The Electronic Journal of Information Systems Evaluation provides critical perspectives on topics relevant to Information Systems Evaluation, with an emphasis on the organisational and management implications
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Journal Article

Considerations for the Adoption of Cloud‑based Big Data Analytics in Small Business Enterprises  pp63-79

Olufemi J. Ajimoko

© Nov 2018 Volume 21 Issue 2, Editor: Prof Shaun Pather, pp63 - 168

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Abstract

This study explores the various adoption criteria that may guide the information technology (IT) professionals in small business enterprises (SBEs) in their decision to adopt cloud‑based big data analytics (CBBDA). The research was guided by three major theories of technology adoption, which were: diffusion of innovation, theory of technology acceptance model, and the theory of technology‑organization‑environment framework. The study was based on a sample of 20 IT professionals from10 SBEs in the state of New Jersey in the United States. The exploratory qualitative research used semi‑structure questionnaires to conduct one‑on‑one interviews with the participants. The results were coded to identify the emergent themes. The study found two categories of CBBDA adoption criteria; they were: (a) internal technology adoption criteria, which were found to be unique to each SBE and (b) external technology adoption criteria, which were found to be uniform to all the SBEs. The internal criteria consisted of technological and organizational factors, while the external criteria consisted of vendor‑related and environmental factors. Further, the study found that some of the prominent internal factors played a dominant role in CBBDA adoption in SBEs. They were: (a) technology/organization alignment and fit; (b) SBE data environment and need; (c) SBE financial standing and (d) SBE owner/top management support. It was also found that no matter how useful the innovation, the lack of SBE owner/top management support can easily obstruct the adoption of CBBDA and other similar future technology.

 

Keywords: big data analytics, cloud computing, cloud-based big data analytics, small business enterprise

 

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Journal Article

Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model  pp28-47

Hemlata Gangwar

© Feb 2020 Volume 23 Issue 1, Editor: Prof Shaun Pather, pp1 - 95

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Abstract

The purpose of this study is to propose a unified model integrating both the technology acceptance model (TAM) and task technology fit (TTF) model, and explore the organizational and environmental fit of the integrated model in order to investigate usage of Big Data Analytics and its effect on business performance. A questionnaire was used to collect data from 280 companies in CPG & Retail, Healthcare, Banking and Telecom in India. The data were analysed using exploratory and confirmatory factor analyses. Further, structural equation modelling was used to test the proposed model. The findings show that the research model for integrating the TAM for adoption and TTF model for utility provides a more comprehensive understanding of Big Data Analytics usage. The study identified task technology fit , individual technology fit, organizational data fit, organizational process fit, and business strategy fit as Tidd important variables for affecting Big Data Analytics usage using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Competitive fit and partner support/customer fit were also found to be directly affecting Big Data Analytics usage, which in turn has significant influence on business performance. The model explained 71.4 percent of Business performance. The integrated model may be used as a guideline to ensure a positive outcome of Big Data Analytics usage in organizations. This study combined both the key ideas of TAM and TTF to show that they were necessary in predicting Big Data Analytics usage and business performance.

 

Keywords: Big Data Analytics, Technology Acceptance Model, Task Technology Fit, Technological Fit, Organizational Fit, Environmental Fit

 

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Journal Issue

Volume 21 Issue 2 / Nov 2018  pp63‑168

Editor: Prof Shaun Pather

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Keywords: Acceptance of Open Learning Resources, Self-efficacy, MOOCs and OERs in India, Technology acceptance, Informal online-learning, Open educational content and higher education, big data analytics, cloud computing, cloud-based big data analytics, small business enterprise

 

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