Profiting from customer relationship management The overlooked role of generative learning orientation

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1 The current issue and full text archive of this journal is available at MD 1678 Profiting from customer relationship management The overlooked role of generative learning orientation Dennis Herhausen and Marcus Schögel Institute of Marketing, University of St Gallen, St Gallen, Switzerland Abstract Purpose This study aims to examine the direct and moderating effects of generative learning on customer performance. Design/methodology/approach The authors test the relationships between customer relationship management (CRM) capabilities, generative learning, customer performance, and financial performance with a cross industry survey of CEOs and senior marketing executives from 199 firms. 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 customer performance. This finding suggests that firms need a well-developed generative learning orientation to fully 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 to cross-sectional surveys. Although several steps were taken to reduce the concern of key informant bias and common method variance, dependent and independent variables were collected from the same source 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 performance due to its direct and interaction effect. Because even small changes in customer performance have a strong impact on financial performance, this finding indicates a remarkable and substantial result for managers. Originality/value Though previous research provides evidence of the adaptive learning consequences of CRM, a review of the literature reveals a lack of studies that analyze the importance of generative learning orientation for successful CRM. Keywords Generative learning, CRM capabilities, Customer performance, Learning, Customer relationship management Paper type Research paper Management Decision Vol. 51 No. 8, 2013 pp q Emerald Group Publishing Limited DOI /MD Introduction Recently, both managers and academics have raised issues about the performance effects of customer relationship management (CRM), defined as a firm s practices for establishing, maintaining, and enhancing long-term associations with customers The authors sincerely thank the anonymous reviewers and Domingo Ribeiro Soriano, Editor of Management Decision, for their insightful comments and suggestions.

2 (Boulding et al., 2005; Reimann et al., 2010; Rigby and Ledingham, 2004). On the one hand, it is claimed that firms profit from their CRM and gain a competitive advantage in the market (Hogan et al., 2002; Mithas et al., 2005; Payne and Frow, 2005). Supporting this position, Palmatier et al. (2006) find ample evidence in a meta-analysis that relationship marketing positively affects firm performance. On the other hand, there is growing skepticism about a direct and unconditional performance effect of CRM and its value for firms (Homburg et al., 2007; Srinivasan and Moorman, 2005). Evidence for this position is provided by the Gartner Group (2003) who find that approximately 70 percent of CRM projects result in either losses or no bottom-line improvements in firm performance. Similarly, many studies report inconclusive findings regarding the performance effect of CRM (for an overview see Reimann et al., 2010). In the light of these conflicting positions, the mechanisms for enhancing CRM performance are not well understood yet, and therefore managers have little guidance on how to focus their CRM efforts. To date, few studies have considered important intervening variables that affect the relationship between CRM and performance. Without identifying these variables, knowledge of the underlying process of performance improvement through CRM remains unclear. In fact, research needs to inspect more thoroughly moderating variables under which CRM results in higher performance (Reimann et al., 2010; Shugan, 2005; Zablah et al., 2004). In particular, the association between CRM and learning remains unclear. We address this shortfall and introduce the firm s learning orientation as a crucial factor for successful CRM. In general, organizational learning can be distinguished into adaptive and generative learning (e.g. Argyris and Schön, 1978; Senge, 1990; Sinkula, 1994; Slater and Narver, 1995). Adaptive learning occurs within a set of recognized and unrecognized constraints that reflect the organization s assumptions about its environment and itself (Slater and Narver, 1995). In contrast, generative learning emerges when the firm is willing to question long-held assumptions about its mission, customers, capabilities, or strategy (Slater and Narver, 1995). This differentiation is meaningful for CRM. While adaptive customer or market-focused learning is an essential and implicit part of all CRM capabilities (e.g. Bohling et al., 2006; Boulding et al., 2005; Jayachandran et al., 2004, 2005; Sun, 2006), generative learning has not been associated with CRM to date. This shortfall has negative consequences for the performance implications of CRM because the value propositions that existing customers 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 they imply a mere adaptive learning orientation (Sun et al., 2006). Consequently, such programs target outdated customer needs, and their performance contribution might decrease over time (Stein and Smith, 2009). Thus, we argue that generative learning which entails exploring and learning new ways of achieving results, critical reflections on shared assumptions, and questioning common perceptions (Atuahene-Gima et al., 2005) contributes to CRM success. Our study builds on existing research that emphasizes the relevance of organizational learning for firms (Sinkula, 1994; Sinkula et al., 1997) and combines CRM capabilities, generative learning orientation, customer performance, and financial performance. We develop hypotheses to argue that a generative learning orientation affects customer performance both directly and through its moderating effect on CRM capabilities. Our results from a cross industry survey of 199 firms show that generative learning Profiting from CRM 1679

3 MD 1680 orientation as well as its interaction with CRM capabilities indeed enhance customer performance, defined by the three key aspects of customer satisfaction, customer loyalty, and customer retention ( Jayachandran et al., 2005). Thus, we contribute to current knowledge by introducing the crucial role of a generative learning orientation for customer relationship success. These findings emphasize the importance of addressing customers latent needs in maintaining beneficial relationships. The rest of our paper is structured as follows. First, we review the theoretical foundations and develop a conceptual model. Second, we specify the study hypotheses. Third, our empirical study is described in which the model is operationalized and tested. Finally, we discuss the results, derive conclusions, and present the implications of our findings. 2. Conceptual development Following Day (2000), differences in customer performance are attributable to differences in underlying assets and capabilities. Therefore, resource-based theory (RBT) and dynamic capability view (DCV) serve as the overarching theoretical frameworks for this study (e.g. Acedo et al., 2006; Barney et al., 2011). RBT views the firm s enduring competitive advantage related to the firm s possession of unique, inimitable resources and capabilities, created over time through complex interactions among the firm s resources, and based on developing, carrying, and exchanging information (Teece et al., 1997). More recently, the focus of much RBT research has been on understanding the outcomes of resource deployment processes often referred to as organizational capabilities (Sirmon et al., 2007). An important part of this literature has highlighted the value of developing organizational capabilities as a means of implementing firm strategies (e.g. Vorhies et al., 2009). Specifically, the firm s CRM capabilities can be considered as core capabilities that provide firms with the means to achieve a more loyal and sustainable customer base (Day, 2000). However, current portrayals of the RBT emphasize its meaning as a contingency theory of organizations (Barney et al., 2011; Ketchen et al., 2007). Strategic resources and capabilities only have potential value, and realizing this potential requires alignment with other important organizational elements (Sirmon et al., 2011). According to this argument, we posit that firms have to align their CRM capabilities and learning orientation to fully benefit from customer relationship activities. Only then firms are able to achieve a high customer performance, which in turn leads to a high financial performance (Boulding et al., 2005). In addition, Sirmon et al. (2007) draw a distinction between the activities of stabilizing, enriching and pioneering. While stabilizing involves improving existing capabilities and enriching involves extending and elaborating current capabilities through activities such as adaptive learning, pioneering is a more advanced process that involves generative learning in order to create novel capabilities. This understanding points towards the DCV, which suggests that some firms are better able than others at enhancing their overall competitive advantage by adding, reconfiguring, and deleting resources or competencies to address rapidly changing environments (Teece et al., 1997). Within the DCV, organizational learning, defined as a process by which organizations learn through interaction with their environment (Cyert and March, 1963), is of central importance (Teece, 2007). Moreover, learning is widely acknowledged as an important success factor for firms (e.g. Sinkula, 1994; Slater and Narver, 1995; Stein and Smith, 2009; Tippins and Sohi, 2003).

4 Organizational learning occurs by detecting a mismatch of outcome to expectation, which disconfirms theory in use (Sinkula, 1994). Consequently, the firm moves to error correction, which results in a change in theory in use. If the subsequent correction leads to a change in organizational norms and if the learning results from proactive organizational behavior and not in direct response to environmental events, then the learning is said to be double-loop or generative and leads to new mental models (Baker and Sinkula, 1999). Thus, generative learning differs from adaptive learning, which occurs within the context of current mental models. Generative learning orientation is the degree to which top management attaches importance to and promotes the development of new skills, the enjoyment of learning, curiosity for new ways of enhancing performance, preference for challenging work, and critical reflection on the assumptions of the organization (Atuahene-Gima et al., 2005). We use this definition and combine a firm s generative learning orientation with CRM capabilities that determine its customer relationship practices. CRM capabilities are defined as the core organizational processes that focus on leveraging long-term associations with customers (Srivastava et al., 1999) and are a fundamental part of marketing (Boulding et al., 2005). Moreover, CRM is seen as a source of competitive advantage in the market which leads to increased customer performance. So far, customer performance only represents a desirable outcome for customer-side consideration of a firm, and neglects the investments necessary to achieve higher customer satisfaction, loyalty, and retention. CRM, however, needs to demonstrate its value to overall performance measures of the firm to point out that its benefits exceed its cost. Thus, all CRM activities need to be linked to financial metrics (Bohling et al., 2006). To account for this requirement, we include financial performance in our framework, defined by overall financial performance, market share, growth, and profitability (Reinartz et al., 2004). Building on the theoretical perspective of RBV, DCV, and organizational learning as well as the definition of our constructs, we next develop testable hypotheses. Profiting from CRM CRM capabilities and customer performance We expect that a firm s CRM capabilities, defined as core organizational processes that focus on establishing, maintaining, and enhancing long-term associations with customers, increase customer satisfaction, loyalty, and retention. Jayachandran et al. (2005) demonstrate the value of four distinct CRM capabilities: Customer relationship orientation, customer-centric management systems, relational information processes, and CRM technology use. Customer relationship orientation reflects the cultural propensity of an organization to undertake CRM. Such an orientation is rooted in the firm s overall culture, guiding the organization s attitude toward both CRM and the implementation of the necessary capabilities (Day, 2000). A customer-centric management system refers to the structure and incentives that provide an organization with the ability to build and sustain customer relationships. Hence, it enables the successful implementation of CRM (Day, 2000). Relational information processes systematize the capture and use of customer information so that the firm s effort to build relationships is not rendered ineffective by poor communication, information loss and overload, or inappropriate information use. These processes provide guidelines to help firms manage customer information, to interact with customers in ways that are consistent with the demands of CRM, and enhance customer performance ( Jayachandran et al., 2005). CRM technology use includes front

5 MD 1682 office applications that support sales, marketing, and services, a data depository, and back office applications that help to integrate and analyze the data. The underlying IT infrastructure highly affects knowledge management, empowering firms not only to store vast amounts of customer data but also providing the necessary tools to capture, manage, and deliver reliable information, both internally and externally (Srinivasan and Moorman, 2005). Therefore, the use of CRM technology boosts the ability of firms to sustain profitable customer relationships (Day and Van den Bulte, 2002). To summarize, given the high plausibility and previous support in the literature (e.g. Jayachandran et al., 2005; Palmatier et al., 2006; Payne and Frow, 2005; Reinartz et al., 2004; Wang and Feng, 2012), we expect that: H1. The firm s CRM capabilities are positively associated with its customer performance. 2.2 Generative learning orientation and customer performance We hypothesize that in addition to its CRM capabilities, a firms learning orientation increases customer satisfaction, loyalty, and retention. It is important to note that the four-dimensional conceptualization of CRM capabilities implicitly includes adaptive learning ( Jayachandran et al., 2005). Adaptive learning is an essential part of a firm s CRM (Sun et al., 2006; Voss and Voss, 2008), incorporated in the definition of relational information processes ( Jayachandran et al., 2004; Selnes and Sallis, 2003), and related to customer-led strategies that emphasize the expressed needs, for example obtained from CRM (Boulding et al., 2005). Thus, CRM includes the process of adaptive learning by 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 associated with unexpressed, latent needs (Narver et al., 2004). While lead the customer strategies are particular relevant for firms that aim to serve new customers and new markets (Slater and Narver, 1998), identifying latent needs also affect satisfaction and retention of existing customers. Customers change their needs continuously, and latent needs may become important to retain relationships and satisfaction (Flint et al., 2002). Firms that succeed in addressing latent needs exhibit a proactive customer orientation in contrast to firms with a responsive customer orientation that only addresses expressed needs (Narver et al., 2004). More importantly, customers explicitly distinguish between firms responsiveness and proactivity, and value firms that are able to proactively anticipate their needs (Blocker et al., 2010; Tuli et al., 2007). The firm s ability to continuously generate intelligence about customers latent needs, and about how to satisfy those needs, is essential for it to create superior customer value (Slater and Narver, 2000). In other words, firms with a generative learning orientation seek to better understand customers unobvious needs in order to respond with offers, products, and services incorporating an adequate value proposition that serves changing needs. We assume that this value proposition will increase both customers satisfaction and loyalty, and lead to long lasting relationships with customers. For these reasons, we expect that: H2. The firm s generative learning orientation is positively associated with its customer performance.

6 2.3 Conditional effect of CRM capabilities and generative learning Recent research on RBT emphasizes the importance of the interaction between the firm s know-what knowledge resources, for example insights from a generative learning orientation, and its complementary know-how deployment capabilities, for example implementation knowledge incorporated in CRM capabilities (Grant, 1996; Sirmon et al., 2007; Sirmon et al., 2011). This notion suggests that the firm s generative learning orientation and CRM capabilities may interact to enable higher customer satisfaction, higher loyalty, and longer customer relationships than its competitors. There are at least three specific reasons why we expect such an interaction. First, the critical appraisal involved in generative learning orientation ensures the quality, relevance, and timely use of customer information within CRM. Sun et al. (2006) investigated information quality in CRM and pointed out the importance of extracting hidden predictive information from large databases to identify valuable customers, predict future behaviors, and estimate customer value. In turn, CRM supports generative learning by providing the necessary implementation processes and 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 be viewed as ambidextrous learning (e.g. Cegarra-Navarro et al., 2011; Lee and Huang, 2012). Specifically, researchers presume that proactivity associated with generative learning and responsiveness associated with adaptive learning can complement each other (e.g. Atuahene-Gima et al., 2005; March, 1991; Slater and Narver, 1998). In other words, the productive capacity of one capability can be enhanced through its interaction with the other. In the context of CRM, customers are constantly evaluating how their changing needs, both expressed and latent, are being met by the firm (Boulding et al., 2005). As customers evaluate, it is likely that their thoughts about these two capabilities coalesce: when the perceived level of proactive (responsive) customer orientation increases, customer attitudes about the efficacy of responsive (proactive) customer orientation will become more positive (Blocker et al., 2010). Third, from the perspective of RBT, generative learning orientation and CRM capabilities may each be viewed as an individual source of competitive advantage. Thus, the interaction between the two possesses the characteristic of asset interconnectedness which makes it particularly difficult for competitors to identify the 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 learning that use existing resources (i.e. CRM capabilities) in new ways (Sirmon et al., 2011). Consequently, a competitor would need to acquire both the interconnected generative learning orientation and CRM capabilities to compete in CRM, and understand managers actions based on generative learning to effectively structure, bundle and leverage a firm s CRM capabilities. In summary and based on the three arguments above, we hypothesize that: H3. The interaction between the firm s CRM capabilities and generative learning orientation is positively associated with its customer performance. Profiting from CRM 1683

7 MD Customer performance and financial performance We also include financial performance measures in our model to demonstrate that benefits of a higher customer performance exceed its respective costs, and thus the value of this concept to managers (Bohling et al., 2006). Previous research has shown that firms who are able to increase customer satisfaction are likely to improve both the level and the stability of net cash flows (Fornell et al., 2006). Furthermore, increasing the loyalty of customers and retaining existing customers are key drivers of firm profitability (Gupta and Zeithaml, 2006). Because of these arguments, we hypothesize that: H4. The firm s customer performance is positively associated with its financial performance. The hypotheses as well as the control variables used in the empirical study are summarized in Figure 1. Next, we describe the research methods, including data collection, measurement, and analysis. 3. Research methods 3.1 Data collection and sampling Primary data for testing our hypotheses were collected via a mail survey of firms in Switzerland operating in consumer and business markets offering both services and goods (including durable and nondurable goods). For an initial set of firms from various 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 into different business units existed) the entire firm. The resulting sample represents an appropriate context for three main reasons. First, Switzerland is a highly developed, de-regulated market with strong competition, a setting were CRM is of particular importance (Boulding et al., 2005). Second, we were interested in general direct and moderating effects of generative learning on customer performance, regardless of industry and firm-specific characteristics. Hence, a cross-industry sample is appropriate (Rindfleisch et al., 2008). Moreover, the empirical results will be less affected by the uncontrollable, idiosyncratic effects of any particular sector, thus allowing for a higher degree of external validity (Tippins and Sohi, 2003). Third, the geographical proximity of the research team with the empirical setting facilitated control over the quality and consistency of the study data. Figure 1. Summary of the hypotheses

8 Given our focus on CRM capabilities and generative learning orientation the survey was 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 from key informants were followed (Kumar et al., 1993; Podsakoff and Organ, 1986), including assurance of confidentiality and anonymity, a self-assessment of the degree of knowledge ( How knowledgeable are you regarding the CRM capabilities of your business 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, and incentivizing participants with a research summary that would be meaningless in case of imprecise answers. Furthermore, respondents were asked to consult with other knowledgeable organizational members when completing the questionnaire. After a follow-up, we received 231 usable questionnaires, for an effective response rate of 15 percent. This response rate is comparable to other top management studies regarding CRM (e.g. Jayachandran et al., 2005; Reimann et al., 2010). We obtained approximately a third of the responses after the follow-up. Because key informant accuracy 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 on a seven-point scale (seven questionnaires) and retained 199 useable surveys. Information on the composition of the final sample appears in Table I. Our sample covers a broad range of firms in terms of industry and size. Approximately 90 percent of participants are chief executive officers, chief marketing officers, or chief marketing and sales officers. Interestingly, only five percent of the participating firms have a dedicated customer relationship position. The mean respondent knowledge score of 6.14 supports the validity of the key informant data. An extrapolation approach to assess nonresponse bias (Armstrong and Overton, 1977) revealed no significant differences between early and late respondents on the main survey constructs and key demographics. Profiting from CRM Measurement CRM capabilities are defined as a firm s capabilities to establish, maintain, and enhance 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. In its entirety, the CRM capabilities measure captured major facets of firm s practices regarding customer-company relationships, as well as the major sub-processes within those facets. The four first-order dimensions include customer relationship orientation, customer-centric management system, relational information processes, and CRM technology use. We developed a pool of items for relational information processes and CRM technology use based on interviews with CRM experts from 12 firms and submitted them to four researchers for review. Refined scales were pretested with 27 members of a MBA class. The final scales used in the survey consisted of three items (relational information processes) and seven items (CRM technology use), respectively. The relational information processes measure includes items that refer to defined processes to constantly generate information about customers, to disseminate customer

9 MD 1686 Table I. Sample composition A. Industries Consumer goods 18 Industrial goods 21 Retail and distribution 14 Financial services 23 IT services 22 Other services 2 B. Position of respondents Chief executive officer 38 Chief marketing officer 39 Chief marketing and sales officer 11 Customer relationship manager 5 Other 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 employees employees ,000 employees 11. 1,000 employees 22 % information within the organization, and to analyze and store customer information. The measure of CRM technology use has seven aspects: relational database, integrated IT infrastructure, intelligent CRM software, sales and marketing staff support, data depository, integration and analysis the of customer data, and promotion of CRM IT infrastructure. Customer relationship orientation and customer-centric management system were measured using scales from Jayachandran et al. (2005). Customer relationship orientation capture the degrees to which employees are encouraged to focus on customer relationships; customer relationships are considered to be a valuable asset; senior management emphasizes the importance of customer relationships; and retaining customers is considered to be a top priority. Customer-centric management system assessed the organization and coordination of the firm around customers and their 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 ways of 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 the top management within the business unit. An existing scale was extended to measure customer performance relative to competitors ( Jayachandran et al., 2005), including the items increasing customer satisfaction, retaining existing customers, and increasing loyalty of customers. Financial performance was measured by a scale borrowed from

10 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 industry and business unit heterogeneity. Following prior research (e.g. Jayachandran et al., 2004, 2005), we collected data on consumer demandingness, competitive intensity, firm size, and industry focus. Customer demandingness refers to the extent to which customers have clout over the firm. In markets where customers are very demanding, firms will be compelled to develop better CRM capabilities without benefiting from them. The scale for consumer demandingness is borrowed from Li and Calantone (1998) and reflects customers demand for product quality and reliability, sophistication in terms of technical specifications, and sensitivity to product cost. Competitive intensity, the extent of interfirm rivalry, might hurt financial performance because as it drives up costs and diminishes profit margins. More specifically, under conditions of high competition, customers have many alternative options to satisfy their needs and wants. Competitive intensity was measured by using the established scale of Jaworski and Kohli (1993). Furthermore we dummy coded each firm as primarily a B2C or B2B business and used employee numbers as an indicator of business unit or firm size to further rule out industry or firm specific influences on financial performance. The respective item indicators for all constructs are contained in the appendix. Profiting from CRM Measure reliability and validity Analyses for each reflective first-order construct revealed that the 44 indicators load significantly on their intended factor, which indicates convergent validity among the items of each scale (all factor loadings. 0.63). Cronbach s alpha and composite reliability of all constructs exceed the recommended minimum of 0.70 and signal scale reliability (Bagozzi and Yi, 1988). Moreover, we checked the significance of the loadings with a bootstrap procedure (500 sub-samples) to obtain t-statistic values. They are all significant. Together with content validity established by expert agreement, these results provide empirical evidence for construct validity. We then assessed discriminant validity of the latent variables using Fornell and Larcker s (1981) criterion, which requires that the square root of each latent variable s average variance extracted (AVE) is at least 0.70 and greater than the latent variable s correlation with any other construct in the model. As we show in Table II, each latent variable meets Fornell and Larcker s criterion in support of discriminant validity. Following the conceptualization of comparable second-order constructs like market orientation ( Jaworski and Kohli, 1993) or marketing capabilities (Morgan et al., 2009), and the recommendations of Jarvis et al. (2003), we conceptualized CRM capabilities as a reflective first-order, formative second-order construct with four sub-dimensions (customer relationship orientation, customer-centric management system, relational information processes, CRM technology). More specifically, we used item parcels to assess the second-order construct (Bagozzi and Edwards, 1998). Following the recommendations of Diamantopoulos and Winklhofer (2001), we evaluated indicator collinearity and external validity for the four CRM factors. All variance inflation factors were well below the common cut-off value of 10, and all four dimensions were significantly correlated with the conceptually related statement Our organization has a 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

11 MD 1688 Table II. Correlations and discriminant validity CRM capabilities (second-order) n.a. * 2 Customer relationship orientation n.a * 3 Customer-centric management system n.a * 4 Relational information processes n.a * 5 CRM technology use n.a * 6 Generative learning orientation * 7 Customer performance * 8 Financial performance * 9 Consumer demandingness * 10 Competitive intensity * 11 Business unit size n.a. * 12 Industry type n.a. * Mean n.a n.a. SD n.a n.a. AVE (%) n.a n.a. n.a. Number of Items n.a n.a. n.a. Notes: All mean values refer to a seven-point format (except business unit size); n.a. ¼ not applicable; italic ¼ correlation is significant at the 0.05 level (two-tailed); * Please find the square-root of the average variance extracted on the main diagonal; The second-order scale CRM capabilities consists of the first-order scales customer relationship orientation, customer-centric management system, relational information processes, and CRM technology use

12 using PLS analysis. The results provided support for the proposed conceptualization of CRM 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 CRM capabilities is displayed in Figure 2. Profiting from CRM 1689 Figure 2. Second-order four-factor model of CRM capabilities

13 MD PLS path model analysis To test our hypotheses, we apply partial least squares (PLS) path modeling to estimate our theoretical model using the software application SmartPLS (Ringle et al., 2005). While other methods of structural equation modeling such as the covariance-based LISREL are indeed more widespread, PLS was finally chosen because it places minimal restrictions on sample size, is tolerant regarding residual distribution, and favors 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 effect between CRM capabilities and generative learning into the pathmodel by applying a commonly-used product-indicator approach (Henseler and Fassott, 2010). Because the data for all variables came from single respondents in a one-time survey, key informant bias and common method variance might influence some postulated relations in the PLS path model. However, given that the constructs measured refer to the present and address salient events, our informants are in high hierarchical positions with long tenure, and we followed established guidelines to increase key informant accuracy, we do 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 items were worded to minimize ambiguity (Podsakoff et al., 2003). We also adopted the marker variable approach (Lindell and Whitney, 2001). More specifically, we applied Lohmöller s (1989) extended PLS algorithm and used several marker variables to estimate the loadings on every item in the PLS path model in addition to each item s loading on its theoretical construct (ct. Sattler et al., 2010). A comparison of the estimated path model relationships with and without each of the additional marker variables shows no notable differences, and all theorized paths maintain their level of statistical significance. Thus, though common method variance cannot be completely ruled out, neither the traditional single-factor test nor the marker variable approach suggests a threat of common method bias. We checked the latent constructs in the path model 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 severe multicollinearity problems (Belsey et al., 1980; Vorhies et al., 2009). 4. Discussion of results In Figure 3, we provide the parameter estimates of the direct and interaction effects from CRM capabilities and generative learning on customer performance and financial performance. H1 examines the effect of a firm s CRM capabilities on customer performance. We argue that firms with high CRM capabilities will achieve superior customer satisfaction, customer loyalty, and customer retention. This argument is supported 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 its customer performance. We predict that firms with a pronounced generative learning orientation better understand customers changing needs and are able to respond with offers incorporating an adequate value proposition that serve these needs, and thus increase customer performance. The coefficient for the path between generative learning and customer performance is positive and significant (b ¼ 0.18, p, 0.05), supporting H2.

14 Profiting from CRM 1691 Figure 3. Results for the structural model H3 explores the relationship between CRM capabilities, generative learning and customer performance. We expect that the contribution of CRM capabilities to customer performance increases if a firm has a high level of generative learning orientation. The coefficient for the interaction between CRM capabilities and generative 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 see greater returns from their CRM capabilities, and vice versa. The corresponding interaction graph is depicted in Figure 4. H4 examines the effect of customer performance on financial performance. The path coefficient reveal that customer performance has a strong positive effect on financial performance (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 not have a significant effects on financial performance. To test whether customer performance mediates the relationships between the antecedents and financial performance, we conduct a mediation analysis. In conformance with the nonparametric PLS path modeling approach, we apply a nonparametric bootstrapping procedure to test the significance of the mediating effects Figure 4. The moderating effect of generative learning on customer performance

15 MD 1692 (Henseler et al., 2009). While the Sobel test is the most commonly used method to assess mediating effects, simulation studies reveal that bootstrapping offers a better alternative, at least in PLS path models, because it does not impose any distributional assumptions (MacKinnon et al., 2002). The results indicate full mediation for generative learning and the interaction of CRM capabilities and generative learning but only partial mediation for CRM capabilities (mediation accounted for 54 percent of variance). Following this finding, we revised the conceptual model and introduced an additional direct path from CRM capabilities to financial performance. The structural model was evaluated using the R 2 for the dependent constructs and the Stone-Geisser Q 2 test for predictive relevance. Both R 2 values and Q 2 values of customer performance (R 2 ¼ 0.534; Q 2 ¼ 0.524) and financial performance (R 2 ¼ 0.444; Q 2 ¼ 0.372) suggest good explanatory power of the model. 5. Conclusion Although extant marketing literature has emphasized the importance of a generative learning orientation for new product performance and market information processes (e.g. Baker and Sinkula, 2007; Slater and Narver, 1995), its relevance for CRM has not received adequate attention yet. Thus, an important contribution of our study is the demonstration that generative learning enhances a firm s customer relationships. The direct effect of generative learning on customer performance is accompanied by a significant interaction between a firm s CRM capabilities and generative learning orientation. This finding supports the complementary nature of insights obtained from generative learning (know-what knowledge resources) and CRM capabilities (know-how deployment capabilities). We conclude that firms need well developed CRM capabilities to fully benefit from translating new insights resulting from generative learning into establishing, maintaining, and enhancing long-term associations with customers, and vice versa. Furthermore, the significant interaction effect underlines recent research that emphasize the importance of balancing different learning 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 the internal positive effects of CRM, to date often neglected in the assessment of its performance implications (Boulding et al., 2005). In addition to help firms better understand customer s needs, shape appropriate responses to customer behavior and effectively differentiate offerings, CRM may also contribute to operational efficiencies. Examples of such contributions include the integration of customer knowledge into superior production processes (Reimann et al., 2010) or the concentration on a profitable customer group (Reinartz et al., 2004). Thus, the benefits of CRM capabilities exceed those that can be measured in terms of customer satisfaction, customer retention, and customer loyalty. The managerial implications of this study are straightforward. Both CRM capabilities and generative learning orientation affect customer performance directly. Furthermore, CRM capabilities affect financial performance directly, and both affect financial performance indirectly via customer performance. Thus, managers should strive for responsiveness to customers expressed needs as well as for proactiveness to customers latent needs to maintain long lasting relationships. We believe that this finding is crucial because many firms seem to have established a dominant focus on

16 responsiveness to customers. In line with this argument, the mean for generative learning orientation in our study (4.53, Table II) was significantly lower than for customer relationship orientation, customer-centric management system, and relational information processes (all p, 0.01). This rating suggests that many firms have opportunities to improve their generative learning orientation. Another important implication for managers is that generative learning orientation is crucial for firms to fully benefit from their competencies in CRM. In addition to its direct effect, generative learning also increases the performance contribution of CRM capabilities. In total and all other variables being equal, an increase of generative learning orientation by one unit (seven-point scale) can command an increase of up to 0.31 in customer performance. In perspective, the average customer performance in our sample was 4.96 (Table II). Thus, an increase by 0.31 equals almost 7 percent of the average customer performance. Since even small changes in customer performance have a strong impact on financial performance (also ct. Fornell et al., 2006; Gupta and Zeithaml, 2006), this finding indicates a remarkable and substantial result for managers. Managers should therefore incorporate double-loop learning that leads to new mental models into their firm s CRM activities. Appropriate measures to increase generative learning include a strong commitment from top management, a shared vision within the responsible department, and more generally open-mindedness for new influences of all employees involved (Sinkula et al., 1997). In doing so, firms can better extract hidden information from large databases, predict future behaviors and preferences, 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 may involve a self-serving bias. To validate our customer performance measure, objective data 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 we followed recommendations to improve data validity (e.g. confidentiality, incentives, clear explanation of usefulness, tests for common method variance) and informants were well qualified, we nevertheless face the usual limitations inherent in key informant survey designs. Third, this work is based on evidence from firms in many different businesses and inherits a cross-sectional nature. Though we used widespread control variables and this type of sample appears appropriate for our research purpose (Rindfleisch et al., 2008), we cannot claim to have identified one specific optimal behavior. Furthermore, we established the relationship between CRM capabilities, generative learning orientation, customer performance, and financial performance at a single moment in time. More appropriate conclusions about causality, i.e. the performance of a given firm shifting its relative emphasis on CRM and generative learning, require a longitudinal study approach and should be undertaken in future research. Beyond these limitations, additional fruitful research directions have emerged from this study. We conceptualize and test an integrative model specifying the links between responsive CRM capabilities, proactive generative learning, and performance outcomes. With the exception of Blocker et al. (2010), generative learning and proactive customer orientation have mainly been associated with new product development and innovation performance in empirical studies. Thus, our quantitative results support Profiting from CRM 1693

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