and the future of retail
Master Thesis
Dilyana Ivanova 1/31/2016 10688005 UVA – ABS Supervisor: Ed Peelen Second reader: Umut Konus EPMS: MarketingStatement of Originality
This document is written by Dilyana Ivanova who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Table of Contents
1. Abstract 2
2. Preface 3
3. Introduction 4
3.1 Problem statement 4
3.2 Academic and managerial contribution 4
4. Literature review 6
4.1 The consumer buying decision-making (BDM) process modelled –
origins and evolution 6
4.2 Touchpoints in the consumer BDM process 8
4.3 Augmented reality, shops and impulse behavior 15
4.4 The impact of changed touchpoints on the consumer BDM process 21
5. The Research 24
5.1 Background 24
5.2 Rethinking the shop display: alternatives to a familiar sight 24
5.3 Consumer decision-making styles 25
5.4 The research setting 27
5.5 Data and method 27
5.5.1 Phase 1 27
5.5.2 Phase 2 29
6. Results 34
7. Discussion, limitations and future research 44
8. Conclusions 46
9. References 47
1. Abstract
Technological advancements and digitalization are transforming the retail sector. By surveying consumer reactions to innovative digital shopping displays in a shopping mall in the Netherlands, this research aims at determining the effect of this new medium on the consumer buying decision-making (BDM) process. The investigation uses a consumer typology called the Consumer Styles Inventory (CSI) to define the consumer types. Two consumer characteristics from the CSI are chosen: impulsiveness and perfectionism. They serve as the backbone for consumer classification. The digital display is studied in relation to personalization, brand focus, and attention attraction; in a mall setting which provides genuine atmospherics. The research finds a relationship between perfectionism and brand focus, as well as a well pronounced connection between openness to personalized marketing messages via mobile and age of the consumer. Based on this recommendations are made for future research and applications of the digital shop displays.
2. Preface
The GlassShopWall
Digital shopping displays are not entirely unknown in marketing; they are however only now making their way into the Dutch retail market.
With the help of Ed Peelen, a University of Amsterdam Professor, connection was established with the GlassShopWall. The firm is ran in conjunction with the expertise of industry and all-round startup professional Peter Joziasse who together with Ed Peelen sits on the board of the ICSB Marketing & Strategy consultancy.
GlassShopWall (GSW) is a company found in 2011 by Wesley Weerdenburg, a ‘serial entrepreneur’ whose professional days began with the co-creation of Microsoft flagship software - Sharepoint. The Dutch enterprise has since won multiple innovation awards mostly associated to its core product – the Glass Shop Wall. In its essence, the GSW is glass window that can switch between digital imagery and pure transparency. The switch is dynamic and seamless and quite a sight to the unsuspecting passer-by. The company owns a patent of this technology and is currently targeting a vast array of businesses ranging from classic retail to government and infrastructure organizations. The company is an Accenture innovation award winner and has put up its first demo store in the shopping mall New Babylon in The Hague. The concept store, named Store of the Future, aims at showing the newest in tech applied to the retail/marketing context.
3. Introduction
Problem statement
In a dynamic retail environment where consumer touchpoints are rapidly growing alongside advancing technology, brick and mortar stores are finding new and innovative ways to retain customers. One of the initiatives retailers are introducing is digital shop displays: a smart shopping window that employs the latest in technology to bridge the gap between the physical and online worlds. This redesigned touchpoint offers an opportunity for marketers to combine atmospherics with personalization and the most abundant information source available to men – the digital universe. The present research aims at studying the influence of the digital displays on the parts of the consumer BDM process where shopping windows are most influential.
Academic and managerial contribution
The research presented here contributes to the limited body of literature investigating the effects of digital shopping displays on consumers and their buying behavior. New technology such as this one has been hardly researched due to the fact that it can be considered an emerging retail approach and is only beginning to gain popularity. To date there has been no specific research focusing on digital displays in isolation from other Visual Merchandising elements. Smart displays create a unique augmented reality by combining the traditional visual stimuli of a storefront window and virtual reality. They therefore have so far undetermined effect on consumers and their buying decision-making process. This research will aim to address that gap.
The managerial implications of the research findings will help shed light on the effects of the new technology on consumer decision-making and will help determine the best potential applications of the digital displays. Next to that, this research will add clarity to how this
new medium can be best implemented to a given end. Conclusions of this research will help managers identify new opportunities created by this augment integrating technology and the traditional shop display. Personalization is one major possibility with this smart window and this research will investigate that possibility with relation to the shop display.
4. Literature review
4.1 The consumer buying decision-making (BDM) process modelled – origins and evolution
To better understand the consumer BDM process one needs context. In order to provide that in full, a peek at the origins of past and contemporary models and their core characteristics will be provided in the paragraphs that follow.
Research on the subject of consumer buying has been an integral part of consumer sciences from the beginning of the 60 and 70 until present day. Various models have been developed to outline this core process and to aid the better understanding of consumer behavior as a whole. The wide usage of models in this area is due to their ability to be a “replica of the phenomena it is designed to present. It specifies the building blocks (variables) and the ways in which they are interrelated.” (Engel, Blackwell & Miniard, 1995:143; cited by C. Erasmus et al 2001).1
The early models describing the consumer buying decision making process were developed at a time where limited research in the discipline of consumer behavior was available. At this point, marketers were those to undertake the majority of studies in the field shaping the start of the consumer behavior discipline. The first model to describe the process was created in 1963 by Howard (Du Plessis et al, 1991:10, cited by C. Erasmus et al 2001). Other models belonging to this stage of research were: Nicosia-model (1966), Howard - Sheth- (1969), Engel, Kollat & Blackwell- (1968), Andreason- (1965), Hansen- (1972) and Markin-models (1968/1974).
Rational decision-making models
An underlying shortcoming of the initial (60s and 70s) models was that they were not developed on a pure theoretical basis. To address this, the next line of models chose to see the purchase decision as a logical problem-solving (rational) process. The definition of purchase decision being: ‘the process of weighing the consequences of product alternatives to come to a final product decision.’ (C. Erasmus et al 2001). In these models, the process of
consumer decision making is seen as a sequence of actions which are preceded by problem definition, which in turn can be triggered by a number of factors. This sequence would eventually lead to a satisfaction or dissatisfaction with the outcome. The most common cognitive decision sequence of these models would be: problem recognition / pre-search stage, information search, alternative evaluation, choice, outcome evaluation (Schiffman & Kanuk, 1994:566-580; Solomon, 1996:268; Du Plessis et al, 1991:27; Foxall, 1983:75 cited by C. Erasmus et al 2001). Among these ‘grand models’ the most widely recognized is the EKB model which uses the aforementioned cognitive sequence.
Bounded rationality
The next step in the evolution of consumer BDM process models came about in the 80s and was marked by an underlying distrust in the extent to which consumers are actually rational in their purchases. Research of the time showed that shoppers are often engaged in ‘non-conscious behavior during consumer decision-making’ (as discussed by Bozinoff, 1982:481 based on work by Lachman et al, 1979). Consumers were found to implement a number of strategies that would aid there purchase decisions, among those would be heuristics which would allow for quick decisions based on limited amounts of information (eg. a brand name). An attempt to address these views was made with the bounded rationality model. It viewed humans as not necessarily possessing all information needed to make a decision, and without an understanding of all alternatives at any given moment. Due to their limited information and processing capabilities individuals often settle for less. This model was developed by Herbert Simon2 and built on three main principles: sequential attention to alternative solution (next best thing), heuristic (alternatives are sought in high probability areas), satisficing (choosing the ‘good enough’ option).
The development in views on consumer rationality lead to the update of one of the ‘grand models’ of consumer decision making - the EKB model. A newer version - the EBM - was introduced in 1995, named after Engel
version the process begins with recognition of a need. This model could be more closely tied to behavioral psychology than its predecessor providing an even stronger theoretical foundation. A need can possess cognitive as well as an emotional meaning and that recognizes the capacity of humans to be irrational. For the purposes of this paper the widely recognized EBM model will be used as reference from this point onwards.
4.1. Touchpoints in the consumer BDM process
The consumer buying process consists of multiple stages throughout which the shopper may come into contact with the brand they would finally opt for. These points of contact with a brand are referred to as touch points. Among the likely touchpoints with a company would be its website, its customer service phone line, physical buildings, self-service machines, etc. Touchpoints essentially form a
link between the consumer and the company and are therefore central to the
consumer experience4.
In the EBM model, there can be multiple touchpoints with a
customer. In the past,
exposure and contact with brands was rather limited: apart from advertisements
and actual store experiences people would have few encounters with a company. At present however, the amount of touchpoints with a brand has skyrocketed. This is of course due to the emerging of the Internet and advancing technology. The increase in touchpoints is expected to continue with the help of further developing technologies such as augmented
reality (AR), Near Field Communication (NFC), IPTV, and “SixthSense” technology.5 Such
customers; an excellent example of this would be IKEA’s ad from 2009 which enables consumers to see how furniture will look in their own homes without having to actually buy it:
Nowadays it is a norm for major companies to have whole teams dedicated to managing their online presence. A website was but a start of this, next to a website a company would now have its own Facebook page, a Twitter account, a LinkedIn page, a Pinterest account
and so forth. A retail survey6 from 2015 done by Channel Advisor revealed that 90% of the
surveyed retail companies have a Facebook account. More ways in which companies may choose to enhance their corporate image is to join forces and be endorsed by bloggers or vloggers who have proved to be sufficiently influential figures in the digital world. Evidence of the significant growth in touchpoints can be found in the upheaval seen in the Customer Experience Management (CEM) market. A 2013 Global CEM report projects market growth of 20.79% in the period of 2012-2016. The key factor driving this growth, the report says, is
the significant increase in customer touchpoints.7
The increased online presence of companies can aid considerably in the first stages of the consumer BDM process. Customized web ads alone provide a textbook example for new and a very low-cost customer touchpoint. Those ads come about to the EBM model particularly strongly in the information search, alternatives evaluation and purchase stages. A person would be immediately ‘spotted’ by cookies and can be approached based on their clickstream data (the record of pages one has visited). To top this off, companies often
encourage buyers to subscribe for emails and newsletters, frequently offering certain incentives, and thus opening a direct one way communication channel to the customer. A channel that can be especially valid in the post-purchase evaluation stage as it continues to connect with the consumer and so to shape their perception of the brand/company by introducing new information. Information is in fact an entire subject on its own when it comes to decision-making and the digital world. The enormous surge in the amount of information available has brought about profound changes in people’s lives and to the retail world.
Growth of information sources and their influence on decision-making:
The information-load paradigm and the effect of ever-increasing amounts of data
In order to better understand what the increase in touchpoints (and underlying increase in the amount of information those touchpoints provide), one must take a look at how humans process information and what findings on the topic the academic world has arrived at.
The information-load paradigm, early research and theoretical foundations
The core belief behind the information-load paradigm is that consumers have limited ability to absorb and process information during any given point in time. Therefore, when exposed to ‘too much’ information they will proceed to make poorer decisions and will experience a so-called information overload. This core belief has been substantiated by several disciplines. For one, it has been proved that human memory has limited processing capabilities. Different approaches have been taken to measure the size of memory span. The most widely recognized of those was developed by Miller (1956). He introduced the term chunk in his famous paper ‘The Magical Number Seven, plus or Minus Two: Some
Limits on our Capacity for Processing Information’. At that time, information theory8 which
originated from applied mathematics, engineering and computer science began to be applied in psychology. Miller observed that some cognitive tasks fit the model of ‘channel capacity’9, but short term memory did not; instead, studies showed that the capacity of short term memory was approximately 7 chunks. Subsequent studies on the subject have
reduced the number to 5-7 (Simon, 1974), and yet later ones to 3-4 (Broadbent, 1975). Further investigations on the topic of human information processing have provided additional support for the limitations of the brain reaching and have unanimously concluded that the individual is a limited information processing system (Newell and Simon 1972, Payne 1976). The idea of limited capacity is in the core of information theory itself and is embodied in the concept of channel capacity.
Information overload and the marketing message
Due to the compelling evidence on the limitations of the human brain, studies have been conducted to investigate the effect of excessive amount of data in a consumer context. Needless to say those limitations are a topic to be considered by marketers. Interestingly, scholars have not been most successful in demonstrating the negative consequences of information overload. The first to research this topic were Jacoby and his associates. They experimented with variations in the number of attributes per brand as well as the number of brands the consumer could choose from (Jacoby, Speller, and Berning 1974; Jacoby, Speller, and Kohn 1974). The initial results seemed to conclude that consumers ‘actually make poorer purchase decisions with more information’. Those claims were severely challenged and Jacoby had to later acknowledge that their research ‘did not generate unambiguous results’. The information load paradigm was further investigated by Scammon (1977), he, however, failed to achieve information overload in his experiments. Later yet, the subject was investigated by Malhotra (1982): the contribution of his research was proving that cognitively complex individuals have lower probability of suffering information overload than cognitively simple ones. His investigation also showed that the relationship between information load and information overload was curvilinear. In his experiment participants did not observe an intensifying effect of the information overload once the threshold into overload was crossed. Two years later Jacoby (1984) published a paper titled ‘Perspectives on Information Overload’10 in which he reviewed existing research on the subject and summed up the findings in two simple conclusions: ‘Can consumers be overloaded? Yes, they can. Will consumers be overloaded? Generally speaking, no’.
Fast-forwarding to 2015, contemporary research in information overload is mostly reviewed in the context of the Internet and the vast amounts of information that are there for consumers to access. Lucian (2015) quite interestingly concludes that while information overload does result in a more confused consumer, post purchase satisfaction grows in
parallel with information overload.11 The author uncovers the significant difference
between perceptions of overload at present and those of the 80s: a tripling in the ideal number of alternatives for consumers (Wilkie 1974 vs Aljukhadar 2013) from 6 to 18. Yet another study on the subject reported an insignificant reduction in choice quality between the two points of alternatives: meaning overload occurred at low to moderate load levels, and applying higher loads did not reduce choice quality.
These findings suggest that consumers are rarely negatively affected by the volumes of information available to them. This is especially useful to consider with respect to digital shopping windows that are a link between the physical and the online world; as they, too, are a potential limitless source of information. The above research points to the conclusion that more information aids the decision-making process and could have an effect on the post-purchase stage of the consumer BDM process and hence the overall marketing outcome (as an antecedent of positive WOM, repeat purchase and so forth). In this sense, understanding the ways consumer process information are critical to selecting the right content of marketing messages: eg. – what to show on the digital display – accordingly, the following paragraph will take a closer look at the coping mechanisms consumers employ when dealing with a lot of data.
Consumer coping mechanisms
Investigations into information overload point to the likely application of heuristics whenever large amounts of information are involved. One such example which has received much attention in relation to information processing is the ‘visual preference heuristic’. It suggests that consumers prefer visual to verbal representation of information in a product selection. The reason behind that is visuals present greater perception of variety than verbal
information and consumers instinctively sense that their needs are more likely to be met when variety is greater. Furthermore, since processing visual information is less systematic it feels easier than that of verbal information12. This insight is helpful to acknowledge especially when considering digital display windows which have (improved) capacity to show both verbal as well as visual information. The existing research therefore points at the fact that the predominant information that will make a digital display successful would be visual rather than written. Understanding the concept of information overload and the situations when it has been observed as well as its consequences is needed when opting for any marketing strategy and communication means. Although common sense would suggest that the incredible amounts of information available to consumers can quickly become overwhelming and an impediment to their decision quality and overall satisfaction, studies have not consistently backed up those assumptions. When deciding what is the optimal way to display information on digital windows one should do so mindfully and in consideration
of the findings on the subject of information overflow.
Consumer buying decision-making process and the Zero Moment of Truth
The increasing number of alternatives available to consumers together with the large amount of information coming from a number of varied touchpoints is impacting the consumer BDM process in a new way. Dellaert and Haubl (2012)13 have investigated the precise effect which recommendations have on the decision making process. The authors have concluded that the large amount of information available to consumers has an effect not only on the way decisions are made but also on the end result of the product search. These findings are confirmed by the research done by Google and the term developed by
them as a result – the Zero Moment of Truth14. This term is used to describe the moment
before the customer actually faces a product (termed the First Moment of Truth by P&G), and aims at reflecting the fact that consumers are already informed about a certain product type/brand before they get to the store. The FMOT came into light in 2005 with the help of a Wall Street Journal Article, and aimed to describe the moment when a customer is standing in front of a shelf full of products and makes a decision of what to buy. This
moment was so critical for P&G that they created a position called Director of FMOT. According to the Procter & Gamble CEO at the time, the best brands would consistently win at two moments of truth: one the FMOT and the other – when the customer is actually
using the product.
Google’s research provides a valuable insight for marketers and helps identify a critical step towards the purchase decision – the ZMOT. According to their research an astounding 70%
of individuals look at product reviews before making a purchase.15 The ZMOT is, in essence,
opening the way to new touchpoints in the consumer BDM process; it reveals the profound effect the online world has on consumer buying behavior and on the consumer BDM process as a whole.
The above literature reviewed shows that consumer decision-making is a complex process which has undergone significant change as a result of the internet. Individuals are able to access vast volumes of information and have an ever-increasing amount of choice. The investigation into ZMOT has identified a critical stage in the purchase process and can be used by marketers to design new types of stimulus to attract consumers.
4.3 Augmented reality, shops and impulse behavior
This chapter will discuss elements that could contribute to augmented reality in a mall setting. Many definitions of augmented reality exist, in their core is the idea of ‘a composite
view of the world’16, achieved by adding digital information to real surroundings. While the
GlassShopWall itself can create an augmented reality in combination with the visual merchandising of a store window (it can be both transparent and digital at the same time, thus revealing a part of the ‘traditional’ display and adding information to it, eg.: discounts, limited deals messages hovering above a real pair of shoes), it can also go a step further and search for a combination with mobile phones consequently making the entire experience more personalized and consumer-focused. An introduction of mobile marketing will be made below, followed by a walk through the physical store reality – shop displays and visual merchandising. The chapter will close off with impulse buying, a key goal of all the aforementioned stimuli thus providing the ‘why’ behind the ‘what’ in this chapter.
Mobile marketing - introduction
The ability to reach consumers on their handheld devices opens doors to an entirely new touchpoint with a steadily growing amount of variations (eg. the IKEA augmented reality ad). The importance of smartphones is reflected in the 2015 PWC Annual Global Total Retail Consumer Survey which has named mobile technology as one of the 4 disruptive forces in retail.17 Despite its huge potential, however, mobile marketing is still a relatively young communication channel. In part, the reason for that lies in the delicate balance which marketers need to maintain between delivering a message and doing that in a non-intrusive way via this new and very personalized touchpoint. There have been many studies trying to investigate mobile acceptance and they have generally returned inconsistent results. As summarized by Persaud et al (2011), research by Heinonen and Strandvik (2007) discovered that gender differences didn’t influence buyers’ experience with mobile media in comparison with other types of media. Age, however, was proved to be a crucial differentiator in that younger consumers are more responsive to digital media. Other research, Barutcu (2007), found ‘no differences in internet and mobile advertising and
coupon usage based on gender, age, income, or education.’ What his research did find was that better educated and younger audiences have more positive attitude towards mobile entertainment. Further research, that of Megdadi and Nusair (2011), found ‘positive relationships between attitudes towards mobile marketing and mobile advertising, discount coupons, and entertainment’, once more hinting at younger generations being more open to mobile technology.
Location-based marketing
An added advantage of mobile technology is that it can provide buyers specific information of interest based on their physical location. This is what is referred to as location-based advertising; a subtype of mobile advertising. Per definition, location-based marketing is: an application, service or campaign that is using geographic location to deliver or enhance a marketing message (Mobile Marketing Association, 2011). To consumers there are two key benefits coming from location-based marketing: locatability and personalization. Locatability means that consumers will receive marketing information at the right time and place for them. Personalization means that the message can be tailored to their exact needs and preferences: this is insured by preset consumer opt-ins and preferences. According to
Chris Treadway, writer of “Facebook marketing: an hour a day”18: ‘Real-time and
location-based marketing in all its forms are the final big Gold Rush of WEB 2.0.’
Physical store stimuli
Shop display window, function and types
Store window displays play an important role in the battle among stores to attract passers-by on the shopping street/mall: they are the first thing a person sees from the outside. While storefronts may vary among shops, some key functions of store displays are to represent the brand as well as products which can be found inside the shop, to communicate the brand personality and taste, to suggest a price level of the products inside and to hint at sales, new arrivals and so forth. To achieve this merchandisers create innovative and distinct displays. There are 2 main types of displays one can encounter:
merchandise-focused displays that aim at representing what the store sells in the most direct manner, and artistic displays that focus on communicating what the store’s image is
and in sending implicit messages to shoppers.19 Successful implementation of artistic
displays can grab a shopper’s attention and provoke their curiosity. On the downside, these displays miss the opportunity to indicate what a shopper can expect to find in the store: types of clothes and prices, potential sales as well as a visual indication of the particular model the clothes have – a cue to whether the clothes will be a good fit for the passer-by. In this sense artistic windows, compared to straightforward merchandise displays, may run into the risk to underperform when shopper’s processing capacity is less or when the shoppers are looking for specific items at a given price point, of a given quality or fit.
Visual merchandising
To get a complete picture of the significance and intention behind window displays, one must look at visual merchandising (VM) and its influence on the buying process as a whole. VM aims at enhancing the perceived image of a store by making it a more attractive place for customers. The idea behind this is: a pleasant atmosphere would have a positive effect on customers’ mood and therefore would help create an affective response to the products displayed, the brands inside as well as the overall reaction to the store itself and the buyers’ purchase intention. When met with positive atmospheric stimuli, people respond with an increased participation in the store and improved customer satisfaction (spies et al. 1997,
cited by Yip 2012)20. There are a number of components which take part in the creation of a
shop’s atmosphere both inside and outside the store, among those are: color combinations, product placement, lightning, scents, usage of props, mannequins, fitting sections and fixtures and so forth. The central sensory channels for atmosphere are sight, scent, sound, and touch.21 Allowing customers close contact with the merchandise is one of the key benefits of a store on the first place. Research shows that elements such as sprayed fragrance can help boost people’s mental imagination and help affective states (Fiore et al., 2000, cited by Yip 2012). Visual stimuli such as colors22 are well-known to marketers and have been used to help induce emotional states and buying intention. Research has found
that appropriate music can affect the cognitive ability of customers, Chebat et al. 200123: especially interesting as music is generally researched as a mood enhancing tool and not a cognitive one. The authors discover that while appropriate music can enhance brain activity but that is not always desirable for a store, as in the case when ‘weak’ arguments are used to persuade the buyer (put simply the offers are not sufficiently attractive or well-suited). In this sense subjecting consumers to environmental stimuli can have twofold effect; one way to measure those stimuli is by evaluating the degree of arousal they induce. The definition of arousal describes it as the ‘degree of novelty (new and surprising elements) and complexity (the number of elements and change in an environment)’, Mehrabian and Russell (1974) cited by Yip 2012. The right amount will induce affective pleasure for consumers, if they are overstimulated however, buyers will have lesser ability to focus and decreased attraction to the surroundings. Designing a well-balanced VM environment isn’t an easy or straight-forward task. Nevertheless, if done well, it could yield one of the most desirable consumer outcomes form a marketer perspective – impulse buying.
Impulse: The Phenomenon
Origins and evolution
The notion of an impulse can be traced centuries back to the times of Plato and Aristotle. In psychology impulses are seen as originating from both conscious and unconscious activity. One well-accepted definition of impulse is: 'a strong, sometimes irresistible, urge—a sudden inclination to act without deliberation', Goldenson 1984, cited by Rook et al. (1987).24
Extensive research on the topic started in the early 1950s, with the start of investigations of consumer behavior that occurred after entering a retail environment. Most loosely impulse buying is defined as unplanned purchase and is, as expected, one of the main goals of various marketing techniques. There are different types mental states/moods and external stimuli/settings that can act as a trigger for impulse buying behavior. Among the internal factors can, ironically, be both positive and negative emotions. Excitement is named as one of the key motivators to spur of the moment shopping during the holiday season, according to a US survey on impulse buying from 2014.25 This survey concluded that shoppers are more easily triggered also when sad or angry (the latter particularly true for younger consumers). Even a feeling such as boredom can
induce unplanned shopping behavior. Looking at the external stimuli, on the other hand, they can come from the retail environment and any potential atmospheric cues or sensory triggers designed to affect the passer-by. In this sense, among the key goals of VM is inducing impulse buying behavior. An investigation of the effect of 4 levels of VM in a store environment: window
displays, in-Store mannequins, floor
merchandising and promotional signage found that while in-store mannequins were not significantly correlated with impulse buying, window displays were. In the above research the authors discovered that the second most impactful Visual Merchandising factor on impulse buying in a retail environment is the store display.26 ‘You will know what you want once you see it’ – a common statement that
describes impulse buying quite eloquently. According to the author of ‘Why We Buy: The Science of Shopping’, Paco Underhill, two-thirds of what is bought in the malls is impulse
buying.27 Needless to say, each piece of information one comes across should be taken with
the proverbial grain of salt. ‘Shocking’ statistics that aim at grabbing attention of the reader can serve on their own as a tool to condone or excuse behavior that may not in all instances be in the best interest of the reader. Even more so when citing information found in books intended for the wider public that is much less critical than the academic community. Having said that, trusting such statistics remains at the reader’s discretion. Numbers aside, impulse buying is one phenomenon that entirely overrides, or at least offers a parallel version to, the classic consumer buying model discussed previously in this paper. The discovery of the ZMOT is symptomatic of fundamental changes in consumer behavior coming from the digital age. It is interesting to contemplate what the future development of this phenomenon will be. Evidence suggests that there is an upward trend in consumer impulse buying driven by smartphones and tablets.28 This is hardly surprising given that these technologies increase the exposure to potential buying situations while decreasing the obstacles or ‘cost’ for the consumer.
There is another element of the digital world which is indirectly related to impulse reactions in general. In an extremely fast-paced environment where individuals are bombarded with information reactions must be fast. Information overload, as discussed in the previous sections, is a constant threat, or should we say reality, and one way people answer to it is by reacting immediately and ‘moving on’ to the next peace of information which awaits their response. Let us imagine the amount of times per day a person gets and email, a Facebook message, a WhatsApp, sms, Skype and so forth. Immediate reactions follow many of the interruptions: a constant string repeating the same behavioral model, stimuli – response. This repetition may quickly become habitual and go into System 2 mode, undoubtedly the
place where impulse resides (System 2 as per Daniel Kahneman).29 An analogy with a Pavlov
immediately – in a retail setting stimuli are everywhere and so it is conceivable that buyers are becoming less and less equipped to resist those stimuli.
What this means for the present research is that impulse is a behavioral response not to be neglected. Consumers’ impulsiveness is, therefore, one characteristic with major significance for the digital shop displays.
4.4 The impact of changed touchpoints on the consumer BDM process
As discussed in the previous chapter, various touchpoints can have substantial impact on the consumer BDM process. Impulse shopping alone is a phenomenon which modifies the traditional process as we know it. Studies covered above (see The Impulse factsheet) show that 88% of all impulse purchases are done because an item was on sale. This therefore means that all touchpoints that communicate such information are incredibly influential on impulse behavior. Among those key touchpoints are location-specific ones (otherwise a purchase cannot be done) such as shop displays and mobile ads; as well as ads online where impulse purchase can also be done on the spot. Impulse behavior is beyond doubt a subject of interest when speaking of retails sales and its relationship with shop displays makes it a topic worth investigating with regard to the present research. The presence of mobile and the prospect of personalized ads are also topics which can be closely tied to a new technology such as a digital display. Their impact on the consumer BDM process is promising and will therefore be of growing interest for retailers. One of the many possibilities these new displays can offer is establishing a connection with one’s mobile and creating a personalized message appealing to that specific person. Personalization and the usage of mobile in a retail context and in connection to the digital displays will, therefore, also be a topic worth exploration.
With the help of the Web, as a whole, consumers can have free and quick access to volumes of product information (multiple new touchpoints), most of which not controlled by companies: forums, reviews, blogs, social media websites and so forth. This knowledge gives significant power to the consumer and allows for a virtually limitless search. Not surprisingly
the internet has become a common, not to say universal, tool for information search and alternatives evaluation. According to a comprehensive study by Shopper Sciences, assigned by Google, the average shopper used 10.4 sources of information to make a decision in 2011, up from 5.3 sources in 2010. The study was of 5000 shoppers and on 12 categories of products.30
In the second and third stages of the consumer BDM process, online information is an especially influential touchpoint. Its impact on consumer behavior has in fact been so significant that the field of digital consumer behavior has come into existence, Solomon (2004). Among the most powerful information sources online are reviews. A Global
Consumer Shopping Habits Survey Report from Channel Advisor dating back to 201131
indicated that online reviews produce considerable influences: 90% of online shoppers read reviews, and 83% of them trust that online reviews influence their buying choices. The report covered the territories of North America, Europe and Australia. Academic research aimed to address the importance of online reviews has confirmed that argument quality, source credibility, and perceived quantity of reviews are important determinants of
behavioral intention.32 Reviews are among the sources that most contribute to the
information overload likely to occur in an online search. As previously demonstrated information overload has received much attention and hasn’t been the most straight-forward subject to investigate. Findings on the topic in an online setting have loosely confirmed those in the offline environment. It has been established that overload has an adverse effect on choice quality in situations where a large amount of attributes are present
and where they are distributed equally among all alternatives. 33 Apart from that, however,
there is no further confirmation of overload occurring, meaning the availability of a large amount of offers to choose from is hardly an issue for consumers. One explanation of this ability of shoppers to cope with large amounts of data is that information is processed not centrally but using heuristics when ‘too much’ of it is present. These mental shortcuts allow for persons to quickly move forward onto the next thing demanding their attention, making reaction times shorter and actual reactions increasingly more automated and less conscious:
exactly the behavioral pattern enhancing impulse. Research on heuristics applied to reviews has confirmed that consumers rely on 2 numerical cues to aid their decision-making: the rating of the source and the number of reviews available on a subject.34 Online retailers have responded to the anticipated information overload which consumers may encounter and have developed decision-aiding tools on their websites in an effort to simplify choice. A study investigating the simple decision aids’ effect on quality and effort associated with consumer decisions concluded that when both sorting and elimination tools are provided decision quality improves.35 This means that there are ways in which consumers can be helped with their online buying.
Although reviews may contribute to a potential overload of information which in turn can have a negative impact on the consumer BDM process, their impact on the buying process is so substantial that they’ve become one of the main reasons for the ZMOT to come into existence: an entirely new stage in the consumer BDM process. This undisputable change in the buying process reveals how significant the impact of all new (and predominantly digital) touchpoints really is. While the ZMOT is a moment focusing entirely on the purchase as an end goal, research shows that touchpoints have a key role in brand consideration as well. This is hardly a surprise since all ‘paid’ touchpoints aim at creating and improving brand visibility, along with increasing sales. A study of the effect of various touchpoints on brand consideration has established that the single most impactful touchpoint was in-store
communication (which includes shopping displays).36 The remaining touchpoints from the
survey ranked as follows: second came peer observation, then brand advertising, after it WOM followed by retailer advertising, finally: traditional earned media on the last place. The authors employed a real-time experience tracking (RET) method ‘by which respondents report on touchpoints by contemporaneous text message’. This research is especially interesting as it points to effects of the digital display one can anticipate. With this in mind brand consideration/focus will be one dependent variable worth exploring when investigating the overall effect of the digital display.
5. The Research
5.1 Background
Economic climate over the last decade has been everything but easy on traditional businesses. The amount of companies that make up Fortune 500 rankings have been subject to unprecedented turnover.37 This turbulent time quite poetically described as ‘creative destruction’ has affected the retail industry immensely. Evidence of that can be found both in amount and size of bankruptcies in retail38 as well as in the increasing M&A activity39 in the industry that seems to be responding to a global ‘adapt or die’ challenge. Alongside this transformative time in the retail sector comes another massive force – the ever-advancing technology, increasingly more accessible and easily customized. This exciting time in human history provides the setting of the present research done in collaboration with a small technology startup in the Netherlands.
5.2 Rethinking the shop display: alternatives to a familiar sight
The retail sector of today is keenly experimenting with its core attributes. Among the most fascinating examples of such active experimentation are stores like the UK chain Selfridges which announced a No Noise campaign leading up to the Christmas season in 2012. During this initiative, special areas in the mall were created where shoppers could take off their shoes, hand in their phones and roam free in an area of de-branded products (see image 1). Another intriguing case of innovation in the sector is the Dutch fashion retailer C&A. The company decided to elegantly link the digital and ‘real’ world by displaying the number of Facebook likes for items from its store (see image 2). Interesting examples of innovation can be found when it comes to shop displays as well. They can range from the rather clunky vitrine of Kate Spade in NYC (see image 3), to the slicker Topshop display in London (see image 4).
It is hard to put a common denominator on all the new ways retail attempts to challenge the customer. One of the clear directions, however, is the merging of the digital and real worlds. Whether by a big screen (TVs) or a small one (mobile), consumers are coming into contact with another reality parallel to the physical world that surrounds them at an increasing rate. At the forefront of stores, display windows are an ideal medium to initiate this exciting interaction with a passer-by. The present research will attempt to shed some light on the success and challenges such innovative displays face.
5.3 Consumer decision-making styles
The most recognized definition of the core concept of consumer decision-making styles is by
Sproles and Kendall (1986)40. The authors define the term as: “a mental orientation
characterizing consumer’s approach to making choices. In essence, it is a basic consumer personality, analogous to the concept of personality in psychology.” Personality traits are seen as enduring factors that can influence behavior. Naturally, those traits are not dominant in each and every situation: fashion consciousness, for example, may be a guiding factor when consumers make choices related to buying apparel or interior design but it does
not necessary manifest in all potential buying situations. In their research41 the two authors
review existing literature on the topic and from that extrapolate 8 consumer styles characteristics (CSI): perfectionistic, brand conscious, novelty-fashion conscious, price-value
1
2
3
conscious, recreational shopping conscious, impulsive, confused by overchoice, habitual/brand-loyal. They test the construct and content validity of these characteristics with a study among 501 students in the US. The eight factor model is confirmed and is since verified in different parts of the world. Firstly in 1992 by Hafstrom et al. in Korea and subsequently in 1993 by S. Durvasula in New Zeland42. Later, in 1995, S. Lysonski and S. Durvasula conduct a multicountry investigation on the applicability of this segmentation to countries in 4 different regions. The conclusions of all these authors point to the fact that the 8 characteristics are applicable to countries other than the US, with developed countries scoring better than developing ones. Based on all this, the CSI are considered applicable to the present research of the Dutch retail market. The 8 factors (=characteristics) are described as follows:
- perfectionistic/high-quality conscious: these consumers are expected to shop more carefully and systematically or by comparison. They are not satisfied by just a ‘good enough’ product;
- brand conscious/price equals quality type: this type refers to consumers who tend to consider that higher quality means higher prices and vice versa. They favor department and specialty stores where brand names and higher prices are prevalent, and tend to buy best-selling and well-advertised brands;
- novelty/fashion conscious: those consumers are excited to shop for new things and keep up-to-date with styles, they often exhibit variety-seeking behavior;
- recreational and hedonistic shopping conciseness type: shoppers for the fun of it; - price conscious/value for money characteristic : refers to consumers that are
concerned with getting the best for their money and are sensitive to sales, likely to be comparison shoppers;
- impulsive/careless orientation: those persons do not plan their shopping and appear unconcerned about how much they spend;
- confused by overchoice consumer: this type of people have difficulty making choices as a result from information overload;
- habitual/brand loyal consumer: this type has favorite stores and brands and tends to stick to their habits.
5.4 The research setting
This investigation will focus on exploring responses to the GlassShopWall in an actual retail setting – a shopping mall in the Hague. The mall is called New Babylon and is a relatively new addition to the shopping enthusiasts in the city. New Babylon is located next to Hague’s central train station and offers an alternative pastime to travelers as well as to those working in the many surrounding office buildings. In this mall is the Store of the Future – the first store in the Netherlands which would employ the GlassShopWall technology. The Store is in itself an intriguing concept attempting to give its audience a journey into the retail world of tomorrow (as unequivocally suggested by the store’s name). It is in effect an active lab where cameras gather consumer data on reactions to the innovative retail environment. In the front of that store is where one GlassShopWall window is located. At the time of the research the GSW showed an informative news-like message referring to the store and brands involved in the Store of the Future project.
5.5 Data and method Phase 1
Because the research questionnaire would be administered in person in the form of a quick interview, the number of questions included needed to be kept to a minimum. The classic CSI would hold 8x5 questions, meaning that if all consumer styles were to be examined the interviews will have above 40 questions each. Needless to say that was not a feasible number of questions for such a setup. To address that a preliminary test was administered which could give indication of the strengths of these characteristics in a small sample of the population. The intention was to pick 2 well represented characteristics (styles), which would be able to adhere to these key criteria:
- be applicable for a vast array of the population;
- have minimum overlap in consumer responses to ensure diversity and avoid bias. From the original 40 questions, 34 were selected based on the highest factor loadings (3 per style) and sent via email to the respondents. The numbers were filled in excel sheets and sent back to the collector, no questionnaire was incomplete. See Appendix I for the full questionnaire. The respondents varied in age and gender: 5 male and 5 female between the ages of 15 and 60. They were all residents of the Netherlands, 30% with a non-Dutch background. The preliminary survey returned the following results:
Perfectionism - 72.7%
Brand loyalty (habitual) – 66% Price-Value Consciousness – 62.7%
Recreational, hedonistic orientation – 58% Novelty / fashion consciousness – 56.7% Impulsiveness – 55.3%
Confused by Overchoice – 53.3% Brand Consciousness – 47.3%
Based on these results the choice was made to investigate the following 2 dimensions: Perfectionism and Impulsiveness. These two were well-represented, and would appear to be on opposing sides of the spectrum: perfectionism as a characteristic of a careful and well-researched buying process employing significant cognitive ability; and impulsiveness - a quick buying decision with uncertain amount of cognitive effort applied. Moreover, the literature review revealed a strong connection between VM and impulse. The GSW as an element of VM would in extension see impulsive buyers among their main target groups.
Phase 2
Due to the nature of the topic of interest – GSW: an innovative and unusual sight for consumers in the Netherlands, it was necessary to pick a type of research which would ensure respondents have a good idea of what the GSW will look like in reality. In this case real life was the best set for this survey (field experiment). Not only because the respondents wouldn’t need to rely on their imagination to answer the questions, but also because they would be in a mall setting and mindset which is exactly the environment where the GSW would be encountered. Furthermore, when writing/reading a questionnaire people tend to get more connected with their minds than their emotions and they may give answers that they deem rational, instead of being completely truthful. Whether voluntary or involuntary is irrelevant. In daily life the majority of our actions are done either unconsciously (System 2) and/or under the influence of emotions and our environment. After the financial crisis in 2008, Behavioral Finance and Behavioral Economics gained traction as recognized alternatives to the lay beliefs about economy and human behavior from classic economic theory. Keeping this in mind, the advantage of a survey which is filled in by the interviewers is that respondents have all the freedom and space to think and are mentally closer to the state of mind commanded by the setting rather than that commanded by the action they are engaged in at the moment (completing a survey), due to the lightness of the engagement (just a conversation). This approach would also ensure that respondents wouldn’t miss/skip a question or would give a question more than one answer (when not permitted). The sample size chosen was 150 people. The study was conducted on 3 separate days by 2 people standing next to the Store of the future in the Hague. The 2 interviewers were located at the opposite side of a mall entrance which would mean that they could observe people walking in the mall and past the store.
The 2 interviewers were equipped with printed versions of the survey and candy (different types to match even the most mature audience, eg – drop, see Appendix III) to give out as a token of appreciation for the cooperative strangers. The questions were administered either in English or in Dutch with an English version filled in by the interviewer. The choice of language was dependent upon the respondent. The days when interviewers were present were both during the week and weekend. This would ensure that people with varying schedules and mindsets would be encountered. The presenters would stop people passing by and introduce themselves as students from the University of Amsterdam investigating the effect of a new type of window display. The first 6 questions from the interview that came from the CSI would use a 5 point likert scale; this model was adopted throughout the rest of the questionnaire as closely as possible. Exceptions were made for age, gender, purpose of mall visit, prior research of the purchase, time spent in front of the window. The rest of the questions were developed together with the GSW and the guidance of The Thesis Supervisor. For the full list of questions, please check Appendix II. On some occasion small notes were added to the questionnaires to supplement additional information that could be helpful in creating a context for the answers.
Conceptual model
Dependent variables definitions
Attention effect is measured with the help of 3 questions and aims at determining the store’s effect on the attention of passers-by;
Brand focus is measured with two questions and aims at determining how mindful of the brands displayed the consumer is as a result the GSW;
Personalization – this variable illustrated the attitude towards potential personalization of the GSW using a connection with a mobile phone.
Independent variables definitions
Digital display evaluation: describes the evaluation passers-by gave to the digital display, ranging from positive to negative
Importance of shopping window: describes how important shoppers perceive shopping windows to be in general;
Goal of the shopping trip: identifies whether the interviewee is looking for something specific or not during their store visit
Hypotheses
H1:
Consumers scoring high on impulsiveness are more likely to have higherattention effect than those scoring low.
Impulse buying behavior is triggered by, among other factors, visual stimuli (eg. shopping windows). Therefore, it is feasible that consumers who display such behavior are more likely to reward their attention and react to the digital shopping display.
H2:
Consumers scoring higher on perfectionism are more likely to look forinformation on brands.
Perfectionism in buying suggests that the consumer makes a conscious choice to purchase a product that offers the best quality – price (P-Q) balance. Brands are among the most obvious of indicators for quality and, often enough, can signal price too. This means brands can provide key information for the perfectionist consumer. Therefore, they would likely have a central place on their minds as mapping tool (not as motivator to buying but as an attribute containing important clues for the P-Q.
H3:
Consumers scoring higher on impulsiveness are more likely to enjoy aninteractive and dynamic shopping by mobile personalization.
Impulsive buyers readily take in and act upon information from their surroundings. This openness to input (and constant ‘interruption’) is likely to be determining for their evaluation of mobile marketing in relation to digital displays.
6. Results
This section will describe the analytical strategy taken: from working with the raw data to obtaining regression results. Since the data was collected by paper, the first step was to enter all results in Excel. From there, SPSS with the PROCESS add-on were used to analyze the data. In SPSS recoding was needed as well as creating new variables from existing ones. Due to the nature of the data and the hypotheses, 3 hierarchical regressions were ran to obtain results. When one of the 3 hypotheses was confirmed a mediation analysis followed conducted using the PROCESS add-on.
Missing Values
Frequencies were run to check for missing cases and errors in the data. Few missing values were identified but could be traced back to the questionnaires: the data was collected via physical questionnaires and hence was entered manually. Upon encounter with any missing questions during data entry into excel (4 respondents out of 160) ‘listwise deletion’ was the chosen option. In this sense the approach of how to handle missing data was chosen already out of SPSS. Apart from that all questionnaires were complete so I was sure that when I missed data in SPSS it was an error of manual entry and not an actual case where the answer of a question was not acquired.
Recoding
In order to analyze the result it was needed to recode the variables in SPSS. Originally the values would be string. In order to get numeric values each of the variables had to be recoded. After recoding the variables, mean variables were created to sum up the results of the various scales.
age variable was recoded as follows: box 10-19 => 1, 20-29 => 2, 30-39 => 3, 40-49 => 4, 50+ => 5
variables attentionattraction, digitalevaluation, displayimportance were measured using likert scales and were recoded into 1 to 5 with 5 for the most positive / desirable answer (example: scoring ‘very positive’ on the digitalevaluation variable would become 5 in the NEWdigitalevaluation variable)
CSI questions were also recoded into scores ranging from 1 to 5
Reliability
Reliability was tested for the following scales:
CSI impulsiveness and perfectionism measures Attention effect
Brand focus effect Table 1: Cronbach’s Alpha
Variable Cronbach’s Alpha Perfectionism 0.912 Impulsiveness 0.926 Attention effect 0.739 Brand focus 0.737
All four variables were found to have a Cronbach’s alpha > .7, which indicates high level of internal consistency.
Computing Scale Means
As the final preliminary step, new variables were created for hypothesis testing. The mean of all items that were used to describe a variable were calculated and those provided the data points for the new variable.
imean and pmean
The variables imean and pmean represent a mean of the results from the sets of 3 questions that measured that dimension. The 3 questions per variable are taken from the CSI inventory described earlier in this paper. These questions have been extensively tested over the span of over 20 years, and account for a high level of scales consistency which was also found in this study. These imean and pmean variables show how strongly an individual displays impulsiveness and perfectionism: the higher the value the stronger a characteristic is displayed.
Correlations
Impulsiveness and perfectionism (imean and pmean)
Key takeaway from this analysis is the negative correlation found between the imean and pmean variables. This shows us that Impulsiveness and Perfectionism have a significant negative correlation. The CSI characteristics were chosen in the hope to avoid overlapping (highly similar) results, and this has been achieved.
Age and gender
Other interesting takeaways from the below table are that gender has limited impact on the rest of the variables; while age is significantly correlated with many of them – especially so with personalization. The high negative score (-.659) shows that younger people are more keen on personalization using mobile devices. Significant correlation is also found for age and display importance, suggesting that older generations tend to place more importance on shopping windows (.340). This could be an explanation of why younger persons seemed to be less impressed by the digital shopping window (negative correlation of -.352). It should be reminded here that the digital display at Store of the Future showed content which was more informative than anything. Furthermore, next to the display was a big stand-alone poster sign advertising muesli products which was placed in such proximity to the screen that the screen was potentially less noticeable and engaging. Additional curious correlations of age reveal that younger people tend to be more impulsive while adults – more perfectionistic. More results involving age demonstrate that it was easier to capture the attention of more mature people as opposed to less mature ones. This could be due to the fact that the latter have been raised with technology unlike persons born before the 90s.
Other significant correlations
Additional higher correlations are seen between prior research and purpose of the shopping trip (.540) – which definitely is not surprising, a more focused shopping trip would often entail prior research. Interestingly, the more people would think shopping windows are important, the less they would be interested in involving mobile personalization: this could be showing that displays are positively perceived the way they are now by many. It would be therefore good to keep the ‘traditional’ feel of a shopping display even when employing novel technology. Fortunately,
personalization via mobile is literally in the hands of each consumer so they can freely choose whether they would like to get involved or not. Lastly, there is a negative correlation between the evaluation of the digital display and the attention variable which could be explained by the fact that the content of the display was informative more than exciting – the persons who spend the most time looking at it may have expected different type of content. This is definitely a point that should be further researched: the impact of the display with varied content.
Table 2: Correlations Correlations imean pmean MEAN attention MEAN brand persona lization prior researrch display importance digital evaluation What brought
you here today? age Gender imean Pearson Correlation 1
Sig. (2-tailed)
N 156
pmean Pearson Correlation -.404** 1 Sig. (2-tailed) 0
N 156 156
MEAN attention Pearson Correlation -0.057 0.102 1 Sig. (2-tailed) 0.483 0.204 N 156 156 156 MEAN brand Pearson Correlation -0.043 .306** .231** 1
Sig. (2-tailed) 0.591 0 0.004 N 156 156 156 156 personalization Pearson Correlation .286** -.223** -.294** -0.016 1
Sig. (2-tailed) 0 0.005 0 0.84 N 156 156 156 156 156 prior researrch Pearson Correlation -0.049 .183* -0.115 0.149 0.091 1
Sig. (2-tailed) 0.541 0.022 0.152 0.064 0.259 N 156 156 156 156 156 156
display importance Pearson Correlation -0.069 0.042 .200* -0.086 -.494** -.286** 1 Sig. (2-tailed) 0.389 0.599 0.012 0.285 0 0
N 156 156 156 156 156 156 156 digital evaluation Pearson Correlation 0.07 -0.094 -.568** -0.098 .338** -0.152 -.194* 1
Sig. (2-tailed) 0.388 0.245 0 0.224 0 0.059 0.015 N 156 156 156 156 156 156 156 156
What brought you here today? Pearson Correlation 0.012 -0.017 0.033 0.011 -.226** .540** -0.035 -.360** 1 Sig. (2-tailed) 0.878 0.837 0.679 0.893 0.005 0 0.664 0
N 156 156 156 156 156 156 156 156 156 age Pearson Correlation -.323** .224** .352** 0.108 -.659** 0.034 .340** -.352** .207** 1
Sig. (2-tailed) 0 0.005 0 0.181 0 0.676 0 0 0.009 N 156 156 156 156 156 156 156 156 156 156 Gender Pearson Correlation -0.057 0.019 0.024 0.141 .183* 0.022 -.158* 0.122 -0.109 -0.128 1
Sig. (2-tailed) 0.483 0.814 0.768 0.079 0.022 0.782 0.049 0.13 0.174 0.112 N 156 156 156 156 156 156 156 156 156 156 156 ** Correlation is significant at the 0.01 level (2-tailed).
After completing the preliminary test, the next step was to move on to hypotheses testing.
To check the described hypotheses, 3 hierarchical regressions were conducted: one for each dependent variable. The regressions were controlled for age, gender and goal of the shopping trip. Each hierarchical regression would return 2 models: model 1 with control variables (age, gender and goal of the shopping trip), and model 2 with the consumer style variable (perfectionism, impulsiveness) as predictors of the respective dependent variable. In addition, the PROCESS add-in to SPSS was used to test for mediation43 if the main relationship (consumer decision style as predictor of personalization/brand focus/attention effect) was confirmed by the regressions.
Regression 1
Results significance
The Regression showed that both models had significant results.
Model 1 (control variables) with p=.000 and F=7.563; and model 2 with p=.000 and F=5.888.
Adjusted R2 for m1 was .360 and that for m2 was .367, which means that age and gender
together explain ~36% of the variance, the remaining 1 variable explains a total of ~36.7% of the model variance.
Beta coefficients
Looking at the individual statistics per variable we see the following: results are not significant for gender (p=.383), purpose of shopping trip (p=.648), and impulsiveness (p=.350). This means that H1 is rejected. Results are significant for age (p=.000).The beta coefficient for this variable is: age β=.368 which means that for each point increase in age there is a 36.8% increase in the DV. Therefore more mature people’s attention was more easily and meaningfully attracted by the screen than that of less mature people. This result could be explained with the fact that the younger generations regard technology as
