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Improving the performance of the operations of ride-hailing platforms (RHPs) by adequately considering drivers’ switching behaviors is becoming crucial for their profitability and sustainability. This study explores how to optimize the operations of RHPs by investigating the impact of commission rates on drivers’ switching behaviors in a dynamic mobility market. Two queue-theory-based mathematical models have been developed to explore the relationship between commission rates, drivers’ switching behaviors, and critical platform parameters in optimizing the operations of RHPs. Numerical examples are presented to demonstrate the applicability of such models in determining the best commission rate to optimize the operations of RHPs in duopoly and fully competitive market conditions. The findings suggest that understanding the intricate relationship between commission rates, drivers’ switching behaviors, and critical platform parameters is significant for RHPs in formulating appropriate strategies and policies to ensure their sustainable operations.

Pedestrian behaviour and safety are essential components of urban sustainability. They are influenced by a complex interplay between various factors from different perspectives, particularly in culturally and linguistically diverse (CALD) communities. This study presents a comprehensive overview of the factors influencing pedestrian behaviour and safety with a focus on CALD communities. By synthesizing the existing literature, the study identifies six key groups of influencing factors: social–psychological, cultural, risk perceptions, environmental, technological distractions, and demographic differences. It discovers that well-designed interventions, such as tailored education campaigns and programs, may effectively influence pedestrian behaviour. These interventions emphasize the importance of targeted messaging to address specific risks (e.g., using mobile phones while crossing the road) and engage vulnerable groups, including children, seniors, and CALD communities. The study reveals that CALD communities face higher risks of pedestrian injuries and fatalities due to language barriers, unfamiliarity with local road rules, and different practices and approaches to road safety due to cultural differences. This study underlines the importance of developing and promoting tailored road safety education programs to address the unique challenges faced by CALD communities to help promote safer pedestrian environments for all.

Evaluating and selecting appropriate contractors is critical to the success of specific construction projects in the building industry. Existing approaches for addressing this problem are unsatisfactory due to the ignorance of the multi-dimensional nature of the evaluation process and inappropriate consideration of existent risks. This study presents a DEA game cross-efficiency model with loss aversion for evaluating and selecting specific contractors. The competitiveness of the evaluation process is modeled using game theory with respect to the adoption of the cross-efficiency model. The attitude of the decision maker toward risks is tackled with the use of loss aversion, which is a phenomenon formalized in prospect theory. As a result, the proposed approach can adequately screen available contractors through prequalification and adequately consider the attitude of the decision maker toward risks, leading to effective decisions being made. An example is presented to demonstrate the applicability of the proposed model in evaluating and selecting appropriate contractors for specific construction projects. The results show that the proposed model is effective and efficient in producing a unique solution for contractor selection through appropriate modeling of the multi-dimensional contractor selection process and adequate consideration of the competition between the contractors and the attitude of the decision maker toward risks in practical situations.

Existing approaches to sustainability assessment are typically characterized as being either “top–down” or “bottom–up.” While top–down approaches are commonly adopted by businesses, bottom–up approaches are more often adopted by civil society organizations and communities. Top–down approaches clearly favor standardization and commensurability between other sustainability assessment efforts, to the potential exclusion of issues that really matter on the ground. Conversely, bottom–up approaches enable sustainability initiatives to speak directly to the concerns and issues of communities, but lack a basis for comparability. While there are clearly contexts in which one approach can be favored over another, it is equally desirable to develop mechanisms that mediate between both. In this paper, we outline a methodology for framing sustainability assessment and developing indicator sets that aim to bridge these two approaches. The methodology incorporates common components of bottom–up assessment: constituency-based engagement processes and opportunity to identify critical issues and indicators. At the same time, it uses the idea of a “knowledge base,” to help with the selection of standardized, top–down indicators. We briefly describe two projects where the aspects of the methodology have been trialed with urban governments and communities, and then present the methodology in full, with an accompanying description of a supporting software system.

PurposeThis study aims to explore the adoption of contact tracing apps through a hybrid analysis of the collected data using structural equation modelling (SEM) and artificial neural networks (ANN), leading to the identification of the critical determinants for the adoption of contact tracing apps in Australia.Design/methodology/approachA research model is developed within the background of the unified theory of acceptance and use of technology (UTAUT) and the privacy calculus theory (PCT) for investigating the adoption of contact tracing apps. This model is then tested and validated using a hybrid SEM-ANN analysis of the survey data.FindingsThe study shows that effort expectancy, perceived value of information disclosure and social influence are critical for adopting contact tracing apps. It reveals that performance expectancy and perceived privacy risks are indirectly significant on the adoption through the influence of perceived value of information disclosure. Furthermore, the study finds out that facilitating condition is insignificant to the adoption of contact tracing apps.Practical implicationsThe findings of the study can lead to the formulation of targeted strategies and policies for promoting the adoption of contact tracing apps and inform future epidemic control for better emergency management.Originality/valueThis study is the first attempt in integrating UTAUT and PCT for exploring the adoption of contact tracing apps in Australia. It combines SEM and ANN for analysing the survey data, leading to better understanding of the critical determinants for the adoption of contact tracing apps.

PurposeUnderstanding the privacy concerns of individuals in the adoption of contact tracing apps is critical for the successful control of pandemics like COVID-19. This paper explores the privacy paradox in the adoption of contact tracing apps in Australia.Design/methodology/approachA comprehensive review of the related literature has been conducted, leading to the development of a conceptual model based on the privacy calculus theory and the antecedent-privacy concern-outcome framework. Such a model is then tested and validated using structural equation modelling on the survey data collected in Australia.FindingsThe study shows that perceived benefit, perceived privacy risk and trust have significant influences on the adoption of contact tracing apps. It reveals that personal innovativeness and trust have significant and negative influences on perceived privacy risk. The study further finds out that personal innovativeness is insignificant to perceived benefit. It states that perceived ease of use has an insignificant influence on perceived privacy risk in the adoption of contact tracing apps.Originality/valueThis study is the first attempt to use the privacy calculus theory and the antecedent–privacy concern–outcome framework for exploring the privacy paradox in adopting contact tracing apps. This leads to a better understanding of the privacy concerns of individuals in the adoption of contact tracing apps. Such an understanding can help formulate targeted strategies and policies for promoting the adoption of contact tracing apps and inform future epidemic control through effective contact tracing for better emergency management.

This paper presents a fuzzy approach for ranking discrete alternatives in multi-attribute decision-making under uncertainty. Linguistic variables approximated by fuzzy numbers were applied for facilitating the making of pairwise comparison by the decision maker in determining the alternative performance and attribute importance using fuzzy extent analysis. The resultant fuzzy assessments were aggregated using the simple additive utility method for calculating the fuzzy utility of each alternative across all the attributes. An ideal solution-based procedure was developed for comparing and ranking these fuzzy utilities, leading to the determination of the overall ranking of all the discrete multi-attribute alternatives. An example is provided that shows the proposed approach is effective and efficient in solving the multi-attribute decision making problem under uncertainty, due to the simplicity and comprehensibility of the underlying concept and the efficiency and effectiveness of the computation involved.

This paper presents a fuzzy multi-criteria group decision making method for evaluating the performance of Cloud services in an uncertain environment. Intuitionistic fuzzy numbers are used to better model the subjectivity and imprecision in the performance evaluation process. An effective algorithm is developed based on the technique for order preference by similarity to the ideal solution and the Choquet integral operator for adequately solving the performance evaluation problem. An example is presented for demonstrating the applicability of the proposed method for solving the multi-criteria group decision making problem in real situations.

Purpose The purpose of this study is to investigate how digital technologies are used for facilitating knowledge sharing and decision-making through enhanced coordination and communication and their impact on job performance in organizations. Design/methodology/approach A conceptual model is developed within the background of the social capital theory through a comprehensive review of the related literature for exploring how digital technologies can improve knowledge sharing and decision-making via enhanced communication and coordination between individuals in organizations for better job performance. This model is then tested and validated based on structural equation modeling of the collected survey data in Australia. Findings This study shows that digital technology enhanced coordination and communication have significant impact on knowledge sharing. It finds out that digital technology driven coordination significantly influences decision-making and digital technology driven knowledge sharing significantly influences decision-making. Furthermore, this study reveals that enhanced decision-making and knowledge sharing can lead to better job performance in organizations. Originality/value To the best of the authors’ knowledge, this study is the first attempt to explore the role of digital technologies in enhancing knowledge sharing and decision-making for better job performance in a digitalized working environment in organizations. The validated model can be used as the foundation to further investigate the changing role of digital technologies in driving knowledge sharing for better performance of individuals and competitive advantages of organizations.

There are increasing challenges for optimally distributing fresh products while adequately considering the uncertain demand of customers and maintaining the freshness of products. Taking the nature of fresh products and the characteristics of urban logistics systems into consideration, this paper proposes an improved genetic algorithm for effectively solving this problem in a computationally efficient manner. Such an algorithm can adequately account for the uncertain demand of customers to select the optimal distribution route to ensure the freshness of the product while minimizing the total distribution cost. Iterative optimization procedures are utilized for determining the optimal route by reducing the complexity of the computation in the search for an optimal solution. An illustrative example is presented that shows the improved algorithm is more effective with respect to the distribution cost, the distribution efficiency, and the distribution system’s reliability in optimally distributing fresh products.