Ambiguity and its coping mechanisms in supply chains lessons from the Covid-19 pandemic and natural disasters
By Saileshsingh Gunessee, Nachiappan Subramanian. International Journal of Operations & Production Management, 2020
153 citations recorded by the source corpus.
PurposeThe first purpose of this paper is to situate and conceptualise ambiguity in the operations management (OM) literature, as connected to supply chain decision-making (SCDM). The second purpose is to study the role of ambiguity-coping mechanisms in that context.Design/methodology/approachThis research uses the behavioural decision theory (BDT) to better embed ambiguity in a generic SCDM framework. The framework explicates both behavioural and non-behavioural antecedents of ambiguity and enables us to also ground the “coping” mechanisms as individual and organisational level strategies. Properties of the framework are illustrated through two “ambiguous” events – the 2011 Thai flood and Covid-19 pandemic.FindingsThree key findings are documented. First, ambiguity is shown to distinctively affect supply chain decisions and having correspondence with specific coping mechanisms. Second, the conceptual framework shows how individual coping mechanisms can undermine rational-based organisational coping mechanisms, leading to “sub-optimal” (poor) supply chain decisions. Third, this study highlights the positive role of visibility but surprisingly organisational “experiential” learning is imperfect, due to the focus on “similar” past experience and what is known.Originality/valueThe paper is novel in two ways. First, it introduces ambiguity – an often neglected concept in operations management – into the supply chain lexicon, by developing a typology of ambiguity. Second, ambiguity-coping mechanisms are also introduced as both individual and organisational strategies. This enables the study to draw distinctive theoretical and practical implications.
Subfields: Management Information Systems, Strategy and Management
View Paper: https://www.semanticscholar.org/paper/834963373819262833033e155460651118b735ab
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Citations in the corpus
- Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review (2022, background): “To address such disruptions, the literature has highlighted the need to consider resilience of supply chains and to further delve on this concept, rather than restricting the discourse to solely risks (Gunessee & Subramanian, 2020).”
- COVID-19 pandemic related supply chain studies: A systematic review (2021, unknown): “Moreover, unlike other previous outbreaks, this pandemic has impacted all the nodes (supply chain members) and edges (ties) in a supply chain simultaneously (Gunessee and Subramanian, 2020; Paul and Chowdhury, 2020a); hence, the flow of the supply chain has been disrupted substantially.”