Understanding the key determinants of IoT adoption for the digital transformation of the food and beverage industry
Industrial Management & Data Systems
ISSN: 0263-5577
Article publication date: 15 June 2023
Issue publication date: 27 June 2023
Abstract
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.
Keywords
Citation
Ali, I., Aboelmaged, M., Govindan, K. and Malik, M. (2023), "Understanding the key determinants of IoT adoption for the digital transformation of the food and beverage industry", Industrial Management & Data Systems, Vol. 123 No. 7, pp. 1887-1910. https://doi.org/10.1108/IMDS-02-2022-0082
Publisher
:Emerald Publishing Limited
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