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The impact of customer relationship management capability on innovation and performance advantages: Testing a mediated model


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Customer relationship management (CRM) and innovation are widely considered to be valuable capabilities associated with competitive advantage. However, there is a lack of research demonstrating how they work together to produce performance advantages. This research investigates the mediating role of innovation between CRM and performance. The authors examine the direct impact of both CRM and innovation on firm performance. Moreover, they investigate the role of innovation as a mediating mechanism to explain the effect of CRM on performance. The authors use structural equation modelling to test the relationships among these constructs. The results support the direct impact of CRM and innovation on performance. Also, the findings indicate that the indirect effect of CRM on firm performance through innovation is significant. These results reinforce the view that developing close relationships with customers enhances a firm's ability to innovate.
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The impact of customer relationship management capability on innovation
and performance advantages: testing a mediated model
Moustafa Battor a;Mohamed Battor a
a Tanta University, Egypt
First published on: 02 February 2010
To cite this Article Battor, Moustafa andBattor, Mohamed(2010) 'The impact of customer relationship management
capability on innovation and performance advantages: testing a mediated model', Journal of Marketing Management,,
First published on: 02 February 2010 (iFirst)
To link to this Article: DOI: 10.1080/02672570903498843
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The impact of customer relationship management
capability on innovation and performance
advantages: testing a mediated model
Moustafa Battor, Tanta University, Egypt
Mohamed Battor, Tanta University, Egypt
Abstract Customer relationship management (CRM) and innovation are widely
considered to be valuable capabilities associated with competitive advantage.
However, there is a lack of research demonstrating how they work together to
produce performance advantages. This research investigates the mediating role
of innovation between CRM and performance. The authors examine the direct
impact of both CRM and innovation on firm performance. Moreover, they
investigate the role of innovation as a mediating mechanism to explain the
effect of CRM on performance. The authors use structural equation modelling
to test the relationships among these constructs. The results support the direct
impact of CRM and innovation on performance. Also, the findings indicate that the
indirect effect of CRM on firm performance through innovation is significant.
These results reinforce the view that developing close relationships with
customers enhances a firm’s ability to innovate.
Keywords innovation; CRM; customer relationship management; performance
Developing a superior customer relationship management (CRM) capability – that is,
creating and managing close customer relationships – is expected to be one of the most
important sources of superior performance in today’s competitive business
environment (Day, 2000; Kale, 2004). Capital One, for example, has significantly
outperformed 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 income
from each customer and enjoyed double the profit margin (Day, 2002). Amazon is
another good example. Nearly 59% of’s sales come from repeat
customers. 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 as
keeping or managing existing ones, which means that existing customers contribute
five times more sales than new customers do (Ko, Kim, Kim, & Woo, 2008).
Therefore, beyond designing strategies to attract new customers and create
transactions with them, organisations recognise the importance of retaining current
ISSN 0267-257X print/ISSN 1472-1376 online
#2010 Westburn Publishers Ltd.
DOI: 10.1080/02672570903498843
Journal of Marketing Management
iFirst, 2010, 1–16
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customers and building lasting customer relationships (Kotler, Armstrong, Saunders,
& Wong, 1999).
Innovation is also an important organisational capability, because successful
new products are engines of growth and provide increased sales, profits, and
competitive strength for most organisations (Pauwels, Silva-Risso, Srinivasan, &
Hanssens, 2004; Sivadas & Dwyer, 2000). Robust findings uniformly suggest that
a positive and direct relationship exists between innovation and superior
performance (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 failure
rate of new products is somewhere between 40% and 75%, and nearly 50% of
the new products that are introduced each year fail. Organisations thus must not
only innovate consistently to remain competitive, but must also seek to reduce
the 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 assimilate
external knowledge, and to transform it into new or improved products (Branzei &
Vertinsky, 2006; Joshi & Sharma, 2004; Marinova, 2004). In that sense, CRM plays
an 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 through
active use of, and learning from, the information collected (Brohman, Watson, Piccoli,
& Parasuraman, 2003). Firms with superior CRM capability are in a better position to
gather and store customer knowledge. They can track customer behaviour to gain
insights into customer’s tastes and evolving needs. Firms with greater deployment of
CRM applications thus will be better able to design and develop innovative products
and 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 and
innovation to performance, insufficient attention has been paid in this literature to
address how they work together to achieve higher performance. Also, although prior
conceptual work has suggested that CRM can enhance an organisation’s innovation,
empirical evidence is sparse. Therefore, the key questions addressed by our research
are how CRM and innovation interact to affect performance and whether CRM
fosters innovation. More specifically, we study the CRM–innovation–performance
chain, and examine both direct and indirect (through innovation) effects of CRM
capability on performance.
The paper is organised as follows. The theoretical background and hypotheses are
presented in the next section. We then describe the research method and the scale
development and validation. Next, we present the results of testing the structural
model. Finally, we discuss the implications and limitations of our study, and offer
directions for future research.
Theoretical background and research hypotheses
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
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the organisation. Drucker (2001, p. 22) proposes that innovation is a different product
or service creating a new potential of satisfaction, rather than an improvement.
Innovation is defined as a process that begins with an idea, proceeds with the
development of an invention, and results in the introduction of a new product,
process, or service to the marketplace (Thornhill, 2006, p. 689). Innovation usually
involves something new. It involves doing new things or finding new ways of doing
things to change the rules of the game (Keskin, 2006; Porter, 1985).
Porter (1985, p. 164) recognises innovation as ‘one of the principal drivers of
competition’. Drucker (2001, p. 21) argues that ‘distinctive capabilities are different
for every organization; they are part of an organization’s personality. But every
organization needs one core capability: innovation’. These quotations leave little
doubt that innovation has been, and will continue to be, a key capability of interest
to firms. Innovation plays a key role in the survival and success of any organisation in
the 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 and
unpredictable environments, new things have to be done or new ways have to be
adopted in doing things that are already being done (i.e. to be innovative) (Keskin,
The impact of innovation on performance has been examined extensively in prior
research, and considerable empirical evidence of a positive impact has been
accumulated (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, conclude
that management research suggests that innovative firms are, on average, twice as
profitable as other firms. Also, a study of 700 companies, which launched a total
number of 13,311 new products between 1976 and 1981, reported that 22% of profits
and 28% of sales growth came from new product launches. The trend was predicted to
rise to 31% profits and 37% sales (Zairi, 1995). Thus the following hypothesis is
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 relationship
marketing was defined as attracting, maintaining, and enhancing customer
relationships (Berry, 2002). Kotler et al. (1999) define CRM as retaining current
customers and building profitable, long-term relationships with them. Recently, Day
(2002) conceptualised CRM as a firm capability that results from a focus on three
organisational components working in concertwitheachother:anorganisational
orientation that makes customer retention a priority; a configuration that includes
the structure of the organisation, its processes for personalising product offerings,
and its incentives for building relationships; and information about customers that is
in-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 used
interchangeably to describe a firm’s ability to outperform its rivals by creating and
managing close customer relationships (Day, 1994).
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CRM capability and performance
Day (1994) described CRM capability as a valuable and difficult-to-imitate source of
superior performance. CRM capability is much more difficult to understand and
imitate than most capabilities because it takes time to develop, relies on the complex
interplay of supporting resources, and is based primarily on tacit knowledge and
interpersonal skills (Hooley, Greenley, Cadogan, & Fahy, 2005). In addition,
building stronger relationships with customers provides the basis for understanding
the evolving requirements of customers and identifying the most appropriate ways of
satisfying customers better than competitors, which can provide greater opportunities
for realising superior performance (Day, 1994).
Compelling evidence exists that retaining customers leads to positive business
performance. In their seminal study, Reichheld and Sasser (1990) find that a 5%
increase in customer retention rates increases profits by 25 to 85%, depending
upon the industry. The cost of keeping current customers is much lower than that
of winning new ones (Reichheld, 1996; Reichheld & Sasser, 1990), and many
new relationships are often unprofitable in the early years (Kotler et al., 1999;
Reichheld, 1996). Only later, when the cost of serving loyal customers falls and
the 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 positive
word 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 between
customer relationship and business performance. For example, a recent special
section in the Journal of Marketing finds that customer-relationship activities
enhance firm performance in eight of the ten papers published (Boulding, Staelin,
Ehret, & Johnston, 2005). Also, Day and Van den Bulte (2002) find that CRM
capability is an important determinant of superior performance. Similar findings are
reported 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 being
increasingly acknowledged in the management literature (e.g. Campbell & Cooper,
1999; Nambisan, 2002). The Marketing Science Institute’s (MSI) 2006–2008 research
priorities include the topic of the customer’s role in innovation as the first research
priority. A survey by the MSI shows that ‘innovation continues to be viewed as the
prime engine of growth, but customers play a much larger role in shaping innovation
strategy and execution [and] at the development level, customer insights are needed to
drive 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 management
strategy (Ko et al., 2008). Porter (1990, p. 86) argues that through building close
relationships with customers, ‘information flows freely and innovations diffuse
rapidly’. Having close relationships with customers can help the firm take advantage
of short lines of communication, a quick and constant flow of information, and an
ongoing exchange of ideas and innovations (Porter, 1990).
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Firms deploying CRM can track customer behaviour to gain insight into customer
tastes and evolving needs (Mithas et al., 2005; Vickery, Jayaram, Droge, & Calantone,
2003). With currently available technology, CRM applications allow the firm to learn
about customer preferences in real time, continuously update the firm’s knowledge of
customer preferences, and analyse customer insights (Sun, 2006). Firms with superior
CRM capabilities are in a better position to collect, organise, and prioritise customer
information and transmit this information to the product-development team. By
integrating this information in the product-development process, firms can create an
incremental innovation or develop a product to satisfy growing or evolving customer
needs (Zahay, Griffin, & Fredericks, 2004). Customer information obtained from
CRM, then, is a valuable intellectual asset for a company to improve its ability to
innovate products that meet or even exceed customers’ requirements (Su, Chen, &
Sha, 2006).
Many examples exist of firms that have successfully used customer knowledge as a
key 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 allowing
innovation to be customer driven, FedEx has developed innovative ways to meet
customers’ needs (Battor, Zairi, & Francis, 2008). Microsoft is another good
example of the positive relationship between customer knowledge and innovation.
Microsoft established beta sites to seek customer knowledge in all development phases
of new software, from generating product specifications to the final check of the
product before its release. For example, more than 650,000 customers tested a beta
version of Microsoft’s Windows 2000 and shared with Microsoft their ideas for
changing some of the product’s features (Prahalad & Ramaswamy, 2000). Microsoft
attributes its sustained success to ‘its vigorous pursuit of customer knowledge in new
product 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 provides
detailed financial and accounting information for 1.8 million firms registered in the
UK (Nachum, 2003). We used a systematic random sampling method to draw a sample
of 1000 companies with more than 50 employees from this database. To improve the
validity of the survey questions and the response rate, we followed the general
recommendations 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 was
conducted to assess the face and content validity of the measurement items.
Consequently, a small number of modifications to the questionnaire were made in
order to clarify the intent of specific questions. Second, a pilot study was performed to
confirm the appropriateness of the survey administration (Hair, Bush, & Ortinau,
Battor and Battor The impact of CRM capability on innovation and performance advantages 5
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2003). After some modifications, the final questionnaire was mailed to CEOs or
managing directors together with a return prepaid envelope and a personalised
covering letter explaining the purpose of the study and its potential value, and
emphasising the confidentiality of the respondents (Dillman, 1978).
We directed the survey to CEOs because previous studies have shown that such
high-level executives are generally reliable in their evaluations of their firm’s activities
and performance (Hooley & Greenley, 2005). We used a three-wave mailing approach
based on the recommendations of Dillman (1978). A total of 204 respondents
returned questionnaires, but 24 were omitted from analyses due to missing data,
leaving a total of 180 completed questionnaires. This response rate is reasonable
given that the targeted respondents were high-level executives who usually operate
under time constraints (Wu, Balasubramanian, & Mahajan, 2004).
We measured the three constructs (CRM capability, innovation, and business
performance) by multiple-item scales adapted from previous studies. All items were
operationalised using a five-point Likert-type scale. While CRM and innovation items
ranged from strongly disagree (1) to strongly agree (5), performance items ranged
from much worse than competitors (1) to much better than competitors (5). All the
scale items are provided in the Appendix.
CRM capability
In conceptualising CRM capability, we follow Day (2002) defining it as a second-order
construct that consists of three first-order components – relationship orientation,
configuration, and customer information – measured by four, four, and six items
respectively. We borrowed or adapted these items from Day (2002), Reinartz, Krafft,
and Hoyer (2004), and Jayachandran, Sharma, Kaufman, and Raman (2005).
The original Booz Allen Hamilton (1982) scale of innovation is used in this study. In
spite of recent attempts to develop an innovation scale, the original Booz Allen
Hamilton (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. Subjective
measures ask respondents to assess their companies’ performance relative to that of
their competitors (Greenley, 1995). For this study, subjective measures were used for
the 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 between
objective measures and subjective responses (e.g. Dess & Robinson, 1984; Jaworski
& Kohli, 1993). Third, objective measures are difficult to compare across companies
due to different accounting conventions (Ottum & Moore, 1997). In obtaining
subjective assessments of a firm’s performance, the measures are more likely to
accurately reflect a firm’s true position when captured as relative to competitors
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rather than as an absolute value (Slotegraaf & Dickson, 2004). Fourth, subjective
measures are particularly useful for assessing the non-financial dimensions of
performance (Stam & Elfring, 2008).
However, we followed prior research and operationalised business performance as
a two-dimensional construct: market performance and financial performance
(e.g. Spanos & Lioukas, 2001). Market performance was assessed with four items
reflecting customer satisfaction, customer retention, market share, and sales growth,
whereas financial performance was measured with return on investment and
profitability. For all these items, respondents were asked to indicate their firm’s
performance relative to their major competitors.
Analysis and results
Adapting Anderson and Gerbing’s (1988) two-step approach, we developed separate
measurement models before conducting tests of the hypothesised relationships
between constructs. First, the psychometric properties (reliability, convergent and
discriminant validity) of the constructs used in this study were evaluated. Then,
structural equation modelling (SEM) was used to test the hypothesised relationships
between 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 alpha
coefficients to estimate the reliability of each scale. We dropped items with low item-
to-total correlation from subsequent analysis. The item-total correlation analysis for
the innovation scale indicated that two items should be excluded from further analysis.
For the CRM construct, the reliability analysis resulted in the configuration
component being dropped from further analysis, as well as one item from the
relationship-orientation component and two items from the customer-information
component due to a low item-to-total correlation. This results in CRM capability
being measured by only two components. In the case of performance, one item was
dropped from the market-performance component. The estimated reliabilities for the
refined 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 by
Nunnally (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. We
performed EFAs using principal components analysis with Varimax rotation. For the
innovation items, EFA yielded a one-factor solution that accounted for 78% of the
variance extracted. For the CRM items, EFA yielded a two-factor solution that
accounted for 81% of the total variance. All items loaded highly on their intended
constructs. Finally, a factor analysis of the business-performance items revealed a two-
factor solution, which accounted for 94% of the variance extracted. All items loaded
highly 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 to
Sujan, Weitz, and Kumar (1994), including all the constructs would result in a model
too complex to be estimated easily. We, therefore, performed three separate
Battor and Battor The impact of CRM capability on innovation and performance advantages 7
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measurement models: CRM, innovation, and business performance. This approach
was selected to allow for a direct test of the dimensionality of the constructs (Hult
et al., 2004), to avoid violating recommended minimal sample-size-to-parameter-
estimate ratios (Baker & Sinkula, 1999), and to assess the reliability, discriminant
validity, and convergent validity of the constructs (Sujan et al., 1994). This approach is
also 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 loaded
significantly on their corresponding latent construct. Also, the three measurement
models fit well as indicated by the CFA results for the CRM construct (w
degrees of freedom [df] ¼11, p¼.026, goodness-of-fit index [GFI] ¼.968, adjusted
goodness-of-fit index [AGFI] ¼.918, Tucker–Lewis index [TLI] ¼.980, comparative
fit index [CFI] ¼.990, root mean square error of approximation [RMSEA] ¼.074),
the innovation construct (w
¼1.932, df ¼1, p¼.165, GFI ¼.993, AGFI ¼.957,
TLI ¼.989, CFI ¼.996, RMSEA ¼.074), and the performance construct (w
df ¼4, p¼.102, GFI ¼.984, AGFI ¼.941, TLI ¼.992, CFI ¼.997, RMSEA ¼.072).
Within the confirmatory factor analysis, we calculated the composite reliability
following the procedures that Fornell and Larcker (1981) suggest. Composite
reliability is similar to Cronbach’s alpha, but it estimates consistency on the basis of
actual construct loadings (White, Varadarajan, & Dacin, 2003). As shown in Table 1,
the composite reliabilities for the three scales ranged from .88 to .92, exceeding
acceptable levels for construct reliability (Bagozzi & Yi, 1988; Fornell & Larcker,
1981; Nunnally, 1978).
To examine the convergent validity for the three constructs, we computed the
average variance extracted (AVE) by the indicators corresponding to each of
the three constructs. The AVE is the amount of variance that is captured by the
construct in relation to the amount of variance due to measurement error. If
the AVE is less than .50, the variance due to measurement error is larger than the
variance captured by the construct, and the validity of the individual indicators, as well
as the construct, is questionable (Fornell & Larcker, 1981). Therefore, convergent
validity is established if the AVE for each construct accounts for .50 or more of the
total variance. As shown in Table 1, the AVE exceeded the recommended level of .50
for CRM (.78), innovation (.71), and performance (.84), providing evidence for
convergent validity. We also found support for convergent validity because all the
standardised factor loadings were relatively high and statistically significant at the 1%
level (Anderson & Gerbing, 1988).
We examined discriminant validity following the procedure recommended by
Fornell and Larcker (1981). They suggest that discriminant validity is established for
a construct if its AVE is larger than its shared variance with any other construct. We
compared the AVE with the highest variance that each construct shared with the other
Table 1 Properties of measurement models.
Average variance
Highest shared
Standardised factor
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.
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constructs. The AVE for each construct was always greater than the highest shared
variance (see Table 1). Collectively, these tests provide support for the robustness of
our measures.
Research-model testing
After confirming the appropriateness of the measurement models, we used structural
equation modelling to test our hypotheses with the maximum likelihood estimation
method. Before testing the hypotheses, we examined a correlation matrix for the scales
of the major constructs (see Table 2). As expected, there is a significant, positive
correlation 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 within
acceptable 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 are
used in order to simplify the model. For each first-order construct, a composite score
was created by averaging the scores of its measurement items. Thus we aggregated the
innovation scale to have a single indicator, the CRM scale to have two indicators
(customer information and relationship orientation), and the performance scale to
have two indicators (financial performance and market performance). Because we
used a single indicator for innovation construct, we fixed its factor loading to the
square root of the construct reliability, and its error variance to (1 construct
reliability) construct variance to account for measurement error (Bagozzi &
Heatherton, 1994).
SEM results of the relationship between the constructs operationalised in this study
are summarised in Table 3. The results of SEM analysis indicate a good overall fit with
the data (w
¼5.853, df ¼3, p¼.119, GFI ¼.987, AGFI ¼.936, TLI ¼.972,
CFI ¼.992, RMSEA ¼.073). Since these indexes are acceptable, we concluded that
the structural model is an appropriate basis for hypothesis testing.
The results support the three hypotheses and, in particular, confirm the mediating
role 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: w
¼5.853, df ¼3, p¼.119, GFI ¼.987, AGFI ¼.936, TLI ¼.972, CFI ¼.992,
RMSEA ¼.073.
Battor and Battor The impact of CRM capability on innovation and performance advantages 9
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relates to firm performance (b¼.29, t¼3.457, p¼.001), providing support for
hypothesis 1. As predicted by hypothesis 2, there is a positive relationship between
CRM capability and performance (b¼.24, t¼2.994, p¼.003). The results also
indicate that CRM capability has a positive direct impact on innovation (b¼.43, t¼
5.112, p¼.001), supporting hypothesis 3. We also examined the mediating effect of
innovation on the relationship between CRM and performance. To test meditation, we
estimated the significance of the indirect and total effects of CRM capability on
The results show that innovation not only has a direct relationship
with performance but also plays a mediating role in the relationship between CRM
capability and performance. This is because the indirect effect of CRM on
performance through innovation is statistically significant (b¼.17, p¼.001). The
total effect of CRM on performance, which is the sum of the direct and indirect effects,
is also significant (b¼.36, p¼.001).
Discussion and conclusion
The primary focus of this study was the simultaneous effects of CRM and innovation
on firm performance. This study suggests that CRM is an antecedent to innovation,
and that CRM and innovation simultaneously contribute to firm performance. The
findings 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 finding
that is consistent with previous research (e.g. Day & Van den Bulte, 2002; Hooley
et al., 2005). CRM practices can be seen as a means to learn about customers’ needs
and how best to create, satisfy, and sustain customers. CRM involves getting close to
customers, understanding their needs and preferences, and determining how to
profitably satisfy those needs. Satisfied and committed customers lead to lower
marketing 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 positive
relationship between innovation capability and performance. Indeed, innovation is a
central strategy pursued by firms for creating value and gaining positional advantages
in competitive markets (Weerawardena, O’Cass, & Julian, 2006). Firms with greater
capacity to innovate are likely to be more successful in responding to their
environments and developing new competences that lead to competitive advantage
and superior performance (Hurley & Hult, 1998). General Electric, DuPont, Procter
& Gamble, and Visa are all companies whose sustained success owes much to
organisational innovation (Hamel, 2006).
This study has shown that innovation capability is a missing link not previously
conceptualised in the context of how CRM contributes to firm performance. The
results provide support for the mediating effect of innovation on the relationship
between CRM capability and performance. Customer knowledge is a competitive
resource within a firm, and the ability to translate that knowledge into innovative
products is considered to result in competitive performance (Thornhill, 2006).
Superior-performing firms are those that can not only gain sufficient understanding
To test the significance of the mediation effect, we obtained total and indirect effects estimates and
significance using the AMOS 6 Bootstrap Estimation procedure.
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of customer requirements and preferences, but can also use that knowledge and
information to innovate products and services.
A major finding of potential interest to managers is that successful development of
innovations can be achieved not only when firms have the adequate financial resources
but also when firms have the important attributes that actually facilitate innovation. A
firm’s ability to develop a thorough understanding of customers’ needs, wants, and
preferences is significant, an understanding that can be made through continuous
communication with customers. Firms without such understanding are less likely to
stand out in terms of innovation capability (Calantone et al., 2002). CRM capability
provides 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 their
limited resources. Firms with more resources may have a greater ability to innovate
(Sorescu, Chandy, & Prabhu, 2003). However, allocating resources is likely to be a
helpful but not sufficient condition for innovation success. It may be possible for firms
to fail even when they have resources, if they do not have broad, deep, and specific
market knowledge (De Luca & Atuahene-Gima, 2007). Innovating firms should create
an environment in which innovation can flourish (Thornhill, 2006). The capability of
building close relationships with customers is an example of the organisational
capabilities 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, our
data is cross-sectional. Cross-sectional studies suffer from an inability to determine the
causes and effects of the variables investigated (Hill & Hansen, 1991). Although the
hypothesised causal ordering is theoretically possible, the cross-sectional design limits
our ability to draw causal inferences. Future research, therefore, could use longitudinal
data 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 single
study is impossible (Damanpour & Schneider, 2006). Different authors use different
operationalisations to capture innovation. We used an outcome-oriented measure of
innovation. Other researchers, however, have developed a scale to measure
innovation-oriented organisational culture (e.g. Hurley & Hult, 1998). Although we
validated our measure of innovation, the existence of multiple methods to measure
innovation warrants attention in future research. Future studies may benefit from the
use 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 or
managing directors) who was likely to be one of the most knowledgeable about the
characteristics of the organisation and its performance (Weerawardena et al., 2006).
The most desirable data-collection procedure, however, is to collect data from
multiple sources (Auh & Menguc, 2006). We recommend that future studies take a
multiple-source data-collection approach (e.g. CEOs and marketing managers).
Fourth, our sample is from a single country, which limits the generalisability of the
findings. Future studies using data from other countries may help increase the
generalisability of our findings.
Finally, we recognise that other variables not examined in our study – such as
market orientation, learning orientation, and total quality management – have been
Battor and Battor The impact of CRM capability on innovation and performance advantages 11
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Appendix: Measures of constructs
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 this
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,
Our employees are willing to treat different customers differently (Day, 2002).
We systematically attempt to customise services and products based on the value of
the customer (Reinartz et al., 2004).
– We reward employees for building and deepening relationships with high-value
customers (Reinartz et al., 2004).
Battor and Battor The impact of CRM capability on innovation and performance advantages 15
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We can create customised offerings to our customers (Day, 2002).
We are organised in a way to respond optimally to customer groups with different
profitability (Reinartz et al., 2004).
– We have technologies that allow for one-to-one communications with potential
customers (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 of
each customer (Reinartz et al., 2004).
Our information systems are integrated across several functional areas (Jayachandran
et al., 2005).
We invest in technology to acquire and manage ‘real time’ customer information and
feedback (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 in
Egypt. He is currently doing his PhD at the University of Bradford. His current research
interests include innovation, relationship marketing, organisational learning, and firm
Corresponding author: Moustafa Battor, Tanta University, Faculty of Commerce, Said Street
31515, Tanta, Egypt.
Mohamed Battor is a research assistant in marketing at the Faculty of Commerce, Tanta
University in Egypt. He received his MBA from Tanta University. Currently, he is doing his
PhD at Malaya University in Malaysia.
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Purpose The aim of this article is to shed light on the impact of intangible resources, such as organizational learning (OL), organizational agility (OA) and organizational innovativeness (OI), on supply chain resilience (SCR). For this, a theoretical model is developed to analyze the development of relationships between chosen resource variables. Design/methodology/approach This study is based on a cross-sectional questionnaire. Survey data were collected from 180 businesses including only medium to senior level managers to ensure a thorough understanding about the company's inner workings and supply chain (SC). The validity of the model is determined using structural equation modeling (SEM) and tested using lavaan package in R. Findings The findings indicate a statistically significant relationship between OL and SCR. Two organizational resource constructs, OI and OA, are found to have a strong mediating effect on this relationship. OL ability mediated by OA and OI results in increased SCR. Research limitations/implications The data cover multiple sectors but are collected from one country. The dataset is also limited in that it is collected from mid- to high-level managers working on manufacturing and supply chain-related departments. Practical implications The authors believe that the results of this study will guide both managers and academics in developing effective measures to avoid SC disruptions due to the Covid-19 pandemic or other comparable risks. Originality/value This is the first study that examines the relationship between OL and SCR. Prior studies have examined the relationship between OA and SCR. However, OL and OI, in particular, have not featured frequently in SCR-related studies. In this regard, this research is also unique in that it examines the mediating role of OA and OI in the relationship between OL and SCR.
Marketing communications have evolved on account of development of greater interaction between organizations due to the availability of new electronic media. Much has been written about the effect of these new communication channels and how they can be used to retain consumers. However little evidence has been presented to show how these channels of communication are used by smaller organizations, particularly when serving business to business customers. This research builds on earlier exploratory work of Brink and Atanassova and Clark to provide empirical evidence of the importance of social media usage for small organizations when serving business customers. However, unlike Brink, this research separates small firms from medium size organizations and focuses on the subcategory of micro organizations (fewer than 50 employees).
The motivation of this research is to assess the relationship existing between Customer Relationship Management (CRM) and Business Performance (BP) alongside the mediating effect of Innovation Capability (IC) as a crucial study in the telecommunication sector in Ghana. The proposed model was blueprinted based on relationship marketing theory, innovation theory, resource-based view theory and related literature of CRM as an independent variable, Business Performance as an independent variable and Innovation Capability as a mediating variable. Data was collected from 579 various departmental heads, branch managers and the permanent staff of six mobile communication companies in Ghana turned out to be analysed by the use of multiple linear regression analysis and Structural Equation Model (SEM). The analysis of the study was enacted by using STATA and AMOS statistical software package to excerpt the results. The results indicated a statistically positive and significant relationship among CRM, innovation capability and business performance. Also, it was found that innovation capability has a significantly positive and partially mediating effect on CRM constructs and business performance.
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This study aims to examine the effect of collaborative networks on business performance. This study tries to find a collaborative network format that can improve business performance. The respondents in this study were 295 owners of the fashion sector SMEs in Central Java, Indonesia. Data analysis used the Structural Equation Modeling (SEM) approach. The results showed that collaborative networks (CN) significantly influence innovation capability (IC), competitive advantage (CA), and business performance (BP). Furthermore, the capability of innovation and competitive advantage also significantly influence business performance. Innovation capabilities and competitive advantages can mediate the relationship between collaborative networks and business performance.
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Although the chocolate market has become increasingly larger and more competitive, no diagnostic measures were found to evaluate relationship marketing from customer perspectives in this very attractive market in the B2C context. Thus, the main purpose of this paper is to obtain validity evidence for the Chocolate Brands Relationship Scale (CBR Scale), a scientific instrument that enables the identification and measurement of the prime aspects perceived by chocolate brands’ customers as relevant in their relationship with such brands. Additionally, we tested the influence of the relationship, evaluated from the validated CBR Scale, with the chocolate consumers’ satisfaction. We conducted a survey with 523 Brazilian consumers, and data were analyzed using Confirmatory Factor Analysis. The CBR Scale is composed of 21 items divided into three factors: Brand Trust, Shopping Experience and Perceived Quality. As theoretical implications, we produce a valid and reliable operational measure, offering a useful starting point from which further theoretical and empirical research of customer relationship management, branding strategies, brand loyalty, and brand experience in the chocolate market can be built. Managerially, the CBR Scale is a valid instrument for practitioners and managers in the chocolate sector to access customers, establishing and developing long-term relationships with them.
This research addresses three questions: (1) Why are some organizations more market-oriented than others? (2) What effect does a market orientation have on employees and business performance? (3) Does the linkage between a market orientation and business performance depend on the environmental context? The findings from two national samples suggest that a market orientation is related to top management emphasis on the orientation, risk aversion of top managers, interdepartmental conflict and connectedness, centralization, and reward system orientation. Furthermore, the findings suggest that a market orientation is related to overall (judgmental) business performance (but not market share), employees’ organizational commitment, and esprit de corps. Finally, the linkage between a market orientation and performance appears to be robust across environmental contexts that are characterized by varying degrees of market turbulence, competitive intensity, and technological turbulence.
Learning and performance goal orientations, two motivational orientations that guide salespeople's behavior, are related to working smart and hard. Working smart is defined as the engagement in activities that serve to develop knowledge of sales situations and utilize this knowledge in selling behavior. It is found that a learning goal orientation motivates working both smart and hard, whereas a performance goal orientation motivates only working hard. The goal orientations also are found to be alterable through supervisory feedback. Furthermore, self-efficacy, salespeople's confidence in their overall selling abilities, is found to moderate some of the relationships with the goal orientations.
Considerable progress has been made in identifying market-driven businesses, understanding what they do, and measuring the bottom-line consequences of their orientation to their markets. The next challenge is to understand how this organizational orientation can be achieved and sustained. The emerging capabilities approach to strategic management, when coupled with total quality management, offers a rich array of ways to design change programs that will enhance a market orientation. The most distinctive features of market-driven organizations are their mastery of the market sensing and customer linking capabilities. A comprehensive change program aimed at enhancing these capabilities includes: (1) the diagnosis of current capabilities, (2) anticipation of future needs for capabilities, (3) bottom-up redesign of underlying processes, (4) top-down direction and commitment, (5) creative use of information technology, and (6) continuous monitoring of progress.
Although the role of market knowledge competence in enhancing new product advantage is assumed widely in the literature, empirical studies are lacking because of an absence of the concept definition. In this study, the authors conceptualize market knowledge competence as the processes that generate and integrate market knowledge. The authors test the conceptual model using data collected from the software industry. The findings show that each of the three processes of market knowledge competence exerts a positive influence on new product advantage. The results also reveal a positive association between new product advantage and product market performance. The findings regarding the antecedents indicate that the perceived importance of market knowledge by top management has the largest impact on the processes of market knowledge competence.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
A critical element in the evolution of a fundamental body of knowledge in marketing, as well as for improved marketing practice, is the development of better measures of the variables with which marketers work. In this article an approach is outlined by which this goal can be achieved and portions of the approach are illustrated in terms of a job satisfaction measure.
Marketing Research, 4/e takes an application-oriented approach, providing students with the tools and skills necessary to solve business problems and exploit business opportunities. This new edition was written to meet the needs of students through additional coverage of qualitative methods, emphasis on applied research projects as well as cases studies or exercises at the end of the chapters. The text is concise, highly readable and value-priced, yet it delivers the basic knowledge needed for an introductory text. The authors provide the student with an exciting, up-to-date text and an extensive supplement package.