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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2020

Open Access 01-12-2020 | Review

The inhibiting effects of resistance to change of disability determination system: a status quo bias perspective

Authors: Wen-Chou Chi, Po-Jin Lin, I-Chiu Chang, Sing-Liang Chen

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

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Abstract

Background

Information systems implementation projects have been historically plagued by failures for which user resistance has consistently been identified as a salient reason. Most prior studies investigated either the causes or the consequences of Resistance to Change (RTC) of medical related Information Systems. In this study, we simultaneously explore the causes and impacts of RTC of Disability Determination System (DDS).

Methods

This study adopts the Status Quo Bias perspective and combines the information systems usage model and Technology Acceptance Model (TAM) as theoretical foundation to investigates the causes and impacts of users’ RTC on their intention to use the DDS. Data were obtained through internet questionnaire survey. Totally, 326 respondents from 22 local governments and 142 hospitals were collected, of which 252 were valid samples and were analyzed using structure model analysis.

Results

The research model is proved with eight out of 11 hypotheses being supported. The antecedents of RTC can explain 21.4% of the RTC variation, and the RTC impacts can explain 57.5% of the variation of intention to use DDS.

Conclusions

Combining the Status Quo Bias perspective and key component of TAM provides an adequate explanation of adopting intention of changing systems and extend the existing knowledge of information systems adoption. The results provide as a reference for managing users’ RTC and enhance the effects and efficiency of new systems adoption.
Literature
1.
Meissonier R, Houzé E. Toward an ‘IT conflict-resistance theory’: action research during IT pre-implementation. Eur J Inf Syst. 2010;19(5):540–61.CrossRef
2.
Bhattacherjee A, Hikmet N. Physicians’ resistance toward healthcare information technology: a theoretical model and empirical test. Eur J Inf Syst. 2007;16(6):725–37.CrossRef
3.
Dent EB, Goldberg SG. Challenging “resistance to change”. J Appl Behav Sci. 1999;35(1):25–41.CrossRef
4.
Van Offenbeek M, Boonstra A, Seo D. Towards integrating acceptance and resistance research: evidence from a telecare case study. Eur J Inf Syst. 2013;22(4):434–54.CrossRef
5.
Claudy MC, Garcia R, O’Driscoll A. Consumer resistance to innovation—a behavioral reasoning perspective. J Acad Mark Sci. 2015;43(4):528–44.CrossRef
6.
Kim HW. The effects of switching costs on user resistance to enterprise systems implementation. IEEE Trans Eng Manag. 2011;58(3):471–82.CrossRef
7.
Samuelson W, Zeckhauser R. Status quo bias in decision making. J Risk Uncertain. 1988;1(1):7–59.CrossRef
8.
Polites G, Karahanna E. Shackled to the status quo: the inhibiting effects of incumbent system habit, switching costs, and inertia on new systemacceptance. MIS Quarterly. 2012;36(1)21–42.
9.
Hsieh PJ. An empirical investigation of patients’ acceptance and resistance toward the health cloud: the dual factor perspective. Comput Hum Behav. 2016;63:959–69.CrossRef
10.
Laumer S, Maier C, Eckhardt A, Weitzel T. Work routines as an object of resistance during information systems implementations: theoretical foundation and empirical evidence. Eur J Inf Syst. 2016;25(4):317–43.CrossRef
11.
Lapointe L, Beaudry A. Identifying IT user mindsets: Acceptance, resistance and ambivalence. In Proceedings of the 47th Hawaii International Conference on System Science (pp. 4619–4628). Washington, DC: IEEE; 2014.
12.
Campbell RH, Grimshaw M. User resistance to information system implementations: a dual-mode processing perspective. Inf Syst Manag. 2016;33(2):179–95.CrossRef
13.
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 1989;13(3):319–340.
14.
Laumer S. ‘Why Do People Reject Technologies – a Literature-Based Discussion of the Phenomena “Resistance to Change” in Information Systems and Managerial Psychology Research’, ECIS 2011 Proceedings; 2011. p. 60.
15.
Sirdeshmukh D, Singh J, Sabol B. Consumer trust, value, and loyalty in relational exchanges. J Mark. 2002;66(1):15–37.CrossRef
16.
Ali M, Zhou L, Miller L, Ieromonachou P. User resistance in IT: a literature review. Int J Inf Manag. 2016;36(1):35–43.CrossRef
17.
Hsieh PJ, Lin WS. Explaining resistance to system usage in the PharmaCloud: a view of the dual-factor model. Inf Manag. 2018;55(1):51–63.CrossRef
18.
Huang TK. Exploring the antecedents of screenshot-based interactions in the context of advanced computer software learning. Comput Educ. 2015;80:95–107.CrossRef
19.
Ajzen I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior. J Appl Soc Psychol. 2002;32(4):665–83.CrossRef
20.
Mahmud I, Ramayah T, Kurnia S. To use or not to use: Modelling end user grumbling as user resistance in pre-implementation stage of enterprise resource planning system. Inf Syst. 2017;69:164–79.CrossRef
21.
Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: an extension of the UTAUT model. Int J Med Inform. 2017;101:75–84.PubMedCrossRef
22.
Cenfetelli RT, Schwarz A. Identifying and testing the inhibitors of technology usage intentions. Inf Syst Res. 2011;22(4):808–23.CrossRef
23.
Alharbi S, Drew S. Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. Int J Adv Comput Sci Appl. 2014;5(1):143–55.
24.
Beglaryan M, Petrosyan V, Bunker E. Development of a tripolar model of technology acceptance: hospital-based physicians’ perspective on EHR. Int J Med Inform. 2017;102:50–61.PubMedCrossRef
25.
Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18(1):39–50.CrossRef
Metadata
Title
The inhibiting effects of resistance to change of disability determination system: a status quo bias perspective
Authors
Wen-Chou Chi
Po-Jin Lin
I-Chiu Chang
Sing-Liang Chen
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-020-1090-7

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