Social Networks and Adaptability to IT-Enabled Change: The Case ofHealthcare Information Technologies Pubblico
Raman, Roopa (2008)
Published
Abstract
Long-term assimilation of information technology (IT) is a persistent challenge for organizations, limiting the business value of these information technologies. Failure to adapt to IT-enabled changes in work processes is one factor limiting long-term assimilation of the technology. In an empirical study, a large hospital faced such long-term assimilation challenges, such that different units within the same organization differed in the extent to which they were able to adapt to IT-enabled changes in work processes. This was the case even though all units were using the same technology features, had access to the same resources, and were subject to the same concurrent implementation effort. In this study, I seek to understand why. Using social network analysis as the methodological lens, I examine the association between intra-unit social structures of knowledge demand and knowledge supply and unit-level variations in adaptability. Results show that structural variations in the knowledge demand and knowledge supply networks across units explain more of the variance in the adaptability of these units, than do other non-relational attributes of the units that have been explored in prior literature. My study also shows that knowledge demand versus knowledge supply networks have distinct social structural characteristics, and do not have the same impact on adaptability to IT-enabled change. In the knowledge demand network, the network structural characteristics of low average incloseness centrality and high network density had a positive effect on adaptability. Network cohesion had a negative effect on adaptability in partial models, when network density was not included in the analysis. In the knowledge supply network, high average eigenvector centrality and high network density had a positive effect on adaptability. The cohesiveness of the knowledge supply network was not found to have any significant effect on adaptability. Findings from this study would be of interest to multiple areas of research in Information Systems and Management as well as to healthcare practice.
Table of Contents
TABLE OF CONTENTS 1. CHAPTER 1: PROBLEM STATEMENT, RESEARCH QUESTION, AND SIGNIFICANCE OF THE STUDY 1 2. CHAPTER 2: LITERATURE REVIEW 9 TECHNOLOGY ACCEPTANCE 9 IS IMPLEMENTATION (including RESISTANCE TO IT) 11 ADAPTABILITY, CHANGE MANAGEMENT 12 SOCIAL NETWORKS and ADAPTIBILITY 14 CHAPTER 3: RESEARCH MODEL 21 KNOWLEDGE DEMAND NETWORKS (KDN) 25 KNOWLEDGE SUPPLY NETWORKS (KSN) 33 CHAPTER 4: METHODS 46 RESEARCH SETTING 46 MEASUREMENT 48 DATA COLLECTION 49 CHAPTER 5: ANALYSIS & RESULTS 63 TREATMENT OF DATA 63 MODEL SPECIFICATION 64 RESULTS 66 6. CHAPTER 6: DISCUSSION & CONCLUSION 71 REFERENCES 96 TABLE OF CONTENTS- LIST OF FIGURES Figure 1: InCloseness centrality 81 Figure 2: Eigenvector centrality 81 Figure 3: Density 82 Figure 4: Distance-based cohesion 82 Figure 5: Research model 83 TABLE OF CONTENTS- LIST OF TABLES Table 1: Network studies related to technology-based change 84 Table 2: Overview of Clinical Information System 87 Table 3: Variable Definitions and Measures 88 Table 4: Reliability analysis (Non-network data) 91 Table 5: OLS Regression Results for TOV 92 Table 6: OLS Regression Results for TMA 93 Table 7: Correlation Matrix with Study Variables (N = 27 Units) 94 Table 8: Summary of findings 95
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