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Drawing on the social learning theory, the purpose of this paper is to examine the antecedents and consequences of users’ excessive online social gaming. Specifically, the authors develop a model to propose that observational learning and reinforcement learning mechanisms together determine excessive online social gaming, which further foster adverse consequences.

The model is empirically validated by a longitudinal survey among users of a popular online social game: Arena of Valor. The empirical data are analyzed using component-based structural equation modeling approach.

The empirical results offer two key findings. First, excessive online social gaming is determined by observational learning factors, i.e. social frequency and social norm, and reinforcement learning factors, i.e. perceived enjoyment and perceived escapism. Second, excessive online social gaming leads to three categories of adverse consequences: technology-family conflict, technology-work conflict and technology-person conflict. Meanwhile, technology-family conflict and technology-work conflict further foster technology-person conflict.

This study contributes to the literature by developing a nomological framework of excessive online social gaming and by extending the social learning theory to excessive technology use.

This study proposes an objective assessment model to evaluate the performance of internal and external capabilities of firms. It facilitates firms to invest appropriate resources to cultivate the organizational capability necessary to meet the requirements of the performance indicators.

This study integrates the concepts of resource-based theory, the organizational capability concept, and conduct a performance analysis to the four perspectives of the BSC by implementing the fuzzy set theory and data employment analysis.

The findings show that the appropriate strategies help allocate available resources and capabilities during the different product life cycle, which provides practical guidelines for firms to achieve sustaining competitive advantage.

The selected factors were focused on four resources and capabilities rather than all possible factors.

An objective assessment model was created based on internal and external competitive performance efficiency in this research field. This model facilitates the ability of the top management to make decisions for resource allocation that will enhance firm's performance.

The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps.

Papers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed.

Five main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper.

Procurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty.

The contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems.

Fresh product loss rates in supply chain operations are particularly high due to the nature of perishable products. The purpose of this paper is to maximize profit through the contract between retailer and supplier. The optimized prices for the retailer and the supplier, taking the fresh-keeping effort into consideration, are derived.

To address this issue, the authors consider a two-echelon supply chain consisting of a retailer and a supplier (i.e. wholesaler) for two scenarios: centralized and decentralized decision making. The authors start from investigating the optimal decision in the centralized supply chain and then comparing the results with those of the decentralized decision. Meanwhile, a fresh-keeping cost-sharing contract and a fresh-keeping cost- and revenue-sharing contract are designed. Numerical examples are provided, and managerial insights are discussed at the end.

The results show that the centralized decision is more profitable than the decentralized decision; a fresh product supply chain (FPSC) can only be coordinated through a fresh-keeping cost- and revenue-sharing contract; the optimal retail price, wholesale price and fresh-keeping effort can all be achieved; and the profit of a FPSC is positively related to consumers’ sensitivity to freshness and negatively correlated with their sensitivity to price.

This research is based on the assumption that demand is relatively stable. It has not addressed when demand is stochastic.

The findings would be useful for managers in fresh food sector in terms of how to deal with suppliers in order to maximize total profit while also provide freshest food to the customers.

Few studies have considered fresh-keeping effort as a decision variable in the modelling of supply chain. In this paper, a mathematical model for the fresh-keeping effort and for price decisions in a supply chain is developed. In particular, fresh-keeping cost-sharing contract and revenue-sharing contract are examined simultaneously in the study of the supply chain coordination problem.

The purpose of this paper is to introduce an integrated approach using failure modes and effects analysis (FMEA), multiple-criteria decision making (MCDM), mathematical modeling and quality function deployment (QFD) techniques, for risk assessment and service quality enhancement in coronary artery bypass grafting (CABG) as a treatment for cardiovascular diseases (CVDs).

First, the disruptions in the CABG process are identified and prioritized following FMEA instructions, using two MCDM techniques, called analytic hierarchy process (AHP) and TOPSIS. Consequently, several corrective activities are identified and weighted on the basis of QFD. Finally, a mathematical model is established to determine the most cost-effective activities for implementation. The approach is developed in a fuzzy environment to reflect the uncertainty and ambiguity of human reasoning.

Regarding the CABG process disruption, a total of 30 failure modes in four main categories were identified and prioritized. Moreover, eight corrective activities were devised and ranked according to their impact on the failure modes. Finally, considering a limited amount of budget, a sensitivity analysis on the mathematical model’s objective function indicated that using 30 percent of the total budget, required to implement all corrective activities, was enough to cover more than 70 percent of the effects of corrective activities on the failure modes.

This paper contributes to the quality risk assessment knowledge by introducing an integrated approach to evaluate and improve healthcare services quality. Also, the case study conducted on the CABG process has not been done by other related studies in the literature.

The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment.

This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes.

The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome.

The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results.

Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

This research has three objectives: one is to develop measuring criteria for ascertaining performance of customs clearance firms, another is to test reliability and validity of the factors, and the third is to analyze the relationship between customer service and firm performance. This research gathered the data from customs clearance firms. Reliability and validity concerned with the collected data are tested by exploratory factor analysis and confirmatory factor analysis and the relationship between variables is tested by analyzing structural equation modeling. The results are as follows. There are no problems in reliability and validity. According to the result of the analysis, customer service is divided into customer focus, customer needs, customer response and flexibility and performance is classified into customer performance and financial performance. The result of empirical tests is as follows. Customer focus has a positive effect on customer performance and financial performance. Flexibility has a positive effect on both types of performance. This means that firms which have discriminative services and a high level of flexibility through collaboration with customers can achieve high levels of customer performance and financial performance.

The purpose of this paper is to provide an approach that a manufacturer can periodically use to measure the performances of its supplier development programs (SDPs). It also quantifies and indicates the specific supplier development outcomes (SDOs) that a manufacturer has to concentrate in improving its SDPs.

The proposed methodology integrates fuzzy analytic hierarchy process (to determine the importance of SDOs), Pareto analysis (to choose the significant SDOs) and fuzzy logic (to periodically determine the performance indices and grades of the SDPs) methods to measure the performances of SDPs. In order to demonstrate the utility of the methodology, a case situation is presented where the performance measures of SDPs running at five key suppliers of an Indian turbine manufacturing company were determined.

By periodically applying the above mentioned methodology, the performance indices of a manufacturer’s SDPs are measured, monitored and managed effectively. It is shown that the methodology is advantageous in clearly indicating the scope of improvement for the SDPs along the specific SDOs.

Since the obtained results in this study are specific to a manufacturing environment, they may not be generalized. However, the generic lists of SDOs mentioned in the paper and the proposed approach can be used in any manufacturing environment for analyzing the performances of SDPs.

The performance analysis of SDPs would help a manufacturer to know more about the functioning of its SDPs along the timeline and provide the scope for improvement.

The proposed approach successfully measures and also grades the performances of SDPs. The results enable the supplier development managers to effectively distribute their investments and efforts along the SDPs.

This paper aims to develop a genetic algorithm (GA)‐based process knowledge integration system (GA‐PKIS) for generalizing a set of nearly optimal fuzzy rules in quality enhancement based on the extracted fuzzy association rules in a supply chain network.

The proposed methodology provides all levels of employees with the ability to formulate nearly optimal sets of fuzzy rules to identify possible solutions for eliminating the number of defect items.

The application of the proposed methodology in the slider manufacturer has been studied. After performing the spatial analysis, the results obtained indicate that it is capable of ensuring the finished products with promising quality.

In order to demonstrate the feasibility of the proposed approach, only some processes within the supply chain are chosen. Future studies can advance this research by applying the proposed approach in different industries and processes.

Because of the complexity of the logistics operations along the supply chain, the traditional quality improvement approaches cannot address all the quality problems automatically and effectively. This newly developed GA‐based approach can help to optimize the process parameters along the supply chain network.