lectura 2

The impact of customer relationship managementcapability on innovation and performanceadvantages: testing a mediated model

Moustafa Battor, Tanta University, EgyptMohamed Battor, Tanta University, Egypt

Abstract Customer relationship management (CRM) and innovation are widelyconsidered to be valuable capabilities associated with competitive advantage.However, there is a lack of research demonstrating how they work together toproduce performance advantages. This research investigates the mediating roleof innovation between CRM and performance. The authors examine the directimpact of both CRM and innovation on firm performance. Moreover, theyinvestigate the role of innovation as a mediating mechanism to explain theeffect of CRM on performance. The authors use structural equation modellingto test the relationships among these constructs. The results support the directimpact of CRM and innovation on performance. Also, the findings indicate that theindirect effect of CRM on firm performance through innovation is significant.These results reinforce the view that developing close relationships withcustomers enhances a firm’s ability to innovate.

Keywords innovation; CRM; customer relationship management; performance

Introduction

Developing a superior customer relationship management (CRM) capability – that is,creating and managing close customer relationships – is expected to be one of the mostimportant sources of superior performance in today’s competitive businessenvironment (Day, 2000; Kale, 2004). Capital One, for example, has significantlyoutperformed First USA with a strategy that leverages their superior CRM capability.Despite being half the size of First USA, Capital One earned 40% more interest incomefrom each customer and enjoyed double the profit margin (Day, 2002). Amazon isanother good example. Nearly 59% of Amazon.com’s sales come from repeatcustomers. This is because Amazon’s strategy is to keep its customers loyal (Peppers,Rogers, & Dorf, 1999). Generally, attracting new customers costs five times as much askeeping or managing existing ones, which means that existing customers contributefive times more sales than new customers do (Ko, Kim, Kim, & Woo, 2008).Therefore, beyond designing strategies to attract new customers and createtransactions with them, organisations recognise the importance of retaining current

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DOI:10.1080/02672570903498843

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Journal of Marketing ManagementVol. 26, Nos. 9–10, August 2010, 842–857

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customers and building lasting customer relationships (Kotler, Armstrong, Saunders,& Wong, 1999).

Innovation is also an important organisational capability, because successfulnew products are engines of growth and provide increased sales, profits, andcompetitive strength for most organisations (Pauwels, Silva-Risso, Srinivasan, &Hanssens, 2004; Sivadas & Dwyer, 2000). Robust findings uniformly suggest thata positive and direct relationship exists between innovation and superiorperformance (e.g. Baker & Sinkula, 1999; Calantone, Cavusgil, & Zhao, 2002;Han, Kim, & Srivastava, 1998; Hult, Hurley, & Knight, 2004; Hurley & Hult,1998; Keskin, 2006; Panayides, 2006; Thornhill, 2006). However, the failurerate of new products is somewhere between 40% and 75%, and nearly 50% ofthe new products that are introduced each year fail. Organisations thus must notonly innovate consistently to remain competitive, but must also seek to reducethe risks associated with innovation (Joshi & Sharma, 2004; Pauwels et al.,2004; Sivadas & Dwyer, 2000).

Organisational capabilities for successful product innovation encompass firms’abilities to understand customer preferences and needs, to acquire and assimilateexternal knowledge, and to transform it into new or improved products (Branzei &Vertinsky, 2006; Joshi & Sharma, 2004; Marinova, 2004). In that sense, CRM playsan important antecedent role in a firm’s ability to innovate. At its most basic level,CRM is a firm’s ability to translate customer data into customer relationships throughactive use of, and learning from, the information collected (Brohman, Watson, Piccoli,& Parasuraman, 2003). Firms with superior CRM capability are in a better position togather and store customer knowledge. They can track customer behaviour to gaininsights into customer’s tastes and evolving needs. Firms with greater deployment ofCRM applications thus will be better able to design and develop innovative productsand services due to an enhanced customer understanding (Brohman et al., 2003;Mithas, Krishnan, & Fornell, 2005).

Although the existing literature has acknowledged the importance of CRM andinnovation to performance, insufficient attention has been paid in this literature toaddress how they work together to achieve higher performance. Also, although priorconceptual work has suggested that CRM can enhance an organisation’s innovation,empirical evidence is sparse. Therefore, the key questions addressed by our researchare how CRM and innovation interact to affect performance and whether CRMfosters innovation. More specifically, we study the CRM–innovation–performancechain, and examine both direct and indirect (through innovation) effects of CRMcapability on performance.

The paper is organised as follows. The theoretical background and hypotheses arepresented in the next section. We then describe the research method and the scaledevelopment and validation. Next, we present the results of testing the structuralmodel. Finally, we discuss the implications and limitations of our study, and offerdirections for future research.

Theoretical background and research hypotheses

Innovation

Many definitions of innovation have been proposed. For example, Hult et al. (2004, p.429) describe innovation as the introduction of new processes, products, or ideas in

Battor and Battor The impact of CRM capability on innovation and performance advantages 843

the organisation. Drucker (2001, p. 22) proposes that innovation is a different productor service creating a new potential of satisfaction, rather than an improvement.Innovation is defined as a process that begins with an idea, proceeds with thedevelopment of an invention, and results in the introduction of a new product,process, or service to the marketplace (Thornhill, 2006, p. 689). Innovation usuallyinvolves something new. It involves doing new things or finding new ways of doingthings to change the rules of the game (Keskin, 2006; Porter, 1985).

Porter (1985, p. 164) recognises innovation as ‘one of the principal drivers ofcompetition’. Drucker (2001, p. 21) argues that ‘distinctive capabilities are differentfor every organization; they are part of an organization’s personality. But everyorganization needs one core capability: innovation’. These quotations leave littledoubt that innovation has been, and will continue to be, a key capability of interestto firms. Innovation plays a key role in the survival and success of any organisation inthe face of today’s seemingly accelerating and changing market environment (Francis& Bessant, 2005; Han et al., 1998). For firms to survive and prosper in turbulent andunpredictable environments, new things have to be done or new ways have to beadopted in doing things that are already being done (i.e. to be innovative) (Keskin,2006).

The impact of innovation on performance has been examined extensively in priorresearch, and considerable empirical evidence of a positive impact has beenaccumulated (e.g. Baker & Sinkula, 1999; Calantone et al., 2002; Han et al., 1998;Hult et al., 2004; Hurley & Hult, 1998; Keskin, 2006; Panayides, 2006; Thornhill,2006). For example, Francis and Bessant (2005), by reviewing the literature, concludethat management research suggests that innovative firms are, on average, twice asprofitable as other firms. Also, a study of 700 companies, which launched a totalnumber of 13,311 new products between 1976 and 1981, reported that 22% of profitsand 28% of sales growth came from new product launches. The trend was predicted torise to 31% profits and 37% sales (Zairi, 1995). Thus the following hypothesis isproposed:

H1: Higher levels of innovation are associated with higher levels of performance.

CRM capability

The origins of CRM can be traced to the relationship-marketing literature.Introduced by Leonard Berry in the early 1980s, the concept of relationshipmarketing was defined as attracting, maintaining, and enhancing customerrelationships (Berry, 2002). Kotler et al. (1999) define CRM as retaining currentcustomers and building profitable, long-term relationships with them. Recently, Day(2002) conceptualised CRM as a firm capability that results from a focus on threeorganisational components working in concert with each other: an organisationalorientation that makes customer retention a priority; a configuration that includesthe structure of the organisation, its processes for personalising product offerings,and its incentives for building relationships; and information about customers that isin-depth, relevant, and available in all parts of the company. The terms customer-linking capability (Day, 1994), customer-relating capability (Day & Van den Bulte,2002), and CRM capability (Srinivasan & Moorman, 2005) are usedinterchangeably to describe a firm’s ability to outperform its rivals by creating andmanaging close customer relationships (Day, 1994).

844 Journal of Marketing Management, Volume 26

CRM capability and performance

Day (1994) described CRM capability as a valuable and difficult-to-imitate source ofsuperior performance. CRM capability is much more difficult to understand andimitate than most capabilities because it takes time to develop, relies on the complexinterplay of supporting resources, and is based primarily on tacit knowledge andinterpersonal skills (Hooley, Greenley, Cadogan, & Fahy, 2005). In addition,building stronger relationships with customers provides the basis for understandingthe evolving requirements of customers and identifying the most appropriate ways ofsatisfying customers better than competitors, which can provide greater opportunitiesfor realising superior performance (Day, 1994).

Compelling evidence exists that retaining customers leads to positive businessperformance. In their seminal study, Reichheld and Sasser (1990) find that a 5%increase in customer retention rates increases profits by 25 to 85%, dependingupon the industry. The cost of keeping current customers is much lower than thatof winning new ones (Reichheld, 1996; Reichheld & Sasser, 1990), and manynew relationships are often unprofitable in the early years (Kotler et al., 1999;Reichheld, 1996). Only later, when the cost of serving loyal customers falls andthe volume of their purchases rises, do relationships generate big returns. Long-term customers buy more, are cheaper to serve, take less of a company’s time,pay less attention to competing brands, provide new referrals through positiveword of mouth, and buy other products offered by the company (Kotler et al.,1999; Reichheld, 1996).

Recently, several studies provide evidence of a positive association betweencustomer relationship and business performance. For example, a recent specialsection in the Journal of Marketing finds that customer-relationship activitiesenhance firm performance in eight of the ten papers published (Boulding, Staelin,Ehret, & Johnston, 2005). Also, Day and Van den Bulte (2002) find that CRMcapability is an important determinant of superior performance. Similar findings arereported by Hooley et al. (2005). Thus the following hypothesis is proposed:

H2: Higher levels of CRM capability are associated with higher levels of performance.

CRM capability and innovation

The role customers can play in idea generation or product conceptualisation is beingincreasingly acknowledged in the management literature (e.g. Campbell & Cooper,1999; Nambisan, 2002). The Marketing Science Institute’s (MSI) 2006–2008 researchpriorities include the topic of the customer’s role in innovation as the first researchpriority. A survey by the MSI shows that ‘innovation continues to be viewed as theprime engine of growth, but customers play a much larger role in shaping innovationstrategy and execution [and] at the development level, customer insights are needed todrive innovation and product and service design’ (MSI, 2006, p. 3).

A new product-development strategy is an information-processing procedure (Liu,Chen, & Tsai, 2005). In that sense, CRM can be considered an innovative managementstrategy (Ko et al., 2008). Porter (1990, p. 86) argues that through building closerelationships with customers, ‘information flows freely and innovations diffuserapidly’. Having close relationships with customers can help the firm take advantageof short lines of communication, a quick and constant flow of information, and anongoing exchange of ideas and innovations (Porter, 1990).


Firms deploying CRM can track customer behaviour to gain insight into customertastes and evolving needs (Mithas et al., 2005; Vickery, Jayaram, Droge, & Calantone,2003). With currently available technology, CRM applications allow the firm to learnabout customer preferences in real time, continuously update the firm’s knowledge ofcustomer preferences, and analyse customer insights (Sun, 2006). Firms with superiorCRM capabilities are in a better position to collect, organise, and prioritise customerinformation and transmit this information to the product-development team. Byintegrating this information in the product-development process, firms can create anincremental innovation or develop a product to satisfy growing or evolving customerneeds (Zahay, Griffin, & Fredericks, 2004). Customer information obtained fromCRM, then, is a valuable intellectual asset for a company to improve its ability toinnovate products that meet or even exceed customers’ requirements (Su, Chen, &Sha, 2006).

Many examples exist of firms that have successfully used customer knowledge as akey component of their innovation strategies. For example, FedEx adopts an outside-in approach to create innovative products. That means FedEx discovers its customers’wants and needs, and focuses innovation activities in those areas. By allowinginnovation to be customer driven, FedEx has developed innovative ways to meetcustomers’ needs (Battor, Zairi, & Francis, 2008). Microsoft is another goodexample of the positive relationship between customer knowledge and innovation.Microsoft established beta sites to seek customer knowledge in all development phasesof new software, from generating product specifications to the final check of theproduct before its release. For example, more than 650,000 customers tested a betaversion of Microsoft’s Windows 2000 and shared with Microsoft their ideas forchanging some of the product’s features (Prahalad & Ramaswamy, 2000). Microsoftattributes its sustained success to ‘its vigorous pursuit of customer knowledge in newproduct development’ (Li & Calantone, 1998, p. 13). Based on the above discussion,the following hypothesis is proposed:

H3: Higher levels of CRM capability are associated with higher levels of innovation.

Research design

Data collection

We used the FAME database of UK companies as our sampling frame. FAME providesdetailed financial and accounting information for 1.8 million firms registered in theUK (Nachum, 2003). We used a systematic random sampling method to draw a sampleof 1000 companies with more than 50 employees from this database. To improve thevalidity of the survey questions and the response rate, we followed the generalrecommendations of Churchill (1979), Conant, Mokwa, and Varadarajan (1990),and Dillman (1978) to design and administer the survey.

The questionnaire used to collect the data was pretested twice (Churchill, 1979;Conant et al., 1990). First, a pretest involving five academics and three executives wasconducted to assess the face and content validity of the measurement items.Consequently, a small number of modifications to the questionnaire were made inorder to clarify the intent of specific questions. Second, a pilot study was performed toconfirm the appropriateness of the survey administration (Hair, Bush, & Ortinau,


2003). After some modifications, the final questionnaire was mailed to CEOs ormanaging directors together with a return prepaid envelope and a personalisedcovering letter explaining the purpose of the study and its potential value, andemphasising the confidentiality of the respondents (Dillman, 1978).

We directed the survey to CEOs because previous studies have shown that suchhigh-level executives are generally reliable in their evaluations of their firm’s activitiesand performance (Hooley & Greenley, 2005). We used a three-wave mailing approachbased on the recommendations of Dillman (1978). A total of 204 respondentsreturned questionnaires, but 24 were omitted from analyses due to missing data,leaving a total of 180 completed questionnaires. This response rate is reasonablegiven that the targeted respondents were high-level executives who usually operateunder time constraints (Wu, Balasubramanian, & Mahajan, 2004).

Measures

We measured the three constructs (CRM capability, innovation, and businessperformance) by multiple-item scales adapted from previous studies. All items wereoperationalised using a five-point Likert-type scale. While CRM and innovation itemsranged from strongly disagree (1) to strongly agree (5), performance items rangedfrom much worse than competitors (1) to much better than competitors (5). All thescale items are provided in the Appendix.

CRM capability

In conceptualising CRM capability, we follow Day (2002) defining it as a second-orderconstruct that consists of three first-order components – relationship orientation,configuration, and customer information – measured by four, four, and six itemsrespectively. We borrowed or adapted these items from Day (2002), Reinartz, Krafft,and Hoyer (2004), and Jayachandran, Sharma, Kaufman, and Raman (2005).

Innovation

The original Booz Allen Hamilton (1982) scale of innovation is used in this study. Inspite of recent attempts to develop an innovation scale, the original Booz AllenHamilton (1982) scale of innovation is widely used in the literature (Darroch,2005). Booz Allen Hamilton identified six categories of products ranging from new-to-the-world products to cost reductions.

Business performance

The literature shows that performance is both objectively and subjectively measured.Objective measures use the absolute values of the firms’ actual performance. Subjectivemeasures ask respondents to assess their companies’ performance relative to that oftheir competitors (Greenley, 1995). For this study, subjective measures were used forthe following reasons. First, objective measures are difficult to gather due to firms’unwillingness to disclose financial information (Haugland, Myrtveit, & Nygaard,2007). Second, many researchers have reported a strong association betweenobjective measures and subjective responses (e.g. Dess & Robinson, 1984; Jaworski& Kohli, 1993). Third, objective measures are difficult to compare across companiesdue to different accounting conventions (Ottum & Moore, 1997). In obtainingsubjective assessments of a firm’s performance, the measures are more likely toaccurately reflect a firm’s true position when captured as relative to competitors


rather than as an absolute value (Slotegraaf & Dickson, 2004). Fourth, subjectivemeasures are particularly useful for assessing the non-financial dimensions ofperformance (Stam & Elfring, 2008).

However, we followed prior research and operationalised business performance asa two-dimensional construct: market performance and financial performance(e.g. Spanos & Lioukas, 2001). Market performance was assessed with four itemsreflecting customer satisfaction, customer retention, market share, and sales growth,whereas financial performance was measured with return on investment andprofitability. For all these items, respondents were asked to indicate their firm’sperformance relative to their major competitors.

Analysis and results

Adapting Anderson and Gerbing’s (1988) two-step approach, we developed separatemeasurement models before conducting tests of the hypothesised relationshipsbetween constructs. First, the psychometric properties (reliability, convergent anddiscriminant validity) of the constructs used in this study were evaluated. Then,structural equation modelling (SEM) was used to test the hypothesised relationshipsbetween constructs. We used a combination of SPSS (V14.0; SPSS, Inc., Chicago, IL)and AMOS (V6.0; SPSS, Inc.) software packages to carry out the analysis.

Reliability and validity of the measurement scales

To establish the internal consistency of the measures, we computed Cronbach’s alphacoefficients to estimate the reliability of each scale. We dropped items with low item-to-total correlation from subsequent analysis. The item-total correlation analysis forthe innovation scale indicated that two items should be excluded from further analysis.For the CRM construct, the reliability analysis resulted in the configurationcomponent being dropped from further analysis, as well as one item from therelationship-orientation component and two items from the customer-informationcomponent due to a low item-to-total correlation. This results in CRM capabilitybeing measured by only two components. In the case of performance, one item wasdropped from the market-performance component. The estimated reliabilities for therefined scales are .90 for innovation, .82 for CRM capability, and .86 for performance.As all scales achieved a Cronbach’s alpha greater than the .70 level recommended byNunnally (1978), the reliability of the measurements is established.

The remaining items for each scale were submitted to an exploratory factor analysis(EFA) to investigate its unidimensionality and underlying factor structure. Weperformed EFAs using principal components analysis with Varimax rotation. For theinnovation items, EFA yielded a one-factor solution that accounted for 78% of thevariance extracted. For the CRM items, EFA yielded a two-factor solution thataccounted for 81% of the total variance. All items loaded highly on their intendedconstructs. Finally, a factor analysis of the business-performance items revealed a two-factor solution, which accounted for 94% of the variance extracted. All items loadedhighly on the appropriate construct and there were no significant cross-loadings.

We subsequently conducted confirmatory factor analysis (CFA). For this research,we chose to separate the measurement model from the structural model. According toSujan, Weitz, and Kumar (1994), including all the constructs would result in a modeltoo complex to be estimated easily. We, therefore, performed three separate


measurement models: CRM, innovation, and business performance. This approachwas selected to allow for a direct test of the dimensionality of the constructs (Hultet al., 2004), to avoid violating recommended minimal sample-size-to-parameter-estimate ratios (Baker & Sinkula, 1999), and to assess the reliability, discriminantvalidity, and convergent validity of the constructs (Sujan et al., 1994). This approach isalso consistent with prior research (e.g. Baker & Sinkula, 1999; Hooley et al., 2005;Hult et al., 2004; Jayachandran et al., 2005).

For the three constructs, the CFA results show that all indicators loadedsignificantly on their corresponding latent construct. Also, the three measurementmodels fit well as indicated by the CFA results for the CRM construct (w2 ¼ 21.742,degrees of freedom [df] ¼ 11, p ¼ .026, goodness-of-fit index [GFI] ¼ .968, adjustedgoodness-of-fit index [AGFI] ¼ .918, Tucker–Lewis index [TLI] ¼ .980, comparativefit index [CFI] ¼ .990, root mean square error of approximation [RMSEA] ¼ .074),the innovation construct (w2 ¼ 1.932, df ¼ 1, p ¼ .165, GFI ¼ .993, AGFI ¼ .957,TLI¼ .989, CFI¼ .996, RMSEA¼ .074), and the performance construct (w2¼ 7.732,df¼ 4, p¼ .102, GFI¼ .984, AGFI¼ .941, TLI¼ .992, CFI¼ .997, RMSEA¼ .072).

Within the confirmatory factor analysis, we calculated the composite reliabilityfollowing the procedures that Fornell and Larcker (1981) suggest. Compositereliability is similar to Cronbach’s alpha, but it estimates consistency on the basis ofactual construct loadings (White, Varadarajan, & Dacin, 2003). As shown in Table 1,the composite reliabilities for the three scales ranged from .88 to .92, exceedingacceptable levels for construct reliability (Bagozzi & Yi, 1988; Fornell & Larcker,1981; Nunnally, 1978).

To examine the convergent validity for the three constructs, we computed theaverage variance extracted (AVE) by the indicators corresponding to each ofthe three constructs. The AVE is the amount of variance that is captured by theconstruct in relation to the amount of variance due to measurement error. Ifthe AVE is less than .50, the variance due to measurement error is larger than thevariance captured by the construct, and the validity of the individual indicators, as wellas the construct, is questionable (Fornell & Larcker, 1981). Therefore, convergentvalidity is established if the AVE for each construct accounts for .50 or more of thetotal variance. As shown in Table 1, the AVE exceeded the recommended level of .50for CRM (.78), innovation (.71), and performance (.84), providing evidence forconvergent validity. We also found support for convergent validity because all thestandardised factor loadings were relatively high and statistically significant at the 1%level (Anderson & Gerbing, 1988).

We examined discriminant validity following the procedure recommended byFornell and Larcker (1981). They suggest that discriminant validity is established fora construct if its AVE is larger than its shared variance with any other construct. Wecompared the AVE with the highest variance that each construct shared with the other

Table 1 Properties of measurement models.

ConstructCompositereliability

Average varianceextracted

Highest sharedvariance

Standardised factorloadings*

CRM .88 78% 13% .80–.95

Innovation .88 71% 13% .76–.89

Performance .92 84% 12% .86–.94

*All factor loadings are significant at the .01 level.


constructs. The AVE for each construct was always greater than the highest sharedvariance (see Table 1). Collectively, these tests provide support for the robustness ofour measures.

Research-model testing

After confirming the appropriateness of the measurement models, we used structuralequation modelling to test our hypotheses with the maximum likelihood estimationmethod. Before testing the hypotheses, we examined a correlation matrix for the scalesof the major constructs (see Table 2). As expected, there is a significant, positivecorrelation among CRM, innovation, and performance. Also, to check for non-normal distributions, we examined the skewness and kurtosis of the final measures.All the measures had normal distributions with deviations from normality withinacceptable ranges, suggesting that the data did not violate the normality distribution(Curran, West, & Finch, 1996).

Following Bagozzi and Heatherton (1994), the reduced forms of the constructs areused in order to simplify the model. For each first-order construct, a composite scorewas created by averaging the scores of its measurement items. Thus we aggregated theinnovation scale to have a single indicator, the CRM scale to have two indicators(customer information and relationship orientation), and the performance scale tohave two indicators (financial performance and market performance). Because weused a single indicator for innovation construct, we fixed its factor loading to thesquare root of the construct reliability, and its error variance to (1 � constructreliability) � construct variance to account for measurement error (Bagozzi &Heatherton, 1994).

SEM results of the relationship between the constructs operationalised in this studyare summarised in Table 3. The results of SEM analysis indicate a good overall fit withthe data (w2 ¼ 5.853, df ¼ 3, p ¼ .119, GFI ¼ .987, AGFI ¼ .936, TLI ¼ .972,CFI ¼ .992, RMSEA ¼ .073). Since these indexes are acceptable, we concluded thatthe structural model is an appropriate basis for hypothesis testing.

The results support the three hypotheses and, in particular, confirm the mediatingrole of innovation. The results indicate that innovation significantly and positively

Table 2 Correlations and descriptive statistics.

Variable CRM Innovation Mean Standard deviation Skewness Kurtosis

CRM 3.096 .852 �.380 .079

Innovation .36* 3.589 .775 �.247 �.847

Performance .32* .35* 3.194 .894 .237 �1.092

*Correlation is significant at the .01 level.

Table 3 Results of the test of structural equation model.

Hypotheses Standardised coefficient t-value p-value

Hypothesis 1: Innovation! Performance .29 3.457 .001

Hypothesis 2: CRM! Performance .24 2.994 .003

Hypothesis 3: CRM! Innovation .43 5.112 .001

Goodness-of-fit statistics: w2 ¼ 5.853, df ¼ 3, p ¼ .119, GFI ¼ .987, AGFI ¼ .936, TLI ¼ .972, CFI ¼ .992,RMSEA ¼ .073.


relates to firm performance (b ¼ .29, t ¼ 3.457, p ¼ .001), providing support forhypothesis 1. As predicted by hypothesis 2, there is a positive relationship betweenCRM capability and performance (b ¼ .24, t ¼ 2.994, p ¼ .003). The results alsoindicate that CRM capability has a positive direct impact on innovation (b ¼ .43, t ¼5.112, p ¼ .001), supporting hypothesis 3. We also examined the mediating effect ofinnovation on the relationship between CRM and performance. To test meditation, weestimated the significance of the indirect and total effects of CRM capability onperformance.1 The results show that innovation not only has a direct relationshipwith performance but also plays a mediating role in the relationship between CRMcapability and performance. This is because the indirect effect of CRM onperformance through innovation is statistically significant (b ¼ .17, p ¼ .001). Thetotal effect of CRM on performance, which is the sum of the direct and indirect effects,is also significant (b ¼ .36, p ¼ .001).

Discussion and conclusion

The primary focus of this study was the simultaneous effects of CRM and innovationon firm performance. This study suggests that CRM is an antecedent to innovation,and that CRM and innovation simultaneously contribute to firm performance. Thefindings provide support for the proposed relationships between CRM, innovation,and firm’s superior performance.

The results show that CRM capability contributes to firm performance, a findingthat is consistent with previous research (e.g. Day & Van den Bulte, 2002; Hooleyet al., 2005). CRM practices can be seen as a means to learn about customers’ needsand how best to create, satisfy, and sustain customers. CRM involves getting close tocustomers, understanding their needs and preferences, and determining how toprofitably satisfy those needs. Satisfied and committed customers lead to lowermarketing costs and increased revenues (Fung, Chen, & Yip, 2007).

Consistent with previous studies (e.g. Baker & Sinkula, 1999; Calantone et al.,2002; Han et al., 1998; Hult et al., 2004; Hurley & Hult, 1998; Keskin, 2006;Panayides, 2006; Thornhill, 2006), our findings provide support for a positiverelationship between innovation capability and performance. Indeed, innovation is acentral strategy pursued by firms for creating value and gaining positional advantagesin competitive markets (Weerawardena, O’Cass, & Julian, 2006). Firms with greatercapacity to innovate are likely to be more successful in responding to theirenvironments and developing new competences that lead to competitive advantageand superior performance (Hurley & Hult, 1998). General Electric, DuPont, Procter& Gamble, and Visa are all companies whose sustained success owes much toorganisational innovation (Hamel, 2006).

This study has shown that innovation capability is a missing link not previouslyconceptualised in the context of how CRM contributes to firm performance. Theresults provide support for the mediating effect of innovation on the relationshipbetween CRM capability and performance. Customer knowledge is a competitiveresource within a firm, and the ability to translate that knowledge into innovativeproducts is considered to result in competitive performance (Thornhill, 2006).Superior-performing firms are those that can not only gain sufficient understanding

1To test the significance of the mediation effect, we obtained total and indirect effects estimates andsignificance using the AMOS 6 Bootstrap Estimation procedure.


of customer requirements and preferences, but can also use that knowledge andinformation to innovate products and services.

A major finding of potential interest to managers is that successful development ofinnovations can be achieved not only when firms have the adequate financial resourcesbut also when firms have the important attributes that actually facilitate innovation. Afirm’s ability to develop a thorough understanding of customers’ needs, wants, andpreferences is significant, an understanding that can be made through continuouscommunication with customers. Firms without such understanding are less likely tostand out in terms of innovation capability (Calantone et al., 2002). CRM capabilityprovides a firm with a better understanding of customers’ current and potential needs,which subsequently increases the possibility of innovation generation.

The failure of firms in product innovation should not be attributed mainly to theirlimited resources. Firms with more resources may have a greater ability to innovate(Sorescu, Chandy, & Prabhu, 2003). However, allocating resources is likely to be ahelpful but not sufficient condition for innovation success. It may be possible for firmsto fail even when they have resources, if they do not have broad, deep, and specificmarket knowledge (De Luca & Atuahene-Gima, 2007). Innovating firms should createan environment in which innovation can flourish (Thornhill, 2006). The capability ofbuilding close relationships with customers is an example of the organisationalcapabilities that the firm needs to have to enhance its ability to innovate.

Limitations and future research directions

Several limitations of our study can be noted to help guide future research. First, ourdata is cross-sectional. Cross-sectional studies suffer from an inability to determine thecauses and effects of the variables investigated (Hill & Hansen, 1991). Although thehypothesised causal ordering is theoretically possible, the cross-sectional design limitsour ability to draw causal inferences. Future research, therefore, could use longitudinaldata to increase confidence in the causal nature of the relationships tested in this study.

Second, innovation is a complex construct, and capturing all its facets in a singlestudy is impossible (Damanpour & Schneider, 2006). Different authors use differentoperationalisations to capture innovation. We used an outcome-oriented measure ofinnovation. Other researchers, however, have developed a scale to measureinnovation-oriented organisational culture (e.g. Hurley & Hult, 1998). Although wevalidated our measure of innovation, the existence of multiple methods to measureinnovation warrants attention in future research. Future studies may benefit from theuse of other measures to clarify the relationships examined in this study.

Third, the data for this study was gathered from a single informant (i.e. CEOs ormanaging directors) who was likely to be one of the most knowledgeable about thecharacteristics of the organisation and its performance (Weerawardena et al., 2006).The most desirable data-collection procedure, however, is to collect data frommultiple sources (Auh & Menguc, 2006). We recommend that future studies take amultiple-source data-collection approach (e.g. CEOs and marketing managers).

Fourth, our sample is from a single country, which limits the generalisability of thefindings. Future studies using data from other countries may help increase thegeneralisability of our findings.

Finally, we recognise that other variables not examined in our study – such asmarket orientation, learning orientation, and total quality management – have been


theorised as determinants of innovation and business performance, and may be used tosupplement the variables included in this study.

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Appendix: Measures of constructs

Innovation

– Our company is often first to the market with new services and products.

– We often introduce new ranges of services and products not previously offered by thiscompany.

– We often add new services and products to our existing ranges.

– We often improve or revise existing services and products.

– We often change our services and products in order to reduce costs.

– We often reposition existing services and products.

CRM capability

Relationship orientation:

– We actively stress customer loyalty or retention programs (Reinartz et al., 2004).

– In our organisation, there is an openness to sharing information about customers (Day,2002).

– Our employees are willing to treat different customers differently (Day, 2002).

– We systematically attempt to customise services and products based on the value ofthe customer (Reinartz et al., 2004).

Configuration:

– We reward employees for building and deepening relationships with high-valuecustomers (Reinartz et al., 2004).


– We can create customised offerings to our customers (Day, 2002).

– We are organised in a way to respond optimally to customer groups with differentprofitability (Reinartz et al., 2004).

– We have technologies that allow for one-to-one communications with potentialcustomers (Reinartz et al., 2004).

Customer information:

– We have comprehensive databases to give a full picture of our customers (Day, 2002)

– We integrate customer information across customer contact points (e.g. mail,telephone, Web, fax, face-to-face) (Reinartz et al., 2004).

– Our databases are designed to ensure data quality (Day, 2002).

– We continuously track customer information in order to assess the lifetime value ofeach customer (Reinartz et al., 2004).

– Our information systems are integrated across several functional areas (Jayachandranet al., 2005).

– We invest in technology to acquire and manage ‘real time’ customer information andfeedback (Reinartz et al., 2004).

Business performance

Financial performance:

– Profitability

– Return on investment

Market Performance:

– Market share

– Customer satisfaction

– Customer retention

– Sales growth

About the authors

Moustafa Battor is a lecturer in marketing at the Faculty of Commerce, Tanta University inEgypt. He is currently doing his PhD at the University of Bradford. His current researchinterests include innovation, relationship marketing, organisational learning, and firmperformance.

Corresponding author: Moustafa Battor, Tanta University, Faculty of Commerce, Said Street31515, Tanta, Egypt.

E [email protected]

Mohamed Battor is a research assistant in marketing at the Faculty of Commerce, TantaUniversity in Egypt. He received his MBA from Tanta University. Currently, he is doing hisPhD at Malaya University in Malaysia.

E [email protected]


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