This research evaluates the effect of customer relationship management (CRM) on customer knowledge and customer satisfaction. An analysis. Please wait while the data is being loaded. . Although these articles do not have all bibliographic details available yet, they can be cited using the year of. Data collection tool is a researcher-made questionnaire of CRM and its on productivity () and the highest effect on customer satisfaction.
Because of direct communication with the actions of an organization, clients are a valuable source for opportunities and threats related to respective industry. In new business processes, a vital position is allocated for achieving customer satisfaction in organization goals and senior managers are well aware that their success in achieving the major goals of the organization depends on customer satisfaction.
Many successful organizations emphasize on the note that maintaining stable relationships with the customers can hold them firm and progressive in competition stage.
The research report of Healthcare Information and Management Systems Society has shown that the quality of care and customer satisfaction in the coming years will have a significant impact on health. Furthermore, effective communication is often associated with improved physical health, more effective chronic disease management, and better health-related quality of life.
On the other hand, failure in CRM affects the patient dissatisfaction and leads to a distrust of system, a sense of patient alienation in the hospital and endangering the survival of the business at the hospital. Therefore examining such valuable concept in hospitals, which their function is based on the patient customeris very necessary. On the other hand, failure in CRM will affect the patient dissatisfaction, distrust towards systems, patient feels alienated in the hospital, and jeopardize business survivability in the future.
Hence, the effect of CRM on the organization's productivity, customer satisfaction, trust and loyalty in the hospitals of Isfahan University of Medical Sciences was studied in this research. The estimated sample size was nurses. The adequacy of sample size has been taken using Kaiser—Meyer—Olkin method.
Data collection tools This study is a correlation descriptive research. A researcher-made questionnaire has two main parts. The first part is based on the functions of CRM and the second part is the variables that CRM have influence on them, the second part of questionnaire consisting of three areas: Organization productivity, customer satisfaction, and customer loyalty and trust. For confirming the validity of questionnaires, expert judgment methods were used so that the developed questionnaire along with explanations of its vocabulary and concepts was given to 10 university professors, five hospital managers and authorities, and six hospital nurse managers and they were asked to express their opinions in relation to the content, structure and style of wording of the questionnaire and its appearance.
After applying the recommendations and its final confirmation by other experts, the content validity of the questionnaire was ensured. We find support for this hypothesis tronic access to the following types of application or data: This variable is the natural log of the test to assess this mediation effect Baron and Kenny This result implies that, Because the dependent variable i.
A firms that report no gains in customer knowledge following linear model may result in predicted probabilities less zero investments in CRM applications. Because the coefficient or greater than one. In addition, a linear model does not of the CRM applications variable is statistically significant allow us to consider the nonlinear nature of the effect of in Column 2 of Table 1, our results suggest partial media- independent variables on the binary dependent variable.
Why Do Customer Relationship Management Applications Affect Customer Satisfaction?
To tion and imply that CRM applications may also have a overcome these estimation problems inherent in the OLS direct effect on customer satisfaction. Improvement in Customer Knowledge Model 2: We considered whether the —. Focusing on CRM servable variables that may have affected our findings applications also avoids aggregation across several IT appli- Boulding and Staelin ; Wierenga, Van Bruggen, and cations, in which applications may be relevant for customer Staelin We used a matching estimator based on satisfaction and others may have a negative or zero impact propensity scores to calculate the treatment effect of CRM implementation on improvement in customer knowledge Banker et al.
Using a hyay, Kekre, and Kalathur Column 2 of Table 1 also procedure that Rosenbaum suggests, we bound the shows that, on average, manufacturing firms have greater matching estimator to evaluate the uncertainty of the esti- customer satisfaction than services firms, a finding that is mated treatment effect due to selection on unobservables.
After matching the propensity scores and thus adjusting Additional Analyses for the observed characteristics, we find that the average CRM effect for improvement in customer knowledge is We conducted additional sensitivity analyses to check the positive and statistically significant. This calculation is robustness of our results. Because Equation 1 uses data based on the assumption that treatment and control groups from InformationWeek sources on dependent and indepen- are different because they differ on the observed variables dent variables, we tested for common method bias using in the data set.
Because no single factor emerged fer on unobserved measures, a positive association between as a dominant factor accounting for most of the variance, treatment status and performance outcome would not neces- common method bias is unlikely to be a serious problem in sarily represent a causal effect Boulding and Staelin These results lend support to our previ- assessment of the robustness of treatment effects due to fac- ously developed theory and conceptual framework.
Because it is not possible to estimate the magnitude of selection bias due to unobserv- Contributions and Research Implications ables with nonexperimental i. First, it builds on pre- calculated the upper and lower bounds on the test statistics vious research that links IT systems and customer satisfac- used to test the null hypothesis of the no-treatment effect tion to contribute to the cumulative knowledge in this for different values of unobserved selection bias.
Under the assumption that the unobserved vari- More specifically, given the paucity of research on the ben- able u is a binary variable, the following expression can be efits gained from CRM technology investments, this study derived Rosenbaum If unobserved variables have no level in the United States Jayachandran et al. Although several studies provide con- unobserved variable makes it twice as likely for a firm to be ceptual support for the effect of CRM applications on cus- in the treatment group than in the control group after we tomer knowledge, our study empirically establishes this control for several observed characteristics.
More broadly, our study provides support for results provide evidence for the robustness of our findings, the emerging view that IT applications affect firm perfor- showing the positive effect of CRM applications on cus- mance by enabling other business processes and capabili- tomer knowledge and, in turn, on customer satisfaction.
Discussion and Conclusion Third, the results of this study suggest that it is impor- Our goal in this research was to study the effect of CRM tant to account for the effect of factors such as supply chain applications on customer knowledge and customer satisfac- integration in the evaluation of returns from CRM applica- tion.
We developed a theoretical model that posits a mediat- tions. We find that though CRM applications are associated ing role of customer knowledge and a moderating role of with customer knowledge and customer satisfaction, they supply chain integration in explaining the effect of CRM are even more beneficial if firms share their customer- applications on customer satisfaction.
We used archival data related information with supply chain partners. This result on CRM applications and a perceptual measure of customer provides empirical support for the importance of supply knowledge on a cross-section of large U.
We matched part of this data set with the sample of colleagues and Rai, Patnayakuni, and Patnayakuni firms common to the ACSI to study the effect of CRMemphasizing the importance of flexibility in the applications on customer satisfaction. Our results We identify several promising areas for further research in suggest that the effect of CRM applications on customer this stream of literature.
These differ- Managerial Implications ences arise because of the differential ability of firms to This study also has several managerial implications. First, negotiate Pareto-optimal contracts across these contexts, the for firms evaluating CRM applications, it is important to large but infrequent purchases in the B2B context com- understand the conditions under which deployment of those pared with small but frequent purchases in the B2C con- applications contribute to improved customer knowledge textand the presence of multiple stakeholders in the buy- and customer satisfaction.
Our results showing the impor- ing center in the B2B context compared with individual tance of supply chain integration in realizing the benefits decision making in the B2C context. Future studies could from CRM applications could be useful to managers who explore whether CRM initiatives in the B2B context have are currently evaluating or implementing CRM applica- an effect on customer satisfaction and other customer-based tions.
Our analysis shows that firms with greater supply metrics. The results imply that firms need to be willing mediated by customer knowledge, note that CRM applica- to share their customer-related information with supply tions merely enable firms to collect customer knowledge.
There is a need for further research to touchpoints in order to benefit in terms of customer satis- trace the causal chain linking CRM applications and cus- faction. An implication of this finding is that managers need tomer satisfaction at a finer level of granularity by specifi- to institute measurement systems to capture the gains in cally accounting for such complementary actions.
The stud- customer knowledge following the implementation of CRM ies by Barua and colleaguesBarua and colleagues applications because gains in customer knowledge are aand Wu, Mahajan, and Balasubramaniam precursor to gains in customer satisfaction.
Conclusion Finally, although many firms justify the implementation This research empirically tested the effect of CRM applica- of CRM applications based on expected gains in customer tions on customer knowledge and customer satisfaction.
Equally, there is a need to consider and supply chain. Our findings provide empirical support for the quantify the potential risks of not implementing CRM conjectures that CRM applications help firms gain customer applications in a competitive environment.
An attractive knowledge and that this knowledge helps firms improve area for further research may be to evaluate the extent to their customer satisfaction. Our research contributes to which the implementation of CRM applications helps firms empirically valid theory by synthesizing insights from the enhance the net present value of their customer base and marketing and information systems literature and by inves- improve the effectiveness of cross-selling and one-to-one tigating the effect of organizational variables that leverage marketing programs Peppers and Rogers Overall, our results suggest that firms that make investments in CRM applications reap significant intangible benefits, such as improved customer knowledge and customer satisfaction.
Achieving such customer- 1We thank an anonymous reviewer for this insightful focused business objectives is a critical ingredient for suc- observation. Bardhan, Shu Lin, and Hsihui Chang ence, 12 2— Journal of Management Information Systems, 21 37— Professional, 3 January—February47— Lehmann, Maria Merino, Rajendra K. Thomas, and Peter C. VerhoefV. A Framework of Service Research, 5 126— Lemon, and Ronald T.
Charting New Directions for Bharadwaj, A. Bitner, Mary Jo, Stephen W. Brown, and Matthew L.
Kannan, and Matthew D. Lemon, and Peter C. Mithas, Sunil, Joni L. Association for Information Brynjolfsson, E. Samba- Enterprise Software Shakeout? Harris, and Ajay K. Value of Information Technology: Are You in Supply Chain Management: An Exploratory Study of North Ready?
Linking Information Technol- ———, Michael D. Agarwal and Laurie Kirsch, eds. Marketing, 60 October7— Association for Information Systems. Strategic Implications of Knowledge as an Relationships: Dicksontional: MIS Quarterly, 27 2—