Channel Habits and the Development of Successful Customer-Firm Relationships in Services

Abstract

Technology advances have profoundly changed the way customers and service organizations interact, leading to a multitude of service channels. This study investigates consumer habits toward service channels in order to understand the influence of these channel habits on perceptions and intentions (perceived switching costs and attitudinal loyalty) and on consumer behavior (service usage and cross-buy). We empirically test the framework in the financial services industry, and the results reveal that physical store habit increases perceived switching costs and that acquired habits toward the physical store and self-service kiosks have a positive influence on attitudinal loyalty. Perceived switching costs positively affect service usage, and attitudinal loyalty positively influences cross-buy. In addition, habits in each channel lead to an increase in the number of services acquired (cross-buy), but online and self-service kiosks channel habits negatively impact service usage, as the lack of physical presence may increase customer uncertainty. Because habits are built on the frequency and stability of channel usage, firms can manage habits by encouraging frequent interactions under stable contexts. In addition, firms should stimulate customer habits toward the physical store as it is central to the promotion of loyalty and for increasing service usage.

Keywords

customer channel habits multichannel management perceived switching costs attitudinal loyalty financial services

The emergence of new service channels (McLean and Wilson 2016; Moe and Ratchford 2018) has increased the number and complexity of interactions and contact points between customers and service organizations (Lemon and Verhoef 2016). Among numerous themes in multichannel customer management, understanding the consequences of channel usage for customer perceptions and behaviors has received significant attention (Ackermann and von Wangenheim 2014; Bilgicer et al. 2015; Li et al. 2015; Venkatesan, Kumar, and Ravishanker 2007; Verhoef and Donkers 2005). However, understanding channel habits is necessary, as recent studies suggest that the study of channel usage and their consequences may capture only incomplete patterns of channel preferences (Polo and Sese 2016). In this study, we propose an in-depth understanding of how customer habits may operate in a multichannel service context.

Habit refers to a person’s disposition to frequently repeat past behavior under a stable context (Neal et al. 2012). Customers may develop habits toward products, services, promotion purchases, or firms, and the study of customer habits continues to present relevant opportunities for research (Shah, Kumar, and Kim 2014). Best and Papies (2017) acknowledge the benefits of understanding how customer habits influence behavioral changes and generate positive outcomes (Carden et al. 2017; Herziger and Hoelzl 2017; Hubert et al. 2017; Liu-Thompkins and Tam 2013; Shah, Kumar, and Kim 2014). In addition, habits are prevalent in consumers’ daily lives, so the study of customer habits may empower companies to influence everyday consumer behavior (Drolet and Wood 2017).

However, recent literature shows that consumer researchers have taken surprisingly little interest in the topic of habits within channel literature, despite its relevance to consumer behavior literature (Drolet and Wood 2017). Barwitz and Maas (2018) is one of the few articles that has studied habits in a multichannel context, albeit from a theoretical point of view. As a result, this is the first attempt to introduce the concept of “channel habits” to empirically measure the consequences of customer habits toward a firm’s different service channels (physical stores, online channels, and self-service kiosks).

This study contributes to the marketing and service literature in three ways. First, it integrates habit research into channel customer behavior, as we contemplate, in a novel way, customer habits toward different channels (physical stores, online channels, and self-service kiosks). Despite the importance of habits in consumer behavior, service research, particularly when focused on multichannel customer management, has not considered this perspective. Apart from a few notable exceptions in related contexts (e.g., Blut, Wang, and Schoefer 2016; Gensler, Verhoef, and Böhm 2012; Wang, Harris, and Patterson 2013, in their attempt to explain the use of self-service technologies), this study is the first to explicitly consider habits in a multichannel service context. Therefore, more research is needed to clarify the effect of customer habits toward different channels.

Second, this research empirically investigates key perceptual and intentional measures that may help explain why habits could be transformed into regular income for service organizations. Liu-Thompkins and Tam (2013) stated that customers’ behavioral loyalty as a consequence of repeatedly patronizing a business may be driven by different factors such as favorable attitudinal loyalty or barriers to switching. Identifying the drivers of purchase behaviors may help customize marketing strategies and allocate marketing resources more efficiently (Liu-Thompkins and Tam 2013; Seetharaman 2004). Thus, in this research, perceived switching costs (i.e., customers’ perceived costs involved in changing from one supplier to another; Heide and Weiss 1995) and attitudinal loyalty (i.e., intention to buy based on favorable evaluations of the products or services; Oliver 1999) are proposed as two key perceptual and intentional measures that will provide more information about the extent to which habits become strong predictors of behavioral customer responses (i.e., cross-buy and service usage) toward a firm (Liu-Thompkins and Tam 2013).

Finally, our empirical study uses a longitudinal customer database (objective customer information) from a financial services company, which provides a high degree of validity for the obtained findings. Herziger and Hoelzl (2017) confirmed that research on habits requires real behavior measurement due to the unconscious nature of habits. Their two empirical studies demonstrated that the effect of habit on consumer behavior is systematically underestimated when measured in hypothetical scenarios. Measuring customer habits therefore demands longitudinal investigations in real scenarios to capture recurring behaviors routinely and reliably. Moreover, collaboration with companies is the only way to offer a rigorous and impactful research that can reveal the depth and significance of customers’ habits (Dholakia and Tam 2017; Drolet and Wood 2017). The approach taken in this study enables us to provide an integrative and more comprehensive understanding of the consequences of channel habits and to gain new insights that help understand multichannel customer behavior.

Background

This study analyzes customer habits into a multichannel service context to understand the consequences of habits toward different channels. Table 1 presents a summary of relevant research on multichannel customer management, and it shows that this study is the first attempt to consider habits in a multichannel services context.

Table 1.

Literature Review of Multichannel Customer Management.

Authors/Year	Context	Theory	Methodology	Customer Habits	Related Variables to Habit	Independent/mediating Variables	Dependent Variables	Main Findings/Future Research Lines
Shankar, Smith, and Rangaswamy (2003)	Travel services industry	Behavioral decision theory, familiarity-liking theory, prospect theory, and disconfirmation theory	Simultaneous equation model, pooled models (3SLS), and recursive system of three equations	—	—	Service encounter satisfaction, overall satisfaction, the Online medium; control variables)	Loyalty	The levels of customer satisfaction for a service chosen online are the same as when it is chosen offline, and loyalty to the service provider is higher when the service is chosen online than offline. In addition, the relationship between overall satisfaction and loyalty is stronger online than offline and there is a positive reciprocal relationship between loyalty and satisfaction online.
Wallace, Giese, and Johnson (2004)	Large regional retailer of specialty outdoor sporting goods	Satisfaction theory	LISREL and three-stage least squares [3SLS]	—	—	Customer multichannel employment, multichannel portfolio of service outputs, disconfirmation, customer satisfaction	Attitudinal and behavioral loyalty	Multiple channel retail strategies enhance the portfolio of service outputs provided to the customer, thus enhancing customer satisfaction and ultimately customer retailer loyalty.
Thomas and Sullivan (2005)	Major U.S. retailer	—	Multinomial logit model, latent class segmentation approach, and Markov switching matrices	—	—	The authors propose six steps to enhance the targeting and management of customers in a multichannel context: Step 1: Estimate a segment-level channel choice model Step 2: Assign each existing customer to a segment and profile the segments Step 3: Predict the probability of channel choice over time Step 4: Develop a segment-specific communications strategy Step 5: Classify first-time customers into existing segments Step 6: Update segment affiliation.
Neslin et al. (2006)	—	—	Theoretical literature review	—	—	Problem pecognition, consumer channel perceptions and preferences (search, purchase, and after sale), and channel strategy	Postevaluation, data, and channel evaluation	The authors identify five major challenges practitioners must address to manage the multichannel environment more effectively: (a) data integration, (b) understanding consumer behavior, (c) channel evaluation, (d) allocation of resources across channels, and (e) coordination of channel strategies.
Falk et al. (2007)	German bank	Status quo bias theory	AMOS Version 5.0	—	—	Trust in the service provider; off-line channel satisfaction; perceived usefulness of SSC; perceived risk of SSC	Intention to use SSC	Off-line channel satisfaction reduces the perceived usefulness and enhances the perceived risk of the online channel. These inhibiting effects represent a status quo bias. Trust in the bank shows both adoption-enhancing effects and an adoption-inhibiting effect.
Venkatesan, Kumar, and Ravishanker (2007)	Apparel manufacturer	Social exchange theory	Shared-frailty hazard model	—	—	Channel-related attributes, purchase-related attributes, frequency-related attributes, and customer heterogeneity	Channel adoption duration	The authors provide evidence that multichannel shopping is associated with higher customer profitability. Other findings reveal that customers who purchase across product categories or with a higher purchase frequency have shorter channel adoption durations. In addition, the proportion of travel cost in current channels is negatively related to the duration to adopt for both single-channel and two-channel customers.
Verhoef, Neslin, and Vroomen (2007)	800 Panelists on channel usage over six product categories	Theory of reasoned action	3SLS	—	—	Search attributes, search and purchase attributes, purchase attributes	search and purchase attractiveness and search and purchase choice	(1) Attribute-based decision making, (2) lack of channel lock-in, and (3) cross-channel synergy are at work in making internet search ⇒ store purchase the most popular form of research shopping.
Ansari, Mela, and Neslin (2008)	Retailer of durable and apparel products	—	Type II Tobit specification and a probit framework	—	Experience effects	Communications by catalog, communications by e-mail	Purchase volume (frequency and order size) and channel selection	(1) Web purchasing is associated with lower subsequent purchase volumes than when buying from other outlets; (2) marketing efforts are associated with channel usage and purchase incidence, offsetting negative web experience effects; and (3) negative interactions occur between like communications (Catalog × Catalog or Email × Email) and between different types of communications (Catalog × Email).
Cortiñas, Chocarro, and Villanueva (2010)	Financial services industry	—	Latent class regression model	—	—	Customer involvement, acquisition of online-managed financial products, acquisition of customer-managed financial products, age, occupation	Multichannel behavior	The owners of liability products that can only be managed through the Internet have a higher tendency to distribute transactions across the different available channels. Multichannel banking is more frequent in customers carrying out large numbers of routine banking transactions such as transfers. Customers with the type of liability contracts that can be customer-managed on the Internet also show higher multichannel banking.
Valentini, Montaguti, and Neslin (2011)	Book retailer, and durable and apparel retailer	—	Model to capture the evolution process, and two Logit channel choice models	—	Inertia (state dependence)	Marketing, channel attributes, social influence, channel integration, individual differences, situational factors	Marketing, channel preferences, inertia (state dependence), unobserved factors	Customers’ channel choice decision processes evolve over time. A significant portion of customers are expected to shift (ranging from 22% [newly acquired customers] to 35% [new channel introduction]). The predominant pattern of evolution is from a more marketing-responsive to a less marketing-responsive decision process.
Wiesel, Pauwels, and Arts (2011)	Data from office furniture products Business to Consumer (B2C)	—	Vector autoregressive model	—	—	Dynamic conceptual model: Interrelationships between marketing activity, online channel, and off-line channel	Profits	Marketing communication activities directly affect both early and later purchase funnel stages (website visits, online and off-line information, and quote requests). In addition, online customer-initiated contacts have substantially higher profit impact than off-line firm-initiated contacts (FICs).
Gensler, Verhoef, and Böhm (2012)	Banking industry	Loss aversion theory and random utility theory	Multinomial logit model formulation	—	Experience effects	Channel attributes, channel experience, stage and product channel associations, channel utility, experience effects, spillover effects	Channel choice	This study evidences the importance of channel experience and spillover effects for explaining consumers’ channel choice intentions in the different stages of the buying process. The results show that both effects influence consumers’ channel choice intentions over and above channel attributes. Importantly, the model results strongly pledge for studying attribute, experience, and spillover effects simultaneously.
Gensler, Leeflang, and Skiera (2012)	Banking industry	—	Hybrid matching method	—	—	Online use, product portfolio, self-selection effects	Customer revenue and cost to serve a customer	Online use improves customer profitability by increasing customer revenue and decreasing cost to serve. Moreover, the revenue effects of online use are substantially larger than the cost-to-serve effects although the effects of online use on customer revenue and cost to serve vary by-product portfolio. Self-selection effects also emerge and can be even greater than online use effects.
Konus, Neslin, and Verhoef (2014)	Dutch retailer of household and personal products	Reactance theory	Binomial probit model	—	State dependence	Channel elimination (catalog), firm preference, channel preference, firm-marketing efforts, costs and benefits of shopping at the firm, utility	Purchase	Channel elimination decreases purchase incidence, especially for customers who were heavy users of the telephone purchase channel that aligns with the catalog search channel. Channel choice for purchases is shifted toward the internet and away from the telephone channel.
Montaguti, Neslin, and Valentini (2016)	European book retailer	Theory of reasoned action, theory of consumer reactance, theory of planned behavior	Randomized fields experiment	—	—	Marketing communication, multichannel behavior, moderating variables: self-selection, increased customer satisfaction, and use higher margin channels	Customer profits	A properly designed marketing campaign increases the number of multichannel customers and increases average customer profitability. The authors also show that, after accounting for self-selection, multichannel customers are more profitable than they would be if they were not multichannel.
Barwitz and Maas (2018)	Motor insurance customers	—	Focus groups, expert interviews, and laddering interviews	√ (theoretical approach)	Inertia	Attributes (quick, richness of interaction, range of options, etc.), values (effectiveness, performance content, pleasure, excitement, etc.)	Consequences (having choice, time pressure, information attainment, purchasing enjoyment, etc.)	Customer habits have been found to influence the choice of interaction sequences along the customer journey. Singular interaction choices, inertia, and customer journey patterns drive customers’ interaction choices. Research shopping, impersonalization, or interactivity reduction are the underlying reasons behind customer journey patterns. Utilitarian, hedonic, cost, sacrifice minimizing, and relational type of value-in-use help more efficiently segment omnichannel customers.
This study	Financial services industry	Theory of planned behavior	SUR model	√ (empirical approach)	—	Physical store habit, online habit, self-service kiosks habit, perceived attitudinal loyalty, perceived switching costs	Service usage and cross-buy	Physical store and online habits are particularly valuable for increasing perceived switching costs, and the acquired customer habits toward the three channels considered (physical stores, online channels, and self-service kiosks) have a positive influence on attitudinal loyalty. Perceived switching costs positively affect service usage only, and attitudinal loyalty positively influences cross-buy. In addition, habits in each channel lead to an increase in cross-buy, but online channel and self-service kiosk habits negatively impact service usage, as the lack of physical presence may increase customer uncertainty.

Customer habits have received special attention in the literature (Henderson, Beck, and Palmatier 2011; Herziger and Hoelzl 2017; Liu-Thompkins and Tam 2013), as they explain a person’s disposition to frequently repeat past behavior under a stable context (Neal et al. 2012). When individuals frequently repeat a given behavior, such as wearing a seat belt when sitting in a car, they will automatically wear a seat belt whenever they sit in a car, as the car represents a relevantly similar context (Shah, Kumar, and Kim 2014). This example helps us identify the two main properties of habit that distinguish it from other concepts: frequency and stability. Habit formation depends on the frequency of previous behaviors in stable contexts.

The first key property of habit is the frequency of action (Liu-Thompkins and Tam 2013; Wood, Tam, and Witt 2005). When a person frequently repeats a given behavior, this person will execute the behavior automatically (Shah, Kumar, and Kim 2014). Due to its subconscious nature, habit involves people using less deliberative processing (rational, effortful, and analytic) and more automatic decision making (experiential, effortless, and holistic; Henderson, Beck, and Palmatier 2011; Hoffman and Novak 2009; Smith and DeCoster 2000). The more frequently a specific behavior is performed, the more likely it is that a habit will develop. Thus, habit formation is strongly related to the frequency of previous behavior.

The second property is the presence of certain contextual stability (Herziger and Hoelzl 2017; Neal et al. 2012; Shah, Kumar, and Kim 2014). The literature confirms that around 45% of customers are prone to repeating behavior when they are in similar contextual situations, with these situations (e.g., interacting with the company through the same channel) providing the stability required for the customer to perform the same behavior again (Shah, Kumar, and Kim 2014; Wood, Quinn, and Kashy 2002). In the presence of one or more similar contexts, habit is triggered (Herziger and Hoelzl 2017). As habits gain strength, the perception of contextual stability brings to customers’ minds the associated response (Labrecque et al. 2017; Neal et al. 2012).

Because of these two key properties of stability and frequency, habit requires minimal awareness; habit is efficient because actions are performed quickly, easily, and with little effort; and habit is characterized by a lack of conscious intention, as it is difficult for habitual customers not to perform the action in the same way that they did in the past (Verplaken and Wood 2006). The literature clearly evidences that “with repetition and practice of a skill in a given context, the cognitive processing that initiates and controls the response becomes automatic and can be performed quickly in parallel with other activities and with allocation of minimal focal attention” (Ouellette and Wood 1998, p. 55).¹

To understand customer habits in depth, it is essential to distinguish them from other related concepts such as state dependence, inertia, or experience effects. State dependence reflects the extent to which previous purchases influence the current purchase (Chintagunta 1998; Jimenez-Martin and Ladrón-de-Guevara 2007). Depending on the customer’s level of state dependence, this variable can be positive or negative. On the one hand, inertia equals positive state dependence, and the previous purchase of a product increases the likelihood that the customer will buy the same product again in the current time period (De Jong, Lehmann, and Netzer 2012). Inertia has its origin in the costs associated with switching, as switching implies breaking a routine and may lead to uncertainty or perceived risks (Jeuland 1979). Inertia reflects a strong persistence of the existing form and function (Polites and Karahanna 2012) or the inability to change an established pattern of buying or selling activities (Yadav and Varadarajan 2005). In contrast to inert customers, noninert customers do not avoid making new purchasing decisions, learning new service routines and practices, or making price comparisons among alternatives. On the other hand, negative state dependence is present in situations when the purchase of the product in the previous period decreases the likelihood that the customer will buy the same product again in the current time period. In such situations, customers may decide to switch in a quest for variety (Chintagunta 1998; McAlister 1982; Seetharaman 2004; Seetharaman, Ainslie, and Chintagunta 1999).

The literature also introduces the term “experience effects” to capture whether the use of a specific channel increases the likelihood that the customer will use the same channel on the next purchasing occasion (Gensler, Verhoef, and Böhm 2012). However, the authors clarify that experience effects appear as a type of channel loyalty.

Although the conceptual differences between habit, negative state dependence, and experience effects seem obvious, habit is frequently confused with inertia (positive state dependence) in the literature (Polites and Karahanna 2012). Inertia is a distinct construct that does not require frequency or stability (Konus, Neslin, and Verhoef 2014; Valentini, Montaguti, and Neslin 2011). The marketing literature indicates that status quo bias is not habitual since it does not require more than one previous action (Falk et al. 2007). Thus, inertia can be developed in the absence of frequently repeated past behavior under a stable context, as the properties that explain customer habits (i.e., frequency and stability) are not contained in inert customer patterns (Falk et al. 2007).

Conceptual Framework

We provide a conceptual framework (see Figure 1) that aims to understand the relationships between channel habits, two key perceptual and intentional measures (perceived switching costs and attitudinal loyalty), and customer behaviors (cross-buy and service usage).

Figure 1.

Conceptual framework.

To do this, we build on the theory of planned behavior. This theory has been used to study the influence of perceptions, attitudes, and behavioral intentions on customer behaviors (Ajzen and Driver 1992). In line with this theory, we study how perceived switching costs and attitudinal loyalty influence customer behavior, as measured by cross-buy and service usage.

Although the theory of planned behavior has shown wide efficacy in predicting behavior (Armitage and Conner 2001), previous research has also revealed that the mechanisms behind behavior predictors are more complex than the theory hypothesizes (Perugini and Bagozzi 2001). Behaviors can follow an automatic repetition of past actions (Ouellette and Wood 1998). As such, past behavior can provide additional explanatory power to the prediction of behavioral intentions and behavior (e.g., Bagozzi et al. 2000; Kidwell and Jewell 2008), and it may also influence perceptual, attitudinal, and behavioral intention dimensions (Ouellette and Wood 1998). When customers have repeated behaviors in the past, they increase the automaticity that follows from repeated performance of the behavior (i.e., habit). These past behaviors are assumed to capture the motivational factors that influence future behaviors. In this study, the theory of planned behavior is used to provide the basis for the hypothesized relationship between switching costs and loyalty (as explanatory variables) on the one hand and cross-buy and service usage on the other hand (as dependent variables).

In our framework, to conceptualize how channel habit influences perceived switching costs and attitudinal loyalty, we argue that channels have specific capabilities (Verhoef, Neslin, and Vroomen 2007) that offer different support to customer goals (Dholakia et al. 2010). Physical stores allow for a rich and multisensory experience (Avery et al. 2012). They also provide rich personal contact that enables customers to develop psychological bonds with service personnel and with the firm (Ackermann and Wangenheim 2014). The online channel complements the physical store by offering efficient service provision, but it lacks personal contact capabilities. The online channel has the highest levels of convenience and accessibility, and it facilitates the search and evaluation effort (Montoya-Weiss, Voss, and Grewal 2003). However, although ease of search can foster cross-buy, it may also increase the likelihood of switching to competitors (Ackermann and Wangenheim 2014). Self-service kiosks, such as ATMs or vending machines, provide a blend of capabilities in comparison to physical stores and online channels. Self-service kiosks provide a physical presence closer to that of retail stores, thus enriching the customer experience, and they have high availability and convenience, at the expense of lack of personal contact, as with the online channels (Patrício, Fisk, and Cunha 2008).

Given the different capabilities of each channel, customer habits toward each channel may influence perceived switching costs in different ways. Perceived switching costs are the “onetime costs that customers associate with the process of switching from one provider to another” (Burnham, Frels, and Mahajan 2003, p. 110). The literature relates perceived switching costs to how easy or difficult it is to look for competitors’ proposals and channels as well as to the extent to which the customer is able to develop a close relationship that makes it harder to switch. It is becoming more difficult to retain customers, owing to increased market competition and the minimal perceived switching costs that have resulted from technological advances (Srinivasan, Anderson, and Ponnavolu 2002).

We also argue that, given the different capabilities of each channel, customer habits toward each channel may influence attitudinal loyalty in different ways. Attitudinal or affective loyalty can be defined as the intention to rebuy or to repatronize the firm consistently in the future, based on favorable evaluations of the product or service (Oliver 1999). Attitudinal loyalty indicates a propensity to display specific behaviors such as a strong intention to buy from the firm, to repeat patronage, or to make recommendations to friends and colleagues (Umashankar, Bhagwat, and Kumar 2017). In addition, attitudinal loyalty persists over time and can occur in a variety of situations (as it is resistant to situational and social conditions; Dick and Basu 1994; Liu-Thompkins and Tam 2013). When a customer is highly comfortable in interacting with the company, attitudinal loyalty often arises. Attitudinal loyalty may make customers behave as a relational partner (Ringberg, Odekerken-Schröder, and Christensen 2007; Umashankar, Bhagwat, and Kumar 2017). Thus, achieving customer attitudinal loyalty may become salient for companies. We therefore propose that perceived switching costs and attitudinal loyalty will impact customer behavioral outcomes and that they may also mediate the relationships between customer channel habits and behaviors (Bolton, Lemon, and Verhoef 2004; Palmatier et al. 2006).

Bolton, Lemon, and Verhoef (2004) showed that the depth and breadth of customer-firm relationships are reflected in different purchase behaviors. The depth dimension represents service usage over time, which consists of a customer’s purchases and use of the services offered by a firm (Lemon and Wangenheim 2009). The breadth dimension refers to cross-buy, which represents a customer buying additional products and services from an existing service provider (Konus, George, and Pancras 2008). Thus, in line with Bolton, Lemon, and Verhoef (2004), who proposed an analysis of how different marketing instruments influence customer perceptions, intentions, and customer behaviors, we build on the particular capabilities of different channels to provide specific hypotheses about the impact of channel habits on customer perceptions and, ultimately, on customer behavior.

Hypothesis Development

The Impact of Channel Habit on Perceived Switching Costs and Attitudinal Loyalty

Physical store habit

We propose that developing a habit of using the physical store will lead to higher perceived switching costs (Dholakia et al. 2010). Physical stores enable customers to carry out a complete range of operations with regard to the company’s products and services, and the stores help customers become more knowledgeable about the offerings of the service firm. In this context, switching to alternative providers implies an important investment in understanding the products and services they offer (Burnham, Frels, and Mahajan 2003). Furthermore, the physical store has stronger capabilities for giving personal advice (Patrício, Fisk, and Cunha 2008) and facilitates the establishment and consolidation of a relationship with individual salespeople (Avery et al. 2012). This is particularly important for reducing perceived risk, which will increase the perceived cost of switching to alternative providers because of fears of not obtaining the same service level. Similarly, the development of stronger bonds through this channel may enable the company to develop deep knowledge of its customers and to implement personalized marketing activities, which are difficult to replicate and become important barriers to switching (Tang and Xing 2001). Hence, we propose Hypothesis 1a:

Hypothesis 1a: Physical store habit will have a positive effect on perceived switching costs.

We argue that physical store habit will lead to an increase in attitudinal loyalty toward the firm. By promoting face-to-face interactions and a strong sense of personal touch, physical stores help companies generate personalized and multisensory customer experiences and establish a close relationship between customers and the firm’s employees and brand (Avery et al. 2012; Bolton, Lemon, and Verhoef 2004). The wider range of operations available in the physical channel also facilitates deeper interactions between the customer and the firm (Patrício, Fisk, and Cunha 2008), which ultimately promote the development of successful long-term relationships (van Doorn et al. 2017). These strong social bonds between the customer and the company generate positive customer attitudes and comfort with the retailer, which make customers perceive their relationship with the company as having a higher value (Chang and Zhang 2016; Wallace, Giese, and Johnson 2004). This capability helps customers find products and services that more closely match their needs, thus providing superior value and promoting the development of loyalty based on favorable evaluations of the service provider. Hence, we propose Hypothesis 1b:

Hypothesis 1b: Physical store habit will have a positive effect on customer attitudinal loyalty.

Online habit

Developing an online channel habit is expected to reduce the cost of switching for customers. The online channel is characterized by its efficiency in terms of convenience, accessibility, and ease of use. However, this ease of use enables customers to look for information on other competitors’ websites and makes it easier to switch (Ackermann and Wangenheim 2014; Dholakia et al. 2010). In addition, the set-up costs of using a new provider’s online channel will not be high, as online channels are often similar and easy to use (Burnham, Frels, and Mahajan 2003). An important part of perceived switching costs is the loss of personal relationships (Burnham, Frels, and Mahajan 2003). In the online channel, the lack of physical presence, personal contact, and human touch impedes the development of social bonds (Tang and Xing 2001), so customers will not feel a cost in terms of loss of personal relationship. In addition, the online channel does not usually promote the development of strong ties with the brand (Ackermann and Wangenheim 2014), partly because of its utilitarian nature and its inability to provide rich sensory experiences (Montoya-Weiss, Voss, and Grewal 2003). This weaker brand identification will make switching easier. Hence, we propose Hypothesis 2a:

Hypothesis 2a: Online habit will have a negative effect on perceived switching costs.

Online channel habit may also lead to a decrease in attitudinal loyalty. The online channel increases efficiency and convenience for customers. However, the lack of a human interface prevents regular personal interactions between the customer and the retailer, thus leading to weaker relationships with employees and to levels of attitudinal loyalty lower than through other channels (Bolton, Lemon, and Verhoef 2004). As noted previously, the online channel usually provides a weaker brand experience because it is based only on visual representation (Avery et al. 2012). This relatively poor customer experience, based solely on convenient development of service operations anytime and anywhere, will not help develop emotional bonds between both parties (Chang and Zhang 2016). The online channel usually gives customers access to their previous purchases and searches, providing useful information that facilitates a more active understanding of their behavior and own needs (Montoya-Weiss, Voss, and Grewal 2003). This information, together with the lack of the personal touch, promotes a focus on the more calculative aspects of the relationship (Campbell and Frei 2016), such as the ratio of input to output. Although it may favor better purchase decisions, this calculative mindset impedes the development of long-term relationships based on attitudinal aspects. We therefore propose Hypothesis 2b:

Hypothesis 2b: Online habit will have a negative effect on customer attitudinal loyalty.

Self-service kiosk habit

When customers develop a habit of using the self-service kiosk, the impact on the development of perceived switching costs can be both positive and negative. On the one hand, the lack of human touch and face-to-face interactions prevents customers from developing strong identification and emotional bonds with the service frontline employees. It also contributes to an increase in the uncertainty of transacting with the firm because a human presence is a safeguard against potential negative outcomes that could arise during the interaction (e.g., lack of information; Avery et al. 2012). Similarly, the usually narrow assortment of products and services available in this channel (Patrício, Fisk, and Cunha 2008) makes it more difficult for customers to find those more tailored to their particular needs; it also limits the ability to cross-buy, which is an important source of perceived switching costs. On the other hand, the physical presence of the kiosk can promote brand awareness and the development of a strong brand identification. As noted by Burnham, Frels, and Mahajan (2003, p. 112), “the affective losses associated with breaking the bonds of identification that have been formed with the brand or company with which a customer has associated” can be very high for the customer. These conflicting effects lead us to an open formulation for Hypothesis 3a:

Hypothesis 3a: Self-service kiosk habit will have an effect on perceived switching costs.

We contend that a self-service kiosk habit may lead to an increase in levels of attitudinal loyalty. The physical presence of this channel requires customers to go to a physical place, which contributes to the formation of a superior and richer experience, ultimately leading to higher perceived value and the development of a positive attitude toward the service firm (Avery et al. 2012). Similarly, this channel is highly accessible, has a flexible schedule, and is convenient and easy to use, which helps satisfy customer needs in a more efficient manner. The customer learned experience will make customers develop positive attitudes and perceive a higher value in the relationship (Chang and Zhang 2016; Wallace, Giese, and Johnson 2004), so it will make customers more loyal. We therefore propose Hypothesis 3b:

Hypothesis 3b: Self-service kiosk habit will have a positive effect on customer attitudinal loyalty.

The Links Between Perceived Switching Costs, Attitudinal Loyalty, and Behavioral Outcomes

Service usage

As noted previously, service usage refers to a customer’s purchases and use of the services offered by a firm (Lemon and Wangenheim 2009). Following our proposed model, perceived switching costs are expected to positively influence service usage. Perceived witching costs are likely to be more effective than attitudinal loyalty in influencing service usage because customers usually consider the costs and the benefits of the service (Bolton, Lemon, and Verhoef 2004). We therefore propose Hypothesis 4a:

Hypothesis 4a: Perceived switching costs will have a positive effect on service usage.

We also consider the linkage between attitudinal loyalty and service usage. As Bolton, Lemon, and Verhoef (2004) highlighted, little research has focused on the effect of customer loyalty on service usage. We propose that customer loyalty will have no influence on service usage. This is because, although loyal customers may prefer one supplier to others, this preference may not translate into higher usage levels since “the usage behavior is mainly driven by the utility provided by the usage of the service” (Bolton, Lemon, and Verhoef 2004, p. 278). Hence, we formulate Hypothesis 4b:

Hypothesis 4b: Attitudinal loyalty will not influence service usage.

Cross-buy

Cross-buy refers to a customer buying additional products and services from an existing service provider that they uses (Konus, George, and Pancras 2008). We propose that perceived switching costs will not influence cross-buy. If customers perceive high switching costs, it is reasonable for them to continue being customers of this company, but they do not necessarily have to buy additional products/services (cross-buy) from the focal firm (Bolton, Lemon, and Verhoef 2004). Specifically, we propose Hypothesis 5a:

Hypothesis 5a: Perceived switching costs will not influence cross-buy.

Attitudinal loyalty will positively influence cross-buy and/or relationship breadth (Bolton, Lemon, and Verhoef 2004). This variable reflects customers’ behavioral intentions to rebuy or repatronize the firms’ products and services, based on their beliefs of service superiority and on favorable attitudes toward the firm (Kursunluoglu 2011; Oliver 1999; Umashankar, Bhagwat, and Kumar 2017). The positive effect of customer loyalty on cross-buy behaviors has already been confirmed in the financial services field (Verhoef, Franses, and Hoekstra 2001). Hence, we propose Hypothesis 5b:

Hypothesis 5b: Attitudinal loyalty will have a positive effect on cross-buy.

Empirical Study

Data Description and Measures

In this study, we used data from a major bank in a European country. We collected objective customer information from the financial entity over 15 months (January 2011 to March 2013, both inclusive) and combined it with subjective information obtained from customers via a survey. Specifically, our database contained the following information for these customers: (a) multichannel data, or the number of contacts that customers developed through the different channels of physical store, the online channel, and self-service kiosks; (b) transactional data that include customer behavioral information about service usage and cross-buy; and (c) customer-level information, including demographics.

To properly measure channel habits, longitudinal investigations in real scenarios are required to capture recurring behaviors routinely and reliably (Dholakia and Tam 2017; Drolet and Wood 2017; Herziger and Hoelzl 2017; Liu-Thompkins and Tam 2013), as consumers’ self-reports are inaccurate due to the unconscious nature of habits. Thus, the only way to offer a rigorous and impactful research is to obtain real data from companies (Dholakia and Tam 2017; Drolet and Wood 2017). Once we were sure of the quality of the customer data, we used the literature to adapt, in an innovative way, the formula of customer habit to channel habit. As noted previously, channel habits are reflected in the frequency and stability of the performed actions (Shah, Kumar, and Kim 2014). Moreover, in contexts conducive to habit formation, characterized by a stable context that offers frequent opportunities to perform the behavior (such as banking), frequency and stability of past behavior can be considered the best measurement of habit (Ouellette and Wood 1998).

Following previous research (Shah, Kumar, and Kim 2014), we measured channel habits (one habit per channel) by multiplying the stability and frequency of channel usage. We used 1 year of data and examined month by month the frequency and stability of the chosen channels. Thus, the measure of channel habits will depend on how these interactions are distributed across channels (i.e., frequency) and over time (i.e., temporal stability). In our context, frequency refers to the number of times that a given channel was used to contact the firm (we used a relative measure to make this comparable across customers and divided this number by the total amount of customer-initiated contacts).² Frequency for channel j would be calculated as the ratio between the number of times channel j was used to contact the bank (e.g., 11 interactions through the online channel) divided by the total number of interactions with the bank through all channels (e.g., 30 interactions through all channels). Thus, the more a channel was used, the higher the frequency. Temporal stability indicates whether channel usage was repeated over time (Shah, Kumar, and Kim 2014). This was calculated as the number of months in which channel j was used in a given year (e.g., a customer interacted with the bank in 9 months of the 12). Thus, when a channel was used repeatedly over time, temporal stability increased. The habit toward channel j would then be calculated as the product of stability and frequency, meaning that higher values are indicative of stronger habits toward the channel.

The objective information from the financial entity was combined with subjective information obtained from a survey on customers’ perceived switching costs and attitudinal loyalty, based on scales derived from the literature. The market research company that usually works with the financial entity was responsible for this survey, which was conducted by telephone in December 2012. They approached a total of 5,848 customers and obtained 2,000 valid responses (a response rate of 34.19%). Respondents had to score statements about the company on a scale from 1 (strongly disagree) to 7 (strongly agree). The scales used to measure perceived switching costs and loyalty are shown in Appendix A, which also gives the values of Cronbach’s α for the variables considered.

We merged the objective data provided by the financial entity with the subjective data from the questionnaire. After removing customers with incomplete information or missing values for key objective variables in the transactional data, we had a final sample of 1,990 customers. The description of the variables that we measured and their descriptive statistics are displayed in Table 2. Appendix B provides the correlation matrix for these variables.

Table 2.

Descriptive Statistics.

Variables		Description	Mean	Standard Deviation
Independent variables	Physical store habit	The level of habit of customer i toward the physical store channel in t0 (between January 2012 and December 2012). This variable has been measured using the following formula: (Number of months that customer i uses this channel in 2012/12) × (Total number of customer-initiated contacts [CICs] in this channel/total number of CICs).	0.37	0.30
	Online habit	The level of habit of customer i toward the online channel in t0 (between January 2012 and December 2012). This variable has been measured using the following formula: (Number of months that customer i uses this channel in 2012/12) × (Total number of CICs in this channel/total number of CICs).	0.18	0.32
	Self-service kiosk habit	The level of habit of customer i toward the self-service kiosk channel in t0 (between January 2012 and December 2012). This variable has been measured using the following formula: (Number of months that customer i uses this channel in 2012/12) × (Total number of CICs in this channel/total number of CICs).	0.15	0.22
Mediating variables	Perceived switching costs	The perception of customer i about the level of perceived switching costs in the relationship measured as the average of three items collected through a survey (from 1 = strongly disagree to 7 = strongly agree) in December 2012 (t1).	4.56	1.73
Mediating variables	Attitudinal loyalty	The perception of customer i about the level of customer attitudinal loyalty with the financial entity measured as the average of 2 items collected through a survey (from 1: strongly disagree to 7: strongly agree) in December 2012 (t1).	5.42	1.64
Dependent variables	Cross-buy	The total number of different products/services that customer i buys/contracts from January 2013 to March 2013 (t2). The company offers 13 different products/services (remote banking, funds, fixed deposits, pension plans, home loan, insurance risk, credit cards, etc.).	3.44	2.21
Dependent variables	Service usage	Total financial requirements (in thousands of euros) of customer i from January 2013 to March 2013 (t2).	181.29	374.64
Control variables	FICs	The number of FICs to customer i from January 2012 to December 2012 (t0; i.e., offers of products/services, promotions, interesting information for customer i, etc.).	0.23	0.25
	CICs	The number of contacts initiated by customer i (excluding complaints) from January 2012 to December 2012 (t0; i.e., informational inquiries about deposits or home loans, connection operations on the internet, inquiries about the prices of shares, etc.).	13.79	24.24
	Relationship duration	The number of years that customer i has been a customer of this financial entity. This variable has been measured in t0.	30.38	14.75
	Cross-buy	The number of different products/services that customer i buys/contracts from January 2012 to December 2012 (t0). The company offers 13 different products/services (remote banking, funds, fixed deposits, pension plans, home loan, insurance risk, credit cards, etc.).	3.49	2.16
	Service usage	Total financial requirements (in thousands of euros) of customer i from January 2012 to December 2012 (t0).	179.76	343.80
	Income	Annual income of customer i measured, from January 2012 to December 2012 (t0), using five categories: (1) salary below €24,000 per year, (2) salary between €24,000 and €35,000 per year, (3) salary between €35,000 and €45,000 per year, (4) salary between €45,000 and €60,000 per year, and (5) salary above €60,000 per year.	2.19	1.22
	Gender	Dummy variable that takes the value 1 for men and 0 for women.	0.53	0.49

Method

To test the proposed conceptual framework and its associated hypotheses empirically, we developed a four-equation seemingly unrelated regression (SUR). The SUR model is a system of linear equations with errors that are correlated across equations for a given individual (Zellner 1962). Using the SUR modeling approach enables more efficient estimations from combining information from different equations, and it alleviates potential endogeneity problems (Autry and Godilic 2010; Ogundari 2014). This model consists of j = 1 … m linear regression equations for i = 1…N individuals.

As we had longitudinal data (a customer observation window of 15 months), in order to respect the causality in the proposed model, we aggregated the data from three different periods: t0, t1, and t2. We included multichannel-related data and customer-level information (including demographics) for the period from January 2012 to December 2012 (t0), customer-relational data from the questionnaire in December 2012 (t1), and customer purchase data that include information about service usage and cross-buy, for January to March 2013 (t2). We also controlled for the effect of additional relevant variables by including the total number of customer-initiated contacts and firm-initiated contacts during t0, the levels of usage and cross-buy in t0, the relationship duration, and a set of demographic variables including income and gender.

The model consists of j = 4 linear regressions, where the first linear regression has perceived switching cost as its dependent variable, the second linear regression explains customer attitudinal loyalty, the third linear regression explains customer usage, and the fourth explains cross-buy. The linear regressions for the SUR model are represented as follows:

S C_{i t 1} = β_{0} + β_{1} p s H_{i t 0} + β_{2} o n H_{i t 0} + β_{3} s s k H_{i t 0} + β_{4} C O N T R O L_{i t 0} + ∊ i t,

A L_{i t 1} = β_{0} + β_{1} p s H_{i t 0} + β_{2} o n H_{i t 0} + β_{3} s s k H_{i t 0} + β_{4} C O N T R O L_{i t 0} + ∊ i t,

U s_{i t 2} = β_{0} + β_{1} S C_{i t 1} + β_{2} A L_{i t 1} + β_{3} p s H_{i t 0} + β_{4} o n H_{i t 0} + β_{5} s s k H_{i t 0} + β_{6} C O N T R O L_{i t 0} + ∊ i t,

C B_{i t 2} = β_{0} + β_{1} S C_{i t 1} + β_{2} A L_{i t 1} + β_{3} p s H_{i t 0} + β_{4} o n H_{i t 0} + β_{5} s s k H_{i t 0} + β_{6} C O N T R O L_{i t 0} + ∊ i t,

where psH_it ₀ represents the level of habit of customer i toward the physical store channel in t0 (between January 2012 and December 2012), onH_it ₀ represents the level of habit of customer i toward the online channel in t0, and sskH_it ₀ represents the level of habit of customer i toward the self-service kiosks channel in t0. SC_it ₁ and AL_it ₁ are the relational variables (perceived switching costs and customer attitudinal loyalty, respectively) measured via the questionnaire, which reflect the level of perceived switching costs and customer loyalty of customer i in t1 (December 2012). Us_it ₂ represents the level of service usage of customer i in period t2, and CB_it ₂ represents the cross-buy behavior of customer i in period t2. CONTROL_it ₀ represents a vector of control variables: CICs_it ₀ represents the number of contacts initiated by customer i in period t0; FICs_it ₀ represents the total number of contacts that customer i receives and customer i’s relationship duration, cross-buy, service usage behavior, income, and gender, all in period t0. Accounting for cross-buy and service usage in t0 provides a further way of addressing endogeneity concerns. Finally, ∊ _it is the error term for customer i in month t. To estimate our model, we used the Stata Version 14 statistical software package.

Findings

Overall, we found strong support for our proposed model, as most of the parameters are significant and point in the expected direction (Table 3). Results confirm that both physical store habit (β = 0.584, p < .05) and online habit (β = −0.751, p < .01) have a significant effect on perceived switching costs. However, we cannot confirm the influence of self-service kiosk habit on perceived switching costs (β = 0.309, p > .10). In addition, physical store habit (β = 0.556, p < .05), online habit (β = −0.719, p < .01), and self-service kiosk habit (β = 0.644, p < .05) significantly influence attitudinal loyalty.

Table 3.

Estimation Results.

Variables	Dependent Variable
	Perceived Switching Costs	Attitudinal Loyalty	Service Usage	Cross-Buy
	Equation 1, R ² = .0549	Equation 2, R ² = .0858	Equation 3, R ² = .2105	Equation 4, R ² = .5253
Intercept	4.162^***	4.649^***	−185.213^***	−0.518^***
Independent variable
Physical store habit	0.584^***	0.556^***	−14.716	2.013^***
Online habit	−0.751^***	−0.719^***	−119.653^**	3.014^***
Self-service kiosk habit	0.309	0.644^***	−174.007^***	2.364^***
Perceived switching costs	—	—	23.527^***	−0.04
Attitudinal loyalty	—	—	−5.024	0.22^***
Control variable
FICs	−0.624^***	−0.669^***	185.691^***	2.172^***
CICs	0.0003^**	0.0003^**	−0.038	0.0004^***
Relationship duration	0.0075^***	0.010^***	−0.457	−0.004^**
Cross-buy	0.089^***	0.153^***	69.255^***	—
Service usage	0.0005^***	0.0003^**	-	0.001^***
Income	−0.034	−0.024	35.463^***	0.171^***
Gender	−0.421^***	−0.568^***	3.306	0.311^***

Note. Significant parameters are highlighted in bold. FICs = firm-initiated contacts; CICs = customer-initiated contacts.

*p < .10. **p < .05. ***p < .01.

Regarding the consequences of our model, Hypothesis 4a was concerned with the relationship between perceived switching costs and service usage, and the results show that perceived switching cost positively and significantly influences service usage (β = 23.527, p < .01), which supports the proposed hypothesis. In line with Hypothesis 4b, attitudinal loyalty does not significantly influence service usage (β = −5.024, p > .10), so Hypothesis 4b is also supported. The opposite happens with cross-buy. We proposed that perceived switching cost does not significantly influence cross-buy, and the results confirm Hypothesis 5a, where perceived switching cost does not affect cross-buy (β = −0.04, p > .10). We can also confirm Hypothesis 5b, as attitudinal loyalty positively and significantly influences cross-buy (β = 0.22, p < .01).

We tested the direct impact of multichannel habit on the behavioral consequences for the financial entity. Online habit (β = −119.65, p < .05) and self-service kiosk habit (β = −174.007, p < .01) significantly influence service usage although physical store habit (β = −14.716, p > .10) does not affect this variable. To conclude, the physical store (β = 2.013, p < .01), online (β = 3.014, p < .01), and self-service kiosk habits (β = 2.364, p < .05) all positively and significantly influence cross-buy.

As we noted above in the Conceptual Framework section, the literature suggests that habits are a strong force affecting behavior and that channel habits can be a better predictor of behavioral responses than other constructs considered in previous research.³ Table 4 summarizes the results of the hypothesis testing. The theoretical and managerial implications of these results are considered in the Discussion section of this article.

Table 4.

Hypothesis Testing Results.

Hypothesis	Channels Considered
Hypothesis	Physical Store	Online Channel	Self-Service Kiosks
Habit influences perceived switching costs	Confirmed (+)	Confirmed (−)	Not confirmed
Habit influences attitudinal loyalty	Confirmed (+)	Confirmed (−)	Confirmed (+)
Perceived switching costs positively influence service usage	Confirmed (+)
Attitudinal loyalty does not influence service usage	Confirmed (no influence)
Perceived switching costs do not influence cross-buy	Confirmed (no influence)
Attitudinal loyalty positively influences cross-buy	Confirmed (+)
Additional tests
Habit influences service usage	No influence	− Influence	− Influence
Habit influences Cross-buy	+ Influence	+ Influence	+ Influence

Mediating Effects

We also analyzed potential mediating effects in our conceptual framework. To increase the understanding of the proposed relationships, we tested whether the central variables (perceived switching costs and attitudinal loyalty) act as mediators in the model. We followed the bootstrapping method with 5,000 subsamples, as proposed by Preacher and Hayes (2008). We used their SPSS, version 22 routine to calculate the total, direct, and indirect effects, and the 95% confidence interval (CI) for the mediating variables. When an interval for a mediating effect does not contain zero, the indirect effect is significantly different from zero with a 95% CI (Preacher and Hayes 2008). Taking into account the CIs obtained, when the value zero is not contained in a path, we can confirm that the indirect effect is statistically significant.

Results confirm that attitudinal loyalty acts as a mediator in the relationship between physical store habit and cross-buy (CI [0.0965, 0.2504], significant at 95%). Attitudinal loyalty also acts as a mediator in the relationship between online habit and cross-buy (CI [−0.2343, −0.0855], significant at 95%) and in the relationship between self-service kiosk habit and cross-buy (CI [0.0877, 0.2846], significant at 95%). Thus, attitudinal loyalty is a key mediating variable in the proposed model because it enables customer multichannel habits to lead indirectly to positive customer behaviors. These behaviors, which include cross-buy purchases, may be of great profitability to the firm.

Results also show that perceived switching cost plays a mediating role in the relationship between physical store habit and cross-buy (CI [−0.1567, −0.0091], significant at 95%). This result reflects the indirect effect of physical store habit on cross-buy through perceived switching cost.

Discussion

Theoretical Implications

Despite the importance of habit in consumer behavior, service research on multichannel customer management has not yet considered this perspective (Blut, Wang, and Schoefer 2016; Liu-Thompkins and Tam 2013; Marinova et al. 2017; Melis et al. 2016; Rafaeli et al. 2017; Shah, Kumar, and Kim 2014; Singh et al. 2017). Thus, although customer habit is a mechanism that strongly affects human behavior, the present research is one of the first studies to integrate customer habits into the multichannel service literature. By building on habit and by combining it with the different and distinguishing capabilities of the different channels, this study provides an understanding of the consequences of channel habits in service settings (Cambra-Fierro et al. 2016; Huang and Rust 2018; Kushwaha and Shankar 2013; Verhoef, Kannan, and Inman 2015). It considers, in an innovative way, two key perceptual and intentional measures (perceived switching costs and attitudinal loyalty) that are proposed to mediate the relationships between channel habits and customer behaviors (Ansari, Mela, and Neslin 2008; Liu-Thompkins and Tam 2013; Shankar, Smith, and Rangaswamy 2003).

The marketing literature has emphasized the importance of the attitudinal consequences of channel usage in explaining subsequent customer behavior (Balasubramanian, Raghunathan, and Mahajan 2005; Neslin et al. 2006; Neslin and Shankar 2009). However, empirical studies have failed to integrate perceptual, intentional, and behavioral measures into the study of the consequences of channel customer behavior (Ansari, Mela, and Neslin 2008; Balasubramanian, Raghunathan, and Mahajan 2005; Bolton, Lemon, and Verhoef 2004; Dholakia et al. 2010; Neslin et al. 2006; Shankar, Smith, and Rangaswamy 2003). In this respect, this study is the first to focus on perceived switching costs and attitudinal loyalty, on two central subjective measures (customer perceptions and intentions), and on explaining how channel habits can trigger behavioral outcomes. The results show that physical store habit leads customers to perceive switching costs as higher and increases their levels of attitudinal loyalty. Customer habit through the online channel makes customers perceive switching costs as lower and leads to lower levels of attitudinal loyalty. Finally, self-service kiosk habit increases attitudinal loyalty because the physical presence of the kiosk generates brand awareness and enriches the brand experience.

With respect to the influence of perceived switching cost and attitudinal loyalty on behavior, we obtained interesting insights. First, perceived switching cost affects service usage only. This result is particularly interesting because it reveals that high perceived switching costs will lead customers to increase the intensity of service usage and the volume of incomes with the same provider, but they do not necessarily drive an increase in the number of different products acquired. This result may be due to customers not knowing yet what switching costs any new products will have. Second, attitudinal loyalty influences cross-buy only. This result was expected, given that loyal customers prefer to continue doing business with the same provider. In such cases, attitudinal loyalty will lead customers to increase the number of products/services with the same company, but these new products do not necessarily drive a significant increase in service usage.

We have analyzed not only the impact of perceived switching costs and attitudinal loyalty on behavior but also the effect of channel habit on customer behavior (directly and indirectly through these two perceptual and intentional measures). Surprisingly, we found a differential impact of channel customer habit on service usage and cross-buy. These novel findings complement previous studies of multichannel customer behaviors (Cambra-Fierro et al. 2016; Kushwaha and Shankar 2013; Verhoef, Kannan, and Inman 2015). In particular, the empirical findings reveal that customer habit in respect of the three channels (physical stores, online channels, and self-service kiosks) has a direct and positive effect on cross-buy. This result indicates that a habit in each channel can lead to an increase in the number of products/services acquired. In the physical store, customer habit may increase cross-buy not only because of the wide range of products/services available but also because channels that create stronger social and economic bonds, such as personal selling or retailer channels, will have higher levels of cross-buy (Bolton, Lemon, and Verhoef 2004). In the online and self-service kiosk channels, cross-buy can easily arise because of their convenience, availability, and accessibility (Dholakia et al. 2010). The wide range available can trigger the acquisition of new products/services.

In general, each channel may influence this cross-buy in a different way. Depending on the level of perceived risk, some channels will be more appropriate than others. For instance, in the physical store, frontline employees can accurately explain to the customer the specificities of a new product or service, such as a mortgage loan or a pension plan, and this personalized attention can result in the customer making an immediate decision. However, the online channel or self-service kiosks can be better suited to products with a lower level of perceived risk, such as cell phone recharges, balance inquiries, or collection of theater tickets.

Our findings also reveal that customer habits in the online channel and in self-service kiosks negatively impact service usage. This result is in line with the literature (Avery et al. 2012; Bolton, Lemon, and Verhoef 2004; Patrício, Fisk, and Cunha 2008), as the lack of physical presence increases customer uncertainty about the company behind the sale and increases the return risk. The online channel is not considered suitable for complex financial services, which may lead to a decrease in service usage. For self-service kiosks, there is also a perception of limited usefulness, owing to the limited operations available, with no access to operations that can lead to an increase in service usage (e.g., savings and loans). Convenience and accessibility may lead to the use of more financial services, but they do not necessarily increase the volume of usage (Patrício, Fisk, and Cunha 2008).

Finally, channel habits also influence customer behaviors indirectly through perceived switching costs and attitudinal loyalty. The mediating effects identified show that physical store habit, online habit, and self-service kiosk habit lead through attitudinal loyalty to cross-buy. Thus, for attitudinally loyal customers, any multichannel habit will drive cross-buy. Perceived switching costs mediate the relationship between physical store habit and cross-buy only. Customers who are used to operating with a firm at a physical store perceive switching costs as higher; if they need a new product or service, they will be more likely to continue doing business with the company and to develop cross-buy.

Managerial Implications

Habits are stable, frequently repeated, and known to be profitable for firms, as they drive customer purchases (Liu-Thompkins and Tam 2013; Mark et al. 2019). If companies misunderstand customer habits, their investments in marketing may become inefficient, impacting on the wrong behavior, the wrong channel, or the wrong customer segment (Herziger and Hoelzl 2017). Managers should therefore carefully monitor and develop proactive strategies to identify and manage channel habits to promote the development of profitable relationships. For instance, the role of frontline employees is crucial; they can teach customers to use, and benefit from the convenience of, each channel as a first element in fostering habits. Firms can also create incentives through gaming strategies in which customers accumulate points each time they use the mobile app (e.g., watching an explicative video or correctly answering questions about the company’s products/services) or can increase their points allocation if they invite friends to use the app. For instance, this strategy has been adopted by the financial entity Banco Bilbao Vizcaya Argentaria [BBVA], through the “BBVA Game.” Because habits are built on the frequency and stability of channel usage, firms can manage habits by encouraging regular interactions under temporal stability. As an example, loyalty programs have been shown to promote the development of habits by encouraging customers to interact with the firm repeatedly in a stable and controlled context (i.e., the program; Henderson, Beck, and Palmatier 2011; Wood and Neal 2009). Currently, gaming strategies seem to be very beneficial for firms (Koivisto and Hamari 2019).

A major managerial implication of our findings is that physical stores continue to play a key role in multichannel relationships with customers. In spite of the myriad of new technology service interfaces such as chat bots and robots, our results indicate that habitual usage of the physical store is still central to the promotion of loyalty, the deepening of the relationship with customers, and ultimately increasing service usage. As such, although automated interfaces that make use of new technology may offer new opportunities to increase interactions and cross-buy, managers should actively pursue strategies to foster physical store habit. As channel habit formation depends on frequency of past usage behavior in stable contexts, firms may try to maintain store context stability to strengthen habit. For example, given the key role of frontline employees in the relationship with customers, firms trying to foster store habit may want to increase employee retention, which has been found to influence the customer-firm relationship (Hogreve et al. 2017). Firms can also promote store habit by actively creating more frequent opportunities for customers to interact with the store, which may require integration of new technologies and redesign of the store concept. These results are in tune with recent developments in retailing. This is especially important given that usage of technology-enabled channels is on the rise, and new technology developments are predicted to dramatically change organizational frontlines in terms of interfaces and ways in which customers and firms interact (Singh et al. 2017). While estimates predict that 25% of shopping malls will shrink in the near future, Amazon.com, the giant of online commerce, has entered physical retail space and is reported to be planning to open 2,000 fresh grocery stores in the next 10 years (Bell, Gallino, and Moreno 2018). According to these authors, new-generation physical stores are quite different from the traditional ones: They are showrooms, “zero-inventory” stores, where customers benefit from a high-touch, high-tech service.

Finally, in spite of the immense opportunities generated by new technologies, our study shows that developing a strong online habit can hamper customer relationship with the firm, with subsequent negative impacts on perceived switching costs, loyalty, and service usage. Firms should therefore carefully balance the efficiency and cross-buy gains of automatic channel habit with their negative impact on customer loyalty and sales. To this end, firms may develop active strategies to reduce online habit strength, for example, by periodically providing incentives for customers to break the online routine and use the store; this is likely to reduce online context stability and promote more frequent interactions with the physical store.

Limitations and Further Research

This empirical study has demonstrated the importance of analyzing channel habits as a key antecedent of a set of relational customer variables that led to other behavioral outcomes during an observational and dynamic period of 15 months. However, the study is not without limitations. First, we developed our research in the context of the financial services industry, and the habit development under study is tied to the channels of a specific provider. This industry has specific characteristics, as it requires a high degree of customer involvement for financial products with a high level of perceived risk (such as mortgages, stocks, savings plans, and pension plans; Cambra-Fierro et al. 2018). This contrasts with other recurrent products and services with very low risk (such as withdrawal of funds and checking an account balance). In the financial services industry, risk perceptions result in anxiety and stimulate the development of risk reduction strategies, such as searching for information, which consequently require a higher level of customer involvement (Cambra-Fierro et al. 2018; Shun-Yao and Ching-Nan 2015). Hence, it would be of value for further research to replicate our model in other contexts to compare the results and to extract solid conclusions for the literature. Second, although we considered three different customer channels (physical stores, online channels, and self-service kiosks), further research could usefully include mobile channels or smartwatches as key novel customer channels for any type of service operation. As these channels are relatively new, we were not able to include them in our empirical analysis. Third, because we measured customer perceptions (perceived switching costs and attitudinal loyalty) using a survey, we transformed longitudinal customer data (channel habit, service usage, and cross-buy) into cross-sectional data (including the averages of each period of time in the database). For future research aiming to replicate our study, it would be advisable to collect more subjective data, taking into account all (monthly) customer information. Fourth, this research has not considered customer characteristics such as shopping goals, preferences, channel usage experience, or the need for touch. These variables could be interesting moderators in the conceptual framework, as they may shape the impact of channel habits on customer perceptions and behavioral responses. Future studies could add to the model’s customer characteristics to increase the contribution of the research. Finally, we acknowledge that, despite the merits of the data and the approach adopted, self-selection in channel usage may still be present. We suggest that future studies perform a cleaner test (e.g., using an experimental design) for the impact of channel usage on behavior and profitability.

We encourage future research to investigate habits using a different methodological approach to examine the robustness of the findings. As noted by Labrecque and Wood (2015), other measures of habit, such as the Self-Report Habit Index or direct tests of cognitive associations, may be better suited for survey research or experimental studies. Finally, future research could usefully pay attention to the drivers of habits. Here, we have considered whether channel habits could lead to perceived switching costs, but perceived switching costs could, for example, also lead to channel habits.

Supplemental Material

Supplemental Material, Executive_summary - Channel Habits and the Development of Successful Customer-Firm Relationships in Services

Supplemental Material, Executive_summary for Channel Habits and the Development of Successful Customer-Firm Relationships in Services by Jesús Cambra-Fierro, Iguácel Melero-Polo, Lia Patrício and F. Javier Sese in Journal of Service Research

Footnotes

Appendix A

Table A1.

Scales Used From the Literature.

Perceived Switching CostsAdapted from Lam et al. (2004) and Burnham, Frels, and Mahajan (2003)	Cronbach’s α
I keep the relationship with this company because both the company and I make a profit from it.	.913
I want to keep the relationship with this company because establishing a new relationship needs more effort.
I want to keep using this company because it is difficult to find another firm like it.
Attitudinal Loyalty Adapted from Auh et al. (2007)	Cronbach’s α
I am a loyal customer to this company.	.874
In the future, I will continue using the services of this company.	.874

Appendix B

Table B1.

Correlation Matrix.

Variable	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1. Physical store habit	1
2. Online habit	−0.480*	1
3. Self-service kiosk habit	−.215*	−.240*	1
4. Perceived switching costs	.146*	−.132*	.049*	1
5. Attitudinal loyalty	.139*	−.118*	.105*	.751*	1
6. Service usage (t2)	.202*	.054*	−.064*	.080*	.082*	1
7. Cross-buy (t2)	.173*	.334*	.050*	.077*	.133*	.414*	1
8. FICs	.346*	.027	−.063*	.023	.045*	.369*	.508*	1
9. CICs	−.200*	.664*	−.109*	−.023	.004	.120*	.405*	.159*	1
10. Relationship duration	.189*	−.182*	−.026	.089*	.123*	.034	−.013	.142*	−.095*	1
11. Service usage (t0)	.223*	.061*	−.061*	.086*	.090*	.811*	.431*	.414*	.145*	.012	1
12. Cross-buy (t0)	.174*	.345*	.039	.045*	.097*	.395*	.966*	.516*	.407*	−.015	.431*	1
13. Income	.087*	.176*	−.015	.033	.092*	.295*	.401*	.371*	.298*	.084*	.349*	.404*	1
14. Gender	.177*	.102*	−.034	−.086*	−.123*	.128*	.253*	.246*	.145*	−.038	.154*	.273*	.041	1

Note. Our discriminant validity analyses demonstrate that switching costs and attitudinal loyalty are two distinct constructs. With the aim of examining the discriminant validity in a comprehensive and rigorous manner, we used four procedures that are widely accepted in the literature of discriminant validity (Farrell 2010; Franke and Sarstedt 2019; Henseler, Ringle, and Sarstedt 2015; Shiu et al. 2009; Voorhees et al. 2016; ): (1) the constrained phi approach, (2) the overlapping confidence intervals approach, (3) the cross-loadings method, and (4) the Fornell–Larcker criterion. FICs = firm-initiated contacts; CICs = customer-initiated contacts.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the financial support for the research, authorship, and/or publication of this article: The authors appreciate the financial support received from the projects ECO2017-83993-P (MICINN, FEDER), SS54_17R (Gobierno de Aragón and Fondo Social Europeo), and JIUZ-2018-SOC-11 (Fundación Ibercaja). Jesús Cambra-Fierro, F. Javier Sese, and Iguácel Melero-Polo are members of the research group Generés (). In addition, Iguácel Melero-Polo acknowledges financial support provided through “Estancias de movilidad en el extranjero “José Castillejo” para jóvenes doctores” (CAS17/00105) and from “Programa CAI-Ibercaja de Estancias de Investigación” to develop part of the research at the University of Porto. The authors also want to show their gratitude to the collaborating bank for providing the data for the analyses. The authors’ names appear in alphabetical order.

ORCID iD

F. Javier Sese

Supplemental Material

Supplemental material for this article is available online.

Notes

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