Scale Development and Construct Validity of Organizational Capital in Customer Relationship Management Context: A Confirmatory Factor Analysis Approach

Abstract

Effective usage and implementation of customer relationship management (CRM) or information technology requires some prerequisite organizational commitments and changes. Improving and deploying the organizational resources, such as top management support and employee training, which are collectively pursued under the umbrella term, that is, organizational capital, appears to be one of the important transformations that organizations need to undertake. Organizational capital is one of the key determinants in improving the results from CRM implementations. This article proposes organizational capital as a multidimensional construct. Further, this article attempts to develop a scale for measuring the organizational capital and test its factorial validity through confirmatory factor analysis (CFA). To test the proposed theoretical model, primary data were used which was generated through a survey of some selected pharmaceutical companies in India. The data were analyzed using exploratory factor analysis (EFA) and subsequently CFA. The results demonstrated that CFA of organizational capital showed an acceptable fit to the data and this construct is best represented by three constituent elements, that is, employee training, customer-centric management system (CMS), and customer relationship orientation (CRO) in current research context. This scale may be used in future research for measurement of this construct as a whole with contextual validity check. Further, implications for theory and practitioners are presented.

Keywords

Organizational capital CRM management support CFA

Introduction

Customer relationship management (CRM) gained prominence in marketing literature and has been researched quite extensively in some industries from past two decades. The CRM as a strategy in general and CRM technology in specific offers some exciting benefits in the form of customer retention and customer value development. Although CRM has lived up to the expectations of delivering these benefits to organizations that implement it, some evidences have revealed the poor performance from CRM systems in recent past. Thus, it suggests that mere implementation of CRM systems might not be enough to achieve tangible benefits. Researchers have argued about various shortcomings that might be a reason for this failure and one of the prominent reasons seems to be the absence of a customer-centric organizational culture. In order to realize the goals related to development and maintenance of long-term customer relationships, the organizations try to deploy every type of resource they have at hand. However, for the successful CRM implementations, it is important that whole organizations should be committed to the task in every way possible. Such commitment requires the presence of customer-centric management system (CMS) which includes support from top management, suitable changes in organizational incentive system and proper training of employees. The same things should be clearly reproduced in policies of the organization. For example, in this context, it may be crucial to consider as which employees are considered to be the champions of the organization (Boulding, Staelin, Ehret, & Johnston, 2005; Payne & Frow, 2005; Srinivasan & Moorman, 2005). Apparently, in such organizations, the employees who bring more visibility into the customer needs are regarded higher. A proper combination of these tangible and intangible resources could be used as a supplementary force to facilitate the implementation of CRM initiatives and it could also help to achieve desired outcomes from CRM. These prerequisites, that is, top management support, employee training, organizational incentive and reward system, and customer relationship orientation (CRO), are collectively referred to as organizational capital in the current study.

The structure of this article is as follows:

The first section of this article presents a review of the relevant studies and the proposed conceptual framework.

The second section provides the detailed account of the methodology used and the statistical procedures adopted.

The final section of the article presents the summary of the findings, conclusion, and directions for future research.

Literature Review, Context, and Conceptual Framework

A firm’s productive capacity can be significantly increased with the higher deployment of organizational capital. Looking at the extant literature related to organizational capital, there seems to be a lack of consensus among researchers about the definition of organizational capital and how it can be measured (Black & Lynch, 2005). Organizations are yet to develop a clear method where it can be put on records of organizations like other tangible assets. Moreover, there are some measures which have been proposed in the past to capture this concept, for example, employee voice, employee training, work design, and so on. Despite some varied perspectives, Evenson and Westphal (1995) have provided a suitable definition of organizational capital which is very much relevant to the current context. They define organizational capital as the knowledge used to combine human skills and physical capital into systems for producing and delivering want-satisfying products.

Regardless of the varied perspectives and measures available, organizational capital may be perceived in different ways in different organizations. All organizations might not put equal thrust on its overall development and deployment. After reviewing the extant literature related to organizational capital and CRM antecedents, in current study organizational capital is referred to as the combined effect of elements like top management support, employee training, organizational incentive and reward system, and CRO. It is all about the customer-centered philosophy followed by the organizations and it is quite obvious that in organizations with higher organizational capital deployment, the organizational champions or in other words employee of the month/year rewards must be weighted by keeping in mind the contribution made by the employee toward retaining the customers, enriching customer experience, and developing stronger relationships. As rightly pointed out, the deployment or change of technology without the appropriate change in the organizational policies in terms of “... work processes and the mindset of the employees” usually results in poor performance of CRM systems (Goodhue, Wixom, & Watson, 2002). Therefore, for a competent CRM strategy and overall augmentation, it is suggested that a firm must take in consideration factors like customer management orientation, integration and alignment of organizational processes, information capture and alignment of technology, and proper implementation plan in addition to financial resources (Coltman, 2007). These four components form a unique organizational resource called as organizational capital. These dimensions are as follows: (a) management support, (b) employee training, (c) organizational reward system, and (d) CRO. All of the major components of organizational capital are important in achieving the benefits from CRM implementations to a greater extant because these components help organizations to be in synchronization with the other organizational variables while they are trying to develop relationships with the customers. The terms organizational capital and CMS have been used interchangeably in some studies.

Research related to CRM advocates that a CMS is a very important determinant of how the firms approach the overall CRM activities. The reason being that the management system is deeply involved in deciding how the internal organizational activities, for example, employee training and incentive system and business processes, should be organized to achieve its objectives. This type of collective mindset directs the efforts and resources of the entire organization for keeping customers at top priority and achieving better relationships with the customers. The following section discusses in detail the four components of organizational capital.

Components of Organizational Capital

Based on past literatures, it is hypothesized that organizational capital is a multidimensional construct and the current study proposes four behavioral components of organizational capital, that is, management support, employee training, organizational reward system, and CRO. Successful CRM deployment requires enterprise-wide effort and commitment. In order to manage this kind of transformation, organizations need to deploy organizational capital. Based on various case studies Goodhue et al. (2002) conclude that “… changing the technology without transforming the organization (the work processes and the mindset of the employees) often leads to less than optimal results.” The most important components of organizational capital that have been researched in some earlier studies include top management support, changes in organizational incentive systems, appropriate employee training, organizational championship, and CRO (Boulding et al., 2005; Srinivasan & Moorman, 2005). The following section provides a detailed account of the four components of organizational capital.

Management Support

Technological implementations are the means and not the ends to achieve relationship marketing goals. Therefore, any sort of technological implementation, be it CRM systems, might not guarantee success unless the organizational management and employees are not involved in making it work according to the organizational goals. The involvement and support from management holds a great importance in this context. With the creation of a corporate culture that identifies relationship management as a crucial element of organizational strategy, management encompasses a very important role in CRM implementations (Day, 1994; Kohli & Jaworski, 1990; Narver & Slater, 1990). If top- or middle-level management of an organization claims its supportive nature toward the CRM systems, then it must reflect in their commitment toward the better implementation of CRM systems (Auh & Menguc, 2005; Jarvenpaa & Ives, 1991; Sabherwal, Jeyaraj, & Chowa, 2006). The commitment from the top management has been identified as one of the key elements in the successful implementation of CRM systems (Alt & Puschmann, 2004). The academic literature related to CRM indicates that there is a lack of studies that investigated the role of management and employees in the implementation of CRM systems (Boulding et al., 2005). Management support is an important prerequisite for CRM implementation and hence is a very crucial constituent of organizational capital.

Employee Training

The second important constituent of organizational capital that could potentially impact the usage and implementation of CRM initiatives in an organization is employee training. It is quite important to understand that how well the employees have been trained for CRM or other technology-based issues and how effectively the corresponding changes have been communicated to them. There is ample evidence clearly indicating that CRM is not only limited to technological implementation and it is rather an organization-wide strategy to create value for customers at a profit (Payne & Frow, 2005). As a part of CRM strategy, sometimes it is very difficult for the organization to make their employees customer-oriented (Sin, Tse, & Yim, 2005). While implementing or using the CRM technology, the customer information needs to be handled effectively and efficiently. This is only possible when the implementation of technology is followed by appropriate organizational changes. Among the various requisite organizational transformations, one is the education and training of employees who are directly or indirectly related to CRM practices (Reinartz, Krafft, & Hoyer, 2004). If the employees are not trained well, the technological part of CRM has greater chances to underperform.

Organizational Incentive and Reward System

The extant literature on CRM and relationship marketing also advocates that before taking any kind of relationship development initiatives, the company must have a customer-centric culture. The customer-centric culture could be displayed by organizations in various ways, for example, creating awareness in the whole organization about customer orientation, rewarding employees for customer satisfaction, celebrating the success of their customer in achieving value, and so on. In other words, such culture is reflected in all relevant activities and departments of the organization. Customer-centric companies would operate their business such a way that reward systems are designed to encourage the employees to win customers and retain them. In these organizations, the champions or heroes of the company are “. those who deliver outstanding value or service to customers” (Buttle, 2004, p. 4). Therefore, in order to realize the CRM investment goals, it is necessary that the organization makes the corresponding changes in incentive systems (Reinartz et al., 2004). Employees who capture and maintain important customer information are highly regarded in high customer-oriented organizations. In such organizations, customer data are greatly valued because the customer data aid in the strategic decision (Boulding et al., 2005). Clearly, a relationship-oriented organization rewards the employees on the basis of maintaining the quality of customer (Day, 2003) apart from routine performance metrics. All these factors can subsequently enhance the performance of the CRM in maintaining the relationships and achieving better relationship outcomes. There are not a substantial number of studies that have examined the reward and incentive systems as a potential antecedent to use of CRM technological applications (Reinartz et al., 2004). This suggests a need to explore this dimension as a part of organizational capital.

Customer Relationship Orientation

The CRO is conceptualized as a subdimension of organizational capital in the current study. The existing literature on relationship marketing has suggested the importance and relevance of CRO in managing the customer relationships (Chalmeta, 2006; Jayachandran, Sharma, Kaufman, & Raman, 2005; Ryals & Knox, 2001). With reference to the current study, CRO is referred to as a belief system that considers customer relationships as a valuable asset, and to achieve such outcomes, the business processes are shaped accordingly (Jayachandran et al., 2005). It is a potential antecedent of the CRM technological implementation and current study proposes that organizations with higher CRO are likely to implement CRM on a higher level. This dimension has not been studied so extensively in earlier studies. Examining it as a part of organizational capital will provide deeper insights with respect to its importance in CRM context.

Figure 1.

Source:The author.

Objectives

Thus, based on the review of the studies mentioned earlier, the objective of this study is to develop a scale that measures the organizational capital as a construct. Further, the study aims to test the factorial validity of organizational capital through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) so that it can be incorporated in future research.

Rationale of the Study

One of the primary reasons for conducting this research is that the extant literature of CRM and other technological implementations suggests that most of these implementations fail because organizations do not accompany these implementations with requisite organizational transformations. One of these prerequisites is the organizational capital which has neither been researched so rigorously nor has a comprehensively scale been presented to capture it as a whole construct. Moreover, there is a lack of such studies in the Indian context.

Methodology

Research Instrument Development

Systematic steps were taken to finalize the measurement scales. The steps are consistent with the Churchill’s (1979) scale development work. The constructs under study were measured through existing scales with the addition of some items by the researcher. First, the items for measurement of the constructs were generated using the previously available literature of organizational capital. Second, the face validity of the questionnaire was established by discussing the questionnaire with a panel of academic experts. Questions difficult to comprehend were either modified or deleted. Finally, the revised questionnaire was piloted to 10 marketing managers from the industry in order to check the wording, clarity, understandability of questions and appropriateness of the research, and overall rationale of the study. Thorough interviews were also held with some the mangers in order to get deeper insights about the research instrument in terms of what actually matters and what kind of information technology is at hand with the managers. The results of all the earlier discussed steps are prerequisite for validating the questionnaire and the same have to be implemented for measure refinement. All the items used to measure the different constructs were anchored on standard five-point Likert scales (1 = strongly disagree; 5 = strongly agree).

Since the organizational capital is comprised of four different subdimensions, these subdimensions were measured using different scales, that is, management support, employee training, organizational incentive and reward system, and CRO. The measurement scales have been adopted from different studies that are mentioned as follows:

Management support (Becker, Greve, & Albers, 2009), employee training (Becker et al., 2009), organizational incentive and reward system (Becker et al., 2009; Reinartz et al., 2004), and CRO (Jayachandran et al., 2005).

Some of the items in the scale have been rephrased after validity check for better understanding of the respondents. The total number of items in these scales is 14.

Research Population and Sample

Research population of this study is the pharmaceutical companies operating in India. Reason for choosing pharmaceutical industry is based on the fact that more emphasis is laid on the relationship management activities with the B2B customers because final consumer cannot be reached directly. However, this does not limit the scope of the scales to one specific industry as the nature of questions asked in this survey is not industry specific. It is important to mention here that the organizations that were surveyed had already some CRM technology in place. The survey data were collected from a cross-section of pharmaceutical industry with some select organizations serving as the sampling units. In order to get the accurate subjects for the survey, purposive sampling was undertaken. Purposive sampling as defined by Maxwell (1997, p. 87) is a type of sampling in which “particular settings, persons, or events are deliberately selected for the important information they can provide that cannot be gotten as well from other choices.”

Based on this premise, the researcher included Bombay Stock Exchange (BSE) pharma companies. The list of companies was obtained from BSE official website. This list included the top companies which are currently operating in Indian pharmaceutical industry in terms of annual revenue and some other lower level companies. The respondents held positions of area sales managers, CRM managers, and other top-level managers who were directly or indirectly involved in relationship management-related activities. With this sampling method, the number of companies was 155 and since two key informants from each company were included, the total number of responses was 310.

Initially an online version of questionnaire was created, and the questionnaire was sent to the target respondents through email and LinkedIn. However, no response was received with this method. Consequently, the hard copy of questionnaires was given in person and some questionnaires were sent through post. However, the questionnaires were self-administered because the survey respondents in these positions were qualified enough to understand the questionnaire. A good response was received with this method which resulted in a combined response rate of approximately 66 percent. In case of any query or feedback, a contact number and email of the researcher were provided. The likely advantages of self-administered survey are that it reduces the researcher bias (Sudman & Bradburn, 1974) and large number of units of research population can be covered with a relatively lower cost. All of the returned questionnaires were found to be usable and were included in the final analysis.

Exploratory Factor Analysis

The organizational capital is conceived as a potential antecedent and a supplementary resource to the successful implementation of CRM in the current study. Theoretically, this study proposed four factors of OC, that is, management support, employee training, employee reward and incentive system, and CRO. However, after performing the EFA and examining the factor extraction, three underlying factors were obtained. The employee training and CRO emerged as separate subdimensions of organizational capital. The third factor evolved as a combination of management support and employee reward and incentive system as the items of these two factors grouped into one factor. This factor was renamed as CMS. The eigenvalues of these factors, that is, employee training, CRO, and CMS are 1.28, 4.69, and 2.70, respectively. These three components of OC explained 72.42 percent of the total variance in the model. The Kaiser–Meyer–Olkin (KMO) value of the rotated solution was 0.808 (p < 0.05) which shows a good fit of the data. The rotation converged in five iterations. Table 1 presents the results of the EFA for organizational capital construct. Also, the communalities table shows that all the values of extraction are higher than the threshold value of 0.4 (see Table 2) indicating a good amount of correlation with other items in the scale (Statwiki.com, 2018).

Table 1.

Exploratory Factor Analysis Results for Organizational Capital

Kaiser–Meyer–Olkin (KMO) = 0.808 (p < 0.05)	OC Components
Kaiser–Meyer–Olkin (KMO) = 0.808 (p < 0.05)	ET	CRO	CMS
OC_ET_EMPTR	0.829
OC_ET_COR	0.809
OC_ET_RGIN	0.808
OC_CRO_EMPENCRG		0.921
OC_CRO_RCTPRTY		0.911
OC_CRO_EMPCS		0.889
OC_CRO_CRVA		0.855
OC_MS_TMINVL			0.829
OC_REW_INCTV			0.827
OC_MS_CEOSP			0.708
OC_MS_TMMTVT			0.702
OC_REW_EMNTH			0.690
Eigenvalues	1.28	4.69	2.70
Variance explained	19.75%	28.39 %	24.27%

Source: The author (SPSS software output).

Notes: Results of exploratory factor analysis results: The extraction method used is principal component analysis (PCA). The rotation converged in five iterations with varimax rotation method.

Moreover, Figure 1 shows the scree plot obtained from EFA of organizational capital construct. A closer examination of the scree plot shows that there are only three components that have an eigenvalue greater than 1 and it is depicted by the intersection of the dotted line with the plot line in Figure 1. This is another evidence for a three factor model of OC (Figure 2).

Confirmatory Factor Analysis

Preliminary development of CFA model is also referred to as measurement model. It is very crucial to specify a correct measurement model before the extracted components from EFA can be used in a structural model somewhere else. This condition is consistent with the structure equation modeling technique such as AMOS (Anderson & Gerbing, 1988; Bagozzi, 1994). Moreover, performing CFA on the elements extracted from EFA gives a more robust measure of tests of unidimensionality of constructs. The CFA was performed using structure equation modeling software AMOS graphics. 20 which is available with the university library. There are various fit indices that have been suggested and used by the previous researchers to assess the overall fit of measurement model before arriving at the final factor loadings. For the current study, goodness of fit (GFI), comparative fit index (CFI) as suggested by Hu and Bentler (1999), the χ2 (chi-square)/df (degrees of freedom) ratio (Bollen, 1989), DELTA2 index (Bollen, 1989), and root mean square error of approximation (RMSEA) were used to check the overall fit of the model. Table 3 presents the results of measurement model fit indices for organizational capital.

Figure 2.

Source:The author.

Table 2.

Communalities

	Initial	Extraction
OC_CRO_EMPCS	1.000	0.836
OC_CRO_RCTPRTY	1.000	0.854
OC_CRO_EMPENCRG	1.000	0.882
OC_CRO_CRVA	1.000	0.757
OC_ET_RGIN	1.000	0.720
OC_ET_EMPTR	1.000	0.719
OC_ET_COR	1.000	0.741
OC_MS_CEOSP	1.000	0.536
OC_MS_TMINVL	1.000	0.816
OC_MS_TMMTVT	1.000	0.576
OC_REW_INCTV	1.000	0.777
OC_REW_EMNTH	1.000	0.477

Source: The author (SPSS software output).

Note: Extraction method: principal component analysis.

Table 3.

Model Fit Indices for Organizational Capital

Fit index	Value
𝒳²	100.97, p < 0.000
𝒳²/df ratio	2.685
GFI	0.951
CFI	0.976
DELTA2	0.977
RMSEA	0.074

Source: The author.

Various researchers have suggested that when the CFI values approach 0.95, it is an indication of a good model fit (e.g., Hu & Bentler, 1998). The CFI of the measurement model for organizational capital is 0.976 which is well above the cutoff value and thus confirming the good fit of the data with the model in observation. According to Bollen (1989) and Gerbing and Anderson (1992), the DELTA2 scores ≥ 0.90 suggest a good model fit. Pertaining to this criterion, the DELTA2 value of this model is 0.977 which is also well above the cutoff value. The 𝒳² statistic evaluates whether the “unrestricted population variance/covariance matrix of the observed variables is equal to the model-implied variance/covariance matrix” (Mueller, 1996, p. 82). Another fit index which is being widely used by researchers these days is the 𝒳^2/df value. The reason for comparing chi-square value to df in the model as suggested by Jöreskog and Sorbom (1993) is that 𝒳² statistic is sensitive to sample size. The values between 0 and 5 for 𝒳^2/df are indicative of a reasonable fit (Mueller, 1996). The measurement model of organizational capital showed a 𝒳^2/df value of 2.68 providing further evidence to the model fit.

Looking at the different fit indices discussed earlier, it can be ascertained that they are very close or well above the established cutoff values and hence indicate that overall the measurement model for organizational capital possesses exceptionally good fit. One of the items was deleted due to relatively lower factor loading. The item that was deleted is represented by code OC_MS_CEOSP (Our CRM implementation has strong CEO support). Figure 3 shows the measurement model of organizational capital and CFA factor loadings are presented in Table 4.

Figure 3.

Source: The author (Output from AMOS graphics).

Table 4.

CFA Measure Loadings

Organizational Capital Components	Loadings
Employee Training (ET)
Our employees receive thorough training in the use of CRM functions.	0.732
We reward employees for building and deepening relationships with high-value customers.	0.83
Regular investments are made in the development of employee CRM skills.	0.769
Customer Relationship Orientation (CRO)
In our company, employees are encouraged to focus on customer relationships.	0.926
In our company, retaining customers is considered to be a top priority.	0.920
In our company, employees are encouraged to focus on customer relationships.	0.884
In our company, customer relationships are considered to be a valuable asset.	0.796
Customer-centric Management system (CMS)
Top management is involved to a large degree in CRM implementation and entrusted with it.	0.999
Employees who acquire customer data and use CRM analysis results are given material and nonmaterial incentives.	0.937
Top management motivates the employees to live the CRM vision.	0.609

Source: The author.

Convergent Validity and Discriminant Validity

Convergent validity of different components of organizational capital was examined using the Fornell and Larcker’s (1981) criterion. This criterion suggests that the dimensions of a construct in a measurement model exhibit a good convergent validity if the average variance extracted (AVE) for each element exceeds the value of 0.50. This cutoff or threshold indicates that the latent construct should explain more variance in the respective indicators than does measurement error. The AVE of all the constructs under study exceeded the cutoff value of 0.50 (see Table 5). The diagonal entries in the table represent the interfactor correlations. This clearly indicates that there is no issue with the convergent validity.

Table 5.

Discriminant and Convergent Validity Using AVE Approach for OC

	AVE	CMS	CRO	ET
CMS	0.749	0.865
CRO	0.780	0.242	0.883
ET	0.603	0.493	0.475	0.776

Source: The author.

Notes: CR: composite reliability; AVE: average variance extracted.

For examining the discriminant validity, some other Fornell and Larcker’s (1981) criteria were utilized. According to their recommendation, the discriminant validity is established if the following condition is met:

The AVE by the construct should be greater than the maximum shared variance (MSV) between a construct and other constructs in the model (Fornell & Larcker, 1981).

The square root of AVE is greater than the interconstruct correlations in the model.

The values displayed in Table 6 clearly indicate that both the conditions for establishing the discriminant validity of the dimensions of organizational capital are very well satisfied. Thus, there are no issues of discriminant validity with the dimensions of the organizational capital construct. These values indicate organizational capital is well captured by a three-factor construct and can be further incorporated in other structural models.

Reliability

The norm for examining the reliability of the constructs as suggested by Fornell and Larcker (1981) is calculating the composite reliability (CR) of each scale. The reliability estimates using the criterion of CR based on the loadings in the measurement model (Bagozzi & Yi, 1988) are presented in Table 6. This criterion gives a more robust measure of reliability of scales than the conventional Cronbach’s α. According to Fornell and Larcker (1981), constructs showing composite reliabilities greater than 0.80 are considered to be indicative of adequate reliability. The composite reliabilities of all the dimensions of organizational capital were well above the Fornell and Larcker (1981) criterion and surpassed the 0.9 value in all cases indicating a very good reliability of each dimension. Moreover, good composite reliabilities of constructs are also an indicator of good convergent validity. As the composite reliabilities of all the constructs exceeded the cutoff value of 0.80, it provides further evidence to the convergent validity.

Table 6.

Composite Reliability

	CR	AVE
CMS	0.896	0.749
CRO	0.934	0.780
ET	0.819	0.603

Source: The author.

Conclusions

This study is believed to have contributed the existing body of knowledge in many ways. There are several constructs which have been researched and put forward as antecedents of CRM implementation. However, the significant contribution of this study remains that it presents a comprehensive and refined scale to assess the organizational capital. It takes into account the multiple aspects such as top management support, employee training, and CRO which need to be considered while organizations are planning to invest in CRM technologies. Although the scale has been empirically tested on one specific industry due to some limitations, considering the nature of questions related to organizational capital, the scale can be administered in various manufacturing industry settings. Moreover, the findings of this study emphasize that employee training remains as one of the crucial tangible features of organizations which facilitates any kind of organizational transformation apart from intangible resources such as top management support. The results also indicate that top management support and organizational incentive and reward system emerged as one construct which was renamed as CMS which encompasses the attributes of both these elements and may be perceived as a single dimension by the managers.

Managerial Implications

This study is believed to serve as an attempt to provide the managers with practical advice as how managers can work on various elements to foster a customer-centric culture which aids them to implement relationship-oriented technologies and also helps them to sustain these relationships for competitive advantage. One of the important aspects of this study is that managers need to take care of every single dimension related to organizational capital because all of these dimensions might provide better results when they are in synchronization. This is also supported by the statistical results which suggest that all the dimensions of the organizational capital loaded strongly on the parent factor. From a marketing research in emerging and developing markets context, this research is believed to be the first attempt in India that is dedicated to the development of a scale for organizational capital.

Limitations and Directions for Future Research

The results of this study may be observed in light of some limitations. One of the limitations is that the sample used for the study was taken from a cross-section of one specific industry; thus, the results may not be generalizable. The future researchers may include a sample from diverse industries. Moreover, future research may be carried in other country settings to further validate the scale. Finally, the future researchers may include more variables which might fit into the scale to capture the concept with some deeper insights.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

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

Appendix A

Questionnaire

SPSS CODE	Organizational Capital (OC)
Management Support
OC_MS_CEOSP	Our CRM implementation has strong CEO support.
OC_MS_TMMTVT	Top management motivates the employees to live the CRM vision.
OC_MS_TMINVL	Top management is involved to a large degree in CRM implementation and entrusted with it.
Organizational Incentive and Reward System
OC_REW_EMNTH	Our organization has created a formal position to reward employees which bring visibility to customer needs.
OC_REW_DPR	Top management informs the employees regularly about high customer orientation.
OC_REW_INCTV	Employees who acquire customer data and use CRM analysis results are given material and nonmaterial incentives.
Employee Training
OC_ET_KNUSE	Employees in the relevant divisions (marketing, service, and distribution) know the use of CRM in their daily work.
OC_ET_RGIN	Regular investments are made in the development of employee CRM skills.
OC_ET_COR	We reward employees for building and deepening relationships with high-value customers.
OC_ET_EMPTR	Our employees receive thorough training in the use of CRM functions.
Customer Relationship Orientation
CRO_EMPCS	Our senior management emphasizes the importance of customer relationships
CRO_RCTPRTY	In our company, retaining customers is considered to be a top priority.
CRO_EMPENCRG	In our company, employees are encouraged to focus on customer relationships.
CRO_CRVA	In our company, customer relationships are considered to be a valuable asset.

References

Alt

Puschmann

(2004). Successful Practices in Customer Relationship Management. 37th Hawaii International Conference on System Science , 1–9.

Anderson

J. C.

Gerbing

D. W.

(1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin , 103(3), 411–423.

Auh

Menguc

(2005). The influence of top management team functional diversity on strategic orientations: The moderating role of environmental turbulence and inter- functional coordination. International Journal of Research in Marketing , 22(3), 333–350.

Bagozzi

R. P.

(1994). Structural equation models in marketing research: Basic principles. In Bagozzi

R. P.

(Ed.), Principles of marketing research (pp. 317–385). Cambridge, MA: Blackwell.

Bagozzi

R. P.

. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science , 16(1), 74–94.

Becker

J. U.

Greve

Albers

(2009). The impact of technological and organizational implementation of CRM on customer acquisition, maintenance, and retention. International Journal of Research in Marketing , 26(3), 207–215.

Black

E. S.

Lynch

L. M.

(2005). Measuring organizational capital in the new economy. In Corrado

Haltiwanger

Sichel

(Eds.), Measuring capital in the new economy . Chicago, IL: University of Chicago Press.

Bollen

K. A.

(1989). Structural equations with latent variables . New York, NY: John Wiley & Sons, Inc.

Boulding

Staelin

Ehret

Johnston

(2005). A customer relationship management roadmap: What is known, potential pitfalls, and where to go. Journal of Marketing , 69(4), 155–166.

10.

Buttle

(2004). Customer relationship management: Concepts and tools . Sydney: Elsevier.

11.

Chalmeta

(2006). Methodology for customer relationship management. The Journal of Systems and Software , 79(7), 1015–1024.

12.

Churchill

(1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research , 16(2), 64–73.

13.

Coltman

(2007). Can superior CRM capabilities improve performance in banking. Journal of Financial Services Marketing , 12(2), 102–114.

14.

Day

G. S.

(1994). The capabilities of market-driven organizations. Journal of Marketing , 58(4), 37–52.

15.

Day

G. S.

(2003). Creating a superior customer-relating capability. MIT Sloan Management Review , 44(3), 77–82.

16.

Evenson

R. E.

Westphal

L. E.

(1995). Technological change and technological strategy. In Behrman

Srinivasan

T. N.

(Eds.), Handbook of development economics (pp. 2211–298). Amsterdam: North-Holland.

17.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research , 18(1), 39–50.

18.

Gerbing

D. W.

Anderson

J. C.

(1992). Monte Carlo evaluations of goodness of fit indices for structural equation models. Sociological Methods and Research , 21(2), 132–160.

19.

Goodhue

D. L.

Wixom

B. H.

Watson

H. J.

(2002). Realizing business benefits through CRM: Hitting the right target in the right way. MIS Quarterly Executive , 1(2), 79–94.

20.

Bentler

P. M.

(1998). Fit indices in covariance structure modeling: Sensitivity to under-parameterized model misspecification. Psychological Methods , 3(4), 424–453.

21.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling , 6, 1–55.

22.

Jarvenpaa

S. L.

Ives

(1991). Executive involvement and participation in the management of information technology. MIS Quarterly , 15(2), 204–227.

23.

Jayachandran

Sharma

Kaufman

Raman

(2005). The role of relational information processes and technology use in customer relationship management. Journal of Marketing , 69(4), 177–192.

24.

Jöreskog

Sörbom

(1993). LISREL 8: Structural equation modeling with the SIMPLIS command language . Chicago, IL: Scientific Software International Inc.

25.

Kohli

A. K.

Jaworski

B. J.

(1990). Market-orientation: The construct research propositions, and managerial implications. Journal of Marketing , 54(2), 01–18.

26.

Maxwell

(1997). Designing a qualitative study. In Bickman

Rog

D. J.

(Eds.), Handbook of applied social research methods (pp. 69–100). Thousand Oaks, CA: SAGE Publications.

27.

Mueller

R. O.

(1996). Basic principles of structural equational modeling: An introduction to LISREL and EQS . New York, NY: Springer.

28.

Narver

J. C.

Slater

S. F.

(1990). The effect of a market orientation on business profitability. Journal of Marketing , 54(4), 20–35.

29.

Payne

Frow

(2005). A strategic framework for customer relationship management. Journal of Marketing , 69(4), 167–176.

30.

Reinartz

W. J.

Krafft

Hoyer

W. D.

(2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research , 41(3), 293–305.

31.

Ryals

Knox

(2001). Cross-functional issues in the implementation of relationship marketing through customer relationship management. European Management Journal , 19(5), 534–542.

32.

Sabherwal

Jeyaraj

Chowa

(2006). Information system success: Individual and organizational determinants. Management Science , 52(12), 1849–1864.

33.

Sin

Tse

Yim

(2005). CRM: Conceptualisation and scale development. European Journal of Marketing , 39(11/12), 1264–1290.

34.

Srinivasan

Moorman

(2005). Strategic firm commitments and rewards for customer relationship management in online retailing. Journal of Marketing , 69(4), 193–200.

35.

Statwiki.com. (2018). Exploratory factor analysis . Retrieved from http://statwiki.kolobkreations.com/index.php?title=Exploratory_Factor_Analysis

36.

Sudman

Bradburn

N. M.

(1974). Response effects in surveys: A review and synthesis . Chicago: Aldine.