Factors influencing user acceptance of public sector big open data

Abstract

In recent years Government departments and public/private organisations are becoming increasingly transparent with their data to establish the whole new paradigm of big open data. Increasing research interest arises from the claimed usability of big open data in improving public sector reforms, facilitating innovation, improving supplier and distribution networks and creating resilient supply chains that help improve the efficiency of public services. Despite the advantages of big open data for supply chain and operations management, there is severe shortage of empirical analyses in this field, especially with regard to its acceptance. To address this gap, in this paper we use an extended technology acceptance model to empirically examine the factors affecting users’ behavioural intentions towards public sector big open data. We outline the importance of our model for operations and supply chain managers, the limitations of the study, and future research directions.

Publication DOI: https://doi.org/10.1080/09537287.2017.1336802
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way
Uncontrolled Keywords: Big open data,operations,public sector,supply chains,use,Computer Science Applications,Strategy and Management,Management Science and Operations Research,Industrial and Manufacturing Engineering
Publication ISSN: 1366-5871
Last Modified: 11 Apr 2024 07:13
Date Deposited: 03 Jul 2019 14:45
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.tan ... 87.2017.1336802 (Publisher URL)
PURE Output Type: Article
Published Date: 2017-12-01
Published Online Date: 2017-07-11
Accepted Date: 2017-05-16
Authors: Weerakkody, Vishanth
Kapoor, Kawaljeet (ORCID Profile 0000-0001-9524-905X)
Balta, Maria Elisavet
Irani, Zahir
Dwivedi, Yogesh K.

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