The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
CRM: conceptualization and scale development
Abstract
Purpose: To develop a reliable and valid measuring scale for customer relationship management (CRM). Design/methodology/approach: A series of studies were conducted for the development and validation of multiple measures for the dimensions of CRM. Once the dimensions of CRM were identified, data from study 1 (n=150 business executives attending a part‐time MBA program) were used to select items based on factor analysis. Then, confirmatory factor analyses was used on data obtained from a mail survey of Hong Kong financial firms in study 2 (n=215) to examine factor structure, as well as to provide evidence of dimensionality, scale reliability and validity. Finally, in study 3, data from 276 business executives attending a seminar on CRM were used to test the scale generalizability of CRM measures in various industries. Findings: A reliable and valid scale was developed to measure the four dimensions of CRM: key customer focus, CRM organization, knowledge management and technology‐based CRM. Research limitations/implications: Since this study was conducted in Hong Kong only, the generalizability of the CRM scale has to be tested in other countries. In addition, cross‐sectional data were used in this study. Future studies should collect time‐series data for the testing of the causal relationship between CRM and business performance. Practical implications: The findings validate the long‐held belief that CRM is a critical success factor for business performance. Firms wishing to improve their relationships with customers need constantly to monitor their behavioral and internal processes. The proposed scale in this study could be used as a diagnostic tool to identity areas where specific improvements are needed, and to pinpoint aspects of the firm's CRM that require work. Originality/value: This is the first study to provide a comprehensive, psychometrically sound, and operationally valid measure of a firm's CRM.
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Cites
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CRM to Social CRM: The Integration of New Technologies into Customer Relationship Management
Choudhury, M. M., & Harrigan, P. (2014) · Journal of Strategic Marketing · doi:10.1080/0965254x.2013.876069 · Cited by 263
Forecasting Social CRM Adoption in SMEs: A Combined SEM-Neural Network Method
Ahani, A., Rahim, N. Z. A., & Nilashi, M. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.05.032 · Cited by 239
Bhat, S. A., & Darzi, M. A. (2016) · International Journal of Bank Marketing · doi:10.1108/ijbm-11-2014-0160 · Cited by 115
Effects of e-CRM on customer–bank relationship quality and outcomes: The case of Thailand
Sivaraks, P., Krairit, D., & Tang, J. C. S. (2011) · The Journal of High Technology Management Research · doi:10.1016/j.hitech.2011.09.006 · Cited by 98
Guerola-Navarro, V., Oltra-Badenes, R., Gil-Gomez, H., & Gil-Gomez, J. A. (2021) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2020.1836992 · Cited by 85
Metadata
- Authors
- Sin, L. Y. M., Tse, A. C. B., & Yim, F. H. K.
- Venue
- European Journal of Marketing
- Publisher
- Emerald
- Published
- 2005-11-01
- Volume
- 39
- Issue
- 11/12
- Pages
- 1264-1290
- ISSN
- 0309-0566 (ISSN-L)
- OpenAlex
- W2019661831
- Cited by
- 631
- References
- 67
- FWCI
- 17.18
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.