Unraveling the Customer Education Paradox

Abstract

Customer education or the extent to which firms are seen as providing customers with the skills and abilities to utilize critical information is often considered a valuable augmentation to a firm’s service offerings. Yet, many firms are hesitant to invest in customer education efforts for fear that it will equip customers with the skills to shop around and possibly switch providers. The purpose of this research is to understand the circumstances under which customer education ties customers more closely to a firm or encourages customers to leave. Specifically, our studies show that an understanding of this paradox of customer education lies in the specificity of customer expertise that is built as a result of customer education initiatives. The results demonstrate that educating customers for firm-specific expertise leads to increased loyalty, while building market-related expertise may decrease customer loyalty. A critical practical implication of our findings therefore is the need for managers to understand the varying effects of enhancing customers’ firm-specific versus market-related expertise and to consider customer education initiatives proactively.

Keywords

customer education customer expertise loyalty switching costs customer centricity

Quite apart from the organizational challenges such as employee training, increased variability in service encounters, and not to mention cost implications, managers have long suspected—although rarely admitted openly—that keeping customers “in the dark” was good for business (The Economist 2014). Maintaining black box or proprietary methodologies, so the logic goes, protects a firm’s competitive advantage. To share too much with customers about the workings of a firm and its market is to equip customers with the expertise to shop around for competing alternatives or even produce the service themselves (e.g., Heilman, Bowman, and Wright 2000).

This status quo is becoming less tenable in today’s market environment as customers prefer transparent and “honest brands” (Liu et al. 2015; NBC News 2016) that make their lives easier (Park, MacInnis, and Eisingerich 2016). Particularly in a service context, there has been an increased emphasis on managing customer equity (Hogan, Lemon, and Rust 2002; Kumar, Lemon, and Parasuraman 2006) and seeking a path to customer centricity (Shah et al. 2006). First, there is emerging evidence that the process of educating customers has a positive impact on customer trust and loyalty. Efforts expended by individuals and organizations to enhance customer education are seen as a valuable augmentation to the core service offering (Bell and Eisingerich 2007; Burton 2002; Hennig-Thurau 2000).

Second, given the increased popularity of cocreation and coproduction business models in service research and practice (e.g., Dong et al. 2015; Mende and Van Doorn 2015; Merlo, Eisingerich, and Auh 2014; Moeller et al. 2013), concerted efforts by firms to educate customers may be necessary to ensure their successful transformation from product-centric to customer-centric organizations. Indeed, customer knowledge has been noted as a “valuable asset” (Shah et al. 2006, p. 115) that can be leveraged for enhanced cross-selling opportunities, while customer knowledge or expertise has been found to be a key determinant of involvement in service coproduction processes (Auh et al. 2007).

Third, customers’ appreciation of a service offering requires customers to be able to understand how a new offering makes a difference to their lives (Moreau, Lehmann, and Markman 2001). To the extent that “…the true essence of the customer centricity paradigm lies not in how to sell products but rather on creating value for the customer and, in the process, creating value for the firm….” (Shah et al. 2006, p. 115), educating customers about a firm’s offerings can be a catalyst for serving customers better, tying them more closely to the firm, and ensuring greater share of wallet.

Yet, despite evidence of its effectiveness and its potential to equip service organizations to compete in a new world of cocreation possibilities, many firms remain resistant to the idea of educating customers. Legal services providers, for example, appear especially intransigent (Robertson and Corbin 2005) and, despite evidence that highlights the value of educating patients, the medical fraternity, for instance, has been slow to embrace the idea (Hausman and Mader 2004). What is the cause of this inertia?

An important reason, and one that forms the core thesis of this article, stems from the uncertainty about the implications of helping build customer expertise in the first place. To what degree will customer education initiatives lead to customer-retention benefits versus equipping customers with the confidence and ability to switch to a competitor? This is the essence of a paradox of customer education highlighted sometime ago by Levitt (1980).

While past research has alluded to the paradox of customer education, prior work has neither attempted to measure it nor offer insights toward its solution. Of the few available studies in customer education, many are either conceptual (e.g., Burton 2002) or do not provide sufficient granularity in their conceptualization of customer education (e.g., Bell and Eisingerich 2007) and customer skill development (e.g., Hennig-Thurau 2000) to tease out the source of this paradox. The evidence for a relationship between customer expertise—the result of customer education—and loyalty is somewhat equivocal with some studies showing a negative effect (e.g., Wirtz and Mattila 2003), others showing a null effect (e.g., Bell and Eisingerich 2007), and yet another group demonstrating a positive relationship (e.g., Bell, Auh, and Smalley 2005; Punj and Staelin 1983). We believe that part of the confusion, and the means by which we might understand the paradox of customer education, lies in the nature or specificity of expertise or knowledge that is built as a result of customer education initiatives. Expertise or knowledge that in general (or market related) is, due to its transferability, likely to equip customers with the skills to shop around for alternatives. Firm-specific expertise, on the other hand, is likely to bond the customer more closely to the firm as customers increase their level of comfort with the way it does business.

In this article, we present the results of two studies. Study 1 explores the implications of educating customers using customer behavioral data. Study 2 manipulated levels of customer education in two different service contexts of varying degrees of customer involvement and customer-firm interdependency (Ma and Dubé 2011) and considered perceived switching costs as a potential mediator. Thus, we make the following critical contributions to the literature. Our focus on different types of customer education extends previous work that has studied customer expertise (e.g., Auh et al. 2007; Bell, Auh, and Smalley 2005) but has not considered how service firms can be proactive in changing or modifying this expertise and its implications for customer loyalty and the customer-centric status of the firm (Shah et al. 2006).

Importantly, by exploring the impact of education on different domains of expertise, we are able to demonstrate the paradox of customer education. We therefore extend previous work, which has investigated the influence of customer education and knowledge but neglected to explore the nuanced implications for customer loyalty (e.g., Eisingerich and Bell 2006, 2008; Hibbert, Winklhofer, and Temerak 2012).

Conceptual Background and Hypotheses

We define customer education as the extent to which a firm is seen as proactively providing customers with the skills and abilities to utilize information (Burton 2002). Education is a process of informing, explaining, and demonstrating concepts to customers (Bell and Eisingerich 2007). To better understand how customer education initiatives influence customer loyalty, we turn to von Hippel’s (1994) theory of information stickiness, which holds that knowledge varies in terms of the ease with which it can be transferred to alternative settings. Thus, we propose a model of customer education, which investigates its impact on customer expertise and knowledge which, in turn, will have implications for loyalty.

Customer Knowledge and Expertise

Customer knowledge can be classified according to type and specificity. The declarative knowledge/procedural knowledge distinction is often employed in assessing the type of knowledge that is gained from education (Brucks 1986). Briefly, declarative knowledge refers to the understanding of concepts, objects, or facts (i.e., “knowing what”), while procedural knowledge refers to the understanding of rules for taking action (i.e., “knowing how”). Knowledge is also often distinguished according to its level of specificity; knowledge can be broad based or product specific (Gregan-Paxton 2001; Punj and Staelin 1983). Broad-based knowledge refers to the understanding of product category information that is relevant to a range of goods or services. Product-specific or specialized knowledge, on the other hand, refers to the understanding of the attributes of individual products, services, or firms.

Over the course of a number of transactions, customer knowledge, or expertise, grows with accumulated exposure to a firm and the market (Brady and Cronin 2001; Eisingerich and Bell 2008). Most research to date has taken the view that as customers accumulate knowledge, they become better able to assess products and services within the market, more capable of making finer distinctions between alternatives, more savvy and discerning, and ultimately better equipped to shop around (Heilman, Bowman, and Wright 2000; Levitt 1980). In other words, expert customers are likely to be less loyal than novice customers. While the logic of these arguments is compelling, often we observe the opposite in many firms and markets. Anecdotal and empirical evidence shows us that some of the most expert customers tend to be the most loyal (e.g., Apple lead users, customers offering advice on how Starbucks can create and deliver better service experiences on My Starbucks Idea, etc.)—a phenomenon observed by Söderlund (2002) for high-performance brands.

We suggest that part of the reason for this is that existing research overlooks the locus of knowledge and expertise. While a great deal of what customers learn will indeed equip them to leave the firm, much of what is learned will bind a customer closer to the organization. In particular, we suggest that knowledge that is broad based, market related, and of a general nature—what Punj and Staelin (1983) call “product class knowledge”—will reduce switching costs and provide customers with the latitude to shop around (Wirtz and Mattila 2003); firm- or service-specific knowledge (Punj and Staelin 1983), on the other hand, will tend to increase loyalty through increased switching costs. Our logic is derived from the theory of information stickiness (von Hippel 1994), namely, firm-specific knowledge is “stickier” than market-related knowledge.

Stickiness of Customer Knowledge

The notion of knowledge stickiness provides some insight into why some knowledge frees a customer to shop around while other knowledge binds a customer more closely to the organization. The stickiness of knowledge is proportionate to the cost of transferring it from one locus to another such that it remains useful to the customer (von Hippel 1994). Firm-specific knowledge will be stickier than market-related knowledge for two main reasons. First, firm-specific knowledge will have a higher tacit component than market-related knowledge (Polanyi 1958). As firms interact with customers in educating them about their service offerings, a great deal of what firms convey will be revealed by their actions (e.g., demonstrating how to use Internet banking or how to set up a new account). As Polanyi (1958, p. 49) notes, “…the aim of a skillful performance is achieved by the observance of a set of rules which are not known as such to the person following them.” Yet, customers will pick up on this tacit knowledge through observation and engagement with the firm, and it is this tacit knowledge accumulated by customers that is fundamentally more difficult to redeploy in an alternative setting.

Second, educating customers about the firm will likely involve the communication of relatively more procedural information than declarative information. Interactions with a firm require customers to learn and perform a set of appropriate behaviors or roles to ensure seamless and efficient transactions (Dong et al. 2015; Shah et al. 2006). While knowledge of service categories, service attributes, and market dynamics (i.e., declarative knowledge) will be useful irrespective of the firm being patronized, how to purchase and use a firm’s services (i.e., procedural knowledge) is likely to be idiosyncratic to each firm. Further, there will be a closer link between knowledge and the skills needed to use this knowledge when it is domain specific (i.e., firm focused; von Hippel 1994). Thus, domain-specific knowledge will tend to build switching costs (Burnham, Frels, and Mahajan 2003). Market-related knowledge, on the other hand, is inherently useful on its own. It can be more easily compartmentalized and communicated independently of the knowledge required to purchase and use the services.

Customer Education, Firm-Specific Expertise, and Market-Related Expertise

Educational efforts will play a role in helping customers know which behaviors to adopt and how to perform in the service delivery process. We define market-related expertise as a customer’s understanding of service category information relevant to a range of providers within a market. Firm-specific expertise refers to the customer’s understanding of the offerings and processes of a particular firm.

Firm-specific information will be nested to some extent in market-related information. For example, in order to understand a specific service of the firm (e.g., a firm’s web-based retail interface), some general background to the service category (e.g., e-commerce) will need to be communicated. Equally, in providing a broad overview of a service category, illustrations of a firm’s services in this category are likely to be used. Any education efforts by a firm, therefore, will tend to boost both market-related expertise and firm-specific expertise of customers. Firms are unlikely to be able to direct their educational efforts with the sort of precision that will only boost one type of knowledge; there is likely to be some cross influence or “spillover” of focused educational initiatives. Nonetheless, we suggest that it is possible for firms to direct their educational initiatives toward intended outcomes by simply focusing more attention on the specific domain. Therefore, we propose the following hypotheses:

Hypothesis 1: Firm-specific customer education will be positively associated with (a) firm-specific expertise and (b) market-related expertise with the positive association between firm-specific education and firm-specific expertise being stronger than the positive association between firm-specific education and market-related expertise.

Hypothesis 2: Market-related customer education will be positively associated with (a) firm-specific expertise and (b) market-related expertise with the positive association between market-related education and market-related expertise being stronger than the positive association between market-related education and firm-specific expertise.

Customer Expertise, Switching Costs, and Customer Purchase Behavior

Firm-specific expertise is more likely to be based on an understanding of procedural elements than market-related information. An organization’s procedures and routines, while often sharing common elements with those of competitors, tend to exhibit many characteristics that are idiosyncratic (Josephson et al. 2016). Thus, customers who have high levels of firm-specific expertise will tend to be more loyal to the firm about which they are knowledgeable. Customers with product-specific skills, for example, have been shown to demonstrate greater calculative commitment to the firm (Hennig-Thurau 2000), while firm-specific expertise is also likely to underpin more efficient and productive customer relationships with the focal organization.

In contrast, as customers’ market-related expertise increases, customers will have greater confidence in evaluating competing alternatives (Seifert et al. 2015). Expert customers can make more fine-grained distinctions between services and their various attributes. More accurate and comprehensive assessment of service attributes allows customers to choose services that best fit their circumstances (Brucks 1986). They can “unbundle” services with confidence and easily switch between providers, cherry picking elements of each firm’s service that best suit their needs. Potentially, they may even self-produce part of the solution (Dong et al. 2015). Finally, customers with a high level of market-based expertise will have a good understanding of levels of performance across competitors within the market. They are likely, therefore, to have increased expectations of service quality and will be more critical of the service delivered by a focal firm.

Marketers’ emphasis on building relationships with customers typically considers ways in which firms might maximize the strength of a relationship relative to competitors (e.g., Park et al. 2010; Park, Eisingerich, and Park 2013). Thus, we consider the impact of firm-specific expertise and market-related expertise on three measures of relationship strength including relationship depth (the number of services held by a customer from the focal firm), purchase growth from the focal firm (the increase in number of services purchased over time), and purchase growth from competitors (the increase in number of services purchased from competitors over time). Taken together, we hypothesize that:

Hypothesis 3: Firm-specific expertise will be positively associated with (a) relationship depth and purchase growth from the focal firm and (b) negatively associated with purchase growth from competitors.

Hypothesis 4: Market-related expertise will be negatively associated with (a) relationship depth and purchase growth from the focal firm and (b) positively associated with purchase growth from competitors.

As a consequence of firm-specific customer education, customers grow expert in a set of behaviors and skills that are specific to a particular service firm and are not easily transferred elsewhere. The knowledge they accumulate is stickier. In other words, the procedural costs of switching between firms grow (Bell, Auh, and Smalley 2005), as customers become more comfortable with a particular way of doing things. We define perceived switching costs as the “onetime costs that customers associate with the process of switching from one provider to another” (Burnham, Frels, and Mahajan 2003, p. 110). Customers with high firm-specific expertise will need to invest relatively more in “setting up” a relationship with a competitor to equivalent levels. Market-related expertise, on the other hand, does not similarly constrain customers. Those with high market-related expertise, because their knowledge is more broad based and transferable, will perceive lower evaluation costs (i.e., costs associated with assessing competitive alternatives) and economic risk costs (i.e., costs of uncertainty about a new provider) as these customers are more familiar with the market as a whole (Burnham, Frels, and Mahajan 2003). Therefore, we hypothesize that:

Hypothesis 5: Firm-specific (market related) customer education is positively (negatively) related to perceived switching costs, which in turn result in stronger (weaker) customer loyalty.

Taken together, we hypothesize that building customer expertise can result in both customer defection and customer retention. Furthermore, by hypothesizing the loyalty impact of different domains of education and expertise, we are able to show that some customer knowledge domains are stickier than others. Importantly, we demonstrate this mechanism at work by arguing that switching costs mediate, to different degrees, the impact of firm-specific and market-related customer education on loyalty (see Figure 1).

Figure 1.

Conceptual model.

Study 1

In Study 1, we test Hypotheses 1–4 using data collected from a global commercial bank. The collaborating firm provided us with contact details for 2,700 randomly selected customers. We matched self-reported data from customers collected at time T ₁ (independent and mediating variables) with customer behavior data collected at time T ₂ (dependent variables).

Method

We first conducted a pretest to generate scale items for market-related and firm-specific customer education and expertise. As noted earlier, most prior research has taken a general approach to measuring education and expertise (e.g., Bell and Eisingerich 2007), so we generated a pool of 16 items—8 items each for the firm-specific and market-related customer education scales—with “different nuances of meaning” (Churchill 1979). Included in the pool were items from existing measures (Bell and Eisingerich 2007) and items contributed by customers in 10 face-to-face interviews. In a further set of interviews (N = 88), we tested our pool of 16 items asking customers to point out any ambiguity in responding to individual items. Items that loaded strongly on their intended factors and had minimal cross loadings were retained while others were discarded. This resulted in two education scales with 4 items each.

In our interviews, we also refined our market-related expertise scale that was adapted from Bell and Eisingerich (2007) whose “expertise” scale drew predominantly from the market-related domain of knowledge. The 3 final items captured the words “understanding,” “knowledge,” and expertise—themes that emerged from our interviews—which we incorporated in the measure of firm-specific expertise when adapting the items for this domain. We then pretested our all scales with 200 randomly selected customers of the collaborating firm. The customers were from one branch deemed representative of the firm’s entire network (i.e., respondents did not differ significantly from the population of customers in terms of relationship length with the focal firm, age, or gender). Due to minor changes in item wording, we did not include pretest response data in our subsequent main analysis. The final scale items are listed in Table 1.

Table 1.

Study 1: Constructs and Measurement.

Constructs	Measurement	Factor Loadings^a
Firm-specific customer education	[Company name] makes an effort to increase my knowledge of the products and services offered by [company name].	.97
	[Company name] tries to improve my skills needed to assess the pros and cons of the different services they offer.	.90
	[Company name] takes the time to provide me with all information I need on [company name’s] way of doing things.	.71
	[Company name] gives me all the tools necessary to make informed decisions about the mix of services I buy from them.	.82
Market-related customer education	[Company name] makes an effort to increase my knowledge of the financial services available in the market.	.96
	[Company name] tries to improve my skills needed to assess the pros and cons of different financial services in the market as a whole.	.94
	[Company name] takes the time to provide me with all information I need on the financial services industry.	.93
	[Company name] gives me all the tools necessary to make informed decisions about opportunities in the financial services market.	.90
Firm-specific customer expertise	My understanding of [company name’s] services and products is much stronger than of other banks.	.96
	I have a much greater knowledge of how [company name] operates than I do of other banks.	.84
	I am expert at getting the most out of my relationship with [company name].	.89
Market-related customer expertise	I can understand almost all the aspects of banking services across the market.	.85
	I possess a good knowledge of financial services and products.	.93
	I have a high level of expertise in the financial services industry.	.79

^aAll factor loadings are significant at p < .001 level.

Our main survey efforts involved two rounds of data collection. In the first round, we asked respondents to indicate the levels of firm-specific and market-related customer education as well as their perceived firm-specific and market-related expertise. The total number of usable responses was 789 (39 questionnaires were eliminated because of excessive missing values). We then followed up with a second questionnaire asking customers to indicate their behavioral loyalty. The second round resulted in 763 usable responses. The response rate is a consequence of the firm communicating frequently with customers via a monthly, tailored newsletter (e.g., containing specific information about customers’ mortgages, pension savings, etc.). Questionnaire data from the second round were matched with survey data obtained in the first round for a total of 763 usable responses. Females represented 47% of the final sample. We assessed whether relationship length, gender, and purchases with the firm differed between respondents and nonrespondents; we found no significant difference across any of these variables. We also compared early and late responses (first 100 vs. last 100) across study variables and found no significant differences between the two groups. Correlations and descriptive statistics for our measures can be seen in Table 2. We collected data on relationship depth by asking customers to indicate the services they held with the firm (“Please indicate and check the services that you currently hold with [firm name]; e.g., deposit/savings account, credit card, personal loan, home loan, insurance services, etc.”). For 20% of customers, response data were cross-checked by the bank and verified that customer responses were in fact in line with their actual number of services held with the firm. The cross-check showed that over 98% of customer responses were correct. For the remaining 2%, customers indicated one or two fewer services than they actually held with the firm.

Table 2.

Study 1: Descriptive Statistics and Correlations.

Variables	AVE	CR	1	2	3	4	5	6	7	8
1. Relationship depth			1.00
2. Purchase growth from focal firm			.27**	1.00
3. Purchase growth from competitors			−.18**	−.70**	1.00
4. Firm-specific customer education	.73	.91	.03	.02	−.06*	1.00
5. Market-related customer education	.87	.96	−.08**	−.17**	.07*	.36**	1.00
6. Firm-specific customer expertise	.81	.93	.13**	.16**	−28**	.41**	.37**	1.00
7. Market-related customer expertise	.74	.90	−.25**	−.36**	.29**	.31**	.66**	.33**	1.00
8. Relationship length			.00	−.06*	.07*	.05	.13**	.01	.08*	1.00
Mean			3.70	1.93	1.82	5.61	4.03	5.92	4.54	13.52
Standard deviation			2.14	0.88	0.94	1.10	1.65	1.31	1.74	12.38

Note. AVE = average variance extracted; CR = composite reliability.

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

We also measured growth in purchases from the focal firm¹ as the change in the number of purchases from the firm over the 6 months between data collections. We asked respondents if the number of services they bought from the firm had decreased (= 1), stayed the same (= 2), or increased (= 3). Again, 20% of customer responses were matched with objective purchase data from the firm and cross-checked for accuracy. Results indicated that customer responses were factually correct (r > 90%). We measured purchase growth from competitors by asking customers to indicate on a scale how their purchases from competing firms had changed in the past 6 months (i.e., “Over the past 6 months, the number of services I have bought from other banks has…decreased = 1, stayed the same = 2, increased = 3, or not applicable, I only bank with [firm name], = 4”). While we were unable to validate purchasing behavior from other banks, we suggest that the accuracy with which customers reported purchasing behavior for the focal bank will be indicative of the accuracy of their reports for competitor purchases. We also controlled for relationship length (in years) and gender (1 = male, 2 = female).

Results

We used structural equation modeling with AMOS 21.0 to test our hypotheses. Although our study used behavioral measures as dependent variables, they were still based on self-reported measures. Therefore, we sought to control for potential common methods bias (CMB) by including a common method factor (Podsakoff et al. 2003). In Table 3, we report the findings from two models, Models 1 and 2. Model 1 does not include a common method factor, while Model 2 does. Both models, Model 1: χ²(129) = 587.36, p < .001, comparative fit index (CFI) = .96, Tucker–Lewis index (TLI) = .95, root mean square error of approximation (RMSEA) = .07, and Model 2: χ²(113) = 342.91, p < .001, CFI = .98, TLI = .97, RMSEA = .05, provide good fit to the data. The χ² difference between Models 1 and 2 was significant, Δχ²(16) = 244.45, p < .001. However, when both models are compared, none of the structural paths became nonsignificant after including the common method factor. In fact, the paths are robust and intact. Thus, we conclude that CMB is not a threat to the study and turn our attention to hypotheses testing. We report the results of our hypotheses testing from Model 2 (model with common method factor included).

Table 3.

Study 1: Hypotheses Testing.

Hypothesized Paths	Model 1: Without Common Method Factor		Model 2: With Common Method Factor
Hypothesized Paths	Γ	t Value	γ	t Value
Firm-specific education → firm-specific expertise	.32	8.67***	.30	8.29***
Market-related education → firm-specific expertise	.28	7.76***		8.42***
Market-related education → market-related expertise	.66	18.03***	.68	17.99***
Firm-specific education → market-related expertise	.07	2.34*	.07	2.10*
Firm-specific expertise → relationship depth	.24	6.36***	.25	6.44***
Firm-specific expertise → purchase growth from focal firm	.33	9.30***	.33	9.28***
Firm-specific expertise → purchase growth from competitors	−.41	−11.02***	−.40	−10.88***
Market-related expertise → relationship depth	−.34	−8.65***	−.35	−8.66***
Market-related expertise → purchase growth from focal firm	−.49	−12.59***	−.49	−12.46***
Market-related expertise → purchase growth from competitors	.34	8.91***	.34	8.73***
Controls
Gender → firm-specific expertise	−.04	−1.06	−.03	−0.91
Gender → market-related expertise	.04	1.28	.04	1.46
Gender → purchase growth from focal firm	−.04	−1.17	−.04	−1.21
Gender → purchase growth from competitors	.02	0.71	.03	0.77
Gender → relationship depth	−.04	−1.18	−.04	−1.21
Relationship length → firm-specific expertise	−.04	−1.05	−.04	−1.07
Relationship length → market-related expertise	−.02	−0.65	−.02	−0.56
Relationship length → purchase growth from focal firm	−.03	−0.98	−.03	−1.00
Relationship length → purchase growth from competitors	.05	1.55	.05	1.58
Relationship length → relationship depth	.02	0.55	.02	0.54
Squared multiple correlation (R ²)
Firm-specific expertise	.23		.25
Market-related expertise	.48		.50
Purchase growth from focal firm	.24		.24
Purchase growth from competitors	.19		.19
Relationship depth	.12		.12
Model fit
(df) χ²	(129) 587.36		(113) 342.91
CFI/TLI/RMSEA	.96/.95/.07		.98/.97/.05

Note. Standardized path coefficients reported. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation.

*p < .05. **p < .01. ***p < .001.

Firm-specific customer education was positively associated with both firm-specific expertise (γ = .30, p < .001) and market--related expertise (γ = .07, p < .05). However, consistent with Hypothesis 1, firm-specific customer education had a significantly stronger positive effect on firm-specific expertise than on market-related expertise, Δχ²(1) = 23.32, p < .001. Regarding Hypothesis 2, our results showed that market-related customer education was positively associated with both market-related expertise (γ = .68, p < .001) and firm-specific expertise (γ = .32, p < .001); yet the impact of the former was significantly greater than the influence of the latter, Δχ²(1) = 45.31, p < .001, lending support to Hypothesis 2.

As predicted, firm-specific expertise was positively associated with relationship depth (i.e., number of services currently held with the firm; γ = .25, p < .001) and purchase growth from the focal firm (i.e., the change in number of services purchased over the period following the survey; γ = .33, p < .001). It is also noteworthy that firm-specific expertise was negatively associated with purchase growth from competitors (i.e., the change in number of services purchased from competitors over the period following the survey; γ = −.40, p < .001. Therefore, Hypothesis 3 was supported. In line with Hypothesis 4, market-related expertise was negatively associated with purchase growth from the focal firm (γ = −.49, p < .001) as well as relationship depth (γ = −.35, p < .001). Market-related expertise was positively associated with purchase growth from competitors (γ = .34, p < .001) as predicted. Gender and relationship length had no significant effect on any of the behavioral loyalty measures (p = ns).

The model explained 24% of the variance in purchase growth from the focal firm, 19% of the variance in purchase growth from competitors, and 12% of the variance in relationship depth. The model was able to explain 25% of the variance in firm-specific expertise and 50% of the variance in market-related expertise. Further, to determine whether firm-specific and market-related customer education had a direct impact on the three behavioral loyalty measures, we included direct paths from firm-specific and market-related customer education to the three behavioral loyalty variables. Since the model without the direct paths (our conceptual model) is a constrained version of the model with the direct paths included, they are nested and a χ² difference test can be used to test for the direct paths’ significance. The χ² difference, Δχ²(6) = 11.93, p > .05, between the unconstrained model, χ²(107) = 330.98, p < .001, and the constrained model, χ² (113) = 342.91, p < .001, was nonsignificant, supporting the constrained model (conceptual model) over the unconstrained model.

Since we did not find support for a direct path from firm-specific and market-related customer education to behavioral loyalty, we tested for the significance of the indirect effects via firm-specific and market-related expertise. The indirect effect of firm-specific customer education via firm-specific expertise on (a) purchase growth from the focal firm (Sobel z = 4.32, p < .001; 95% confidence interval [CI] [.04, .09]), (b) purchase growth from competitors (Sobel z = −6.73, p < .001; 95% CI [−.14, −.08]), and (c) relationship depth (Sobel z = 3.91, p < .001; 95% CI [.07, .21]) were all statistically significant. Similarly, the indirect effect of market-related education via market-related expertise on (a) purchase growth from the focal firm (Sobel z = −8.99, p < .001; 95% CI [−.19, −.12]), (b) purchase growth from competitors (Sobel z = 5.41, p < .001; 95% CI [.06, .14]), and (c) relationship depth (Sobel z = −7.15, p < .001; 95% CI [−.39, −.21]) were also all statistically significant.²

Finally, we calculated the total effects for each indirect path (total of 12) in our conceptual model. The results are shown in Table 4. We computed the total effects that firm-specific customer education and market-related customer educations have on each of the three behavioral loyalty measures. The total effect of firm-specific customer education on purchase growth from the focal firm is .07 (a + d), while the same effect of market-related customer education is −.22 (g + j). We performed similar calculations for purchase growth from competitors and relationship depth. The total effect of firm-specific customer education on purchase growth from competitors is −.10 (b + e), while the same effect of market-related customer education is .10 (h + k). For relationship depth, the total effect of firm-specific customer education on number of services possessed is .05 (c + f), while the same effect of market-related customer education is −.16 (i + l). According to the results from Table 4, the sum of total effects³ on customer loyalty that originate from firm-specific education (a–f) is .22, while the aggregate of total effects from market-related education (g–l) is −.48.

Table 4.

Study 1: Total Effects.

Independent Variables	Mediator	Dependent Variables	Total Effect
Firm-specific customer education	Firm-specific expertise	Purchase growth from focal firm¹	.10 (a)
Firm-specific customer education	Firm-specific expertise	Purchase growth from competitors²	−.12 (b)
Firm-specific customer education	Firm-specific expertise	Relationship depth³	.08 (c)
Firm-specific customer education	Market-related expertise	Purchase growth from focal firm	−.03 (d)
Firm-specific customer education	Market-related expertise	Purchase growth from competitors	.02 (e)
Firm-specific customer education	Market-related expertise	Relationship depth	−.03 (f)
Market-related customer education	Firm-specific expertise	Purchase growth from focal firm	.11 (g)
Market-related customer education	Firm-specific expertise	Purchase growth from competitors	−.13 (h)
Market-related customer education	Firm-specific expertise	Relationship depth	.08 (i)
Market-related customer education	Market-related expertise	Purchase growth from focal firm	−.33 (j)
Market-related customer education	Market-related expertise	Purchase growth from competitors	.23 (k)
Market-related customer education	Market-related expertise	Relationship depth	−.24 (l)

¹Change in number of services purchased from focal firm. ²Change in number of services purchased from competitors. ³Number of services used.

Discussion

Our findings from Study 1 demonstrate that firm-specific and market-related expertise have opposing effects on customer loyalty to the firm. This provides further validation of the paradox of customer education to which Levitt (1980) alluded. Our results from Study 1 further indicate that customer education—irrespective of how it is directed—significantly increases both the firm-specific and market-related expertise of customers although, as hypothesized, the effect is not symmetrical. Interestingly, our analysis of total effects shows that market-related education has, all things being equal, a stronger overall negative impact on loyalty than firm-specific education has positive (−.48 vs. .22). This finding lends some credence to managers’ concerns that educating customers may equip them to ultimately leave the firm. Nonetheless, there would appear to be scope for firms to craft educational campaigns to focus more specifically on firm-specific skills and knowledge. Firm-specific education (despite also increasing a customer’s market-related expertise) significantly increased customer loyalty to the firm (in terms of increasing relationship depth, increasing number of services purchased from the firm, and decreasing the number of services purchased from competitors).

We wanted to investigate whether Study 1 results would be replicated with actual customer education using an experimental manipulation. Further, drawing on our arguments about knowledge “stickiness,” we were intrigued as to whether switching costs played a role in explaining the varying effects of firm-specific education and market-related education on customer loyalty across service contexts that differed in terms of customer involvement and the degree of interdependency between customer and firm in the value creation process (Ma and Dubé 2011). We therefore conducted a second study to address these issues.

Study 2

Study 2 was designed to enable us to educate customers (through a manipulation) and also investigate a potential process mechanism—perceived switching costs—as an explanation for the effects of customer education on customer loyalty. The two service contexts (medical services and hospitality) in Study 2 allowed us to investigate whether the level of customer involvement and customer-firm interdependency influenced the results.

Method

Study 2 used a 2 (high vs. low market-related customer education) × 2 (high vs. low firm-specific customer education) × 2 (service setting: cancer clinic vs. café) between-subjects experimental design. The cancer clinic and café service settings were chosen based on a pretest (N = 48), which showed that the two were of equal familiarity to respondents but differed in interest to them. Two hundred and twenty-five students took part in Study 2 as part of a regular course. Respondents were randomly assigned to one of the eight conditions. Measures (with anchors 1 = not at all, 7 = completely) and their reliabilities are shown in Table 5.

Table 5.

Study 2: Measure Reliabilities.

Measures	Items	Cronbach’s α
Firm-specific expertise	My understanding of [Company name’s] services and products is much stronger than of other [coffee/cancer care] providers.	.97
	I have much greater knowledge of how [Company name] operates than I do of other [coffee/cancer care] providers.
	I feel I would be an expert at getting the most out of a relationship with [Company name].
Market-related expertise	I can understand almost all aspects of [coffee and coffee products/cancer care services] across the market.	.99
	I possess a good knowledge of [coffees and coffee products/cancer care services].
	I have a high level of expertise in the [coffee industry/cancer care market].
Attitudinal loyalty	I would be highly interested in trying the products and services offered by [Company name].	.98
	I would like to purchase the products and services offered by [Company name].
	I would make an effort on behalf of [Company name] to help it succeed.
Switching costs	[Company name] offers service benefits I would not receive from another [coffee company/cancer clinic].	.98
	I would find it hard to switch and use another [coffee company/cancer clinic] instead of [Company name].
	I would find it difficult to use another [coffee company/cancer clinic] other than [Company name].
	Using [Company name] gives me privileges I would not receive from another [coffee company/cancer clinic].

First, respondents were asked to indicate their familiarity with the coffee industry/cancer care market (1 = not at all familiar, 7 = very familiar), attitude toward the service setting (“To what extent do you view the coffee industry/cancer care market as 1 = bad/negative, 7 = good/positive; r = .93; and 1 = boring/irrelevant to me, 7 = interesting/relevant to me; r = .97), and prior market expertise (“I possess good knowledge of the coffee industry/cancer clinic market,” “I am quite experienced in the coffee/cancer clinic area”; 1 = strongly disagree, 7 = strongly agree; r = .96).

They were then asked to read a short text containing paragraphs that varied in terms of how educational they were about the industry and the focal firm. Thus, we aimed to manipulate respondents’ objective level of knowledge. The Web Appendix shows sample excerpts for the high market/high firm and low market/low firm customer education in the café and cancer clinic conditions. Next, respondents were asked to respond to a set of questions that included perceived firm-specific expertise, perceived market-related expertise, attitudinal loyalty, and perceived switching costs (see Table 5 for items and scale reliabilities). Attitudinal loyalty was measured using a 3-item scale from Zeithaml, Berry, and Parasuraman (1996). Perceived switching costs was measured using a 4-item scale adapted from Burnham, Frels, and Mahajan (2003). Finally, respondents rated the believability of the excerpt (“How believable is the description?” “How trustworthy is the description?”; 1 = not at all believable/not at all trustworthy, 7 = very believable/very trustworthy; r = .60), their attitude toward (“To what extent do you view the café/cancer clinic as”; 1 = bad/negative, 7 = good/positive; r = .76), and interest in the service firm (1 = boring/irrelevant to me, 7 = interesting/relevant to me; r = .89).

Results

Manipulation Checks

A set of 2 (high vs. low market-related customer education) × 2 (high vs. low firm-specific customer education) × 2 (service setting: cancer clinic vs. café) analyses of variances (ANOVAs) on the manipulation checks revealed that the manipulations were successful (see Table 6 for details). The manipulation check for firm-specific customer education revealed the expected main effect of firm-specific education: M _high = 4.97 vs. M _low = 2.09, F(1, 225) = 421.21, p < .001; cancer clinic: M _high = 4.89 vs. M _low = 2.12, p < .001; Café: M _high = 5.05 vs. M _low = 2.04, p < .001. The manipulation check for market-related customer education also revealed the expected main effect of market-related education: M _high = 5.31 vs. M _low = 2.20, F(1, 225) = 454.33, p < .001; cancer clinic: M _high = 5.29 vs. M _low = 2.22, p < .001; Café: M _high = 5.32 vs. M _low = 2.18, p < .001. In addition, the results showed that the café service setting was higher in customer relevance than the cancer clinic setting, M _café = 5.37 vs. M _clinic = 3.51; F(1, 225) = 64.84, p < .001. No other effects were observed.

Table 6.

Study 2: Manipulation Checks and Hypotheses Testing.

Means	Café				Cancer Clinic
Customer Education	High Firm/High Market (N = 28)	High Firm/Low Market (N = 29)	Low Firm/High Market (N = 28)	Low Firm/Low Market (N = 29)	High Firm/High Market (N = 28)	High Firm/Low Market (N = 29)	Low Firm/High Market (N = 27)	Low Firm/Low Market (N = 27)
Manipulation checks
Firm-specific education	5.07^a	5.03^a	2.05^b	2.03^b	4.88^a	4.90^a	2.21^b	2.06^b
Market-related education	5.60^a	2.28^b	5.05^a	2.09^b	5.39^a	2.19^b	5.19^a	2.26^b
Interest in service setting	5.29^a	5.47^a	5.39^a	5.33^a	3.32^b	3.45^b	3.57^b	3.69^b
Confounds
Familiarity of service setting	3.46^a	3.48^a	3.43^a	3.55^a	3.18^a	3.24^a	3.26^a	3.33^a
Attitude toward service setting	4.63^a	4.50^a	4.63^a	4.79^a	4.56^a	4.55^a	4.50^a	4.57^a
Excerpt believability	5.86^a	5.78^a	5.77^a	5.86^a	5.88^a	5.81^a	5.98^a	6.09^a
Attitude toward firm	5.41^a	3.95^c	4.64^b	2.43^d	5.45^a	4.77^b	4.70^b	2.48^d
Interest in firm	5.38^a	4.07^b	3.48^b	2.50^c	5.34^a	4.10^b	3.43^b	2.44^d
Dependent variable
Attitudinal loyalty	4.75^b	6.35^a	1.43^d	2.72^c	5.00^b	6.31^a	1.47^d	2.73^c
Variables relevant to the underlying logic of the hypotheses
Firm-specific expertise	6.25^a	5.46^a	3.17^b	1.74^c	6.39^a	5.24^a	3.15^b	1.69^c
Market-related expertise	6.24^a	2.91^b	5.29^a	1.51^c	6.30^a	3.05^b	5.75^a	1.46^c
Switching costs	4.17^b	5.32^a	2.50^c	2.07^c	4.68^b	5.55^a	2.78^c	2.31^c

Note. Means with different superscript alphabets are significantly different, p < .05.

Confound Checks

As illustrated in Table 6, a set of 2 × 2 × 2 ANOVAs on variables that might produce potential confounds showed that there were no differences across the conditions in service setting familiarity, attitude, and excerpt believability. In our analyses, we also controlled for attitude toward and interest in the service firm.

Effects of Firm-Specific and Market-Related Customer Education on Expertise

Consistent with our theorizing and replicating our earlier study, a 2 × 2 × 2 ANOVA on customers’ firm-specific expertise and market-related expertise revealed main effects of firm-specific customer education, firm-specific expertise: M _high = 5.84 vs. M _low = 2.44, F(1, 225) = 723.59, p < .001, and market-related customer education, market-related expertise: M _high = 4.74 vs. M _low = 3.53, F(1, 225) = 91.22, p < .001, respectively. Further, using regression analysis, we found that firm-specific education had a stronger impact on firm-specific expertise than on market-related expertise (β_{firm specific} = .68 vs. β_{market related} = .29, p < .001). Conversely, market-related education had a stronger impact on market-related expertise than on firm-specific expertise (β_{firm specific} = .22 vs. β_{market related} = .71, p < .001).⁴ The main effect of service setting was not significant, M _café = 4.15 vs. M _clinic = 4.12; F(1, 225) = 0.08, p = ns. The two-way interaction between firm-specific and market-related customer education was not significant at the standard .05 level, F(1, 225) = 3.50, p = .06. No other effects were observed.

Effects of Firm-Specific and Market-Related Customer Education on Attitudinal Loyalty

Consistent with our theorizing, a 2 × 2 × 2 ANOVA on attitudinal loyalty showed that customers’ attitudinal loyalty was stronger when firm-specific education levels of the service firm were high versus low, M _high = 5.60 vs. M _low = 2.09, F(1, 225) = 807.42, p < .001; cancer clinic: M _high = 5.66 vs. M _low = 2.10, p < .001; café: M _high = 5.55 vs. M _low = 2.08, p < .001. In contrast, and in line with our theorizing, attitudinal loyalty was weaker for high versus low levels of market-related education, M _high = 3.16 vs. M _low = 4.53, F(1, 225) = 121.85, p < .001; cancer clinic: M _high = 3.24 vs. M _low = 4.52, p < .001; caféL M _high = 3.09 vs. M _low = 4.53, p < .001.

Evidence for Proposed Mechanism

A set of 2 × 2 × 2 ANOVAs on customers’ perceived switching costs revealed a main effect of firm-specific education; respondents had stronger perceived switching costs for the firm that offered high versus low levels of firm-specific education, M _high = 4.93 vs. M _low = 2.42; F(1, 225) = 264.96, p < .001. In contrast, no such effect was observed for market-related education, M _high = 3.53 vs. M _low = 3.81; F(1, 225) = 3.23, p = ns. We also observed a significant main effect of service setting for perceived switching costs, M _café = 3.51 vs. M _clinic = 3.83; F(1, 225) = 4.19, p < .05, and a two-way interaction between firm-specific and market-related customer education, F(1, 225) = 22.56, p < .001. Using Hayes’s (2012) bootstrapping technique, we revealed that perceived switching costs mediated the influence of firm-specific customer education on attitudinal loyalty such that higher levels of firm-specific customer education led to stronger perceived switching costs, which in turn resulted in greater attitudinal loyalty (for detailed results, see Table 7A). In contrast, however, and contrary to our formal prediction, market-related customer education did not significantly affect perceived switching costs (see Table 7B) and, thus, we did not observe a mediating role of switching costs in the relationship between market-related customer education and attitudinal loyalty. Thus, support for Hypothesis 5 was mixed.

Table 7.

Study 2: Mediation Analyses.

(A) Firm-Specific Education
Mediator variable model (predicting switching costs)	R ²	F(1, 223)	B (SE)	T	CI [LL, UL])	CI [LL, UL])
Firm-specific customer education	.35	129.48***	.58 (.05)	11.38***	[.48]	[.68]
Dependent variable model (predicting attitudinal loyalty)	R ²	F(2, 222)	B (SE)	T	CI [LL, UL])	CI [LL, UL])
Switching costs	.53	169.99***	.43 (.07)	6.15***	.30	.58
Firm-specific customer education			.53 (.07)	7.52***	.39	.67
Total effect of firm-specific education on attitudinal loyalty	R ²	F(1, 223)	B (SE)	T	CI [LL, UL])	CI [LL, UL])
Firm-specific customer education	.44	65.22***	.78 (.05)	15.52***	.69	.88
Direct effect of firm-specific education on attitudinal loyalty
Firm-specific customer education			.53 (.07)	7.52***	.39	.67
Indirect effect of firm-specific education on attitudinal loyalty
Switching costs			.25 (.05)		.17	.35
(B) Market-Related Education
Mediator variable model (predicting switching costs)	R ²	F(1, 223)	B (SE)	T	CI [LL, UL])	CI [LL, UL])
Market-related customer education	.01	0.56	−.05 (.06)	−0.75	−17	.08
Dependent variable model (predicting attitudinal loyalty)	R ²	F(2, 222)	B (SE)	T	CI [LL, UL])	CI [LL, UL])
Switching costs	.43	137.01***	.75 (.05)	15.39***	.65	.84
Market-related customer education			−.21 (.06)	−3.75***	−.32	−.10
Total effect of market-related education on attitudinal loyalty	R ²	F(1, 223)	B (SE)	T	CI [LL, UL])	CI [LL, UL])
Market-related customer education	.05	11.29***	−.24 (.07)	−3.36***	−38	−.10
Direct effect of market-related education on attitudinal loyalty
Market-related customer education			−21 (.06)	−3.75***	−.32	−.10
Indirect effect of market-related education on attitudinal loyalty
Switching costs			−.03 (.05)		−.13	.06

Note. SE = standard error.

*p < .05. **p < .01. ***p < .001.

Discussion

Study 2 manipulated firm-specific and market-related customer education in two different service contexts with varying requirements for customer-firm interdependency in value creation. Consistent with Study 1, the results of Study 2 showed that firm-specific (market related) education had a stronger influence on firm-specific (market related) expertise than on market-related (firm specific) expertise. Moreover, Study 2 showed that customers’ attitudinal loyalty was stronger (weaker) when firm-specific (market related) education levels of the service firm were high. In addition, Study 2 offered evidence for the role of switching costs in helping to explain the positive effect of firm-specific customer education on attitudinal loyalty. Contrary to our expectation, however, switching costs did not mediate the effect of market-related expertise on attitudinal loyalty.

General Discussion

While many firms claim to share information openly with their customers, few firms share critical information with customers (NBC News 2016) and fewer firms still equip customers with the tools needed to make full use of critical information (Hausman and Mader 2004). The hesitation of many firms to not proactively engage in customer education initiatives is backed by a body of literature that suggests firms may better protect their competitive advantage by leaving customers in the dark (Heilman, Bowman, and Wright 2000). There is considerable merit in these studies; we find empirical support in Study 1 for the potentially overall negative effect of customer education. Yet, we also find evidence across our two studies that loyalty can be enhanced when education is directed toward building firm-specific expertise, and the total effects of customer education in our experimental setting are positive. This critical finding offers important implications for marketing practice and theory.

Managerial Implications

While we have been able to demonstrate that customer education can have both positive and negative effects on customer loyalty, the total effects appear to vary by industry. In the retail-banking context, the net effect was negative while the net effect in both the café and cancer clinic contexts was positive, albeit within an experimental context. The positive total effects of education in cancer clinics may be a function of relatively high levels of customer-firm interdependency; however, this does not explain the positive total effects in the café setting (a low interdependence context, similar to banks). That said, banks differ from cafés in other important ways. First, banking services (e.g., insurance products, loans) tend to be seen as “grudge” purchases—“a mundane, nonconspicuous mode of consumption that typically exists outside of the paraphernalia of consumer culture” (Loader, Goold, and Thumala 2015, p. 858). Banks provide services that customers need but may not necessarily want (Berry and Bendapudi 2007). Customers in developed economies have almost no option but to purchase banking services, and these services are seen as a means to an end (e.g., credit cards facilitate purchases of other things) rather than aspirational purchases in their own right. Second, in many developed countries, retail banks are seen as oligopolistic and exploitative in the pursuit of large profits, often breeding resentment and a cynicism among customers. Finally, the café setting is likely to involve a far more somatic, intimate service relationship than the retail-banking context. While there will undoubtedly be exceptions, customers will tend to have warmer, more intimate feelings for their favorite café and cup of coffee than a new savings account or mortgage offers from a bank. Encounters between customers and their café, therefore, will be characterized by greater intimacy and communion than encounters between customers and a bank. Taken together, these differences are likely to increase customers’ likelihood of shopping around with an increase in market-related knowledge in a banking context. Managers might take into account the intimacy of their service offering where, perhaps, the act of educating customers may be perceived as a service augmentation and then invest appropriately in the training of employees to be able to deliver such initiatives.

Critically, our results showed that customer education (directed toward firm-specific elements of the service) has a strong, positive effect on customer loyalty and that distinction between firm-specific and market-related customer education is indeed possible. Customers find such a distinction natural, so there appears to be an opportunity for managers to build customer education campaigns paying careful attention to the mix of market-related and firm-specific elements. Our findings demonstrate that any educational efforts improve both firm-specific and market-related expertise. Managers might, however, craft educational initiatives such that they are anchored in the idiosyncrasies of the firm or, at the very least, make it clear to customers how “things work” in their own firms.

We should note that we are by no means advocating keeping customers in the dark about market-related opportunities, especially where they are essential for customer need fulfillment (e.g., alternative treatment regimens or nontreatment possibilities for sick patients). As Study 2 showed, educating customers about the market does not necessarily reduce their switching costs overall. Further, in conditions of high customer-firm interdependence or where the service is experiential, customer education may have customer-retention effects overall as service providers might be perceived by customers as more honest and caring.

Because of Internet access and social media, customers’ ability to “self-educate” has increased substantially. Even if firms choose not to invest in market-related customer education or try to avoid it, customers are likely to become increasingly informed about how competitors and markets operate over time. This may have implications for the way in which service providers engage with customers; static, one-size-fits-all approach customer education is inadvisable. At the very least, it suggests that firms should be aware of the changing levels of expertise of customers within their market and how they can play a part in shaping its development (Bell, Auh, and Smalley 2005; Bell and Eisingerich 2007).

Further, our relatively parsimonious model might benefit from the inclusion of additional, well-established drivers of loyalty (e.g., customer satisfaction, trust, etc.) in order to show the additional variance in loyalty explained by our customer expertise variables. Moreover, service employees are likely to play a key role in identifying customers’ receptivity to educational efforts. This has implications for managers needing to align service employees’ values and behavior with the firm’s overall educational mission (Ostrom et al. 2010; Sirianni et al. 2013) to ensure employees’ customer orientation (Yoo and Arnold 2016) and emotional competence (Delcourt et al. 2016) for delivering on the customer education promise.

Theoretical Implications and Future Research Directions

Customer knowledge is seen as a valuable asset to firms in their pursuit of customer centricity (Shah et al. 2006). The current research provides scholars with a typology of customer knowledge that can account for the nuance in the relationship between expertise and loyalty. While this typology could undoubtedly be extended (e.g., to customer life-cycle stages, purchase decision phases), we suggest that considering market-related and firm-specific education as a foundation provides researchers with the basis for understanding where education is likely to lead to stronger relationship outcomes and where it will lead to weaker outcomes. Future studies might consider the interactive effects of market-related and firm-specific education on customers’ expertise and their willingness to engage in positive word of mouth on social media platforms such as Facebook (Eisingerich et al. 2015). To the extent that each mutually reinforced the other’s main effect on customer expertise, one might observe increased (or decreased) gaps between customer-retention and customer-defection effects of education.

In previous research, a number of causes of information stickiness have been advanced including the nature of the knowledge itself, such as its codifiability, heterogeneity, and completeness (Turner and Makhija 2006); the volume of knowledge transferred; and the attributes of the senders and receivers of this knowledge (von Hippel 1994). Our finding, that perceived switching costs mediate (to different degrees) the impact of firm-specific and market-related customer education on loyalty, demonstrates the difficulty of redeploying certain types of knowledge elsewhere. It is likely, therefore, that knowledge codifiability—the ease with which knowledge can be articulated, partitioned, and catalogued for use in other settings (Turner and Makhija 2006)—is a key determinant of knowledge stickiness. In addition to this more general contribution to the literature on knowledge, we demonstrate this mechanism at work across varied service settings, thus answering the call for studies into the “transferability” of customer education across industries and contexts (Hennig-Thurau 2000, p. 74).

Notably, we found that it is possible to educate customers both in low-interdependency settings (i.e., cafés) as well as higher interdependency (cancer clinic) settings. However, one wonders about the exact returns to customer education (Hennig-Thurau 2000), particularly in high interdependency settings where education efforts may be costly and demand increased efforts in employee training. Important questions thus remain about potential cost-benefit trade-offs and the impact of customer education on a service firm’s future growth (Chun et al. 2015). When does it not payoff to educate one’s customers? Furthermore, we are conscious of the fact that both Studies 1 and 2 employed subjective measures of customer expertise, which might raise a question about our ability to generalize our findings. Wirtz and Mattila (2003), however, who studied market-related expertise, compared the impacts of objective versus subjective expertise on customer loyalty, showing that objective knowledge led to a significant decrease in loyalty while subjective knowledge did not. Potentially, therefore, our subjective measures of loyalty may be a conservative estimate of the true impact of expertise.

Footnotes

Authors’ Note

The authors contributed equally to this research. The order of authorship was decided by rolling the dice.

Acknowledgment

The authors thank their editor Mary Jo Bitner and anonymous reviewers for their insightful comments and helpful suggestions throughout the review process.

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) received no financial support for the research, authorship, and/or publication of this article.

Notes

Supplemental Material

The online [appendices/data supplements/etc.] are available at

References

Auh

Seigyoung

Bell

Simon J.

McLeod

Colin S.

Shih

Eric

(2007), “Co-Production and Customer Loyalty in Financial Services,” Journal of Retailing, 83 (3), 359–370.

Bell

Simon J.

Eisingerich

Andreas B.

(2007), “The Paradox of Customer Education: Customer Expertise and Loyalty in the Financial Services Industry,” European Journal of Marketing, 41 (5/6), 466–486.

Bell

Simon J.

Auh

Seigyoung

Smalley

Karen

(2005), “Customer Relationship Dynamics: Service Quality and Customer Loyalty in the Context of Varying Levels of Customer Expertise and Switching Costs,” Journal of the Academy of Marketing Science, 33 (2), 169–183.

Berry

Leonard L.

Bendapudi

Neeli

(2007), “Health Care a Fertile Field for Service Research,” Journal of Service Research, 10 (2), 111–122.

Brady

Michael K.

Cronin

Joseph J.

Jr . (2001), “Customer Orientation: Effects on Customer Service Perceptions and Outcome Behaviors,” Journal of Service Research, 3 (3), 241–251.

Brucks

Merrie

(1986), “A Typology of Consumer Knowledge Content,” Advances in Consumer Research, 13 (June), 58–63.

Burnham

Thomas A.

Frels

Judy K.

Mahajan

Vijay

(2003), “Consumer Switching Costs: A Typology, Antecedents, and Consequences,” Journal of the Academy of Marketing Science, 31 (2), 109–126.

Burton

Dawn

(2002), “Consumer Education and Service Quality: Conceptual Issues and Practical Implications,” Journal of Services Marketing, 16 (2), 125–142.

Churchill

Gilbert A.

Jr . (1979), “A Paradigm for Developing Better Measures of Marketing Constructs,” Journal of Marketing Research, 16 (1), 64–73.

10.

Chun

HaeEun H.

Park

C. Whan

Eisingerich

Andreas B.

MacInnis

Deborah J.

(2015), “Strategic Benefits of Low Fit Brand Extensions: When and Why?” Journal of Consumer Psychology, 25 (4), 577–595.

11.

Delcourt

Cécile

Gremler

Dwayne D.

van Riel

Alalrd C. R.

van Birgelen

Marcel J. H.

(2016), “Employee Emotional Competence: Construct Conceptualization and Validation of a Customer-Based Measure,” Journal of Service Research, 19 (1), 72–87.

12.

Dong

Beibei

Sivakumar

Evans

Kenneth R.

Zou

Shaoming

(2015), “Effect of Customer Participation on Service Outcomes: The Moderating Role of Participation Readiness,” Journal of Service Research, 18 (2), 160–176.

13.

Eisingerich

Andreas B.

Chun

Hae Eun

Liu

Yeyi

Jia

Bell

Simon J.

(2015), “Why Recommend a Brand Face-to-Face But Not on Facebook? How Word-of-Mouth on Online Social Sites Differs from Traditional Word-of-Mouth,” Journal of Consumer Psychology, 25 (1), 120–128.

14.

Eisingerich

Andreas B.

Bell

Simon J.

(2008), “Perceived Service Quality and Customer Trust: Does Enhancing Customers’ Service Knowledge Matter?” Journal of Service Research, 10 (February), 256–268.

15.

Eisingerich

Andreas B.

Bell

Simon J.

(2006), “Relationship Marketing in the Financial Services Industry: The Importance of Customer Education, Participation and Problem Management for Customer Loyalty,” Journal of Financial Services Marketing, 10 (4), 86–97.

16.

Gregan-Paxton

Jennifer

(2001), “The Role of Abstract and Specific Knowledge in the Formation of Product Judgments: An Analogical Learning Perspective,” Journal of Consumer Psychology, 11 (3), 141–158.

17.

Hausman

Angela V.

Mader

Deanna

(2004), “Measuring Social Aspects in the Physician/Patient Relationship,” Health Marketing Quarterly, 21 (3), 3–26.

18.

Hayes

A. F.

(2012), Process: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling, [white paper], (accessed October 10, 2013), [available at http://www.afhayes.com/public/process2012.pdf.]

19.

Heilman

Carrie M.

Bowman

Douglas

Wright

Gordon P.

(2000), “The Evolution of Brand Preferences and Choice Behaviors of Consumers New to a Market,” Journal of Marketing Research, 37 (May), 139–155.

20.

Hennig-Thurau

Thorsten

(2000), “Relationship Quality and Customer Retention through Strategic Communication of Customer Skills,” Journal of Marketing Management, 16 (1-3), 55–79.

21.

Hibbert

Sally

Winklhofer

Heidi

Temerak

Mohamed S.

(2012), “Customers as Resource Integrators: Toward a Model of Customer Learning,” Journal of Service Research, 15 (3), 247–261.

22.

Hogan

John E.

Lemon

Katherine N.

Rust

Roland T.

(2002), “Customer Equity Management: Charting New Directions for the Future of Marketing,” Journal of Service Research, 5 (August), 4–12.

23.

Josephson

Brett W.

Johnson

Jean L.

Mariadoss

Babu J.

Cullen

John

(2016), “Service Transition Strategies in Manufacturing: Implications for Firm Risk,” Journal of Service Research, 19 (2), 142–157.

24.

Kumar

Lemon

Katherine N.

Parasuraman

(2006), “Managing Customers for Value: An Overview and Research Agenda,” Journal of Service Research, 9 (November), 87–94.

25.

Levitt

Theodore

(1980), “Marketing Success through Differentiation—Of Anything,” Harvard Business Review, 58 (1), 83–91.

26.

Liu

Yeyi

Eisingerich

Andreas B.

Auh

Seigyoung

Merlo

Omar

Chun

Hae Eun

(2015), “Service Firm Performance Transparency: How, When, and Why Does it Pay off?” Journal of Service Research, 18 (4), 451–467.

27.

Loader

Ian

Goold

Benjamin

Thumala

Angelica

(2015), “Grudge Spending: The Interplay Between Markets and Culture in the Purchase of Security,” The Sociological Review, 63 (4), 858–875.

28.

Zhenfeng

Dubé

Laurette

(2011), “Process and Outcome Interdependency in Frontline Service Encounters,” Journal of Marketing, 75 (3), 83–98.

29.

Mende

Martin

van Doorn

Jenny

(2015), “Coproduction of Transformative Services as a Pathway to Improved Consumer Well-Being: Findings from a Longitudinal Study on Financial Counseling,” Journal of Service Research, 18 (3), 351–368.

30.

Merlo

Omar

Eisingerich

Andreas B.

Auh

Seigyoung

(2014), “Why Customer Participation Matters,” MIT Sloan Management Review, 55 (2), 81–88.

31.

Moeller

Sabine

Ciuchita

Robert

Mahr

Dominik

Odekerken-Schröder

Gaby

Fassnacht

Martin

(2013), “Uncovering Collaborative Value Creation Patterns and Establishing Corresponding Customer Roles,” Journal of Service Research, 16 (4), 471–487.

32.

Moreau

Page C.

Lehmann

Donald R.

Markman

Arthur B.

(2001), “Entrenched Knowledge Structures and Consumer Response to New Products,” Journal of Marketing Research, 38 (1), 14–29.

33.

NBC News (2016), “Consumers Prefer ‘Honest’ Brands—And Are Willing to Pay Extra for Them,” (accessed October 24, 2016), [available at http://www.nbcnews.com/business/consumer/consumers-prefer-honest-brands-are-willing-pay-extra-them-n644916.]

34.

Ostrom

Amy L.

Bitner

Mary Jo

Brown

Stephen W.

Burkhard

Kevin A.

Goul

Michael

Smith-Daniels

Vicki

Demirkan

Haluk

Rabinovich

Elliot

(2010), “Moving Forward and Making a Difference: Research Priorities for the Science of Service,” Journal of Service Research, 13 (February), 4–36.

35.

Park

C. Whan

Eisingerich

Andreas B.

Park

Jason Whan

(2013), “Attachment-Aversion (AA) Model of Customer-Brand Relationships,” Journal of Consumer Psychology, 23 (2), 229–248.

36.

Park

C. Whan

MacInnis

Deborah J.

Eisingerich

Andreas B.

(2016), Brand Admiration: Building a Business People Love. New York, NY: Wiley.

37.

Park

C. Whan

MacInnis

Deborah J.

Priester

Joseph

Eisingerich

Andreas B.

Iacobucci

Dawn

(2010), “Brand Attachment and Brand Attitude Strength: Conceptual and Empirical Differentiation of Two Critical Brand Equity Drivers,” Journal of Marketing, 74 (November), 1–17.

38.

Podsakoff

Philip M.

MacKenzie

Scott B.

Lee

Jeong-Yeon

Podsakoff

Nathan P.

(2003), “Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies,” Journal of Applied Psychology, 88 (5), 879–903.

39.

Polanyi

Michael

(1958), Personal Knowledge: Towards a Post-Critical Philosophy. London, UK: Routledge.

40.

Punj

Girish N.

Staelin

Richard

(1983), “A Model of Consumer Information Search Behavior for New Automobiles,” Journal of Consumer Research, 9 (4), 366–380.

41.

Robertson

Michael

Corbin

Lillian

(2005), “To Enable or to Relieve? Specialist Lawyers’ Perceptions of Client Involvement in Legal Service Delivery,” International Journal of the Legal Profession, 12 (1), 121–143.

42.

Seifert

Matthias

Siemsen

Enno

Hadida

Allegre L.

Eisingerich

Andreas B.

(2015), “Effective Judgmental Forecasting in the Context of Fashion Products,” Journal of Operations Management, 36 (May), 33–45.

43.

Shah

Denish

Rust

Roland T.

Parasuraman

Staelin

Richard

Day

George S.

(2006), “The Path to Customer Centricity,” Journal of Service Research, 9 (2), 113–124.

44.

Sirianni

Nancy J.

Bitner

Mary Jo

Brown

Stephen W.

Mandel

Naomi

(2013), “Branded Service Encounters: Strategically Aligning Employee Behavior with the Brand Positioning,” Journal of Marketing, 77 (November), 108–123.

45.

Sobel

M. E.

(1982), “Asymptotic Intervals for Indirect Effects in Structural Equations Models,” in Sociological Methodology, Leinhart

, ed. San Francisco, CA: Jossey-Bass, 290–312.

46.

Söderlund

Magnus

(2002), “Customer Familiarity and Its Effects on Satisfaction and Behavioral Intentions,” Psychology and Marketing, 19 (October), 861–879.

47.

The Economist (2014), “The Openness Revolution,” (accessed March 10, 2015), [available at http://www.economist.com/news/business/21636070-multinationals-are-forced-reveal-more-about-themselves-where-should-limits.]

48.

Turner

Karynne L.

Makhija

Mona V.

(2006), “The Role of Organizational Controls in Managing Knowledge,” Academy of Management Review, 31 (1), 197–217.

49.

von Hippel

Eric

(1994), “Sticky Information and the Locus of Problem Solving: Implications for Innovation,” Management Science, 40 (April), 429–439.

50.

Wirtz

Jochen

Mattila

Anna S.

(2003), “The Effects of Consumer Expertise on Evoked Set Size and Service Loyalty,” Journal of Services Marketing, 17 (7), 649–665.

51.

Yoo

Jaewon (Jay)

Arnold

Todd J.

(2016), “Frontline Employee Customer-Oriented Attitude in the Presence of Job Demands and Resources: The Influence Upon Deep and Surface Acting,” Journal of Service Research, 19 (1), 102–117.

52.

Zeithaml

Valarie A.

Berry

Leonard L.

Parasuraman

(1996), “The Behavioral Consequences of Service Quality,” Journal of Marketing, 60 (April), 31–46.

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