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While the metaverse is promised to be the next big step for the Internet, this new technology may also bear negative impacts on individuals and society. Drawing on innovation resistance literature, this article explores the reasons for metaverse resistance.

The study is based on 66 semi-structured interviews, and the subsequent data were analysed thematically.

The findings revealed 11 reasons for metaverse resistance: lack of understanding, lack of regulation, addiction avoidance, claustrophobia, loss of social ties, disconnection from reality, privacy concerns, extreme consumer society, unseen benefits, infeasibility and nausea.

By understanding the various reasons for metaverse resistance managers and policymakers can make better decisions to overcome the challenges facing this innovation, rather than adopting a “one-size-fits-all” approach.

While the literature has mainly adopted a positive perspective on the metaverse, this research offers a more nuanced view by identifying the reasons why consumers may resist the metaverse. Furthermore, this study introduces for the first-time “addiction-driven-innovation-resistance (ADIR)” as a potential reason for metaverse resistance, which may also apply to other cases of innovation resistance, when new innovations are perceived as being “too good” and therefore potentially addictive.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

Using a real-time road network combined with historical traffic data for Al-Salt city, the paper aims to propose a new federated genetic algorithm (GA)-based optimization technique to solve the dynamic vehicle routing problem. Using a GA solver, the estimated routing time for 300 chromosomes (routes) was the shortest and most efficient over 30 generations.

In transportation systems, the objective of route planning techniques has been revised from focusing on road directors to road users. As a result, the new transportation systems use advanced technologies to support drivers and provide them with the road information they need and the services they require to reduce traffic congestion and improve routing problems. In recent decades, numerous studies have been conducted on how to find an efficient and suitable route for vehicles, known as the vehicle routing problem (VRP). To identify the best route, VRP uses real-time information-acquired geographical information systems (GIS) tools.

This study aims to develop a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences.

Furthermore, developing a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. An adaptive genetic algorithm (GA) is used to determine the optimal time route, taking into account factors that affect vehicle arrival times and cause delays. In addition, ArcGIS' Network Analyst tool is used to determine the best route based on the user's preferences using a real-time map.

In regarding to operational efficiency and safety improvements, multiple tower crane service scheduling problem is one of the main problems related to tower crane operation but receives limited attention. The current work presents an optimization model for scheduling multiple tower cranes' service with overlapping areas while achieving collision-free between cranes.

The cooperative coevolutionary genetic algorithm (CCGA) was proposed to solve this model. Considering the possible types of cross-tasks, through effectively allocating overlapping area tasks to each crane and then prioritizing the assigned tasks for each crane, the makespan of tower cranes was minimized and the crane collision avoidance was achieved by only allowing one crane entering the overlapping area at one time. A case study of the mega project Daxing International Airport has been investigated to evaluate the performance of the proposed algorithm.

The computational results showed that the CCGA algorithm outperforms two compared algorithms in terms of the optimal makespan and the CPU time. Also, the convergence of CCGA was discussed and compared, which was better than that of traditional genetic algorithm (TGA) for small-sized set (50 tasks) and was almost the same as TGA for large-sized sets.

This paper can provide new perspectives on multiple tower crane service sequencing problem. The proposed model and algorithm can be applied directly to enhance the operational efficiency of tower cranes on construction site.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

This study aims to examine whether there exists any relationship between corporate biodiversity reporting decision (CBRD) and corporate environmental performance (CEP).

The primary sample contains 442 firm-year observations over a period of 13 years (2008–2020) for 34 listed Finnish companies. Based on both legitimacy theory and voluntary disclosure theory, 2 logit regression models are estimated to test the CBRD–CEP nexus. CBRD is a dichotomous variable. Three proxies for CEP, namely propensity to emit greenhouse gas (GHG), propensity to consume water and propensity to generate waste are employed.

This study finds that firms having higher propensity to consume water and generate waste are inclined to release biodiversity-related information. The findings support legitimacy theory suggesting that firms with inferior environmental performance may decide on reporting biodiversity information for legitimation purpose.

The study uses Finnish data and hence, the results may lack in generalizability to other national contexts.

The results of this study should be valuable to policy makers for formulating mandatory biodiversity reporting standards to ensure disclosure of standard, extensive and authentic biodiversity-related information by companies. The results should also be valuable to corporate managers and eco-friendly investors.

Corporate biodiversity reporting (CBR) is an under-researched area of environmental accounting literature. Using the Finnish context, this paper extends the existing literature by investigating whether any association exists between CBRD and CEP, which has not been examined before.

The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

Commercial entities have recently expressed growing interest in commercialising indigenous knowledge (IK) due to its enormous economic and intrinsic value. As this happens, custodial communities must not be disadvantaged in the process. This paper aims to understand the legal framework of the commercialisation of IK to identify the opportunities and factors impeding or affecting the commercialisation of indigenous knowledge in Kenya.

The study used a qualitative research approach. An extensive exploratory literature review of existing legal instruments was done to establish the progress and gaps for commercialising indigenous knowledge in Kenya.

The study shows that the legal framework of IK in Kenya is inadequate. There are no well-established frameworks and policies to protect IK in Kenya, and thus, host communities are subjected to exploitation. The diversity of tribes and communities makes it challenging to have a clear framework, mainly because IK is a devolved function. The study identifies the Protection of Traditional Knowledge and Cultural Expressions Act 2016, The National Museums and Heritage Act 2006 and the Natural Products Industry as the key milestones towards commercialisation of IK, while inadequate documentation of IK, communal ownership and inadequate legislation were identified as the main impediments to commercialisation of IK in Kenya.

Owing to the diverse cultures and tribal communities in Kenya, the research could not access all the literature on all traditional IK in Kenya, and very few case studies have been conducted in Kenya.

The gaps identified in the legal framework can form a basis for legislation, policy change, actions and research needed to improve the commercialisation of IK.

The paper underscores the importance of balancing economic empowerment with preserving cultural integrity and protecting indigenous rights in commercialisation.

The first purpose is to assess the quality of containerized multimodal export and the second is to develop and demonstrate the design of a service network with quality approach.

The article used the structural equation model to develop a model to measure the quality of multimodal transportation for containerized exports and finalized the model with an alternative approach. The evolutionary algorithm had been used to design a service network based on quality.

Provided factors affecting quality of multimodal transportation and reverse to one hypothesis, the construct variation in cost, time shape and quantity did not affect the quality of multimodal transportation for containerized exports. The model without variation construct was finalized by exploring causality.

This research had scope till container loading onto the vessel and assessed the quality for containerized cargo only, and second research purpose is limited by assumed values of fitness function and the limited number of nodes, in service network design demonstration.

This research provided a tool to measure the quality of multimodal transportation for containerized exports and demonstrated the field application of the model developed in service network design. This approach included all factors applicable across the container movement. The integrated approach of the article provided an organized method to design a service network for containerized exports.

This work provided the tool to assess the quality of multimodal transportation for containerized exports and developed an approach to design a service network of multimodal transportation based on quality. This approach has considered the factors of multimodal transportation comprehensively in contrast to the optimization approaches based on operation research techniques.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.