Reexamining the Impact of Information Technology Investments on Productivity Using Regression Tree- and MARS-Based Analy
Several studies have investigated the impact of investments in IT on productivity. In this chapter, we revisit this issue and reexamine the impact of investments in IT on hospital productivity using two data mining techniques, which allowed us to explore
- PDF / 367,298 Bytes
- 18 Pages / 439.37 x 666.142 pts Page_size
- 39 Downloads / 177 Views
Reexamining the Impact of Information Technology Investments on Productivity Using Regression Tree and MARS-Based Analyses Myung Ko and Kweku-Muata Osei-Bryson Several studies have investigated the impact of investments in IT on productivity. In this chapter, we revisit this issue and reexamine the impact of investments in IT on hospital productivity using two data mining techniques, which allowed us to explore interactions between the input variables as well as conditional impacts. The results of our study indicated that the relationship between IT investment and productivity is very complex. We found that the impact of IT investment is not uniform and the rate of IT impact varies contingent on the amounts invested in the IT Stock, Non-IT Labor, Non-IT Capital, and possibly time.
1 Introduction Evaluating the true impact of IT on organizations has been a constant concern for both researchers and practitioners and numerous studies have investigated this issue for more than 3 decades. Several recent studies have reported that a positive relationship between IT and productivity at the firm level (e.g., Lichtenberg 1995; Brynjolfsson and Hitt 1996; Hitt and Brynjolfsson 1996; Dewan and Min 1997; Mukopadhyay et al. 1997; Menon et al. 2000; Shao and Lin 2001; Kudyba and Diwan 2002; Shin 2006). While previous studies made significant contribution to IT & productivity research, these studies focused on examining the impact of
M. Ko (*) Department of Information Systems and Cyber Security One UTSA Circle, The University of Texas at San Antonio, San Antonio, TX 78249, USA e-mail: [email protected] K.-M. Osei-Bryson Department of Information Systems, Virginia Commonwealth University, 301 W. Main Street, Richmond, VA 23284, USA e-mail: [email protected]
K.-M. Osei-Bryson and O. Ngwenyama (eds.), Advances in Research Methods for Information Systems Research, Integrated Series in Information Systems 34, DOI: 10.1007/978-1-4614-9463-8_9, © Springer Science+Business Media New York 2014
109
110
M. Ko and K.-M. Osei-Bryson
Table 1 Capabilities of data mining techniques Capability
RT
MARS
Ability to detect interactions Estimate values of the coefficient for each variable Identify order of importance of variable Ability to build a model by partitioning a variable
Yes No Yes Yes
Yes Yes Yes Yes
IT investment on productivity in terms of its existence or nonexistence. We suggest that, at current stage of IT & productivity research, the appropriate research question that should be addressed is not “does IT impact productivity?” but “under what conditions do investments in IT impact productivity?” In this study, we use two popular data mining techniques, regression trees (RT) and multivariate adaptive regression splines (MARS), to explore our research question. Our reasons for using this pair of techniques are primarily because they have capabilities for discovering nonlinear relationship between the response and predictor variables (Deichmann et al. 2002) and identifying interactions and conditional relationships between t
Data Loading...
