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The omnichannel revolution: device switching and its effect on cross- channel free riding intentions


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The omnichannel revolution: device switching and its effect on cross- channel free riding intentions

University of Amsterdam Faculty of Economics and Business

MSc Business Administration – Digital Marketing track

Author: Maxim Wakker Student number: 11791632 Date: 27-01-2022 – Final version Thesis Supervisor: F. Javier Sese EBEC approval number: 20220127090107



Statement of Originality

This document is written by Student Maxim Wakker who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

UvA Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.



Table of Contents

Abstract... 7

1. Introduction ... 8

2. Literature review ... 12

2.1 Omnichannel marketing ... 12

2.1.1 Definition and evolvements ... 12

2.1.2 Customer retention strategies in an omnichannel setting ... 13

2.2 Channel choices along the customer journey... 14

2.2.1 Omnichannel segmentation ... 14

2.2.2 Channel choices ... 15

2.2.3 Customer journeys and cross-channel effects ... 16

2.3 Cross-channel free riding behaviour ... 17

2.3.1 Origin and definition ... 17

2.3.2 Motives ... 17

2.3.3 Retention... 18

2.3.4 Device choice and webrooming ... 19

2.4 Summary ... 20

3. Conceptual framework and hypotheses ... 21

3.1 Conceptual framework ... 21

3.2 Hypothesis development ... 22

3.2.1 Device switching and cross-channel free riding intentions ... 22

3.2.2 Customer loyalty ... 23

3.2.3 Product type ... 24

3.3.4 Product price ... 25

3.3 Hypothesis overview ... 26

4. Methodology ... 27

4.1 Research methods and design ... 27

4.1.1 Study 1 ... 27

4.1.2 Study 2 ... 28

4.2 Pre-test ... 29

4.3 Sample and participants ... 30

4.4 Measurements ... 32

4.4.1 Dependent variable ... 32



4.4.2 Independent variables ... 32

4.4.3 Control variables ... 33

5. Results ... 35

5.1 Study 1 ... 35

5.1.1 Data examination ... 35

5.1.2 Reflective constructs ... 35

5.1.3 Descriptive statistics ... 36

5.1.4 Preliminary results ... 37

5.1.5 Regression analysis ... 38

5.1.6 Assumptions and robustness checks ... 39

5.2 Study 2 ... 40

5.2.1 Randomization check ... 40

5.2.2 Data examination ... 41

5.2.3 Reflective constructs ... 41

5.2.4 Descriptive statistics ... 41

5.2.5 Preliminary results ... 42

5.2.6 Regression analysis ... 43

5.2.7 Assumptions and robustness checks ... 45

5.3 Hypotheses outcomes ... 46

6. Discussion ... 47

6.1 Answering the research question ... 47

6.2 Theoretical implications... 48

6.3 Managerial implications ... 49

6.4 Limitations and recommendations for future research ... 50

7. Conclusion ... 52

Bibliography ... 53

Appendices ... 61

Appendix I. Survey ... 61

Appendix II. Pre-test survey for experiment ... 66

Appendix III. Experiment design... 68

Appendix IV. Descriptive statistics for normality in study 1. ... 74

Appendix V. Correlation matrix for reliability of variables in study 1... 75



Appendix VI. VIF tables for multicollinearity in study 1. ... 76

Appendix VII. Additional regressions for robustness study 1. ... 77

Appendix VIII. Descriptive statistics for normality in study 2. ... 77

Appendix IX. Correlation matrix for reliability in study 2. ... 78

Appendix X. VIF tables for multicollinearity in study 2. ... 78

Appendix XI. Heteroscedasticity plot for study 2. ... 79

Appendix XII. Plots of residuals for normality check of study 2. ... 80

Appendix XIII. Additional regressions for robustness study 2. ... 81



List of tables

Table 1. Overview of hypotheses. ... 26

Table 2. Overview of preliminary questions to select respondents for either study 1 or 2. ... 28

Table 3. Overview of different groups for study 2. ... 29

Table 4. Results of pre-test manipulation check. ... 30

Table 5 Summary statistics for both survey and experiment separately. ... 31

Table 6. Pairwise Pearson correlation matrix of the relevant variables of study 1. ... 37

Table 7. Hierarchical logistic regression with main independent variable freeriding. ... 39

Table 8. Pairwise Pearson correlation matrix of the relevant variables of study 2. ... 42

Table 9. Hierarchical OLS regression of main dependent variable freeriding. ... 44

Table 10. Overview of outcomes of hypotheses. ... 46

List of figures

Figure 1. Conceptual framework. ... 22

Figure 2. Difference in means of cross-channel free riding intentions between devices in study 1. .... 37

Figure 3. Difference in means of cross-channel free riding intentions between devices in study 2. .... 43




The omnichannel revolution in which more devices and touch points are fully integrated in the customer journey, is in full swing. The availability of increasingly more devices accelerated this process as customers use different devices for different purposes continuously. Consequently, many firms face the problem that customers use their free online services without continuing to purchase.

This thesis aims to discover the impact of switching from a mobile to a fixed device on cross-channel free riding intentions. More specifically, how do the intentions of customers to switch retailers change when they use a more fixed device and plan to buy that specific product in a physical store? The hypotheses with respect to this question are tested by analysing online data from both a survey and an experiment which are collected through Qualtrics. The results show that customers have higher intentions to switch retailer in a webrooming setting when they use a more fixed device for search purposes. This relation is moderated by customer loyalty in such a way that for loyal customers this effect is not observed. Product type and product price do not seem to significantly moderate this relation. The findings show that the use of different devices matter in an omnichannel environment.

Academics can use this empirical study as a first framework to study device switching and cross- channel free riding jointly rather than separately. Marketers can implement the findings by designing marketing campaigns more efficiently across the different devices and retain more customers inside the firms’ channels.

Key words: omnichannel marketing, cross-channel free riding, device switching, webrooming



1. Introduction

Omnichannel marketing is the standard for many firms in the current digitized world and is still growing at a rapid pace (Verhoef et al., 2015). The continuous development of the omnichannel integration has caused more customers to adopt multichannel behaviour and take advantage of channel-specific characteristics (Verhoef et al., 2007). Analysing a large sample of 46.000 shoppers Sopadjieva et al. (2017) found that 73% of the customers use multiple channels during their shopping journey. They also identified that omnichannel customers spend on average 4% more in store than other customers and 10% more online than customers who use only a single channel. This

demonstrates the significance of omnichannel integration and the retention of such valuable customers inside the firm.

Creating a strong customer experience is now one of the leading objectives within firms (Lemon & Verhoef, 2016). A strong customer experience in a retailing context exists of the

customer’s positive cognitive, affective, emotional, social and physical response to a retailer (Verhoef et al., 2009). As customers interact through more channels, setting up a coherent omnichannel

integration is critical for firms to accomplish this objective. Once a seamless interaction between multiple channels has been reached, organizations have greater opportunities to interact with their customers (Käuferle & Reinartz, 2015). Customers that are emotionally fully connected are 52% more valuable, on average, than customers who are only highly satisfied (Magids et al., 2015). Therefore, the customer journey is a process in which firms try to interact with customers as much as possible to keep them connected.

As people are now better able to switch channels along their respective customer journeys (Watson et al., 2015), it creates several problems for organizations. Cross-channel effects have become stronger and there is an increased chance a firm might lose the customer during switching (Neslin et al., 2006). This poses a significant threat to many organizations as in that case the marketing efforts in their own channel do not find its way inside the firm. The phenomenon of consumers switching from one retailer channel to another channel of another retailer, is called cross- channel free riding (Van Baal & Dach, 2005) and this will be the focal point of this thesis.

The increased use of the internet forms the foundation for cross-channel free riding behaviour to materialize (Chevalier, 2002). Research from 2007 by comScore already showed that 89% of consumers use the online space to search for information, but only less than 7% of the sales take place through that channel. Eliminating omnichannel integration is not the way to tackle this problem as 90% of customers actually want a cross-channel shopping experience (Readycloud, 2019).

Accordingly, the Interactive Advertising Bureau (IAB) Data Center of Excellence and the

Winterberry Group (2016) reported that roughly 70% of the responding marketers and executives named cross-device audience recognition as the topic that would require most of their attention in the



recent future. Highlighting switching behaviour and taking appropriate actions are therefore becoming more crucial to survive in an omnichannel environment.

With the rapid growth of omnichannel integration, there is an increased need to effectively allocate resources across channels and better understand cross-channel effects (Kushwaha & Shankar, 2020). Kushwaha & Shankar (2020) found that cross-channel effects are significant and asymmetric as some channels more often occur earlier in the customer journey and therefore carry more positive spillover effects to other channels along the journey. Companies like Apple and Nike sell their products through 4 or 5 different channels and understanding the effects of their effort in one channel on the outcomes in other channels is crucial since all channels are evolving rapidly (Gauri, 2013).

Cross-channel effects are also apparent in device switching. People tend to turn to a less mobile device to make purchases (De Haan et al., 2018). A survey by Gallup (2015) showed that 74%

of US adults use their desktop or laptop to make their purchase online. On the other hand, 60% of shoppers began their product search on a mobile device (Readycloud, 2019), but most sales still take in the physical store. A recent publication by the US Department of Commerce (2021) showed that in the US, retail sales have increased dramatically after the dip during the COVID pandemic. Web penetration is only at 18.6% which means that even though ecommerce is growing, it is still important to review retail sales more extensively. Because of these recent movements, it will be especially interesting to highlight the behaviour where the search process starts online but the actual purchasing is done in a physical store. This behaviour is called webrooming and it has become far more

pronounced over recent years (Kleinlercher et al., 2020).

Even though omnichannel customers may provide more monetary value (Sopadjieva et al., 2007; Kumar & Venkatesan, 2005), they also yield problems. Customers benefit from the free access to product information from one retailer, but then place an order with another retailer that offers other (price) benefits (Van Baal & Dach, 2005). Cross-channel free riding behaviour is a major problem for firms and therefore much research has been conducted to find why people engage in the

behaviour. Most studies have focused on the two most common directions of behaviour within cross- channel free riding. One is webrooming and the other is showrooming where consumers search through a retailer’s physical store but turn to a competitor’s online store to purchase the product (Gensler et al., 2017). Heitz-Spahn (2013) looked into possible motives to engage in cross channel free riding and concluded that shopping convenience, flexibility and price comparison are important drivers. Chiu et al. (2011) detected that multichannel self-efficacy and attractiveness of competitor’s offline retail store positively influenced webrooming intentions, while within-firm lock-in had a negative influence.

The growing influence of omnichannel integration and the rise of mobile e-commerce provides new problems in relation to cross-channel free riding. The work of De Haan et al. (2018) highlights the importance of different devices in an omnichannel environment. Conversion rates for more fixed devices like laptops and desktops are significantly higher for mobile devices like phones



and tablets. Also, the work of Xu et al. (2017) showed that device switching is an important aspect to consider when multiple devices are available to a consumer. However, what remains untouched by these studies is how the usage of different devices affects the level of cross-channel free riding

behaviour when the consumer has webrooming intentions. Previous research on device switching only specified the online sphere where search and purchase are done online. On the other side with respect to cross-channel free riding behaviour, shopping motives have been identified that show why people switch to another retailer when they switch channels (Chiu et al., 2011; Heitz-Spahn, 2013; Chou et al., 2016).

Therefore, this thesis focuses on the level of cross-channel free riding intention when using either a mobile device or fixed device when searching for information and subsequently buying the product in the competitor’s physical store. Webrooming is preferred in this setting because the search phase specifically focuses on online usage of devices. Also, it is the most common switching

behaviour (Chiu et al., 2011), which was supported by a recent study of JRNI (2019). The study showed that 74% of the consumers in the US and UK engaged in webrooming behaviour, while only 57% in showrooming. Accordingly, a lot of shops face the difficulties of consumers using their websites for the wrong intentions as webrooming has become common practice in omnichannel consumer behaviour (Herhausen et al., 2019).

The main relationship between the type of device and the level of cross-channel free riding intention in a webrooming setting is extended with relevant moderators. Customer loyalty is an important factor for businesses as it locks the customer inside the firm (Anderson et al., 2002). As customers have more experience with a specific firm it also reduces the risks associated with shopping online and in store. However, in the light of webrooming behaviour this relation still contains many questions and therefore is especially interesting for research. Also, product type and product price are important aspects to consider in the light of cross-channel free riding behaviour as they can provide more detailed answers to practitioners how to use the insights from the main relation in reality. These factors have shown to generate interesting effects on research on similar topics (De Haan et al., 2018;

Heitz-Spahn, 2013; Chiu et al., 2011) and are therefore investigated in this thesis. Consequently, in order to provide specific answers to the problems regarding type of device and cross-channel free riding and its moderating effects, the following question will be answered in this thesis:

How does the type of device (mobile vs fixed) influence the level of cross-channel free riding intention in a webrooming situation and how is this relation moderated by customer loyalty, product type and

product price?

The proposed research question will contribute to the literature on omnichannel retailing and mobile marketing. Firstly, this thesis provides insights on the influence of cross-device usage in a webrooming situation. Much research has previously focused purely on cross-channel free riding



(Heitz-Spahn, 2013; Chiu et al., 2011; Maggioni et al., 2020) or the webrooming and showrooming phenomenon (Kleinlercher et al., 2020; Flavián et al., 2020). On the other side of device switching, most research focused on only conversion through online channels (De Haan et al., 2018; Xu et al., 2017). This thesis will therefore contribute to this literature by focusing on the consumer path to purchase across different types of devices and how this influences the decision to buy a specific product in a physical store. Secondly, this research contributes to the literature by providing extra information under which circumstances the chances are either higher or lower a person will engage in cross-channel free riding behaviour. The study will therefore provide a framework to review cross- channel free riding when a customer engages in webrooming and device choice jointly rather than separately.

Practically, marketers can specifically use the insights from this study to understand how the use of different devices influences customers to change retailers when they commit to buy the product in the offline store. Also, by focusing on customer loyalty, product type and product price

practitioners will be provided with more answers specifically to their product or service. Websites and marketing campaigns can therefore be designed more efficiently on each device separately to retain as many customers within the retailer’s channels. Therefore, important solutions are provided in this study that will help companies deal with the increasing problem of cross-channel free riding.

This thesis is structured as follows. First, there will be a comprehensive elaboration on the existing literature in the field of omnichannel marketing and cross-channel free riding behaviour in relation to different devices. This information flows accordingly into the construction of the conceptual framework and development of hypotheses in the next chapter. In section 4, the methodology will be explained where the data collection through both a survey and an experiment will be elaborated and analyzed. This will be naturally followed by the results section and consists of a full analysis of the data of both studies to see if the hypotheses are accepted. Next, there will be a discussion on the obtained results and implications to the existing literature and the practical world, after which a conclusion will briefly summarize the entirety of the thesis.



2. Literature review

This chapter will focus on the relevant existing literature on the several topics that are important to understand cross-channel free riding behaviour and device usage. Firstly, omnichannel marketing will be defined in a broad sense before exploring the different literature on customer retention strategies.

Next, consumers will be highlighted through their channel selections along their respective customer journeys. Lastly, the concept of cross-channel free riding behaviour will be laid out with an explicit focus on webrooming and device choice. At the end, there will be a short summary in order to highlight the research gaps that are investigated in this thesis.

2.1 Omnichannel marketing

2.1.1 Definition and evolvements

The world of retailing has changed dramatically over the past decade and this is mainly caused by the continuous advancements in technology (Verhoef et al., 2015; Oh et al., 2012). The retail industry is moving towards so-called omnichannel retailing which takes a broader perspective on channels and focuses on how shoppers are influenced and move through several channels along the customer journey (Verhoef et al., 2015). Rather than just having an offline and online solution in parallel, in an omnichannel setting, firms provide a seamless and simultaneous shopping experience in both physical stores and through multiple online channels (Sopadjieva et al. 2017; Lazaris & Vrechopoulos, 2014).

As a result, the customers consider all the retailer's sales and marketing channels as one entity (Herhausen et al., 2015).

While multichannel integration means a clear distinction between the offline and online store, in an omnichannel context the customer can roam freely between the online, mobile devices and physical store, all during the same transaction process (Rosenblum & Kilcourse, 2013). Still, the terms multichannel and omnichannel are often used interchangeably but increasingly more literature is available that separate the two concepts and define omnichannel marketing as a distinguished paradigm (Simone & Sabbadin, 2018).

Early studies have shown there is little cannibalization between online and offline channels.

Cao & Li (2015) found that channel integration is positively related to sales growth, while Pozzi (2013) highlighted that the introduction of an online store increases overall sales with limited effect on the sales in the physical store. Similarly, Piercy (2012) investigated the cross-channel effects of the offline and online store, but concluded that there are both significant positive and negative effects.

Consequently, it is not guaranteed that offering your service through more than one channel provides higher returns. Still, the fact that the integration of several channels increases the joint value, provides a large incentive to develop more channels (Tse & Yim, 2001).



More firms are continuously trying to implement new omnichannel strategies due to the observed benefits and technological advancements (Verhoef et al., 2015). The customers' changed shopping patterns are driven by new innovative technologies which enhance the omnichannel experience such as mobile devices, creative software, mobile applications and payments, e-coupons and digital flyers (Rosenblum & Kilcourse, 2013). Such new technologies not only allow the retailers to prosper more effective and personalized promotions but also optimize their prices. As a result, new in-store technologies are available in addition to the mobile devices: virtual screens and aisles, virtual mirrors-fitting room, self-service displays, vending machines and QR codes (Rosenblum & Kilcourse, 2013). Alternatively, through in-store Wi-Fi networks, firms can communicate with their customers through their mobile devices and also track their behaviour (Verhoef et al., 2015). With implementing such new innovations in the offline stores, the retailers are trying to improve their omnichannel strategy. The importance and role of such innovations is continuously growing and shows that the focus of marketers should not only be on the online channels (Simone & Sabbadin, 2018).

Another important development in omnichannel marketing is the integration of interactive channels with traditional mass advertising channels (Verhoef et al. 2015). An example is how Vodafone and other telecom providers are using mobile apps to interact with TV viewers during shows. Such possibilities provide enormous opportunities for firms to connect with many more people than what previously was known.

2.1.2 Customer retention strategies in an omnichannel setting

Aron (1999) looked at multichannel retailers and discovered that they have an advantage when it comes to customer acquisition over online only retailers. Consumers find their way easier to retailers that are visible both online and offline and such retailers therefore spend half the money as online- only retailers to attract new customers. Turning to customer retention, the opposite is true.

Multichannel retailers have a much harder time keeping customers inside the firm and Jaffe (2000) argued that they have to spend five times more money to retain customers compared to online-only retailers.

However, many things have changed since the period that these papers have been written.

Omnichannel marketing has taken off and increasingly more channels are integrated.

Correspondingly, retailers have struggled to clearly segment customers in order to know where to focus their resources (Neslin & Shankar, 2009). In order to clearly understand the desires of the customer, customer feedback metrics (CFM) have become more important to predict and subsequently increase customer retention (De Haan et al., 2015).

Technological developments on for example social media, caused firms to have a lot less control over the customer journey, which may result in the customer leaving the firm’s channels (Rapp et al., 2015). This highlights the importance for omnichannel players to create a strong



customer experience as this is of great importance to keep customers engaged over their complete customer journeys (Lemon & Verhoef, 2016). A strong customer experience in a retailing context exists of the customer’s cognitive, affective, emotional, social and physical response to a retailer (Verhoef et al., 2009). Such an experience consists of separate contacts between the firm and the customer along several touch points over a customer’s decision process or purchase journey (Homburg et al., 2017; Verhoef et al., 2009).

A recent study by Gao et al. (2021) showed how an incoherent customer experience can lead to a reduction in customer retention. This would mean that when a company fails to effectively create a customer experience through both its online and offline touchpoints, there is an increased chance the customer leaves the firm’s channels. This incoherency is measured through the cognitive effort it takes to switch between channels. However, if a channel is perceived to be transparent, convenient or seamless, this negative effect can be mitigated.

Pekovic & Rolland (2020) also described the importance of creating a strong customer experience to increase customer loyalty. They found that firms may benefit from investing in

customer experience, even if they do not invest in all dimensions, but only when the dimensions they select substitute for or complement the others that are already used. Cambra-Fierro et al. (2021) further researched this topic and concluded that creating not only a coherent but also a stable customer experience is crucial for customer retention.

2.2 Channel choices along the customer journey 2.2.1 Omnichannel segmentation

It is also important to take an individual approach to omnichannel marketing and investigate the individual decision processes and choices. Fully understanding your customers and their choices allows firms to adapt their channels more precisely and efficiently. However, every customer is different and firms should focus on segmenting customers to create a clearer picture on where to focus their resources (Neslin & Shankar, 2009).

Researchers have therefore long tried to segment multichannel or omnichannel customers because of their different shopping behaviours (Verhoef et al., 2015). Kumar & Venkatesan (2005) already identified customers who shop across multiple channels provide higher sales, higher share of wallet, have higher customer value and a higher likelihood of staying active than other customers.

Another important finding in their paper is that multichannel shoppers have a higher familiarity to the retailer and have higher purchase frequencies.

Older research by Okumara (2002) showed that multichannel shoppers are more likely to be between 18 and 34 years old. Lee & Kim (2010) however, revealed that age has become less of a factor for multichannel shopping recently and shows older age groups have found their way to work with multiple channels at once as well. Additionally, they discovered that multichannel shoppers are



more likely to respond to promotional advertisements and folders which makes them an even more attractive segment to target.

A more recent study by Sopadjieva et al., (2017) showed that omnichannel customers spend 4% more than the average customer and 10% more than customers who only use a single channel. In addition, omnichannel customers are also considered to be more loyal. Such customers logged 23%

more repeat shopping trips to the retailer’s stores and were also more likely to spread positive word- of-mouth than those who used a single channel. These findings highlight that customer who shop in multiple channels differ significantly from other shoppers in terms of characteristics and drivers.

Therefore, clearly segmenting such groups provides opportunities to develop new channel strategies to fully take advantage of these characteristics.

2.2.2 Channel choices

Along with segmenting omnichannel customers it is important to understand the channel choices customers make to really be able to generate successful channel strategies. However, what makes creating these strategies difficult is that decision processes evolve over time (Valentini et al., 2011).

Therefore, strategies have to be constantly reviewed to check if they are still applicable and relevant.

Valentini et al., (2011) argued that only consumers that are highly responsive to marketing efforts will alter their decision processes. New consumers are more responsive to marketing compared to mature customers. Consequently, new customers are more prone to be positively influenced by marketing efforts and less attention could be paid on for example service experience as compared to mature customers. Consistent with this outcome, Venkatesan et al. (2007) concluded that customers' channel choice, channel switching behaviour, and price effects are extremely complex concepts and depend on many factors such as product category and customer shopping traits.

When reviewing channel choices, historic experiences affect the consumers’ attitude towards other channels. Kim and Park (2005) discovered that the consumers’ prior attitude toward an offline retailer could predict the attitude toward the online channel of that retailer. So, this means that positively affecting consumers' attitudes towards offline stores may then have a positive effect on the consumers’ attitude towards online stores and online information searches. The most important factors to positively increase such attitudes are store image and service consistency. This is also in line with recent research by Mark et al. (2019) who found that email communication has a significant positive effect on the frequency of purchases in the offline channel.

However, while the effect of the web channel on the offline channel is mainly positive, the introduction of a physical store has mixed effects on other channels (Shankar & Kushwaha, 2020).

Both Avery et al. (2012) and Pauwels & Neslin (2015) discovered similar results that the introduction of physical stores of a retail firm reduces the sales of the catalogue channel without affecting sales on the online channel. Nevertheless, the results showed that in most cases it will not hurt but benefit the



company to introduce new channels. Shankar & Kushwaha (2020) identified asymmetric cross- channel effects much like we could expect from the previous studies. However, what is interesting is that they found asymmetry in both the direction and magnitude between a persuasive channel (agent), informative channel (web) and balanced channel (call centre). It demonstrates that customer channel migration is a dynamic process where customers are repeatedly making choices between online and offline channels (Sullivan & Thomas, 2004).

2.2.3 Customer journeys and cross-channel effects

As omnichannel marketing has evolved rapidly, the more singular channel choice processes which just have been described do not fully take the perspective of the complete customer journey. The full customer journey takes into account the relevance of prior and also subsequent interactions (Barwitz

& Maas, 2018). Customers' channel choices should therefore be reviewed across the three different stages of the customer journey: pre-purchase, purchase and post-purchase.

Throughout this process, customers do not use the same channel for all of their interactions (Venkatesan et al., 2007). Within the customer journey there has been much research on the

progression between stages and the omnichannel behaviour of customers. Verhoef et al., (2007) were one of the first to provide evidence of the research shopper phenomenon, where people search for information in the pre-purchase stage, but do the actual purchase through another channel. However, as firms have even more tried to streamline the transition between channels, the underlying motives may have decreased too, such as cross-channel synergies and insufficient channel lock-in (Homburg et al., 2017).

Gensler et al. (2012) found that the channel which is used in the previous stage of the customer journey is positively related to the choice of channel in the next phase. This is in line with research by Melis et al. (2015) who concluded that people tend to stick with their previous decision as a result of loyalty to a certain channel or retailer. Satisfaction can therefore be seen to positively influence channel engagement after previous experiences with a channel and act as a stickiness factor (Rego et al., 2013).

Herhausen et al. (2015) looked at the benefits of channel integration and found it positively affected perceived risk reduction, quality enhancement of online stores and also lower cannibalization of offline stores. These results show the importance for companies to have a complete channel integration over the full customer journey where each channel plays an important role both independently and interdependently.

This translates well into the research which has been earlier described by Lemon & Verhoef (2016) who explain the importance in today’s world to generate a strong customer experience. They highlighted the importance of mapping the customer journey and identifying the critical touch points.

Only by having a seamless experience across channels through channel integration over the full



customer journey, companies are able to create a stronger customer experience and increase firm value.

2.3 Cross-channel free riding behaviour 2.3.1 Origin and definition

The seamless integration of channels however, is posing several problems to firms. As a consequence, more consumers are taking advantage of channel-specific characteristics now that the channels have developed much more (Verhoef et al., 2007). Consumers now take an omnichannel approach and use different channels interchangeably to satisfy their shopping needs (Konuş et al., 2008). Also, the increased availability of products and retailers, the diversification of shopping channels and the increased use of mobile devices to browse and purchase have further established the need for comparative evaluations within a purchase decision (Wang et al., 2015; Maggioni et al., 2020).

Now that consumers can more easily switch channels during the decision-making process, the likelihood of switching retailers increases as well (Heitz-Spahn, 2013). This is how the chances of consumers acting in free riding increases. Free riding can take place when a company is unable to charge for its services separately, like providing product information (Heitz-Spahn, 2013). This was already researched by Singley & Williams (1995), who argued that product information is fairly similar to public goods, as it is nearly impossible to restrict this information to purchasers only.

Therefore, non-purchasers can also use this information to their advantage. As a result, retailers will be less motivated to invest time and effort in promoting its products and can also lower the morale of the sales force (Singley & Williams, 1995). Similarly, Tang & Xing (2001) discovered that free riding reduces sales effectiveness and customer service.

Cross-channel free riding occurs when customers gather information from channel X of Company A but they purchase from Company B through channel Y for example (Chou et al., 2016).

Cross-channel free riding can take many different variations, however the migration from the online web shop to the offline store or vice versa is the most common (Maggioni et al., 2020). When a consumer migrates from an online channel to an offline channel between search and purchase, it is called webrooming (Aw, 2019). Showrooming occurs when a customer first uses the offline channel but completes the purchase in an online channel (Gensler et al. 2017).

2.3.2 Motives

Reasons why consumers engage in cross-channel free riding behaviour has been extensively

researched. One of the first models that has been created to explain consumers’ switching behaviour is the push-pull-mooring (PPM) model by Bansal et al. (2005). This model emphasises three factors that influence consumers’ switching intentions. The push factors motivate people to leave an origin, the pull factors draw potential migrants to a specific destination and mooring factors that prevent migration from occurring (Bansal et al., 2005; Chiu et al., 2011). Based on this model Chiu et al.



(2012) discovered important new motives related to cross-channel free riding. Multichannel self- efficacy was revealed to be a push effect. This means that consumers who have confidence to employ multiple channels for different purposes also are more likely to engage in cross-channel free riding behaviour. Attractiveness of the competitor’s offline store is described as a pull effect and within firm lock-in as a mooring effect.

Along all stages of the decision process consumers are constantly trying to fulfil both their utilitarian and hedonic needs. They do so at the lowest costs relative to the possible benefits (Konuş et al., 2008; Noble et al., 2005). Therefore, consumers engage in cross-channel switching behaviour because they want to maximise their utility through using different channels (Chiou et al., 2012;

Heitz-Spahn, 2013). Heitz-Spahn (2013) found in her paper that shopping convenience, flexibility and price comparison are the top motives for why consumers adopt cross-channel free riding behaviour.

Consumers that engage in this behaviour are also more focused on utilitarian factors such as price and schedule issues. This is confirmed by Machavolu & Raja (2014) who found that in both situations of showrooming and webrooming, price is constantly identified as the primary motivator.

From previous research it seems plausible that most people are aware when they engage in cross-channel free riding behaviour or even plan to do so. Maggioni et al. (2020) found that cross- channel behaviour is not always a planned or intentional action. Such behaviour may not always come from consumer desire to play with a retailer and maximise value from trading off benefits through multiple channels. Maggioni et al. (2020) reported that approximately 22% of consumers engage intentionally in cross-channel switching. In total three segments were identified: the planned channel switchers, the opportunistic channel switcher and the forced channel switcher. Such consumers tend to be driven by price and are often younger and have a higher rate of in-store mobile usage to search for information and evaluate purchase decisions.

2.3.3 Retention

Consumers that engage in cross-channel free riding present important problems to many firms as they lose would-be customers that therefore reduce profits. Chou et al. (2016) specifically looked at how such customers can be retained inside the firm. Their study showed that even if someone perceives risk through a specific channel or if another channel is very attractive, the consumer may not switch if specific company factors moor them to the current channel provider. This emphasises that investing in service quality and other value improvements can result in higher profits. Therefore, firms should target customers engaging in free-riding with offerings that highlight service quality and value, which can be done by further developing customer service operations for example.

Managers should also implement a channel-differentiated promotion strategy in order to attract and retain cross-channel customers (Heitz-Spahn, 2013). These customers are, as previously explained, more valuable than other customers and firms should do everything to attract and retain



such customers (Kumar & Venkatesan, 2005; Sopadjieva et al., 2017). As cross-channel free riders do not necessarily focus on physical and cognitive constraints and therefore prefer visiting different stores and websites for the best deal, firms should offer different promotions in each channel

separately (Heitz-Spahn, 2013). Moreover, Wang et al. (2021) found that online retailers should allow webrooming behaviour but optimally not disclose any extra information than needed. Offline retailers on the other side can decide whether they disclose information depending on the size of the

investment cost.

2.3.4 Device choice and webrooming

As the smartphone is becoming increasingly more popular in the retail environment, marketers are also using it more often to meet the new demands of online shoppers (Shankar et al., 2016). Already in 2015, firms spend half of all their digital spending on mobile advertising and about 24% of digital revenue is from mobile sources (eMarketer, 2015). Shoppers that value access to information turn to the mobile phone during the search phase of the customer journey (Holmes et al., 2014). Also, convenience and savings are the primary motivations for the use of a mobile phone for shopping (Shankar et al. 2016). Additionally, the mobile optimized and search engine friendly websites are more likely to increase search and discovery. Therefore, sticky applications and features help increase customer spending (Kim et al., 2015; Shankar et al., 2016).

Mobile devices make it much easier shifting back and forth between deliberation and implementation as it provides direct information that may lead to both the abandonment or

acceleration of shopping intentions (Shankar et al., 2016). On the other side, people still switch away from mobile devices to finalize their purchases. Xu et al. (2017) were one of the first to investigate how switching of devices can have a different effect on purchase deliberations. De Haan et al. (2018) came to the similar conclusion that when people switch to a more fixed device, the conversion rates were higher. This follows from previous literature where the laptop or desktop provides more convenience when it comes to doing a secured transaction (Chin et al., 2012).

Still, the developments with regard to mobile possibilities have accelerated the chances of people engaging in cross-channel free riding and thus webrooming as well. People that engage in webrooming benefit the most from comparative channel advantages. The internet is often preferred to use for search purposes as it provides fast access to a large amount of information and this stimulates product evaluations (Verhoef et al., 2007). On the other hand, such consumers prefer the offline store for the actual purchase due to its enhanced service quality and lower purchase risks (Kleinlercher et al., 2020).

Consumers that engage in webrooming consider product characteristics as the most important factor that influences their purchase decisions. They also often have a clear idea what product they will acquire once they are in the physical store (Maggioni et al., 2020). Combining online search and



offline purchase for a target product, compared to search and purchase offline, increases purchase intention, search process satisfaction and choice confidence (Flavián et al., 2016). Also, compared to showrooming, webrooming induces smart shopper feelings and confidence in having selected the right option (Flavián et al., 2019).

Kleinlercher et al., (2020) discovered that receiving sales advice is the one of the most important drivers of webrooming and that price advantages are more productive than assortment advantages in order to lead customers from the online to offline store. Webrooming is more likely to take place for products with strong search characteristics and fast technological change (Heitz-Spahn, 2013; Van Baal and Dach, 2005). Webroomers also engage in longer purchasing processes using multiple online touchpoints to acquire and analyse information and therefore such ‘smart shoppers’

are hard to influence (Maggioni et al. 2020).

2.4 Summary

The existing literature shows there is a large amount of research available on the topics of

omnichannel marketing, cross-channel free riding and device choices. Still, after carefully examining the literature, it shows that there is a research gap to be explored that combines these several subjects.

The works of Kleinlercher et al. (2020) and Maggioni et al. (2019) emphasise the importance of webrooming and explain different motives why people engage in this behaviour. However, they take a very broad perspective and do not look more specifically at the different devices that are available for the searching phase. De Haan et al. (2018) and Xu et al. (2017) focus on these different devices that are available to search for products online, but they only highlight differences in conversion rates between the devices as the purchase still happens online.

Combining the previously mentioned research with the work of Heitz-Spahn (2013) and Chiu et al. (2011) as important references for cross-channel free riding intentions, it is possible to highlight the research gap. Multiple devices are nowadays used to search for products online, but still the largest number of purchases take place in the physical store. Currently, there is no work that emphasises the importance of devices in the searching phase and how subsequently their purchase decisions are influenced when they decide to buy the product offline.

This thesis will therefore focus on this gap by examining how the use of a more fixed device in the searching process, like a laptop or desktop, consequently affects the chances of someone buying the product in a different retailer’s store. Based on the literature, important moderators of this relation will be investigated. De Haan et al. (2018) already pointed out the effects of customer experience, product price and perceived risks on device switching. Heitz-Spahn (2013) also looked at product categories and product price in relation with cross-channel free riding behaviour. This shows the different dimensions that play a role in consumers’ switching behaviour and will be subsequently discussed in the conceptual framework.



3. Conceptual framework and hypotheses

This chapter presents the conceptual framework that was created based on the available literature.

First the framework will be carefully elaborated, after which the hypotheses will be laid out and explained.

3.1 Conceptual framework

The aim of this thesis is to understand how the use of a fixed device as compared to a mobile device affects the intentions to engage in cross-channel free riding and more specifically in a webrooming situation. As examined by previous studies, cross-channel free riding intentions cannot be explained by device choice exclusively (De Haan et al., 2018; Heitz-Spahn, 2013; Chiu et al., 2011). In order to provide more information on the constructed relation, customer loyalty, product type and product price are included as moderators. The insights generated through these moderators will help both practitioners and researchers to better understand the proposed main relationship and how it may affect them more specifically.

Customer loyalty is an important factor related to customer satisfaction and customer retention and can explain why people are more willing to pay for specific products or services

(Anderson et al., 2002). Therefore, many different variations related to this subject have been added to many frameworks regarding the topic of cross-channel free riding behaviour. Chiu et al. (2011) looked at lock-in factors and concluded they have a negative effect on cross-channel free riding intentions. Customer loyalty is a factor that locks the customer inside the firm, because it increases indirect costs to switch to another retailer and for that reason it is a very relevant component (Johnson et al., 2003). Also, when looking at different devices, customer loyalty and experience play an

important role. De Haan et al. (2018) showed that previous experiences with a retailer, which can be seen as a proxy for loyalty, affected conversion rates through device switching.

The type of product is also a relevant factor that can take many different variations that affect the level of cross-channel free-riding through device choice. Heitz-Spahn (2013) analysed high and low frequency products and concluded that low frequency products are more prone for consumers to adopt cross-channel free riding behaviours on. De Haan et al. (2018) evaluated product categories and the risks associated with those when investigating its effect on device switching. Such research highlights that product categories play an important role in the proposed framework.

The price of the product is the last moderator that is included in the model. Price has been consistently identified to be a primary driver of cross-channel free riding behaviour (Machavolu &

Raju, 2014). Consumers that are price-conscious aim to minimise the price for a specific product and therefore people are more likely to search through multiple retailers (Verhoef et al., 2007). Research by Heitz-Spahn (2013) and Maggioni et al. (2020) for example investigated the influence of price on cross-channel free riding intentions. Especially relevant to this study is how De Haan et al. (2018)



focused on price as a moderator that influences device switching. The vast amount of research that uses price as a moderator shows the importance to include the variable in this framework as well.

In summary, figure 1 presents the complete proposed conceptual framework with the expected directions of the relations, where the main relation is between type of device and cross- channel free riding intention. This relation is then moderated by customer loyalty, product type and product price.

Figure 1. Conceptual framework.

3.2 Hypothesis development

Although the examined field of study is relatively new, there has been a lot of recent research on this specific topic to formulate expected directions of relations. The expected directions from the

framework in figure 1 will be further elaborated in this section.

3.2.1 Device switching and cross-channel free riding intentions

Mobile devices and device switching have only recently gotten more academic attention due to the broad adoption and increased possibilities to transform the customer journey (De Haan et al., 2018).

Consumers perceive mobile devices to provide more ease and flexibility when it comes to searching for information, while more fixed devices give a sense of security when consumers are shopping online and making payments (Chin et al., 2012). Also, besides convenience and (time) savings as the fundamental drivers of mobile search, mobile devices provide a sense of immediacy to offer

incredibly quick access to all the possible information (Shankar et al., 2016). When looking at the distinct people that engage in webrooming, the specific form of cross-channel free riding behaviour this thesis focuses on, Viejo-Fernandez et al. (2018) found that such people travel to the offline store with an extensive knowledge of the product. Therefore, such people already have a good idea of what they want to buy specifically.



De Haan et al. (2018) described several channel flows that exist along the customer journey.

More importantly, they discuss how different channel flows can work independently along different channels. For example, the information flow can take place on a mobile device, while the ownership flow (i.e., the purchase) can occur on a more fixed device. This translates into the results of a study by Xu et al. (2017) who discovered that revenue increases when shoppers go from a smaller device (mobile, tablet) to a larger device (laptop, PC). On the other side, a negative effect was found for the reverse relation. De Haan et al. (2018) found similar results in their paper where they observe that switching from a more mobile device to a less mobile device results in a significantly higher conversion rate.

So, people that plan to engage in webrooming already have a good idea of what they want to buy (Viejo-Fernandez et al., 2018). Also, people that choose to do their search on a more fixed device are already more certain of their future purchase decisions, because of the higher conversion rates associated with these devices (Xu et al., 2017; De Haan et al., 2018). However, people that use a more fixed device for their product search are more likely to take their time to do so because the actual search is more convenient on a laptop compared to a more mobile device (Chin et al., 2012).

Therefore, a consumer will conduct a more thorough search on a fixed device and visit more retailers as a result to come to their purchase decision. A mobile device on the other hand, is more likely to be used for its convenience in situations where a consumer is more time constrained for example

(Shankar et al., 2016). Time-pressured consumers are less likely to engage in cross-channel free riding behaviour and conduct search and purchase more often through the same retailer (Maggioni et al., 2020). Consequently, people should be more likely to switch retailers when searching on a more fixed device and subsequently buy the product in store through a different retailer. Therefore, in this thesis the following relation is expected:

H1: When a customer uses a more fixed device (laptop, PC) compared to a more mobile device (mobile phone, tablet) during the search phase, the person has a higher intention to engage in cross-channel free riding.

3.2.2 Customer loyalty

As previously mentioned, customer loyalty is an important driver of customer retention and why customers are willing to pay more for specific products or services (Anderson et al., 2002). Customer loyalty can serve as a lock-in factor that increases personal switching costs of customers. One of the main arguments is that the costs associated with doing business with the company have decreased due to lower perceived risks (Marakanon & Panjakajornsak, 2017). Experienced customers generally have more expertise about the product or service (Alba & Hutchinson, 1987). Loyal customers therefore experience less risks as they also have a higher perceived quality level of the company which



increases satisfaction (Tzavlopoulos et al., 2019). Chiu et al. (2011) considered customer loyalty as a lock-in factor and concluded it decreases cross-channel free riding intentions.

Trust in a mobile retailer is also an essential factor than influences the intentions to engage in shopping activity with that specific vendor (Marriot & Williams, 2018). If a customer has more trust in a mobile retailer, the lower the perceived risks associated with financials concerns are (Beatty et al., 2011). If consumers feel that online retailers are opportunistic and unpredictable, their levels of trust reduce, therefore lowering their overall intention to engage in online shopping activities with that retailer (Hong & Cha, 2013).

De Haan et al. (2018) also focused on perceived risks in relation to research on devices. They argued that more experienced customers are more familiar with the structure and features of specific websites, which increases the ease of navigating through the different stages of the purchase journey.

The researchers found that higher experience with the retailer decreases the observed higher

conversion rates on more fixed devices, bringing the two devices closer together. When taking a more holistic perspective, smartphone purchases rely more on affective experiences (Kaatz et al., 2019). It means that customers using a mobile device have to trust other components such as personal emotions towards a retailer in order to substitute for the lack of data available from mobile devices (Grewal et al., 2018).

So, people that have more experience with a specific company have reduced risks when searching for information on the retailer’s website, as they have a higher expertise scrolling around on the website (Alba & Hutchinson, 1987; De Haan et al., 2018). Their higher level of trust will also result in lower perceived risks which increases the chances they engage in shopping activity with that retailer on a more mobile device (Marriot & Williams, 2018). In general, more loyal customers have even lower perceived risks as they have a higher perceived quality of the product or service

(Tzavlopoulos et al., 2019). Consequently, loyal people will feel less of a need to switch to a more fixed device when they want to extensively search for specific product information. Also, because they are more familiar with the product, they have an even lower need to engage in a more detailed information search, which is normally done on a fixed device. As a consequence, this thesis argues that more loyal customers will be spread more evenly over fixed and mobile devices. Therefore, the following relation is expected:

H2: The observed higher intention to engage in cross-channel free riding for a more fixed device is lower when a customer is more loyal towards a specific company.

3.2.3 Product type

In omnichannel marketing, there has been extensive research into how different product categories can affect specific intentions and relations. For example, Kushwaha & Shankar (2013) showed that multichannel customers are the most valuable segment but only for hedonic products. They highlight



the fact that multichannel customers have a higher promotion focus as opposed to a prevention focus.

Promotion focus is then associated with hedonic product attributes and therefore there is a clear regulatory fit between multichannel customers and hedonic products. Other research by Heitz-Spahn (2013) looked at the relation between cross-channel free riding behaviour and product categories. She found that low frequency, high financial value products are more prone for consumers to adopt cross- channel free riding behaviours on.

When looking at differences in risks, De Haan et al., (2018) looked at how functional risk influences the observed relation between a higher conversion rate for more fixed devices. Functional risk is the risk that the product does not meet performance expectations and can subsequently be reduced by searching for specific information about the product. They concluded therefore that fixed devices enable a more sophisticated information search compared to a mobile device and therefore this risk is lower as well. Consequently, the authors argued and proved that for products with high functional risks more consumers switch to a more fixed device for the actual purchase stage, meaning they trust such devices more.

From a product type standpoint, utilitarian products are bought without second guessing and have little emotional and sensory attachment (Strahilevitz & Myers, 1998). On the other hand,

hedonic products can be described to have a more affective and sensory experience of sensual fantasy, fun and pleasure (Hirschman & Holbrook, 1982). Therefore, the functional risk for hedonic products can be perceived as higher compared to utilitarian products. Consequently, customers switch to a more fixed device in order to reduce such risks. Also, because it is harder to justify the purchase of hedonic products, price discounts will have a stronger positive effect on purchase likelihood for hedonic than utilitarian products (Kivetz & Zheng, 2016). Therefore, people will more likely want to do better price research for hedonic goods, which can be better done through a more fixed device.

Accordingly, due to the regulatory fit of hedonic products and multichannel shoppers, the need to reduce functional risks and the need for justification of hedonic products, the following relation is expected:

H3: The observed higher intention to engage in cross-channel free riding for a more fixed device is higher when a product is categorized as hedonic rather than utilitarian.

3.3.4 Product price

Consumers that are price-conscious aim to minimise the price for a specific product (Verhoef et al., 2007). Therefore, consumers are more likely to adopt cross-channel free riding intentions for types of products that are more expensive. This means that they are willing to take more time to review products through multiple websites if the product is more expensive (Heitz-Spahn, 2013). This is also highlighted by Wu & Wang (2005) who show the effect of good information searching on the risk reduction for more expensive items.



When looking into price sensitivity and mobile marketing, Wang et al. (2015) discovered that consumers who start to adapt to mobile commerce technologies generally only make habitual

purchases on such devices, because such purchases involve less risky transactions. This again

highlights the importance of perceived risks in choosing a mobile or fixed device. More specifically, it emphasises the financial risk that customers experience when choosing a device for online shopping.

Financial risk is the risk of losing money due for example to fraud, dubious payment methods and undelivered goods (Featherman & Pavlou, 2003). Habitual purchases often involve lower value transactions from familiar retailers which reduces this risk.

Marriot & Williams (2018) confirmed that financial risk is a significant contributor to overall perceived risk in a mobile environment. Also, this risk is likely to be higher on a mobile device due to the larger implications with regard to security risks (Chin et al., 2012). Because more expensive transactions have higher financial risk, customers have a greater desire to decrease that risk, and thus the advantage of a more fixed device is increased (De Haan et al., 2018). Given the greater need to use a more fixed device for searching product information for a more expensive product to decrease the risk associated with such products, the following relation is expected:

H4: The observed higher intention to engage in cross-channel free riding for a more fixed device is higher when the price of the product is higher too.

3.3 Hypothesis overview

Table 1. Overview of hypotheses.

H1 When a customer uses a more fixed device (laptop, PC) compared to a more mobile device (mobile phone, tablet) during the search phase, the person has a higher intention to engage in cross-channel free riding.

H2 The observed higher intention to engage in cross-channel free riding for a more fixed device is lower when a customer is more loyal towards a specific company.

H3 The observed higher intention to engage in cross-channel free riding for a more fixed device is higher when a product is categorized as hedonic rather than utilitarian.

H4 The observed higher intention to engage in cross-channel free riding for a more fixed device is higher when the price of the product is higher too.



4. Methodology

This chapter describes the two research methods that have been used to investigate the relation between the type of device and cross-channel free riding intentions. The chapter starts with an

overview of the data collection on both studies, after which both studies are elaborated on with regard to their research design. The pre-test which was conducted will be explained next and the sample and participants in the section after. Lastly, the measurements used for the research model are laid out.

4.1 Research methods and design

This thesis used two different studies to draw its main conclusions from the research model. The primary study focused on historic customer journeys and provided actual search and purchase decisions by people through the use of a survey. In order to further verify the drawn relations from this study, an experiment was conducted as study 2 which recreated a specific search and purchase scenario. Both studies used a cross-sectional design where the data is collected through Qualtrics. The survey administration started on November 4th, 2021 and ended on November 21st, 2021. The total number of respondents equalled 452, however only 349 people fully completed the questions.

All participants read and checked an informed consent before entering the survey. This informed consent included the purpose of the study and stated that the results are stored anonymously.

On average, the respondents took 4 to 5 minutes to complete either the survey or experiment.

4.1.1 Study 1

In study 1, survey responses were collected to discover historic search and purchase decisions. The proposed model in this thesis focuses on webrooming behaviour, which contains a combination of both online and offline information. A survey is therefore the most valid way to obtain data because it reveals actual actions by customers. Lemon & Verhoef (2016) recommended mapping the journey from the customer’s perspective and therefore much research in the field of omnichannel marketing and customer experience have used questionnaires to gather data (Kleinlercher et al., 2019; Maggioni et al., 2020; Herhausen et al., 2019). The survey specifically focused on the most recent experiences people have had when they searched for information online and bought the product (or a very similar one) in the physical store. The survey is divided into four sections and a slightly condensed version is presented under Appendix I. The sections are:

1. General questions on the product and store.

2. Type of device used and cross-channel free riding behaviour.

3. Moderating questions about customer loyalty, product type and product price.

4. Questions on control variables like attitude towards online shopping, age, gender and education.


28 4.1.2 Study 2

An experiment as study 2 is conducted because not all respondents might have a complete memory of a previous engagement in webrooming. Therefore, the people that know they have engaged in this behaviour before but cannot recall the journey, entered an experiment through the same link. Here, a possible customer journey was replicated in order to assess the main effect of device usage on cross- channel free riding intentions. Even though the answers may not replicate true purchase decisions, it does focus on search and purchase intentions. The use of an experiment in combination with a survey to measure cross-channel free riding has previously been done by Heitz-Spahn (2013). By integrating the experiment in the survey, the dropout percentage will be a lot lower and more respondents are able to provide valuable information. The starting sequence of the survey and allocation into each group is shown in table 2.

Table 2. Overview of preliminary questions to select respondents for either study 1 or 2.

Q1: Do you remember ever having bought a product in a physical store after having searched for this product online?

No Yes

Q2: Do you remember which product this was and how you searched for it online?

No Not in

target group Enter experiment


Question not displayed

Enter survey

A scenario approach was used with a 2x2x2 factorial design. Scenario analysis is “a method for predicting the possible occurrence of an object or the consequences of a situation, assuming that a phenomenon or a trend will be continued in the future” (Yuan et al., 2017, p.514). This approach was used because it would allow for the manipulation of different devices for search purposes. Because respondents are likely to be familiar with the scenario of searching information on their devices, imagining the situation would not be too complicated. Table 3 shows the different groups that respondents can be allocated to and a shortened version of the experiment itself is presented under Appendix III.



Table 3. Overview of different groups for study 2.

Experimental variable

Mobile device Fixed device Utilitarian Hedonic Utilitarian Hedonic Cheap Group 1: Group 3: Group 5: Group 7:

€20 chair €90 watch €20 chair €90 watch Expensive Group 2: Group 4: Group 6: Group 8:

€149 chair €545 watch €149 chair €545 watch

4.2 Pre-test

The scenarios revolve around the search on a specific device of a hedonic product (a watch) or a utilitarian product (a chair), which is either cheap or expensive, and subsequently the purchase of the product in a physical store. These products were chosen because regardless of the price, most people observe a watch as a hedonic product and a chair as a utilitarian product. To confirm this thought process, a manipulation check through a pre-test was executed with 18 different respondents and is shown under Appendix II. Respondents were asked to review the same products for all different prices whether they are observed as either hedonic or utilitarian. The measurement scale of Voss et al.

(2003) (Cronbach’s alpha = 0.95) was slightly adapted and used for this purpose.

The results of the manipulation check are presented in table 4. With regard to the reliability of the measures, all measures passed the Cronbach’s Alpha threshold of 0.7. Only 2 dimensions scored below 0.8 which may be the result of relatively low number of respondents and the fact that these measures score low for the opposite type of product (i.e., low hedonic reliability for the expensive chair and low utilitarian reliability for the expensive watch).

The results provide evidence for a successful manipulation. All mean differences between the hedonic (HED) and utilitarian (UT) scale are significant and in the correct predicted direction.

Consequently, price is irrelevant whether people observe a chair as a utilitarian product and a watch as a hedonic product. The mean scales for prices show that the more expensive chair and watch are also objectively perceived as expensive because their mean scale is for both products higher than 4.278.

The same holds for the cheaper chair and watch which are perceived as cheap.



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