Search results

This study embarks on a comprehensive investigation into the intricate relationship between consumer trust in e-commerce platforms and the adoption of Green Supply Chain Management (GSCM). It delves into a multifaceted analysis of how these dynamics influence the landscape of online shopping, with a specific focus on four critical dimensions: the efficiency of online purchasing processes, the fulfillment of product delivery commitments, the convenience associated with e-platform utilization, and the safeguarding of consumers' personal information.

This research employs sophisticated Structural Equation Modeling (SEM) techniques, facilitated by SPSS and SmartPLS software, to meticulously analyze the amassed data and subject the formulated hypotheses to rigorous testing. The empirical foundation of this study draws from a sample of 377 randomly selected online shoppers, providing a robust basis for its insights.

At its core, this research is squarely focused on unraveling the dynamics of consumer trust within e-commerce platforms and highlighting the pivotal role played by GSCM in making online shopping more ecologically responsible and sustainable. Of paramount importance is the novel dimension introduced by this study the integration of trust in e-commerce platforms, GSCM practices, and the multifarious dimensions of online shopping all within a unified conceptual framework. Trust on e-commerce platforms leads to GSCM. GSCM determines online shopping satisfaction, i.e. efficiency, fulfillment, convenience, and privacy. Finally, GSCM mediates between trust on e-commerce platforms and online shopping satisfaction.

This holistic approach represents a ground-breaking contribution to the existing body of literature. It presents a fresh perspective on the intricate interactions that define the contemporary e-commerce landscape.

This initial research integrates trust in e-commerce platforms, GSCM, and online shopping in a single framework through UTAUT2.

In situations where the crew is reduced, the optimization of crew task allocation and sequencing (CTAS) can significantly enhance the operational efficiency of the man-machine system by rationally distributing workload and minimizing task completion time. Existing related studies exhibit a limited consideration of workload distribution and involve the violation of precedence constraints in the solution process. This study proposes a CTAS method to address these issues.

The method defines visual, auditory, cognitive and psychomotor (VACP) load balancing objectives and integrates them with workload balancing and minimum task completion time to ensure equitable workload distribution and task execution efficiency, and then a multi-objective optimization model for CTAS is constructed. Subsequently, it designs a population initialization strategy and a repair mechanism to maintain sequence feasibility, and utilizes them to improve the non-dominated sorting genetic algorithm III (NSGA-III) for solving the CTAS model.

The CTAS method is validated through a numerical example involving a mission with a specific type of armored vehicle. The results demonstrate that the method achieves equitable workload distribution by integrating VACP load balancing and workload balancing. Moreover, the improved NSGA-III maintains sequence feasibility and thus reduces computation time.

The study can achieve equitable workload distribution and enhance the search efficiency of the optimal CTAS scheme. It provides a novel perspective for task planners in objective determination and solution methodologies for CTAS.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

Fully-actuated unmanned aerial vehicles (UAVs) are a growing and promising field of research, which shows advantages for aerial physical interaction. The purpose of this paper is to construct a force sensor-denied control method for a fully-actuated hexarotor to conduct aerial interaction with accurate force exerted outward.

First, by extending single-dimension impedance model to the fully-actuated UAV model, an impedance controller is designed for compliant UAV pose/force control. Then, to estimate the interaction force between UAV end-effector and external environment accurately, combined with super-twisting theory, a nonlinear force observer is constructed. Finally, based on impedance controller and estimated force from observer, an interaction force regulation method is proposed.

The presented nonlinear observer-based impedance control approach is validated in both simulation and environments, in which the authors try to use a fully-actuated hexarotor to accomplish the task of aerial physical interaction finding that a specified force is able to be exerted to environment without any information from force sensors.

A solution of aerial physical interaction for UAV system enabling accurate force exerted outward without any force sensors is proposed in this paper.

This paper aims to investigate how perceived working conditions affect employee performance, including safety compliance and task performance, through employee well-being (i.e. job satisfaction) in the context of the coal mining sector in China.

This paper uses the job demands-resources model to test the relationships between working conditions, including job demands (work pressure as a challenge demand and perceived risks and hazards in the workplace and ineffectiveness of the safety system as hindrance demands), job resources (interpersonal harmony), job satisfaction and performance. This study adopts a two-wave design with a three-month lag to reduce possible common method bias.

Employees who experienced high level of challenge demands, e.g. time pressure workload, reported higher levels of task performance, and this positive relationship seemed to be robust. There is a direct effect of perceived ineffectiveness of the safety system on task performance, while the relationship between perceived risks and hazards and task performance was fully mediated by job satisfaction. Challenge demands, i.e. work pressure, did not impact much on employees’ well-being, and thus job satisfaction did not mediate the relationship between work pressure and performance. Perceived ineffectiveness of the safety system was negatively associated with safety compliance. This result is not surprising since a lack of effective safety system reflects management’s ignorance of workplace safety, which demotivates employees to enact safe behaviors. In contrast, the presence and implementation of an effective safety system would be interpreted by employees as management exhibiting a high level of commitment. Work pressure was positively not negatively related to safety compliance. One possible explanation for this finding is that the effects of work pressure on safety compliance behaviors might be dependent on contextual factors such as safety climate. Interpersonal harmony moderated the relationships between work pressure and employee performance (both safety compliance and task performance) and the relationship between perceived risks and hazards and task performance, but the role of interpersonal harmony appeared more complex. There was no significant correlation between challenging job demands and individual employee performance when there were higher levels of interpersonal harmony. The relationship between perceived risks and hazards, a hindrance job demand and task performance became positive as interpersonal harmony increased but negative as interpersonal harmony decreased.

This paper provides a robust integrative theoretical framework that better explains the various types of job demands and job resources in the working environment of coal mining sector in China and their relationships to employee performance. The findings also offer valuable guidance for managers trying to identify effective ways to enhance employee performance and safety in the workplace.

This study examines the relationship between higher education improvement and tacit knowledge importance. In this context, the scarcity of empirical and theoretical studies on acquiring tacit knowledge to enhance academic performance in higher education suggests that this research area holds significant importance for experts and policymakers. Consequently, this study aims to explore the factors that influence academic research performance at Chinese universities by acquiring tacit knowledge.

To achieve the study aims, the current approach utilizes the research technique based on the socialization, externalization, internalization and combination (SECI) model and knowledge management (KM) theory. To analyze the study objective, the authors collected data from post-graduate students at Chinese universities and analyzed it using structural equation modeling (SEM) to test the model and hypotheses.

The results indicated that social interaction, internalization and self-motivation have a positive impact on academic research performance through the acquisition of tacit knowledge. Furthermore, the findings suggest that academic researchers can acquire more knowledge through social interaction than self-motivation, thereby advancing research progress.

This study addresses the critical issues surrounding the acquisition of tacit knowledge and presents a comprehensive framework and achievements that can contribute to achieving exceptional academic performance.

The current study focuses on the relationship between total quality management (TQM) and sustainable performance (SP) and examines how TQM practices can facilitate firms' achievement of sustainable performance. Knowledge management (KM), with its four dimensions, i.e. knowledge creation (KCR), knowledge acquisition (KAC), knowledge sharing (KSH) and knowledge application (KAP), is also an essential factor for organizations. Therefore, this study also focuses on the mediating role of KM in the relationship between TQM and sustainable performance.

This study used a survey method to collect data from the managers of 485 manufacturing SMEs working in five major industrial cities in Pakistan. Collected data were analyzed through PLS-SEM with the help of smart-PLS.

The study's findings reveal that TQM practices positively influence the environmental and economic sustainability of the firm. At the same time, there is no evidence that TQM practices positively affect the social sustainability of the firm. Results further elaborate that TQM practices significantly affect all four dimensions of KM. Moreover, KM positively affects the two dimensions of SP, i.e. economic and social sustainability, but surprisingly, the impact of KM on environmental sustainability is not found. Finally, results indicate the significant mediating role of KM between TQM and SP.

This study contributes to bridging research gaps in the literature and advances how TQM, directly and indirectly, helps firms improve sustainable performance via the mediating role of KM.

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

The purpose of this research is to determine how supply chain management (SCM) might be less affected by COVID-19 by using innovative technologies such as the Internet of Things (IoT), eco-friendly corporate practices and other digital advancements. It strongly emphasizes the use of technology to improve supply networks’ and Omani firms’ performance.

Using a mixed-methods research strategy, this study integrates both qualitative and quantitative approaches. It involves a survey and interviews with supply chain and IT managers from various industries in Oman to gather data and evaluate the impact of technology on SCM.

This study finds that IoT capabilities, smart technologies (STs) and green practices significantly mitigate COVID-19 impacts on SCM. The performance of the supply chain and the business are both improved by these technologies’ positive effects on integrating various supply chain elements, such as suppliers, internal processes and customer relations.

The main constraint of this study is its concentration on businesses in Oman, potentially restricting the applicability of its findings to broader contexts. Future studies could investigate similar frameworks across various geographic and industry settings.

The findings suggest that incorporating STs into SCM is crucial for enhancing operational efficiency and resilience against disruptions such as COVID-19. This offers valuable insights for managers and policymakers in adopting technology-driven strategies for SCM.

This study highlights the significant role of technology in sustaining supply chains during pandemics, thereby supporting economic stability and societal well-being. It underscores the importance of technological advancements in maintaining supply chain continuity in challenging times.

By empirically examining the effect of emerging technologies on enhancing SCM in the context of the COVID-19 pandemic, specifically in the Oman market, this research makes a unique contribution to the body of knowledge.