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To meet environmental performance (EP) goals, this study aims to identify the complex interaction between green Industrial Internet of Things (GIIoT), circular economic practices (CEPs) and dynamic capabilities (DC). This study analyzes how technological, operational and cultural compatibilities enhance GIIoT adoption.

Data were collected from diverse Chinese manufacturing firms (n = 339) through a quantitative survey. The research model was proposed, and hypotheses were tested using structural equation modeling. Moreover, the robustness of the structural model is further tested using Fuzzy Set Qualitative Comparative Analysis and importance performance map analysis.

The empirical results indicate that higher organizational compatibilities boost GIIoT adoption and EP. DC was assessed as a higher-order construct to examine its mediation of GIIoT adoption and EP. DC positively mediates GIIoT adoption-EP. Similarly, CEP’s positive impact on EP, partially mediating the relationship between GIIoT adoption and EP, has also been proved.

This research bridges current understanding and contributes useful insights for fostering environmental sustainability inside manufacturing firms and advances the theoretical understanding of technology adoption, sustainable development and dynamic capacity theory. It illuminates the way forward to harmonize and successfully integrate technology, CEP and EP. This research advances the area and gives decision-makers practical advice for creating sustainable and technologically sophisticated organizations.

The prevalence of digital reading and the widespread use of social media among young people demands systematic exploration of the effects of social media addiction on students' reading practice. This paper aims to explore the effects of social media addiction on reading preferences, in-depth reading and sustained attention.

Survey and analysis methods are employed.

For many, social media provides an ideal platform of connection and expression; however, prolonged social media use holds the danger of becoming a behavioral addiction that threatens to undermine one's reading practice. Social media use tends to have a more significant impact on leisure reading than on academic reading. Obsessive engagement with social media hurts reading concentration and in-depth reading. While a majority (70.4%) of those surveyed believed that chronic social media use carries more harm than good on their learning, only half (50.1%) agreed or strongly agreed that today's students are too indulged in social media and need forceful control of it.

Implications of the effects of social media on reading practice are discussed, and directions for future research are suggested. It is likely that social media will continue to be seductive, attracting new generations of young people. Future research should explore prevention strategies.

This study investigates whether and how chief executive officers (CEOs) with personal risk-taking preference (expressed in owning a pilot license) will act differently when they are vested with additional power serving as board chairs.

Regressions analyses are performed using a sample of Standard and Poor’s (S&P) 1,500 firms with available data during 1996–2009. CEO's risk-taking outcomes are measured using firms' total risk, idiosyncratic risk and research and development expenditures (R&D) investment.

Firms led by pilot CEOs have greater firm risks, yet CEO duality attenuates the relationship. Further channel tests show that CEO duality suppresses CEO's risk-taking tendencies through managers' reputation concerns.

The findings highlight the importance of incorporating human factors into consideration of appropriate governance structures for a firm. Future studies can expand the existing data and further explore the relationship between human factors and governance structures on other firm strategies.

Regulators may focus mainly on regulatory setting based on the “best practice” of governance yet overlook human influence in corporate dynamics. For shareholders, hiring managers with distinct styles will change corporate outcomes but different governance mechanisms could be devised to adapt to CEOs with various personalities.

Prior studies show that both CEO personal preferences and firms' governance structure affect corporate policies, and this paper complements prior studies by exploring how the two may interact to shape corporate policy and its outcomes. This paper also adds to the literature showing that CEO duality could serve a disciplinary role.

The purpose of this paper is to create the inventory scheduling model for supply chain system.

Aiming at the problems of analysis and modeling of multi‐agent system, an agent‐oriented Petri net (AOPN) model is proposed by introducing the characteristics of agent based on Petri net, and formalization definition of AOPN is presented. Combined with the inventory scheduling in supply chain system, inventory scheduling model based on AOPN is constructed. On the basis of the inventory scheduling model, its program model is designed. Then the reachability of the inventory scheduling model is analyzed and its soundness is verified.

The AOPN model and inventory scheduling model based on AOPN in supply chain system are found.

The reasonable basic function design of various agents are main limitations.

A very useful tool of analysis for the modeling of inventory scheduling in supply chain system.

The paper presents a new modeling method of inventory scheduling in supply chain system. This paper is aimed at researchers and engineers.

This paper aims to optimize the charging schedule for battery electric buses (BEBs) to minimize the charging cost considering the time-of-use electricity price.

The BEBs charging schedule optimization problem is formulated as a mixed-integer linear programming model. The objective is to minimize the total charging cost of the BEB fleet. The charge decision of each BEB at the end of each trip is to be determined. Two types of constraints are adopted to ensure that the charging schedule meets the operational requirements of the BEB fleet and that the number of charging piles can meet the demand of the charging schedule.

This paper conducts numerical cases to validate the effect of the proposed model based on the actual timetable and charging data of a bus line. The results show that the total charge cost with the optimized charging schedule is 15.56% lower than the actual total charge cost under given conditions. The results also suggest that increasing the number of charging piles can reduce the charging cost to some extent, which can provide a reference for planning the number of charging piles.

Considering time-of-use electricity price in the BEBs charging schedule will not only reduce the operation cost of electric transit but also make the best use of electricity resources.

Presents over sixty abstracts summarising the 1999 Employment Research Unit annual conference held at the University of Cardiff. Explores the multiple impacts of globalization on work and employment in contemporary organizations. Covers the human resource management implications of organizational responses to globalization. Examines the theoretical, methodological, empirical and comparative issues pertaining to competitiveness and the management of human resources, the impact of organisational strategies and international production on the workplace, the organization of labour markets, human resource development, cultural change in organisations, trade union responses, and trans‐national corporations. Cites many case studies showing how globalization has brought a lot of opportunities together with much change both to the employee and the employer. Considers the threats to existing cultures, structures and systems.

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods.

This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems.

This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies.

This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

Underground logistics system (ULS) is recognized as sustainable alleviator to road-dominated urban logistics infrastructure with various social and environmental benefits. The purpose of this study is to propose effective modeling and optimization method for planning a hub-and-spoke ULS network in urban region.

Underground freight tunnels and the last-mile ground delivery were organized as a hierarchical network. A mixed-integer programming model (MIP) with minimum system cost was developed. Then a two-phase optimization schema combining Genetic-based fuzzy C-means algorithm (GA-FCM), Depth-first-search FCM (DFS-FCM) algorithm and Dijkstra algorithm (DA), etc. was designed to optimize the location-allocation of ULS facilities and customer clusters. Finally, a real-world simulation was conducted for validation.

The multistage strategy and hybrid algorithms could efficiently yield hub-and-spoke network configurations at the lowest objective cost. GA-FCM performed better than K-means in customer-node clustering. The combination of DFS-FCM and DA achieved superior network configuration than that of combining K-means and minimum spanning tree technique. The results also provided some management insights: (1) greater scale economies effect in underground freight movement could reduce system budget, (2) changes in transportation cost would not have obvious impact on ULS network layout and (3) over 90% of transportation process in ULS network took place underground, giving remarkable alleviation to road freight traffic.

Demand pairs among customers were not considered due to lacking data. Heterogeneity of facilities capacity parameters was omitted.

This study has used an innovative hybrid optimization technique to address the two-phase network planning of urban ULS. The novel design and solution approaches offer insights for urban ULS development and management.

This study aims to obtain the more precise decision map to fuse the source images by Coefficient significance method. In the area of multifocus image fusion, the better decision map is very important the fusion results. In the processing of distinguishing the well-focus part with blur part in an image, the edge between the parts is more difficult to be processed. Coefficient significance is very effective in generating the better decision map to fuse the multifocus images.

The energy of Laplacian is used in the approximation coefficients of redundant discrete wavelet transform. On the other side, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient.

Due to the shift-variance of the redundant discrete wavelet and the effectiveness of fusion rule, the presented fusion method is superior to the region energy in harmonic cosine wavelet domain, pixel significance with the cross bilateral filter and multiscale geometry analysis method of Ripplet transform.

In redundant discrete wavelet domain, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient of source images.