Customer loyalty

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Customer loyalty

in a multichannel retail environment

June 16th_{, 2019}

K.H. ter Borg

Faculty of Economics and Business Department of Marketing

Nettelbosje 2, 9747 AE Groningen, The Netherlands,

K. de Vriezestraat 54, 9741AH, Groningen Cellphone: +31650237728

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Abstract

Since the introduction of the internet, more and more retailers have started to sell their products and services through multiple sales channels. This has caused a major transformation within the retail environment, since everything is made transparent. Nowadays, it is so easy to switch channels and stores that it has become a challenge for retailers to build strong relationships with their customers. The goal of this research is to identify how multichannel retailers can optimize customer loyalty in both their online and offline sales channels. It attempts to explore what the influences are of different drivers of loyalty towards the online and offline sales channels of multichannel retailers. The effect of channel integration, service quality, assortment variety, price perception and image is examined in both online and offline channels to see whether there are differences in these effects between online and offline sales channels. This study is based on a survey conducted on 206 customers in the context of multichannel clothing retail. Findings show that loyalty in online sales channels is directly influenced by service quality, online assortment and online image. On the other hand, offline loyalty is affected by online assortment, offline price perception and offline image. So, an important finding of this research is the directly related positive spillover effect from online assortment variety on offline loyalty. The model presented in this research, also accounts for mediating effects. Findings show that online assortment variety negatively influences offline image and positively influences online image. This works the same for assortment variety offline; it negatively influences image online and positively influences image offline. Image in turn, positively influences loyalty towards both online and offline channels. Also, the effect of channel integration on loyalty is mediated by both online and offline image. Based on the differences among drivers of customer loyalty between online and offline channels, this study provides channel specific practical implications for multichannel retailers, in order to optimize loyalty in both sales channels.

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Preface

As a Marketing Intelligence student, I have always been fascinated by consumer shopping behaviour and especially how this relates to online and offline shopping channels. In this master thesis, I’ve had the opportunity to build a complete and comprehensive research on this topic. Striving for completeness challenges me as a researcher to dive into different parts of an extensive amount of literature, building a complete model and trying to measure all of the concepts in one single survey. This has led to a research project that turned out to be one of the most informative experiences of my career as a student.

I would like to thank my supervisor Arnd Vomberg for his time and effort, to guide through the different stages of writing a master thesis. Especially structuring the different concepts and helping out to decide which type of analysis could be useful in this setting. Doing academic research and dealing with survey data in RStudio in this specific way was new to me but with the right tips from my supervisor and a whole lot of effort, I can say that I am proud of the end result. I hope you will enjoy reading it as much as I enjoyed writing it.

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4.4 Control variables ... 24 4.4.1 Channel preference ... 24 4.4.2 Amount of shopping ... 25 5. Results ... 27 5.1 Data analysis ... 27 5.2 Power Analysis ... 28 5.3 Measurement assessment ... 29 5.4 Statistical analysis ... 30 5.5 Main effects ... 33 5.5.1 Control variables... 34 5.6 Mediating effects ... 34

5.6.1 Control variables in mediation ... 37

5.7 Overview of the results ... 38

6. Discussion and conclusions ... 39

6.1 Academic contribution ... 41

6.2 Managerial contribution ... 42

6.3 Limitations and directions for future research ... 43

7. References ... 46

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1. Introduction

1.1 Starting point

Today’s shoppers have endless choice and frequently move between different retailers. So, in order to be successful, retailers need more than great products to stand out. Loyalty is the critical factor in multichannel retailing. Customer loyalty used to be one-way traffic, now companies really have to get their acts together (De Groot, 2019). Before the introduction of the internet consumers were only able to buy products from a store, a market or via direct sales (e.g. phone sales) (Press, 2015). These buying processes all entailed human contact whereas nowadays in online shopping, this is not a necessity anymore (Konus, Verhoef & Neslin, 2008). Since the first online purchases in 1994, internet sales accelerated rapidly (Shop Direct, 2014). In just three years later, internet sales pioneer Dell was the first company reaching a million-dollar revenue in one day through an online sales channel (Netonomy, 2013). More and more brick and mortar stores started to recognize this profitable growing sales channel and added online sales channels to their portfolio, introducing multichannel retailing.

Multichannel retailing is defined as “selling products or services through more than one sales channel”, this can be an online or an offline sales channel (Van Baal & Dach, 2005, p. 75). An online sales channel in this case, refers to the Internet based sales channels (e.g. mobile and website). The offline channel entails traditional sales channels like physical stores and catalog. The multichannel strategy has gained a lot of attention in literature and practice (Konus et al., 2008; Neslin & Shankar, 2009).

The research of this thesis focuses on multichannel retailers in the Netherlands. Dutch retailers started to become multichannel with the success of the Internet sales of Dutch online store Bol.com. Some retailers that recognized the power of the online channel too late have gone bankrupt, shopping mall chain V&D for example (Otto, 2017). Other retailers flourish with their multichannel strategy (e.g. supermarket Albert Heijn) (van Joolingen, 2016). Recent figures of Dutch multichannel retailers show an incredible growth in 2018 compared to 2017 of revenue with almost 26%, while pure online players grew with 13% (CBS, 2018).

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shoppers purchase from one channel, online or offline. Multichannel shoppers use different types of online and offline channels to purchase their product (Konus et al., 2008). Previous research has suggested that compared to single channel shoppers, multichannel shoppers are spending more money, returning to the (online) store and repeating purchases. Moreover, some researchers say they also have the tendency to exhibit greater retailer loyalty than single channel shoppers (Dholakia, Zhao, & Dholakia, 2005; Kumar & Venkatesan, 2005; Lee & Kim, 2010). Given the intensified competition in the retail industry, it becomes more and more important to build stronger customer loyalty, which can be done through a combination of online and offline channels (Frasquet, Molla Descals, & Ruiz-Molina, 2017). However, too little research has been done on the understanding of what drives customer loyalty in a multichannel world (Liu, Lobschat & Verhoef, 2018).

1.2 Problem statement

Selling your products through multiple channels entails quite a challenge. Due to the increasing transparency created by the internet, consumers’ expectations grow each day, while retailers fail to reach these expectations (Reichheld & Schefter, 2000). When a large online retailer like Zalando offers free deliveries and deals with faulty product deliveries immediately and without hesitation, customers will also expect the same level of service from every other retailer. Moreover, switching stores has never been easier (Srinivasan, Anderson & Ponnavolu, 2002). This has caused a major decline in loyalty towards certain brands and stores (Davis-Sramek, Droge, Mentzer & Myers, 2009). As the performance levels differ among sales channels, the central question of this study is:

How can loyalty be optimized towards both online and offline sales channels of a multichannel retailer? According to Wallace, Giese & Johnson (2004), customer loyalty consists of product

loyalty and retailer loyalty. While product loyalty has extensively been studied in the past (e.g. Jacoby & Chestnut, 1978a; Oliver, 1997), less research has been done on the loyalty towards a retailer (Zentes, Morschett & Schramm-Klein, 2008). Because acquisition costs in retailing are high (Wallace et al., 2004; Das, 2014), and the costs of serving a new customer are 5 or 6 times higher than serving a loyal customer (Ndubisi, 2005; Pfeifer, 2005), it is of vital importance to establish a solid customer-retailer relationship.

1.3 Relevance

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retail setting. This research seeks to develop an integrative model which considers both online and offline channels as individual channels and accounts for cross channel effects. It has both practical and theoretical implications. It examines the drivers of customer loyalty in a multichannel environment, which helps multichannel retailers to optimize their sales channel mix in order to generate loyalty. Theoretically, this paper extends the literature in the understanding of what drives customer loyalty, and more specifically, a model is developed that suits especially in the context of a retailer. The aim is to identify the importance of several selected constructs, which are established drivers of customer loyalty in a single channel, driving loyalty in multiple sales channels.

1.4 Research questions

As stated before, there is no doubt that loyalty has become more and more important for retailers that operate in multiple channels at the same time. Retailers are currently not only competing with their local competitors but, they compete with competitors across the whole world. To answer the main question of how multichannel retailers can optimize their individual sales channels in order to build loyalty, the following research questions are investigated:

(1) What is the influence of the following factors: price perception, quality, image and perceived channel integration on the online and offline loyalty, and what are the differences?

(2) In what way do the drivers of one channel of a retailer influence the loyalty towards the other sales channel of the same retailer (how do spillover effects relate to each other between online and offline sales channels)?

(3) Which drivers of customer loyalty are most important in prediction of loyalty towards multichannel retailers?

1.5 Structure

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2. Literature review

This second chapter of the research presents an overview of the existing literature in the field of customer loyalty. First, the existing general literature about customer loyalty in a multichannel setting is reviewed critically. Thereafter a table is presented, summarizing the existing literature on the drivers of customer loyalty (see table 1). Further sections dive deeper into the different constructs and the existing knowledge and develop hypotheses to test the developed conceptualizations.

2.1 Customer loyalty

In this section customer loyalty is defined and conceptualized. Furthermore, it provides arguments to why customer loyalty is divided into online loyalty and offline loyalty. Thereafter the literature about customer loyalty is reviewed.

Defining customer loyalty in general has proven to be difficult for researchers, mainly due to imprecise and varying conceptualizations of the construct (Martin, Ponder & Lueg, 2009). In general, there are two categories of definitions of the construct. The first category of researchers defines loyalty as behavioural loyalty. Behavioural loyalty is the consistency in revealed brand choice across several purchase occasions (Hariharan, Desai, Talukdar, & Inman, 2018). These customers may be spuriously loyal as they may purchase not because of their loyalty and commitment but because of situational constraints like a situation where online one brand is available at a retail store (Jaiswal & Niraj, 2011), or situations where they stay with a retailer till they find a better alternative somewhere else (Dick & Basu 1994). For that reason, this thesis focuses on the way the second category defines customer loyalty: attitudinal loyalty. An attitudinally loyal customer has some attachment or commitment to the retailer and is not easily swayed by a slightly more attractive alternative (Shankar, Smith & Rangaswamy, 2003). Furthermore, attitudinal loyal customers show higher intentions to repurchase, they are more resistant to counterpersuasion and negative opinions of others, willing to pay a higher price and make recommendations to friends and family (Shankar et al., 2003; Srinivasan et al., 2002).

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senses to evaluate the product, have personal service provided by humans, the possibility to pay with cash, to acquire the product immediately and to shop for social experiences and entertainment (Zhang, Farris, Irvin, Kushwaha, Steenburgh & Weitz, 2010). On the other side, physical stores require a certain time and energy to visit the store, to carry the product home and stores might not be open at convenient times.

Online sales channels overcome these difficulties by making it possible to shop wherever and whenever you want and have the products delivered at your home. Online stores have the possibility to show broader assortment selections and customers can shop in any physical location that is comfortable for them. Customers can obtain as much information about the product they want and easily compare prices among stores. Online sales channels make it possible to extract data and tailor offering according to customer needs (Zhang et al. 2010). However, they do not have the specific benefits that physical stores have (e.g. touching products, human contact, delivery times, etc.).

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In addition, negative relationships between using the Internet and loyalty have been discovered by other papers as well (e.g. Ansari et al., 2005).

These previous researches show that there is no consensus about whether loyalty is increased by different sales channels and what drives customers to be loyal towards these different sales channels. Most factors influencing customer loyalty, have been studied either towards loyalty to the retailer as a whole (e.g. Wallace et al., 2004), or studied as single channels (e.g. Clottey, Collier & Stodnick, 2008) without considering sales channel differences. In contrast, this thesis examines these drivers of customer loyalty in a multichannel setting. Because performance and images can differ across sales channels (Kwon & Lennon, 2009b), the distinction between online and offline loyalty is made. Some papers already made this distinction, suggesting there are differences in loyalty across channels (e.g. Frasquet et al., 2017; Kwon & Lennon, 2009b) however, this research considers different drivers of loyalty that are indicated by other researches (e.g. Clottley et al. 2008) in order to extend our understanding of what drives loyalty in these different sales channels.

Table 1 below presents an overview of the existing literature on the selected drivers of customer loyalty. It summarizes the focus and the findings of the research on the different drivers of customer loyalty. This helps structuring the literature. The end of the table presents where the current study contributes to these previous studies.

2.1.1 Choice of factors

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retail business, there are a lot of multi brand shops. Comparison on individual products (brands) is not relevant because this research focuses on the sales channel, not on the products (brands).

However, a retailer can distinguish itself on the range of products that it offers in its different sales channels, which is the assortment. Therefore, this research does take this factor into account in our quality (of assortment) factor in the conceptual model. In addition to the model of Clottey et al. (2008), the perceived channel integration and a price perception component are added. These are included as it is indicated by several studies as a driver of customer loyalty (see table 1), which makes our model more complete. The variables in the model are tested on the loyalty of multiple channels as customers are nowadays more and more shopping in multiple channels.

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Table 1 - Literature overview

Authors Category Focus Findings

Kim et al. (2002)

Quality of service

The influence of several metrics of architectural quality of the internet store on customer satisfaction and loyalty is studied. They performed a large-scale survey about four different business domains: virtual malls, online stock brokerages, search portals, and online network games.

Three architectural measures (firmness, convenience, delight) positively influence customer satisfaction which in turn, positively influences customer loyalty in all four business domains.

Montoya-Weiss et al., (2003). Quality of service (multichannel)

This article looks at what drives customers’ use of an online channel in a multichannel service provider environment. They research online channel use and overall satisfaction with the service provider. They empirically test their hypothesis in two contexts: financial and university services.

They find that the service quality online is influenced by website design and that service quality in both online and offline sales channels determine the level of overall satisfaction with the service provider.

Oliveira (2007)

Quality of service (offline)

This article looks at the link between e-service quality and customer loyalty. They surveyed customers of an e-banking service in Portugal (n=5942).

Found strong empirical evidence for the relationship between e-service quality (represented by website quality) and customer loyalty in an online service setting.

Clottey et al., (2008)

Quality of service (offline)

Studies the determinants of customer loyalty for a large retailer (physical store) in the U.S.by surveying 972 customers.

Service quality, product quality and brand image drive customer loyalty. This research measured customer loyalty by a customer’s willingness to recommend the retailer’s products to other people.

Lin & Sun (2009)

Quality of service (online)

Assesses how customer satisfaction and loyalty can be associated with each other and how these are affected by external factors (technology acceptance factors, website service quality) and internal factors (specific holdup cost), in an online channel environment.

Website service quality positively influences customer satisfaction and e-loyalty directly Huang (2009) Quality of service (offline)

Researches the influence of 5 dimensions of service quality on the impact on perceived quality of the retail brand. Used surveys on randomly selected supermarket shoppers in Taiwan (n=218).

They found that service quality has 5 dimensions (physical aspects, reliability, personal interaction, problem solving and policy) that affect both perceived overall service quality and perceived quality of the retail store brand. Sousa &

Voss (2012)

Quality of service (online)

Researches the influences of online service quality on (i) online channel loyalty; and (ii) channel behaviour. Measured based on an online questionnaire towards a sample of actively present customers (n=5942) of the online service and their transactional data across all available channels from a bank in Portugal.

Online quality has a strongly positive impact on loyalty towards the online channel; however, it did not have an effect on customer channel behaviour. Online service quality is not a really effective driver of channel behavior but it is a key driver of loyalty intentions towards online channels.

Lin (2012) Quality of service (online)

Explores the effect of service quality in multiple channels on customer loyalty on mobile phones while accounting for online(website) and mobile channels. Surveyed (n=102) multichannel (mobile & website) shoppers in Taiwan.

Findings show that customer loyalty on mobile phones is influenced by e-service responsiveness, e-service tangibility and the degree of empathy in e-service channels. These findings are found for both the direct effect as through the effect of the same construct in a mobile setting.

Lund & Marinova (2014) Quality of service (multi-channel)

This research develops a conceptual model based on objective service performance and direct marketing on on-site retail revenue and remote retail revenue. They used different data sources 1. using time series data of a pizza retailer across five pizza stores (marketing data and performance/delivery data). 2. survey data of the customers

Findings show that performance of the service (e.g. delivery times) has different effects on retailer revenues across channels. In physical stores the objective service performance positively affects a retailer’s revenue. At Internet based channels, there is no significant effect on revenue development.

Das (2014) Quality of service (offline)

Studies the direct and indirect effects of a retailer’s personality through purchase intention and perceived quality on customer loyalty towards a retail store. Used a survey on shoppers from nine non-food retail stores in India (n=365).

Found that retailer personality, purchase intention and perceived quality influences store loyalty.

Devaraj et al. (2001)

Quality/Price Studies the relationships between service quality, service satisfaction, product quality, and customer loyalty. They used 3 types of data sources: archival data on product quality and customer purchases, survey data (n=504) and Consumer Reports in the automotive industry.

They find that product quality perceptions positively significantly influence customer loyalty and that price negatively influences repurchase decisions but positively influence product quality perceptions.

Hoch et al., (1999)

Assortment This research investigates what the impact of variety perception and organisation of display is on the customer satisfaction and store choice.

This research found that assortment variety perceptions and organization positively influence customer satisfaction and store choice. Customers tend to be more satisfied when stores are carrying assortments that offer high variety and that are displayed in a well-organized way.

Broniarczy k et al. (1998)

Assortment This research performs and experimental study on (n=229) participants. It examines how the perceptions of assortment are formed by consumers when dealing with assortment reductions.

They find that retailers can make reductions in their assortment without negatively influencing the perceptions about the assortment and store choice, as long as lower preferred items are removed, and space of the category stays the same.

Cox & Cox (1990)

Price Study retail price perception and its influence on store-price image and patronage behaviour

It shows that supermarkets can build a lower price image when the prices are advertised as price reductions.

Jiang & Rosenbloo m (2005)

Price Researches the influences of price perceptions on customer loyalty (intention to return) towards the online channel. They performed a survey (n=416) on 416 online retailers with more than 250.000 respondents. They used the aggregated data of the 416 e-tailors collected by BizRate.com.

Findings show a positive influence of price perception on customer satisfaction and loyalty when price perception is measured on a comparative basis.

Han & Ryu (2009)

Price Examines the effects between physical facilities, price perceptions, satisfaction and loyalty in the context of a restaurant. Survey based measures (n=279)

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Martin et al. (2009)

Price Examines the influences of price fairness on customer loyalty in the context of a retailer increasing its prices.

They find that loyalty positively affects price fairness perceptions when price increases are low. When price increases are high, this does not hold anymore. If post purchase loyalty increases by price fairness depends on the presence of price increases and on how justifiable these increases are.

Konuk (2018)

Price This study analyses the relationship between price fairness, satisfaction, trust, and purchase intentions towards organic food. Survey data was collected from a sample of participants in Turkey (n=349).

The paper found a positive significant relationship between price fairness and satisfaction. Also, price fairness influences trust and also purchase intentions.

Mazursky & Jacoby (1986b)

Image The study examines how environmental cues influence the formation of store image.

The findings of this research show that customers rely on different kind of objective cues to assess the different aspects of image. They confirm that consumers simplify complex reality into associations with the store. Yun &

Good (2007)

Image This study investigates several store attributes of online retailers that build positive perceptions of the online store image, which in turn determines their sense of loyalty towards the online sales channel.

A good online store image positively influences online loyalty.

Kwon & Lennon (2008)

Image (multichannel)

They examined how offline image influences online beliefs and attitudes which in turn influences online and offline purchase intentions accounting for cross channel effects. They performed 2 experiments

Findings show that prior offline image influences online brand beliefs and that online performance influences offline brand beliefs. Prior offline image lowers the impact of bad online performance on offline brand beliefs.

Verhagen & van Dolen, (2009)

Image (multichannel)

This study investigates the influence of store image on the purchase intentions in a multichannel setting. They surveyed (n= 630) customers of a large music retail store in the Netherlands.

They found that the offline and online store perceptions directly influenced online purchase intentions.

Bendoly et al. (2005)

Channel integration

Did a survey on (n= 1598) multichannel shoppers from 3 different retailers selling clothing, electronics, and music. It researches the influences of perceived channel integration on the risk that is believed to be associated with product availability and their impact on purchasing decisions.

Channel integration has two factors: physical- and informational integration. Channel integration increases loyalty by reducing the chance that a customer switches firms in case of product availability failures.

Pentina & Hasty (2009)

Channel integration

This article examines the role of a multichannel coordination strategy on the impact on online performance and if outsourcing of e-commerce functions were needed in the first stages of channel integration. They did this by analysing the content of 50 multichannel retailers.

Channel integration improves the sales of online channels.

Lee & Kim (2010)

Channel integration

Surveyed 706 multichannel customers of different kinds of retailers. Assesses the dimensionality of a multichannel retailer’s cross-channel integration practices and effectiveness.

Five factors of channel integration are extracted :1. Consistency of the information in both channels, 2. Degree to which you can select channels freely, 3. Marketing via email, 4. Mutuality of channels, 5. Appreciation of in-store customer service. Three of the five dimensions of multichannel integration were positively related to customer loyalty.

Schramm-Klein et al. (2011)

Channel integration

Surveyed 981 customers of a variety of multichannel retailer in different industries. Studied the perceived channel integration and the influence on customer loyalty. They also include image trust, and evaluations of retail channels in their model.

Channel integration positively influences customer loyalty. Image and trust mediate this relationship. Channel integration can enhance the movement of customers to more efficient sales channels.

Chiu et al. (2011)

Channel integration

Surveyed customers that have experience with showrooming or webrooming when shopping computer products (n= 716). The research explores antecedents that may contribute to consumer switching behaviours.

Channel integration does not significantly influence channel lock-in, meaning it does not prevent customers from showrooming.

Gallino & Moreno (2014)

Channel integration

They analysed the effect of the implementing a BOPS (=buy online, pick up in store) system. They used a using a privately-owned database from multiple sales channels (online and offline).

Channel integration reduces the sales in online channels and increases sales and traffic in physical stores.

Herhausen et al. (2015)

Channel integration

Examines the impact of channel integration on perceived service quality and risk of the online store, which in turn influences the search intention, purchase intention and willingness to pay of the online channel, offline channel and overall of the retailer. It uses an experimental research design where they manipulate online and offline channel integration in the context of clothing retailers.

A direct effect of channel integration on online service quality was found. An indirect effect on behavioural response was found for both online and offline channels. Furthermore, the effect of channel integration is stronger for online shopper that have less experience with shopping.

Frasquet & Miquel (2017)

Channel integration

Investigated the perceived channel integration on online and offline loyalty and on customer satisfaction. Surveyed multichannel apparel shoppers (n=761) and applied a scale development process.

Found that channel integration positively influences loyalty online and offline both direct and through partial mediation of satisfaction.

Current study: Channel integration, Quality of Service and Assortment, Price & Image

This research extends the theory by investigating and empirically tests the existing drivers of customer loyalty in a multichannel setting whereby direct influences of online and offline loyalty are compared.

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3. Conceptual background and hypothesis

The main goal of this study is to test the effect of several drivers of customer loyalty on both the online and offline sales channel of a retailer, in order to find an optimized strategy for multichannel retailers. Therefore, the next chapters will build hypotheses on previous research, these hypotheses are graphically shown in figure 1 below.

Figure 1 - Conceptual framework

3.1 Drivers of customer loyalty

In order to research whether established drivers of loyalty also hold in a multichannel retail environment, this section illuminates different drivers of customer-retailer loyalty. It starts off with quality, followed by price and image and ends with perceived channel integration.

3.1.1 Quality of Service

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assortment. Service quality has been extensively researched (Fisk, Brown, & Bitner, 1993) and its importance is widely recognized by companies because it affects customer satisfaction and loyalty (Kim, Lee, Han, & Lee, 2002). Service quality can be defined as “a global judgment, related to the overall superiority of the service at a specific retail store” (Parasuraman, Zeithaml, & Berry, 1988, p. 42). Service quality is a multidimensional construct that entails different dimensions for different contexts, this is further discussed in section 4.3.2 (Yang, Jun & Peterson, 2004). As the performance of these quality dimensions differs among channels (Kwon & Lennon, 2009a), this research considers online and offline service quality.

Online service quality can be defined as overall customer evaluations and judgments regarding the excellence and quality of e-service delivery in the virtual marketplace (Santos, 2003). For online sales channels, service quality is especially important because it distinguishes them from antecedents that are easier to compare like product features, assortment, or price (Liu et al., 2018).

According to Lund and Marinova (2014) service performance has different effects on retailer revenues across channels. They point out that in physical stores the objective service performance positively affects a retailer’s revenue while in Internet based channels, there is no significant effect. However, in contrast, there is a lot of evidence against this insignificant result. For example, Oliveira (2007) found a strong and significant link between website service quality and online loyalty in an e-banking context. Also, Lin (2012) has found that service quality improves the customer loyalty in an online/mobile context. Moreover, Hoff et al., (1998) state that returning customers and thus loyalty, is caused mainly by an online company providing better services than their competitors. In addition, Collier and Bienstock (2006) state that getting an order in the expected amount of time and receiving it in the expected quality has a positive impact on online satisfaction, which in turn affects customer loyalty (Lin & Sun, 2009). These are a few examples of the extensive amount of evidence of the relationship between online service quality and online loyalty. Therefore, the following hypothesis is made:

H1.a: The perceived online service quality of a retailer, positively influences the customer loyalty

towards the online channel of the multichannel retailer.

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does not include expectational aspects. It has been proved valid also in the retail setting (Babakus, Bienstock & Van Scotler, 2004; Wallace et al., 2004). Generally, service quality is recognized as a key driver of customer loyalty (Martinelli & Balboni, 2012). According to Vazquez, Rodriguez-Del Bosque, Diaz, & Ruiz, (2001), perceived service quality in offline channels rely on 4 factors: reliability, physical aspects, personal interaction and policies. Huang (2009) adds a 5th dimension to these 4 namely: problem solving. This research shows that retail service quality indeed positively affects the perceived quality of the retailer, which in turn increases customer loyalty (Huang, 2009). Sirohi et al. (1998) confirm in a retail setting that service quality has a direct effect on store loyalty. Hence, the following hypothesis is developed:

H1.b: The perceived offline service quality of a retailer positively influences the customer loyalty

towards the offline channel of the multichannel retailer.

3.1.2. Assortment variety

Part of the policy component of service quality, is the variety of assortment. Retailers are concerned about the perceived variety of their assortment, as customers value variety (Hoch, Bradlow, Wansink, 1999). Moreover, retailers are worried that when customers do not find their desired products, they will be less likely to return when they make subsequent purchases (Hoch et al. 1999). For this reason, retailers must optimize their assortment offering (Liyun, 2011), meaning larger assortments would fit more customer’s needs (Spassova & Isen, 2013). Reibstein, Youngblood, and Fromkin (1975) have shown that since customers tend to be more satisfied when they select products from a larger set of choices, perceptions about minimal assortment may negatively impact customers attitude. This is especially relevant for physical stores where assortment is often kept at its minimum in order to save costs (Hoch et al., 1999). Assortment in online stores is usually much larger as stock keeping costs are lower, however on the other hand, choice overload may play a role in the perceived assortment (Wan, Menon & Ramaprasad, 2003; Scheibehenne, Greifeneder & Todd, 2010).

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Langerak & Donkers, 2007a). All this research has been done in a single channel setting. No research has been found in a multichannel setting.

In addition, the preference for a certain (offline) store depends for an important part on the provided assortment (Arnold and Tigert, 1982; Arnold et al., 1983). According to Briesch, Chintagunta, and Fox (2009), store choice decisions are even more sensitive to changes in assortment variety than to changes in price. A larger assortment turns out to have a higher attractivity towards customers, as it improves the perception of the provided variety (Chernev, 2003). It gives customers the feeling they will find what they need, as they have more options to choose from (White and Hoffrage, 2009; Berger et al., 2007). Several authors have found the relationship between loyalty intentions towards a store and the perception of a wide variety of the assortment (e.g, Sirohi et al., 1998; Hoch et al., 1999; Baker et al., 2002; Verhoef et al, 2007a). Based on these previous findings and the existing differences between online and offline assortments, the following hypotheses are proposed:

H1.c1: The perceived online assortment variety of a retailer, positively influences the customer’s

loyalty towards the online channel of that retailer.

H1.d1: The perceived offline assortment variety of a retailer, positively influences the customer’s

loyalty towards the offline channel of that retailer.

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to buy in the other channel (Balasubramanian, Raghunathan & Mahajan, 2005). This is called the research-shopper phenomenon (Verhoef et al., 2007b). Thus, a spillover effect between the assortment of one channel towards the loyalty of the other channel is expected. This leads to the following hypotheses:

H1.c2: The perceived online assortment variety of a retailer, positively influences the customer’s

loyalty towards the offline channel of that retailer.

H1.d2: The perceived offline assortment variety of a retailer, positively influences the customer’s

3.1.3 Price Perception

Price perception is another important antecedent of customer satisfaction and loyalty. Price perceptions relate to evaluations of fairness of the cost of a product or service (Singh & Sirdeshmukh, 2000). Consumers form an overall price perception of a store based on different mechanisms. This can be consciously (based on perceptual processes) or unconsciously through latent memories (Coutelle & Desmet, 2006; Monroe & Lee, 1999). This means retailers can manipulate their overall perception of their pricing policy by using product prices as signals in their marketing communication (Cox et al., 1990). Researchers say that price perceptions are foundations of overall store price image (Desai & Talukdar, 2003; Lourenco, Gijsbrechts & Paap, 2015). This is relevant to the current research, as it focuses on the overall price perception on the store level.

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online channel. Which is an indicator of customer loyalty (Taylor, 1997). In other words, when a customer is not fully satisfied with the service quality or product quality, but the price is much lower than at other retailers, so it is a fair price, customer loyalty can still be positive (Martin et al., 2009).

Differences in ability to compare prices within online and offline channels are influencing channel loyalty as well. Higher competition and price dispersion in online channels may cultivate higher price sensitivity (Liu et al., 2018). This suggests there are differences in likeliness to switch between online and offline channels which leads to less loyalty. Therefore, the following two hypotheses are made:

H2.a: The higher the price perception (better price fairness) of the online channel, the higher the

customer loyalty towards the online channel.

H2.b: The higher the price perception (better price fairness) of the offline channel, the higher the

customer loyalty towards the offline channel.

3.1.4 Retailer image

A lot of research in the past has been focussing on brand image of the brand of a product (e.g. Kwon et al., 2009; Aghekyan-Simonian, Forsythe, Kwon & Chattaraman, 2012). However, in this research the focus is on the retailer being a brand, often referred to as retailer image, vendor image or store image. The image of the retail brand is considered to be influencing the perception of the customer about the goods and services that are offered (Zeithaml and Bitner, 1996). Back in the 1950s, Boulding (1956) stated in his book that humans are only able to cope with a certain number of complex situations. The human brain attempts to simplify these situations to summarize complexity and values into symbolic images (Boulding, 1956). One of the first researchers that used the concept of store image was Martineau (1958), who defined store image as: “a store defined in customers’ mind partly based on functional attributes and partly based on psychological attributes” (Martineau, 1958, p. 47).

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Yoo & Chang (2005) for example, developed 12 components of store image based on previous research. These components consist of quality, salesperson service, store facility, price, assortment, promotion, advertisement, convenience of shopping convenience of location, credit service, store atmosphere and store brand. They studied these attributes in different store types and found that store atmosphere had the largest influence on store loyalty in department stores. Store location convenience, convenience of shopping and salesperson service were also important factors of store loyalty in department stores. For discount stores, quality of goods, advertisement, store atmosphere and credit policy where important drivers of loyalty. Moreover, Lindquist (1974) who studied the store image literature, combined models from 19 different studies and developed 9 different attributes of image. These attributes are: merchandise, service, clientele, physical facilities, comfort, promotion, store atmosphere, institutional and post-transaction satisfaction (Lindquist, 1974).

Furthermore, the store image of a multi-channel retailer may be multi-faceted. Store image can be formed based on information of the retailer of all channels the customer has experienced, where other customers may online have a specific set of brand images and expectations each of the retailer’s individual channels (Kwon & Lennon, 2009a). For this reason, brand image is considered in both online and offline sales channels.

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The previously stated researches provide enough evidence to assume that image has not only a direct effect on its own channel but can also influence loyalty towards the other channel. This has led to the following hypotheses:

H3.a: A high perceived channel image of the online channel of a retailer, positively influences the

H3a.1: A high perceived channel image of the online channel of a retailer, positively influences

the loyalty towards the offline channel of that retailer.

H3.b: A high perceived channel image of the offline channel of a retailer, positively influences the

loyalty towards the offline channel of that retailer.

H3b.1: A high perceived channel image of the offline channel of a retailer, positively influences

the loyalty towards the online channel of that retailer.

3.1.5 Mediation effects

As stated earlier, the image of a retailer is a multidimensional construct which is influenced by parts of other concepts in this research. Previous research has shown that service quality of a retailer influences the image of a retailer (Wu, Yeh & Hsiao, 2011; Yoo & Chang, 2005; Mazursky & Jacoby, 1986). The service quality of a retailer can leave behind positive or negative cues within the mind of a customer, these in turn, influence the image of the retailer (Lindquist, 1974), which can both influence the loyalty towards a retailer (Clottey et al., 2008). This can result in a mediating effect within our model, these effects are shown in figure 2 below. Hence, the hypotheses that the effect of service quality on loyalty is mediated by retailer image are proposed below:

H3.c: The effect of service quality online on loyalty towards the online channel is mediated by

retailer image online.

H3.d: The effect of service quality offline on loyalty towards the offline channel is mediated by

retailer image offline.

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important attribute of image. Within a list of 9 image attributes of which assortment and low prices account for 86% of the variance in store choice, showing the mediating role of assortment. Not only in store choice, but also in loyalty intentions assortment plays dominant role in the perception of the image and variety of the assortment (Bell, 1999). Amine & Cadenat (2003) found in their research that the perception of the assortment can influence the image of the store. Accordingly, it can be identified that assortment can not only influence loyalty but also increase the image. Hence, the mediating effect in both online and offline sales channels, results in the following hypothesis:

H3.e: The effect of the perceived assortment variety online on loyalty towards the online channel

is mediated by the retailer image online.

H3.f: The effect of the perceived assortment variety offline on loyalty towards the offline channel

is mediated by the retailer image offline.

Figure 2 - Mediation effects

3.1.6 Channel integration

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it is interesting to look at how important perceived channel integration is in predicting multichannel loyalty.

Channel integration can be defined as “the management of different sales channels to offer customers a seamless experience across all of the firm’s sales channels” (Goersch, 2002, p. 749; Chatterjee, 2010). The way that customers perceive this channel integration is found to be multidimensional (Frasquet et al., 2017). Frasquet et al. (2017) proposed that this depends on two dimensions: reciprocality (refers to the opportunity to cross between online and offline channels) and coordination (referring to the alignment of online and offline offerings).

As both channels combine their beneficial characteristics, they both profit from it (Montoya-Weiss, Voss & Grewal, 2003) and this might lead to channel synergies (Robey, Schwaig & Jin, 2003; Chan & Pan, 2005). By integrating channels, retailers can allow customers with options to deliver or return their products using their choice of sales channels (Carey, 2004; Frattaroli, 2009). An example of this is Alibaba’s Hema stores in China (food retail) which allow customers to buy in store, pay by phone and have the groceries delivered at home or picked up. Each store also operates as a fulfilment centre for orders that came through the online channel (CNBC, 2018). It is considered that channel integration will generate a reinforced loyalty of the overall store and can also lead to the increase of usage of individual channels of a multichannel retailer. (McGoldrick & Collins, 2007).

Some research studies already showed that the perceived channel integration can influence overall loyalty towards a retailer. For example, Schramm-Klein, Wagner, Steinmann & Morschett (2011) found in their research that perceived channel integration influences customer trust, retailer image and channel portfolio, which increases customer loyalty. Moreover, Bendoly, Blocher, Bretthauer, Krishnan and Venkataramanan (2005) find in their research that channel integration increases customer loyalty by reducing the likelihood of switching firms in case of product availability failures in one of the sales channels. However, little is known about how it affects the individual channels.

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positively influences the loyalty towards the online and offline sales channel of a retailer. Accordingly, the following hypotheses are developed:

H4.a: The perceived channel integration positively influences the loyalty towards the online

channel of a retailer.

H4.b: The perceived channel integration positively influences the loyalty towards the offline

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4. Methodology

4.1 Design and participants

Because the aim of this research is to investigate standardized and systematic comparisons, an online survey was conducted to collect cross-sectional data from multichannel shoppers. Besides, considering the short amount of time and a lack of financial resources to collect more or longitudinal empirical data, collecting survey data is the most appropriate method. The survey is performed on attitudes towards multichannel retailers in the clothing industry. Clothes are one of the bestselling online product categories (Moore, 2012) which is why most people are likely to have experience with both shopping offline and online channels in this context. Furthermore, the apparel industry is characterized as highly competitive, with massive price competition, assortment decisions and a lot of image is attached to certain brands and stores which is where our model integrates these facets. The clothing and fashion industry is rapidly changing in terms of their assortment and customer journey with interference of technology. Clothes are bought on a frequent basis, not as frequently as fast-moving consumer goods like food but also not as little als luxury goods like a car. Apart from this, shopping for clothes is a product category that is mostly shopped through online and offline channels (Konus et al., 2008). This makes it interesting to see what drives customers to be loyal to a retailer, considering both online and offline channels.

The participants will be attained through random sampling via both social media and in person. The participants will be located in the Netherlands because our survey is about Dutch multichannel retailers. Through this random selection method, it is managed to reach 257 participants, of which 206 were found to be qualified for analysis. Thus, the sample size is 206 participants. The self-reporting role of the participant is best suitable for researching attitudes (like customer loyalty etc.) (Franc & Brkljačić, 2006).

4.2 Procedure

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a list of multichannel retailers that operate in the clothing industry in the Netherlands. They had to choose which retailer they have experiences preferably in both online and offline sales channel. After this, the participant filled in the questionnaire with the chosen retailer in mind. It is necessary to perform this research on perceptions of existing retailers because this research is measuring attitudes (e.g. image) and loyalty towards the retailer and this would be impossible with a fake or unknown retailer. The questionnaires were asked to be filled in from their smartphone or computer from their own place, where they are comfortable.

4.3 Measurement

The following part describes the measures that are used to obtain the required information from the participants. Indicators of latent variables are drawn from multiple questionnaires.

4.3.1 Service Quality of the retailer

SERVQUAL has been one of the most influential measurements of service quality and is established by Parasuraman, Zeithaml, and Berry (1985). SERVQUAL as a construct, is based on 10 initial dimensions: tangibles, reliability, responsiveness, communication, credibility, security, competence, courtesy, understanding the customer and access. However, many researchers have argued that the initial dimensions where not particularly suitable for online service quality settings. Therefore, they have combined several parts from other models into a better suitable model for only settings. They used parts from SERVQUAL, TAM (Technology Acceptance Model; Davis, 1989), and EUCS (End User Computing Satisfaction, Venkatesh & Davis, 2000; Zeng, Hu, Chen & Yang, 2009). This led to the following dimensions: access, ease of navigation, efficiency, flexibility, reliability, personalization, security, responsiveness, assurance/trust, site aesthetics and price knowledge (Yang et al., 2004; Kaynama & Black 2000; Loiacono, Watson, & Goodhue 2002). Jun, Yang & Kim (2004), have scaled down these dimensions to: Ease of use, Access and Reliable/prompt responses. These dimensions are adapted and scaled down the number of items to only the ones which were suitable in a retail setting. Furthermore, the appreciation of free returns is added as this is another indicator of online service quality (Bower & Maxham, 2012). This resulted in a service quality construct with 7 items divided under 4 dimensions (reliable/prompt responses, access, ease of use and free returns) for online service quality (see table 2 below).

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policies, physical aspects, personal interaction, reliability and problem solving (Diallo & Seck, 2018). The current research uses these dimensions in our measurement of service quality for offline channels. Policies are excluded because these measures are about assortment and assortment is already included as an independent variable, which would cause overlap. Moreover, Jun et al., 2004 found that reliable/prompt response was the most important factor for service quality online. This was highly significant with the highest beta coefficient; the rest of the factors were less important and it is decided to focus only on the most important factors. This resulted in a 7-item scale of different dimensions of service quality (physical aspects, reliability, personal interaction and problem solving), as can be seen from table 2 below. They are measured on a 5-point Likert scale ranging from 1= strongly agree to 5= strongly disagree.

4.3.2 Perceived assortment variety

For the perceived variety of assortment, the measures used by Prediger & Huertas Garcia (2018) are used. These measures apply best in our context as Prediger & Huertas Garcia (2018) use a definition of perceived assortment variety that is in line with our definition. Furthermore, these measures can apply in both online and offline sales channels. A 5-point Likert scale, ranging from 1= strongly agree to 5= strongly disagree, is used to measure the 4 questions.

4.3.3 Price perception of the retailer

The way that price is perceived by customers is measured on store level. Lombart & Louis (2014) provided measures of price image on store level which include more value for money components, which is in line with our definition of price perception. However, this research does not include for example perceived prices of shipping costs or whether the store provides a lot of discounts (find articles with questions about this). Price perception is measured in four questions on a 5-point Likert scale, ranging from 1= strongly agree to 5= strongly disagree. The questions apply for both online and offline price perception.

4.3.4 Image of the retailer

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questions. However, it is not possible to ask that much questions about image. This is why it is decided to include the simpler measure from Clottey et al. which contains two items that can apply to both online and offline settings and is suitable for retail settings. To develop a more complete scale, two more items are added which measure the general image of the online or offline store. These can be found in table two. Image is also measured on a 5-point Likert scale, ranging from 1= strongly agree to 5= strongly disagree.

4.3.5 Perceived channel integration of the retailer

The current research measures perceived channel integration on the scale from Schramm-Klein et al. (2011). They argue that perceived channel integration is a complex construct and it is difficult to collect direct evaluations of perceived channel integration. However, they managed to make a scale including 6 questions, which measure the extent to which both channels are perceived as integrated, which applies to the clothing retail industry. The bonus card option is left out of the questions as this does not apply to our setting. This construct is measured on a 5-point Likert scale ranging from very easy (1) to not possible (5).

4.3.6 Customer loyalty online and offline channel

In order to measure the customer loyalty online and offline a 4-item scale is used that applies for both online and offline sales channels. This is assessed through a 7-point Likert scale ranging from 1= very strongly agree to 5= very strongly disagree. The scale is adapted from earlier research by Frasquet et al. (2017) as they use the same definition of (attitudinal) loyalty.

4.4 Control variables

4.4.1 Channel preference

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The amount of shopping in a certain shopping channel shows how much a person uses a certain channel. The more someone uses a certain channel, the more he is susceptible to be loyal towards a certain channel (Xue, Hitt & Harker, 2007). This is why amount of shopping is added as a control variable in the analysis.

Table 2 - Survey questions

Construct Question Translated Questions Scale

-Perceived channel integration: Adapted from Schramm-Klein et al. (2008)

How would you assess the possibility of the following at store XYZ?

1. Collection/return to physical outlets of goods ordered from catalogue outlets

2. Collection/return to physical outlets of goods ordered over the Internet

3. Product information about all channels available in all channels

4. Price information about all channels available in all channels

5. Orientation information about all channels via visibility of assortment and services

6. Pointers to alternative channels in all channels

Hoe zijn mogelijkheden van de volgende stellingen bij winkel XYZ?

1. Afhalen/ terugbrengen van kleding gekocht in de ene winkel naar een ander filiaal van dezelfde keten

2. Afhalen/ terugbrengen van kleding besteld via de online winkel naar de fysieke winkel

3. Productinformatie verkrijgen over bepaalde online/offline kleding in zowel de online winkel als de fysieke winkel 4. Prijsinformatie verkrijgen over online/offline kleding in zowel fysieke als de online winkel.

5. Assortiment Informatie verkrijgen over online/offline kleding in zowel fysieke als de online winkel.

6. Zichtbare verwijzingen naar webshop in fysieke winkel en naar fysieke winkel in de webshop.

5-point Likert scale (1=very easy; 5=impossib le) -Service quality online Adapted from Jun et al., (2004) Free returns (Bower & Maxham, 2012) Reliable/prompt responses

1. The product/service I ordered was delivered to me within the time promised by the online retailer 2. The online retailer responded to my inquiry promptly

Access

3. If I want to, I could easily contact a customer service representative over the telephone

4. For more information, I could turn to the online retailer’s chat rooms, bulletin boards, or others

Ease of use

5. The Web site address was easy to remember 6. The organization and structure of online catalogues were logical and easy to follow

Free returns

7. I like the fact that I can return my clothes for free to this retailer.

Betrouwbare/ snelle antwoorden

1. Het product die ik heb besteld, is binnen de door de online verkoper beloofde tijd geleverd

2. De online winkel reageerde snel op mijn order/vraag

Toegankelijkheid

3. Als ik dat zou willen, kan ik eenvoudig via de telefoon contact opnemen met een medewerker van de klantenservice

4. Als ik meer informatie wil of een vraag heb kan ik terecht bij chatrooms van de online winkel, FAQ, contactformulier of andere hulpmiddelen

Makkelijk te gebruiken

5. Het websiteadres was gemakkelijk te onthouden

6. De opzet van online catalogus is logisch en gemakkelijk te navigeren

Gratis retourneren

7. Ik stel het op prijs dat ik bestelde kleding bij deze winkel gratis kan retourneren. 5-point Likert scale (1=strongly agree; 5=strongly disagree) -Service quality offline Adapted from (Vazquez et al., 2001) Physical aspects

1. The physical facilities at this store look visual appealing

2. The store layout makes it easy for customers to move around the store.

Reliability

3. When the store promises to do something at a certain time, it will do so.

Personal Interaction

4. Employees in this store give prompt service to customers.

5. Employees in this store are very customer friendly. 6. Employees in this store are always willing to help customers.

Problem Solving

7. Employees of this store are able to handle customer complaints directly and immediately.

Fysieke aspecten

1. Deze winkel is mooi ingericht.

2. De winkel is zo opgezet dat ik mij makkelijk kan verplaatsen.

Betrouwbaarheid

3. Wanneer de winkel iets belooft te doen, dan gebeurt dit ook

Persoonlijke interactie

4. Medewerkers in deze winkel geven snelle service aan klanten. 5. Medewerkers van deze winkel zijn klantvriendelijk

6. Medewerkers in deze winkel zijn altijd bereid om klanten te helpen

Probleemoplossend

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1. It seems that this online store has a great variety of products

2. This online store seems to have a wide variety of brands

3. Everything I need seems to be at this online store

4. This online store has my favourite products available

1. Deze online winkel heeft een grote variëteit aan producten. 2. Deze online winkel heeft een groot assortiment aan verschillende merken.

3. Alles wat ik nodig heb is in deze online winkel aanwezig.

4. Mijn favoriete producten zijn aanwezig in deze online winkel

5-point Likert scale (1=strongly agree; 5=strongly disagree) -Assortment offline Adapted from Prediger & Huertas Garcia (2018)

1. It seems that this offline store has a great variety of products

2. Everything I need seems to be at this offline store .3 This offline store seems to have a wide variety of brands

4. This offline store has my favourite products available

1. Deze fysieke winkel heeft een grote variëteit aan producten. 2. Alles wat ik nodig heb, is in deze fysieke winkel aanwezig. 3. Deze fysieke winkel heeft een groot assortiment aan verschillende merken.

4. Mijn favoriete producten zijn aanwezig in deze fysieke winkel. 5-point Likert scale (1=strongly agree; 5=strongly disagree) - Online Price Adapted from Lombart & Louis (2014)

1. I am certain to find particularly attractive prices in this store

2. I can control my spending in this store

3. I think that this store makes important efforts to offer me attractive prices

4. In this store, I found that the prices were rather expensive (reverse scored)

1. Ik kan in deze webshop rekenen op aantrekkelijke prijzen. 2. Ik kan mijn uitgaven onder controle houden in deze webshop. 3. Deze webshop doet hard zijn best om mij aantrekkelijke prijzen te bieden.

4. In deze webshop vond ik dat de prijzen vrij hoog waren. (Omgekeerd gescoord). 5-point Likert scale (1=strongly agree; 5=strongly disagree) - Offline Price Adapted from Lombart & Louis (2014)

1. I am certain to find particularly attractive prices in this store

2. I can control my spending in this store

3. I think that this store makes important efforts to offer me attractive prices

4. In this store, I found that the prices were rather expensive (reverse scored)

1. Ik kan in deze winkel rekenen op aantrekkelijke prijzen 2. Ik kan mijn uitgaven onder controle houden in deze winkel. 3. Deze winkel doet hard zijn best om mij aantrekkelijke prijzen te bieden.

4. In deze winkel vond ik dat de prijzen vrij duur waren (omgekeerd gescoord). 5-point Likert scale (1=strongly agree; 5=strongly disagree) - Offline Image Adapted from Clottey et al., (2008)

1. This store is believed to be better compared to other stores nearby

2. This store has a very good image

4. In general there is good publicity about this physical store

5. In general, people talk about this physical store in a positive way.

1. Deze fysieke winkel staat goed bekend in vergelijking met andere winkels in de buurt.

2. Deze fysieke winkel heeft een goed imago.

3. Over het algemeen komt deze fysieke winkel positief in de publiciteit.

4. Doorgaans wordt er positief gepraat over deze fysieke winkel op social media. 5-point Likert scale (1=strongly agree; 5=strongly disagree) - Online Image Adapted from Clottey et al., (2008)

1. This store is believed to be better compared to other stores nearby

2. This store has a very good image

3. In general, there is good publicity about this online store

4. In general, people talk about this online store in a positive way.

1. Deze online winkel staat goed bekend in vergelijking met andere webwinkels.

2. Deze online winkel heeft een goed imago.

3. Over het algemeen komt deze online winkel positief in de publiciteit.

4. Over het algemeen wordt er positief gepraat over deze online winkel op social media.

5-point Likert scale (1=strongly agree; 5=strongly disagree) -Offline Loyalty Adapted from (Frasquet et al., 2017)

- I say positive things about the physical stores of XYZ to other people.

- I would recommend the physical stores of XYZ to those who seek my advice about such matters. - I would encourage friends and relatives to use the XYZ physical stores.

- I intend to visit the physical stores of XYZ more often.

- Ik praat positief over de fysieke winkels van XYZ tegen anderen. - Ik zou de fysieke winkels van XYZ aanbevelen aan mensen die mijn advies over dergelijke zaken vragen.

- Ik moedig vrienden en familie aan om de XYZ fysieke winkels te gebruiken.

- Ik zou de fysieke winkels van XYZ vaker willen bezoeken

7-point Likert scale (1=strongly agree; 7=strongly disagree) -Online Loyalty Adapted from (Frasquet et al., 2017)

- I say positive things about the online store of XYZ to other people.

- I would recommend the online stores of XYZ to those who seek my advice about such matters. - I would encourage friends and relatives to use the XYZ online stores.

- I intend to visit the online stores of XYZ more often.

- Ik praat positief over de online winkel van XYZ tegen anderen. - Ik zou de online winkel van XYZ aanbevelen aan mensen die mijn advies over dergelijke zaken vragen.

- Ik moedig vrienden en familie aan om de XYZ online winkel te gebruiken.

- Ik zou de online winkel van XYZ vaker willen bezoeken.

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5. Results

5.1 Data analysis

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Table 3 - Demographic statistics

Variable Count Percentage

Age <20 21-25 26-30 31-40 41> # 8 120 24 7 47 % 3,88 58,25 11,65 3,40 22.82 Gender Male Female # 100 106 % 48,55 51,45 Education Lower School High School Practical Education Bachelor’s degree Master’s degree University PhD # 1 29 34 101 40 1 % 0,49 14,08 16,50 49,03 19,42 0,49

Amount of online shopping per month

<1x per month 1x per month 2x per month 3x per month >4x per month # 125 49 19 10 3 % 60,68 84,47 9,22 4,85 1,46

Amount of offline shopping per month

<1x per month 1x per month 2x per month 3x per month >4x per month # 107 63 25 7 4 % 51,94 30,58 12,14 3,40 1,94 5.2 Power Analysis

Customer loyalty