Factors influencing the adoption of mobile-based AI services in Tanzanian manufacturing SMEs

Fredrick Ishengoma and Elia John

Vilakshan - XIMB Journal of Management, 2024, vol. 22, issue 2, 234-252

Abstract: PurposeThis study aims to establish a comprehensive framework for adopting mobile-based artificial intelligence (AI) services in Tanzanian manufacturing small and medium enterprises (SMEs).Design/methodology/approachThe methodology involved conducting a literature review and using the combination of Mobile Services Acceptance Model and Innovation Diffusion Theory (IDT) as a theoretical foundation. This synthesis delves into the current knowledge on technology adoption, organizational behavior and innovation diffusion, creating a solid conceptual basis. Expert review was used for framework validation to ensure the framework's accuracy.FindingsThis study shows that the factors influencing the adoption of mobile-based AI services in Tanzanian manufacturing SMEs include perceived usefulness, perceived ease of use, context, personal initiatives and characteristics, trust, infrastructure, cost, mobility, power distance, compatibility, observability and trialability.Research limitations/implicationsThe framework provides valuable insights tailored to Tanzanian sociocultural and economic nuances. However, its generalizability is limited due to its specificity to Tanzanian manufacturing SMEs.Practical implicationsThe framework outlined in this research provides SME leaders, policymakers and technology implementers with valuable guidance to make informed decisions during the adoption process.Originality/valueThis study introduces a novel lens for understanding technology adoption. This study's focus on the Tanzanian context and its nuanced examination of contributing factors add to its originality and practical significance.

Keywords: Adoption; Artificial intelligence; MSAM; Small and medium enterprises; Tanzania (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:xjmpps:xjm-11-2023-0214

DOI: 10.1108/XJM-11-2023-0214

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