ANTECEDENTS AND CONSEQUENCES OF MOBILE AUGMENTED MEDIA ADOPTION

THE FUTURE OF BRICKS-AND-MORTAR:

ANTECEDENTS AND CONSEQUENCES OF MOBILE AUGMENTED MEDIA ADOPTION

Mark O. Slijkhuis

University of Groningen

August 2011

The author thanks Professor Dr. Laurens M. Sloot, University of Groningen, for his helpful comments, connections, and advice. Comments by PhD Candidate Peter S. van Eck, University of Groningen, were helpful in revising this paper. Financial support by EFMI Business School was critical for the realization of this research project. The author acknowledges the anonymous marketing professionals helpful in constructing the second part of the conceptual framework. The beautiful cover art that enlivens this paper was created by Joke Holtrust.

Send correspondence to Mark O. Slijkhuis, University of Groningen, Faculty of Economics and Business, Department of Marketing, P.O. Box 800, 9700 AV Groningen, The Netherlands, Tel.

+31 6 39 75 16 45 (e-mail: m.o.slijkhuis@student.rug.nl).

University of Groningen / Faculty of Economics and Business

THE FUTURE OF BRICKS-AND-MORTAR:

ANTECEDENTS AND CONSEQUENCES OF MOBILE AUGMENTED MEDIA ADOPTION ¹

Scientific theoretical and empirical research

by

MARK O. SLIJKHUIS

MSc Business Administration Specialization Marketing

Summertime 2011

Master’s Thesis

+31 6 39 75 16 45 m.o.slijkhuis@student.rug.nl

student number: 1539299

1 Preferred reference: Slijkhuis, M.O. (2011), “The Future of Bricks-and-Mortar: Antecedents and Consequences of Mobile Augmented Media Adoption”, unpublished master’s thesis for master’s degree, University of Groningen, the Netherlands.

ABSTRACT

As consumer adoption of smartphones accelerates around the globe, interactive mobile applications enable marketing practitioners to deliver a new generation of personalized in-store mobile services to their valued customers. However, academic research into the prospects of personalized mobile media for retail marketing is scarce. This study therefore conceptualizes a new typology of mobile media for retailing and introduces the concept of mobile augmented media (MAM). Based on prior studies in related areas of research, a holistic conceptual framework is developed aiming to explain both the predictors of consumer adoption and the managerial consequences of MAM for retailers.

Antecedents of adoption are subsequently empirically investigated using a cross-sectional survey of 407 primary grocery shoppers in the Netherlands. The results show that the consumer readiness variables of motivation and ability are key mediators between the predictors of adoption (individual and technology characteristics) and the likelihood of MAM trial. These results allow for the mitigation of the negative effects of individual characteristics by the development of managerial tactics aimed at improving the actionable technology characteristics and consumer readiness variables. Through a comprehensive literature study and several in-depth interviews with professionals in the European supermarket industry, further light is shed on the organization-wide consequences of MAM for grocery retailers. Although the scale of the investments requires a large user base, promoting MAM adoption can prove to be very beneficial for retailers. MAM has the potential to enhance customer shopping value, improve operational efficiency, and generate cross-channel synergy. Practitioners can apply the findings of this study for the development of mobile media management strategies and the promotion of consumer MAM trial and adoption. Market research and customer feedback are essential tools for an individual retailer to offer an optimized mix of MAM functionality. As this study only represents an initial attempt to investigate this new phenomenon in personalized in-store marketing, there are numerous avenues for future research.

Keywords: mobile applications, mobile augmented media, personalized communications, in-store

marketing, RFID and NFC technology, consumer technology adoption, operations management.

TABLE OF CONTENTS

MOBILE AUGMENTED MEDIA IN RETAILING ... 9

Personalized Marketing and CRM ... 9

Electronic Commerce ... 10

Mobile Commerce... 11

RFID and NFC ... 12

Augmented and Virtual Reality ... 13

Conclusion: Mobile Media Typology ... 15

CONCEPTUAL FRAMEWORK (STUDY 1) ... 18

Consumer Readiness – MAM Trial ... 18

Consumer Characteristics ... 20

Technology Characteristics... 23

Consumer and Technology Characteristics – MAM Adoption... 26

DATA AND METHODOLOGY ... 29

Research Design ... 29

Survey Development ... 30

Data Collection Procedure ... 31

Sample Characteristics ... 32

Measure purification ... 34

Analysis approach ... 35

RESULTS AND DISCUSSION OF FINDINGS ... 38

Mediating Variables (H2-H4) ... 38

Consumer Characteristics (H5-H11) ... 39

Technology Characteristics (H12-H18) ... 40

MAM Trial (H1) ... 40

Discussion of Validated Framework ... 46

CONCEPTUAL FRAMEWORK (STUDY 2) ... 49

Customer Value ... 49

Operations Management ... 51

Learning and Growth ... 53

Financial Results ... 55

Consumer and Situation Moderators ... 56

MANAGERIAL IMPLICATIONS (STUDY 1) ... 58

LIMITATIONS AND FUTURE DIRECTIONS ... 60

Improving the Research Design ... 60

Expanding the Conceptual Model ... 61

CONCLUDING REMARKS ... 62

Research Conclusions ... 62

Future Vision ... 63

REFERENCES ... 64

APPENDIX A ... 78

APPENDIX B ... 81

APPENDIX C ... 88

INTRODUCTION

According to the Marketing Science Institute (MSI), the leveraging of new media and channels to deliver value to customers is one of the main priorities for research by marketing scholars in 2010- 2012 (MSI, 2010). These new media and channels enable marketers to more effectively create, communicate, deliver, and exchange value propositions. Moreover, convergence of digital media and channels gives rise to additional opportunities and dilemmas that lead marketers to pursue new frontiers. In response, this research paper focuses on an important technological development in retail: the use of mobile augmented media by traditional bricks-and-mortar retailers to enhance customer experience, improve customer shopping value, and create cross-channel synergy.

Since the commercialization of the personal computer in the nineteen eighties and the Internet in the nineteen nineties, a trend towards convergence of communication networks and digital media content has been set. Moreover, in the past two decades the widespread adoption of the mobile phone and more recently the GPS-enabled smartphone, has led to an explosion in the use of broadband mobile communications. Location-aware marketing (LAM) became a reality and allowed for (targeted) location-specific advertising to be delivered to a mobile device. When combined, these trends and technologies guided the development of augmented reality (AR) and mobile augmented reality (MAR). These new technologies blur the line between the real and virtual environment by enhancing our five senses when we experience the physical world. AR applications, whether shaped as a see-through interface or a self-updating database, enrich or augment our environment by presenting virtual, environment-specific, and real-time information. The content made accessible by AR applications turns media into what this paper characterizes as ‘augmented media’. More specifically, the focus of this research is on the mobile variant for which the term

‘mobile augmented media’ (MAM) will be introduced. The development of content for MAM is still in its infancy and will gain momentum when the consumer and retail adoption of radio-frequency identification (RFID) and near field communication (NFC) becomes more widespread. Mobile NFC is technology with similar properties as RFID, however, NFC allows for two-way communication between a mobile device and its surroundings. Virtual information can subsequently be integrated with the bricks-and-mortar (retail) environment. An extended conceptualization of MAM and its enabling technologies will be given in the next section titled ‘Mobile Augmented Media in Retailing’.

The implications of these developments for retail atmospheres and retail marketing in

general are at present unclear. However, Curtin, Kauffman, and Riggins (2007) explain that B2C

marketing applications of these technologies seem to have great potential; they call for more

research into this area. This research therefore aims to (1) conceptualize MAM and investigate the prospects of mobile media for retailing, (2) theoretically and empirically explore the predictors of MAM adoption by consumers, and (3) theoretically and empirically investigate its organization-wide consequences for retailers. Retailers can apply the findings of this study for the development of comprehensive customer experience management strategies and when promoting MAM adoption by their customers. MAM adoption can prove to be advantageous for both the retailer’s operations and its customers’ shopping experiences. For example, MAM applications can allow a retailer to integrate virtual and traditional retail environments and thereby improve its cross-channel synergy. The customer benefits by having an integrated shopping experience with enhanced utilitarian and hedonic shopping value. With regard to the adoption process and organization-wide consequences, an empirical focus is given to grocery retailers. Due to their broad and deep assortments and a customer shopping experience which is highly utilitarian in nature, convenience and information are of high importance to their shoppers. Relevance of product information is of particular importance to consumers when they weight decision criteria leading up to product choice. For example, Overby and Lee (2006) found that a utilitarian shopping motive is strongly related to a customer’s preference towards the online retailer. Moreover, the availability, browsability, and searchability of information on the Internet, provides a rationale for the research shopping phenomenon (Verhoef, Neslin, and Vroomen, 2007). Making information and functionality typically provided by an online retailer available in a brick-and-mortar store using MAM, can therefore be most attractive to enhance utilitarian shopping experiences, such as grocery shopping. By relating marketing’s academic constructs and insights to the opportunities technological progress offers, this research aspires to assist retailers in developing a comprehensive, multi-channel, customer experience management strategy; contribute to scientific knowledge as state of the art marketing research; and motivate other researchers and practitioners to further expand the frontiers of marketing.

The remainder of this paper is structured as follows. First, personalization of electronic media,

MAM, and technologies that enable MAM will be explored. This literature review will lay the

foundation for the structuring of four mobile media varieties into a concise typology for retail

marketing. Hypotheses regarding the antecedents of consumer adoption of MAM are subsequently

structured into a conceptual model to guide empirical research. A description of the data and

methodology provides the basis for the analysis and discussion of findings on the predictors of MAM

adoption by consumers. Several in-depth interviews with retail professionals in the European

supermarket industry will shed further light on the anticipated managerial consequences of MAM

adoption. Finally, there is room to discuss and summarize results, managerial implications, future

research opportunities, limitations, and a conclusion regarding the overall role of MAM in retailing.

MOBILE AUGMENTED MEDIA IN RETAILING

As a didactic introduction to the conceptual framework, this section presents an overview of marketing practices, technological developments, and academic studies which build the foundation for the technologies and business strategies supporting MAM in retailing. First, recent literature on the personalization of marketing and CRM will be discussed. Thereafter, two transaction and communication channels enabled by the Internet, e-commerce (clicks-and-mortar) and m-commerce, and two groups of technological tools, mobile identification (RFID and NFC) and mixed-reality media (augmented and virtual reality), will briefly pass in review. These channels and tools lead up to two dimensions and four advanced mobile media types which retailers can use for communication and transaction purposes.

Personalized Marketing and CRM

The information technology (IT) revolution introduced extraordinary improvements in the ability of firms to collect and analyze huge amounts of customer data. Initially, the use of database marketing enabled a company to better identify and analyze smaller segments of its customer population to create a more valuable proposition for one or more of these sub-segments and thereby increase the impact of marketing campaigns (Kumar and Reinartz, 2006; Reutterer, Mild, Natter, and Taudes 2006). However, the recent availability of more sophisticated customer database technology, data mining techniques, and technology-facilitated transaction systems, provides retailers with detailed information (e.g. POS data) on a customer-level and the ability to customize marketing activities to the needs of every single customer (Kumar and Reinartz, 2006; Verhoef, Venkatesan, McAllister, Malthouse, Krafft, and Ganesan, 2010). This technological progress guided a new paradigm in marketing where a company can live up to the promises of customer centricity by implementing customer relationship management (CRM) systems and supporting routines, structures, and organizational processes (Becker, Greve, and Albers, 2009; Jayachandran, Sharma, Kaufman, and Raman, 2005; Shah, Rust, Parasuraman, Staelin, and Day, 2006). In this new paradigm, the role of customer intelligence has become essential for marketing’s ability to target the right customer, with the right offer, at the right time (Hoekstra, Leeflang, and Wittink, 1999; Hoekstra and Verhoef, 2010).

Cross-functional integration of processes, people, operations, and marketing capabilities, which is enabled through information, technology, and applications, is what allows CRM to flourish (Payne and Frow, 2005).

With regard to the extent of CRM adoption, Kumar and Reinartz (2006) explain that there are

different levels (or generations) of CRM implementation: (1) functional (supporting specific

functions), (2) customer-facing (single view of the customer across channels), and (3) strategic (strategic implications and company-wide adoption). As will be elaborated upon at the construction of the consequence-side of the conceptual framework, customer-facing front-end CRM adoption is essential to the success of MAM. Customer experience management at the service interface and multi-channel integration are therefore important as well (Payne and Frow, 2005; Verhoef, Lemon, Parasuraman, Roggeveen, Tsiros, and Schlesinger, 2009). Moreover, customer-level value metrics (e.g. customer lifetime value (CLV) and recency, frequency and monetary value (RFM) scores) and company-level customer value metrics (e.g. customer equity) are key instruments to evaluate the effectiveness of MAM campaigns (Gupta, Lehmann, and Stuart, 2005; Gupta, 2009; Kumar, Della Pozza, Petersen, and Shah, 2009; Kumar and Reinartz, 2006).

The creation of personalized marketing, or ‘segments of one’ (one-to-one marketing), and customized offers for every customer is what CRM intends to accomplish (Ansari and Mela, 2003;

Montgomery and Smith, 2009). Optimizing CLV and customizing offerings and promotions to individual customer needs is in sharp contrast to the conventional mass marketing (one-to-many marketing) approach. State of the art research on customer engagement hints at further added or subtracted value from the non-transactional part of the interaction (behavioural manifestation with a brand or firm focus) with or between consumers (many-to-many marketing) (Kumar, Akzoy, Donkers, Wiesel, Venkatesan, and Tillmanns, 2010; Van Doorn, Lemon, Mittal, Naß, Pick, Pirner, and Verhoef, 2010; Verhoef, Reinartz, and Krafft, 2010). As will be discussed further on in this paper, MAM can offer a valuable tool and opportunity for retailers to manage customer engagement. This research will show how new technological developments (e.g. RFID, NFC, and MAM) and more advanced marketing practices will drive superior personalization and more sophisticated CRM in bricks-and- mortar retailing.

Electronic Commerce

New technologies are also radically changing the way businesses provide meaningful and valuable propositions to their online customers. Electronic commerce (e-commerce), such as Internet-enabled commerce and mobile commerce (m-commerce), arises from the integration of advanced computing power, telecommunications, and Internet-technology and has expanded conventional retail practices in the past two decades. Specialist in online consumer research and retailing Forrester Research, Inc.

expects that online retailing in both the United States and Western Europe will continue on a double- digit growth trajectory in the first half of this decade, reaching an annual turnover of $279 billion and

€134 billion, respectively, in 2015 (Forrester Research, Inc., 2011). Forrester Research, Inc. further

expects that by 2015 more than 50 percent of total U.S. retail sales are influenced by the Internet

(Forrester Research, Inc., 2011). As this research shows, e-commerce (or e-tailing) is a powerful player in the retail industry and together with the World Wide Web in general will continue to grow and have an effect on the sales of traditional bricks-and-mortar retailers. However, in the face of these competitive e-tailers, traditional bricks-and-mortar retailers add online channels and turn themselves into ‘clicks-and-bricks’ retailers to maintain their customer appeal. Bernstein, Song, Jing- Sheng, and Zheng (2008) study this development and show that in an oligopoly setting, clicks-and- mortar arises as the equilibrium channel structure. This equilibrium does not necessarily imply higher profits for the firms involved, but it emerges as a strategic necessity (prisoner’s dilemma) to the benefit of their customers. With regard to potential of cannibalization by this additional channel, research by Geyskens, Gielens, and Dekimpe (2002) indicates that for B2C transactions cannibalization on existing channels is an overstated threat, and in order to avoid cannibalization a strategy contingent approach should be taken. Kumar and Venkatesan (2005) extend these findings by providing evidence for synergy across communication channels and added customer value of multi-channel shoppers. An extended review of multi-channel management, its contingencies for success, and implications will be given when the consequence-side of the conceptual framework is constructed. The potential of mobile commerce, another novel Internet-enabled channel that retailers need to consider when devising a multi-channel strategy, is explored in the next paragraph.

Mobile Commerce

comScore, Inc., a global leader in measuring the digital world, has announced in its ‘2010 Mobile Year

in Review’ that smartphone adoption has accelerated in both the U.S. and Europe. U.S. smartphone

adoption reached 27 percent of mobile subscribers in December 2010, an increase of 10 percentage

points versus the previous year. European adoption reached 31 percent, also up nearly 10

percentage points versus December 2009 (comScore, Inc., 2011). Moreover, according to this study,

6 percent of mobile Americans and 29 percent of mobile Europeans browsed the mobile web in

December 2010. Across regions, mobile browsing and application usage is growing in the range of 7-9

percentage points per year. However, Japan is far surpassing the U.S. and European countries in this

regard. More than 75 percent of mobile subscribers in Japan are connected media users (used their

browser, accessed applications, or downloaded content). Japan also saw nearly 10 percent of its

mobile audience make a purchase with their mobile wallet in December 2010. This rapid proliferation

of mobile Internet devices is radically changing the way businesses provide meaningful and valuable

propositions to their customers, and it is creating an unprecedented opportunity for e-commerce to

leverage the benefits of mobility (Kleijnen, De Ruyter, and Wetzels, 2004; Nysveen, Pedersen, and

Thorbjørnsen, 2005). In conjunction, the transition from ‘2G’ (voice-only) telecommunication

systems to ‘3G’ (high data speed) and ‘4G’ (ultra-broadband data speed) systems is accelerating around the globe, and supporting smartphone technology is advancing quickly (4G Americas, 2010).

The above trends can be summarized as the proliferation of mobile devices, convergence of mobile telecommunications networks and the Internet, the transition to third-generation telecommunications technology, and the introduction and adoption of corresponding applications and services. These four trends are the forces which Norman Sadeh already identified, back in 2002, in his book titled ‘M-commerce: Technologies, services and business models’, as the underpinnings of the emergence of m-commerce (Sadeh, 2002). Mobile e-commerce, commonly referred to as m- commerce, can be defined as the mobile character of wireless devices that support the ability to purchase goods and conduct electronic service transactions (Kleijnen et al., 2004; Sadeh, 2002).

Because it can deliver a unique value proposition where both the dimensions of time and place of purchase are flexible, m-commerce can be regarded as a distinct channel and not as an extension of e-commerce (Balasubramanian, Peterson, and Jarvenpaa, 2002). When combined, these changes and trends are heralding the emergence of so-called ‘u-commerce’. U-commerce can be defined as ubiquitous, universal, unique, and unison commerce, where traditional barriers have been broken down, and networks are used to support personalized and uninterrupted communications and transactions providing unparalleled customer value (Nysveen et al., 2005; Watson, Pitt, Berthon, and Zinkhan, 2002; Wu and Hisa, 2008). The ‘ESNA project’ by EUREKA, a pan-European organization to foster and coordinate innovation, is an effort to develop wireless network structures and applications which support u-commerce through an ‘Internet of Things’ (EUREKA, 2011). As more objects and appliances are embedded with sensors and gain the ability to communicate, connectivity expands from interpersonal to inter-things. An intelligent refrigerator, for example, will have RFID technology to scan its own contents, detect food going bad, order groceries automatically, and suggest recipes using the products contained within.

RFID and NFC

Radio-frequency identification (RFID) refers to any method of wireless identification and tracking

technology that uses tags or materials which reflect back a unique portion of the radio waves

beamed at them to enable the identification of the object to which the tag is attached (Chaffey and

Wood, 2005; RFID Journal, 2011). In recent years, automatic identification procedures have become

popular in a wide variety of industries to provide information about people, animals, goods, and

products in transit (Finkenzeller, 2010). RFID uses a ‘passive tag’, which draws power from the reader

and is cheaper and smaller than an ‘active tag’, which uses a battery to broadcast the identification

signal to the reader (Curtin et al., 2007). In most cases, these tags currently have an effective read

range of less than 30 feet (Finkenzeller, 2010). RFID can be regarded as an extension of existing barcode and QR-code technology, and its adoption is gaining momentum with applications in retail (e.g. Wal-Mart and Target) and other industries (e.g. United States Department of Defense, livestock tracking and automotive anti-theft systems) (Bacheldor and Sullivan, 2004; Ngai, Moon, Riggins, and Yi, 2008). Research agency and expert in RFID technology usage IDTechEx, Inc. has estimated that 1.98 billion RFID tags were sold in 2009 (IDTechEx, Inc., 2010). In 2010, retailers (mostly in the U.S.) are expected to have used about 300 million RFID tags on pallets and cases in response to mandates by major retailers (IDTechEx, 2010). However, researchers and analysts expect a further exponential increase in use since tag costs are expected to decrease from their current level of $0.50 each to about $0.05 each within five to ten years (Curtin et al., 2007). For example, as Fusaro (2004) demonstrates, clothing retailers have demonstrated interest in tagging products sold and worn by their customers. A concern regarding widespread RFID-usage, ventilated by both researchers and advocacy groups, is the possible violation of privacy by commercial organizations and government agencies (Bits of Freedom, 2011; Curtin et al., 2007). Application of RFID tags in personal or personalized items will result in a flood of privacy sensitive usage data, and embedding tags in passports might, when read ranges increase, allow for location tracking of individuals. For marketers, the vast amount of generated data provides an opportunity to employ powerful data mining techniques and improve marketing capabilities.

Near Field Communication (NFC) is an advanced, short-range, high-frequency, wireless communication technology with similar properties as RFID, however, NFC allows for two-way communication between a mobile device and its surroundings (Finkenzeller, 2010). In contrast to RFID, NFC uses an ‘active tag’ which has an internal power source to broadcast the identification signal to the reader (Curtin et al., 2007). Moreover, NFC tags currently have a significantly smaller range (less than 5 inches) than RFID tags (Finkenzeller, 2010). In Japan, NFC is already extensively used to purchase items and services with a mobile ‘smart’ wallet (comScore, Inc., 2011). comScore, Inc. (2011) expects manufacturers of smartphones to increasingly integrate NFC with their mobile devices and consumers to rapidly adopt this technology. This would enable retailers to provide a wide variety of services, ranging from mobile payment systems to advanced product information applications and displays, and individualized promotion systems.

Augmented and Virtual Reality

Augmented reality (AR) is a technology which allows computer generated virtual imagery to exactly

overlay physical objects in real time (Zhou, Duh, and Billinghurst, 2008). Although only recently

applications on smartphones have made AR accessible and practical for consumers, the first AR

interface was already conceptualized by Ivan Sutherland in the nineteen sixties (Sutherland, 1965).

Sutherland engineered the first virtual reality and augmented reality head-mounted display (HMD) system back in 1968; and even though there were some AR experiments for aircrafts in World War II, his work was the first true AR application with computer generated graphics. A HMD refers to a display system worn on the head, similar to a pair of glasses, which projects digital information.

Azuma (1997) provides a commonly accepted definition of AR as a technology which (1) combines real and virtual imagery, (2) is interactive in real time, and (3) registers the virtual imagery with the real world. Current research implies there are useful applications for AR technology in many domains, such as engineering, entertainment, and education (Hughes, Stapleton, Hughes, and Smith, 2005;

Magerkurth, Cheok, Mandryk, and Nilsen, 2005; Zhou e.a, 2008).

Milgram and Kishino (1994) developed a taxonomy of so-called ‘mixed reality’, which displays the merging of the real and virtual worlds along a ‘virtuality continuum’, ranging from completely real to completely virtual environments. They refer to augmented reality as ‘any case in which an otherwise real environment is augmented by means of virtual objects’. AR utilizes similar technology as ‘virtual reality’ (VR), however, as VR focuses on replacing reality and completely immersing the user in a virtual environment, AR only aims to subtly supplement it and allows the user to interact with the virtual images using real objects in a seamless way (Tamura, Yamamoto, and Katayama, 2001; Zhou e.a., 2008). A recent practical application of AR (e.g. Layar and Tour Wrist) is the use of GPS technology (Global Positioning System) and the backside camera of a smartphone to create a see-through interface where location-specific online information is projected onto physical reality.

Consumers are therefore required to observe reality through a digital interface. This use of AR technology, although very creative and entertaining, is impractical for daily use without a HMD (Feiner, MacIntyre, Höllerer, and Webster, 1997). Nevertheless, even if a fully operation AR-capable HMD system would be available, aesthetics and practical issues could prevent widespread consumer adoption. It would therefore be more realistic and practical to augment mobile media with real-time environment-specific information, though without the use of a see-through interface. Moreover, GPS technology only enables broad spatial identification (e.g. LAM) and does not support identification on a product-level, nor does it enable in-store marketing to the extend RFID and NFC do. A broad vision of mobile AR technology to communicate digital information is therefore not limited to GPS and a see-through interface. For the purpose of this study, this broad vision will be labelled ‘mobile augmented media’ (MAM). Mobile augmented media is the use of mobile media and context identification technology (e.g. GPS, RFID, or NFC) to subtly enrich a physical environment with relevant, real-time, virtual information.

In a bricks-and-mortar or clicks-and-mortar retail setting, MAM can be useful in providing

shoppers with real-time information from the offline and online (shopping) environment. Thereby

the advantages of online shopping (e.g. social interaction via network sites and the availability, browsability, and searchability of information) can be combined with the advantages of a physical retail setting (e.g. the ability to try the product). Figure 1 depicts the relationship between AR and VR in a retail setting. The overlap in the constructs ‘store’ and ‘website’, represented by augmented reality, forms the foundation of this research paper. Augmented media has in this framework the potential to bridge the gap between the different transaction and communication channels, and thereby contributes to creating an integrated shopping experience for consumers.

FIGURE 1

Relationship between AR and VR in Retailing

Recent studies on customer engagement (e.g. Van Bruggen, Antia, Jap, Reinartz, and Pallas, 2010) extend this line of thinking and argue for the recognition of ‘channel multiplicity’ when considering channels through which products as digital representations of customer experiences (e.g.

trade in World of Warcraft accounts) can be communicated and exchanged. Marketers are therefore motivated to consider VR and AR as distinct channels (e.g. for exchanging customer-to-customer product reviews) when developing customer experience management strategies. Although the development of content for MAM is still in its infancy and will gain more momentum when the adoption of RFID and NFC become more widespread, this technology has the potential for improving cross-channel synergy and customer shopping value for both single- and multi-channel retailers. A comprehensive literature review and systematic empirical study into the antecedents and consequences of MAM adoption will be presented further on in this paper.

Conclusion: Mobile Media Typology

In order to organize the preceding conceptualization of marketing concepts, communication

channels, advanced information display techniques, and object identification tools, this paragraph

outlines a mobile media typology. This theoretical model defines four mobile media types, as presented in Figure 2, and is organized around two dimensions: (1) time-space specificity and (2) personalization of marketing. Time-space specificity refers to the degree to which a transaction or communication channel is flexible (or constrained) spatially and temporally. A channel can be considered spatially flexible when it does not need a fixed location to be operational. A channel can be considered temporally flexible when it does not have a fixed time at which it is and isn’t operational. A channel can be regarded as spatially and temporally flexible when it can be used as a communication or transaction medium anywhere, anytime. For example, as explained in the first part of this section, m-commerce and u-commerce deliver services whenever and wherever the customer wants.

FIGURE 2

Mobile Media Typology for Retailing

As explained in the discussion on one-to-many, one-to-one, and many-to-many marketing, personalization of marketing refers to an extreme form of product differentiation where an organization tries to make a unique value proposition for every single customer. This value proposition includes the channel through which a product or service is communicated and delivered.

As previously discussed, using an advanced CRM system would enable an organization to customize these promotions and offerings to individual customer needs. The resulting mixed reality media continuum delineates four types of mobile media: personalized augmented media, personalized virtual media, mass virtual media, and mass augmented media. Table 1 gives a short description and some examples of each of these advanced mobile media types.

TABLE 1

Advanced Mobile Media Types for Retailing

Mobile media type

Description Examples

Personalized augmented media

A type of media which communicates a highly personalized message (customer, shopping basket, or history specific) using augmented reality technology while the receiver of this message is in a physical (bricks-and-mortar) retail environment.

Mobile augmented media (as defined in this paper); in-store, real-time, personalized, communication displays (enabled by RFID and NFC)

Personalized virtual media

A type of media which communicates a highly personalized message (customer, shopping basket, or history specific) using virtual reality technology while the receiver of this message is in a virtual (website) retail environment.

Personalized (virtual) shopping environment; personalized product and check-out suggestions

Mass virtual media

A type of mass media which communicates a context unspecific message using virtual reality technology while the receiver of this message is in a virtual (website) retail environment.

Virtual (website) reality retail environment (ultimate clicks- and-bricks synergy)

Mass augmented media

A type of mass media which communicates location specific, though context unspecific content using augmented reality technology while the receiver of this message is in a

physical (bricks-and-mortar) retail environment.

Augmented reality (e.g. Layar)

to locate competing retailers

(enabled by GPS) without real-

time, personalized content

CONCEPTUAL FRAMEWORK (STUDY 1)

As the previous section elaborated on the technological developments and academic research which serve as a foundation for a new typology of mobile media in retailing, this part focuses one of those identified mobile media categories: mobile augmented media (MAM). Now that MAM has been fully conceptualized, its antecedents of adoption by consumers and consequences of implementation by retailers need to be investigated. The scarcity of systematic scholarly research in personalized mobile media requires the conceptual framework to be constructed using prior studies in related areas of research. Figure 3 gives an overview of this effort, and both the constructs and relational hypotheses explaining the adoption process will be further explored in this section. After discussing the results of empirical research into the antecedents of adoption, a combination of a literature study and several in-depth interviews with professionals in the European supermarket industry will shed further light on the anticipated consequences of MAM adoption for retailers.

Consumer Readiness – MAM Trial

The Theory of Reasoned Action (TRA) (Ajzen and Fishbein, 1980; Sheppard, Hartwick, and Warshaw,

1988), the Technology Acceptance Model (TAM) (Davis, 1989), and the Theory of Planned Behaviour

(TPB) (Ajzen, 1991) presume that attribute-based beliefs drive customer intentions (partially

mediated by customer attitudes) to use an innovation for the first time. However, as Meuter, Bitner,

Ostrom, and Brown (2005) confirm, consumer readiness mediates this relationship between

technology or customer attributes and customer trial. Consumer readiness refers to a state or

condition in which a consumer is prepared and likely to try out new technologies (Liljander, Gillberg,

Gummerus, and Riel, 2006; Meuter e.a., 2005). For the purpose of this study and in accordance to

the often used MAO framework (Batra and Ray, 1986), consumer readiness is conceptualized as

motivation (both intrinsic and extrinsic), ability, and opportunity. Although a consumer might

evaluate MAM positively, a lack of consumer readiness can therefore nonetheless explain a failure to

try. All three of these mediating variables are dependent on the perceived context as created by the

retailer introducing the MAM application. Consequently, in contrast to diffusion, which refers to the

accumulated levels of users of an innovation a market (Mahajan, Muller, and Bass, 1990), this study

investigates individual consumer adoption. Studies on the adoption of self-service technologies

(SSTs), e-commerce, and m-commerce will guide the development of hypotheses on the factors

affecting the adoption of MAM in retailing. H1: Motivation (extrinsic and intrinsic), ability, and

opportunity mediate the relationship (a) between the consumer characteristics and the likelihood of

MAM trial, and (b) between the technology characteristics and the likelihood of MAM trial.

FIGURE 3

Antecedents and Consequences of Mobile Augmented Media Adoption in Retailing (Conceptual Framework)

Motivation. Motivation, both intrinsic (hedonic) and extrinsic (utilitarian), plays a vital role in predicting the usage of technology-based products and services (Barczak, Ellen, and Pilling, 1997;

Davis, 1989). For example, the adoption of multimedia mobile devices and the use of an e-commerce environment are strongly influenced by both intrinsic (enjoyment) and extrinsic (status) motivations (Bridges and Florsheim, 2008; Pagani, 2004). Adventure and convenience are, with regard to MAM, a potential intrinsic and extrinsic reward for usage, respectively. It can therefore be expected that the trial of MAM applications is directly affected by both intrinsic and extrinsic motivations. H2:

Motivation (extrinsic and intrinsic) relates positively to the likelihood of MAM trial.

Ability. Ability refers to the extent to which consumers perceive themselves to have the necessary skills, resources, and confidence to make an outcome happen (Ellen, Bearden and Sharma, 1991; Jones, 1986; MacInnis and Jaworski, 1989). Potential users of MAM are unlikely to try this new feature when they don’t possess a MAM-capable smartphone, have a lack of technology skills (mobile literacy), or there is uncertainty about the directions or requirements for usage. For example, usage rates of m-commerce and SSTs are improved when clear instructions are given and an easy to use application is developed (Curran and Meuter; 2005; Davis, 1989; Shankar, Venkatesh, Hofacker, and Naik, 2010). Moreover, self-efficacy (perceived confidence in the ability to engage in a task) is also shown to positively influence adoption of technology based services (Van Beuningen, De Ruyter, Wetzels, and Streukens, 2009). Trial rates of MAM applications are therefore likely to be higher when potential users have a high degree of perceived ability and self-efficacy. H3: Ability relates positively to the likelihood of MAM trial.

Opportunity. Customer and technology characteristics affect the degree to which a retail environment is perceived to provide an opportunity for using MAM. The availability of an assortment where technologies like RFID and NFC are integrated into products and services is a precondition for enjoying the benefits of MAM. Furthermore, from the perspective of the customer, the availability of time (time pressure) affects the decision process by altering how much and to what degree information is searched (Beatty and Smith, 1987) and processed (Wright, 1974). Both the breadth and length of the ‘window of opportunity’ are therefore expected to have a positive effect on MAM trial. H4: Opportunity relates positively to the likelihood of MAM trial.

Consumer Characteristics

Several antecedents, customer and technology characteristics, can explain the formation of an

attitude by consumers towards MAM. While retailers have only indirect control over the customer

characteristics, technology characteristics are influenced by the MAM application developed. This

paragraph focuses solely on the most prominent attitude-based antecedents (consumer

characteristics) and their relationships with a consumer’s readiness regarding the prospect of using MAM in a retail setting. As confirmed by Meuter e.a. (2005), consumer readiness mediates the relationship between customer attitudes and technology trial. This research will therefore build upon their work by suspecting an indirect effect of consumer characteristics on MAM trial.

Technology readiness. Technology readiness (TR) (related to personal innovativeness, attitude towards technology, and technology anxiety) refers to the attitude towards and propensity to embrace and use new technologies for accomplishing goals in home life and at work (Bobbitt and Dabholkar, 2001; Parasuraman, 2000). Due to an increase in the use of SSTs, e-commerce, and m- commerce, customers have to interact with technology-based systems rather than service employees (Meuter, Ostrom, Roundtree, and Bitner, 2000; Reinders, Dabholkar, and Frambach, 2008). However, consumers differ in their degree of inertia, mental enablers, and inhibitors that collectively determine their predisposition to use new technologies (Lin, Shih, and Sher, 2007; Parasuraman, 2000). The construct of TR is based on four dimensions: (1) optimism, (2) innovativeness, (3) discomfort, and (4) insecurity (Parasuraman, 2000). Optimism and innovativeness are positive drivers of TR and encourage consumers to use technological products and services. Discomfort and insecurity are negative drivers and make consumers reluctant to use technology. Several studies on the TAM model and the adoption of SSTs give credence to the expectation that TR has a positive effect on perceived ability and motivation for MAM trial (e.g. Lin e.a., 2007; Meuter, Ostrom, Bitner and Roundtree, 2003). H5: Technology readiness relates positively to consumer readiness.

Need for information. According to literature on the process of decision making (e.g. Beatty and Smith, 1987; Guo, 2001; Hoyer and MacInnis, 2010; Laurent and Kapferer, 1985), the following seven factors determine the motivation (need) for external information search: (1) market characteristics (e.g. Russo (1977): information availability), (2) situational variables (e.g. Noble, Griffith and Weinberger (2005): hedonic or utilitarian shopping motivation), (3) product importance (e.g. Moore and Lehmann (1980): perceived purchase risk: performance, financial, physical (safety), social, psychological, and time risk), (4) knowledge and experience (e.g. Peracchio and Tybout (1996):

discrepancy of information), (5) individual differences (e.g. Beatty and Smith (1987): situational involvement), (6) conflict and conflict resolution strategies (e.g. Iyengar and Lepper (2000): satisficing versus optimizing), and (7) perceived costs and benefits of search (e.g. Punj and Staelin (1983):

search time). However, as Moorthy, Ratchford, and Talukdar (1997) show, because higher enduring

involvement leads to more product class knowledge and experience and therefore a lower need for

information, the relationship between enduring product class involvement and MAM usage might

have an inverted u-shape. In conclusion, as MAM delivers personalized, context specific information,

it can be expected that the use of MAM is positively dependent on the personal (motivation) and

contextual need (opportunity) for such information (search). H6: Need for information relates positively to consumer readiness.

Need for interaction. As a commonly used variable in literature on SST adoption, need for interaction refers to the desire to retain personal contact with others during a service encounter (e.g.

Dabholkar, 1996; Reinders e.a., 2008). MAM can supply consumers with personal product recommendations, allows for automatic payment (using NFC and RFID), and encourages dynamic customer-technology interaction. Consequently, when a customer has a high need for personal interaction, the motivation to learn (ability) about using new e-services (e.g. MAM) may be reduced (Curran and Meuter, 2005; Meuter e.a., 2005; Reinders e.a., 2008). Moreover, a high need for personal interaction can also be expected to decrease the desire to try a new e-service (e.g. MAM) (Meuter e.a., 2000). H7: Need for interaction relates negatively to consumer readiness.

Distrust of marketing. Drake and Ritchie (2007) show that the practice of deceptive advertising creates distrust, which then undermines the credibility of subsequent advertising from both the same source and second-party sources. Studies on m-commerce (e.g. Shanker e.a., 2010) have used this scepticism of marketing as an inhibitor of adoption. Moreover, consumer attribution of marketing intent (attribution theory) as firm-serving behaviour is often used as a rationalization for negative consumer response to advertising (Campbell, 1995). However, Forehand and Grier (2003) found the expressed motive of the firm to mediate this relationship. Retailers using MAM as a promotional tool should therefore avoid outwardly expressing customer-serving motives (unbiased recommendations) when firm-serving benefits are clear to their customers. A built-in feature which allows customer-to-customer interaction (e.g. product recommendations) could also mitigate this problem. In conclusion, distrust of marketing is expected to have a negative effect on motivation for MAM trial. H8: Distrust of marketing relates negatively to consumer readiness.

Privacy concerns. Secure (RFID) technology to ensure personal data privacy when tracking individual customer buying behaviour for e/m-retailers and bricks-and-mortar retailers, has been described as a prerequisite for consumer trust and adoption (Ohkubo, Suzuki, and Kinoshita, 2005;

Peltier, Milne, and Phelps, 2009; Urban, Amyx, and Lorenzon, 2009). Empirical proof for customer

privacy concerns in retailing can be found in research on the adoption of LAM and loyalty programs

(e.g. Xu, Luo, Carroll, and Rosson, 2011; Leenheer, Van Heerde, Bijmolt, and Smidts, 2007). Although

this group is generally quite small, some customers are unwilling to provide retailers with personal

identifiers because of a need for control and a negative attitude towards direct marketing (Leenheer

e.a., 2007; Noble and Phillips, 2004; Phelps, D’Souza, and Nowak, 2001; Phelps, Nowak, and Ferrell,

2000). Adoption of MAM applications can therefore be hindered by customer concerns about privacy

infringements. Consequently, when retailers implement MAM technology, they should secure their

(RFID) technology and communicate privacy protection guarantees to their customers. H9: Privacy concerns relate negatively to consumer readiness.

Previous experience. The development of MAM technology and mobile content is still in its infancy, however, smartphone adoption rates are increasing rapidly (comScore, 2011), and LAM and AR applications (e.g. Layar) are developed and used by millions of smartphone users worldwide (Layar, 2010; Xu e.a., 2011). Studies on technology diffusion (e.g. Mahajan e.a., 1990) and related areas such as adoption of SSTs (e.g. Reinders e.a., 2008; Meuter e.a., 2005) have confirmed that previous use of related technology has a positive effect on attitude towards trial (self-confidence) and ability. Moreover, satisfaction and enjoyment of previous (related) customer experience affects future expectations (motivation) (Mittal, Kumar, and Tsiros, 1999). These studies give therefore credence to the expectation that previous satisfactory experience with related technologies has a positive effect on customer readiness (motivation and ability for MAM trial). H10: Previous experience relates positively to consumer readiness.

Demographics. It is well established in literature on technology diffusion that demographic variables affect technology adoption (e.g. Venkatraman, 1991; Greco and Fields, 1991). In this respect, the segmentation based on age is often used: the Millennials (or Net Generation) (1983- 2000), the Generation X (or Road Warriors) (1966-1982) and the Baby Boomers (or Concerned Parents) (1946-1965) (Junco and Mastrodicasa, 2007). These segments, for example, are found to differ in the degree to which they adopt and engage with mobile technology in retailing (Shankar e.a., 2010; Zeithaml and Gilly, 1987). The Millennials are found to be extensive users of technology and have great familiarity with digital communications and media. For the purpose of this research, age, sex, education, and income will be used as demographic explanatory variables of MAM trial (consumer readiness). Although prior research on the adoption of e-commerce, mobile technology, and SSTs has not been consistent regarding the effect of individual demographic variables, it can be expected that consumers who are younger, male, have a higher education (ability), and earn a higher income (opportunity) will be more likely to try MAM (Igbaria and Parasuraman, 1989; Meuter e.a., 2003; Meuter e.a., 2005; Porter and Donthu, 2006; Shanker e.a., 2010; Sim and Koi, 2002;

Venkatraman, 1991). H11: The demographic variables (a) age (younger), (b) sex (male), (c) education (higher), and (d) income (higher) are positively related to consumer readiness.

Technology Characteristics

Besides consumer characteristics, technology characteristics are expected to explain the formation of

an attitude by consumers towards MAM. This paragraph focuses solely on the most prominent

attribute-based antecedents and their relationships with a customer’s readiness regarding the

prospect of using MAM in a retail setting. As confirmed by Meuter e.a. (2005), consumer readiness mediates the relationship between technology attributes and technology trial. This research will therefore build upon their work by suspecting an indirect effect of technology characteristics on MAM trial. The technology characteristics used are adopted from Rogers (1983): compatibility, relative advantage, complexity, observability, and trialability. Studies on a wide range of innovation diffusion processes (e.g. electronic banking, mobile services, and SSTs) have empirically confirmed their relevance and explanatory power (e.g. Dabholkar, 1996; Howcroft, Hamilton, and Hewer, 2002;

Kleijnen, e.a. 2004; Lockett and Littler, 1997; Meuter e.a., 2005; Moore and Benbasat, 1991; Verhoef and Langerak, 2001). For the purpose of this study, the framework of Rogers (1983) is extended with

‘perceived control’ and ‘perceived security’ as additional technology characteristics. Because MAM applications give users control over their retail experience, collect, store, and transport personal information (privacy risk), and allow for mobile payment (using NFC) (financial risk), perceived control and perceived security become relevant factors in determining a customer’s attitude towards MAM technology.

Compatibility. Compatibility refers to the extend to which an innovation is consistent with the needs, values, norms, and behaviours (lifestyle) of consumers (Gatignon and Robertson, 1991).

For example, the adoption of a new fashion becomes more attractive when the fashion can be integrated with existing norms (Agins, 2007). Widespread diffusion of an innovation is unlikely when it’s only compatible with a relatively small percentage of the public. Furthermore, Theotokis, Vlachos, and Pramatari (2008) find that the intention to use SSTs is stronger for services that have a higher level of customer-technology contact (CTC) compared to those with a lower level of CTC. In conclusion, when the use of MAM is perceived to be compatible with regular shopping routines and lifestyles, motivation and opportunity can be expected to increase. H12: Perceived compatibility of MAM relates positively to consumer readiness.

Relative advantage. Relative advantage refers to the degree to which consumers perceive a new product or service as being better than its predecessors or substitutes (Rogers, 1983). Perceived

‘usefulness’ is a related (TAM) construct often used in research on the adoption of SSTs and e-

commerce (e.g. Curran and Meuter, 2005; Davis, 1989; Porter and Donthu, 2006). MAM applications

can be expected to increase convenience, time-savings, fun, and service quality (personalized

recommendations add value to the product or service). The perceived relevance of the assortment

increases because only those products that relate to the needs of the individual customer are

displayed via MAM on the customer’s smartphone. Not only can the number of options consumers

are faced with affect the perceived assortment attractiveness, perceived choice difficulty, and the

chance of buying, it also affects what consumers choose (a preference towards hedonic products)

(Iyengar and Lepper, 2000; Sela, Berger, and Liu, 2008). In conclusion, the perceived relative

advantage will encourage consumers to learn about MAM (ability) and motivate them to try it out.

H13: Perceived relative advantage of MAM relates positively to consumer readiness.

Complexity. Complexity refers to the extend to which an innovation is complicated and difficult to understand or use (Hoyer and MacInnis, 2010). Products with an extensive amount of features might appear useful, yet the sheer amount of them makes the product also appear to be overly complicated and confusing (Thompson, Hamilton, and Rust, 2005). Perceived ‘ease of use’ is a related (TAM) construct and often used in research on the adoption of SSTs and e-commerce (e.g.

Curran and Meuter, 2005; Davis, 1989; Porter and Donthu, 2006). A complicated MAM application is more difficult to understand (ability), makes the benefits (motivation) less apparent, and therefore discourages consumers from trying. H14: Perceived complexity of MAM relates negatively to consumer readiness.

Observability. Observability refers to social relevance and the degree to which an innovation is visible and easily communicable to the social group of which the customer is part (Hoyer and MacInnis, 2010; Levy and Weitz, 2009). In general, consumers are more likely to adopt an innovation when they can observe others using it (Fisher and Price, 1992). Observability can help in the learning process (ability) and shows consumers the positive outputs, which increases their motivation. An easily communicable MAM application can therefore support customer readiness for MAM trial. H15:

Perceived observability of MAM relates positively to consumer readiness.

Trialability. Trialability refers to the extend to which an innovation can be tried on a limited basis or the costs and commitment required to adopt it (Levy and Weitz, 2009). Because at the early stages of diffusion there is a limited user base on which to rely on for reviews, trialability can, with regard to the diffusion model of innovation (Rogers, 1983), be quite important to innovators and early adopters. The ability to test the efficacy of MAM creates the opportunity for users to learn about its workings (ability) and recognize its benefits (motivation). The trialablity of MAM applications can therefore be expected to increase the likelihood for consumers to adopt MAM on a limited basis. H16: Perceived trialability of MAM relates positively to consumer readiness.

Control. Control refers to the extend to which an innovation is providing consumers with

perceived influence over the buying process or its outcomes (Bateson and Hui, 1987; Dabholkar,

1996). Because consumers are less dependent on the ability and willingness of service employees to

provide correct information or an honest product recommendation, using MAM might contribute to

a feeling of being in control over buying process outcomes (e.g. satisfaction) and improve the value

of the offering (Bateson and Hui, 1987). However, due to being uncomfortable with the technology

involved, others may decline trial because they feel less in control (Lee and Allaway, 2002). Based on

research on the adoption of SSTs (e.g. Dabholkar, 1996; Lee and Allaway, 2002) and m-commerce

(e.g. Kleijnen, De Ruyter, and Wetzels, 2007), perceived control can be expected to motivate consumers to try MAM. H17: Perceived control of MAM relates positively to consumer readiness.

Security. As noted previously, secure (RFID) technology to ensure personal data privacy when tracking individual customer buying behaviour for e/m-retailers and bricks-and-mortar retailers is a prerequisite for consumer trust and adoption (Ohkubo, Suzuki, and Kinoshita, 2005; Peltier, Milne, and Phelps, 2009; Urban, Amyx, and Lorenzon, 2009). In this context, security refers to perceived uncertainty or risk associated with the use of MAM applications. The application collects and stores personal information, and consumers are therefore likely to feel threatened by the possible consequences of privacy infringements. Subsequently, this threat reduces the attractiveness (motivation) of trial, especially for those concerned with privacy (Xu e.a., 2011). The application of other technologies related to MAM (e.g. automatic billing via NFC) can further increase the (financial) risk of adoption. However, privacy protection laws could prevent the retailer (opportunity) from effectively integrating the MAM application with, for example, the supply chain and in-home appliances. H18: Perceived security of MAM relates positively to consumer readiness.

Consumer and Technology Characteristics – MAM Adoption

Because the consequences illustrated in the conceptual framework are unlikely to fully materialize without customer acquaintance with and repeated use of the technology, the distinction made in the innovation adoption process (Rogers, 1983) between trial and repeated use is for the purpose of this research highly relevant. For example, enhanced search efficiency due to a better assortment overview is only enabled through MAM when consumers are experienced with a MAM application and its search functionality. The need for long-term customer usage also holds with regard to achieving the other potential benefits (managerial consequences) for the retailer introducing the application (e.g. more efficient internal operations or improved overall financial results). Moreover, a major challenge for technology adoption is getting consumers to try the innovation and experience its advantages. Several studies on the adoption of SSTs conclude that consumers who have had previous experience with SSTs have a more positive attitude toward SSTs and also have an increased likelihood of trying out new SSTs in the future (e.g. Keaveney and Parthasarathy, 2001; Meuter e.a., 2005). In addition, customer experience with different types of SSTs grants a more positive attitude toward service providers who offer such options (Curran, Meuter, and Surprenant, 2003). The initial experience with MAM is therefore expected to positively influence repeated use (adoption).

However, in contrast to the regular innovation adoption theory (Rogers, 1983), TAM, TRA,

TPB, and the customer trial model by Meuter e.a. (2005), this study introduces a process of dynamic

interaction between trial, consumer readiness, consumer characteristics (excluding demographics),

technology characteristics, and MAM adoption. More specifically, this conceptual model expects the

trial of MAM to trigger an updating process in which the new experiences alter the initial degree of

consumer readiness. Through these processes, consumer attitudes and attribute-based beliefs are

modified as well. These dynamic attitude updating processes are similar to those described in studies

on the dynamic development of customer satisfaction (e.g. Bolton and Drew, 1991) and the effect of

critical incidents on satisfaction assessments (e.g. Van Doorn and Verhoef, 2008). As the proposed

updating processes will not be empirically tested due to the shortly explained methodological

constraints of this study, they are labelled propositions instead of hypotheses. Moreover, because of

the likely existence of other explanatory factors of MAM adoption besides those included in the

present model, a conventional direct effect of MAM trial on MAM adoption is also presumed. Figure

4 gives an overview of the conceptualized MAM adoption process, the antecedent attitude- and

attribute-based predictors, their mutual relationships, and the hypotheses formed. P1: MAM trial

leads to MAM adoption partly through the mediating effect of the revision of (a) consumer readiness,

(b) consumer characteristics, and (c) technology characteristics. P2: The updated (a) consumer and (b)

technology antecedent predictors have the same effect (signs) on MAM adoption as they have had on

consumer readiness.

FIGURE 4

Antecedent Predictors of Consumer Trial and Adoption of MAM Technology (Conceptual Model)

DATA AND METHODOLOGY

To empirically test the conceptual framework, a self-administered consumer survey was conducted to explore the antecedent predictors and mediating variables of the adoption process. This section will describe the research design, survey development process, data collection procedure, sample characteristics, measure purification, and analysis approach as applied by this paper.

Research Design

Descriptive survey research with a cross-sectional design is extremely popular in marketing research and generally considered as appropriate for investigating to what extend the relationships put forward by an exploratory literature study need consideration in managerial decision making (Cooper and Schindler, 2006; Malhotra, 2010). A well designed experiment capable of controlling and manipulating individual variables could be useful in further determining the causal nature of these relationships (Cooper and Schindler, 2006; Malhotra, 2010); it would allow management to design an optimal MAM application.

As can be concluded from the preceding development of the mobile media typology, the implementation of MAM in a retail setting requires advanced RFID/NFC technology and extensive integration between shopping channels. Moreover, smartphones carried by customers need to be able to identify and read RFID/NFC chips. However, no Dutch retailer has currently invested in MAM by developing advanced mobile applications and integrating RFID/NFC chips with shelves and products to allow for identification on product-level. Mobile network operators and mobile phone manufacturers at present also offer only a limited number of smartphones capable of identifying and reading RFID and/or NFC chips. This research will therefore only gather data on the intention of MAM trial by presenting a hypothetical MAM application to consumers. These consumers are subsequently asked to fill out a questionnaire on prospective service features, socio-demographics, attitudes, and adoption intentions. As will be noted in the section on limitations and future research suggestions, while a controlled experiment can serve as an appropriate addition to this survey data, the aforementioned limitations, in combination with budgetary constraints, do not allow for the controlled and realistic execution an experimental design calls for.

Although reducing external validity of the research, an empirical focus is given to grocery

retailers. As previously discussed, due to their broad and deep assortments and a customer shopping

experience which is highly utilitarian in nature, convenience and information are of high importance

to their customers. Relevance of product information is of particular importance to consumers when

they weight decision criteria leading up to product choice. For example, Overby and Lee (2006) found that a utilitarian shopping motive is strongly related to a customer’s preference towards the online retailer. Moreover, the availability, browsability, and searchability of information on the Internet, provides a rationale for the research shopping phenomenon (Verhoef e.a., 2007). Making information and functionality typically provided by an online retailer available in a brick-and-mortar store using MAM, can therefore be most attractive to enhance utilitarian shopping experiences, such as grocery shopping.

Survey Development

A questionnaire was developed to explore the relationships and variables hypothesized in the conceptual framework. This self-administered cross-sectional survey instrument consisted of four sections: MAM functionality, attitudes and behaviour, intention of trial, and socio-demographics. A hypothetical MAM application for grocery shopping (‘Shoppy’) was introduced to customers via a short description of its functionalities. Shoppy has twelve main functionalities, half of these functions relate specifically to product-level personalization enabled by RFID/NFC technology.

A multistep process was used to develop the survey. Previous research on technology adoption, as presented by the literature study, provided pre-validated scales for almost all measures.

The survey was reviewed on face validity by two experienced academic researchers. A convenience sample of 12 participants pretested the survey on length and clarity. After this process several items were added and deleted, and some of the wording was revised. For example, the concept and definition of a ‘smartphone’ was unclear to some respondents and therefore replaced by the regular term ‘mobile phone’. Appendix A (Table A1) gives an overview of the final items included in the questionnaire and the academic sources from which these items were adopted.

Items on socio-demographics (e.g. income and education) were measured using scales and

decile distributions published by CBS (Statistics Netherlands). Data published by EFMI and CBL (two

leading knowledge institutes in the Dutch food industry) was used for developing scales on shopping

behaviour and supermarket segmentation. All items measuring a respondent’s interest in Shoppy’s

functionality were assessed using five-point Likert scales ranging from ‘not interested at all’ to ‘very

interested’. These items on the functionality of Shoppy were included for two reasons. First, they

further clarify the concept of MAM to the respondent. When asked to rate their interest in the

functionalities offered by Shoppy, the newly introduced concept of MAM is further conceptualized

and visualized by the participant. Second, statistical analysis can help with determining the

functionalities of interest to different respondents and therefore to the supermarket chains they

patronize. Items included to investigate the attitudes, intentions, and behaviours of respondents