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Social Customer Relationship Management and Differentiation Strategy Affecting Banking Performance Effectiveness



J. Wongsansukcharoen, J. Trimetsoontorn and W. Fongsuwan
 
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ABSTRACT

This research aims to develop structural equation modelling of variables that affect the banking performance effectiveness of Thai Commercial Bank branches in the financial service sector by gathering quantitative data. The population of the study covers all 2,068 Thai Commercial Bank branches in Bangkok, Thailand (as of 31 July 2012). This research defined the Thai banks for data collection using stratified sampling (first step) and simple sampling (second step). Primary data were collected using a self-administered survey of 65 managers and 185 marketing officers. Data were analysed using Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM). It was found that significant relationships existed between social customer relationship management (social CRM) and differentiation strategy and banking performance effectiveness. The key success factors of social CRM were found to have direct influence on banking performance effectiveness (p<0.01) and indirectly through the mediation of differentiation strategy (p<0.001).

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  How to cite this article:

J. Wongsansukcharoen, J. Trimetsoontorn and W. Fongsuwan, 2013. Social Customer Relationship Management and Differentiation Strategy Affecting Banking Performance Effectiveness. Research Journal of Business Management, 7: 15-27.

DOI: 10.3923/rjbm.2013.15.27

URL: https://scialert.net/abstract/?doi=rjbm.2013.15.27
 
Received: January 28, 2013; Accepted: March 02, 2013; Published: May 28, 2013



INTRODUCTION

The development of the ASEAN Economic Community (AEC) in 2015 is an important step in the process of ASEAN integration. As financial integration is part of this process, it is essential to reap the benefits by creating a mutually reinforcing “virtuous circle” of financial integration (Kanithasen et al., 2011). One of the most relevant features in the current competition has been the enormous increase of uncertainty in the financial sector (Carbonara and Caiazza, 2010). The crisis has spread globally, hitting Europe especially. Banks confront a more profoundly uncertain business environment than most of them have ever faced (Ferguson, 2009).

The Bank of Thailand has estimated that financial economics and investments will see an increase in competition. Therefore, it is important to adapt to the increasing risk. The banking industry must be able to develop within the more challenging and severe environment. Every sector involved should co-operate and synergize to build a more effective and sustainable system, rather than focusing on short-term gain. As for the Bank of Thailand, it aims to build “a strong financial economy, sustainable and nationwide development”. This study scopes the problem and research area to look for a concept that could lead to creating a more effective banking industry by using modern business strategies, i.e., social customer relationship management (social CRM) (Greenberg, 2009, 2010) and differentiation strategy (Porter, 1980a, b; 1996). This study is to investigate for strategies used in various parts of the world to set organizations’ directions, particularly in the banking and financial service sectors. In order to integrate holistically, this research based on Porter (1980a, b; 1996) competitive strategy as a core element. The objective of this study is to obtain new knowledge by integrating social CRM and differentiation strategy with research into the Thai banking and financial service industry. This concept has never been researched. Furthermore, this study aims to create new knowledge that can increase banking performance effectiveness by using strategic planning that can respond to customers’ needs using a full range of financial and investment services, as well as giving professional advice and world-class financial and investment consulting. This will achieve the objective of building value and fairness for customers, shareholders (business partners) and employees and will also allow the further development of the whole Thai banking and financial services industry, enabling it to reach a higher potential.

This being the case, it follows that there are gaps in our knowledge and understanding about the key success factors of social CRM and differentiation strategy and how to measure the effectiveness of the factors that affect banking performance effectiveness.

The objectives of the study are:

To study the direct and indirect influences of variables that affect the banking performance effectiveness of the Thai banking industry and
To develop structural equation modelling of variables that affect the banking performance effectiveness of the Thai banking industry

LITERATURE REVIEW

Social customer relationship management (Social CRM): Today’s customers are using the “oh-so-social-Web” (Bernoff and Li, 2008) to connect with one another and to share experiences and information on products, services, companies and brands. As a result of the widespread growth of online social networking and user-generated-content sites, a shift in the balance of power is occurring (Bernoff and Li, 2008; Parameswaran and Whinston, 2007; Pitt et al., 2002; Rezabakhsh et al., 2006). These social networks have come into play in business. Social CRM recognizes that instead of managing customers, the role of the business is to facilitate collaborative experiences and dialogue that customers value (Baird and Parasnis, 2011). Social CRM (or CRM 2.0) is a philosophy and a business strategy, supported by a system and a technology, designed to engage the customer in collaborative interaction that provides mutually beneficial value in a trusted and transparent business environment (Greenberg, 2009, 2010). They reflect the social environment of the customer. Therefore, the development of CRM systems into social CRM systems is important (Hart and Kassem, 2012). Social CRM applications have great potential for businesses to communicate and interact within social networks and improve the quality and quantity of interactions with customers, suppliers and partners, as well as boost reputation and overall brand loyalty (Ayanso, 2012).

Social CRM (Greenberg, 2009, 2010) is thus the combination of social media and Customer Relationship Management (CRM) and is consequently more than an extension of traditional CRM. Therefore, the goal of social CRM is to use new social and collaborative technologies to establish customer value and thus solve business problems (Hart and Kassem, 2012). In addition, social CRM can deliver insights that will help drive real customer-centric innovation (Woodcock et al., 2011).

Table 1: Differences between traditional CRM and social CRM
Image for - Social Customer Relationship Management and Differentiation Strategy Affecting Banking Performance Effectiveness
Adapted from Greenberg, 2009, p. 8; 2010, p. 414

This research analysed the role of social CRM (Greenberg, 2009, 2010) in terms of exogenous latent variables and as a new strategy for the customer-organization relationship, finding that social CRM is a potential driver for long-term relationships with customers and identifying factors that influence performance effectiveness. Based on Greenberg (2009, 2010), the differences between traditional CRM and social CRM presented in the Table 1.

Based on the above, the hypotheses relative to banking performance effectiveness are:

H1: Social CRM will have a positive impact on banking performance effectiveness
H2: Social CRM will have a positive impact on differentiation strategy

Image for - Social Customer Relationship Management and Differentiation Strategy Affecting Banking Performance Effectiveness
Fig. 1: Conceptual framework

Differentiation strategy: According to Porter (1980a, b) framework, a business can also pursue superior performance by differentiating its products and services from those of its rivals (Parnell, 2011; Parnell and Hershey, 2005). A differentiation strategy involves the firm creating a product or service that is considered to be unique in some aspect that the customer values (Spencer et al., 2009). Additionally, the differentiation strategy is effectively implemented when the business provides unique or superior value to the customer through product quality, product features or after-sales support (Allen et al., 2007; Hahn and Powers, 2010). Thus, the essence of this strategy is choosing to perform activities differently than rivals do (Porter, 1996).

Based on the above, the hypothesis relative to banking performance effectiveness is:

H3: Differentiation strategy will have a positive impact on banking performance effectiveness

Banking performance effectiveness: In this study, banking performance effectiveness is an endogenous latent variable. To measure banking performance effectiveness, each respondent in this study was asked to evaluate his/her Thai Commercial Bank branch’s current banking performance in the local financial service sector relative to its principal competitors with respect to the following six items: (1) sales growth, (2) customer retention, (3) Return on Investment (ROI), (4) market share, (5) cost reduction and (6) overall performance (Gordon et al., 2008; Kunc and Bhandari, 2011; Olotu et al., 2011; Pertusa-Ortega et al., 2010; Sin et al., 2002; Tse et al., 2004).

Conceptual framework: The main purpose of this study was to explore the effects of social CRM and differentiation strategy on banking performance effectiveness among the Thai banking industry. Conceptual framework of this study presented in the Fig. 1.

RESEARCH METHODOLOGY

Questionnaire design: The methodology of all the constructs in the conceptual framework was based on the literature. This research adopted and modified 27 items developed by HBR Analytic Services (2010), Akroush et al. (2011), Reimann et al. (2010) and Vella and Caruana (2012) to measure social CRM. For differentiation strategy, this research adopted and modified 5 items developed by Parnell (2011), Pertusa-Ortega et al. (2010), Hahn and Powers (2010) and Salavou (2010).

Table 2: Summary of measurement items
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SM: Social media, CRM: Customer relationship management, Differ: Differentiation strategy

In this study derived 6 items from Kunc and Bhandari (2011), Merchant (2007), Pertusa-Ortega et al. (2010), Gordon et al. (2008) and Sin et al. (2002) for banking performance effectiveness. All items were seven-point Likert-type scales. In this study modified and developed all the aforementioned items (38 items) into 15 items for calculation. Following this, all the items for calculation were adapted to fit with the model, resulting in 10 measurement items (please refer to Table 2).

Data collection: The population of the study covers all 2,068 Thai Commercial Bank branches in Bangkok, Thailand (as of 31 July 2012), which Thai commercial bank listing presented in Table 3. This research defined the Thai banks for data collection using stratified sampling (first step) and simple sampling (second step). The study utilized quantitative methodologies. A questionnaire was used as the study tool, comprising two parts: first, general information about Thai Commercial Bank branches in Bangkok; and second, three latent variables comprising (1) social customer relationship management (Social CRM), (2) differentiation strategy and (3) banking performance effectiveness.

Out of the 510 questionnaires distributed, a total of 250 completed questionnaires were returned: a response rate of 49.02%. Primary data were collected using a self-administered survey of 65 managers and 185 marketing officers. In the total sample, 69 (27.6%) of the respondents were male and 181 (72.4%) were female in the Thai Commercial Bank branches. The responses to the questions capturing focal constructs used a seven-point Likert scale (rating statements 1-7; 1 = strongly disagree and 7 = strongly agree). Table 4 displays the characteristics of respondents.

Reliability and validity construct: This research first conducted Confirmatory Factor Analysis (CFA) and subsequently reliability analysis to measure Cronbach’s alphas for the scale items to ensure internal consistency (Laroche et al., 2013).

Table 3: Thai commercial bank branches
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Bank of Thailand (2012)

Multi-item measures were developed based on Cronbach’s alpha >0.70 (Nunnally, 1978). This study then calculated Cronbach’s alphas for each construct. As shown, the reliability measure ranged from 0.838 to 0.935 (Table 5).

According to Wheaton et al. (1977) and Hair et al. (1999), widely used measures of model fit include CMIN/df (χ2/df). “The CMIN/df ratios in the range of 2 to 1 or 3 to 1 are indicative of an acceptable fit between the hypothetical model and the sample data” (Carmines and McIver, 1981); thus, the ratio should be close to 1 for correct models. “...a CMIN/df ratio more than 2.00 represents an inadequate fit” (Byrne, 1989). The goodness of fit (GFI) and the adjusted goodness of fit (AGFI) indices are at least 0.90; GFI and AGFI values of 1 indicate a perfect fit (Joreskog and Sorbom, 1984). For the Comparative Fit Index (CFI) (Bentler, 1990), CFI values close to 1 indicate best fit. The root mean square residual (RMR) and the Root Mean Square Error of approximation (RMSEA) are both less than 0.05. The smaller the RMR is the better; an RMR value of 0 indicates a perfect fit. For the Root Mean Square Error of Approximation (RMSEA), a value of about 0.05 or close to 0 indicates a perfect fit (Browne and Cudeck, 1993). Thus, all the variables that measured latent constructs in this model achieved convergent validity (Fig. 2).

Table 4: Characteristics of respondents
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Table 5: Results of CFA for measurement model
Image for - Social Customer Relationship Management and Differentiation Strategy Affecting Banking Performance Effectiveness

Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM): For this study, AMOS version 21 was used for confirmatory factor analysis and structural equation modelling. Table 5 shows the factor loadings, R-squares and Cronbach’s alpha of each item. Figure 2 shows the results of the confirmatory factor analysis using AMOS: CMIN (Chi-square) = 39.503, df = 31, CMIN/df = 1.274, GFI = 0.969, CFI = 0.995, RMR = 0.023 and RMSEA = 0.033, which are all considered excellent and significantly above the acceptable thresholds suggested by Wheaton et al. (1977), Joreskog and Sorbom (1984), Byrne (1989), Bentler (1990), Browne and Cudeck (1993) and Hair et al. (1999). Table 6 shows regression weights of all variables in this model. Table 7 shows standardized regression weights and Table 8 shows squared multiple correlations or R-squares of each variable in this model.

Table 6: Regression weights: (Group number 1-Default model)
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***p<0.001, CRM: Customer relationship management, CR: Customer retention, MS: Market share, OP: Overall performance, ROI: Return on investment, SM: Social media

Table 7: Standardized regression weights: (Group number 1 - Default model)
Image for - Social Customer Relationship Management and Differentiation Strategy Affecting Banking Performance Effectiveness
CRM: Customer relationship management, CR: Customer retention, MS: Market share, OP: Overall performance, ROI: Return on investment, SM: Social media

Table 8: Squared multiple correlations: (Group number 1-Default model)
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CRM: Customer relationship management, CR: Customer retention, MS: Market share, OP: Overall performance, ROI: Return on investment, SM: Social media

Image for - Social Customer Relationship Management and Differentiation Strategy Affecting Banking Performance Effectiveness
Fig. 2: Confirmatory factor analysis of social CRM, differentiation strategy and banking performance effectiveness df: Degrees of freedom, p: “p-value” associated with discrepancy function (test of perfect fit), CMIN/DF: Minimum value of the discrepancy function divided by degrees of freedom, GFI: Goodness of fit index, CFI: Comparative fit index, RMR: Root mean square residual, RMSEA: Root mean square error of approximation

RESULTS AND DISCUSSION

This research tested the proposed hypotheses using structural equation modelling. For the conceptual framework, it was found that this structural equation modelling complies with empirical data: CMIN (Chi-square) = 39.503, df = 31, CMIN/df = 1.274, GFI = 0.969, CFI = 0.995, RMR = 0.023 and RMSEA = 0.033. The results of the model estimation are shown in Fig. 3. For Thai Commercial Bank, social CRM have direct influence on banking performance effectiveness (p<0.01) and indirectly through the mediation of differentiation strategy (p<0.001). It is concluded that the model can explain 62.3% of the banking performance effectiveness variation (p<0.001). In this study found social CRM; it can explain 55.5% of the differentiation strategy variation (p<0.001) (Fig. 3).

First of all, this research can conclude that social CRM (Greenberg, 2009, 2010) is an excellent tool for organizations. This study developed a new model for social CRM. In terms of business structure, social CRM gives the organization the opportunity to acquire new customers and good employees. Finally, this research can conclude that social CRM has high potential and endless possibilities. The differentiation strategy construct, as a mediator/intervening variable. The results show that the differentiation strategy was significantly (p<0.001) related to banking performance effectiveness. As an endogenous latent variable, the banking performance effectiveness construct comprises four manifest variables. In this study, the structural equation modelling shows that customer retention, market share, return on investment and overall performance were significantly (p<0.001) related to performance effectiveness (Fig. 3).

Image for - Social Customer Relationship Management and Differentiation Strategy Affecting Banking Performance Effectiveness
Fig. 3: Results for the structural modelling of social CRM, differentiation strategy and banking performance effectiveness df: Degrees of freedom, p: “p-value” associated with discrepancy function (test of perfect fit), CMIN/DF: Minimum value of the discrepancy function divided by degrees of freedom, GFI: Goodness of fit index, CFI: Comparative fit index, RMR: Root mean square residual, RMSEA: Root mean square error of approximation

Cost reduction and sales growth were not significantly related to banking performance effectiveness.

The conclusion of previous studies found that significant relationship were sought between social CRM with organization performance (Greenberg, 2009; 2010; Baird and Parasnis, 2011; Woodcock et al., 2011) and the summary of previous studies found that significant relationship between differentiation strategy with organization performance (Porter, 1980a, b; 1996, Parnell, 2011; Spencer et al., 2009; Hahn and Powers, 2010). Based on these previous studies, the finding of this research that social CRM and differentiation strategy affecting banking performance effectiveness, are consistent with the research of Greenberg (2009; 2010), Baird and Parasnis(2011), Woodcock et al. (2011), Porter, (1980a, b, 1996), Parnell (2011), Spencer et al. (2009) and Hahn and Powers (2010).

CONCLUSION

The banking industry faced with the most uncertain environment in its history. Until this time, the financial industry had been characterized as a free flow of trades and exchanges between one entity and another, with importance placed on having a participative relationship (Carbonara and Caiazza, 2010).This study proceeded to introduce social customer relationship management as an ability prominent influence on banking performance effectiveness.

In this research, social customer relationship management and differentiation strategy affecting banking performance effectiveness among 65 managers and 185 marketing officers of Thai commercial bank branches in the financial service sector. Data were analysed using confirmatory factor analysis and structural equation modelling. It was found that significant relationships existed between social customer relationship management and differentiation strategy and banking performance effectiveness. The key success factors of social CRM were found to have direct influence on banking performance effectiveness (p<0.01) and indirectly through the mediation of differentiation strategy (p<0.001). Finally, the banking industry needs to adjust in order to gain a competitive edge by using social CRM and differentiation strategy to service consumers and businesses (both in public and private sectors), focusing on the business’s effectiveness, strategy, customer insight and building long-term relationships with customers (both retail and public enterprises). This is important given the highly competitive situation at present and will be even more so with the ASEAN Economic Community (AEC) coming in 2015.

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