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Information Technologies and Business Value: An Analytic and Empirical Investigation
Abstract
An important management question today is whether the anticipated economic benefits of Information Technology (IT) are being realized. In this paper, we consider this problem to be measurement related, and propose and test a new process-oriented methodology for ex post measurement to audit IT impacts on a strategic business unit (SBU) or profit center's performance. The IT impacts on a given SBU are measured relative to a group of SBUs in the industry. The methodology involves a two-stage analysis of intermediate and higher level output variables that also accounts for industry and economy wide exogenous variables for tracing and measuring IT contributions. The data for testing the proposed model were obtained from SBUs in the manufacturing sector. Our results show significant positive impacts of IT at the intermediate level. The theoretical contribution of the study is a methodology that attempts to circumvent some of the measurement problems in this domain. It also provides a practical management tool to address the question of why (or why not) certain IT impacts occur. Additionally, through its process orientation, the suggested approach highlights key variables that may require managerial attention and subsequent action.
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Cites
A Simple Test for Heteroscedasticity and Random Coefficient Variation
Breusch, T. S., & Pagan, A. R. (1979) · Econometrica · doi:10.2307/1911963 · Cited by 5,647
Cited by
Bharadwaj, A. S. (2000) · MIS Quarterly · doi:10.2307/3250983 · Cited by 5,042
Big data analytics and firm performance: Effects of dynamic capabilities
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.009 · Cited by 2,474
Zhu, K., & Kraemer, K. L. (2005) · Information Systems Research · doi:10.1287/isre.1050.0045 · Cited by 1,723
Pavlou, P. A., & El Sawy, O. A. (2006) · Information Systems Research · doi:10.1287/isre.1060.0094 · Cited by 1,311
RAVICHANDRAN, T., LERTWONGSATIEN, C., & LERTWONGSATIEN, C. (2005) · Journal of Management Information Systems · doi:10.1080/07421222.2005.11045820 · Cited by 1,238
Enterprise agility and the enabling role of information technology
Overby, E., Bharadwaj, A., & Sambamurthy, V (2006) · European Journal of Information Systems · doi:10.1057/palgrave.ejis.3000600 · Cited by 1,063
Investment in Enterprise Resource Planning: Business Impact and Productivity Measures
Hitt, L. M., Wu, D. J., & Zhou, X. (2002) · Journal of Management Information Systems · doi:10.1080/07421222.2002.11045716 · Cited by 877
Zhu, K., & Kraemer, K. L. (2002) · Information Systems Research · doi:10.1287/isre.13.3.275.82 · Cited by 779
Bai, B., Law, R., & Wen, I. (2008) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2007.10.008 · Cited by 749
Drnevich, P. L., & Kriauciunas, A. P. (2011) · Strategic Management Journal · doi:10.1002/smj.882 · Cited by 681
Gattiker, T. F., & Goodhue, D. L. (2005) · MIS Quarterly · doi:10.2307/25148695 · Cited by 642
Assessing business value of big data analytics in European firms
Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2016.08.011 · Cited by 538
Schryen, G. (2013) · European Journal of Information Systems · doi:10.1057/ejis.2012.45 · Cited by 420
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
LAI, F., LI, D., WANG, Q., & ZHAO, X. (2008) · Journal of Supply Chain Management · doi:10.1111/j.1745-493x.2008.00064.x · Cited by 261
Raghunathan, S. (1999) · Decision Support Systems · doi:10.1016/s0167-9236(99)00060-3 · Cited by 222
Information Technology Impacts on Firm Performance: An Extension of Kohli and 1
Sabherwal, R., & Jeyaraj, A. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.4.4 · Cited by 200
Digitally transforming work styles in an era of infectious disease
Kodama, M. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102172 · Cited by 116
Customization and real time information access in integrated eBusiness supply chain relationships
Klein, R. (2007) · Journal of Operations Management · doi:10.1016/j.jom.2007.03.001 · Cited by 108
The Business Value of IT: A Synthesis and Framework of Archival Research
Masli, A., Richardson, V. J., Sanchez, J. M., & Smith, R. E. (2011) · Journal of Information Systems · doi:10.2308/isys-10117 · Cited by 98
Mishra, D., Luo, Z., Hazen, B., Hassini, E., & Foropon, C. (2019) · Management Decision · doi:10.1108/md-03-2018-0324 · Cited by 87
An empirical analysis of inventory turnover behaviour in Greek retail sector: 2000–2005
Kolias, G. D., Dimelis, S. P., & Filios, V. P. (2011) · International Journal of Production Economics · doi:10.1016/j.ijpe.2010.04.026 · Cited by 76
Helping Small Businesses become more Data-Driven: A Field Experiment on eBay
Bar-Gill, S., Brynjolfsson, E., & Hak, N. (2023) · doi:10.2139/ssrn.4407497
Metadata
- Authors
- Barua, A., Kriebel, C. H., & Mukhopadhyay, T.
- Venue
- Information Systems Research
- Published
- 1995-03
- Volume
- 6
- Issue
- 1
- Pages
- 3-23
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2045449564
- Cited by
- 1,131
- References
- 1
- FWCI
- 23.68
- Type
- journal article
- Language
- en
- Open access
- closed
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