Business intelligence (BI) and Customer Relationship Management (CRM) projects are difficult to maintain and manage, since they consist of different systems that need to work together in a collaborative manner. For example, inaccurate data from one location can cause most reports to be generated incorrectly, which in turn can cause meaningless results. This situation may result in the failure of BI and CRM systems. This is most likely due to the fact that the process is not planned correctly and needs are not adequately analyzed. For this reason, systems must always be checked and maintained. Additionally, users’ acceptance of this new technology must be examined. In this study, techniques of using BI applications in CRM were examined. While previous studies on the adoption of BI and CRM systems in businesses have used TAM or variants formed from the variables of different theoretical models, in this study an alternative model was proposed based on the Technology Acceptance Model 3 (TAM3), which is an extended form of the TAM's social impact processes and cognitive impact process variables. Thus, this study aims to fill in the gap in the literature and provide a detailed explanation. In order to investigate users’ acceptance of BI and CRM systems, an adapted questionnaire was administered to 90 employees at institutions operating in capital markets. Findings showed that by order of importance perceived usefulness, perceived ease of use and behavioral intention to use are the key constructs for promoting the usage of BI and CRM systems in such sectoral context. Besides this, the author offered suggestions to technology managers and institutions operating in the capital markets concerning new technology, adoption of BI and CRM systems under similar sectoral circumstances.
Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies, Decision Sciences, 28(3), 557-582.
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155.
Alkanat, F. (2011). Data analysis (business intelligence) and modeling in the service sector (Doctoral dissertation), YTU.
Alshawi, S., Missi, F., & Irani, Z. (2011). Organisational, technical and data quality factors in CRM adoption—SMEs perspective. Industrial Marketing Management, 40(3), 376-383.
Altas, D., Cilingirturk, A. M., & Gulpinar, V. (2013). Analyzing the process of the artificial neural networks by the help of the social network analysis. New Knowledge Journal of Science, 2(2), 80-91.
Ashraf, A. R., Thongpapanl, N., & Auh, S. (2014). The application of the technology acceptance model under different cultural contexts: The case of online shopping adoption. Journal of International Marketing, 22(3), 68-93.
Brown, S. A., Massey, A. P., Montoya-Weiss, M. M., and Burkman, J. Y. (2002). Do I really have to? User acceptance of mandated technology, European Journal of Information Systems, 11(4), 283-295.
Calisir, F., Gumussoy, C. A., & Bayram, A. (2009). Predicting the behavioral intention to use enterprise resource planning systems: An exploratory extension of the technology acceptance model. Management research news, 32(7), 597-613.
Chatterjee, S., & Hadi, A. S. (2015). Regression analysis by example. John Wiley & Sons.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
Chen, I.J., & Popovich, K. (2003). Understanding customer relationship management (CRM): People, process and technology. Business Process Management Journal, 9(5), pp.672-688.
Cosic, R., Shanks, G., & Maynard, S. (2012). Towards a business analytics capability maturity model. In ACIS 2012: Location, Location, Location: Proceedings of the 23rd Australasian Conference on Information Systems 2012, 1-11.
Davis, F. D., & Venkatesh, V. (2004). Toward preprototype user acceptance testing of new information systems: implications for software project management. IEEE Transactions on Engineering management, 51(1), 31-46.
Davis, J., Edgar, T., Porter, J., Bernaden, J., & Sarli, M. (2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering, 47, 145-156.
Dutot, V. (2015). Factors influencing Near Field Communication (NFC) adoption: an extended TAM approach. The Journal of High Technology Management Research, 26(1), 45-57.
Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135-153.
Esgin, E., (2015). An analysis of the effects of users’ different characteristics on the acceptance of e-transformation in a public institution. Journal of Human Sciences, 12(1), 761-789.
Ghazanfari, M. J. S. R. M., Jafari, M., & Rouhani, S. (2011). A tool to evaluate the business intelligence of enterprise systems. Scientia Iranica, 18(6), 1579-1590.
Giovannini, C. J., Ferreira, J. B., Silva, J. F. D., & Ferreira, D. B. (2015). The effects of trust transference, mobile attributes and enjoyment on mobile trust. BAR-Brazilian Administration Review, 12(1), 88-108.
Goh, T. T. (2011). Exploring gender differences in SMS-based mobile library search system adoption. Journal of Educational Technology & Society, 14(4), 192.
Grandón, E. E., Nasco, S. A., & Mykytyn, Jr. P. P. (2011). Comparing theories to explain e-commerce adoption. Journal of Business Research, 64(3), 292-298.
Grublješi?, T., & Jakli?, J. (2015). Busi
In-Text Citation: (Sönmez, 2018)
To Cite this Article: Sönmez, F. (2018). Technology Acceptance of Business Intelligence and Customer Relationship Management Systems within Institutions Operating in Capital Markets. International Journal of Academic Research in Business and Social Sciences, 8(2), 392–414.
Copyright: © 2018 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
