Factors affecting adoption of cloud computing technology by organizations of Saudi petro-chemical supply chains

  • Mohammad Kashif Jalal Syed

    Student thesis: Doctoral Thesis


    Supply chains are faced with the daunting tasks of keeping their operational costs to a minimum low while providing the best customer services. To achieve this objective organizations as part of the supply chains adopt different technologies. Adopting technologies is an expensive venture, especially its maintenance. A major chunk of IT investments goes into maintenance of current IT. Cloud computing technology emerges as a saviour to organizations of all sizes in any supply chain as it allows organizations to use required IT without actually owning it. The purpose of this project is to determine the factors affecting adoption of cloud computing technology by organizations of the Saudi petrochemical supply chains. However, there is no technology adoption model that may be applied to organizations as part of supply chains within the context of Saudi Arabia. Hence, a new technology adoption model “Technology-Supply Chain-Environment” (TSE) is developed that not only takes the technological, supply chain, and environmental variables into consideration but also considers the moderating impact of cultural and other variables on the adoption of cloud computing technology.
    A sequential explanatory mixed methodology was employed to achieve the objective of this study. In quantitative phase, data were collected through a self-administered online questionnaire based survey that generated 303 valid responses from mid-to-senior level decision-making supply chain practitioners from a range of organisations belonging to Saudi petrochemical supply chain. The derived research questions were tested using various data analysis techniques including principal component analysis and structural equation modelling. During the qualitative phase, 48 semi-structured interviews were conducted to have deeper understanding of the results from the earlier phase. Further to the semi-structured interviews, a focus group discussion session with nine experts from industry and academics was also conducted to understand the impact of cultural variables on the adoption of cloud technology in the same context.
    The relationships of independent and dependent variables were simultaneously tested on both behavioural intention and direct adoption. The results indicated that some variables were strongly related to intention to adopt while some others were strongly related to direct adoption. Security concerns, facilitating conditions, trading partner power, and complexity significantly and positively affected intentions to adopt cloud technology. Relative advantage, compatibility, and behavioural intentions significantly and positively affected adoption of cloud technology. Top management support and trading partner readiness were not found significant predictors of either behavioural intention or direct adoption of cloud technology. Trading partner power was the most significant factor for the intention to adopt and also to direct adoption of cloud technology. The proposed model explained 67% of the variance in the intentions and 57% in the direct adoption of cloud technology.
    This research focused only one industry, Saudi petrochemical industry. Further research would be required to apply similar models on different industries. The findings reveal the important role of cloud computing service providers to enable end-users to better evaluate the use of cloud computing. It also reveals that top management support is no longer a driver as organisations are starting to adopt cloud computing services on the basis of flexible and more agile IT resources in order to support business growth and hence the whole supply chain.
    The TSE model is the first of its kind in which supply chain variables are integrated and it is hoped that this model will open up the way for future research in constructing new models for technology adoption within supply chains.
    Date of AwardJul 2017
    Original languageEnglish
    Awarding Institution
    • University of Portsmouth
    SupervisorAlessio Ishizaka (Supervisor), Martyn Roberts (Supervisor) & Martin Read (Supervisor)

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