Abstract

Keywords

۱٫ Introduction

۲٫ Literature review

۲٫۱٫ Employee-based brand equity

۲٫۲٫ Customer relationship management dimensions

۲٫۲٫۱٫ Customer orientation

۲٫۲٫۲٫ Knowledge management

۲٫۲٫۳٫ CRM organization

۲٫۲٫۴٫ Technology based CRM

۳٫ Methodology

۴٫ Analysis of results

۴٫۱٫ Common method Bias

۴٫۲٫ Descriptive statistics

۴٫۳٫ Measurement model

۴٫۴٫ Structural model

۵٫ Discussion and conclusion

۶٫ Limitations and future research

Appendix A. Factor loadings of remaining items based on CFA results

References

Abstract

     The purpose of this study was to explore the effect of customer relationship management (CRM) dimensions on employee-based brand equity. By looking at the past literature, it can be noticed that the majority of earlier studies on brand equity examined it from customers’ perspective, while the employees’ perspective received less emphasis. Thus, the required data was obtained using a survey tool from 164 employees in the ride-hailing industry in Saudi Arabia. The obtained data was analyzed by the Covariance-Based Structural Equation Modeling (CB-SEM) method to test the hypothesis and reach at conclusions. The findings showed that CRM in general has a positive effect on employee-based brand equity. Specifically, it was found that CRM organization has a significant positive impact on employee-based brand equity. It was also verified that knowledge management and customer orientation have positive effects on overall employee-based brand equity. Finally, the results confirmed that technology-based CRM has a significant positive impact on employee-based brand equity. These results add to the body of literature on this topic and verify the significance of customer relationship management dimensions in affecting employee-based brand equity.

Introduction

     The topic of brand equity has received a noteworthy attention from several scholars since the last decades. The examination of brand equity has mainly been centered towards determining the factors that could improve brand value (Dinçer, Bozaykut-Buk, Emir, Yuksel, & Ashill, 2019). Although the concept has many definitions in the previous literature, there is a universal agreement that brand equity symbolizes the tangible and intangible value of a firm’s products or services that are devised from the unique name of the brand (Srivastava & Shocker, 1991). A strong brand equity provides noteworthy implications to firms, their clients, and to the investors as well. It enables the service providers to generates several benefits, such as: charging higher prices than the rivals for similar products, maintaining business customers on the long term even though competitors peruse price cutting strategies, and withstanding negative information about the failure of a certain product or service (Gelb & Rangarajan, 2014). High brand equity also permits firms to use the same brand name when they successfully introduce a new product. As the competition has increased recently among businesses and due to the imitation of a brand’s offerings, the acquisition of a reliable and trustworthy brand name has been regarded as the key distinguishing element. According to Augusto and Torres (2018); and Phung, Ly, and Nguyen (2019), a firm that acquires high brand equity tends to have greater trust from its consumers. Moreover, Poulis and Wisker (2016) tested the effect of employee-based brand equity on organizational performance and found that the attachment of employees to a brand represents the main factor that contributes to better financial outcomes.

Results and analyses

     As stated above, the collected data were analyzed using the CB-SEM approach. In order to check the likely existence of common-method variance bias among constructs, this paper relied on the CFA and single factor test of Harman (1960), and full collinearity test of Kock and Lynn’s (2012). When all of the measurement items of the constructs were constrained to be loaded on one single common factor, the overall variance explained by a general latent variable was lower than the Harmin’s threshold value of 50%, indicating that the final model is considered free from common method bias. Additionally, all of the Variance Inflation Factors (VIFs) in a full collinearity test were less than 5, suggesting that there is no indication of existing pathological collinearity (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003).