How customer experiences across the offline, conventional online and mobile-online channels affect customer loyalty towards the firm
Master thesis Date: 30 June 2014
Author: Tadas Treigys (10605304)
University of Amsterdam, Faculty of Economics and Business
Under the supervision of Dr. Umut Konus, Assistant Professor of Marketing at the University of Amsterdam Business School
2 Table of contents Table of contents 2 Abstract 3 Introduction 4 Literature review 7
Growth in online and multichannel marketing 7
Conventional online and mobile-online channels in general 10
Customer loyalty to the firm and the multichannel context 13
Banking and distribution of books and entertainment tickets in the multichannel context 20
Development of hypotheses 22
Research methodology 26
Results of data analysis 30
Discussion 38
Managerial implications 43
Limitations and ideas for future research 46
Appendix 46
3 Abstract
In the last few years, the immense growth in the volume and popularity of purchasing online has intensified the competition between firms and increased the value of having good
understanding about the subtleties of the internet based business and its position in the multichannel business context. This study was intended to reduce certain literature gaps by providing some clarifications regarding issues such as how different channels compare in terms of customer perceptions, adoption intentions and especially the influence on attitudinal loyalty to the firm. This was studied in the contexts of banking, book retail and entertainment ticket distribution. The results suggest that, in terms of attitudinal loyalty, customers who use just one channel of a bank or a seller do not differ significantly from those who use two or more channels. Some differences between conventional online and mobile-online channels are observed in terms of how the satisfaction with them influences loyalty intentions. The relationship between the customer satisfaction with mobile-online channel and the attitudinal loyalty intentions towards the firm is shown to be very weak. These and other findings are discussed and their managerial implications are considered at the end of the study.
4 Introduction
The purpose of this study is to investigate how customer experiences of using the offline, conventional online and mobile-online channels of the same firm influence their loyalty intentions towards that firm. The term “channel” is used in the marketing/retailing literature to refer to a medium through which a seller and a customer interact (Neslin et al., 2006) or via which their transaction takes place (Wallace et al., 2004). In this study, the term “offline channel” refers to non-internet based business channels such as brick-and-mortar stores. Whereas “conventional online” is used to refer to the internet websites accessed by customers via a computer while “mobile-online” represents those internet business channels (websites or apps) that are accessed via a mobile phone or a tablet device. Each of these channels differs from the other two in terms of how customers can access them and what kind of goods/services can be provided through them. Such differences make it interesting to study the potential outcomes for businesses when these channels are combined.
This thesis will focus on three kinds of businesses operating across those three channels: banks, book retailers and entertainment (e.g. concerts, theatre, museum, cinema, sports events etc.) ticket distributors. The banking sector is chosen to represent service providers whereas the reason for including book retailers and entertainment ticket distributors is that a variety of channels are adopted by them but companies differ in terms of what and how many channels they use. Therefore, comparing different channels with regard to how they fare in terms of customer satisfaction and promotion of loyalty to the firm may be expected to provide some interesting implications. At least several key reasons exist for the managerial and academic relevance of this topic. In terms of managerial importance, more and more businesses are trading across multiple channels and the knowledge of how different channels interact in influencing customer perceptions, satisfaction and loyalty intentions has genuine practical applications (Kwon and Lennon, 2009). For example, knowing more about how conventional
5 online and mobile-online channels interact may make it easier for managers to evaluate whether operating through both channels would be worthwhile for their businesses. Also, knowing whether online and offline channels complement or negatively influence each other or maybe have no significant relationship is valuable as well. For example, major book sellers such as Waterstones or Barnes & Noble already operate via all of the above three channels but most of their smaller competitors do not. Thus, knowing more about the interrelationships between the channels could help such firms in their considerations about possible adoptions of additional channels. Moreover, if a particular channel was found to dominate against other channels in terms of customer satisfaction, managers might be more motivated to analyse carefully what makes that particular channel stand out and whether its most effective practices could be applied by other channels as well.
Furthermore, if a particular channel was found to be particularly effective in promoting customer loyalty, then managers whose firms are not currently utilizing that channel would have a reason to consider adopting it or at least try to derive some ideas for potential
adjustments to the existing loyalty programs or campaigns that are run through the currently used channels. Or, if a particular channel appeared to be ineffective promoting loyalty, managers could be encouraged to consider abandoning it and transferring its resources to those channels that would use them more effectively. Such knowledge could be particularly valuable to those firms that, for instance, have very limited budgets and cannot afford to use more than a few channels and therefore need to select the right ones for their purposes very carefully. Another example could be a marketing manager who is given a limited amount of funding to run a campaign designed to boost customer loyalty to the firm and needs to select the target channel that could be expected to the most conducive.
In terms of a more theoretical contribution, one of the arguments is that the availability of mobile-online channel is a relatively recent phenomenon. Its emergence was enabled by the
6 introductions of smartphones and tablet devices. Unsurprisingly, research on the mobile-online channel in relation to matters such as customer loyalty remains very limited and multiple questions still need answers. Thus, a thesis topic that involves attention to the mobile-online channel arguably has scientific relevance in and of itself. Moreover, previous studies have not clarified sufficiently the specific distinctions between the conventional online and the mobile-online channels in terms of the customer experiences and attitudes towards these two channels. One could argue that expanding on the current theoretical knowledge is necessary for researchers to be able to make solid practical recommendations that will be applicable in the mobile-online channel in the future.
The previous studies have also failed to investigate the multichannel retail environment in sufficient detail. The reason is that they usually have tended to either study just one particular channel and/or either the offline or online retail contexts but not both of them together. This issue has been frequently emphasized (Toufaily et al., 2013; Lin, 2011; Dhalokia, 2010; Hsieh et al., 2012) as the one that desperately needs to be addressed by future research. Thus, one of the objectives of this thesis is to address this dearth of literature by exploring the multichannel business environment in such a way that both online and offline contexts will be involved. It also means that customer loyalty tendencies will be studied from the perspective where offline and online environments are combined- this will address the academic need for studies where the customer loyalty is investigated in both rather than just in one of the
contexts.
It could be potentially valuable to understand whether the phenomenon of customer loyalty manifests itself differently in different channels and whether it depends on what or how many different channels consumers use. For example, this thesis aims to reduce the literature gap (Lee and Kim, 2010) on whether and how the consumers who use two or more channels of the same seller differ (in terms of loyalty intentions towards the firm) from consumers who
7 use only one channel. Another question yet to be addressed is whether either of the offline, conventional online and mobile-online channels has significantly stronger influences on customer loyalty than the other channels. As mentioned in the above discussion of managerial relevance, such knowledge could be applied in the context of multichannel business when, for example, developing/planning a customer loyalty program or campaign or making some adjustments to the existing practices or channels.
Researchers like Neslin and Shankar (2009) have previously suggested relevant future research questions such as whether satisfied customers are more willing to make use of the availability of multiple channels or whether such availability is what actually contributes to increasing customer satisfaction. One could argue that research on what kind of relationship exists between the variables has potential for considerable managerial significance. The knowledge of whether it is customer satisfaction that makes that them use multiple channels or whether those channels are essential for customer satisfaction could be valuable to, for example, managers of a firm that currently operates in a single channel only and considers whether it would be beneficial to trade via others as well. That is another topic this thesis is intended to address.
Literature review
In this section, the literature on the recent developments within online and multichannel retail will be overviewed. Subsequently, the review of concepts of conventional online and mobile-online channels and the discussion of customer loyalty both in general and in the
multichannel context will follow. Then the literature regarding specifically multichannel banking, book retail and ticket distribution will be discussed.
Growth in online and multichannel marketing
In the last few years, the popularity of online shopping has been growing rapidly and consumers are spending vast amounts of money buying online. For example, Sabbath et al.
8 (2013) estimated that the global online retail spending rose from $236 in 2007 to $521 billion in 2012. This accounts for a substantial addition to the spending in the brick-and-mortar stores. For example, Rigby (2011) predicted that in the near future online sales would account for around 20 per cent of all retail sales in the US.
This growth in the consumer inclination to shop online has encouraged more and more businesses to start trading online (van Birgelen et al., 2006). According to Kwon and Lennon (2009), multichannel retailing has become a widespread trend for the once-traditional in-store retailers. The literature (Levy and Weitz, 2009; Coelho and Easingwood, 2008) defines multichannel retailing as the set of processes involved in selling services or goods to consumers through more than one channel. Zhang et al. (2010) describe multichannel retailers as those businesses whose main source of revenue is multichannel retailing
activities. Schröder and Zaharia (2008) argue that selling through multiple channels can help retailers to increase the scope of their sales market in geographical, time-related, and goods-related terms.
The Direct Marketing Association predicted (2005) that, by 2011, 40 per cent of US retailers would sell through at least three channels and 42 per cent through at least two channels. Such retailers hope that online sales would complement their physical stores’ revenues
substantially. For instance, the US multichannel retailer Nordstrom estimates that its
customers who use more than one channel spend up to four times as much as those who shop only through one channel (Clifford 2010). Kushwaha and Shankar (2013) study shows that the average multichannel buyer spends more than the average single channel user. Neslin and Shankar (2009) argue that this result may be already considered an empirical generalization because it has been replicated by multiple past studies.
Schramm-Klein and Morschett (2005) argue that no single channel can meet all the needs of the contemporary consumer and multichannel selling is the only way to provide highest
9 degree of convenience and satisfaction to consumers. Other reasons mentioned in the
literature for the expansion of multichannel retail include electronic channels’ lower cost access to new markets, possible economies of scale (Zhang et al, 2010) and higher profitability than in brick-and-mortar channel (Panteva and Stampfli, 2012).
In their studies of multichannel financial services, Fernandez-Sabiote and Roman (2012) show that there is a positive relationship between the offline quality perceptions and the online quality perceptions. Meanwhile Yang et al. (2013) suggest that higher satisfaction with the offline channel leads to higher use of the online channel while higher satisfaction with the online channel reduces the use of the offline channel. Such results from past research show that further answers are still needed to clarify many issues related to the multichannel retail environment and customer loyalty. According to Young et al. (2013), the lack of clear understanding of the mechanism of cross-channel interactions inhibits both academics and practitioners from making more detailed and accurate predictions related to customer adoption of channels. It also is difficult to design the linking features between offline and online channels appropriately to achieve higher customer acceptance.
The findings of the past research have been mixed when it comes to the interaction of different channels. Yang et al. (2013) describe the past literature as either showing
multichannel synergies or de-synergies. For example, van Birgelen et al. (2006), Montoya-Weiss et al. (2003) and Steinfield (2004) found that multichannel strategy led to channel cannibalization and reduced sales. Meanwhile other studies showed that, for example, the interaction of offline and online channels led to only limited cannibalization (Biyalogorsky and Naik, 2003; Deleersnyder et al., 2002; Pauwels and Neslin 2008). Other researchers suggest that satisfaction with one channel enhances the consumer willingness to try other channels of the same seller (Strebel et al., 2004; Verhoef et al., 2007; Varhagen and van Dolen, 2009).
10 Conventional online and mobile-online channels in general
Before the emergence of smartphones and tablet devices, online shopping was understood as making a purchase online via a computer (Turban et al., 2004). However, nowadays, when discussing online retail in the context of multichannel shopping, it might be useful to make a clear distinction between online shopping using a computer (desktop or laptop) and shopping via a mobile phone/smartphone or a tablet device. According to Gu et al., (2013:329), “it is both theoretically and practically necessary and meaningful” to distinguish between them.
In this study, the term “conventional online” is chosen to refer to the online shopping channel accessed via a computer. According the Collins English Dictionary (Collins, 2014),
conventional means “following the accepted customs and proprieties” or “established by accepted usage or general agreement”. It could be argued that accessing the internet via a computer could be seen as a conventional way because it was the original way of online browsing whereas accessing the internet via a smartphone or a tablet device is a more recent/contemporary phenomenon.
According to Shankar et al. (2010) and Shankar and Balasubramanian (2009), what
differentiates the mobile-online channel from the conventional online and other channels is that a smartphone can be a constant companion for most people and, due to the ultra-portability of the device, consumers can access the mobile-online channel at any time and anywhere. Moreover, the device’s location-sensitivity enables location-specific marketing through the mobile-online channel.
2007 is now widely considered to be the revolutionary year (Frommer, 2011) in the mobile phone industry as the first iPhone smartphone with features such as a large touchscreen was released by Apple that year. Rivals in the mobile phone industry such as Samsung, Nokia, Sony Ericsson and HTC immediately started copying Apple. Unsurprisingly, features such as Wi-Fi and support for downloadable third-party applications became taken for granted and
11 the industry norm in just a few years. These features enabled the implementation and
proliferation of the mobile-online channel. Whereas tablet devices attracted a widespread consumer interest from 2010, with Apple’s iPad being credited as the device that defined what a tablet device should be about (Gruman, 2011; Barker, 2012). Considered together, smartphones and tablet devices provided a more compelling reason for businesses to consider the implementation of the mobile-online channel in their operations.
When thinking about the differences between the conventional online and mobile-online channels, one of the most interesting ways to compare them is arguably by analysing how they differ in terms of functionality and the technology they use. For example, computer website versions of online stores often differ considerably (see the comparison table below) from the phone/tablet versions (Adipat et al., 2011; Shankar et al, 2010). To complete a transaction on their phone, customers are often required to have a dedicated application installed on their device whereas computer users do not face such issues. Moreover, mobile website versions have less advanced user interface than the usual computer versions because smartphones and tablets have smaller screens and lower display resolutions, smaller
computational power and memory capacity etc (Gu et al., 2013; Ozok and Wei, 2010).
Table 1: How conventional and mobile-online channels compare functionality-wise
Conventional Online Channel Mobile-Online Channel Device used for access Desktop or laptop computer Smartphone or tablet Type of internet
connection used
Cable broadband or Wi-Fi 3G/4G mobile internet or Wi-Fi Device screen size Mostly between 10-26 inches Mostly between 3-10 inches Software required to
access the channel
Web browser Web browser and/or a dedicated app Website content Full (i.e. unrestricted by the
technical limitations applicable to smartphones/tablets)
Usually limited, less detailed than on the computer website version
Usability, website navigation
Full (i.e. unrestricted by the technical limitations applicable to smartphones/tablets)
Usually slower, constrained by smaller screen size, device’s lesser processing power etc.
12 Such differences might lead to significant differences in customer experiences across these two shopping channels. Wagner et al. (2013) suggest that, due to the differences between the functionality of computers and their versions of websites and the functionality of
smartphones/ tablet devices and their versions of websites, conventional online and mobile-online channels each can be preferred over the other channel under different
circumstances/for different purposes.
One could argue that having further and better understanding of the differences between the conventional online and the mobile-online shopping experiences is still needed in explaining why majority of online shoppers are not using their phones and or tablets for shopping. This has not been studied extensively and one of the key reasons for that is that the use of the mobile-online channel is not particularly widespread yet. For instance, Butler (2014) states that mobile-online retail sales accounted for around six per cent of all retail sales in the United Kingdom in 2013. McCorkindale and Morgoch (2013) study revealed that only around a quarter of the Fortune 500 companies had a mobile-ready website version.
However, despite these currently low rates, the future research needs to address the literature dearth because the mobile-online channels can be expected to proliferate and, as Shankar and Balasubramanian (2009) suggest, change the retail paradigm. This prediction can be backed up by statistics- smartphone sales are overtaking PC computer sales (Canalys, 2012); moreover, in May 2013, 34 per cent of American adults owned a tablet device and 56 per cent had a smartphone (Pew Research Center, 2013). 17 (Smith, 2012) per cent of
smartphone owners reported conducting most of their online browsing via that device rather than their personal computer. Forester Research (2011) estimated that the value of sales via the mobile-online channels in the United States reached three billion dollars in 2010 and could be expected to reach 31 billion dollars by 2016. Such facts imply that in the future the
13 mobile-online channel is likely to have the potential to reach vast numbers of consumers, perhaps someday even attracting more buyers than the computer-online channel.
Customer loyalty towards the firm and the multichannel context
Given that this study focuses particularly on customer loyalty towards the firm, this section of the literature review will be the most extensive. It would probably be a good idea to start by discussing why loyalty is important enough to justify studying it. First of all, establishing customer loyalty is something many businesses struggle with and there are still multiple questions for academics with regard to what prevents businesses from achieving it and what solutions could be applied in practice to gain it more effectively.
The growth in the number of online sellers has led consumers to having an overwhelming choice. Competition is intense so being able to achieve customer loyalty can be invaluable to the firm’s survival, especially within those product categories where the competition is the fiercest. However, establishing customer loyalty might be very difficult (especially in the online context) because consumers can look around without getting too attached to a
particular seller as accessing other sellers only takes a few seconds and clicks. Therefore, it is not surprising that, as Kwon and Lennon (2009) claim, more than 75 per cent of consumers search for products/services in one channel but end up purchasing them from another. Choi (2010) claims that only around two per cent of the visitors of an average retail website end up buying something from that particular website and only one per cent make repeat purchases. There are multiple reasons for the importance of customer loyalty. For instance, having a loyal base of customers usually makes it easier to have a stable stream of revenues.
According to Seybold (1998), more loyal customers tend to be less price-sensitive and make more cross-purchases. Moreover, a firm with more loyal customers may also have lower marketing costs as various studies show that retaining existing customers tends to be substantially cheaper than acquiring new ones. Pfeifer (2005) suggests that customer
14 acquisition costs five times more than retention whereas the Chartered Institute of Marketing in the U.K. (2010) states that customer acquisition can be between three and 30 times as costly as retention.
Given its importance to businesses, it is not surprising that the topic of customer loyalty in general is widely discussed in the marketing literature as well as in the literature of some other fields. In their state of the art literature review, Toufaily et al. (2013) state that the conceptualization of loyalty has over time moved from the behavioural to the cognitive approach and that the recent literature tends to be based on the processual approach. According to the authors, the researchers who follow this approach tend refer to Oliver’s (1999) theory of loyalty phases (cognition, affection, conation and action phases). The following definition of customer loyalty by Oliver (1997:392) is also frequently cited: “a deeply held commitment to rebuy or repatronize a preferred product/service consistently in the future, thereby causing repetitive same-brand of same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behaviour”.
Srinivasan et al. (2002) define e-loyalty as the customer's intention to continue buying from a particular website and not to switch to another for the same item. Meanwhile positive kind of word-of-mouth is described as a recommendation of a pleasant use experience to others. Xu et al. (2011) suggest that the main indicators of customer loyalty are continuous buying from a company, increasing business with it over time, and providing positive word-of-mouth. Here it could be argued that the definitions of e-loyalty and loyalty in general seem very similar or almost identical as they mention the same key manifestations of loyalty. However, as Liu et al. (2010) point out, the factors that influence customer loyalty in the offline context (e.g. parking facilities, store location) may be very different from the ones that matter most in the online context (e.g. website security, delivery service etc.). For example,
15 Xu et al. (2011) argue that the extent of actual personal interaction with customers is
significantly lower in the online than in the offline context so its quality is likely to be a less significant determinant of online loyalty than of offline loyalty.
Consumers may use different criteria in their minds when evaluating their experiences with different channels. This may arguably explain the differences in satisfaction levels a customer may have with different channels. Even if a company is satisfying customers with its
performance in one particular channel, it does not mean that other channel(s) will be as satisfying as well. Although firms may aim for a close channel integration and consistency of operations, certain characteristics and circumstances related to each channel may have to be taken into account and addressed with solutions unique to that particular channel. On the other hand, Schramm-Klein et al. (2011) and McGoldrick and Collins (2007) show that generally the level of integration between channels is a significant factor in determining customer perceptions and future behavioural intentions. The more consumers perceive the different channels of the same seller as consistent and well integrated, the more positive perceptions of the firm they have and report stronger future loyalty intentions towards it. Karjaluoto and Huhtamaeki (2010) argue that there are four possible levels of e-commerce integration that multichannel retailers can achieve and the highest one is at the point when the firm’s electronic channels are fully integrated with the traditional channels (e.g. brick-and-mortar or catalogue etc.). Lee and Kim (2010) also propose that there are four levels (reinforcement, synergy, reciprocity, and complementarity) of channel integration. The highest one is that of complementarity- it is achieved when a retailer understands the strengths and weaknesses of each channel and implements for each channel individual appropriate strategies to address them.
When it comes to the topic of how multichannel shopping availability influences customer loyalty, the literature seems to be divided. On the one hand, there has been a number of
16 studies suggesting that (Shankar et al., 2003; Hitt and Frei, 2002; Campbell and Frei 2006; Danaher et al., 2003; Wallace et al., 2004; Boehm, 2008) multichannel availability boosts customer loyalty. On the other hand, Asnari et al. (2008) and Wright (2002) suggest that the opposite is the case. Moreover, most of the studies were based on the offline channels such as brick-and-mortar stores, catalogues, telephone sellers etc. There remains a gap in the
literature on the multichannel environment where both online and offline channels are involved and how they interact in influencing customer loyalty intentions.
Past studies suggest that, in the context of online shopping, perceived service usefulness, shopping satisfaction, and past online shopping experience are the main determinants of repurchase intentions (Chen et al., 2009 and Tsai and Huang, 2007). Other studies have suggested that the quality of a website (Ganguly et al. 2009; Rhee et al., 2009; and Schaupp et al., 2009) played a key role in influencing shopper loyalty intentions. Walsh et al. (2010) suggest that customer loyalty is determined by customer evaluations of the seller on the same criteria both in the online and offline contexts. Those criteria are customer’s perceived satisfaction, and perceived trustworthiness and competence of the seller.
Table 2: The summary of the key determinants of customer loyalty to each channel, as suggested in the literature
Conventional online Mobile-online Offline Perceived service usefulness
Perceived satisfaction Website quality
Attitude towards online shopping Past shopping experiences
Perceived value Perceived ease of use Perceived trustworthiness Perceived Satisfaction Perceived satisfaction Perceived trustworthiness/competence Service quality Perceived value
Physical characteristics (e.g. location, parking facilities) When it comes to research on customer loyalty antecedents in the mobile-online channel specifically, research (Lin and Wang, 2006; Wei et al., 2009; Cho et al., 2007) shows that mainly the same factors- i.e. perceived value, perceived ease of use, trust and satisfaction- dominate. Other researchers (Balabanis et al., 2006) argue that satisfaction plays more significant role online than in the offline context. The argument is that in the
brick-and-17 mortar context customers may be forced by certain switching barriers (e.g. long distance to alternative stores) to continue buying from the same store even if they are not satisfied with it whereas in the online context such constraints may not exist or at least be reduced greatly. Thus, satisfaction can be expected to be a much stronger factor in determining online customer loyalty intentions.
DeLone and McLean (DeLone and McLean, 2003 and DeLone and McLean, 2004) model suggests that online businesses’ success is determined by the following factors: system, information, and service quality, usage, user satisfaction, and net benefit. This model implies that the customer attitude (e.g., satisfaction) and subsequent behaviour (e.g., actual purchase or purchase intention) are dependent on their beliefs about the information, system, and service quality. Whereas in the literature on offline commerce, the often-cited model is the following: quality → value → satisfaction → loyalty (Wang, 2008). Kim et al. (2012) propose a rather similar model for studying online shopper behaviour:
quality → value → customer satisfaction → repurchase intention. This is a model whereby quality and value are the antecedents, whereas satisfaction and loyalty- the outcomes. Kassim and Abdullah (2010) found service quality to have a significant impact on customer satisfaction, which in turn has a significant influence on trust. Customer satisfaction and trust subsequently have significant effects on loyalty through word of mouth, which was shown to be an antecedent of repeat visits or repurchase intentions. Abdul-Muhmin (2010) suggests that satisfaction with previous online shopping and attitude toward online shopping are significant determinants of repeat purchase intention. Moreover, satisfaction and experience with online purchasing are key forces affecting the attitude toward buying online. Whereas factors such as prices and product quality are significant determinants of customer
18 As we can understand, the concept of customer satisfaction features prominently in the
research on customer loyalty. However, this concept can be argued to be very broad and possibly confusing as there are many possible perspectives from which the topic can be studied. For example, Polites et al. (2012:2) point out that that “a consumer may be (dis)satisfied with characteristics of a website itself, the vendor, product(s), or service(s) associated with that website, the electronic channel as a whole, a single transaction conducted through the site, or the aggregation of all past transactions that have taken place”. Thus, unless researchers specify clearly which perspective they take, we can only assume that they are considering the overall customer satisfaction. Moreover, differences in the perspectives taken may arguably explain certain conflicting results of the past studies regarding the interrelationship between customer satisfaction and their loyalty intentions.
Another important factor that may influence customer satisfaction and loyalty intentions is whether consumer’s desire to shop online is driven by hedonistic or utilitarian values (Bridges and Florsheim, 2008, Overby and Lee, 2006 and To et al., 2007). Based on these two different kinds of values, shoppers can be divided into two separate categories whereby different customer needs and expectations dominate: utilitarian shoppers can be described as rational and goal-orientated people who will mainly value website features that are practical, convenient and time-saving. Whereas hedonistic customers may give a lot of importance to characteristics such website aesthetics, visual stimulation, entertainment (Bridges and Florsheim, 2008; Kuan et al., 2008; Sorce et al, 2005; Gupta and Kim, 2009; and To et al., 2007). Thus, for instance, a website that is perceived as very good by utilitarian shoppers may be rated as less satisfying by hedonistic customers because they will refer to different criteria when making judgements. A website that caters more to the utilitarian shoppers can be arguably expected to have more loyal customers among utilitarian shoppers than among hedonistic shoppers.
19 Kwon and Lennon (2009) state that the literature tends to suggest measuring customer loyalty on two dimensions. Namely, attitudinal and behavioural. The behavioural dimension should measure consumers’ actual behaviour such as whether they are making repeat purchases and engaging in positive word-of-mouth over time. Meanwhile the attitudinal dimension would be supposed to gauge customer opinions (e.g., whether they were satisfied by previously purchased products or services) and intentions (e.g. whether they have plans/willingness to buy from the same firm in the future).
According to the theories of reasoned action (Fishbein and Ajzen 1975) and planned behaviour (Ajzen 1991), an intention can be seen as a precursor to the actual behaviour. In the context of online commerce, customer intention to buy from a particular online store could be seen as the precursor to the actual behaviour of making a purchase. Understandably, businesses would like to achieve both attitudinal and behavioural loyalty and the latter is arguably highly unlikely if a customer is not loyal attitudinally. On the other hand, as Sousa and Voss (2012) demonstrate, attitudinal loyalty does not guarantee behavioural loyalty. This may be further illustrated with Reichheld’s (1993) estimation that between 65-85 per cent of customers who defect claim they were satisfied with their former seller- i.e. attitudinal loyalty measures could provide support for the expectations that customers will come back when actually they do not return. Mittal and Lassar (1998) found similar results, whereby satisfaction was positively related to loyalty but a reported high satisfaction score did not guarantee a customer’s loyalty. The findings suggest that dissatisfied customers would certainly defect meanwhile those who are satisfied would not necessarily remain loyal in the future.
The gap between intentions and actual behaviour could probably be attributed the existence of a number of other variables apart from satisfaction that influence customer loyalty
20 such as online shopping attitude (George, 2002; Monsuwe et al., 2004), online store
environment cues (Chang and Chen, 2008), product presentation formats (Kim and Lennon 2008), culture (Moon et al., 2008), as well as customer’s personality (Bosnjak et al., 2007). This implies that satisfaction may not be sufficient to retain customers if some other factors are not favourable to the seller.
The recent emergence of mobile-online shopping channel means that online shoppers do not necessarily have to buy via their computers as smartphones and tablet devices can be used as well. The existing research on the mobile-online channel and, more specifically, topics such as how it is different from the conventional online channel and how consumer experiences with those two channels differ in influencing consumer loyalty to the seller appears very limited.
In terms of managerial relevance, an overwhelming majority of retailers are yet to include the mobile-online channel in their operations. Actually, some major retailers are not even trading via the conventional online channels. Thus, the findings of the studies investigating online channels and their influences on customer loyalty might be useful for the retail managers when considering whether their firms would be likely to benefit from trading via
conventional online and mobile-online channels.
The effects on customer loyalty intentions can be arguably expected to have a significant weight when considering the feasibility of adopting a particular retail channel. Thus, the findings on how customer experiences with the computer online and mobile-online channels influence their attitudinal loyalty to the firm would have practical significance.
Banking and distribution of books and entertainment tickets in the multichannel context The banking sector was one of the first to implement multichannel operations featuring both conventional online and mobile-online channels (Laukkanen, 2007). Researchers (Laukkanen and Pasanen, 2008; Kim et al., 2009, Kwiatkowski, 2010) seem to believe that, despite the
21 lower than previously predicted take-up by customers, the electronic banking solutions have the potential to overshadow the traditional banking methods such as visiting branches or using telephone services and become an essential part of successful multichannel banking service providers(Klaus and Nguyen, 2013). According to Cortinas et al. (2010), past studies tended to study internet banking in general but they did not study multichannel users’
behaviour. Therefore, there is a gap in the literature with regard to multichannel banking services. Mallon (2010) argues that the availability of mobile banking service may be key in customer acquisition and building of their loyalty. Whereas Masrek et al. (2012) suggest that provision of mobile banking services only increases customer satisfaction but does not boost customer loyalty. Aldas-Manzano et al. (2012) show that satisfaction with online
banking(conventional online) channel is positively related to customer loyalty intentions towards a bank. Thus, it could be argued that measuring customer satisfaction with the mobile-online banking channel would be a useful addition to complement the past research because then conventional online and mobile-online channels could be compared.
When it comes to multichannel book retail, conventional online and mobile-online channels are playing increasingly important role. According to Bowker Market Research report (2013), the share of books sold via the internet in the UK increased from 25.4 per cent in 2010 to 37.7 per cent in 2012. During the same period in the US, the increase was from 25.1 to 43.8 per cent. So the proliferation of the adoption of electronic channels (both conventional online and mobile-online) by book retailers can be expected to get more and more widespread. Thus, it could be argued that, given this growth, the importance of addressing literature gaps on the conventional online and mobile-online channels is increasing as well.
Whereas the literature on entertainment ticket distribution appears very limited. According to Slack et al. (2008), multichannel distribution has become a norm for event ticket distribution. Major online ticket distributors such as Ticketmaster sell via both the conventional and
22 mobile-online channels but the literature related to multichannel ticket distribution continues to discuss only the offline and the conventional online channels. Therefore, there is a need for studies that pay attention to the mobile-online channel as well because there is only a very limited number of studies related to ticket distribution via the mobile-online channel. Studies by Alfawaer et al. (2011), Smiths et al. (2012) can be given as examples.
Development of hypotheses
In this section, the hypotheses of this study will be outlined together with brief overviews of relevant past studies.
Hypothesis 1: Satisfaction levels with conventional online and mobile-online channels are
positively correlated.
Although these two channels have some considerable functional differences, they should have some important similarities as well. If a firm carefully designs its mobile website version, it should make sure that some degree of consistency is maintained and at least the key features are transferred from the computer version to the mobile version. Then it would be reasonable to expect that if consumers find the computer version satisfying, they should see the mobile version as reasonably good as well. For example, Lin et al. (2011) suggest that customer trust in the conventional online channel is transferred to the mobile-online channel. Lin (2012) shows that customer satisfaction with the conventional online channel on the dimensions such as assurance, reliability and ease of use is positively related to the customer perceptions of the mobile-online channel.
Hypothesis 2: Those customers who use more than one channel of the same company have
stronger attitudinal loyalty than those who use just one channel.
Someone who has been satisfied with one channel can be expected to be more likely than those who have been disappointed to be willing to also give a try to another channel of the same company (Strebel et al., 2004; Verhoef et al., 2007; Varhagen and van Dolen, 2009).
23 Laviriere et al. (2011) found that satisfaction had stronger impact on multichannel users’ loyalty intentions than on single channel users’. According to other literature, (Schramm-Klein et al., 2011; McGoldrick and Collins, 2007; Karjaluoto and Huhtamaeki, 2010) consumer’s willingness to use multiple channels of the same firm may be seen as a sign that the firm has a reasonable level of channel integration and consistency, which have been shown to be positively related to customer satisfaction and loyalty intentions.
Hypothesis 3: There is a positive relationship between the daily amount of time spent
browsing the internet and the satisfaction with the conventional online and mobile-online
channels.
Those who browse the internet more will accumulate more experience with the usage of the internet. They should be feel more comfortable and confident and be more knowledgeable about shopping online or carrying out other kinds of transactions safely than those who use the internet less. They should be less likely to be deterred from using online channels due to lack of trust or some kind of fears related to the safety of shopping online (Myazaki et al., 2001). Citrin et al. (2000) and Naseri et al. (2011) show that online shopping is more
common among those who use the internet for a greater number of non-shopping applications and that broader experience with online browsing provides users with the necessary skills and confidence for adoption of online shopping. Therefore, it seems reasonable to expect that higher browsing frequency should lead to a higher probability that the user will be satisfied with shopping via conventional online of mobile online channels.
Hypothesis 4: Compared with females, males are more satisfied with conventional online and
mobile-online channels and are more inclined to use them regularly in the future
According to Okazaki and Mendez (2013), females tend to be more apprehensive and less confident than males about shopping online because of the higher tendency to worry about privacy and data security issues (Garbarino and Strahilevitz 2004). Also, according to
24 Riquelme and Rios (2010), males more than females tend to find mobile banking convenient and have more inclination to use it. Moreover, women find online shopping in general less practical than men of the same age and internet use experience do (Rodgers and Harris 2003; Hassan, 2010). Falk et al. (2007) also found that satisfaction with the offline channel had more positive effects on the perceptions of the online channels for men than for women. Thus, all in all, it seems reasonable to hypothesize that males are likely to be more satisfied than females with both conventional online and mobile-online channels and want to use them regularly in the future as well.
Hypothesis 5: Older people are less satisfied with online channels (both conventional online
and mobile-online channels) than their younger counterparts and are less interested in using
them in the future
Floh and Treiblmaier (2006) state that older people tend to value website quality less than younger people do. Hernandez et al. (2011), Walsh et al. (2008) and Lassar et al. (2005) show that age does not play a role in influencing consumer online channel adoption and loyalty intentions. Whereas Sorce et al. (2005) found that younger consumers were more likely than older consumers to perceive it positively and agree that online shopping was convenient. Joines et al. (2003) argued that older consumers tended to prefer offline stores are were less satisfied with online shopping. As there is no consensus in the literature, it seems reasonable to check this relationship between age and satisfaction. Especially as there is also a
particularly prominent gap in the research on how consumer demographic characteristics influence their experiences with the mobile-online channel.
Hypothesis 6: There is a positive relationship between customer satisfaction with the
mobile-online channel and attitudinal loyalty towards the bank/the seller
Generally, various studies have confirmed (Kwon and Lennon, 2009; Chen et al., 2009; Tsai and Huang, 2007) the positive relationship between customer satisfaction and loyalty but
25 there is gap in the literature regarding this relationship in the online context and particularly in mobile-online. Thus, testing the above hypothesis would have academic relevance. Hypothesis 7: There is a positive relationship between the intention to use conventional
online and mobile-online channels regularly in the future and the intention to stay with the
same bank/seller
In the past literature on customer loyalty (Kwon and Lennon, 2009; Mittal and Lassar, 1998; Xu et al., 2011), the intention to continue to buy from the same seller regularly was seen as an indication of attitudinal loyalty. It could be argued to be reasonable to hypothesize that the intention to keep using the conventional online and mobile-online channels is related to attitudinal loyalty, i.e. more loyal customers may be expected to use the channels more frequently. Due to the lack of past literature, it seems significant to test whether this hypothesis is correct in the mobile-online context.
Hypothesis 8: There is a positive relationship between the future intention to use the
conventional online channel regularly and the future intention to use the mobile-online
channel regularly
One the one hand, it could be hypothesized that, if a customer is dissatisfied with a computer version of a particular website, it is difficult to expect him or her to like the mobile version because both versions are interrelated and have many things in common. However, usually there are some functional differences between the two versions and they may make customers perceive their experiences with each channel somewhat differently. Thus, it seems reasonable to check whether and what relationship exists between the two channels.
Hypothesis 9: There is a positive relationship between the intention to increase the usage of
service/purchasing and the intention to use conventional online and mobile-online channels
26 According to the literature (Xu et al., 2011; Kwon and Lennon, 2009; Mittal and Lassar, 1998; Sousa and Voss, 2012), the intention to buy more or increase usage of the services of a particular firm in the future can be perceived as a strong indicator of attitudinal loyalty. When a customer who is mainly using the conventional and mobile-online channels intends to buy more/use more services in the future, it seems reasonable to expect that the use of those channels will certainly be more frequent/regular.
Hypothesis 10: There is a positive relationship between the daily amount of time spent
browsing the internet and the extent of intention to use the conventional online and mobile-
online channels regularly in the future.
It seems reasonable to hypothesize that those who spend more time browsing the internet will develop a stronger tendency to use the conventional and mobile-online channels for banking or book/entertainment ticket shopping. If a person is spending a lot of time on online
activities, using these two channels may be intuitively expected to be perceived by him or her as more convenient than the offline alternatives.
Research methodology
In this part the methodology of the study will be briefly described. First of all, research was conducted in the form of a questionnaire-based survey. Self-administered online
questionnaire was used. Survey method was chosen over an experiment or a case study as it seemed to be the most viable and convenient method for data collection, considering the thesis topic. The questionnaire was intended to be short enough to take no more than a few minutes to fill and prevent the issues related to the phenomenon of respondent survey tiredness. Non-probability self-selection sampling method was used.
The sample size of at least 200 was initially targeted and eventually 278 responses were received of which 249 (111 males and 138 females from 20 countries (although the majority
27 of respondents were from the Netherlands, with their ages ranging from 18 to 71) were complete and valid.
One of the key objectives of the thesis was to explore the multichannel business environment so the questionnaire arguably had to be designed in such a way that it asked about consumer experiences across at least several different channels. As can be understood from the channel use frequencies tables (Tables 3 and 4 in the appendix), the respondents turned out to be truly multichannel customers as on, average, they reported using all of the three channels of the study to some extent.
The first section of the questionnaire was about banking services. The respondents were first asked whether they use their bank’s online banking service (through the conventional online channel accessed via a computer), mobile service (through the mobile-online channel accessed via a smartphone or a tablet device) and in-branch/ telephone services (offline channel). The survey intentionally involved both offline and online contexts in order to address the dearth of studies (Toufaily et al., 2013; Lin, 2011; Dhalokia, 2010; Hsieh et al., 2012) that combine both of them.
Then they were asked to estimate how many times out of ten of using the bank’s service they would use each of the channels. This question was asked to determine to what extent the respondents could actually be considered to be multichannel users. Answers could range from 0 to 10 whereby “0” means that a particular channel is not used at all, “10” implies that a particular channel is the only one that is being used and the estimations for the channels have to add up to 10 (e.g. 3+3+4+0=10).
When choosing between quantitative and qualitative data collection methods, the former seemed more convenient and potentially more accurate/consistent for the purposes of this study. Thus, to gather data in a more quantitative way for the study, respondents were asked to rate their experiences with each channel they have used on a Likert-type scale (widespread
28 in research questionnaires) of 1-7, where 1 stands for a “very dissatisfying” experience and 7- for a “very satisfying” experience. Then they were asked how likely they thought they were to use each of the channels regularly in the future. The scale of 1-7 was used again, where 1 stands for “very unlikely” and 7- “very likely”.
Subsequently, they were asked to evaluate the likelihood of staying with their current bank and not switching to another bank. Then they were asked how likely they thought they were to increase their usage of the bank’s services in the future. For both questions, the scale of 1-7 was used, where 1 stands for “very unlikely” and 7- “very likely”. The last question was on how likely they thought they would be to be willing to recommend the bank to their friends or family members, where 1 stands for “very unlikely” and 7- “very likely”. These last three questions above were each based on one of the three key attitudinal loyalty indicators suggested in the literature in order to make sure that attitudinal loyalty was measured appropriately in this study.
In the second section of the questionnaire, respondents were asked whether they bought books or entertainment tickets more frequently (99 respondents claimed to purchase books more frequently whereas the other 150 purchased more entertainment tickets than books). Then, depending on the answer, the same sequence of questions like in the first section were asked about buying books or entertainment tickets. Respondents were first asked to consider the seller they purchase from the most and state whether they have used the seller’s
conventional online, mobile-online and offline channels and then they were asked to estimate how many times out of ten of buying from the seller they would use each of the channels. Answers could range from 0 to 10 whereby “0” means that a particular channel is not used at all, “10” implies that a particular channel is the only one that is being used and the
29 Then the next question asked the respondents how they rated their experience with the
channels they have used on a scale of 1-7, where 1 stands for “very dissatisfying” and 7- for “very satisfying”. Then they were asked how likely they thought they were to use each of the channels regularly in the future. The scale of 1-7 was used again, where 1 stands for “very unlikely” and 7- “very likely”.
Subsequently, they were asked to evaluate the likelihood of staying with their current seller and not switching to another one. Then they were asked how likely they thought they were to increase their purchasing from the seller in the future. For both questions, the scale of 1-7 was used, where 1 stands for “very unlikely” and 7- “very likely”. The last question was on how likely they thought they would be to be willing to recommend the seller to their friends or family members, where 1 stands for “very unlikely” and 7- “very likely”.
At the end of the survey, the respondents were asked to indicate their characteristics such as age, gender, country of residence and the number of hours per day they spend browsing the internet.
Subsequent quantitative analysis of the survey data using the SPSS 22.0 Statistics software was supposed to make it possible to derive a number of important inferences such as whether there is significant interrelationship between consumer experiences with the three channels and how those experiences influence their loyalty intentions, and how different channels compare in terms of their impact etc. Meanwhile the analysis of data on the respondent demographics and other characteristics was intended to be used to investigate whether there are any relationships between customer characteristics, customer experiences across different channels and their loyalty intentions. Thus, the study could be arguably seen as one that focuses on the attitudinal kind of customer loyalty.
30 Results of data analysis
In this section the results of data analysis are reported. The outcomes of hypotheses testing are summarized in the Table 5 below and it is followed by further discussion.
Table 3: Summary of the results of hypotheses testing Fully Supported hypotheses Partially supported
hypotheses
Rejected hypotheses
H1 H5, H6, H7, H8, H9 H2, H3, H4, H10
Hypothesis 1 was tested using the Spearman’s correlation analysis. The justification for using Spearman’s correlation analysis was that the data analysed were ordinal and this kind of analysis is widely recommended for analysing the relationship between ordinal variables. Results for bank customers: As can be seen in Table 6, the correlation between the
satisfaction with conventional online and the satisfaction with mobile-online channel suggests a significant relationship, r = .241, n = 134, p<.01.
Results for book/entertainment ticket buyers: As can be seen in Table 7, the correlation
indicates a significant relationship as well, r = .333, n = 52, p< .05. Thus, all in all, the data supported the hypothesis.
Hypothesis 2 was tested using the T-Test. T-Test analysis is recommended for comparing the means of two independent samples to see if there is a relationship between them, which was exactly what was needed to test this hypothesis so this kind of analysis seemed appropriate. Results for bank customers: The multichannel customers reported numerically higher
likelihood (M = 5.78, SD = 1.37) than the single channel customers (M = 5.50, SD = 1.63) to stay with the same bank and not switch in the future. The difference was not statistically significant (as can be seen in Table 8), t(247) = -1.15, p = .252.
Multichannel customers reported numerically higher likelihood (M = 4.81, SD = 1.25) than the single channel (M = 4.45, SD = 1.45) to increase their use of the bank’s services. The difference was not statistically significant, t(247) = -1.64, p = .102.
31 Multichannel customers reported numerically higher likelihood (M = 4.96, SD = 1.34) to recommend their bank to other people than single channel customers (M = 4.55, SD = 1.16). The difference was not statistically significant, t(50) = -1.54, p = .129.
Results for the buyers of books and entertainment tickets: Multichannel buyers reported
numerically higher likelihood (M = 5.18, SD = 1.29) than single channel buyers (M = 5.00, SD = 1.46) to continue buying from the same seller in the future. As can be seen in Table 9,
the difference was not statistically significant, t(247) = -1.05, p = .297.
Multichannel buyers reported numerically higher likelihood (MD = 4.72, SD = 1.17) than single channel buyers (M = 4.54, SD = 1.22) to increase purchasing from the current seller in the future. The difference was not statistically significant, t(247) = -1.18, p = .241.
Multichannel buyers reported numerically higher likelihood (M = 5.14, SD = 1.04) than single channel buyers (M = 4.94. SD = 1.31) to recommend the seller to other people. The difference was not statistically significant, t(171) = -1.31, p = .192. Thus, all in all, the data provided no support for the hypothesis.
Hypothesis 3 was tested using the MANOVA analysis. This kind of analysis was chosen because it is recommended for comparing multivariate means of several groups when there are two or more dependent variables. The dependent variables were the satisfaction with conventional online and the satisfaction with mobile-online channels. The independent variable indicating the daily amount of time spent browsing the internet was divided into the following three groups: 1 = 0-3 hours, 2 = 4-6 hours, 3 = 7-12 hours.
As can be seen in Table 10, a one way MANOVA revealed no significant multivariate main effect of the amount of time spent browsing the internet, Wilks’ λ = .835 with an associated F(8,78) =.922 and p = .503.
As can be seen in Table 11, the univariate main effects showed no significant relationships between the amount of time spent browsing the internet and the following: bank customer
32 satisfaction with the conventional online channel, F(2, 1.164) = 1.82, p = .175; bank
customer satisfaction with the mobile-online channel, F(2, .297) = .217, p = .805; book/ entertainment ticket buyer satisfaction with the conventional online channel, F(2, .596) = 2.157, p = .128; book/ entertainment ticket buyer satisfaction with the mobile-online channel, F(2, .240) = .377, p = .688. Thus, based on the above results, it can be concluded that the amount of time spent browsing the internet has no significant effect on satisfaction with the conventional online and mobile-online channels and the hypothesis has to be rejected.
Hypothesis 4 was tested using the T-Test. T-Test analysis is recommended for comparing the means of two independent samples to see if there is a relationship between them, which was exactly what was needed to test this hypothesis so this kind of analysis seemed appropriate. The results can be found in the Table 12. Results for bank customers: Females actually reported numerically higher satisfaction with the conventional online channel (M = 6.00, SD = .744) than males (M = 5.93, SD = .80). The difference was not significant statistically, t(236 )= -.75, p = .456.
Males actually reported numerically lower (M= 5.70, SD = 1.06) satisfaction with the mobile-online channel than females (M= 6.05, SD = .82). The difference was statistically significant, t(116) = -2.17, p < 0.05.
Males reported numerically higher likelihood (M = 6.47, SD = .91) than females (M = 6.33, SD = 1.06) to use the conventional online channel regularly. The difference was not
significant statistically, t(235) = 1.07, p = .288.
Males actually reported numerically lower (M = 5.32, SD = 1.83) likelihood than females (M = 5.38, SD = 1.89) to use the mobile-online channel regularly in the future. The difference was not statistically significant, t(247) = -.287, p = .774.
33 Results for book/ entertainment ticket buyers: Males actually reported numerically lower
satisfaction (M = 5.98, SD = .75) with conventional online channel than females (M = 6.08, SD = .73). The difference was not statistically significant, t(235) = -1.05 , p = .235.
Males reported numerically higher (M = 6.47, SD = .91) satisfaction with the mobile-online channel than females (M = 6.33, SD = 1.06). The difference was not statistically significant, t(52) = .37, p = .714.
Males actually reported numerically lower likelihood (M = 6.25, SD = .99) than females (M= 6.29, SD = 1.00) to use the conventional online channel regularly in the future. The difference was not statistically significant, t(247) = -.30 , p = .767.
Males actually reported numerically lower likelihood (M = 4.63, SD = 1.72) than females (M = 4.72, SD = 1.57) to use the mobile-online channel in the future. The difference was not
statistically significant, t(247) = -.45, p = .653. Thus, all in all, the data provided no support to the hypothesis.
Hypothesis 5 was tested using the T-Test. T-Test analysis is recommended for comparing the means of two independent samples to see if there is a relationship between them, which was exactly what was needed to test this hypothesis so this kind of analysis seemed appropriate. Respondents were divided into two age groups- 1(younger) = 18-28 year olds and 2(older) = 29-80 year olds. The results for bank customers: Younger customers actually reported numerically lower satisfaction (M = 5.96, SD = .79) with the conventional online channel than older customers (M = 5.98, SD = 0.66). As can be seen in Table 13 the difference was not statistically significant, t(236) = -0.08, p = .938.
Younger customers reported numerically higher (M = 5.97, SD = 0.95) satisfaction with the mobile-online channel than older customers (M = 5.39, SD = .78). The difference was significant statistically, t(136) = 2.45, p <0.05.
34 Younger customers actually reported numerically lower (M = 6.37, SD = .97) likelihood than older customers (M = 6.50, SD = 1.11) to use the conventional online channel regularly in the future. The difference was not statistically significant, t(247) = -.78, p = .435.
Younger customers reported numerically higher (M = 5.40, SD = 1.85) likelihood than older customers (M = 5.11, SD = 2.00) to use the mobile-online channel regularly in the future. The difference was no statistically significant, t(247) = .93, p = .351.
The results for book/ entertainment ticket buyers: Younger customers actually reported
numerically lower likelihood (M = 6.26, SD = 1.03) than older customers (M = 6.34, SD = .78) to use the conventional online channel regularly in the future. The difference was not statistically significant, t(247) = -.50, p = .618.
Younger customers actually reported numerically lower likelihood (M = 4.64, SD =1.64) than older customers (M = 4.86, SD = 1.65) to use the mobile-online channel regularly in the future. The difference was not statistically significant, t(247) = -.81, p = .42.
Younger customers reported numerically higher (M = 6.05, SD = .73) satisfaction with the conventional online channel than older customers (M= 6.00, SD = .78). The difference was not statistically significant, t(235) = .36, p = .722.
Younger customers actually reported numerically lower (M = 5.81, SD = .77) satisfaction with the mobile-online channel than older customers (M = 6.29, SD = .49).The difference was not statistically significant, t(52)= -1.59, p = .119. Thus, all in all, the hypothesis can only be considered partially supported as it was confirmed as correct on only one dimension out of eight.
Hypothesis 6 was tested using the Spearman’s correlation. The justification for using Spearman’s correlation analysis was that the data analysed were ordinal and this kind of analysis is widely recommended for analysing the relationship between ordinal variables. The results for bank customers: As can be seen in Table 14, the correlation between the
35 satisfaction with the mobile-online channel and the likelihood to stay with the same bank does not indicate a statistically significant relationship, r =.129, n = 138, p = .132.
As can be seen in Table 15, The correlation between the satisfaction with the mobile-online channel and the likelihood to increase the usage of the banks services does not indicate a statistically significant relationship, r = .088, n = 138, p = .305.
As can be seen from Table 16, the correlation between the satisfaction with the mobile-online channel and the likelihood to recommend the bank to other people indicates a statistically significant relationship, r = .207, n = 138, p<.05.
The results for book/ entertainment ticket buyers: As can be seen in Table 17, the correlation
between the satisfaction with the mobile-online channel and the likelihood to recommend the seller to other people does not suggest a statistically significant relationship,
r = .160, n = 54, p = .248.
As can be seen from Table 18, the correlation between the satisfaction with the mobile-online channel and the likelihood to continue buying from the same seller does not suggests a
statistically significant relationship, r = .161, n = 54, p = .244.
As can be seen in Table 19, the correlation between the satisfaction with the mobile-online channel and the likelihood to increase purchasing from the seller in the future does not suggest a statistically significant relationship, r = .022, n = 54, p = .877. Thus, all in all, the hypothesis can only be considered partially supported.
Hypothesis 7 was tested using the Spearman’s Correlation. The justification for using Spearman’s correlation analysis was that the data analysed were ordinal and this kind of analysis is widely recommended for analysing the relationship between ordinal variables. The results for bank customers: As can be seen in Table 20, the correlation between the
36 the same bank does not suggest a statistically significant relationship, r = .121, n = 249, p = .056.
As can be seen in Table 21, the correlation between the likelihood to use the mobile-online
channel regularly and the likelihood to remain with the same banks suggests a statistically significant relationship, r = .228, n = 249, p< .001.
The results for book/entertainment ticket buyers: as can be seen in Table 22, the correlation between the likelihood to use the conventional channel regularly and the likelihood to continue buying from the seller suggests a statistically significant relationship, r = .183, n = 249, p<0.01.
As can be seen in Table 23, the correlation between the likelihood to use the mobile-online channel regularly and the likelihood to continue buying from the same seller does not suggest a statistically significant relation, r = .028, n = 249, p = .661. Thus, all in all, the hypothesis can only be considered partially supported.
Hypothesis 8 was tested using the Spearman’s Correlation. The justification for using Spearman’s correlation analysis was that the data analysed were ordinal and this kind of analysis is widely recommended for analysing the relationship between ordinal variables. The results for bank customers: As can be seen in Table 24, the correlation between the likelihood
to use conventional online channel regularly and the likelihood to use the mobile-online channel regularly does not suggest a statistically significant relationship, r = .044, n = 249, p = .494.
The results for book/entertainment ticket buyers: As can be seen in Table 25, the correlation
between the likelihood to use the conventional channel regularly and the likelihood to use the mobile-online channel regularly in the future suggests a significant relationship, r = .227, n = 249, p<.001. Thus, all in all, the hypothesis can be considered partially supported.
