Irina Harris

Understanding consumer heterogeneity is crucial for analysing attitude formation and its role in innovation diffusion. Traditional top-down models struggle to reflect the nuanced characteristics and activities of the consumer population, while bottom-up approaches like agent-based modelling (ABM) offer the ability to simulate individual decision-making in social networks. However, current ABM applications often lack a strong theoretical foundation. This study introduces a novel, theory-driven ABM framework to examine the heterogeneity of consumer attitude formation, focusing on electric vehicle (EV) adoption across consumer segments. The model incorporates non-linear decision-making rules grounded in established consumer theories, incorporating Rogers’s Diffusion of Innovations, Social Influence Theory, and Theory of Planned Behaviour. The consumer agents are characterised using UK empirical data, and are segmented into early adopters, early majority, late majority, and laggards. Social interactions and attitude formation are simulated, micro-validated, and optimised using supervised machine learning (SML) approaches. The results reveal that early adopters and early majority are highly responsive to social influences, environmental beliefs, and external events such as the pandemic and the war conflict in performing pro-EV attitudes. In contrast, late majority and laggards show more stable or delayed responses. These findings provide actionable insights for targeting segments to enhance EV adoption strategies.

Background: One of the ongoing challenges in freight transport operations is to balance efficiency, effectiveness, and sustainability through the integration of sustainable practices to minimize the environmental impact. When it comes to truck payload and sustainability, the emphasis is on optimizing space, and minimizing empty miles and the wastage of resources. Ensuring that truck loads meet their targets has many challenges, and our empirical research examines the factors influencing the payloads of recycled fibre across the network in the UK paper industry. Methods: A mixed method approach includes interviews, business process analysis, the identification of opportunity areas, a site visit, simulation, and viability analysis to assess factors as part of the sustainable solution. Results: The research identified aspects related to processes, data availability and fragmentation, consistent procedures, practices, and operational considerations. Refining cage-loading procedures, enhancing baling processes and the visibility of upstream processes, and establishing robust information-sharing mechanisms improve efficiency and support sustainability. Conclusions: The empirical research extends the knowledge related to freight efficiency movements on the road and focuses on practical actions in utilizing recycled fibre’s carrying capacity.

Purpose: Vendor selection is the main activity in a sourcing decision, which is a strategic decision in that it leads enterprises to eliminate costs and improve their performance. However, an inappropriate selection may compromise the financial and operational status of the enterprise. But vendor selection is a complex, multi-criteria decision-making process because different and conflicting criteria have to be considered and assessed in order to find consistent suppliers. Consequently, evaluating and selecting the best vendor is the key to successful business. Traditionally, vendors are normally selected on the basis of traditional criteria (TC), such as costs and quality, neglecting resilience criteria (RC) (e.g. agility and flexibility). Thus, enterprises ultimately realize that a selecting method which involves TC as the only ones is inefficient and needs to be changed. The paper aims to discuss this issue.Design/methodology/approach: This study was set in motion by a problem in practice. It aims to provide a user-friendly decision-making tool for selecting the best vendor from a group which submitted their tenders for implementing a proposed radio frequency identification (RFID)-based passport tracking system (Dukyilet al., 2017). The main traditional and resilience (“trasilience” henceforth) selection criteria were identified in a unified framework in collaboration with experts in the institution. Next, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) algorithm was used to determine the relative importance of each criterion and the weights thus obtained were integrated into the ELimination Et Choix Traduisant la REalité (ELECTRE) algorithm. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm was also applied, to evaluate the performance of vendors and to select the best one. The qualitative evaluation of the criteria and the vendors was based on four decision makers. Finally, the Spearman’s rank correlation coefficient (SRCC) approach was applied to obtain the statistical difference between the ranking orders obtained from the two algorithms.Findings: The efficiency of the proposed decision-making tool was evident from the real-case study of six tenders submitted for implementing a RFID-based passport tracking system. The SRCC also turned out a “very strong” association value between TOPSIS and ELECTRE.Practical implications: The developed trasilient decision-making tool can easily be used to solve similar vendor or supplier selection problem. Moreover, other criteria can be added to fit other cases. Later, the tool was made available to the institution under study for solving future evaluation problems.Originality/value: The literature shows that none of the previous papers presented an integrated trasilient approach that considers RC and TC simultaneously. This study presents a new trasilience tool for selecting a vendor.