Profiting from customerrelationship management

The overlooked role of generative learningorientation

Dennis Herhausen and Marcus SchogelInstitute of Marketing, University of St Gallen, St Gallen,

Switzerland

Abstract

Purpose – This study aims to examine the direct and moderating effects of generative learning oncustomer performance.

Design/methodology/approach – The authors test the relationships between customerrelationship management (CRM) capabilities, generative learning, customer performance, andfinancial performance with a cross industry survey of CEOs and senior marketing executives from 199firms. Partial least squares are used to estimate the parameters of the resulting model.

Findings – The results reveal that generative learning affects customer performance directly.Moreover, the interaction of CRM capabilities and generative learning contributes to customerperformance. This finding suggests that firms need a well-developed generative learning orientation tofully benefit from translating new insights resulting from CRM capabilities into establishing,maintaining, and enhancing long-term associations with customers, and vice versa.

Research limitations/implications – The main limitations are those that typically apply tocross-sectional surveys. Although several steps were taken to reduce the concern of key informant biasand common method variance, dependent and independent variables were collected from the samesource at a single moment in time.

Practical implications – Ceteris paribus, an increase of generative learning orientation by one unit(seven-point scale) can command an increase of up to 7 percent of the average customer performancedue to its direct and interaction effect. Because even small changes in customer performance have astrong impact on financial performance, this finding indicates a remarkable and substantial result formanagers.

Originality/value – Though previous research provides evidence of the adaptive learningconsequences of CRM, a review of the literature reveals a lack of studies that analyze theimportance of generative learning orientation for successful CRM.

Keywords Generative learning, CRM capabilities, Customer performance, Learning,Customer relationship management

Paper type Research paper

1. IntroductionRecently, both managers and academics have raised issues about the performance effectsof customer relationship management (CRM), defined as a firm’s practices forestablishing, maintaining, and enhancing long-term associations with customers

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0025-1747.htm

The authors sincerely thank the anonymous reviewers and Domingo Ribeiro Soriano, Editor ofManagement Decision, for their insightful comments and suggestions.

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Management DecisionVol. 51 No. 8, 2013pp. 1678-1700q Emerald Group Publishing Limited0025-1747DOI 10.1108/MD-08-2012-0582

(Boulding et al., 2005; Reimann et al., 2010; Rigby and Ledingham, 2004). On the onehand, it is claimed that firms profit from their CRM and gain a competitive advantage inthe market (Hogan et al., 2002; Mithas et al., 2005; Payne and Frow, 2005). Supporting thisposition, Palmatier et al. (2006) find ample evidence in a meta-analysis that relationshipmarketing positively affects firm performance. On the other hand, there is growingskepticism about a direct and unconditional performance effect of CRM and its value forfirms (Homburg et al., 2007; Srinivasan and Moorman, 2005). Evidence for this position isprovided by the Gartner Group (2003) who find that approximately 70 percent of CRMprojects result in either losses or no bottom-line improvements in firm performance.Similarly, many studies report inconclusive findings regarding the performance effect ofCRM (for an overview see Reimann et al., 2010).

In the light of these conflicting positions, the mechanisms for enhancing CRMperformance are not well understood yet, and therefore managers have little guidanceon how to focus their CRM efforts. To date, few studies have considered importantintervening variables that affect the relationship between CRM and performance.Without identifying these variables, knowledge of the underlying process ofperformance improvement through CRM remains unclear. In fact, research needs toinspect more thoroughly moderating variables under which CRM results in higherperformance (Reimann et al., 2010; Shugan, 2005; Zablah et al., 2004). In particular, theassociation between CRM and learning remains unclear.

We address this shortfall and introduce the firm’s learning orientation as a crucialfactor for successful CRM. In general, organizational learning can be distinguished intoadaptive and generative learning (e.g. Argyris and Schon, 1978; Senge, 1990; Sinkula,1994; Slater and Narver, 1995). Adaptive learning occurs within a set of recognized andunrecognized constraints that reflect the organization’s assumptions about itsenvironment and itself (Slater and Narver, 1995). In contrast, generative learningemerges when the firm is willing to question long-held assumptions about its mission,customers, capabilities, or strategy (Slater and Narver, 1995). This differentiation ismeaningful for CRM. While adaptive customer or market-focused learning is anessential and implicit part of all CRM capabilities (e.g. Bohling et al., 2006; Bouldinget al., 2005; Jayachandran et al., 2004, 2005; Sun, 2006), generative learning has not beenassociated with CRM to date. This shortfall has negative consequences for theperformance implications of CRM because the value propositions that existingcustomers seek from firms can change quite rapidly and in significant ways (Flint et al.,2002). Many customer relationship programs fail to detect these changes because theyimply a mere adaptive learning orientation (Sun et al., 2006). Consequently, suchprograms target outdated customer needs, and their performance contribution mightdecrease over time (Stein and Smith, 2009).

Thus, we argue that generative learning – which entails exploring and learning newways of achieving results, critical reflections on shared assumptions, and questioningcommon perceptions (Atuahene-Gima et al., 2005) – contributes to CRM success. Ourstudy builds on existing research that emphasizes the relevance of organizationallearning for firms (Sinkula, 1994; Sinkula et al., 1997) and combines CRM capabilities,generative learning orientation, customer performance, and financial performance. Wedevelop hypotheses to argue that a generative learning orientation affects customerperformance both directly and through its moderating effect on CRM capabilities. Ourresults from a cross industry survey of 199 firms show that generative learning

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orientation as well as its interaction with CRM capabilities indeed enhance customerperformance, defined by the three key aspects of customer satisfaction, customer loyalty,and customer retention (Jayachandran et al., 2005). Thus, we contribute to currentknowledge by introducing the crucial role of a generative learning orientation forcustomer relationship success. These findings emphasize the importance of addressingcustomers’ latent needs in maintaining beneficial relationships.

The rest of our paper is structured as follows. First, we review the theoreticalfoundations and develop a conceptual model. Second, we specify the study hypotheses.Third, our empirical study is described in which the model is operationalized andtested. Finally, we discuss the results, derive conclusions, and present the implicationsof our findings.

2. Conceptual developmentFollowing Day (2000), differences in customer performance are attributable todifferences in underlying assets and capabilities. Therefore, resource-based theory(RBT) and dynamic capability view (DCV) serve as the overarching theoreticalframeworks for this study (e.g. Acedo et al., 2006; Barney et al., 2011). RBT views thefirm’s enduring competitive advantage related to the firm’s possession of unique,inimitable resources and capabilities, created over time through complex interactionsamong the firm’s resources, and based on developing, carrying, and exchanginginformation (Teece et al., 1997). More recently, the focus of much RBT research hasbeen on understanding the outcomes of resource deployment processes often referredto as organizational capabilities (Sirmon et al., 2007). An important part of thisliterature has highlighted the value of developing organizational capabilities as ameans of implementing firm strategies (e.g. Vorhies et al., 2009). Specifically, the firm’sCRM capabilities can be considered as core capabilities that provide firms with themeans to achieve a more loyal and sustainable customer base (Day, 2000).

However, current portrayals of the RBT emphasize its meaning as a contingencytheory of organizations (Barney et al., 2011; Ketchen et al., 2007). Strategic resources andcapabilities only have potential value, and realizing this potential requires alignmentwith other important organizational elements (Sirmon et al., 2011). According to thisargument, we posit that firms have to align their CRM capabilities and learningorientation to fully benefit from customer relationship activities. Only then firms are ableto achieve a high customer performance, which in turn leads to a high financialperformance (Boulding et al., 2005). In addition, Sirmon et al. (2007) draw a distinctionbetween the activities of stabilizing, enriching and pioneering. While stabilizing involvesimproving existing capabilities and enriching involves extending and elaboratingcurrent capabilities through activities such as adaptive learning, pioneering is a moreadvanced process that involves generative learning in order to create novel capabilities.This understanding points towards the DCV, which suggests that some firms are betterable than others at enhancing their overall competitive advantage by adding,reconfiguring, and deleting resources or competencies to address rapidly changingenvironments (Teece et al., 1997). Within the DCV, organizational learning, defined as aprocess by which organizations learn through interaction with their environment (Cyertand March, 1963), is of central importance (Teece, 2007). Moreover, learning is widelyacknowledged as an important success factor for firms (e.g. Sinkula, 1994; Slater andNarver, 1995; Stein and Smith, 2009; Tippins and Sohi, 2003).

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Organizational learning occurs by detecting a mismatch of outcome to expectation,which disconfirms theory in use (Sinkula, 1994). Consequently, the firm moves to errorcorrection, which results in a change in theory in use. If the subsequent correction leadsto a change in organizational norms and if the learning results from proactiveorganizational behavior and not in direct response to environmental events, then thelearning is said to be double-loop or generative and leads to new mental models (Bakerand Sinkula, 1999). Thus, generative learning differs from adaptive learning, whichoccurs within the context of current mental models. Generative learning orientation isthe degree to which top management attaches importance to and promotes thedevelopment of new skills, the enjoyment of learning, curiosity for new ways ofenhancing performance, preference for challenging work, and critical reflection on theassumptions of the organization (Atuahene-Gima et al., 2005).

We use this definition and combine a firm’s generative learning orientation with CRMcapabilities that determine its customer relationship practices. CRM capabilities aredefined as the core organizational processes that focus on leveraging long-termassociations with customers (Srivastava et al., 1999) and are a fundamental part ofmarketing (Boulding et al., 2005). Moreover, CRM is seen as a source of competitiveadvantage in the market which leads to increased customer performance. So far, customerperformance only represents a desirable outcome for customer-side consideration of afirm, and neglects the investments necessary to achieve higher customer satisfaction,loyalty, and retention. CRM, however, needs to demonstrate its value to overallperformance measures of the firm to point out that its benefits exceed its cost. Thus, allCRM activities need to be linked to financial metrics (Bohling et al., 2006). To account forthis requirement, we include financial performance in our framework, defined by overallfinancial performance, market share, growth, and profitability (Reinartz et al., 2004).Building on the theoretical perspective of RBV, DCV, and organizational learning as wellas the definition of our constructs, we next develop testable hypotheses.

2.1 CRM capabilities and customer performanceWe expect that a firm’s CRM capabilities, defined as core organizational processes thatfocus on establishing, maintaining, and enhancing long-term associations withcustomers, increase customer satisfaction, loyalty, and retention. Jayachandran et al.(2005) demonstrate the value of four distinct CRM capabilities: Customer relationshiporientation, customer-centric management systems, relational information processes,and CRM technology use. Customer relationship orientation reflects the culturalpropensity of an organization to undertake CRM. Such an orientation is rooted in thefirm’s overall culture, guiding the organization’s attitude toward both CRM and theimplementation of the necessary capabilities (Day, 2000). A customer-centricmanagement system refers to the structure and incentives that provide anorganization with the ability to build and sustain customer relationships. Hence, itenables the successful implementation of CRM (Day, 2000). Relational informationprocesses systematize the capture and use of customer information so that the firm’seffort to build relationships is not rendered ineffective by poor communication,information loss and overload, or inappropriate information use. These processesprovide guidelines to help firms manage customer information, to interact withcustomers in ways that are consistent with the demands of CRM, and enhancecustomer performance (Jayachandran et al., 2005). CRM technology use includes front

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office applications that support sales, marketing, and services, a data depository, andback office applications that help to integrate and analyze the data. The underlying ITinfrastructure highly affects knowledge management, empowering firms not only tostore vast amounts of customer data but also providing the necessary tools to capture,manage, and deliver reliable information, both internally and externally (Srinivasanand Moorman, 2005). Therefore, the use of CRM technology boosts the ability of firmsto sustain profitable customer relationships (Day and Van den Bulte, 2002). Tosummarize, given the high plausibility and previous support in the literature(e.g. Jayachandran et al., 2005; Palmatier et al., 2006; Payne and Frow, 2005; Reinartzet al., 2004; Wang and Feng, 2012), we expect that:

H1. The firm’s CRM capabilities are positively associated with its customerperformance.

2.2 Generative learning orientation and customer performanceWe hypothesize that in addition to its CRM capabilities, a firms learning orientationincreases customer satisfaction, loyalty, and retention. It is important to note that thefour-dimensional conceptualization of CRM capabilities implicitly includes adaptivelearning ( Jayachandran et al., 2005). Adaptive learning is an essential part of a firm’sCRM (Sun et al., 2006; Voss and Voss, 2008), incorporated in the definition of relationalinformation processes ( Jayachandran et al., 2004; Selnes and Sallis, 2003), and relatedto customer-led strategies that emphasize the expressed needs, for example obtainedfrom CRM (Boulding et al., 2005). Thus, CRM includes the process of adaptive learningby helping firms to better understand expressed needs of customers (Stein and Smith,2009; Sun et al., 2006).

Additionally, generative learning contributes to customer-related outcomes.Generative learning goes beyond customer-led strategies and is rather associatedwith unexpressed, latent needs (Narver et al., 2004). While lead the customer strategiesare particular relevant for firms that aim to serve new customers and new markets(Slater and Narver, 1998), identifying latent needs also affect satisfaction and retentionof existing customers. Customers change their needs continuously, and latent needsmay become important to retain relationships and satisfaction (Flint et al., 2002). Firmsthat succeed in addressing latent needs exhibit a proactive customer orientation incontrast to firms with a responsive customer orientation that only addresses expressedneeds (Narver et al., 2004). More importantly, customers explicitly distinguish betweenfirms responsiveness and proactivity, and value firms that are able to proactivelyanticipate their needs (Blocker et al., 2010; Tuli et al., 2007). The firm’s ability tocontinuously generate intelligence about customers’ latent needs, and about how tosatisfy those needs, is essential for it to create superior customer value (Slater andNarver, 2000). In other words, firms with a generative learning orientation seek tobetter understand customers’ unobvious needs in order to respond with offers,products, and services incorporating an adequate value proposition that serveschanging needs. We assume that this value proposition will increase both customers’satisfaction and loyalty, and lead to long lasting relationships with customers. Forthese reasons, we expect that:

H2. The firm’s generative learning orientation is positively associated with itscustomer performance.

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2.3 Conditional effect of CRM capabilities and generative learningRecent research on RBT emphasizes the importance of the interaction between thefirm’s know-what knowledge resources, for example insights from a generativelearning orientation, and its complementary know-how deployment capabilities, forexample implementation knowledge incorporated in CRM capabilities (Grant, 1996;Sirmon et al., 2007; Sirmon et al., 2011). This notion suggests that the firm’sgenerative learning orientation and CRM capabilities may interact to enable highercustomer satisfaction, higher loyalty, and longer customer relationships than itscompetitors. There are at least three specific reasons why we expect such aninteraction.

First, the critical appraisal involved in generative learning orientation ensures thequality, relevance, and timely use of customer information within CRM. Sun et al.(2006) investigated information quality in CRM and pointed out the importance ofextracting hidden predictive information from large databases to identify valuablecustomers, predict future behaviors, and estimate customer value. In turn, CRMsupports generative learning by providing the necessary implementation processesand helping organizations to better align to the evolving needs of customers (ct.Landroguez et al., 2011).

Second and more generally, CRM capabilities implicitly include adaptive learning(Jayachandran et al., 2005). Thus, its interaction with generative learning can beviewed as ambidextrous learning (e.g. Cegarra-Navarro et al., 2011; Lee and Huang,2012). Specifically, researchers presume that proactivity associated with generativelearning and responsiveness associated with adaptive learning can complement eachother (e.g. Atuahene-Gima et al., 2005; March, 1991; Slater and Narver, 1998). Inother words, the productive capacity of one capability can be enhanced through itsinteraction with the other. In the context of CRM, customers are constantlyevaluating how their changing needs, both expressed and latent, are being met bythe firm (Boulding et al., 2005). As customers evaluate, it is likely that theirthoughts about these two capabilities coalesce: when the perceived level of proactive(responsive) customer orientation increases, customer attitudes about the efficacy ofresponsive (proactive) customer orientation will become more positive (Blocker et al.,2010).

Third, from the perspective of RBT, generative learning orientation and CRMcapabilities may each be viewed as an individual source of competitive advantage.Thus, the interaction between the two possesses the characteristic of assetinterconnectedness which makes it particularly difficult for competitors to identifythe source of a firm’s observed performance advantage (Teece et al., 1997). Moreover,valuable and difficult-to-imitate strategic actions may arise out of generative learningthat use existing resources (i.e. CRM capabilities) in new ways (Sirmon et al., 2011).Consequently, a competitor would need to acquire both the interconnected generativelearning orientation and CRM capabilities to compete in CRM, and understandmanagers’ actions based on generative learning to effectively structure, bundle andleverage a firm’s CRM capabilities. In summary and based on the three argumentsabove, we hypothesize that:

H3. The interaction between the firm’s CRM capabilities and generative learningorientation is positively associated with its customer performance.

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2.4 Customer performance and financial performanceWe also include financial performance measures in our model to demonstrate thatbenefits of a higher customer performance exceed its respective costs, and thus thevalue of this concept to managers (Bohling et al., 2006). Previous research has shownthat firms who are able to increase customer satisfaction are likely to improve both thelevel and the stability of net cash flows (Fornell et al., 2006). Furthermore, increasingthe loyalty of customers and retaining existing customers are key drivers of firmprofitability (Gupta and Zeithaml, 2006). Because of these arguments, we hypothesizethat:

H4. The firm’s customer performance is positively associated with its financialperformance.

The hypotheses as well as the control variables used in the empirical study aresummarized in Figure 1. Next, we describe the research methods, including datacollection, measurement, and analysis.

3. Research methods3.1 Data collection and samplingPrimary data for testing our hypotheses were collected via a mail survey of firms inSwitzerland operating in consumer and business markets offering both services andgoods (including durable and nondurable goods). For an initial set of firms fromvarious industries, we purchased addresses from a commercial provider (n ¼ 1,548).The unit of analysis is a business unit within a firm or (if no specialization intodifferent business units existed) the entire firm. The resulting sample represents anappropriate context for three main reasons. First, Switzerland is a highly developed,de-regulated market with strong competition, a setting were CRM is of particularimportance (Boulding et al., 2005). Second, we were interested in general direct andmoderating effects of generative learning on customer performance, regardless ofindustry and firm-specific characteristics. Hence, a cross-industry sample isappropriate (Rindfleisch et al., 2008). Moreover, the empirical results will be lessaffected by the uncontrollable, idiosyncratic effects of any particular sector, thusallowing for a higher degree of external validity (Tippins and Sohi, 2003). Third, thegeographical proximity of the research team with the empirical setting facilitatedcontrol over the quality and consistency of the study data.

Figure 1.Summary of thehypotheses

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Given our focus on CRM capabilities and generative learning orientation the surveywas mailed to the senior manager responsible for marketing (ct. Jayachandran et al.,2005; Reimann et al., 2010; Reinartz et al., 2004). Recommendations for valid data fromkey informants were followed (Kumar et al., 1993; Podsakoff and Organ, 1986),including assurance of confidentiality and anonymity, a self-assessment of the degreeof knowledge (“How knowledgeable are you regarding the CRM capabilities of yourbusiness unit?” with 1 ¼ “Very low knowledge” and 7 ¼ “Very high knowledge”),clear explanations of the usefulness of the research to the respondent’s firm, andincentivizing participants with a research summary that would be meaningless in caseof imprecise answers. Furthermore, respondents were asked to consult with otherknowledgeable organizational members when completing the questionnaire.

After a follow-up, we received 231 usable questionnaires, for an effective responserate of 15 percent. This response rate is comparable to other top management studiesregarding CRM (e.g. Jayachandran et al., 2005; Reimann et al., 2010). We obtainedapproximately a third of the responses after the follow-up. Because key informantaccuracy is driven by the hierarchical position of the respondent (Homburg et al., 2012)and by the competency of the respondent regarding the issue of interest (Kumar et al.,1993), we eliminated surveys from respondents in an inappropriate position in the firm(25 questionnaires) and from those who rated their relevant knowledge as below five ona seven-point scale (seven questionnaires) and retained 199 useable surveys.Information on the composition of the final sample appears in Table I. Our samplecovers a broad range of firms in terms of industry and size. Approximately 90 percentof participants are chief executive officers, chief marketing officers, or chief marketingand sales officers. Interestingly, only five percent of the participating firms have adedicated customer relationship position. The mean respondent knowledge score of6.14 supports the validity of the key informant data. An extrapolation approach toassess nonresponse bias (Armstrong and Overton, 1977) revealed no significantdifferences between early and late respondents on the main survey constructs and keydemographics.

3.2 MeasurementCRM capabilities are defined as a firm’s capabilities to establish, maintain, andenhance long-term associations with customers. In operationalizing CRM capabilities,we followed previous research (e.g. Jayachandran et al., 2005; Reimann et al., 2010;Wang and Feng, 2012) and measured CRM capabilities as a second-order construct. Inits entirety, the CRM capabilities measure captured major facets of firm’s practicesregarding customer-company relationships, as well as the major sub-processes withinthose facets. The four first-order dimensions include customer relationship orientation,customer-centric management system, relational information processes, and CRMtechnology use.

We developed a pool of items for relational information processes and CRMtechnology use based on interviews with CRM experts from 12 firms and submittedthem to four researchers for review. Refined scales were pretested with 27 members ofa MBA class. The final scales used in the survey consisted of three items (relationalinformation processes) and seven items (CRM technology use), respectively. Therelational information processes measure includes items that refer to defined processesto constantly generate information about customers, to disseminate customer

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information within the organization, and to analyze and store customer information.The measure of CRM technology use has seven aspects: relational database, integratedIT infrastructure, intelligent CRM software, sales and marketing staff support, datadepository, integration and analysis the of customer data, and promotion of CRM ITinfrastructure. Customer relationship orientation and customer-centric managementsystem were measured using scales from Jayachandran et al. (2005). Customerrelationship orientation capture the degrees to which employees are encouraged tofocus on customer relationships; customer relationships are considered to be a valuableasset; senior management emphasizes the importance of customer relationships; andretaining customers is considered to be a top priority. Customer-centric managementsystem assessed the organization and coordination of the firm around customers andtheir needs and specific incentives that enable the firm to focus on CRM.

A scale for generative learning orientation was adapted from Atuahene-Gima et al.(2005). This scale measures the extent to which challenging work is important; new waysof achieving results are explored and learned; shared assumptions are critically reflected;and perceptions of the market and the competition are questioned; all items refer to thetop management within the business unit. An existing scale was extended to measurecustomer performance relative to competitors (Jayachandran et al., 2005), including theitems “increasing customer satisfaction”, “retaining existing customers”, and “increasingloyalty of customers”. Financial performance was measured by a scale borrowed from

%

A. IndustriesConsumer goods 18Industrial goods 21Retail and distribution 14Financial services 23IT services 22Other services 2

B. Position of respondentsChief executive officer 38Chief marketing officer 39Chief marketing and sales officer 11Customer relationship manager 5Other 7

C. Annual revenue of the business unit,$25 million 23$25 million-$99 million 38$100 million-$249 million 17$250 million-$999 million 13.$1,000 million 9

D. Business unit size,50 employees 850-199 employees 36200-499 employees 23500-1,000 employees 11.1,000 employees 22

Table I.Sample composition

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Reinartz et al. (2004) consisting of the items “achieving overall financial performance”,“attaining market share”, “attaining growth”, and “current profitability”.

In addition, four covariates were incorporated in the survey to control for industryand business unit heterogeneity. Following prior research (e.g. Jayachandran et al.,2004, 2005), we collected data on consumer demandingness, competitive intensity, firmsize, and industry focus. Customer demandingness refers to the extent to whichcustomers have clout over the firm. In markets where customers are very demanding,firms will be compelled to develop better CRM capabilities without benefiting fromthem. The scale for consumer demandingness is borrowed from Li and Calantone(1998) and reflects customers demand for product quality and reliability, sophisticationin terms of technical specifications, and sensitivity to product cost. Competitiveintensity, the extent of interfirm rivalry, might hurt financial performance because as itdrives up costs and diminishes profit margins. More specifically, under conditions ofhigh competition, customers have many alternative options to satisfy their needs andwants. Competitive intensity was measured by using the established scale of Jaworskiand Kohli (1993). Furthermore we dummy coded each firm as primarily a B2C or B2Bbusiness and used employee numbers as an indicator of business unit or firm size tofurther rule out industry or firm specific influences on financial performance. Therespective item indicators for all constructs are contained in the appendix.

3.3 Measure reliability and validityAnalyses for each reflective first-order construct revealed that the 44 indicators loadsignificantly on their intended factor, which indicates convergent validity among theitems of each scale (all factor loadings . 0.63). Cronbach’s alpha and compositereliability of all constructs exceed the recommended minimum of 0.70 and signal scalereliability (Bagozzi and Yi, 1988). Moreover, we checked the significance of theloadings with a bootstrap procedure (500 sub-samples) to obtain t-statistic values.They are all significant. Together with content validity established by expertagreement, these results provide empirical evidence for construct validity. We thenassessed discriminant validity of the latent variables using Fornell and Larcker’s(1981) criterion, which requires that the square root of each latent variable’s averagevariance extracted (AVE) is at least 0.70 and greater than the latent variable’scorrelation with any other construct in the model. As we show in Table II, each latentvariable meets Fornell and Larcker’s criterion in support of discriminant validity.

Following the conceptualization of comparable second-order constructs like marketorientation (Jaworski and Kohli, 1993) or marketing capabilities (Morgan et al., 2009),and the recommendations of Jarvis et al. (2003), we conceptualized CRM capabilities asa reflective first-order, formative second-order construct with four sub-dimensions(customer relationship orientation, customer-centric management system, relationalinformation processes, CRM technology). More specifically, we used item parcels toassess the second-order construct (Bagozzi and Edwards, 1998). Following therecommendations of Diamantopoulos and Winklhofer (2001), we evaluated indicatorcollinearity and external validity for the four CRM factors. All variance inflationfactors were well below the common cut-off value of 10, and all four dimensions weresignificantly correlated with the conceptually related statement “Our organization hasa strong orientation towards customer relationships” external to the index ( p , 0.01).Subsequently, we examined the loadings of the dimensions on the second-order factor

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Table II.Correlations anddiscriminant validity

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using PLS analysis. The results provided support for the proposed conceptualization ofCRM capabilities as a formative second-order construct. The weights were all positive(0.33, 0.19, 0.44, 0.29) and significant ( p , 0.01). The final measurement model for CRMcapabilities is displayed in Figure 2.

Figure 2.Second-order four-factor

model of CRM capabilities

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3.4 PLS path model analysisTo test our hypotheses, we apply partial least squares (PLS) path modeling to estimateour theoretical model using the software application SmartPLS (Ringle et al., 2005).While other methods of structural equation modeling – such as the covariance-basedLISREL – are indeed more widespread, PLS was finally chosen because it placesminimal restrictions on sample size, is tolerant regarding residual distribution, andfavors the estimation of interaction effects (e.g. Chin et al., 2003; Chin and Newsted,1999; Hair et al., 2012; Henseler and Chin, 2010). We incorporate the interaction effectbetween CRM capabilities and generative learning into the pathmodel by applying acommonly-used product-indicator approach (Henseler and Fassott, 2010). Because thedata for all variables came from single respondents in a one-time survey, key informantbias and common method variance might influence some postulated relations in thePLS path model. However, given that the constructs measured refer to the present andaddress salient events, our informants are in high hierarchical positions with longtenure, and we followed established guidelines to increase key informant accuracy, wedo not expect that key informant bias is a severe problem in our data (Homburg et al.,2012). Several steps were taken to reduce the concern of common method variance.Respondents were assured anonymity, encouraged to respond candidly, and itemswere worded to minimize ambiguity (Podsakoff et al., 2003). We also adopted themarker variable approach (Lindell and Whitney, 2001). More specifically, we appliedLohmoller’s (1989) extended PLS algorithm and used several marker variables toestimate the loadings on every item in the PLS path model in addition to each item’sloading on its theoretical construct (ct. Sattler et al., 2010). A comparison of theestimated path model relationships with and without each of the additional markervariables shows no notable differences, and all theorized paths maintain their level ofstatistical significance. Thus, though common method variance cannot be completelyruled out, neither the traditional single-factor test nor the marker variable approachsuggests a threat of common method bias. We checked the latent constructs in the pathmodel for multicollinearity. All variance inflation factors have a value of less than 2,which is clearly below the critical value of 10. Thus, we perceive no severemulticollinearity problems (Belsey et al., 1980; Vorhies et al., 2009).

4. Discussion of resultsIn Figure 3, we provide the parameter estimates of the direct and interaction effectsfrom CRM capabilities and generative learning on customer performance and financialperformance. H1 examines the effect of a firm’s CRM capabilities on customerperformance. We argue that firms with high CRM capabilities will achieve superiorcustomer satisfaction, customer loyalty, and customer retention. This argument issupported by the significant and positive coefficient of the parameter (b ¼ 0.62;p , 0.01).

H2 explores the relationship between a firm’s generative learning orientation and itscustomer performance. We predict that firms with a pronounced generative learningorientation better understand customers’ changing needs and are able to respond withoffers incorporating an adequate value proposition that serve these needs, and thusincrease customer performance. The coefficient for the path between generativelearning and customer performance is positive and significant (b ¼ 0.18, p , 0.05),supporting H2.

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H3 explores the relationship between CRM capabilities, generative learning andcustomer performance. We expect that the contribution of CRM capabilities tocustomer performance increases if a firm has a high level of generative learningorientation. The coefficient for the interaction between CRM capabilities andgenerative learning is positive and significant (b ¼ 0.13, p , 0.05) and supports H3.The result indicates that firms with a high generative learning orientation will seegreater returns from their CRM capabilities, and vice versa. The correspondinginteraction graph is depicted in Figure 4.

H4 examines the effect of customer performance on financial performance. The pathcoefficient reveal that customer performance has a strong positive effect on financialperformance (b ¼ 0.46, p , 0.01). Additionally, we find that customer demandingness,as a covariate, has a significant negative impact on customer performance (b ¼ 20.12;p , 0.10), while competitive intensity, business unit size, and industry type do nothave a significant effects on financial performance.

To test whether customer performance mediates the relationships between theantecedents and financial performance, we conduct a mediation analysis. Inconformance with the nonparametric PLS path modeling approach, we apply anonparametric bootstrapping procedure to test the significance of the mediating effects

Figure 4.The moderating effect of

generative learning oncustomer performance

Figure 3.Results for the structural

model

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(Henseler et al., 2009). While the Sobel test is the most commonly used method to assessmediating effects, simulation studies reveal that bootstrapping offers a betteralternative, at least in PLS path models, because it does not impose any distributionalassumptions (MacKinnon et al., 2002). The results indicate full mediation for generativelearning and the interaction of CRM capabilities and generative learning but onlypartial mediation for CRM capabilities (mediation accounted for 54 percent ofvariance). Following this finding, we revised the conceptual model and introduced anadditional direct path from CRM capabilities to financial performance. The structuralmodel was evaluated using the R 2 for the dependent constructs and the Stone-GeisserQ2 test for predictive relevance. Both R 2 values and Q2 values of customer performance(R 2 ¼ 0.534; Q2 ¼ 0.524) and financial performance (R 2 ¼ 0.444; Q2 ¼ 0.372) suggestgood explanatory power of the model.

5. ConclusionAlthough extant marketing literature has emphasized the importance of a generativelearning orientation for new product performance and market information processes(e.g. Baker and Sinkula, 2007; Slater and Narver, 1995), its relevance for CRM has notreceived adequate attention yet. Thus, an important contribution of our study is thedemonstration that generative learning enhances a firm’s customer relationships. Thedirect effect of generative learning on customer performance is accompanied by asignificant interaction between a firm’s CRM capabilities and generative learningorientation. This finding supports the complementary nature of insights obtained fromgenerative learning (know-what knowledge resources) and CRM capabilities(know-how deployment capabilities). We conclude that firms need well developedCRM capabilities to fully benefit from translating new insights resulting fromgenerative learning into establishing, maintaining, and enhancing long-termassociations with customers, and vice versa. Furthermore, the significant interactioneffect underlines recent research that emphasize the importance of balancing differentlearning types (e.g. Cegarra-Navarro et al., 2011; Lee and Huang, 2012).

We also find a direct effect from CRM capabilities to financial performance.Although not hypothesized, this finding adds to the literature by underlining theinternal positive effects of CRM, to date often neglected in the assessment of itsperformance implications (Boulding et al., 2005). In addition to help firms betterunderstand customer’s needs, shape appropriate responses to customer behavior andeffectively differentiate offerings, CRM may also contribute to operational efficiencies.Examples of such contributions include the integration of customer knowledge intosuperior production processes (Reimann et al., 2010) or the concentration on aprofitable customer group (Reinartz et al., 2004). Thus, the benefits of CRM capabilitiesexceed those that can be measured in terms of customer satisfaction, customerretention, and customer loyalty.

The managerial implications of this study are straightforward. Both CRMcapabilities and generative learning orientation affect customer performance directly.Furthermore, CRM capabilities affect financial performance directly, and both affectfinancial performance indirectly via customer performance. Thus, managers shouldstrive for responsiveness to customers’ expressed needs as well as for proactiveness tocustomers’ latent needs to maintain long lasting relationships. We believe that thisfinding is crucial because many firms seem to have established a dominant focus on

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responsiveness to customers. In line with this argument, the mean for generativelearning orientation in our study (4.53, Table II) was significantly lower than forcustomer relationship orientation, customer-centric management system, andrelational information processes (all p , 0.01). This rating suggests that many firmshave opportunities to improve their generative learning orientation.

Another important implication for managers is that generative learning orientationis crucial for firms to fully benefit from their competencies in CRM. In addition to itsdirect effect, generative learning also increases the performance contribution of CRMcapabilities. In total and all other variables being equal, an increase of generativelearning orientation by one unit (seven-point scale) can command an increase of up to0.31 in customer performance. In perspective, the average customer performance in oursample was 4.96 (Table II). Thus, an increase by 0.31 equals almost 7 percent of theaverage customer performance. Since even small changes in customer performancehave a strong impact on financial performance (also ct. Fornell et al., 2006; Gupta andZeithaml, 2006), this finding indicates a remarkable and substantial result formanagers. Managers should therefore incorporate double-loop learning that leads tonew mental models into their firm’s CRM activities. Appropriate measures to increasegenerative learning include a strong commitment from top management, a sharedvision within the responsible department, and more generally open-mindedness fornew influences of all employees involved (Sinkula et al., 1997). In doing so, firms canbetter extract hidden information from large databases, predict future behaviors andpreferences, and eventually identify valuable customers.

Though our findings are suggestive, we need to acknowledge some limitations.First, we rely on survey data for our dependent and independent variables which mayinvolve a self-serving bias. To validate our customer performance measure, objectivedata on customer behavior from the participating firms would have been desirable.However, due to data security reasons, we were not able to collect such data. Second,we were only able to collect data from one key informant from each firm. Although wefollowed recommendations to improve data validity (e.g. confidentiality, incentives,clear explanation of usefulness, tests for common method variance) and informantswere well qualified, we nevertheless face the usual limitations inherent in keyinformant survey designs. Third, this work is based on evidence from firms in manydifferent businesses and inherits a cross-sectional nature. Though we used widespreadcontrol variables and this type of sample appears appropriate for our research purpose(Rindfleisch et al., 2008), we cannot claim to have identified one specific optimalbehavior. Furthermore, we established the relationship between CRM capabilities,generative learning orientation, customer performance, and financial performance at asingle moment in time. More appropriate conclusions about causality, i.e. theperformance of a given firm shifting its relative emphasis on CRM and generativelearning, require a longitudinal study approach and should be undertaken in futureresearch.

Beyond these limitations, additional fruitful research directions have emerged fromthis study. We conceptualize and test an integrative model specifying the linksbetween responsive CRM capabilities, proactive generative learning, and performanceoutcomes. With the exception of Blocker et al. (2010), generative learning and proactivecustomer orientation have mainly been associated with new product development andinnovation performance in empirical studies. Thus, our quantitative results support

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the importance of addressing customers’ latent needs in maintaining beneficialrelationships developed with qualitative studies (e.g. Flint et al., 2002; Tuli et al., 2007).Furthermore, the results suggest that the benefits of CRM capabilities and generativelearning support each other. However, achieving and maintaining a combination ofresponsiveness (i.e. CRM capabilities) and proactiveness (i.e. generative learning) isdifficult and resource-intensive (Ketchen et al., 2007). This calls attention to moreresearch efforts to understand the different organizational contexts in which firms areable to combine both capabilities in an effective way to achieve ambidexterity and gaintheir respective CRM benefits simultaneously. Overall, this study suggests thatscholars should begin to rethink the traditional assumptions about the role ofresponsiveness and proactiveness in CRM and their impact on maintaining beneficialrelationships. Hopefully, the link of generative learning and CRM developed andsupported in this study will stimulate researchers in future research endeavor.

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Appendix

Scale and measurement items a/AVE/CR

Item loadings

CRM capabilities (new second-order scale) (formative)Customer relationship orientation 0.74Customer-centric management system 0.85Relational information processes 0.79CRM technology use 0.77

Customer relationship orientation (Jayachandran et al., 2005) (reflective) 0.84/0.68/0.89Our employees are encouraged to focus on customer relationships 0.84In our organization, customer relationships are considered to be a valuable asset 0.90Our senior management emphasizes the importance of customer relationships 0.90In our organization, retaining customers is considered to be a top priority 0.64

Customer-centric management system (Jayachandran et al., 2005) (reflective) 0.73/0.50/0.83We focus on customer needs while designing business processes 0.71In our organization, various functional areas coordinate their activities toenhance the quality of customer experience 0.70A key criterion used to evaluate our customer contact employees is the quality oftheir customer relationships 0.70In our organization, employees receive incentives based on customer satisfactionmeasures 0.65In our organization, business processes are designed to enhance the quality ofcustomer interactions 0.78We organize our company around customer-based groups rather than product orfunction-based groupsf

Relational information processes (new scale based on interviews) (reflective) 0.84/0.75/0.90We have defined processes to constantly generate information about ourcustomers 0.89We have defined processes to disseminate customer information within theorganization 0.86We have defined processes to analyze and store customer information 0.86

CRM technology use (new scale based on interviews) (reflective) 0.94/0.72/0.95Relational databases or data warehouse provide a full picture of individualcustomer histories 0.86Customer information is delivered through a highly integrated IT infrastructure 0.89The CRM software triggers or supports new ways to meet customer needs 0.88Front office applications support our sales and marketing staff 0.82We use sophisticated ways of data depository 0.76Back office applications integrate and analyze the obtained customer data 0.83The use of CRM IT infrastructure is promoted within the organization 0.91

Generative learning orientation (Atuahene-Gima et al., 2005) (reflective) 0.83/0.66/0.89The opportunity to do challenging work is important for us 0.77We are always exploring and learning new ways of achieving results 0.81We are not afraid to reflect critically on shared assumptions 0.89We continually question our perceptions of the market and the competition 0.77We prefer to work on tasks that force us to learn new thingsf

We strive for the opportunity to extend the range of our abilitiesf

(continued )

Table AI.Scale items for construct

measurement

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Corresponding authorDennis Herhausen can be contacted at: dennis.herhausen@unisg.ch

Scale and measurement items a/AVE/CR

Customer performance (Jayachandran et al., 2005) (reflective) 0.79/0.72/0.88Relative to your competitors, how has your organization performed with respect to:Increasing customer satisfaction 0.89Retaining existing customers 0.88Increasing loyalty of customers (new item) 0.78

Financial performance (Reinartz et al., 2004) (reflective) 0.93/0.82/0.95Relative to your competitors, how has your organization performed with respect to:Achieving overall financial performance 0.95Attaining market share 0.92Attaining growth 0.90Current profitability 0.87

Consumer demandingness (Li and Calantone, 1998) (reflective) 0.79/0.69/0.87Consumers are demanding for product quality and reliability 0.83Consumers are sophisticated in terms of technical specifications 0.84Consumers are sensitive to product cost 0.82

Competitive intensity ( Jaworski and Kohli, 1993) (reflective) 0.77/0.51/0.84Competition in our industry is cutthroat 0.74There are many “promotion wars” in our industry 0.68Anything that one competitor can offer others can match readily 0.81Price competition is a hallmark of our industry 0.71One hears of a new competitive move almost every day 0.63Business unit size n.a.Number of employees in business unitIndustry type n.a.

Notes: (1) Business to business versus; (2) business to consumer; Customer performance and financialperformance were surveyed with a Likert scale (1 ¼ much worse than competitors; 7 ¼ much betterthan competitors); Except business unit size, and industry type all other items were surveyed with aLikert scale (1 ¼ strongly disagree; 7 ¼ strongly agree); All items were administered in German;f ¼ Items deleted from scaleTable AI.

MD51,8

1700

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