The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Understanding CRM adoption stages: empirical analysis building on the TOE framework
Abstract
Abstract Despite the considerable benefits of CRM, there is a paucity of research considering CRM adoption stages (evaluation, adoption, and routinization). Thus, we developed a conceptual model using the technology-organisation-environment (TOE) framework to examine the antecedents that affect CRM adoption stages in firms. We used partial least squares structural equation modelling (PLS-SEM) with data from 277 firms to test the conceptual model. Data quality and integration, top management support and competitive pressure positively influence CRM evaluation. Technology competence, data quality and integration, top management support and CRM evaluation positively influence CRM adoption. However competitive pressure negatively affects CRM adoption. Finally, competitive pressure and CRM adoption positively influence CRM routinization. Interestingly, each included construct influences every CRM adoption stage differently.
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Cites
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
Modern Methods for Business Research
(1998) · doi:10.4324/9781410604385 · Cited by 6,534
Drazin, R. (1991) · The Journal of Technology Transfer · doi:10.1007/bf02371446 · Cited by 2,122
Zhu, K., Kraemer, K. L., & Xu, S. (2006) · Management Science · doi:10.1287/mnsc.1050.0487 · Cited by 1,440
Kuan, K. K. Y., & Chau, P. Y. K. (2001) · Information & Management · doi:10.1016/s0378-7206(01)00073-8 · Cited by 1,335
Understanding the determinants of RFID adoption in the manufacturing industry
Wang, Y.-M., Wang, Y.-S., & Yang, Y.-F. (2010) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2010.03.006 · Cited by 912
Lessons for Guidelines from the Diffusion of Innovations
Rogers, E. M. (1995) · The Joint Commission Journal on Quality Improvement · doi:10.1016/s1070-3241(16)30155-9 · Cited by 560
A conceptual model for the process of IT innovation adoption in organizations
Hameed, M. A., Counsell, S., & Swift, S. (2012) · Journal of Engineering and Technology Management · doi:10.1016/j.jengtecman.2012.03.007 · Cited by 442
Exploring the role of customer relationship management (CRM) systems in customer knowledge creation
Khodakarami, F., & Chan, Y. E. (2014) · Information & Management · doi:10.1016/j.im.2013.09.001 · Cited by 321
Xu, S., Zhu, K., & Gibbs, J. (2004) · Electronic Markets · doi:10.1080/1019678042000175261 · Cited by 253
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Pillai, R., & Sivathanu, B. (2020) · Benchmarking: An International Journal · doi:10.1108/bij-04-2020-0186 · Cited by 422
Big data analytics adoption: Determinants and performances among small to medium-sized enterprises
Maroufkhani, P., Tseng, M.-L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102190 · Cited by 423
Nguyen, T. H., Le, X. C., & Vu, T. H. L. (2022) · Journal of Open Innovation: Technology, Market, and Complexity · doi:10.3390/joitmc8040200 · Cited by 261
Enabling AI capabilities in government agencies: A study of determinants for European municipalities
Mikalef, P., Lemmer, K., Schaefer, C., Ylinen, M., Fjørtoft, S. O., Torvatn, H. Y., Gupta, M., & Niehaves, B. (2022) · Government Information Quarterly · doi:10.1016/j.giq.2021.101596 · Cited by 240
Ghaleb, E. A. A., Dominic, P. D. D., Fati, S. M., Muneer, A., & Ali, R. F. (2021) · Sustainability · doi:10.3390/su13158379 · Cited by 134
Metadata
- Authors
- Cruz-Jesus, F., Pinheiro, A., & Oliveira, T.
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 109
- Pages
- 1-13
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W2925610025
- Cited by
- 271
- References
- 14
- FWCI
- 37.08
- 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).
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