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The influence of online review adoption on supply chain performance: a hybrid simulation study

Huang, Shupeng 2021. The influence of online review adoption on supply chain performance: a hybrid simulation study. PhD Thesis, Cardiff University.
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Abstract The development of E-commerce leads to the popularity of online review adoption. Customers who purchase online can seek information about products in online reviews posted by others. Although online reviews have become a norm, research mainly focuses on their value from marketing perspectives, with fewer studies linking online reviews to supply chain management. As supply chain management is vitally important to E-commerce, this thesis aims to examine the influence of adopting online reviews on supply chain management. This thesis first conducted a systematic literature review and summarised the mechanisms explaining how adopting online reviews can influence supply chain performance. After that, the thesis followed a positivism research paradigm and conducted hybrid simulations (system dynamics and agent-based modelling) to compare if the influence of online reviews varies between different supply chain configurations. Specifically, the thesis first built a forward uncapacitated supply chain model as a base to investigate how online review adoption can influence supply chain performance. After that, this base model was extended to two further supply chain configurations. One studied the influence of adopting online reviews under the effect of supply chain capacity constraint which is an important factor for production and scheduling in supply chains. Also, the model was extended to consider closed-loop supply chains, given the significance of returns in E-commerce operations. In each scenario, the simulation experiments were conducted in RStudio and analysed by ANalysis Of VAriance (ANOVA). This thesis found that the mechanisms by which online reviews can influence supply chain performance are twofold. The adoption of online reviews can enhance the supply chain communication efficiency and effectiveness. Their adoption can also increase the sensing capabilities of the company. The former mechanism is called ‘connecting-tool mechanism’ while the latter is called ‘data-source mechanism’. This thesis then focused on the connecting-tool mechanism and compared its realisation between different supply chain configurations. In the base model, the adoption of online reviews mainly influenced supply chain demand and revenue, directly determining the supply chain performance. However, when capacity constraints and reverse product flows were considered into the model, the influence of online review adoption became complicated. The results indicated that not only supply chain revenue but also the lost sales and the cost of the reverse supply chain can be affected by online review adoption, and such compound influences from online reviews were contingent on the different contextual factors and led to nonlinear and diverse impacts on supply chain performance. The findings in this thesis contributed to the understanding on how online reviews can influence supply chain performance, enabling researchers to gain deeper insights into the causal relationships between online reviews and supply chain operations. In addition, a generic modelling framework called OR-SCM framework was proposed, providing guidance of mathematical models in this field. For practical implication, the thesis results raised the awareness of complexity in the consequences of online review adoption in supply chains for managers and informed them that, to make better decisions and improve supply chain performance, the adoption decisions of online reviews should be made based on different contextual factors related to supply chains.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Business (Including Economics)
Subjects: H Social Sciences > H Social Sciences (General)
Uncontrolled Keywords: online reviews, supply chain, inventory management, system dynamics, agent-based modelling, hybrid simulation, R
Last Modified: 10 Dec 2022 02:31

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