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• • 6.8k Downloads • Abstract Customer equity drivers (CEDs)—value equity, brand equity, and relationship equity—positively affect loyalty intentions, but this effect varies across industries and firms. We empirically examine potential industry and firm characteristics that explain why the CEDs–loyalty link varies across services industries and firms in the Netherlands. The results show that (1) some previously assumed industry and firm characteristics have moderating effects while others do not and (2) firm-level advertising expenditures constitute the most crucial moderator because they influence all three loyalty strategies (significant for value equity and brand equity; marginally significant for relationship equity), while three industry contexts (i.e., innovative markets, visibility to others, and complexity of purchase decisions) each influence two of the three loyalty strategies. Our results clearly show that specific industry and firm characteristics affect the effectiveness of specific loyalty strategies. With markets becoming increasingly competitive, firms are devoting considerable attention to the issue of loyal customers—specifically, how to attain them and maximize their value (e.g., Watson et al.

Among the many studies that have tried to better understand customer loyalty, one of the most influential is the study of Rust et al. ( ) and their customer equity model. Their follow-up article (i.e., Rust et al. ) further solidifies the value of said model among academics. The Rust et al. ( ) model proposes that three customer equity drivers (CEDs) are crucial components of loyalty intentions: value equity (VE), brand equity (BE), and relationship equity (RE).

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In turn, research has provided convincing support for the proposed impact of CEDs; for example, Ou et al. ( ) generalize this positive link to 13 services industries. Despite the positive support for a CEDs–loyalty link, studies have widely indicated that loyalty strategies are not equally effective across industries and firms (e.g., De Haan et al.; Eisenbeiss et al.; Kumar et al.; Rust et al. ( ) empirically show that there are substantial variations in the effects of CEDs across industries and firms. However, explanations for cross-industry and cross-firm variations remain scarce.

As Table shows, multiple studies have investigated the cross-customer variation in the effectiveness of customer loyalty determinants (i.e., satisfaction, commitment, and trust). They examine, for example, demographics, relationship length, switching costs, and consumer confidence as customer-level moderators to explain the variation (e.g., Mittal and Kamakura; Ou et al. Studies have also investigated industry-level (e.g., Seiders et al. ) or firm-level (i.e., Verhoef et al. ) moderators. Overall, however, few industry- and firm-level moderators have been systematically identified and empirically examined. This constitutes a research gap in the customer relationship management (CRM) literature.

Examining this gap is crucial because situational theory indicates that identifying the contexts relevant to individual reactions is as important as understanding individual cognitive processes; it also assumes that customers in different contexts employ different comparison standards in decision making (Eisenbeiss et al.; Longshore and Prager ) Thus, our main objective is to develop a framework of industry and firm characteristics as moderators and empirically examine the extent to which they explain cross-industry and cross-firm variations on the link between CEDs and loyalty intentions. Studies Main effects Moderators VE BE RE Customer characteristics Firm characteristics Industry characteristics Country characteristics Mittal and Kamakura ( ) ✓ ✓ Verhoef et al. ( ) ✓ ✓ ✓ Nijssen et al.

( ) ✓ ✓ ✓ Gustafsson et al. ( ) ✓ ✓ ✓ Seiders et al. Install Deb Package On Arch Linux Wiki.

( ) ✓ ✓ ✓ Bell et al. ( ) ✓ ✓ Cooil et al. ( ) ✓ ✓ Chandrashekaran et al. ( ) ✓ ✓ Verhoef et al. ( ) ✓ ✓ ✓ ✓ Voss et al.

( ) ✓ ✓ ✓ Eisenbeiss et al. ( ) ✓ ✓ ✓ Frank et al. ( ) ✓ ✓ ✓ ✓ ✓ ✓ Nagengast et al. ( ) ✓ ✓ Ou et al. ( ) ✓ ✓ ✓ ✓ Keiningham et al. ( ) ✓ ✓ Summary of previous studies 14/15 a 3/15 a 7/15 a 12/15 a 2/15 a 5/15 a 1/15 a Current study ✓ ✓ ✓ ✓ ✓ ✓ ×. AIn these fractions, the denominator refers to the number of previous studies included in Table 1; the numerator refers to the number of topics studied in the previous studies.

Take VE as a main effect for illustration. Fourteen out of fourteen studies have examined VE as a main effect Rust et al.

( ) initially discussed some industry characteristics as moderators. They argued that VE is more important for homogeneous markets, BE for visible goods/services, and RE for contractual settings. Building on their discussion and the CRM literature, we derive multiple important industry- and firm-level moderators and theorize their impact on the link between CEDs and loyalty. In the following section, we further detail how we select these moderators and formulate the hypotheses.

Next, we use three data sources to examine the impact of these moderators: (1) a large-scale customer dataset (including 8924 customer responses of 95 leading companies across 18 services industries in the Netherlands), (2) an expert survey consisting of 178 responses from 88 managers and business consultants, and (3) external sources, including data from ACNielsen on firms’ annual advertising expenditures and from firms’ annual revenue reports. We use a multi-level model with four levels to estimate the proposed moderating effects. Overall, this study provides three contributions to the extant literature. The first contribution pertains to a research gap in theorizing and testing the contextual moderators in CRM. Specifically, we theoretically explain cross-industry and cross-firm variations of the effectiveness of CEDs by identifying a framework of multiple industry and firm characteristics. Consequently, we empirically show that the effects of CEDs are indeed influenced by some theoretically assumed industry and firm characteristics.

For example, the effect of RE decreases in innovative markets and for heavy advertisers. This is a significant contribution in the CRM literature, which currently lacks a systematic investigation of industry- and firm-level moderators’ impacts (Kumar et al.; Rust et al. The second contribution is on the methodological level.

We use multiple data sources, including customer data, expert data, and secondary data, which allow us to explore how customers react to different contexts and to achieve a better understanding of how these reactions ultimately influence the role of CEDs on customers’ loyalty decisions in specific contexts. We also control for customer-level moderators while investigating the joint moderating effects of industry- and firm-level characteristics on the link between CEDs and loyalty intentions. To our knowledge, no studies have simultaneously included all three levels as moderators.

This is a critical gap because prior research has indicated that customers’ decision making is influenced by their personal characteristics and also their economic system (Johns; Molloy et al. The third contribution is managerial. Our findings help managers across different services industries determine which industry and firm characteristics actually have a contingent impact and which loyalty strategies are most/least effective in a specific context.

At the industry level, we provide more insight into how firms should adapt CEDs to their industry environment to enhance loyalty intentions. For example, while firms are able to use RE to increase loyalty intentions by 15% on average, this effect decreases to 7.6% in innovative markets. At the firm level, we provide more insight into how firms should effectively combine CEDs and their specific firm characteristics. For example, the average effect (15%) of using RE decreases to 11.4% for heavy advertisers. A study that closely parallels our own is that of Ou et al.

( ), though our work differs in several ways. Specifically, we have a different aim (moderating role of industry and firm characteristics vs.

Moderating role of consumer confidence only), simultaneously incorporate moderators at several levels (multiple industry-, firm- and customer-level moderators vs. Only one customer-level moderator), and apply multiple datasets. We also adopt a new consumer survey, an expert survey, and external sources and provide more nuanced insights relevant for managers. Theoretical background and hypotheses. Moderators Step 1 Step 2 Step 3 Step 4 Industry-level moderators from Rust et al. : not considered Bold: the moderators chosen for this manuscript In the initial step, we critically evaluated Rust et al.’s ( ) presumed moderators and their testability.

First, customer involvement is more of a customer- than an industry-level moderator and has already been tested in prior studies (e.g., Homburg and Giering ). Thus, we included customer involvement in our model as a customer-level control variable. Second, some moderators tend to be relevant to RE. For example, as Rust et al. ( ) argue that customers receive large benefits from RE, the implication then follows that firms find wisdom in steering RE and providing large benefits (i.e., through loyalty programs; Dorotic et al. ) to maintain loyalty. Furthermore, a strong brand community between customers tends to involve customer commitment (Bagozzi and Dholakia ), which is one component of RE (Rust et al.

Therefore, we do not include these two suggested moderators in the model. Third, some moderators are rather difficult to test and/or are not applicable in consumer services industries (e.g., business-to-business [B2B] settings), and our focus is on consumer markets. For example, the importance of recycling seems more important in product industries. This issue is also common among inter-generational brands (e.g., cars). Thus, we initially selected seven of Rust et al.’s ( ) presumed moderators: (1) the presence of differences between competitors, (2) the importance of customer learning, (3) the difficulty of evaluating quality prior to consumption, (4) innovative markets, (5) contractual settings, (6) visibility to others, and (7) the complexity of purchase decisions. In the second step, we considered the existing literature on CRM. We mainly found studies that discuss the moderating role of competitive intensity and market dynamics (e.g., Seiders et al.; Voss et al.

), which are also included. In the third step, we critically evaluated the resulting nine industry-level moderators to determine any theoretical overlap among them (Evans ). For example, competitive intensity and the presence of differences between competitors are strongly related, as competition is usually more intense in industries with homogeneous goods/services (Menguc and Auh ). As competitive intensity is a frequently studied and well-established industry moderator, we focused on that factor. Next, the difficulty of evaluating quality prior to consumption and the importance of customer learning are related. If customers face problems with evaluating goods/services, they eventually learn about the performance of these goods/services during consumption.

Given this theoretical overlap, we focused on the difficulty of evaluating quality prior to consumption, as this concept is more clearly discussed in the literature (e.g., Fischer et al. However, this moderator may have less impact on existing customers, who are the main respondents of this study. Therefore, we used the moderator as a control variable. Finally, we expect that innovative markets and market dynamics are two sides of the same coin: firms in dynamic markets rapidly introduce innovative goods/services to meet sudden changes in customer demand (Slater and Narver ). In other words, rapidly introducing innovative goods/services is a phenomenon of innovative markets. Thus, we chose to focus on innovative markets, as this moderator is discussed more heavily in Rust et al.’s ( ) framework. In summary, we chose to investigate five industry moderators: (1) innovative markets, (2) contractual settings, (3) visibility to others, (4) complexity of purchase decisions, and (5) competitive intensity.

Researchers maintain that contexts at the meso- and micro-levels play a role in influencing the relationships we examine (Bamberger ). In addition to industry characteristics, prior research has uncovered heterogeneity between firms regarding the effect sizes of CEDs on loyalty intentions (Ou et al. In the fourth step, given industry characteristics as the meso-level context influencing customer perceptions of loyalty strategies, we assume that firm characteristics lie at the micro-level; therefore, we also included firm characteristics in our framework. We chose to include two firm characteristics as crucial marketing variables: (1) market position and (2) firm-level advertising expenditures. Many firms strive to be market leaders, while they also use advertising to differentiate themselves from competitors (e.g., Fischer et al.

Regarding market position, research assumes that market leaders have more advantages over followers. For example, when market leaders promote goods/services, they have a greater influence on competitors/followers and draw followers’ market share. In turn, followers’ promotions do not easily influence leaders’ market share (Hoeffler and Keller ). However, there is a strong debate within CRM about whether brands with a high market share can actually benefit from loyalty-based strategies (e.g., Dowling and Uncles; Ehrenberg et al. As a result, how to choose effective context-specific loyalty strategies is still an unresolved puzzle for both market leaders and followers. Regarding firm-level advertising expenditures, market power theory argues that heavy advertisers are able to improve brand recognition and reputation to charge higher prices and maintain a given scale of sales (e.g., Kaul and Wittink ), assuming a strengthened effect of BE and weakened effect of VE for heavy advertisers.

Despite BE and VE, customer relationships are crucial for building strong ties for services industries (e.g., Mende et al. However, little is known about how firm-level advertising expenditures might influence the effect of CRM. As a result, because decisions about the amount of money to spend on advertising influence firm performance (Fischer et al. ), we take an initial step of empirically examining the interaction effects of firm-level advertising expenditures and CEDs on loyalty intentions.

Drivers Gateway Mt3422 Xp. 1 Conceptual model Next, we hypothesize the impact of the industry and firm characteristics on the link between CEDs and loyalty intentions. Drawing on multiple theories in the CRM literature, we develop the hypotheses by exploring how customers react to these contexts and theorizing how these reactions, in turn, influence the role of CEDs on customers’ loyalty decisions in specific contexts. We did not hypothesize the moderating impact of the industry and firm characteristics on all CEDs, as some impacts are rarely theorized or mentioned in the CRM literature. Having no strong underlying theories or empirical findings may give uncertain directions of the moderating impact. As such, we left some moderating impacts open as an empirical question. For example, prior studies have mainly found a weakened effect of VE in competitive industries and paid little attention to the impact of competitive intensity on the effects of BE and RE (e.g., Seiders et al.; Voss et al. Thus, we have little idea of how competitive intensity moderates the effects of BE and RE.

On the one hand, we might surmise that BE and RE are more important in competitive industries, as both are difficult to imitate and should be important differentiators for firms in competitive industries (Rust et al. On the other hand, we might assume that BE and RE are less important in competitive industries, as competitors intensively react to and imitate each other’s marketing strategies (Gatignon ), implying that the strength of BE and RE as differentiators could decrease. Similarly, while there is sufficient evidence of the moderating impact of innovative markets on the effects of VE and BE, we do not know how innovative markets moderate the effect of RE on loyalty intentions. On the one hand, the effect of RE might be strengthened in innovative markets. Customers perceiving high RE are likely to trust firms, which decreases customer uncertainty of the goods/services performance in innovative markets (Palmatier et al. On the other hand, the effect of RE might be weakened in innovative markets.

Customers in innovative markets constantly search for new services and tend to pay more attention to value rather than their relationships with the firm (De Luca and Atuahene-Gima ). In addition, regarding contractual settings, in their framework, Rust et al. ( ) mention its moderating impact only on RE, not on VE or BE. Industry characteristics. Competition is usually more intense in industries with homogeneous rather than heterogeneous goods/services (Menguc and Auh ). When the offerings of goods/services are virtually the same, VE is difficult to build and becomes the basic requirement of all firms operating in competitive industries (Reimann et al.; Rust et al. As a result, firms may be less likely to benefit from implementing VE because the perceived differences in VE are so tiny that customers can hardly use VE to differentiate between firms.

Customers thus may pay less attention to VE when they make loyalty decisions, implying that the effect of VE is weaker in competitive industries (e.g., Seiders et al. We thus formulate the following hypothesis for VE.

Innovative markets Innovative markets tend to have more new ideas, a higher level of innovative activities, and a larger amount of R&D expenditures than do less innovative markets (Pleatsikas and Teece ). As a consequence, firms in innovative markets frequently introduce new goods/services to the market (Pleatsikas and Teece ) to improve customers’ lives through better quality, usefulness, and ease (e.g., Hauser et al. )—all components of VE. Customers in innovative markets thus expect better value from new goods/services, implying that VE is a critical criterion in particular for these customers in their decisions to purchase new goods/services. As such, we expect that VE is more important for customers in innovative markets. Regarding BE, customers perceive uncertainty and risks in purchasing new goods/services (Littler and Melanthiou ).

For example, customers are uncertain about information regarding new goods/services and choosing among alternatives (Urbany et al. BE thus might be more important in innovative markets. Erdem and Swait ( ) indicate that BE, such as strong and innovative brands, functions as a credibility signal, reducing information search costs and perceived risks in innovative markets. Together, we expect that VE and BE are more important for customers in innovative markets. Contractual settings In the CRM literature, the distinction between contractual and non-contractual settings is deemed important because customers may behave differently if they are contractually bound to a specific firm (Fader and Hardie ).

In a contractual setting, customers and firms have an agreement on terms and conditions stated in the contract and the agreement is valid for a period of time (Gulati ). Customers in contractual settings tend to be locked in during the contractual period, but customers in non-contractual settings have the freedom to buy goods/services simultaneously from multiple firms (Burnham et al.; Wirtz et al. Because of the assumed behavioral difference between contractual and non-contractual settings, in accordance with Rust et al. ( ), we expect that RE is a more important strategy for firms in contractual than in non-contractual settings. RE is a way to “glue” customers to the firm.

Contractual relationships lock customers in, providing the firm with more opportunities to glue existing customers to the firm because the firm has a direct connection with customers and thus knows exactly who they are and can collect more information about them. For example, firms may provide specific and personalized offerings (e.g., loyalty programs, affinity programs, special recognition and treatment, community-building programs, knowledge-building programs).

Customers thus perceive the uniqueness of RE provided by contractual firms, which helps firms create unique relationships with customers (Bowen and Jones ) and increases customer attachment/commitment to postpone the termination of the contract (Rust et al. Evidence indicates that specific relationship constructs, such as commitment, have an effect on loyalty, particularly in contractual settings (e.g., Verhoef ). Compared with contractual settings, firms in non-contractual settings face more problems in identifying customers and collecting sufficient information about them.

Consequently, these relationship-building strategies are less effective in non-contractual settings (e.g., Dowling and Uncles ). In summary, we expect that RE is a stronger strategy to enhance loyalty intentions in contractual than non-contractual settings. Visibility to others Visibility to others is the extent to which others notice customers’ use of goods/services (Fisher and Price ). Social comparison cue theory proposes that when people notice social comparison cues (e.g., visible goods/services), their public self-consciousness tends to be high, and thus they are concerned about what others think of them (Bearden and Rose; Fisher and Price ).

If so, customers are likely to pay more attention to publicly noticeable attributes/elements of visible goods/services, as other people’s perceptions of what one uses or buys is important in this context. This indicates that if CEDs are perceived as being publicly noticeable, they should become more important for customers and, thus, for their loyalty intentions. Specifically, VE is customers’ objective assessments of the value of goods/services (Rust et al.

The Oxford Learner’s Dictionary defines objectivity as “the fact of not being influenced by personal feelings or opinions but consider only facts that can be proved,” which implies that VE tends to be assessed by facts presented (e.g., quality and prices of the goods/services). This indicates that the value of goods/services is commonly shared, meaning that the majority of other customers can easily recognize the value; as such, VE tends to be publicly noticeable. Consequently, the notion of public recognition implies that customers may pay more attention to VE in visible goods/services. BE is even more publicly noticeable than VE, acting as a symbol of social status and identity (Rust et al. To maintain or indicate their perceived status and identity, customers are concerned about how others think of which brand they use or buy.

As a result, customers tend to consider BE more relevant in decision making when the usage of goods/services is more visible (Fischer et al.; Rust et al. This behavior is rooted in the need to make a good impression (Bearden and Rose ) and to be accepted by desired reference groups (Bearden and Etzel ). Finally, RE is less likely to be publicly noticeable than VE and BE because the interaction between customers and the firm is embedded in customers’ minds (Aurier and N’Goala ). For example, some stores warmly greet customers or actively provide help from frontline staff as tools for building customer relationships.

Loyalty programs are also a popular customer relationship tool, providing customers useful customized coupons or special offers. The interactions with the frontline staff in the stores are meant to improve customer attachment; the offers provided by the loyalty program are meant to inform customers that stores understand their needs.

Customer attachment or knowledge of needs is likely to be experienced and recognized by customers themselves only, as neither aspect can be easily observed by many other customers and thus is less likely to be used for social comparison purposes. Thus, we expect the following. Complexity of purchase decisions The complexity of purchase decisions is characterized by an ongoing and motivated cognitive process in which multiple important sources of information are integrated to produce an outcome (Wood and Bandura ). That is, customers undergo a more elaborate decision process, which may lead them to evaluate the “whole package” of the offering more intensively. If so, we expect that all three CEDs are crucial for customers in the context of purchase decision complexity, as CEDs are relevant to most of the important firm characteristics perceived by customers and may help customers reduce the complexity of purchase decisions (Rust et al.; Vogel et al.

Specifically, Rust et al. ( ) propose that VE is more important for customers when purchase decisions are complex, as customers tend to evaluate the components (i.e., price, quality, and convenience) of VE carefully to derive the most utility. Regarding BE, customers encountering complex purchase decisions spend more time in collecting information to decrease risks of the future performance of goods/services (Wood and Bandura ). BE provides signal credibility, which may help customers reduce the complexity of purchase decisions, as credibility decreases search costs and also guarantees the quality of goods/services (e.g., Erdem et al. Last, RE creates trust in firms (Rust et al. ), which strengthens customers’ beliefs in firms’ beneficent behavior in the future (Aurier and N’Goala ) and thus may also increase the importance of RE in complex purchase decisions. Thus, we formulate the following hypotheses.

Market position We use the term market position to examine how market leaders and followers differ in terms of the effectiveness of CEDs on loyalty intentions. Market leaders outsell market followers and have the largest percentage of market share in the corresponding industries (Hoeffler and Keller ). We empirically explore the moderation effect of market position, rather than formulating hypotheses, as double jeopardy theory (e.g., Ehrenberg et al. ) and information accessibility theory (Feldman and Lynch; Knapp et al. ) propose opposite moderation effects of market position. The former proposes that CEDs are less important for market leaders, and the latter proposes that they are more important. Specifically, in the customer loyalty literature, research has discussed the link between market share and customer loyalty (e.g., Ehrenberg et al.

Researchers have empirically demonstrated the existence of the double jeopardy phenomenon: The key idea is that market leaders (market followers) have more (fewer) buyers who are also more (less) loyal (Ehrenberg et al. One implication of this empirical regularity is that market leaders should put low expectations on their marketing programs’ ability to influence loyalty intentions because their customers already have strong loyalty intentions. In other words, market leaders are more likely to encounter the ceiling effect than market followers when investing in marketing strategies. As a result, double jeopardy theory proposes that the CEDs–loyalty link becomes weaker for market leaders. In contrast, information accessibility theory proposes that the CEDs–loyalty link is stronger for market leaders than for market followers.

This theory argues that the ability to access information is crucial for customers as input to judgment and decisions (Feldman and Lynch; Knapp et al. One advantage of being a market leader is that customers tend to have more knowledge of market leaders than followers (Hoeffler and Keller ). More knowledge increases information accessibility, and thus customers have better encoding ability to retrieve relevant firms’ information (Keller ).

Moreover, knowledge increases customers’ confidence in evaluating goods/services and willingness to translate retrieved information into decision making (Park et al. Advertising expenditures “Advertising” here refers to non-price advertising, which excludes price discounts (Kaul and Wittink ).

We expect that for heavy advertisers, VE becomes weaker while BE becomes stronger. In terms of VE, market power theory (Mitra and Lynch ) suggests that advertising decreases price sensitivity because advertising creates differentiation, which enables firms to charge higher prices (Kaul and Wittink ). Because price is one component of VE, this theory implies that customers who are exposed to more advertising become less sensitive to perceived VE and thus are less likely to translate perceived VE into loyalty intentions. Consequently, firms with intensive expenditures may experience a weakened effect of VE.

In terms of BE, non-price advertising aims to establish high brand familiarity (Kaul and Wittink ), an extrinsic cue that signals brands (Pieters and Wedel ). Mere exposure theory proposes that customers form brand preferences partly from familiarity triggered by advertising, which implies that the more customers are exposed to advertising, the more familiar they are with brands, and the more likely they are to consider those brands in their decision making (McAlister et al. Brand preferences may be even more salient for firms with intensive advertising expenditures, as these firms provide a sufficient number of exposures to enhance the wear-in effect of advertising (Vakratsas and Ambler ). Thus, we assume that the effect of BE is strengthened for firms with intensive advertising expenditures. We do not expect advertising to have a moderating effect on the RE–loyalty link, because RE is mainly built over time when customers interact with firms through loyalty programs, special recognition and treatment programs, affinity programs, and community programs (Rust et al. Such programs are strategies of customer relationship management and usually distinct from the functions of advertising.

In summary, we only hypothesize the moderating effect of advertising expenditures on the VE–loyalty and BE–loyalty links. Data To examine the heterogeneity of the effects of CEDs on loyalty intentions at the industry and firm level, we used three types of data sources. The first data source is part of a large-scale measurement of customer performance in the Netherlands. The data include 8924 customer responses of 95 leading companies across 18 services industries (i.e., insurance, health insurance, banking, mobile phone, landline phone, energy providers, gasoline providers, travel agencies, holiday resorts, airlines, supermarkets, health/beauty retailing, department stores, electronic retailing, do-it-yourself retailing, furnishing retailing, e-booking, and online retailing). For each industry, the survey provides respondents with a list of firms (between four and 11).

Respondents can choose the companies (maximum of three) they are currently a customer of and then repeatedly answer questions about the chosen firms. The sample size per industry was between 303 and 781 customers, with men comprising 46.4% of our collective sample. In terms of age, 22.9% of the respondents were between the ages of 18 and 29 years, 24.8% were between 30 and 39 years, 20.1% were between 40 and 49 years, 25.3% were between 50 and 64 years, and 7.0% were more than 65 years.

In terms of household income, 48.9% of respondents fell into the category of €30,000 to €60,000 per year. The second data source is an expert survey in which 88 respondents (managers and business consultants) gave 178 responses regarding their opinions on the studied industry characteristics.

One respondent could provide multiple responses to different industries. The managers came from the leading firms of multiple industries in the Netherlands (e.g., Aegon, ING bank, KPN, Wehkamp, Ziggo). The business consultants included principals, consultants, and business analysts from leading consultancies in the Netherlands (e.g., BCG, Deloitte, Ernst & Young, McKinsey).

We e-mailed 2000 questionnaires to the experts and informed them that we would donate €2 to the charity organizations of their choice upon completion of one questionnaire. As a result, 88 experts provided 185 responses, seven of which were incomplete. Therefore, the complete responses are 178, for a response rate of 4.4%. We provide additional information of the expert survey in. The third data source consists of external sources, including data from ACNielsen on firms’ annual advertising expenditures and from firms’ annual revenue reports. Measurement of variables.

Measurement of loyalty intentions Following Rust et al. ( ), we measured loyalty intentions with self-reported probabilities of repurchasing.

The respondents allocated 100 points over the firms of each industry, which allowed us to measure the loyalty shares among competitors in each industry. For example, suppose that a respondent is interested in three supermarkets. For his or her next purchase, he or she allocates 100 points over these three supermarkets, for example, 40, 30, and 30.

These numbers indicate the probabilities of his or her next visit to these supermarkets—that is, 40% of the probability to visit the first supermarket but only 30% to visit the last two supermarkets. This measurement of loyalty intentions reflects the increasing polygamous loyalty in customer behavior nowadays (Cooil et al. The range of loyalty intentions was between 0 and 100. Main variables M SD Sample size 1 2 3 4 5 6 7 8 9 10 11 Loyalty intentions 42.81.30**.34**.39**.13**.003.01 −.01.06** −.08**.11**.14** 1. VE 4.98 1.09 8924.73 c.58**.55**.03** −.04**.13** −.01.09**.10** −.08** −.17** 2. BE 4.75 1.09 8924.70.60**.11** −.07**.03** −.02 +.08**.04** −.01 −.03** 3. RE 4.11 1.24 8924.85.04** −.02.04** −.04**.07**.05** −.02* −.06** 4.

Advertising expenditures 0.2 a 0.16 94 d n.a. −.20** −.07** −.06**.07**.21** −.11** −.13** 5. Market position n.a.

75 b, d n.a..13**.23** −.26** −.07** −.03**.04** 6. Competitive intensity 5.59 0.74 18.89.44**.03**.06** −.28** −.20** 7. Innovative markets 3.86 0.72 18.85 −.14** −.26** −.01 −.08** 8. Complexity of purchase decisions 4.38 0.9 18.87.30** −.20** −.13** 9. Visibility to others 3.63 1.10 18.94 −.30** −.59** 10. Difficulty of evaluating quality 4.43 0.63 18.77.31** 11.

Contractual settings n.a. Not applicable ** P. Industry characteristics We collected industry characteristics mainly from the expert survey, except in the case of contractual settings.

ICC(2) indicates that the average agreement rate of experts on industry characteristics was.67 across 18 industries. We employed seven-point Likert scales to assess these characteristics; the relevant questions appear in. We used PCA to examine whether these industry characteristics are unidimensional, as industry characteristics may theoretically correlate with each other and cause estimation problems due to multicollinearity (e.g., Evans ). For innovative markets, we originally used eight measures. Three measures ended up with multiple factors, and thus we excluded them. Shows that the five industry characteristics ended up in the expected separate components. The reliability (Cronbach’s α) for the items related to industry characteristics is sufficient, with values between.77 and.94 (see Table ).

For each industry, we averaged the factor scores of each expert’s opinions on the corresponding industry characteristics and employed this information in our data analysis. Finally, with the definition of contractual settings mentioned previously (Fader and Hardie ), we coded contractual settings as 1 and non-contractual settings as 0. Firm characteristics We collected firm characteristics from external sources and ACNielsen. We measured firms’ market position by their revenue ranking in the correspondent industries.

We collected revenue information from firms’ annual reports. We then coded the market position by the ascending sequence of firms’ revenues. Namely, we coded firms with the highest revenues in the corresponding industries as 1 and considered them the market leader.

We coded the remaining firms, which we considered market followers, in an ascending sequence (i.e., 2, 3, 4, and so on). In addition, ACNielsen provides firms’ annual expenditures in non-price advertising activities. Because we are interested in the relative advertising expenditures between competitors in an industry, we divided each firm’s advertising expenditures by the total amount of advertising expenditures in the corresponding industry. Model specification We estimated a multi-level model with four levels to test the conceptual model, as the original data are hierarchical with four levels: customer responses (first level), customers (second level), firms (third level), and industries (fourth level).

We expect that the first level (customer responses) is less straightforward than the other three, and thus we explain why its inclusion is necessary. Some respondents in our data repeatedly gave responses to multiple firms if they are currently a customer of these firms. Not including this level would produce correlated errors within customers and lead to inconsistent estimates. This is similar to the idea of repeated measures in the multi-level model (e.g., Hox ). We use e ijmn in Eq. To account for the correlated errors within respondents.

We used a generalized linear model (GLM) because it does not need to meet the assumptions of normally distributed dependent variables or the homoscedastic variance of errors required in standard regression models. We used one of the GLMs, family(binomial) and link(logit). The family(binomial) refers to a binomial distribution of the dependent variable. Proportions are assumed to have a binomial distribution (Baum; Moore and McCabe ). Our dependent variable, loyalty intentions, is proportional because the variable measures self-reported probabilities of repurchasing with multiple firms. The link(logit) refers to a logit transformation of the dependent variable, which assumes a linear relationship between this dependent variable and its predictors. We specify our model as follows.

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