741 publications from this institution
Multi-tier sustainable supply chain management investigation is in its relative infancy. The design and implementation of studies in this field need careful consideration. This paper uses a relationship perspective to examine three-tier levels of supply chains. Specifically, the paper introduces and supports the use of various antecedent factors for effective sustainable adoption of lower tier (third-tier) companies or sub-suppliers. A study design and analytical methodology, using rough set theory and a clustering approach, is introduced to identify relationships amongst the three levels. Data acquisition is currently in process in a developing country, Egypt, in the textile industry to evaluate the relationships and develop theory. Initially, in this paper an illustrative set of data is used to illustrate the effectiveness and novelty of the design and approach to arrive at some potential theoretical relationships. The approach and methodology can be useful to researchers seeking to do general supply chain and sustainability research in multi-tier settings; or other complex research that can benefit from simplified and exploratory empirical quantitative research.
Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalize the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.
