical Implications 6.3. Limitations and Guidance for Future Research

* The following structure is a first draft and might be subject to modifications.


34

WORK PLAN

# Time Task Phase’s description Status

1 01.09.18 – 30.10.18 Exposé

• Handing in three topic proposals • Selecting a thesis topic • Developing the topic with a narrow focus and reading the corresponding literature • Literature Review in the form of a table • Elaborating the exposé

Completed

31.10.18 Deadline Master Thesis Exposé hand-over

2 01.11.18 – 11.11.18

Literature Review & Research Design

• Elaborating the written literature review • Developing the online questionnaire design with the software Sphinx • Pre-test

To follow

3 12.11.18 – 01.11.18 Field Research

• Distributing the online survey • Finalizing the chapter “methodology” • Getting familiar with SmartPLS for analysis

To follow

02.11.18 – 05.12.18 Buffer

4 06.12.18 – 22.12.18 Data Analysis

• Analysis, elaboration and discussion of collected data To follow

5 23.12.19 – 04.01.19 Drawing conclusions

• Formulating implications, conclusions, limitations and guidance to conduct further research

To follow

6 05.01.19 – 16.01.19 Finalization of the thesis

• Revision, proofreading, and printing • Preparing master thesis defence presentation

To follow

17.01.19 – 21.01.19 Buffer 22.01.19 Deadline Master Thesis hand-over

22.01.19 – 23.01.19 Master Thesis Defence


35

REFERENCES

Ailawadi, K. L., & Farris, P. W. (2017). Managing Multi- and Omni-Channel Distribution:

Metrics and Research Directions. Journal of Retailing, 93(1), 120–135.

https://doi.org/10.1016/j.jretai.2016.12.003

Arnold, M. J., & Reynolds, K. E. (2003). Hedonic shopping motivations. Journal of

Retailing, 79(2), 77–95. https://doi.org/10.1016/S0022-4359(03)00007-1

Aurier, P., Evrard, Y., & N’Goala, G. (2004). Comprendre et mesurer la valeur du point de

vue du consommateur. Recherche et Applications En Marketing, 19(3), 1–20.

https://doi.org/10.1177/076737010401900301

Baxendale, S., Macdonald, E. K., & Wilson, H. N. (2015). The Impact of Different

Touchpoints on Brand Consideration. Journal of Retailing, 91(2), 235–253.


Beck, N., & Rygl, D. (2015). Categorization of multiple channel retailing in Multi-, Cross-,

and Omni-Channel Retailing for retailers and retailing. Journal of Retailing and

Consumer Services, 27, 170–178. https://doi.org/10.1016/j.jretconser.2015.08.001

Blázquez, M. (2014). Fashion Shopping in Multichannel Retail: The Role of Technology in

Enhancing the Customer Experience. International Journal of Electronic Commerce,

18(4), 97–116. https://doi.org/10.2753/JEC1086-4415180404

Boardman, R., & McCormick, H. (2018). Shopping channel preference and usage

motivations: Exploring differences amongst a 50-year age span. Journal of Fashion


36

Marketing and Management, 22(2), 270–284. https://doi.org/10.1108/JFMM-04-2017-

0036

Bodhani, A. (2012), “Shops offer the e-tail experience”, Engineering and Technology, Vol. 7

No. 5, pp. 46-50

Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the Age of Omnichannel

Retailing. MIT Sloan Management Review, 54(4), 23–29.

https://doi.org/http://dx.doi.org/10.1108/17506200710779521

Galipoglu, E., Kotzab, H., Teller, C., Yumurtaci Hüseyinoglu, I. Ö., & Pöppelbuß, J. (2018).

Omni-channel retailing research – state of the art and intellectual foundation.

International Journal of Physical Distribution and Logistics Management (Vol. 48).

https://doi.org/10.1108/IJPDLM-10-2016-0292

Gensler, S., Verhoef, P. C., & Böhm, M. (2012). Understanding consumers’ multichannel

choices across the different stages of the buying process. Marketing Letters, 23(4), 987–

1003. https://doi.org/10.1007/s11002-012-9199-9

Hagberg, J., Jonsson, A., & Egels-Zandén, N. (2017). Retail digitalization: Implications for

physical stores. Journal of Retailing and Consumer Services, 39(August), 264–269.

https://doi.org/10.1016/j.jretconser.2017.08.005

Hagberg, J., Sundstrom, M., & Egels-Zandén, N. (2016). The digitalization of retailing: an

exploratory framework. International Journal of Retail and Distribution Management,

44(7), 694–712. https://doi.org/10.1108/IJRDM-09-2015-0140

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A Primer on Partial Least

Squares Structural Equation Modeling. 2nd Ed. Thousand Oaks: Sage.


37

Harris, P., Dall’Olmo Riley, F., & Hand, C. (2018). Understanding multichannel shopper

journey configuration: An application of goal theory. Journal of Retailing and

Consumer Services, 44(May), 108–117. https://doi.org/10.1016/j.jretconser.2018.06.005

Herhausen, D., Binder, J., Schoegel, M., & Hermann, A. (2015). Integrating Bricks with

Clicks: Retailer-Level and Channel-Level outcomes of Online-Offline Channel

Integration. Journal of Retailing, 91(2), 309–325.


Hsiao, C. C., Yen, H. J. R., & Li, E. Y. (2012). Exploring consumer value of multi-channel

shopping: A perspective of means-end theory. Internet Research, 22(3), 318–339.

https://doi.org/10.1108/10662241211235671

Huré, E., Picot-Coupey, K., & Ackermann, C. L. (2017). Understanding omni-channel

shopping value: A mixed-method study. Journal of Retailing and Consumer Services,

39(August), 314–330. https://doi.org/10.1016/j.jretconser.2017.08.011

Kaczorowska-Spychalska, D. (2017). Consumer perspective of omnichannel commerce.

Management, 21(2), 95–108. https://doi.org/10.1515/manment-2017-0007

Kang, J., & Park-Poaps, H. (2011). Motivational antecedents of social shopping for fashion

and its contribution to shopping satisfaction. Clothing and Textiles Research Journal,

29(4), 331–347. https://doi.org/10.1177/0887302X11422443

Kim, H., Fiore, A. M., Niehm, L. S., & Jeong, M. (2010). Psychographic characteristics

affecting behavioral intentions towards pop-up retail International Journal of Retail &

Distribution Management Article information :, (February).

https://doi.org/10.1108/09590551011020138


38

Kumar, V., & Reinartz, W. (2016). Creating Enduring Customer Value. Journal of

Marketing, 80(2), 36–68. https://doi.org/10.1509/jm.15.0414

Larke, R., Kilgour, M., & O’Connor, H. (2018). Build touchpoints and they will come:

transitioning to omnichannel retailing. International Journal of Physical Distribution

and Logistics Management, 48(4), 465–483. https://doi.org/10.1108/IJPDLM-09-2016-

0276

Lazaris, C., & Vrechopoulos, A. (2013). From Multichannel to “Omnichannel” Retailing:

Review of the Literature and Calls for Research. 2nd International Conference on

Contemporary Marketing Issues,(ICCMI)., (JUNE 2014), 6.

https://doi.org/10.13140/2.1.1802.4967

Lazaris, C., Vrechopoulos, A., Katerina, F., & Doukidis, G. (2014). Exploring the

“Omnichannel” Shopper Behaviour. AMA SERVSIG, International Service Research

Conference, 13-15 June, (2007), 1–5. https://doi.org/10.13140/2.1.1278.2089

Leroi-Werelds, S., Streukens, S., Brady, M. K., & Swinnen, G. (2014). Assessing the value of

commonly used methods for measuring customer value: A multi-setting empirical study.

Journal of the Academy of Marketing Science, 42(4), 430–451.

https://doi.org/10.1007/s11747-013-0363-4

Lewis, J., Whysall, P., & Foster, C. (2014). Drivers and Technology-Related Obstacles in

Moving to Multichannel Retailing. International Journal of Electronic Commerce,

18(4), 43–68.


39

Melero, I., Sese, F. J., & Verhoef, P. (2016). Recasting the Customer Experience in Today’s

Omni-channel Environment. Universia Business Review, (Second Quarter), 1–20.

https://doi.org/10.3232/UBR.2016.V13.N2.01

Michon, R., Yu, H., Smith, D., & Chebat, J. C. (2007). The shopping experience of female

fashion leaders. International Journal of Retail and Distribution Management, 35(6),

488–501. https://doi.org/10.1108/09590550710750359

Park, S., & Lee, D. (2017). An empirical study on consumer online shopping channel choice

behavior in omni-channel environment. Telematics and Informatics, 34(8), 1398–1407.

https://doi.org/10.1016/j.tele.2017.06.003

Picot-Coupey, K., Huré, E., & Piveteau, L. (2016). Channel design to enrich customers’

shopping experiences: Synchronizing clicks with bricks in an omni-channel perspective

– the Direct Optic case. International Journal of Retail and Distribution Management

(Vol. 44). https://doi.org/10.1108/IJRDM-04-2015-0056

Piotrowicz, W., & Cuthbertson, R. (2014). Introduction to the Special Issue Information

Technology in Retail: Toward Omnichannel Retailing. International Journal of

Electronic Commerce, 18(4), 5–16. https://doi.org/10.2753/JEC1086-4415180400

Puccinelli, N. M., Goodstein, R. C., Grewal, D., Price, R., Raghubir, P., & Stewart, D.

(2009). Customer Experience Management in Retailing: Understanding the Buying

Process. Journal of Retailing, 85(1), 15–30. https://doi.org/10.1016/j.jretai.2008.11.003

Rigby, D. K. (2011). The Future of Shopping. Harvard Business Review, 89(12), 65–76.

Rintamäki, T., Kanto, A., Kuusela, H., & Spence, M. T. (2006). Decomposing the value of

department store shopping into utilitarian, hedonic and social dimensions: Evidence


40

from Finland. International Journal of Retail & Distribution Management, 34(1), 6–24.

https://doi.org/10.1108/09590550610642792

Saghiri, S., Wilding, R., Mena, C., & Bourlakis, M. (2017). Toward a three-dimensional

framework for omni-channel. Journal of Business Research, 77(March), 53–67.

https://doi.org/10.1016/j.jbusres.2017.03.025

Simone, A., & Sabbadin, E. (2017). The New Paradigm of the Omnichannel Retailing: Key

Drivers, New Challenges and Potential Outcomes Resulting from the Adoption of an

Omnichannel Approach. International Journal of Business and Management, 13(1), 85.

https://doi.org/10.5539/ijbm.v13n1p85

Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From Multi-Channel Retailing to Omni-

Channel Retailing. Introduction to the Special Issue on Multi-Channel Retailing.

Journal of Retailing, 91(2), 174–181. https://doi.org/10.1016/j.jretai.2015.02.005

towards a measure of omni-channel shopping value in the ... › fb07 › fileadmin › datas ›...

Documents