Determinants of mobile supply chain management system diffusion: A structural equation analysis of manufacturing firms

Felix T.S. Chana, Alain Yee Loong Chong

Research output: Journal PublicationArticlepeer-review

105 Citations (Scopus)


Mobile supply chain management (SCM) is gaining recognition as a major source of cost reduction and supply chain performance improvement. The current literature related to mobile SCM needs to be extended further in order to provide insights into how manufacturing firms can implement mobile SCM successfully. Specifically, there is a need to provide empirical and systematic analysis of the variables that can explain the various stages of mobile SCM diffusion. A review of recent literature suggests that existing e-supply chain technology adoption literature is not strongly grounded in theory. A theoretical model with six hypotheses was proposed based on the technology-organisation- environment (TOE) framework and innovation diffusion theory (IDT). This study draws its survey responses from a group of manufacturing firms in order to investigate the factors that affect the diffusion of mobile SCM. The results show that the variables derived from TOE and IDT can explain mobile SCM diffusion well. However, interorganisational relationships (IORs) play a crucial role in determining the success of mobile SCM routinisation. This is one of the first known empirical studies on the factors influencing the diffusion of mobile SCM. The results of this study will help decision makers better understand the implementation process of mobile SCM and formulate strategies for successful diffusion of mobile SCM.

Original languageEnglish
Pages (from-to)1196-1213
Number of pages18
JournalInternational Journal of Production Research
Issue number4
Publication statusPublished - 2013


  • Information technology
  • Manufacturing management
  • Mobile supply chain management
  • Structural equation modelling
  • Survey research/design

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


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