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Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.

A new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.

The proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.

Competitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.

Vendor selection is the main activity in a sourcing decision, which is a strategic decision in that it leads enterprises to eliminate costs and improve their performance. However, an inappropriate selection may compromise the financial and operational status of the enterprise. But vendor selection is a complex, multi-criteria decision-making process because different and conflicting criteria have to be considered and assessed in order to find consistent suppliers. Consequently, evaluating and selecting the best vendor is the key to successful business. Traditionally, vendors are normally selected on the basis of traditional criteria (TC), such as costs and quality, neglecting resilience criteria (RC) (e.g. agility and flexibility). Thus, enterprises ultimately realize that a selecting method which involves TC as the only ones is inefficient and needs to be changed. The paper aims to discuss this issue.

This study was set in motion by a problem in practice. It aims to provide a user-friendly decision-making tool for selecting the best vendor from a group which submitted their tenders for implementing a proposed radio frequency identification (RFID)-based passport tracking system (Dukyil et al., 2017). The main traditional and resilience (“trasilience” henceforth) selection criteria were identified in a unified framework in collaboration with experts in the institution. Next, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) algorithm was used to determine the relative importance of each criterion and the weights thus obtained were integrated into the ELimination Et Choix Traduisant la REalité (ELECTRE) algorithm. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm was also applied, to evaluate the performance of vendors and to select the best one. The qualitative evaluation of the criteria and the vendors was based on four decision makers. Finally, the Spearman’s rank correlation coefficient (SRCC) approach was applied to obtain the statistical difference between the ranking orders obtained from the two algorithms.

The efficiency of the proposed decision-making tool was evident from the real-case study of six tenders submitted for implementing a RFID-based passport tracking system. The SRCC also turned out a “very strong” association value between TOPSIS and ELECTRE.

The developed trasilient decision-making tool can easily be used to solve similar vendor or supplier selection problem. Moreover, other criteria can be added to fit other cases. Later, the tool was made available to the institution under study for solving future evaluation problems.

The literature shows that none of the previous papers presented an integrated trasilient approach that considers RC and TC simultaneously. This study presents a new trasilience tool for selecting a vendor.

Academic success and failure are relevant lifelines for economic success in the knowledge-based economy. The purpose of this paper is to predict the propensity of students’ academic performance using early detection indicators (i.e. age, gender, high school exam scores, region, CGPA) to allow for timely and efficient remediation.

A machine learning approach was used to develop a model based on secondary data obtained from students’ information system in a Nigerian university.

Results revealed that age is not a predictor for academic success (high CGPA); female students are 1.2 times more likely to have high CGPA compared to their male counterparts; students with high JAMB scores are more likely to achieve academic success, high CGPA and vice versa; students from affluent and developed regions are more likely to achieve academic success, high CGPA and vice versa; and students in Years 3 and 4 are more likely to achieve academic success, high CGPA.

This predictive model serves as a classifier and useful strategy to mitigate failure, promote success and better manage resources in tertiary institutions.

This study aims to propose and test a model explicating the antecedents and consequences of knowledge sabotage.

Data obtained from 330 employees working in the Turkish retail and telecommunication sectors were analyzed by means of the Partial Least Squares Structural Equation Modeling technique.

Co-worker knowledge sabotage is the key factor driving knowledge sabotage behavior of individual employees, followed by co-worker incivility. Interactional justice suppresses individual knowledge sabotage, while supervisor incivility does not affect it. Co-worker knowledge sabotage reduces job satisfaction of other employees, which, in turn, triggers their voluntary turnover intention. Contrary to a popular belief that perpetrators generally benefit from their organizational misbehavior, the findings indicate that knowledge saboteurs suffer from the consequences of their action because they find it mentally difficult to stay in their current organization. Employees understate their own knowledge sabotage engagement and/or overstate that of others.

Managers should realize that interactional justice is an important mechanism that can thwart knowledge sabotage behavior, promote a civil organizational culture, develop proactive approaches to reduce co-worker incivility and strive towards a zero rate of knowledge sabotage incidents in their organizations. Co-worker incivility and co-worker knowledge sabotage in the workplace are possible inhibitors of intraorganizational knowledge flows and are starting points for job dissatisfaction, which may increase workers’ turnover intention.

This study is among the first to further our knowledge on the cognitive mechanisms linking interactional justice and uncivil organizational behavior with knowledge sabotage and employee outcomes.

Drawing on COR theory, the purpose of this paper is to contribute to the scarce literature of work-family interference, workplace incivility, gender and psychological distress at the practical and academic juncture.

The linear (structural equation modeling (SEM)) and non-linear (artificial neural network (ANN)) techniques were applied to the survey data from a sample of Nigerian health workers (n=447) to investigate the relationships between the aforementioned variables.

The results from SEM and ANN revealed that work-family conflict (WFC), family-work conflict, supervisor incivility and coworker incivility have positive impacts on psychological distress. A multi-group moderation analysis suggests that women were more likely to experience psychological distress.

The work-family interference and incivility are pervasive and gendered problems in the workplace. This paper strives to enhance the understanding of the nature of the relationship in an African work setting. The implications for making the workplace better and safer are discussed.

Flock leadership theory explores how different collective learning capacities emerge when interacting individuals work through challenges. While research has link other leadership styles with knowledge management systems (KMS), studies examining flock leadership and KMS are largely lacking. This paper aims to investigate the nexus between flock leadership, knowledge content quality, perceived usefulness of KMS, KMS use for sharing and KMS use for retrieval.

Using survey data (n = 442) from health-care establishments in Jordan, the authors apply structural equation modeling (SEM).

The results show the following: flock leadership influences knowledge content quality; knowledge content quality influences perceived usefulness of KMS; perceived usefulness of KMS influences KMS use for sharing and retrieval; and knowledge content quality and perceived usefulness of KMS mediates the link between flock leadership and KMS use for sharing and retrieval.

Existing research considered KMS through a narrow lens, without consideration of the social context (collective capacities) that surrounds knowledge workers. This study responds to call for research concerning the motion that says technical and adaptive capacity are the underlying norm that configures flock behaviors. Implications for research and practice are discussed.

The purpose of this paper is to identify and describe the drivers of lean approaches and successful management of wearable technology start-ups. The paper is a descriptive study that employed a case study methodology based on semi-structured interviews with ten start-ups’ managers in Wearable Technology 2017 conference. Participants were selected based on convenience sampling and the pre-set criteria. The current study contributes to this field through the main findings, which suggest that four stages need to be considered by starts-up for a successful market readiness, including the time of entry and overcoming market entry barriers, product attributes, product development process, and commercialization. Finally, findings were categorized in the form of an iterative learning loop model and also, practical strategies and methods were recommended for successfully going through each stage.

Little empirical attention has been paid to the effects of electronic word-of-mouth (eWOM), electronic referral (eReferral), familiarity and cultural distance on behavioral outcomes, especially within the context of educational tourism. Based on the social network theory, this paper aims to explore the effects of eReferral, eWOM, familiarity and cultural distance on enrollment intention.

Survey data (n = 931) were obtained from educational tourists using a judgmental sampling technique. Linear modeling and artificial intelligence (i.e. artificial neural network [ANN]) techniques were used for training and testing the proposed associations.

The results suggest that eReferral, eWOM, familiarity and cultural distance predict intention to enroll both symmetrically (linear modeling) and asymmetrically (ANN). The asymmetric modeling possesses greater predictive validity and relevance.

This study contributes theoretically and methodologically to the management literature by validating the proposed relationships and deploying contemporary methods such as the ANN. Implications for practice and theory are discussed.

是否在线推荐, 在线口碑, 熟悉度, 还有文化距离决定参加意图吗？人工智能科技的应用

很少有研究检测过在线口碑（eWOM）, 在线推荐（eReferral）, 熟悉度, 和文化距离对行为结果的影响, 特别是在教育旅游的领域里。本论文基于社交网络理论探索eReferral, eWOM, 熟悉度, 和文化距离对参加意图的作用。

研究样本数据为931位教育旅游的游客, 通过判断抽样技术。本论文通过线性建模和人工智能（即人工神经网络）技术来培训和检测提出的关系。

研究结果表明, eRefferal, eWOM, 熟悉度, 和文化距离对参与意图起到决定作用, 其中包括对称（线性建模）和非对称地（人工神经网络）方式。非对称建模将增加有效性和相关性的决定度。

本论文通过证实提出的关系和采用现代方法, 比如人工神经网络, 对管理文献做出理论和实践的贡献。本论文还讨论了对实践和理论的启示。

The purpose of this paper is to propose an innovative integration method based on decision-theoretic rough set and the extended VlseKriterijuska Optimizacija I Komoromisno Resenje (VIKOR) methods to address the resilient-sustainable supplier selection and order allocation (SS/OA) problem.

Specifically, a two-stage approach is designed in this paper. First, the decision-theoretic rough set is employed to calculate the rough number for coping with the subjective uncertainty of data and assigning the weights for a resilient-sustainable evaluation criterion. On this basis, the supplier resilient-sustainable performance is ranked in combination with the extended VIKOR method. Second, a novel multi-objective optimization model is proposed that applies an improved genetic algorithm to select the resilient-sustainable supplier and allocate the corresponding order quantity under a multi-tier supplier network.

The results reveal that joint consideration of resilience and sustainability is essential in the SS/OA process. The method proposed in this study based on decision-theoretic rough sets and the extended VIKOR method can handle imprecise information flexibly, reduce information loss and obtain acceptable solutions for decision-makers. Numerical cases validate that this integrated approach can combine resilience and sustainability for effective and efficient SS/OA.

This paper provides industry managers with a new perspective on SS/OA from a resilience and sustainability perspective as a basis for best practices for industry resilience and sustainability. The proposed method helps to evaluate the resilient-sustainable performance of potential suppliers, which is applicable to solving real-world SS/OA problems and has important practical implications for the resilient-sustainable development of supply chains.

The two interrelated priorities of resilience and sustainability have emerged as key strategic challenges in SS/OA issues. This paper is the first study of this issue that uses the proposed integrated approach.