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<p>This study explores smart technology adoption in industrial settings by focusing on the Industrial Internet of Things (IIoT). Despite the rapid growth of IIoT research, there is still a lack of understanding of how different factors work together to influence IIoT adoption, which, in turn, motivates the present study to fill that gap. Our goal, therefore, is to identify and investigate the factors driving IIoT implementation. To do so, we conducted a meta-analysis that synthesizes data from 92 studies, encompassing 391 effect sizes across diverse sectors. We also crafted a theoretical framework that integrates seven foundational theories, namely, diffusion of innovation (DOI) theory, motivation-opportunity-ability (MOA) theory, theory of planned behavior (TPB), technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), technology-organization-environment (TOE) theory, and value-based adoption model (VAM), alongside seven moderators, including continent, domain, firm size, gross domestic product (GDP), industry, period, and systems. In doing so, our findings reveal the impact of more than 20 factors spread across these theories on IIoT adoption. More specifically, we discovered a stronger inclination toward IIoT in manufacturing than retailing and other sectors, larger enterprises than small and medium enterprises (SMEs), post-2018 than pre-2018 period, and industrial than environmental domain. Overall, these insights can equip academics and practitioners alike with a more comprehensive understanding of IIoT backed by a rich pool of meta-analytic evidence.</p>

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Industrial Internet of Things (IIoT)

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Wagner Júnior Ladeira,
  • Weng Marc Lim,
  • Fernando de Oliveira Santini,
  • Tareq Rasul,
  • Bernardo Frantz,
  • Jean Carlos de Oliveira Rosa,
  • Debdutta Choudhury

摘要

This study explores smart technology adoption in industrial settings by focusing on the Industrial Internet of Things (IIoT). Despite the rapid growth of IIoT research, there is still a lack of understanding of how different factors work together to influence IIoT adoption, which, in turn, motivates the present study to fill that gap. Our goal, therefore, is to identify and investigate the factors driving IIoT implementation. To do so, we conducted a meta-analysis that synthesizes data from 92 studies, encompassing 391 effect sizes across diverse sectors. We also crafted a theoretical framework that integrates seven foundational theories, namely, diffusion of innovation (DOI) theory, motivation-opportunity-ability (MOA) theory, theory of planned behavior (TPB), technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), technology-organization-environment (TOE) theory, and value-based adoption model (VAM), alongside seven moderators, including continent, domain, firm size, gross domestic product (GDP), industry, period, and systems. In doing so, our findings reveal the impact of more than 20 factors spread across these theories on IIoT adoption. More specifically, we discovered a stronger inclination toward IIoT in manufacturing than retailing and other sectors, larger enterprises than small and medium enterprises (SMEs), post-2018 than pre-2018 period, and industrial than environmental domain. Overall, these insights can equip academics and practitioners alike with a more comprehensive understanding of IIoT backed by a rich pool of meta-analytic evidence.