Customer experience: The differential effects of
underlying experience dimensions
Philip Tiekink
Customer experience: The differential effects of
underlying experience dimensions
University of Groningen Faculty of Economics and Business
Master thesis MSc Marketing Marketing Management
June 22, 2015
Author: Philip Tiekink Tjotter 19 8446 DX Heerenveen p.tiekink@gmail.com Student number: 2488221
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Management summary
Purpose – despite extant research has linked customer experience to both financial and
non-financial customer metrics, an understanding of how various dimensions of customer experience differently influence various customer metrics remains absent. This paper provides more detailed insights into the relationship between customer experience and customer value by in depth analyzing the impact of various experience dimensions. It specifically aims to clarify the following: “Do different components of customer experience have different
weights in customer value depending on the value component?”. By answering this managers
are guided on which experience dimensions to use to influence different components of customer value. Particularly, this study explores the differential effects of sensory, affective, behavioral, and intellectual experiences on customers’ share-of-wallet, cross-buying, up-buying, and loyalty intentions.
Methodology – a randomized cross-sectional survey was conducted to collect the data.
Specifically, mall-intercept personal interviews were administrated which yielded 211 responses. Respondents had to rate their particular experiences – sensory, affective,
behavioral, and intellectual – with the firm, and were subsequently asked to indicate their past purchase behavior followed by an expression of their loyalty intentions. The data were
analyzed using regression analyses comprising ordinary least squares and Poisson regression.
Findings - the findings of the cross-sectional study reveal the existence of differential
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Preface
As a student of the master Marketing Management my personal interest is primarily on customer management and the accompanying creation of customer loyalty. I therefore was highly motivated to write my thesis about the relationship between customer experience and customer metrics. I greatly enjoyed performing this task and the gained knowledge is certainly of value for future marketing positions.
I would like to take this opportunity to thank the people who were of great help in guiding and supporting me. First and foremost, my thanks go to my supervisors Evert de Haan and Prof. dr. Peter Verhoef. Their expertise and skills in the field of customer
management and academic research in general, have been invaluable for writing this thesis. Next, I would like to thank my thesis group members for the joint support along writing the thesis. Lastly, I hope you enjoy reading this thesis and that it may give you some valuable insights with regard to experience marketing.
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Table of contents
Management summary ... i Preface ... iii Table of contents ... iv 1 Introduction ... 1 2 Theoretical background ... 5 2.2 Customer value ... 72.2.1 Cross-buying, up-buying and loyalty intentions... 7
2.2.2. Share of wallet ... 8
3 Hypotheses development ... 10
3.1 The influence of customer experience on customer value ... 10
3.2 The differential effects of underlying customer experience dimensions ... 12
3.3 Conceptual framework ... 18 4 Methodology ... 19 4.1 Procedure ... 19 4.2 Measurement of variables ... 19 4.3 Validation of measures ... 21 4.4 Statistical model ... 23 5 Results ... 25
5.1 Total effect of customer experience ... 25
5.2 Differential effects of experience dimensions ... 25
6 Conclusions and recommendations ... 29
6.1 Discussion ... 29
6.2 Managerial implications ... 31
6.3 Research limitations and future research ... 32
7 References ... 35
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Introduction
The importance of creating superior customer experiences has grown dramatically among marketing practitioners. Customer experience can be created by shaping a customer’s sensory, affective, intellectual, and behavioral experiences with the firm (Brakus, Schmitt & Zarantonello, 2009). Customer experience has become a central aspect in developing
marketing strategies and firms increasingly engage in experience marketing to create a sustainable competitive advantage. This has instigated the Marketing Science Institute to list customer experience as a top research priority for the period 2014-2016 (Marketing Science Institute, 2014). As a result, these developments have urged scholars to investigate the profitable relationship between customer experience and financial performance (Gupta & Zeithaml, 2006; Ittner & Larcker, 1998). Also, because nowadays marketing managers are required to show the profitability of their marketing investments and are under increasing pressure to justify their marketing expenditures (Ramani & Kumar, 2008; Van Heerde et al., 2013). Yet despite the growing importance of customer experience in the academic marketing literature, most studies investigate only the total effect of customer experience and thus ignore specific effects of various underlying customer experience dimensions (Klaus & Maklan, 2013). Consequently, marketing managers are unaware of the differential effects that various dimensions of customer experience may have on important marketing outcomes. For instance, do mere affective customer experiences yield higher financial returns than experiences in which intellectual aspects predominate? Thus, although firms are able to create distinctive customer experiences, they lack guidance on which customer experience dimensions to use to influence different components of customer value. Specifically, little is known about which experience dimensions can best prevent customers from shopping at the competition, and which dimensions are most influential in encouraging customers to cross-buy and/or up-buy with the focal firm. Hence, research is required to investigate the impact of customer
experience on financial outcome variables such as customer lifetime value, customer profitability and cross-buying (De Haan, Verhoef & Wiesel, 2015), while particularly distinguishing the effects of underlying experience dimensions (Brakus et al., 2009).
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examines whether different underlying experience dimensions have a differential effect on the aforementioned customer metrics and thus aims to clarify the following:
“Do different components of customer experience have different weights in customer value depending on the value component?”
Table 1 provides an overview of this study’s contribution to the literature. Previous studies have investigated the positive relationship between customer experience and customer behavior. To illustrate, scholars have measured the effect of customer experience on either unobservable customer metrics such as satisfaction and loyalty intentions (Srivastava & Kaul, 2009; Brakus et al., 2009), on mere financially-oriented observable metrics (Ittner & Larcker, 1998; Gruca & Rego, 2005), or on both (Bolton, 1998). However, to the best of my
knowledge no empirical research has yet focused on how different components of customer experience influence different components of customer value (i.e. measuring the effect of various dimensions of customer experience rather than the total effect). Generally, scholars have conceptualized and validated a measure of customer experience and tested the effect of the experience construct on several outcomes (e.g., Brakus et al., 2009). Nevertheless, they measured the total effect of customer experience and ignored specific effects of underlying customer experience dimensions. Though Klaus and Maklan (2013) measured the influence of different customer experience dimensions (see table 1), they used an experience quality scale which ignores important aspects of the customer experience such as sensory feelings.
Moreover, the authors measured the effect of customer experience dimensions on loyalty intentions, satisfaction and word-of-mouth (i.e. unobservable customer metrics), while this study is focused on both unobservable and financial customer metrics.
Given this situation, the key contribution of this study is to examine the differential effects of underlying customer experience dimensions on various customer metrics. Both customer experience and customer value consist of multiple dimensions which may influence each other differently. Therefore, this study investigates the positive effect of customer experience on customer value by in depth analyzing the impact of various experience dimensions. Thus, how different dimensions of customer experience affect different dimensions of customer value. To do so, this study explores (1) the overall usefulness of customer experience in influencing customer value, (2) whether differences between
experience dimensions exist, and (3) how various experience dimensions differently influence various customer metrics. By answering this I provide more detailed insights into the
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different experiences to influence different components of customer value.
Data were gathered by means of a cross-sectional survey. Specifically, mall-intercept personal interviews were conducted with customers of the brands Nike and Adidas. In total, 211 questionnaires were collected and the data were analyzed using confirmatory factor analysis, simple regression analyses, and Poisson regression analyses. The results of this study show that overall customer experience is positively related to customer value. A greater customer experience will increase a customer’s SOW, cross-buying, up-buying, and loyalty intentions. In addition, by evaluating the effects that pertain to each experience dimension rather than the aggregate results, this study demonstrates that the effects vary across
dimensions. Notably, the affective dimension exerts the strongest influence on SOW, cross-buying, and up-cross-buying, which highlights the superiority of emotional experiences in creating customer value. Furthermore, it turns out that both the sensory and affective dimension are the main determinant of customers’ loyalty intentions. Thus, managers can use different customer experiences to influence different components of customer value. The pre-eminence of the affective dimension does not imply that firms should neglect the other experience dimensions. Rather, when all experience dimensions are at a sufficient level, the vast majority of
marketing resources can best be allocated to those dimensions which render the highest profit. The remainder of this paper is organized as follows. The next section describes the theoretical background of this study. Then the hypotheses are developed and the conceptual framework is presented. Next, the data collection procedure and research design are
described. Finally, this paper ends by demonstrating the results followed by a discussion of the main findings.
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Literature overview on effect of customer experience
Effect of customer experience on outcome metrics Multiple outcome variables Measurement Study Unobservable customer metrics Financial/observable
(customer) metrics Total effect of CE
Effect of
dimensions of CE
Bolton (1998)
Ittner & Larcker (1998)
Mittal & kamakura (2001)
Verhoef, Franses & Hoekstra
(2001)
Anderson, Fornell &
Mazvancheryl (2004)
Gruca & Rego (2005)
Brakus et al. (2009)
Srivastava & Kaul (2009)
Klaus & Maklan (2013)
De Haan et al. (2015)
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Theoretical background
2.1 Customer experience
Customer experience has become a new competitive battleground for firms (Pine & Gilmore, 1998). Nowadays consumption goes beyond mere information processing.
Specifically, Holbrook and Hirschman (1982) were one of the first to argue that consumption entails experiential aspects. In addition to mere cognition, fantasies, feelings and fun have shaped the experiential view of consumption. Most notably, Pine and Gilmore (1998) shed some more light on this new experiential approach. The authors theorized that experiences are a new economic offering. They propose the progression of economic value in which the stage experiences emerges after staging services, goods and commodities. Similarly, Schmitt (1999) has emphasized the inevitability of experiential marketing for firms and distinguishes sensory, affective, cognitive, physical, and social-identity experiences in which customers engage when interacting with a firm.
Despite the emerging interest in the topic, a commonly shared definition of customer experience remains absent. Gentile, Spiller and Noci (2007) argue that customer experience derives from a set of interactions. These interactions can be between a customer and a firm, but also between a customer and a product. Furthermore, the authors underscore that the customer experience is personal and entails the customers’ engagement at different levels. Consequently, Gentile et al. (2007) conceptualize customer experience as a multidimensional construct consisting of a sensorial, emotional, cognitive, pragmatic, lifestyle, and relational component. This conceptualization is highly consistent with Schmitt’s (1999) strategic experiential modules and previous views about the multidimensionality of customer experience (Holbrook & Hirschman, 1982; Verhoef et al., 2009; Lemke, Clark & Wilson, 2010). Moreover, the customer experience involves the customer’s subjective and internal response to the firm (Meyer & Schwager, 2007; Brakus et al., 2009). Thus, “the customer experience is holistic in nature and involves the customer’s cognitive, affective, emotional, social, and physical responses to the firm” (Verhoef et al., 2009, p.32). In addition, this experience is created by both direct and indirect encounters with the firm (Meyer &
Schwager, 2007; Klaus & Maklan, 2013). Lastly, the total customer experience includes the search, purchase, consumption and after-sale phases of the experience.
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al., 2007), and, on the other hand, the scale has shown to be reliable and valid (Brakus et al., 2009). The authors propose a four-factor model consisting of sensory, affective, behavioral, and intellectual items. They excluded the social dimension given that it correlated strongly with the affective dimension. More specifically, the sensory dimension engages consumers’ senses consisting of touch, smell, hearing, taste, and vision (Krishna, 2012). The affective dimension relates to affect and emotions and implies the consumer’s valenced affective reaction to situations (Richins, 1997). Next, the behavioral dimension is characterized by the physical aspects of consumption (Brakus et al., 2009). Finally, the intellectual dimension derives from cognitive science and focuses on information processing (Slepian, Masicampo & Ambady, 2015).
These dimensions can be distinguished in terms of consumers’ consciousness and the origin of the stimulus (see figure 1). On the one hand, consumers’ emotions (i.e. affective dimension) and cognitions (i.e. intellectual dimension) both derive from our brains and are internal processes. Nevertheless, emotions mainly occur unconsciously, whereas information processing originated from memory is a rational process which consciously emerges (Hoyer, MacInnis & Pieters, 2013). On the other hand, consumers’ perceptions (i.e. sensory
dimension) and bodily experiences (i.e. behavioral dimension) are primarily driven by external stimuli (Krishna, 2012; Brakus et al., 2009). Without the presence of the product and/or service, sensory and behavioral experiences cannot occur and hence are considered mere external processes. However, the behavioral and sensory dimension differ with regard to consumers’ consciousness. To illustrate, consumers consciously make use of a product and/or service and experience the physical aspects of consumption. In contrast, stimulation of the senses occur entirely unconscious (e.g., one does not deliberately activate his/her smell or hearing). Customer experience Int er n al Conscious E xt er na l Intellectual Behavioral Affective Sensory Unconscious
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2.2 Customer value
In response to the commonly employed product-centric metrics, scholars in the field of customer management have emphasized the necessity to incorporate customer-centric metrics (Verhoef & Lemon, 2013; Rust, Lemon & Zeithaml, 2004; Gupta, 2009). Traditionally, valuation of firms is based on product-centric metrics generally defined at the product-market level. However, as argued by Gupta (2009), these metrics are short-term oriented and do not always guarantee a return on marketing expenditures. Therefore, customer-centric metrics have gained more attention among scholars the last couple of years (Kumar, George & Pancras, 2008; Stahl et al., 2012).
One of those is CLV which has become an important metric in customer relationship management (Donkers et al., 2007). Gupta (2009, p.171) defines CLV as “the present value of all future profits or cash flows obtained from a customer over his/her life of relationship with a firm.” Most notable, CLV is calculated at the individual customer level, and takes into account the possibility that a customer may churn in terms of a retention probability (Rust et al., 2004; Gupta, 2009). Basically, two primary methods of calculating CLV are proposed in the academic literature - namely, the simple retention model and the Markov migration model (Stahl et al., 2012). The simple retention model assumes that at some point the customer terminates the relationships (i.e. lost-for-good situation). In contrast, the Markov migration model assumes that a customer may defect for a period of time, after which, the firm can win back this customer (i.e. always-a-share situation) (Berger & Nasr, 1998). Donkers et al. (2007) have reviewed a variety of models ranging in complexity to predict CLV. They concluded that although more complex models have some added value over simple models, simple models perform rather well. Based on these results, the simple retention model
apparently seems sufficient to compute CLV. However, CLV consists of multiple components which may be influenced differently by various dimensions of customer experience (Gupta, 2009; Stahl et al., 2012). Therefore, this study measures the effect of customer experience dimensions on the underlying components of CLV. These components (cross-buying, up-buying and loyalty intentions) are subsequently discussed.
2.2.1 Cross-buying, up-buying and loyalty intentions
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and/or services from the same firm (Shah et al., 2012). As an example, in addition to one’s savings account, buying loans and/or stocks from the same financial service provider (Gupta & Zeithaml, 2006). Despite research has found that customer cross-buy not always lead to higher profitability and may even result in unprofitable cross-buying (Shah et al., 2012), in most cases, customer cross-buy leads to increased purchase frequencies and higher customer profitability (Reinartz, Thomas & Bascoul, 2008; Reinartz & Kumar, 2003). This study
follows latter reasoning and approaches customer cross-buying as a valuable customer metric. As previously noted, a customer’s margin can be enhanced by selling complementary products to current customers (i.e. cross-buying). In addition, these margins can also be enhanced by selling the same product and/or service, however, a more expensive version. This revenue-enhancing technique, called up-buying, implies buying higher-priced substitutes of the same product and/or service category from the same firm (Bauer, Hammerschmidt & Braehler, 2003). As a consequence, customers’ revenues go up due to higher spending per transaction which subsequently increases their CLV (Gupta & Zeithaml, 2006; Bauer et al., 2003).
Lastly, a customer’s CLV consists of an estimated retention probability commonly referred to as loyalty intentions. In general, customer loyalty is defined as the decision to repurchase a brand repeatedly in the future (Oliver, 1999) and is classified on two main aspects, namely - attitudinal and behavioral loyalty (Kumar et al., 2013; Jacoby, 1975).
Customers who exert attitudinal loyalty (i.e. loyalty intentions) are emotionally attached to the brand which results in a deeply held intention to repurchase the brand in the future (Dick & Basu, 1994). In contrast, behavioral loyalty implies the actual repurchase of a brand instead of an indicated intention to perform this behavior (Hawkins & Vel, 2013). Most notable, the difference between both loyalty expressions is that regarding behavioral loyalty the customer is not necessarily emotionally attached to the brand (Dick & Basu, 1994). To illustrate, alternative factors (e.g., product is sold out or economic situation) could influence a customer’s behavioral loyalty, whereas in the case of attitudinal loyalty, pure loyalty is measured which is not biased by the context. Hence, this study measures attitudinal loyalty (or loyalty intentions) rather than behavioral loyalty as a component of CLV.
2.2.2. Share of wallet
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Hypotheses development
This section introduces the conceptual model of this study and develops the associated hypotheses. In particular, this study investigates the positive relationship between customer experience and customer value, followed by an in depth examination of the differential effects of different experience dimensions on different components of customer value.
3.1 The influence of customer experience on customer value
A positive customer experience entails that customers are motivated to continue their business with the firm. Through positively stimulating consumers’ senses, firms accomplish that consumers are willing to experience such stimulation again (Brakus et al., 2009). For instance, a pleasant smell during the experience can enhance product evaluations which results in increased spending (Krishna, 2012). Besides, Mitchell, Kahn and Knasko (1995) show that congruent ambient scents yield that customers extend their searching process and engage in more variety seeking (i.e. cross-buying). Furthermore, ambient music can positively influence a customer’s mood during the shopping process (Krishna, 2012). In addition,
feelings and emotions play a significant role in consumers’ decision-making. In an
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& Labroo, 2011). Consequently, behavioral experiences may positively influence our thoughts and subsequent behavior.
Given these findings research has linked customer experience to several marketing outcomes. For instance, customer experience can be considered a key determinant of customer satisfaction (Brakus et al., 2009; Klaus & Maklan, 2013). Namely, positive experiences with a firm increases customers’ satisfaction levels (Shankar, Smith &
Rangaswamy, 2003). More specifically, Klaus and Maklan (2013) measured the influence of each experience dimension separately and concluded that emotional aspects exert the greatest influence on customer satisfaction. Next, customer loyalty has also been considered an important outcome of customer experience. Brakus et al. (2009) found that the effect of experience on loyalty is greater than the effect of experience on satisfaction. Hence, the authors argue that customer experience is a strong predictor of repurchase behavior. This is consistent with the findings of Ramaseshan and Stein (2014) who connected brand experience with both attitudinal and behavioral loyalty. Besides, De Haan et al. (2015) show in a
longitudinal study that a positive customer experience measured in terms of customer feedback metrics can actually predict customer retention.
However, what is the use of these mere non-financial measures? Research has shown that customer satisfaction measures are key determinants of customer purchase behavior, and that a positive relationship exists between customer satisfaction and accounting performance (Ittner & Larcker, 1998). This is in line with more recent research on the positive impact of customer satisfaction on shareholder value (Anderson, Fornell & Mazvancheryl, 2004) and future cash flows (Gruca & Rego, 2005). Next, in addition to satisfaction, customer loyalty also yields financial returns. Morgan and Rego (2006) have demonstrated the valuable relationship between repurchase intentions and business performance. Besides, repurchase intentions (i.e. loyalty intentions) are one of the most important drivers of CLV which subsequently affects the profitability of the firm (Gupta & Zeithaml, 2006). Because of the positive relationship between customer experience and non-financial customer metrics like satisfaction and loyalty intentions, which in turn are directly linked to financial measures at the customer and/or firm level, this study proposes the existence of a direct and positive relationship between customer experience and financial customer metrics.
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development of an emotional attachment to the brand which forces customers to maintain an ongoing relationship (Thomson et al., 2005). Besides, a longitudinal examination of Cooil et al. (2007) discovered that changes in customers’ satisfaction levels are positively related to their SOW’s over time. Lastly, the more positive the customer experience with one product and/or service, the more motivated a customer is in experiencing other products and/or services from the same firm (Shah et al., 2012). This study follows above reasoning and hypothesizes:
H1. The greater the positive experience a customer has with the firm, the greater will be
his/her cross-buying, up-buying, loyalty intentions, and SOW with the respective firm.
3.2 The differential effects of underlying customer experience dimensions
The customer experience involves the customer’s sensory, affective, intellectual, and physical responses to the firm (Brakus et al., 2009; Verhoef et al., 2009). Affective responses are characterized by emotional thoughts and feelings generated by consumers (Hoyer et al., 2013). Although consumers often consider multiple brands, they ultimately choose one brand due to their emotional attachment to that brand (Keller, 1993). Namely, consumers are highly emotional and their subsequent behavior is heavily influenced by these emotions (Pawle & Cooper, 2006). Consumers’ emotional attachment to a firm characterizes their commitment to the relationship with that firm. The more consumers become emotionally attached, the more they are willing to continue the relationship with the firm (i.e. greater commitment) which ultimately results in greater loyalty (Thomson et al., 2005). An advanced outcome of consumers’ emotional involvement with a firm relates to the concept of ‘brand love’. Batra, Ahuvia and Bagozzi (2012) argue that the more a firm evokes feelings from its customers, the more these customers get in love with the firm. Furthermore, the authors emphasize the positive outcomes of brand love which relate to greater loyalty intentions, positive word-of-mouth and ignorance of negative information. The latter outcome entails that though the customer-firm relationship is affected by negative cues, the customer will not consider switching to another firm due to the loving relationship. The difference between brand love and mere satisfaction concerns consumer’s “willingness to declare love and involves integration of the brand into the consumer’s identity” (Carroll & Ahuvia, 2006, p.81).
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satisfied or very satisfied (Reichheld, 1996). Hence, scholars have emphasized that merely increasing customer satisfaction is not sufficient to outperform competition due to
polygamous loyalty (Fournier, 1998; Kim & Lee, 2010). To increase a customer’s SOW and to achieve monogamous loyalty, firms need to create profound emotional bonds with their customers (Kim & Lee, 2010). Because the affective dimension is best able to influence consumers’ feelings and emotions compared to other dimensions ( e.g., the mere cognitive focus of the intellectual dimension) (Brakus et al., 2009), this study believes that positive emotional experiences with a firm can greatly contribute to the development of amiable relationships (i.e brand love) which in turn affect customers’ SOW (Batra et al., 2012). In addition, Kim and Lee (2010) found a positive relationship between emotional loyalty and SOW which further highlights the importance of the affective dimension in influencing a
customer’s share of spending. Hence, this study formulates the following hypothesis:
H2. From the customer experience dimensions, the affective dimension exerts the strongest
influence on customers’ SOW.
Although customers can become emotionally attached to a brand and neglect competing brands, this does not necessarily have to imply that they also engage in cross-buying with the focal firm. Customers can develop inner feelings and emotions solely related to one product and/or service (Richins, 1997), whereas information processing (i.e.
intellectual dimension) entails that customers may become aware of additional products and/or services. As argued by Homburg, Koschate and Hoyer (2006), intellectual experiences provide customers the ability to store high amounts of information about the firm which also includes information about additional product and/or service categories. To further strengthen above reasoning this study relies on the widely-accepted elaboration likelihood model (Petty & Cacioppo, 1986; Petty, Cacioppo & Schumann, 1983).
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compared to peripheral route processing (Petty & Cacioppo, 1986).
In an experimental setting Petty et al. (1983) have demonstrated that under high involvement consumers exert the cognitive effort required to process information via the central route rather than the peripheral route. Thus, highly involved consumers are motivated to engage in a diligent consideration of all product- or issue-relevant information about a firm. In addition, research has shown that consumers’ involvement with an object is a function of the interest in the object, personal relevance of an object, knowledge of the object, and past experiences with the object (Bloch & Richins, 1983). Most notably, intellectual experiences can foster consumers’ knowledge and thinking about a firm due to its cognitive nature (Brakus et al., 2009). Hence, this study hypothesizes that greater intellectual/cognitive experiences increase consumers’ involvement with the firm, which consequently urges
consumers to process information about the firm via the central route (Bloch & Richins, 1983; Petty et al., 1983). This thorough information processing may yield valuable information about additional product and/or service categories. Subsequently, the more positive the perceived cognitive information, the more likely consumers engage in cross-buying by
actually purchasing additional products and/or services due to heightened satisfaction (Bolton, 1998; Bolton & Lemon, 1999; Mittal & Kamakura, 2001). Although emotional and sensory experiences may also enhance consumers’ interest and consequent involvement with the firm, these experiences lack cognitive guidance on informational aspects to discover potential cross-buy opportunities. To illustrate, emotional experiences will encourage consumers to focus mainly on emotional aspects while thinking of the firm (e.g., brand love) (see Batra et al., 2012), which consequently hinders a person’s diligent and thoughtful consideration of issue-relevant information (Baumgartner, Sujan & Padgett, 1997; Petty et al., 1983). In addition, because behavioral experiences are primarily related to the consumption process rather than the search and purchase process, these experiences also provide little information in detecting cross-buy opportunities. Thus, because the intellectual dimension of customer experience is best able to make customers aware of additional products and/or services due to the resultant cognitive abilities (Homburg et al., 2006; Hoyer et al., 2013; Brakus et al., 2009), this study puts forward the following hypothesis:
H3. From the customer experience dimensions, the intellectual dimension exerts the strongest
influence on customer cross-buying.
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(1902) has demonstrated that by repeatedly pairing a neutral stimulus (bell), which produces no response, with an unconditioned stimulus (food), which automatically produces a response (salivation), the dog learns to associate the bell with the food. As a result, although initially the bell alone did not make the dog salivate, in time, by associating the bell with the food (as a consequence of repeatedly pairing it) the dog began to salivate by hearing the bell alone. This experiment illustrates that sensory stimulation, when perceived favorable, leads to repeated (loyal) behavior in the long term (e.g., salivate when ringing the bell). This is partially consistent with the assertion of Brakus et al. (2009: p.65) that “ if a brand stimulates the senses, a stimulation-seeking organism may strive to receive such stimulation again”.
From the perspective of the senses, merely touching a product leads to greater hedonic experiences and positively influences persuasion (Peck & Wiggins, 2006), increases
perceived ownership of the product (Peck & Shu, 2009), and actually affects consumer behavior (Krishna, 2012). In addition, Bosmans (2006) demonstrates that ambient scents (congruent with the product category) during the customer experience have a strong and positive impact on consumers’ product evaluations which consequently may foster loyalty. Besides, pleasant ambient scents enhance brand memory in terms of recall and recognition (Morrin & Ratneshwar, 2003). Subsequently, Morrin, Krishna and Lwin (2011) have
investigated the durability of these enhanced memories. They found that although scent-enhanced memory is susceptible to retroactive interference (later exposure to similar
information), part of the lost information can be restored by using a scent-based retrieval cue. These findings highlight the relevance of scents in enhancing and maintaining brand memory which, as a result, has a positive impact on customer loyalty (Taylor, Celuch & Goodwin, 2004; Rust et al., 2004; Vogel, Evanschitszky & Ramaseshan, 2008). Next, the importance of audition marketing has increased dramatically and research has shown the positive effect of it on customer behavior (Krishna, 2012). Specifically, music in advertising is positively related to attitude formation and involvement (Park & Young, 1986; MacInnis & Park, 1991). The former has been shown to impact loyalty if attitudes are accessible from memory and held with confidence (Berger & Mitchell, 1989). Besides, consistent with Pavlov (1902), hearing pleasant sounds (e.g., the bell in the dog experiment) can lead to consistent behavior in the future. Lastly, from the perspective of vision, the findings of Krishna and Elder (2011) demonstrate that visually depicting a product increases purchase intentions.
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al., 2013; Bosmans, 2006), this study proposes the sensory dimension as a main driver of loyalty intentions. Hereby, I follow both arguments of Pavlov (1902) and Brakus et al. (2009), that a pleasurable sensory experience encourages an entity to perform the same behavior consistently in the future in order to receive such stimulation again due to a stimulation-seeking organism.
Nevertheless, equally or even more important is the role of emotions in strengthening loyalty. Namely, positive emotions can greatly enhance customer loyalty through intimacy and passion (Yim, Tse & Chan, 2008). Similarly, according to Batra et al. (2012) greater loyalty intentions are one of the main outcomes of advanced feelings a customer has with the firm. Hence, because of the salient role of emotions in pursuing loyalty (Thomson et al., 2005; Kim & Lee, 2010), this study also considers the affective dimension as a key determinant of loyalty intentions. In contrast, although positive information processing (i.e. intellectual dimension) increases customers’ satisfaction levels, mere satisfaction is not sufficient to build long-term relationships with customers (Fournier, 1998). Likewise, Yim et al. (2008) argue that positive affect results in enduring and holistic bonds between a firm and its customers, whereas mere cognitive inputs are short-term oriented and specific to a particular experience. Thus, given the above, this study proposes the following opposing hypotheses:
H4a. From the customer experience dimensions, the sensory dimension exerts the strongest
influence on customers’ loyalty intentions.
H4b. From the customer experience dimensions, the affective dimension exerts the strongest
influence on customers’ loyalty intentions.
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processes, in the absence of regulatory resources, affect-free choices are hard to make. Besides, positive feelings toward a product elicit strong interpersonal agreement, provoke spontaneous thoughts towards the product, and speed up the judgment process (Pham, 2004). These outcomes cause that customers are more apt to repeatedly buy the same product which enhances their product loyalty. Subsequently, enhanced product loyalty may strengthen the contagion process which forces consumers to purchase the latest versions (often more expensive) of a product rather than outdated versions (i.e. up-buying). Contagion theory suggests that people perform certain behaviors due to their interactions with other people who performed similar behaviors (Burt, 1987). These contagion effects occur because people desire to adhere to perceived norms (e.g., buying the latest version of a product) (Van den Bulte & Wuyts, 2007; Rapp et al., 2013). Thus, positive emotional experiences with a product increases one’s loyalty to that product, which may make people more susceptible to the influence of contagion. Consequently, the stronger the contagion effects, the more likely consumers substitute outdated products for renewed (more expensive) products. When
consumers are not loyal to a product contagion effects are likely less influential, because these consumers have little interest in the product. This lowers the probability that external
pressures can influence them to execute an act of up-buy.
Furthermore, whereas mere knowledge derived from the cognitive system is not enough to accomplish product loyalty, sensory experiences, on the other hand, can be a strong predictor of product loyalty. To illustrate, people who perceived a favorable sensory
stimulation with a product, strive to reach such stimulation again (Brakus et al., 2009). In addition, positive sensory experiences enhance product memory which may prompt customers to buy the same product again (Morrin & Ratneshwar, 2003; Morrin et al., 2011). Notably, through sensory experiences customers directly interact with a certain product, which consequently increases the perceived ownership (Peck & Shu, 2009). The latter may lead to an inner obligation among customers to continuously buy the same product. Again, the developed product loyalty may make contagion effects more influential which increases the chance of up-buying. Therefore, this study puts forward the affective and sensory dimension as the main determinants of product loyalty and consequent up-buying. Yet because it is not clear which one exerts the strongest influence, two opposing hypotheses are formulated:
H5a. From the customer experience dimensions, the affective dimension exerts the strongest
18 H5b. From the customer experience dimensions, the sensory dimension exerts the strongest
influence on customer up-buying.
3.3 Conceptual framework
The conceptual model is presented below. The dependent variables are cross-buying, up-buying, SOW, and loyalty intentions which jointly constitute customer value. The
independent variables are the four dimensions of customer experience and a composite measure of customer experience capturing all four experience dimensions.
Figure 2: conceptual model
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Methodology
The conceptual model is tested in the Dutch consumer sport market. Data are collected by means of a cross-sectional survey and derives from a sample of customers of the brands Nike and Adidas. These global brands are familiar to many people and most of them are customer of one or both. Besides, all underlying experience dimensions measured in this
study are applicable to both brands. This also holds true with regard to the outcome variables.
4.1 Procedure
Mall-intercept personal interviews are administered in the city centre of Groningen in the Netherlands. Respondents are intercepted while they are shopping with the request to fill in a short questionnaire (see Appendix A). Preceding the survey respondents are first asked whether they have purchased one of the focal brands in the past year. If so, the questionnaire is administered. If this is not the case, respondents are thanked for their time and are excluded from the survey. When a respondent is both customer of Nike and Adidas, the brand from which he/she recently purchased the most products is used in the survey. Respondents are further asked to indicate the positivity of their total customer experience, including the search, purchase, consumption and after-sale phases, rather than rating a particular experienced event. In addition, given that respondents’ SOW, cross-buy and up-buy are based on actual
(remembered) purchase behavior, respondents are asked to indicate their total sports-related purchases regarding multiple sports brands rather than directly asking the numbers of the outcome metrics. Subsequently, for the relevant brand (Adidas or Nike) the outcome metrics are calculated which increases the accuracy of the figures (Malhotra, 2006). Finally, to rule out the possible effect of relationship duration (instead of the positivity of the customer experience) on the outcome variables, all respondents are asked to evaluate their consumption behavior from the past two years.
4.2 Measurement of variables
The independent variables are measured using a 7-point Likert-scale. Customer experience is measured through items focused on the underlying experience dimensions (1 =
totally disagree, 7 = totally agree) and are adopted from Brakus et al. (2009). These items
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customer experience. Hence, this study slightly adjusts the scale by transforming it to a
positively worded version. Next, the outcome variables are measured by using (indicated) past purchase behavior. Respondents have to indicate in a table the number and variety of products purchased per sport brand. The outcome variables are subsequently calculated only for the brands Nike and Adidas (depending on each respondent). More specifically, SOW is operationalized in terms of total category spending adapted from Kim and Lee (2010). The proportion of respondents’ expenditures at Nike/Adidas is divided by their total expenditures for sports-related products. Next, in line with Verhoef et al. (2001), cross-buying is measured by a summation of the number of different products purchased from the same brand.
Furthermore, up-buying is measured by calculating how many times it occurred that someone substituted a product for a similar higher-priced product from the same brand. When
respondents have multiple purchases per category per brand, they are asked to indicate whether the last purchase(s) was/were more expensive than the first one(s). Respondents express their loyalty intentions by indicating the probability of buying a Nike/Adidas product again in the future (Gupta & Zeithaml, 2006). Finally, this study controls for gender, age and income.
21 Table 2
Descriptive statistics (n = 211)
Variable Summary statistics
Age 25 year (average), 15 year (minimum age), 65 year (maximum age)
Gender Male (46%), female (54%)
Income <€30,000 (61,1%), approximately €30.000 (19,4%), €30.000–€60.000,
(12,8%), >€60.000 (6,7%)
Mean SD Minimum Maximum
Sensory 5.10 1.06 1 7 Affective 4.85 1.32 1 7 Behavioral 4.88 1.20 1 7 Intellectual SOW Cross-buying Up-buying Loyalty intentions 4.46 61.17 2.73 .63 74.65 1.17 32.26 1.97 1.05 21.25 1 3 1 0 10 7 100 10 6 100 4.3 Validation of measures
To check whether the four-factor solution proposed by Brakus et al. (2009) is
satisfactory, confirmatory factor analysis is conducted to establish convergent validity of the constructs. Because the Kaiser-Meyer-Olkin measure of sampling adequacy (.825) and the Bartlett’s Test of Sphericity (p < .05) show that the variables are likely to factor well, factor analysis is appropriate. Based on the Eigenvalues (>1) derived from the principal component analysis (PCA) a three-factor solution is preferred. However, moving to a four-factor model substantially increases the cumulative variance explained (from 69,8% to 77%). Moreover, the fourth factor explains more than seven percent of the total variance (which is above the minimum accepted five percent), and hence a four-factor model is used. After Varimax rotation, a clear factor model emerged (see Table 3). Only sensory items loaded on the first factor, factor 2 consists of only intellectual items, only behavioral items loaded on factor 3, and only affective items loaded on the fourth factor.
22 Table 3
Factor matrix PCA
Factor
1 2 3 4
This brand makes a positive impression
on my visual sense or other senses .843 .254 .095 .202
I find this brand positively interesting
in a sensory way .661 .395 .115 .255
This brand does not appeal to my
senses in a positive way .832 .136 .195 .201
This brand induces positive feelings
and sentiments .407 .219 .253 .725
I do not have positive emotions for this
brand .068 .069 -.052 .859
This brand is an emotional brand in a
positive way .450 .123 .225 .727
I engage in positive physical actions
and behaviors when I use this brand .160 .133 .896 -.012
This brand results in positive bodily
experiences .070 .188 .760 .185
This brand is not action oriented in a
positive way .130 .133 .827 .062
I engage in a lot of positive thinking
when I encounter this brand .168 .860 .235 .189
This brand does not make me think in a
positive way .140 .852 .232 .124
This brand positively stimulates my
curiosity and problem solving .372 .758 .053 .020
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Finally, to test the internal consistency of the scale reliability analysis is conducted. To be consistent with the widely accepted criterion of .7 (Nunnally, 1978), all cronbach’s alphas are highly sufficient. This entails that the items of the separate constructs – sensory items (.84), affective items (.82), behavioral items (.82), and intellectual items (.86) – are adequately related to each other.
4.4 Statistical model
The data are analyzed by conducting multiple linear regression (ordinary least squares) and Poisson regression. Similar studies (e.g., Klaus & Maklan, 2013; Brakus et al., 2009) followed the same approach in measuring the effect of customer experience. In order to use regression analysis an infinite dependent variable is required. To achieve this, the original finite outcome variables (loyalty intentions and SOW) are transformed to infinite variables by means of natural log. In the case of loyalty intentions (LI) the following formula is used: ln(LI / (100 – LI)), whereby 0 and 100 are re-coded in respectively 1 and 99. The other dependent variables (cross-buying and up-buying) are already infinite. Nevertheless, the latter dependent variables consist of count date which makes it not realistic to assume an error term with a normal distribution, and hence Poisson regression is more appropriate than ordinary least squares (OLS) regression. First, to measure the total effect of customer experience on the outcome variables, the means of the underlying experience dimensions are aggregated to an overall customer experience rating. Second, to explore the differential effects of the
underlying experience dimensions and to determine if they significantly differ from one another, the standardized betas of the dimensions (i.e. factor scores) are compared to one another. Because Poisson regression only produces unstandardized coefficients, in these cases, the standardized coefficients are calculated manually (e.g., regression coefficient[age] x (std dev[age]/std dev[y]). The econometric models of this study are shown below:
SOWi = α0 + α1SEi + α2AEi + α3BEi + α4IEi + α5INi + α6GEi + α7AGEi + εi (1)
CBi = β0 + β1SEi + β2AEi + β3BEi + β4IEi + β5INi + β6GEi + β7AGEi + εi (2)
UBi = γ0 + γ1SEi + γ2AEi + γ3BEi + γ4IEi + γ5INi + γ6GEi + γ7AGEi + εi (3)
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Where,
SOWi: share of wallet calculated for customer i,
CBi: cross-buy outcome calculated for customer i,
UBi: up-buy outcome calculated for customer i,
LIi: loyalty intentions indicated by customer i, SEi: sensory experience evaluated by customer i, AEi: affective experience evaluated by customer i, BEi: behavioral experience evaluated by customer i, IEi: intellectual experience evaluated by customer i, INi: income for each customer i,
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5
Results
This section describes the main results of this study. Table 5 presents the total effect of customer experience in which the experience dimensions are aggregated. Table 6 presents the parameter estimates of the underlying experience dimensions and highlights the differential effects. In the case of the total effect of customer experience (i.e. experience dimensions aggregated) the beta signs (β) represent the unstandardized coefficients. In the case of the differential effects of experience dimensions (i.e. factor scores) the beta signs (β) represent the standardized coefficients.
5.1 Total effect of customer experience
From table 5 it can be read that customer experience is positively related to all
outcome variables. A greater customer experience will increase a customer’s SOW (β = .726; p < .01), cross-buying (β = .164; p <.01), up-buying (β = .625; p < .01), and loyalty intentions (β = .890; p < .01). The F-statistic and Omnibus Test of the different regression models also indicate a good overall model fit. Consequently, hypothesis 1 is supported. Furthermore, to compare the strength of the effect of customer experience on the various outcome metrics, for
the sake of simplicity, a multivariate linear model is estimated1. Noteworthy is the effect of
customer experience on loyalty intentions (R2 adjusted = .188), which is clearly stronger than
the effect of customer experience on the other dependent variables.
5.2 Differential effects of experience dimensions
The second part of this study is dedicated to measuring the impact of the underlying experience dimensions on the dependent variables. To avoid multicollinnearity, the factor scores derived from the factor analysis are used in the regression analyses. From table 6, we notice that the affective dimension exerts the greatest influence on customers’ SOW (β = .293; p < .01). The sensory and behavioral dimension do not significantly affect customers’ SOW (p > .1), and although the intellectual dimension has a slightly significant effect (p < .1), the effect is clearly less strong (β = .114). To determine whether the difference in explanatory power between the affective and intellectual dimension is statistically significant, the original
SOW model (SOWi = α0 + α1SEi + α2AEi + α3BEi + α4IEi + α5INi + α6GEi + α7AGEi + εi) is
transformed to the following: SOWi = α0 + α1SEi + α2AEi + α3BEi + α4(IEi + AEi)+ α5INi +
α6GEi + α7AGEi + εi. As can be seen, the parameter of the intellectual dimension is merged
1
26 Table 5
Estimation results customer experience
Variable Model 1 Model 2 Model 3 Model 4
SOW Cross-buy Loyalty Up-buy
Parameter t value Parameter Wald Parameter t value Parameter Wald
Estimate Estimate Estimate Estimate
(unstandardized) (unstandardized) (unstandardized) (unstandardized)
Intercept -3.691*** -3.241 -.126 .172 -2.292*** -3.009 -3.621*** 30.853 Customer experience .726*** 3.928 .164*** 10.980 .890*** 7.199 .625*** 30.031 Control Gender .546 1.640 .084 .920 -.242 -1.085 -.406** 4.974 Age .030 1.302 .005 .869 .003 .223 .029*** 8.641 Income -.011 -.951 .003 .977 -.003 -.363 -.005 .693 Model fit
R-squared .099 N/A .203 N/A
Adj.
R-squared .082 N/A .188 N/A
F-statistic 5.653*** N/A 13.061*** N/A
Omnibus
Test N/A 15.803*** N/A 45.751***
***p < .01 **p < .05
Note: the parameters in model 1 and 3 are estimated by using OLS regression. The parameters in model 2 and 4 are estimated by using Poisson regression.
with the parameter of the affective dimension. When there is still a remaining effect of the
affective dimension (α2AEi) on SOW, the difference in strength between both dimensions is
significant. The new model is estimated by using OLS regression and shows that the
remaining effect of the affective dimension is slightly significant (α2 = .179; p <.1).
Therefore, hypothesis 2 is supported. Hypothesis 3 relates to the superior influence of the intellectual dimension on cross-buying. Contrary to the expectations, the positive influence of the intellectual dimension is less strong (β = .029; p > .1) than the other dimensions.
Besides, the effect of the intellectual dimension is not significant. Only the influence of the sensory and affective dimension on cross-buying is significant, and thus hypothesis 3 is rejected.
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however, much weaker than the effect of the aforementioned dimensions. To determine whether the positive influence of sensory experiences (β = .418; p < .01) is significantly stronger than the positive influence of emotional experiences (β = .323; p <.01) on loyalty intentions, the original loyalty model is transformed to the following: LIi = η0 + η1SEi +
η2(AEi + SEi) + η3BEi + η4IEi + η5INi + η6GEi + η7AGEi + εi. The regression output shows an
insignificant remaining effect of the sensory dimension (η1 = .095; p > .1) which indicates that
the effect of the sensory dimension is not significantly stronger than the effect of the affective dimension on loyalty intentions. Hence, it is not possible to determine whether hypothesis 4a or hypothesis 4b is true, even though the sensory dimension has a larger standardized beta.
Finally, this study proposed two opposing hypotheses regarding up-buying. Again, the sensory and affective dimension are expected to exert the strongest influence. The results demonstrate that all experience dimensions are positively related to up-buying. As expected, the affective dimension exerts the strongest influence on up-buying (β = .411; p < .01). However, unlike the expectations, the effect of the sensory dimension (β = .217; p < .05) is less strong than that of the intellectual dimension (β = .256; p < .01). Thus, hypothesis 5b is rejected. To ascertain whether the effect of the affective dimension is significantly stronger than the effect of the intellectual dimension, the following model is estimated using Poisson regression: UBi = γ0 + γ1SEi + γ2AEi + γ3BEi + γ4(IEi+ AEi) + γ5INi + γ6GEi + γ7AGEi + εi.
The model shows that the remaining effect of the affective dimension is not significant (γ2 =
28 Table 6
Estimation results underlying experience dimensions
Variable Model 1 Model 2 Model 3 Model 4
SOW Cross-buy Loyalty Up-buy
Parameter t value Parameter Wald Parameter t value Parameter Wald
Estimate Estimate Estimate Estimate
(standardized) (standardized) (standardized) (standardized)
Intercept .118 .149 .663 9.858 1.977 3.870*** -.601 2.047 Main effects Sensory dimension .236 (.097) 1.460 .074 (.038) 2.895* .723 (.418) 6.982*** .226 (.217) 5.609** Affective dimension .715 (.293) 4.438*** .104 (.053) 5.668** .559 (.323) 5.420*** .429 (.411) 18.271*** Behavioral dimension .098 (.040) .605 .060 (.030) 1.903 -.10 (-.006) -.100 .203 (.194) 4.314** Intellectual dimension .278 (.114) 1.692* .057 (.029) 1.705 .292 (.168) 2.780*** .268 (.256) 7.277*** Control variables Gender .491 (.101) 1.467 .089 (.023) 1.017 -.205 (-.059) -.954 -.384 (-.184) 4.324** Age .023 (.075) .987 .005 (.021) .777 .007 (.032) .470 .028 (.217) 7.400*** Income -.012 (-.082) -1.090 .003 (.026) .855 -.007 (-.071) -1.048 -.006 (-.095) 1.002 Model fit R-squared .136 N/A .300 N/A Adjusted R-squared .106 N/A .276 N/A
F-statistic 4.529*** N/A 12.309*** N/A
Omnibus Test N/A
16.525** N/A 48.841*** ***p < .01 **p < .05 *p < .1
29
6
Conclusions and recommendations
The final section of this paper discusses the main findings and proposes some
meaningful implications for marketing practitioners. Also, the shortcomings of this research are discussed and suggestions for future research are proposed.
6.1 Discussion
This study examined the positive relationship between customer experience and customer value by in depth analyzing the impact of various experience dimensions. The goal of this study was threefold: (1) to evaluate the salient role of customer experience in
influencing customer value, (2) to identify whether and how various experience dimensions differently influence various customer metrics, and (3) to provide guidance to managers on which customer experience dimensions to use to achieve different outcomes. This study produced several key findings.
First, in line with previous research (Klaus & Maklan, 2013; Ittner & Larcker, 1998; Bolton, 1998; Mittal & Kamakura, 2001), customer experience has a positive impact on both unobservable and observable customer metrics. Specifically, a greater customer experience will increase a customer’s SOW, cross-buying, up-buying, and loyalty intentions. This suggests that it is useful for firms to improve the experiences customers have with them to outperform competition, encourage variety-seeking, increase spending, and achieve long-term relationships. Thus, customer experience can be regarded as an influential construct in
regulating customer value, and therefore is a key determinant of consumer behavior (Klaus & Maklan, 2013). Notably, this study found that among the outcome variables customer
experience has the strongest impact on loyalty. This aligns with Brakus et al. (2009) view that customer experience is a strong predictor of actual buying behavior.
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a positive relationship between favorable perceived cognitive information and actual cross-buying (Bolton, 1998). However, this study’s results are in line with the notion of Verhoef et al. (2001) that the qualities ascribed to one product are not akin to the decision to buy
complementary products. Thus, it may still be the case that positive intellectual experiences motivates consumers to process information more deeply about the firm which makes them more aware of additional products (see elaboration likelihood model of Petty et al., 1983), however, the positive perceived cognitive information about one product category may not transfer to other categories. In contrast, the findings of this study show that the affective and sensory dimension have the strongest effect on cross-buying. The former effect may be due to the very strong connection between the affective dimension and SOW. When customers declare love to the firm and integrate the firm into their identity, they will probably only buy products from that respective firm which by itself results in cross-buying (Carroll & Ahuvia, 2006; Batra et al., 2012). The unexpected strong effect of the sensory dimension may be the consequence of an extension of the search process due to positively perceived sensory stimuli which results in more variety seeking (Mitchell et al., 1995).
Another notable finding is that positive sensory and affective experiences are the strongest determinants of customers’ loyalty intentions. More specifically, the effect of the sensory dimension is even stronger. Hence, positive sensory experiences motivates customers to perform the same behavior consistently in the future, because they want to experience the same sensory stimuli again. This study thus agrees with Brakus et al. (2009) that if a firm is able to satisfactory stimulate customers’ senses, a stimulation-seeking organism strives to reach such stimulation again. Furthermore, regarding the affective dimension, the findings underscore the critical role that emotions play in achieving pure loyalty (Thomson et al., 2005).
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category. An explanation for this may relate to prospect theory which states that losses loom larger than gains of the same magnitude (Novemsky & Kahneman, 2005). The difference between up-buying and cross-buying is that, in most cases, up-buying is more expensive and people consequently loose more money. Because people give more weight to losses than to gains, up-buying decisions are perceived more riskier which makes people more reluctant to upgrade to higher-priced products (Hoyer et al., 2013). This may entail that people first need to think positive about the product by receiving sufficient cognitive information, before a risky act of up-buy will occur.
All in all, customer experience is positively related to customer value. In addition, this study shows that differences exist between the explanatory power of underlying experience dimensions. Thus, the different components of customer experience have different weights in customer value depending on the value component. Noteworthy is the superiority of the affective dimension which has, except for loyalty intentions, the strongest influence on all customer metrics. Hence, the creation of interpersonal emotional bonds with customers is important whatever the goal may be. This conclusion is in line with Klaus and Maklan (2013), whose findings also show that the emotional dimension of customer experience has the most significant influence of all dimensions.
6.2 Managerial implications
Most promising for managers is that they can use different customer experiences to influence different customer metrics. More specifically, to outperform competition and to lure customers away from competing firms, marketers should create profound emotional bonds with their customers. This can be accomplished by intensifying customers’ emotional experiences with the firm in order to amplify positive feelings and affect. For instance, marketers could develop emotionally-laden advertisements, or engage in cause-related marketing by supporting social causes. Next, when the aim of the firm is to sell a wider variety of products or to encourage customers to buy the latest more expensive products of their assortment, again, customers’ emotional experiences should be strengthened. Moreover, the results show a positive and significant effect of the intellectual dimension on up-buying. Thus, in addition to merely affecting customers’ feelings, cognitive inputs are required to steer customers to upgrade to higher-priced products. In practice, marketers’ efforts to align new (more expensive) products with emotional aspects should be combined with the
32
endorsed by famous football players with whom people have strong interpersonal attachment. Lastly, to increase repeat purchases and to enlarge customers’ loyalty, marketers should both provoke positive feelings and stimulate the senses. Marketers can thus introduce or increase investments in sensory marketing to assure that customers return to the firm. From a retailing perspective, marketers may endeavor to develop congruent ambient scents or play positive perceived music in the shop. Also, online retailers can benefit from sensory marketing by constructing visually-distinctive websites. Moreover, the positive influence of sensory
experiences emphasizes the promising role of product development for firms whose objective is to build long-term relationships with customers. Namely, haptic perception is an important determinant of sensory experiences (Krishna, 2012).
Overall, firms should always strive to optimize the total customer experience by improving customers’ sensory, affective, behavioral, and intellectual experiences with the firm (Verhoef et al., 2009). However, the findings of this study indicate that the effects vary across experience dimensions and that some experiences are more valuable than others. Consequently, to improve the return on customer experience expenditures, marketers could target their spending toward experience dimensions which are most likely to pay off. Thus, bearing in mind the increasing pressure on marketing managers to justify their marketing expenditures (Ramani & Kumar, 2008), it would be more effective to optimize particular dimensions of customer experience rather than distributing marketing resources equally among experience dimensions. Depending on the goal, firms could focus their vast majority of marketing expenditures on optimizing customers’ emotional experiences, and to a lesser extent sensory experiences, because these experiences yield the greatest results. Nevertheless, this does not say that firms should neglect the other experience dimensions. Namely, without providing cognitive information about the properties of a product and/or service (i.e.
intellectual dimension), or when bodily experiences (i.e. behavioral dimension) are perceived unfavorable due to a low quality of the product, the total customer experience is disturbed in a such a way which cannot be healed by superior emotional experiences. However, when all experience dimensions are at a sufficient level, the remaining marketing resources can best be allocated to those dimensions which render the highest profit.
6.3 Research limitations and future research
Although meaningful findings are produced, as with any research, this research has some clear limitations and also provides directions for further research.
33
focused on sports-related purchases, especially sportswear, which is just a minor part of consumers’ total purchases. Moreover, this study was aimed at tangible product offerings which, however, clearly differ from intangible services. To illustrate, customers are more involved in the production and consumption of services which may alter the order of
effectiveness of experience dimensions (Zeithaml, Parasuraman & Berry, 1985). Thus, future research may replicate this study in other sectors and extend it to the domain of services marketing. The second limitation of this study is that the dependent variables consist of customers’ remembered purchase behavior and self-stated retention. Although the theory of reasoned action states that intentions lead to behavior (Fishbein & Ajzen, 1975), combining customers’ retention intentions with actual repeat purchase behavior is a better proxy of customer loyalty (Dick & Basu, 1994). Similarly, customers may have faced difficulties in recalling their total sports-related purchases from the past two years. Hence, future research may tackle this issue by using actual company databases for the measurement of the
dependent variables (e.g., Bolton & Lemon, 1999; Verhoef et al., 2001). The next limitation also relates to the cross-sectional design of the study. Data were collected at a single point in time which entailed that customers had to remember their past experiences with the firm. Because customers’ memories decay rapidly, further investigations may conduct longitudinal research using advanced marketing research techniques such as real-time experience tracking to measure customer experience (Macdonald, Wilson & Konus, 2012). Furthermore, due to limited data availability, some important variables are omitted which may have influenced the dependent variables. For example, customers’ perceptions of quality and price are one of the primary determinants of repurchase behavior which have not been taken into account in this study (Vogel et al., 2008). Finally, another limitation worth mentioning is that the outcome variables were somewhat correlated. This may have been an alternative explanation for the superior influence of the affective dimension.
This study measured the impact of experience dimensions on several outcome metrics. An important avenue for future research is to investigate how and to what extent firms can influence the development of different customer experiences. Thus, examining the
antecedents of the underlying experience dimensions. Next, future research may investigate moderating variables which could strengthen the influence of experience dimensions on several customer metrics. Likely, emotional experiences are more influential for people possessing limited self-regulatory resources (Shiv & Fedorikhin, 1999). Besides, another interesting moderator to explore is relationship duration. Namely, when consumption
34
affective factors decreases (Homburg et al., 2006). Therefore, emotional experiences are probably more important in the early stages of the relationship, whereas intellectual
