How to keep free-riders in the retailers channels : which factors influence cross-channel free-riding behaviour and can prevent offline shoppers to switch to a competitors online channel?

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HOW TO KEEP FREE-RIDERS IN THE RETAILERS CHANNELS:

‘WHICH FACTORS INFLUENCE CROSS-CHANNEL FREE-RIDING BEHAVIOUR AND

CAN PREVENT OFFLINE SHOPPERS TO SWITCH TO A COMPETITORS ONLINE

CHANNEL?’

Amsterdam Business School

Executive programme in management studies

Strategic Marketing Management Track

Final version master thesis, 24-8-2015

AUTHOR

Carolijn Daamen, 10499431

SUPERVISOR

Drs. Frank Slisser

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-Statement of Originality

This document is written by Carolijn Daamen 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.

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-ABSTRACT

Purpose

The purpose of this study is to examine what factors influence consumer cross-channel free-riding behaviour from an offline retailers channel, the physical store, to a competitors online channel. Afterwards the strength of the significant factors are analysed to find out which factors are the strongest indicators for such behaviour.

Methodology

This research focuses on the shopping experience of Dutch shoppers in the physical store and subsequent buying behaviour, specifically for experience goods. Differences in the assessment of the instore shopping experiences between free-riders and non-free-riders will be compared. Consumer free-riding behaviour will be explained by means of the Push-Pull-Mooring Model (Bansal et al, 2005). Survey data was collected from 272 Dutch shoppers through an online questionnaire. The results were analysed by SPSS using independent samples tests and a logistic regression analysis.

Findings

This study finds that high price perception, low service quality and speed and high alternative attractiveness are factors positively influencing cross-channel free-riding behaviour from the retailers offline channel to a competitors online channel. The presence of instore promotions negatively influences such behaviour. The presence or lack of instore promotions is the strongest predictor, followed by price perception, alternative attractiveness, speed of service and finally service quality.

Value

Past research has mainly focussed on consumer cross-channel free-riding behaviour from a retailers online channel to the offline channel of a competitor. Unique insights were generated which help retailers manage their multi-channel strategies and ultimately make sure to keep their shoppers in their channels, either offline or online.

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-TABLE OF CONTENTS

1. INTRODUCTION ... - 6 -

1.1 Topic ... - 6 -

1.2 Research question ... - 7 -

2. LITERATURE REVIEW ... - 9 -

2.1 Multi-channel organizations ... - 9 -

2.2 Multi-channel shoppers ... - 10 -

2.3 Channel choice ... - 11 -

2.3.1 Online channel choice and consumer segments ... - 12 -

2.3.2 Offline channel choice ... - 14 -

2.4 Customer migration and retention ... - 15 -

2.5 Consumer free-riding behaviour ... - 16 -

2.6 PPM Model ... - 18 -

2.7 Conceptual model ... - 19 -

2.8 Hypotheses ... - 21 -

3. METHODOLOGY ... - 23 -

3.1 Research design ... - 23 -

3.2 Data collection and sample ... - 23 -

3.3 Survey design ... - 24 -

3.3.1 Constructs and measures overview ... - 24 -

3.4 Pre-test ... - 25 -

4. RESULTS ... - 26 -

4.1 Data preparation ... - 26 -

4.1.1 Reliability of scales ... - 26 -

4.1.2 Normality ... - 27 -

4.1.3 Scale means ... - 29 -

4.2 Data analysis ... - 30 -

4.2.1 Respondents ... - 30 -

4.2.2 Hypotheses testing ... - 31 -

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-4 DISCUSSION ... - 36 -

5.1 Conclusion ... - 36 -

5.2 Managerial implications ... - 38 -

5.3 Limitations and future research suggestions ... - 38 -

REFERENCES ... - 40 -

APPENDIX 1. SURVEY ... - 44 -

APPENDIX 2. DISTRIBUTION OF DATA ... - 48 -

APPENDIX 3. RESPONDENTS ... - 51 -

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-1. INTRODUCTION

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1.1 Topic

Nowadays consumers have many opportunities to interact with retailers and brands through various channels such as the internet, social media, mobile apps, online web shops and the conventional offline stores.

Consumer channel decision making has therefore become more and more complex and consumer channel switching risks have greatly increased. There are so many moments throughout the customer journey that consumers are able to switch channels and more importantly to switch to a competitors’ channel. The

customer journey is defined as the sequence of events, whether designed or not, that customers go through to learn about, purchase and interact with company offerings, including commodities, goods, services or

experiences (Norton and Pine, 2013). Consumers inform themselves in one retailer channel and purchase through another channel of that same retailer or they inform themselves through one retailer channel and purchase the product though a competitors’ channel. The latter is called consumer free-riding behaviour (van Baal, Dach, 2005).

Throughout the years single-channel organisations have evolved into multi-channel organisations.

Subsequently multi-channel organisations grew into more integrated cross-channel organisations and finally many organisations are aiming for omni-channel strategies. An introduction to these terms is described in the following section.

Multi-channel organisations (http://www.diract-it.nl/multichannel-crosschannel-en-omnichannel/ 4-2-2015) offer different channels through which consumers learn about and purchase their products. Consumers make a choice between these channels. These channels could have different offerings, prices, service, etc. Or, as Levy and Weitz (2009) state, multi-channel retailing is the set of activities involved in selling merchandise or services to consumers through more than one channel. Cross-channel goes a bit further and offers consumers a more uniform offer across these channels (http://www.diract-it.nl/multichannel-crosschannel-en-omnichannel/ 4-2-2015). According to Chatterjee (2010) cross-channel strategies are those that integrate multiple channels allowing cross-channel movements of products, money and information. Omni-channel shopping behaviour does not have a scientific definition yet but is defined by marketing professionals as (http://www.diract-it.nl/multichannel-crosschannel-en-omnichannel/ 4-2-2015) when consumers experience the same offerings, prices, services, experiences, etc. in all channels and use these channels simultaneously and/or switch

continuously between these channels. Consumers experience the different channels as one channel altogether. Omni-channel marketing and retailing is a trending topic at the moment. Many managers claim it to be the most effective way of customer retention in these challenging times, where consumers have the option to choose from various channels at the same time and through various stages in their customer journey processes. Little research however covers what successful omni-channel strategies entail and why this increases customer retention. Omni-channel is very complicated because it entails offering a seamless experience across all channels that could be used and experienced simultaneously. Risks to loose consumers

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-are therefore increasing and strategies could easily be executed wrongly. This might cause consumers to switch retailer, channel(s) and maybe even brands. Examples of brands that have successfully implemented omni-channel strategies are for example Starbucks and Sephora (http://multiomni-channelmerchant.com/must-reads/5- (http://multichannelmerchant.com/must-reads/5-excellent-examples-omnichannel-retailing-done-right-14052014/ 4-2-2015). The Starbucks reward app does a great job engaging consumers to the brand and give them the opportunity to experience Starbucks when and from whatever device they prefer. Sephora makes sure their frequent consumers are rewarded through the ‘My beauty bag’ program that gives consumers an overview of their purchase history, easy re-order

opportunities, personalized shopping lists and opportunities to easily check the status of reward points. Not much research has been done on omni-channel shopping behaviour and how organizations (retailers and brands) can implement and execute successful Omni-channel strategies. To gain a better understanding of what makes up a successful omni-channel marketing strategy more knowledge of consumer channel choice decision making and furthermore why consumers switch channels and why they choose to switch to

competitors’ channels should be developed. Based on those insights managers can better design their channel strategies and customer channel experience to improve customer retention during the shopping experience. Much research has been done on customer channel choice, factors influencing multi-channel behaviour and consumer channel switching. Less research has been done on consumer free-riding in multi-channel

environments. Following the recent articles of Heitz-Spahn (2013) and Chiu et al (2011) it seems evident that more research on multi-channel free-riding behaviour should be done to gain more and better insights on what drives and/or cross channel free-riding so retailers can form customer retention strategies in the multi-channel environment. This group of consumers is very difficult to retain as they are very thrift driven. But it would be interesting to know what factors, apart from the expected factor of price, are important to shoppers and could keep them from switching.

Chiu et al (2011) conducted research on consumers switching from online to offline channels. No research has been done on the opposite situation: consumer free-riding when consumers switch from on offline (instore) retailer to another retailers online channel. Many retailers struggle with how to prevent their stores to become showrooms for free-riders that ultimately purchase at a competitors’ online channel and how to retain these consumers in their channels.

This thesis will focus on consumer free-riding behaviour and how to prevent free-riders from switching from an offline retailers’ channel to the online channel of the competition.

1.2 Research question

Thus, the research question to be answered in this thesis is:

‘Which factors influence cross-channel free-riding behaviour and can prevent offline shoppers to switch to a competitors online channel?’

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-Results should generate more insights to answer the following sub questions:

What are the most important factors driving consumer cross-channel free-riding behaviour from the retailers offline channel to the competitors online channel?

And thus, how can retailers prevent free-riders from switching to competitors’ online channels when shopping offline?

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-2. LITERATURE REVIEW

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2.1 Multi-channel organizations

Since the arrival of the internet many organisations transformed into multi-channel organizations. According to Zhang et al (2010) multi-channel retailing is the set of activities involved in selling merchandise or services to consumers through more than one channel. So customers using more than one channel to interact with firms are multi-channel customers (Rangaswamy, 2005).

Traditional single channel organizations only had one channel through which consumers interacted with the retailer (for example a conventional offline store). With the arrival and growing popularity of online shopping these firms suddenly felt the urge to add a website for information communication purposes and to offer consumers the possibility to purchase through catalogues, call centres and later on online and thus became multi-channel organizations (Haydock, Amire, 2000). These multiple channels can be used in alternating ways in the shopping process for one particular product or customers choose channels depending on different

situations. However, not many firms knew how to manage these multi-channel customers.

Understanding the decision processes and factors that affect the choices of multi-channel customers are very important because research has shown that multi-channel consumers provide higher revenues, higher share of wallet, have higher past customer value and have a higher likelihood of being active than other customers (Kumar, Venkatesan, 2005). Venkatesan et al (2007) found evidence that multi-channel shopping is associated with higher customer profitability. This implies that when managing these multi-channel customers well and keep them in the channels they will generate higher revenues and this could possibly result in higher profitability.

Being a multi-channel organization was and still is considered essential for sustained growth in the current competitive environment(Wind and Mahajan, 2002).Traditional retailers that moved into e-commerce attract twice as many online visitors to their websites as online only retailers do (Aron, 1999). This suggests that consumers find their way to the online channel because of the offline presence and familiarity with the retailer. This is confirmed by Aron (1999) who states that online only retailers have to spend twice as much as multi-channel retailers on customer acquisition. However with multiple multi-channels it is more difficult to keep customers in the channels. Jaffe (2000) argues that multi-channel players spend over five times what online only players do on customer retention. It is thus of great importance that multi-channel retailers understand customer multi-channel behaviour in order to minimize these costs and increase customer retention. During the 90’s more and more retailers started adopting multi-channel strategies because of these earlier discussed opportunities and benefits in this paragraph. But with the arrival of more channels and more ways of interacting with retailers a need for integration of channels arises. According to Chatterjee (2010) multi-channel firms have two strategic options: managing multi-channels independently (for example buy instore and pick up instore) or offering integrated channels (buy online and pick up instore or purchase instore and get products

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-delivered at home). The latter is called cross-channel shopping. Chatterjee (2010) states that retailer

satisfaction is higher for cross-channel compared to multi-channel retailers. And, very importantly, high-thrift customers are less likely to search for alternative competitive offerings in cross-channel environments than in multi-channel environments.

Offering multiple (cross) channels does not only lead to positive benefits. Offering and integrating multiple channels is a complex process because the presence of and experience in one channel could affect purchase intention in the other channels. Piercy (2012) for example states that customers have a higher propensity of engaging in negative cross-channel behaviour (punishing ofﬂine channels for poor service online) than positive cross-channel behaviour (preferring companies online who have an ofﬂine presence). So having an offline presence does not reassure online purchases and poor online service has a very negative effect on offline purchase intentions. Based on this one might say that risks for the firm are at a minimum level when only offering a single channel. However, as stated earlier multi-channel retailing could lead to higher profitability (Venkatesan et al, 2007).

A great stimuli for multi-channel organizations to transform into cross-channel organizations is the fact that certain cross-channel services (for example order online and pick up in store) offer more convenience, greater confidence, and control in product search (Chatterjee, 2010). So integration of channels increases the value of channels (Tse & Yim, 2001) and could thus, when performed better than competitors, result in customer retention.

2.2 Multi-channel shoppers

In 2002 Stoenbachler & Gordon conducted research on why consumers are single or multi-channel shoppers. They proposed a framework to research the antecedents of perceived risk, past direct marketing experience, motivation to buy from a channel, product category and web design, which are predictors of channel buyer behaviour for multi-channel, single channel and not buying. They suggested more research on other variables that may influence whether consumers are multi-channel consumers or not should be done. Complete insights are necessary for firms as these type of consumers need to be marketed differently. Kumar and Venkatesan (2005) found that customers who shop across different product categories and those who shop online are more likely to purchase across multiple channels. According to the article an important factor driving multi-channel shopping behaviour is convenience. Retailers should therefore synchronize product and customer information across all channels as inconsistency in information can lead to inconvenience and will ultimately drive customers away from the firm. According to Kumar and Venkatesan (2005) other characteristics of multi-channel shoppers are familiarity to the retailer (old customers are more likely to purchase in the retailers’ other channels) and purchase frequencies (high purchase frequency is associated with multi-channel shopping). These findings can be used for (new) channel strategies as these findings offer insights on what drives multi-channel shopping.

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-According to Lee & Kim (2010) multi-channel shoppers are more likely to be female with higher educations and incomes. They also state that age has become less of an indicator for multi-channel shopping. However, previous research has shown (Okumara, 2002) that most multi-channel shoppers fall in the age group of 18-34 years old. According to Lee & Kim (2010) multi-channel shoppers are more likely to receive promotional advertisements and folders and are also more likely to respond to these advertisements.

2.3 Channel choice

Insights on what drives the consumer to choose a particular channel are essential in formulating successful channel strategies. Because of the historical developments from single to multi and cross-channel organizations many researchers started tackling the subject of consumer channel choice and researched why consumers did or did not choose certain channels. This is necessary because it generates a better understanding of how to acquire new customers and how to retain current ones in increasingly complex customer journeys where consumers can be reached where and whenever. The customer journey is defined as the sequence of events, whether designed or not, that customers go through to learn about, purchase and interact with company offerings, including commodities, goods, services or experiences (Norton and Pine, 2013).

Typically consumers define the value of a shopping channel by the level of service, convenience, quality and price provided by the channel (Tse, Yim, 2001). So the choice of one channel over the other greatly depends on the evaluation of these factors. According to Tse & Yim (2001) retailers can influence channel choice by increasing these levels compared to competing channels. More specifically according to Kim and Park (2005) consumers’ prior attitude toward the offline retailer predicted the attitude toward the online version of the retailer. So positively increasing consumer attitude toward the offline store may positively influence

consumers’ attitude toward the online store and online information search. Kim and Park (2005) name store image and service consistency between or among multi-channels as factors that may be beneficial for retailers to enhance the consumers attitude toward the online store.

These channel choices and their decision processes do not always stay the same; channel decision processes might change over time. According to Valentini et al (2011) decision processes evolve over time. Consumers that change their decision process do so when they are highly responsive to marketing. Less responsive to marketing are customers that are more mature and new customers are more receptive to marketing. This means that retailers should treat new customers differently than more mature customers when it comes to influencing and predicting channel decisions. Hence, new customers can be positively influenced by marketing efforts whereas more mature customers need to be satisfied with the service experience of the channel of their choice.

As described above customers have different wants and needs and are more or less receptive to retailer initiatives. These preferences should be respected (Schoenbachler, Gordon, 2002) instead of forcing them into certain channels. Instead retailers should understand shopper preferences and channel migration (switching) and adjust their channels and the integration of channels to those preferences. According to Sullivan & Thomas

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-(2004) customer channel migration is a dynamic process in which a current customer repeatedly makes choices to frequent one of a retailers options, for example online, offline, catalogue, etc.

Keen et al (2004) built evidence that the offline channel is still the most popular channel for consumers to do their purchases because most purchases are still done offline. However, under certain circumstances,

consumers tend to have a preference for different channels. For example distance to channel (when an offline store is not in the vicinity) and perceived channel quality influence channel choice (Oppewal et al, 2013).

2.3.1 Online channel choice and consumer segments

In 2013 83% of Dutch internet users between the age of 12-75 admitted they used the internet to shop. This means that 10,3 million Dutch citizens shop online; which is even 0,4 million more than the year before in 2012. It seems that online shopping is still increasing. Of those 10,3 million online shoppers 60% is a frequent online shopper and these numbers do not differ among cities and rural areas

(http://www.cbs.nl/nl-NL/menu/themas/vrije-tijd-cultuur/publicaties/artikelen/archief/2014/2014-4076-wm.htm, 27-2-15). As the

amount of (frequent) online shoppers still increases, and retailers use more and more evolving online channels such as smartphones, tablets and online applications to reach shoppers, it is important for multi-channel retailers to know why and when shoppers use these channels to improve and optimize these channels to offer a seamless experience across all channels.

Keng Kau et al (2003) did research on the types of shoppers that shop online. According to the article online surfers and shoppers can be categorized in the following 6 categories:

1) on-off shoppers (those who like to shop online, but prefer offline)

2) comparison shopper (those who compare features, prices and brands before making a purchase decision) 3) traditional shoppers (those who do not shop online)

4) dual shoppers (like to compare, but mostly rely on internet for information gathering) 5) E-laggard (has lower interest in seeking information on the internet)

6) Information surfer (loves banner ads and clicks on them and, has good navigation skills and prior experience) But also consumer characteristics, such as neuroticism, conscientiousness, extraversion, openness and

agreeableness play a role in whether consumers are likely to shop online (Bosjnak et al, 2007) and thus possibly what type of online shopper they are. Age used to be a demographic factor that was a predictor of online shopping intention because online shoppers are usually young people (Naseri and Elliot, 2011). However a growing number of older people are adopting online shopping (Lian and Yen, 2014). They are becoming more knowledgeable about internet use and build up online shopping experiences. Which are also good indicators of online shopping intentions according to Naseri and Elliot (2011). This group of older consumers is a growing potential market. And finally they name social connectedness as a predictor of the adoption of online shopping.

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-Dickerson and Gentry (1983) wrote that innovators, in that particular article the subject was the adoption of computer use, usually tend to be socially well connected. Naseri and Elliot (2011) found that social

connectedness is indeed a factor that explains online shopping intentions.

According to Stoenbachler and Gordon (2002) internet shoppers seek privacy, a secure environment to

purchase products, technical reliability, up-to-date content and timely delivery of products ordered. Monsue et al (2004) state that based on their literature review attitudes towards online shopping and the intention to shop online are based on ease of use, usefulness and enjoyment. But not only on these factors, because exogenous factors like consumer traits, situational factors, product characteristics, previous online shopping experiences and trust in online shopping are named as factors determining online shopping intentions.

According to Keng Kau et al (2003) the top reasons for buying online are convenience, unique merchandise and competitive prices. Brashear et al (2009), who did research among online shoppers in six different countries, present findings that show that these consumers are all similar in their desire for convenience, have more favourable attitudes toward direct marketing and advertising and are wealthier and heavier users of both e-mail and the internet. Chiang and Dholokia (2003) also found convenience to influence the consumer intention to engage in online shopping, but also the type of product. So when offline shopping is perceived as

inconvenient, consumers are more likely to buy those goods online. And goods that are defined as experience goods are less likely to be bought online compared to search goods. Lin (2007) explained shopper intentions by 3 competing theoretical models: Technology Acceptance Model (Davis, 1989), Theory of Planned Behaviour Model (Ajzen, 1989) and the Decomposed Theory of Planned Behaviour Model (Taylor, 1995). The latter specifically found that perceived usefulness, ease of use and compatibility (the degree to which the online environment fits with the user’s values, previous experiences and current needs; Rogers, 1983) are significant predictors of attitude towards online shopping. To be able to attract and satisfy consumers online retailers should spend a great effort into their websites to maximize these factors.

Situational variables such as distance-to-store, time pressures, clarity of the store and type of product are also reasons for choosing the online channel (Chocarro et al, 2013). When a store is not nearby and the time available to shop is scarce consumers are more likely to shop online. Clarity of the organization is a situational variable which influences channel preference the most. Store untidiness is a factor that makes consumers turn to the online channel, no matter what product category. Retailers that spend time on well designed and laid out websites thereby encourage online purchases.

Previous experience has earlier been named as a predictor for online shopping behaviour. One would expect that previous online shopping experiences should be positive in order to stimulate returning customers. Gounaris et al (2010) found that e-service quality has a positive effect on e-satisfaction and it also has a direct and indirect influence on behavioural intentions such as repeat visits and purchasing. And it also has a positive influence on word-of-mouth communication.

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-2.3.2 Offline channel choice

According to Gilly and Wolfinbarger (2000) online and offline shopping are viewed different by consumers in two ways: the freedom and control offered by online environments and the experiential qualities offered by offline shopping. So experiencing the products is a very important benefit drawing consumers to the shop floor. This is confirmed by Bhatnagar et al (2002) who claims people might not buy online because they would like to touch and feel something before buying. The author also states that risks associated with online shopping (for example credit card fraud) and problems with returning products that fail to meet the expectations are among reasons for not buying online. According to Levin et al (2005) offline shopping is preferred when shoppers need and want personal service and are interested in see-touch-handle products. Also Chiang and Dholakia (2007) state that offline shopping intentions are higher for experience goods than for search goods.

As mentioned in previous paragraph online and mobile shopping offers consumers several advantages (for example convenience, speed, ease of use), but brick and mortar stores can provide exciting, entertaining, and emotionally engaging shopping experiences as well as face-to-face personal interaction with store staff (Anderson and Eckstein, KPMG 2013). These shopping enjoyments are predictors of customer’s willingness to continue to shop in the retailers’ traditional brick and mortar store(s) (Johnson et al, 2015). The article states store factors that predict shopping enjoyment are atmosphere, price, leisure, design and service.

Store preference is created by increasing the satisfaction levels of the following instore attributes (Thang and Tan, 2003): merchandising (product mix, value for money, availability), accessibility (ease of travel, parking, duration of travel), reputation (history, value for money, reliability, word-of-mouth), in-store service (congeniality, advice on purchase, gift wrapping, convenience of payment), store atmosphere (decorations, layout, ease of movement, display of merchandise) and promotions (advertisements, promotions, special events). By doing so retailers could create store preference and possibly store loyalty.

Attitude towards a store is also influenced by the emotional responses to the shopping environment. Yoo et al (1998) conducted research on if these previously mentioned store characteristics induce shoppers’ in-store emotions. Their results show that store characteristics induce shoppers’ instore emotions and influence store attitudes. Some of the effects were direct (such as location) and others (product assortment, value,

salespersons’ service, after-sales service, store facilities) were mediated by the emotional responses triggered by those characteristics. For example when stores offer a wide variety of products, good value, and

accommodating facilities shoppers experience positive emotions like pleasure, excitements, contentment, pride and satisfaction. When store staff offer excellent service shoppers feel pleased, excited, content and attractive. When the retailer offers great after sales service shoppers feel pleasure, pride, attractiveness and contentment. Negative emotions, such as anger, anxiety, displeasure and nullification, were evoked by incompetent or unkind store staff and unaccommodating facilities.

In summary most important drivers for offline shopping preferences are primarily to be able to experience products in the retailers store and to receive personal service.

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-2.4 Customer migration and retention

As described earlier multi-channel customers use different channels to learn about and acquire products or product information. In the previous paragraphs the drivers of online and offline shopping preferences have been described. These insights make it easier for retailers to adjust their channels to the preferences of their customers. However, these channels have been described separately and many customers combine these channels in their purchase processes or using them simultaneously. Retailers should know when customers migrate to other channels.

Customer channel migration is a dynamic process in which a current customer repeatedly makes choices to frequent one of a retailers channel options, for example choosing between the online web shop, store and mobile online platforms (Sullivan and Thomas, 2004). According to Ackermann and von Wangenheim (2014) customer-initiated channel migration in the airline industry is the voluntary migration of a customer’s channel setting if the majority of transactions in the consecutive period occur in the additional or substitute channel in a 2 (direct vs indirect) by 2 (online vs offline) channel matrix. Direct vs indirect refers to booking either with the airline direct or through an agency. It would be interesting to know whether these results hold under different circumstances, for example buying directly from the brand (for example Nike.com) or buying the same sneakers from for example sneaker retailer Footlocker. Customers switching from the offline to the online channels show higher levels of cross-buying and those that switch from indirect offline to direct online channels reveal higher levels of both sales and cross-buying behaviour (according to Verhoef (2005) this is add-on selling, so buying/selling more products or services at the same time from/at the same party). These results suggest that brands should put more emphasis on attracting consumers from offline retailer channels to their online channels.

Many firms try to steer customers to their channels. However, all customers have different purchase patterns and retailers need to investigate the differences between customers in a multi-channel environment and to know when and why they have these patterns. And more importantly, what causes them to switch to another channel of another retailer? For example more loyal customers are less responsive to migration strategies than less loyal customers (Trampe et al, 2014). And multichannel organizations usually have more loyal customers, because they offer a wider range of services which lead to higher customer satisfaction and ultimately to higher customer loyalty. So retailers should put emphasis on creating loyal customers because they are less likely to switch to a different retailer.

Other factors that cause customers to switch (migrate) or to stay loyal to the firm are quality, satisfaction, value, trust, commitments, price and the attractiveness of alternatives (Bansal et al (2005). Trust has been named before as a factor that motivates or demotivates consumers to shop online (Stoenbachler and Gordon, 2002). So low trust with the current channel or retailer could lead to channel migration within the retailers’ channels or from one retailers’ channel to another retailers’ channel. As stated before satisfaction is also an important determinant of customer loyalty (Wallace et al, 2004), but it also leads to a decrease in the attractiveness of alternatives. Either the attractiveness of alternative channels or competitors’ channels. But

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-according to the article alternative attractiveness also influences satisfaction. For example when there are low switching costs or investments attached to switching to another channel, the amount of competitors has increased or customers have been in direct contact with competitors, they are more likely to become less satisfied with the current retailer. Furthermore Bensal et al (2004) state that subjective norms, switching costs, prior switching behaviour and variety seeking determine the probability of switching to another party. Consistent with price perception as mentioned by Bansal (2005) are sales promotions that stimulate shoppers to buy at that particular moment (Alvarez and Casielles, 2005). According to the article consumers would even buy products they would not normally buy. So sales promotions (discounts or free products) could keep shoppers from buying another product or buying at another store or through a different channel. Furthermore monetary savings as the result of loyalty programs are a predictor of customer loyalty. So retail loyalty

programs (saving points, coupons, special benefits) could be used to prevent customers from going to other channels or competitors. Liu (2007) states that loyalty programs are an important form of Customer Relations Management. It however is a costly way of customer retention and needs the long-term commitment of a firm to make it successful.

2.5 Consumer free-riding behaviour

Channel choice and switching challenges that retailers face are also influenced by the customer journey phases. The choice of channel might depend on what the specific needs for a consumer are along the different touch points in the customer journey. For example according to Baal & Dach (2005) 20 percent of multi-channel customers switch retailers when they switch channels between the information collection stage and the purchase transaction stage. This means that for example consumers gain information from one retailer and subsequently purchase the product from another retailer; this is called free-riding. Chiu et al (2011) refer to cross-channel free-riding when consumers use one retailers’ channel to obtain information or evaluate products and then switch to another retailers’ channel to complete the purchase. According to this article cross-channel free-riding erodes profits and is one of the most important issues that firms face in the multi-channel era. According to van Baal and Dach (2005) for every fourth online purchase a retailer provided unpaid information in its store. According to this article only 1.8% of the consumers completed their purchases in the retailers online channels they visited offline to collect information. An explanation for these online purchases could be price, but another explanation could also be the absence of an online channel. It is also unclear how many shoppers postpone their purchase instore but decide later on (for example at home) to purchase the product and do this online.

Consumer free-riding brings many negative consequences for multichannel retailers (Singley, 1995). This type of consumer behaviour could possibly reduce both productivity and profits of full-service retailers because it negatively influences productivity of store sales staff and instore generated revenues.

Free-riding is more likely when consumers adopt cross-channel rather than single-channel behaviour (Heitz-Spahn, 2013). The article also finds that free-riders do not visit just two channels (pre-purchase and purchase),

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-but visit more channels before making the actual purchase. And in contrast with the earlier mentioned theory that multichannel consumers are more loyal (Wallace et al, 2004), Heitz-Spahn (2013) states that multichannel shoppers are less loyal and are more likely to initiate free-riding behaviour. That research shows that when retailers add more channels, consumers are likely to free-ride. This could possibly highlight a negative outcome of multi-channel retailing. However as stated before, multi-channel consumers account for more revenues than do single channel consumers. Firms should find ways to retain potential free-riders in their channels. According to Heitz-Spahn (2013), to understand why consumers adopt cross-channel free-riding behaviour, it is necessary to address shopping convenience, flexibility and price comparison as the top three motives. The cross-channel free-riding consumer segment is more focused on utilitarian shopping motives such as price and schedule issues (refers to earlier mentioned flexibility and convenience) compared to hedonic shopping motives (website design, ergonomics and store design). These do not seem to have any explanatory power for free-riding behaviour according to their findings. And consumer free-riding is more likely for products with low-frequency but high value such as electronics, furniture and appliances. This is confirmed by van Baal and Dach (2005) who find that free-riding behaviour is more likely for products that change technology rapidly or products that are purchased infrequently.

Consumer personality characteristics have also been named as possible indicators of free-riding behaviour. Burns (2007) states that consumers who are more assertive and aggressive in the marketplace have more positive attitude towards free-riding than consumers who are not assertive and aggressive. So for store sales staff this means they should be alert to consumers who are assertive and aggressive because these are the consumers that need to be persuaded to buy with the specific retailer.

Chiu et al (2011) conducted research on cross-channel free-riding intentions of consumers who do online information searches at one retailer and switch to another retailers’ offline store to make the purchase. According to this article cross-channel free-riding intention can be explained by three factors: multi-channel self-efficacy, within-firm lock-in, and the attractiveness of competitors’ offline retail store. Self-efficacy is described in the literature (Bandura, 1986) as people’s judgements of their capabilities to organize and execute courses of action required to attain designated types of performances. Thus, in the context of multi-channel behaviour, to what extent do people find they are able to perform such shopping behaviour? Neslin et al (2006) states that within-firm lock-in is the ability of a company to retain consumers across both search and purchase processes. For example Chiu et al (2011) names switching costs as a possible within-firm lock-in factor. According to Wang (2008) consumer free-riding behaviour does not have to damage retailers’ businesses. Under certain circumstances they even benefit from free-riding. As consumer free-riding increases the online channel of the retailer becomes more competitive and raises prices. So this reduces the competition with the offline retailers. However, this is a timely process and there are still many retailers that do not have an offline channel (and can therefor compete on low pricing and might not change that). Many researches therefor still confirm that, whenever not well managed, free-riding erodes profits (Chiu et al, 2011).

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-Kucuk et al (2010) claims that consumer free-riding from a full service retailer (particularly a wallpaper retailer) to an online only retailer can be prevented by explaining the value of offline retail services provided and why a retailer charges more than an online only retailer. Manufacturers could also play a role by offering suggested retail prices because this leaves less room for price differentiation. And last but not least these full service retailers need to innovate their business models continuously to stay ahead of online only retailers in offering consumer experiences, service, etc.

Nowadays consumers can choose from so many different retailers and their channels and are even able, with the arrival of smartphones, mobile apps and tablets, to easily compare and switch channels during the

customer journey. It makes it more likely retailers lose their shoppers to competition throughout the customer journey. According to Heitz-Spahn (2013) 67% of the respondents adopted free-riding behaviours for the purchase of a durable good and more than 50% of them had engaged in some cross-channel free-riding

behaviour. There is a need for further research on customer retention of (potential) free-riders in multi-channel environments.

2.6 PPM Model

A common model used to explain consumer migration and thus consumer free-riding intentions is the Push Pull Model (Bansal et al, 2005). Historically a model used to explain human migration (Lee, 1966) it has also been used by for example Bansal et al (2005) and Chiu et al (2011) as a model to explain customer migration and switching behaviour.

Predictors of consumer switching behaviour are researched as a push, pull or mooring effect on consumer service provider switching (Bansal et al 2005) and free-riding behaviour from an online retailer to an offline competitor channel (Chiu et al, 2011). Push factors are factors that motivate people to leave an origin (Stimson and Minnery, 1998) and factors at the origin that have a negative influence on the quality indicators of life (Moon, 1995). According to Moon (1995) pull factors are positive factors that draw prospective migrants to the destination. Dorigo et al (1983) describe pull factors as the attributes of distant places that make them

appealing. Even though there could be strong push or pull factors people might not migrate due to contextual of situational factors, these factors are called mooring factors (Lee, 1966). Gardner (1981) described mooring effects as obstacles that prevent people from migrating.

In the context of consumer free-riding behaviour from online to an offline retailer Chiu et al (2011) state that a push factor is multichannel self-efficacy, because when people find they are able to perform particular behaviour it motivates people to actually become cross-channel free-riders. This factor might have weighed heavier when internet usage was not yet as popular and often used as it is nowadays. Attractiveness of the competitor retailer is described as a pull effect, because it draws consumers to another retailers’ offline channel. And finally the article states that within firm lock in is a mooring effect because it creates reluctance to switch. It would be interesting to know whether these same factors hold for the opposite situation where offline consumers switch to another retailers’ online channel. Probably factors that play a role in the instore

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-experience will effect cross-channel free-riding behaviour. Further research is needed in this and will be the main focus of this thesis.

Even though this model was designed to analyse human migration it has been proven to be an effective model for explaining consumer channel switching behaviour as described above.

2.7 Conceptual model

As stated before the specific situation of consumer free-riders visiting an offline retailers’ channel but who then switch to another retailers’ online channel to finalize the purchase has not been researched yet. To test factors that influence cross-channel free-riding behaviour from an offline channel to a competitors online channel the PPM Model will be used to identify the most important Push, Pull and Mooring factors that influence cross-channel free-riding behaviour from offline to a competitors online cross-channel. Factors from the multicross-channel behaviour, consumer migration and free-riding literature are combined with the PPM Model to measure the most significant push, pull and mooring factors in the specific situation of consumers visiting a retailers’ offline channel and the intention to then switch to a competitors’ online channel. This will gain insights for retailers to prevent consumers from free-riding behaviour and to form strategies for customer retention in their offline and online channels.

Based on the literature review push factors, factors that push consumers towards other retailers’ channels, are high price perception, low service quality, low integration of channels, low convenience and low store

attractiveness. Price has been named as a factor that predicts the perceived value of the channel and predicts consumer free-riding intentions in general and influences channel choice and loyalty (Tse, Yim (2001), Keng Kau et al (2003), Johnson et al (2015), Bansal et al (2005), Heitz-Spahn (2005)). A high price perception at the offline retailers’ channel could therefor explain switching behaviour to another retailer and in particular the online channel because consumers often think that the most competitive prices are found online. This factor is expected to be the strongest reason for free-riding behaviour from offline to online because free-riding behaviour is often linked to high thrift seeking (searching for low prices and discounts) consumers in the literature.

Another factor that is expected to cause consumers to switch to another retailers online channel is low service quality. The quality of service could create consumer loyalty, store preference and prevent switching

behaviours (Tse, Yim, (2001), Oppewal et al (2013)) and is determined by for example personal service from store staff (Levin et al (2005), Yoo et al (1998), Anderson and Eckstein (KPMG 2013), Johnson et al (2015), Thang and Tan (2003)) . So dissatisfaction with service factors that are usually the reason for offline shopping are expected to influence free-riding behaviours, specifically to an online channel where personal service is less important. Incorporated in service quality is also speed of service because it is expected that when retailers do not quickly (or at all) offer personal service to their customers the likelihood of losing these customers will increase because it decreases convenience for the customer. And convenience influences channel choice and

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-free-riding intentions (Chatterjee (2010), Tse and Yim (2001), Keng Kau et al (2003), Brashear et al (2009), Chiang et al (2003)).

The fourth factor expected to positively influence cross-channel free-riding behaviour is when the integration of the retailers channels is perceived as low (Kumar and Venkatesan (2005), Chatterjee (2010), Bhatnagar et al (2002)). Nowadays many retailers have multiple channels that can reach consumers when and whenever. These channels should be carefully managed to offer a seamless experience across all channels. For example the ease of using multiple channels from one retailer, efficient and care-less return policies, speedy and convenient product delivery, good stimulation of using the online channel in an offline shopping environment (applications, mobile websites, comparison information) result in consumer convenience and good integration of channels during the shopping experience does not leave any room for customers to migrate to another retailers channel. Chatterjee (2010) found evidence that high-thrift (low price seeking) consumers engaging in cross-channel behaviour are less likely to search for competitive offerings online (and also offline) than multi-channel consumers. This suggests that the more integrated multi-channels are the less likely high-thrift shoppers will go and purchase elsewhere.

Because of the use of smartphones customers are able to check and compare retailers reputation (prices, services, etc.) real time during the shopping process. Negative reviews, for example on prices, service, competitive position, are available and visible to consumers (Lin et al (2011), Thang and Tan (2003)). High quality reviews and a high number of reviews have a positive effect on the purchasing intention of online shoppers (Lin et al, 2011). Nowadays many people use their smartphones to compare retailers’ prices and reviews while shopping. As these reviews could have a positive influence on consumer perception of the retailers reputation and thus purchase intentions this factor has been included in the model. So negative reviews on the retailers reputation are expected to positively influence cross-channel free-riding behaviour from offline to online.

The final push factor that will be tested is the attractiveness of the store. Store attractiveness influences the choice of an offline channel and store preference and positive store attractiveness stimulates customers to also use the other channels of the retailers (Chocarro et al (2013), Bhatnagar et al (2002), Levin et al (2005),

Johnson et al (2015), Yoo et al (2013), Trampe et al (2014). Store attractiveness is defined by store tidiness, store atmosphere and design. These factors are of great importance to the shopping experience and could in general keep customers from shopping online at all. So it is expected that low store attractiveness will result in customers switching to another retailers online channel.

Logically the attractiveness of another retailers channel will attract consumers to these channels (Chiu et al, 2011). Chiu et al (2011) also showed that the attractiveness of the competitors offline channel serves as a pull factor to consumers. The same is expected in this thesis, but in the opposite direction; when the online competitors’ channel is more attractive in general than the retailers offline channel.

Expected mooring effects for cross-channel free-riding from offline to online are promotions offered instore and positive prior experience with the retailer. High switching costs are named as a mooring effect in the article

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-of Bansal et al (2011). Switching costs are the costs associated with leaving one party for another. In this specific case that could mean that consumers miss out on a promotion that they are made aware of instore (price discounts, free products, etc.). Bansal et al (2011) also name low variety seeking and infrequent prior switching behaviour as mooring factors influencing channel migration intentions. So frequent positive prior visits to the offline retailer suggest low prior switching behaviour and high retailer loyalty and is therefore expected as a factor negatively influencing free-riding behaviour.

Based on the above the conceptual model applied in this research is as follows:

2.8 Hypotheses

Resulting from the conceptual model the following hypotheses will be applied:

o H1a. High consumer perception of price of the offline retailers’ channel has a positive effect on cross-channel free-riding behaviour to a competitors’ online channel (see page 19).

o H1b. Low service quality of the offline retailers’ channel has a positive effect on cross-channel free-riding behaviour to a competitor’s online channel (see page 19).

o H1c. Low speed of service in the offline retailers’ channel has a positive effect on cross-channel free-riding behaviour to a competitor’s online channel (see page 19).

o H1d. Low integration of retailers’ channels has a positive effect on cross-channel free-riding behaviour to a competitor’s online channel (see page 20).

o H1e. Negative reviews of retailers’ reputation has a positive effect on cross-channel free-riding behaviour to a competitor’s online channel (see page 20).

o H1f. Low store attractiveness of the retailers offline channel has a positive effect on cross-channel free-riding behaviour to a competitor’s online channel (see page 20).

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-o H2. Attractiveness of the competitor’s online channel has a positive effect on cross-channel free-riding behaviour from the retailers’ offline channel to the competitor’s online channel (see page 20)

o H3a. Promotions offered during the visit of the retailers’ offline channel have a negative effect on cross-channel free-riding behaviour to a competitor’s online channel (see page 20).

o H3b. Positive prior experiences with the retailers offline channel have a negative effect on cross-channel free-riding behaviour to a competitor’s online cross-channel (see page 21).

As stated in the previous paragraph high price perception is expected to be the strongest factor causing consumers to engage in free-riding behaviour from the retailers offline channel to the competitors online channel. As free-riders are usually thrift seeking consumers, high price perception as the strongest factor would be a logical result. It is therefore interesting to find out what factors are strongest following price in order to find out what retailers should do to keep these shoppers in their channels.

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-3. METHODOLOGY

___________________________________________________________________________________________

3.1 Research design

The research approach that is applied in this thesis is deductive research. Previous theories from the literature are tested to answer the research question and subsequently also the sub questions. This thesis will generate more insights into consumer free-riding behaviour, particularly losing consumers to the competitors’ online channel after having visited the retailers’ offline channel, by studying the relationships between variables. These variables are identified from the literature as variables explaining free-riding and switching behaviour. Because of these characteristics this thesis is an explanatory study. The research method used is the ‘Survey’ method as this is a suitable method for explanatory studies. Research needs to be done on what factors have an influence on cross-channel free-riding behaviour and what the individual strength of the proposed variables is. Therefor structured data needs to be collected and the survey method seems most appropriate. Because of the timeframe of this thesis, this study is a cross-sectional study.

3.2 Data collection and sample

The population selected for this research is all Dutch male and female people that have had prior shopping experience with shopping for ‘experience products’. ‘Experience products’ are products that consumers feel they need to touch, smell or try on and those that require an offline presence at least at the final purchase stage (Levin, Levin & Weller, 2005, Chiang & Dholskia 2003, Lynch, Kent & Srinivasan 2001). Because the starting point in this thesis is the offline shopper, it makes sense to focus on ‘experience products’ that consumers would want to experience before buying either offline or online. In the sample the actual shopping experience of shoppers that purchased offline and at a competitors online channel is compared to gain insights into the differences and possible reasons for free-riding behaviour. Respondents have been asked questions about past experiences when shopping in a store after which they either bought instore or at a different retailer online. The first group is the group that shops instore and eventually bought the product from that same retailer either instore or from the retailers’ online channel. The second group is the group that shops instore but decided to buy the product from a different retailer online.

To select a sample to collect data a non-probability sampling technique called convenience sampling and snowball sampling has been used. Due to the time restrictions of this research these sampling techniques have been selected because they allowed for easy and fast access. Respondents were self-selected from personal networks and they were asked to forward the invitation to other (at least 2 to at least double the initially selected amount) potential respondents that met the participation criteria. Invitations to participate in the survey were send through email. The size of the sample that was aimed for was n=100 (Green, 1991). Two groups were to be compared in this research and both groups should consist of at least n=50 respondents. In this research 75 respondents per group was aimed for. Unfortunately it was more difficult than expected to fill

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-both groups with enough respondents within a short timeframe. In the end the first group had more than 100 respondents, and the second group (the free-riders) was filled with only 50 respondents after a lot of effort. Apparently most people did not have a free-riders experience that they had or could think of.

3.3 Survey design

Surveys were used to collect the data. To build the survey questions were used from prior research literature and were, when necessary, slightly adapted to the context of this thesis research question. By using items and measures from previous studies criteria for validity of the survey are met. Control variables were used to identify the two groups that will be compared. But also to gain more insights into the characteristics of age groups and possible differences in shopping behaviour for the different experience goods.

The different items and measures for the constructs in the conceptual model will be explained in this section. The survey can be found in Appendix 1. All constructs of the conceptual model will be measured on a seven point Likert scale.

3.3.1 Constructs and measures overview

In the following table an overview of the different constructs from the conceptual model, the items, measures and references can be found that were used in the survey.

Constructs Items Measures Reference

Price I pay a better price for the product(s) in this store

than I would at a competitors store 7 point likert Bansal et al (2005)

Service Quality The people who work in this store were very helpful 7 point likert Johnson et al (2015)

The people in this store were very friendly

Speed of Service The people who work in this store offered fast

service 7 point likert Yu et al (2011)

Store attractiveness The store is well designed 7 point likert Johnson et al (2015)

The store has a good appearance

The atmosphere in this store is entertaining

The atmosphere in this store is stimulating

Reputation _{I have always had a good impression of the retailer}

I visited.

7 point likert Nguyen et al (2001) In my opinion, the retailer I visited has a good

image in the minds of consumers

I believe that the retailer I visited has a better

image than its competitor.

Integration of channels The store allows me to choose where to shop for

merchandise 7 point likert Lee et al (2010)

The store allows me to choose a way of returning

the merchandise

_{The store allows me to arrange/choose delivery}

options

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The store allows me to arrange various service

options

The store allows me to check out new products

online/via apps

Alternative attractiveness All-in all competitors would be much more fair

than the retailer of the physical store I visited first 7 point likert Bansal et al (2005)

Overall competitors policies would benefit me

much more than the policies of the retailer of the physical stores I visited first

In general, I would be much more satisfied with the services offered by competitors than the services provided by the retailer of the physical store I visited first

Overall, competitors would be better to do

business with than with the physical store I visited

Positive prior experience My past dealings with the store before I visited the

store left a positive impression.

7 point likert Tax et al (1998) _{My past dealings with the store before I visited the}

store were negative.

_{My past dealings with the store before I visited the}

store left me satisfied.

_{My past dealings with the store before I visited the}

store left me dissatisfied.

Promotions The store offered good price discounts /

promotions 7 point likert Johnson et al (2015)

3.4 Pre-test

A pre-test was conducted to make sure the introduction in the cover letter and the questions were clear and well understood to make sure relevant data could be generated later on in the process.

Five people took part in the pre-test and based on their results and feedback after completing the survey the initial survey was slightly adjusted. Most commonly asked question after completion was: ‘Is it correct I only had to fill out 3 questions?’. Apparently most people referred to an online shopping experience only and were therefor excluded from the rest of the survey as data on the instore shopping experience only is needed. Most people did not read the cover letter well or were not made well enough aware of the fact they needed to refer to an instore experience for experience goods after which they had bought the product either at that particular store or at another retailer online.

More explanation was added to the questions in the questionnaire so it would be clear to respondents that they were about to be excluded from further participation in the case of answering no to certain control questions. It is an extra reminder to respondents of what particular experience is relevant for filling out this survey. After these adjustments the final survey was sent out to the sample group and results were generated.

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-4. RESULTS

__________________________________________________________________________________

In this chapter the results of the data collected will be presented. The chapter will start with an explanation of how the data was prepared for data analysis. In the second part of this chapter the data will be analysed by different means to test hypotheses and research questions.

4.1 Data preparation

Through the survey collection method 190 respondents took part in the survey. Unfortunately not all the respondents showed useful data. Many respondents were excluded from taking part in the early stages of the survey. Shoppers were excluded when:

- they did not buy an ‘experience product’ in the past

- they did not experienced an ‘experience product’ instore before purchasing - they did not buy the product afterwards instore or from another retailer online

The data of these respondents were deleted from the data set. Ultimately Group 1 (shoppers who shopped instore and bought the product there) contained 94 respondents and Group 2 (shopper who shopped instore but bought the product from another retailer online) contained 32 respondents. Missing values in the data were excluded from analysis . Finally, SPSS will be used to analyse the data.

In the following sections requirements of the data will be checked in order to further use the data for analysis.

4.1.1 Reliability of scales

The reliability of all constructs is tested, results can be found below. Only price perception, speed of service and promotions are not tested for reliability as these constructs have a one-item scale.

Service quality

Cronbach Alpha score for this construct is .863. This is sufficient as the score is greater than 0.7. The Corrected Item – Total Correlation scores are all above 0.30, which is also sufficient. As this is a two-item scale the Cronbach’s Alpha when items deleted is not relevant.

Integration of channels

Cronbach Alpha score for this construct is .816. This is sufficient as the score is greater than 0.7. The Corrected Item – Total Correlation scores are all above 0.30, which is also sufficient. And the Cronbach Alpha scores if

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-items deleted differ less than 0.1 from the Cronbach Alpha score, so these -items are all good. This means that this construct is reliable and can be used for further testing.

Reputation

Cronbach Alpha score for this construct is .779. This is sufficient as the score is greater than 0.7. The Corrected Item – Total Correlation scores are all above 0.30, which is also sufficient. And the Cronbach Alpha scores if items deleted differ less than 0.1 from the Cronbach Alpha score, so these items are all good. This means that this construct is reliable and can be used for further testing.

Store attractiveness

Alternative attractiveness

Prior experience

Cronbach Alpha score for this construct is .627. This is not sufficient as the score is not greater than 0.7. The Corrected Item – Total Correlation scores are all above 0.30, which is sufficient. And the Cronbach Alpha scores if items deleted indicate that when one item is deleted the Cronbach’s Alpha score will become .839. Therefor item 1 (PriorExp1Nega) will be excluded from the construct so it will be reliable.

4.1.2 Normality

In order to choose and execute the correct statistical tests a check on the distribution of the data is necessary. To test whether the data is normally distributed, and thus whether parametric testing is possible, the

Kolmogorov-Smirnov test is used. These test have been done on both groups (Group 1: not a free-rider, group 2: free-rider) within the different variables. When the Kolmogorov-Smirnov test is significant for both groups, it

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-means that the data differs significantly from a normal distribution and non-parametric testing is needed. The distributions of the data can be found in the Appendix 2.

Price Perception (PricePercTOT)

The Kolmogorov-Smirnov test for group 1 shows D(109)=.181, p<.05 and for group 2 D(47)=.208, p <.05. This means that both scores deviated significantly from normal. Because the data is not normally distributed a non-parametric test must be used.

Service Quality (ServQualTOT)

Speed of Service (ServSpTOT)

Integration of channels (ChanIntTOT)

The Kolmogorov-Smirnov test for group 1 shows D(120)=.108, p<.05 and for group 2 D(45)=.105, p>.05. This means that the group 1 scores deviates significantly from normal, whereas the group 2 score does not deviate significantly from normal. Because the data is not entirely normally distributed a non-parametric test will be used.

Reputation (RepTOT)

The Kolmogorov-Smirnov test for group 1 shows D(109)=.140, p<.05 and for group 2 D(45)=.154, p<.05. This means that both scores deviated significantly from normal. Because the data is not normally distributed a non-parametric test must be used.

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-Store Attractiveness (-StoreAttrTOT)

The Kolmogorov-Smirnov test for group 1 shows D(108)=.134, p<.05 and for group 2 D(46)=.155, p<.05. This means that both scores deviate significantly from normal. Because the data is not normally distributed a non-parametric test must be used.

Alternative Attractiveness (AltAttrTOT)

The Kolmogorov-Smirnov test for group 1 shows D(109)=.085, p>.05 and for group 2 D(46)=.133, p<.05. This means that group 1 does not deviate significantly from normal, but the scores of group 2 do. Because the data is not entirely normally distributed a non-parametric test will be used.

Prior Experience (PriorExpTOT)

Promotions (PromoTOT)

4.1.3 Scale means

Scale means are needed to do further testing. Please find below in table 1 an overview of the variables and their scale means:

Table 1. Scale means

N Mean

Price perception total 156 4,2500

Service quality total 156 5,2276

Speed of service total 154 4,9732

Integration of channels total 153 4,7268

Reputation total 154 5,2457

Store attractiveness total 154 5,1201 Alternative attractiveness total 155 3,6349