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Understanding the key determinants of IoT adoption for the digital transformation of the food and beverage industry

journal contribution
posted on 2024-04-16, 06:00 authored by Imran AliImran Ali, M Aboelmaged, K Govindan, M Malik
Purpose: Research on the Internet of Things (IoT) has gained momentum in various industry contexts. However, the literature lacks broad empirical evidence on the factors that influence users' intention to adopt this cutting-edge technology, especially in the food and beverage industry (F&BI) – a significant yet unexplored setting. Therefore, the authors aim to extend the “Unified Theory of Acceptance and Use of Technology (UTAUT)” model by coupling it with perceived collaborative advantage, organizational inertia and perceived cost and explore the key determinants of IoT adoption for the digital transformation of the F&BI. Design/methodology/approach: This study employs a cross-sectional quantitative approach, where a sample of 307 usable responses was drawn from the senior managers of the Australian F&BI. Findings: The authors have found that performance expectancy, perceived collaborative advantage, effort expectancy, social influence and facilitating conditions have a strong positive influence on the behavioural intention to adopt IoT for the digital transformation of the F&BI. Furthermore, while high perceived costs and organizational inertia are often considered negative factors in adopting new technology, our results reveal the insignificant influence of these factors on the adoption of IoT, which is interesting. The findings also suggest that age and voluntariness significantly moderate most of the relationships, while gender is an insignificant moderator. Originality/value: The study provides several novel insights into the existing body of knowledge by extending the UTAUT model with three variables and applying it in a unique context.

History

Volume

123

Issue

7

Start Page

1887

End Page

1910

Number of Pages

24

eISSN

1758-5783

ISSN

0263-5577

Publisher

Emerald

Language

en

Peer Reviewed

  • Yes

Open Access

  • No

Acceptance Date

2023-03-13

External Author Affiliations

Swinburne University of Technology; Yonsei University, South Korea; University of Southern Denmark; University of Sharjah, UAE; Shanghai Maritime University, China

Era Eligible

  • Yes

Journal

Industrial Management and Data Systems

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