No abstract

This study aims to investigate and identify the factors affecting the empowerment and implementation of knowledge management in organizations as well as the impact of knowledge management on organizational performance. This study also examines the mediating role of human capital in the relationship between knowledge management and performance of Kabul Steel Plant, which is the largest steel plant in Afghanistan. The research model was developed through the literature review. The initial data were collected through a questionnaire containing 48 questions. Participants were 108 managers and administrative staff of the company. The collected data were analyzed by using the SPSS and SmartPLS software. The hypotheses regarding the impact of strategy and technology on knowledge management were rejected by using correlation analysis and t-test statistic. Finally, the findings showed the positive effects of variables of structure, culture, leadership, and trust on knowledge management in an organization. Also, knowledge management influences the organizational performance, both directly and through the mediating variable of human capital. This research encourages the managers and employees of organizations to use the available organizational resources to implement knowledge management in organizations and improve knowledge management practices and human resources that are the most valuable resources of any organization in order to remain competitive in the markets.

No abstract

The selection process of appropriate Performance Appraisal (PA) methods for organizations in today's dynamic and agile environments along with its funding scales is a complex problem. Performance appraisal in modern organizations has become a part of the strategic approach toward integrating business policies and human resource activities. The existence of multiple criteria in the decision-making procedure makes finding the optimal PA method more challenging. The current study tackles a PA method assessment by applying a multiple criteria decision analysis method i.e., MULTIMOORA integrated Shannon's entropy significance coefficient. A case study on the optimal PA method selection is analyzed by identifying the criteria and alternatives based on the literature and expert comments of the case-study employing two approaches, that is, MULTIMOORA and Entropy MULTIMOORA. The final rankings of the suggested methods are compared to TOPSIS and TOPSIS integrated Shannon's entropy methods utilizing correlation coefficients of the final ranks. Eventually, by identifying the optimal PA approach i.e., 360-degree feedback, the selected optimal method employed in the case study and results are demonstrated and described with a comprehensive example.

One of the main factors in the success of projects is communications management and proper and timely distribution of information among all internal and external project stakeholders. Despite of emphasizing on the importance of project communications in the literature, there are few studies identifying factors influencing project communications. This research aims to address this shortcoming by identifying and determining sequences and relationships factors affecting project communications and their clustering. The informed communication strategy allows managers to structure the information flow in a better and more controlled manner and to avoid the costs caused by lack of effective and timely communication. The present study is conducted to help clarify the views of the organization's managers and project managers on project communications, and to identify factors affecting it and how they effectively communicate to successfully accomplish the projects. First a number of factors influencing project communications are identified on the basis of previous studies and interviews with experts and project managers working in oil, gas and power plant construction megaprojects in Iran. Then, these factors are analyzed by using the combination of fuzzy DEMATEL and Interpretive Structural Modeling (ISM) techniques. The relationships and sequence between the indicators are determined so that it can be effective in project communications planning and project success through providing an insight for senior managers and project managers.

KEYWORDSMulti-objective supplier selection problem; Coverage; Fuzzy logic; MOPSO; NSGA-II.Abstract. In this paper, a fuzzy multi-objective model is presented to select and allocate order to the suppliers in uncertain conditions, considering multi-period, multisource, and multi-product cases at two levels of a supply chain with pricing considerations. Objective functions considered in this study as the measures to evaluate the suppliers are the purchase, transportation, ordering costs, and timely delivering (or deference shipment quality, or wastages) which are amongst major quality aspects. Partial and general coverage of suppliers with respect to distance and nally suppliers' weights makes the amounts of product orders more realistic. Deference and coverage parameters in the model are considered as uncertain and random triangular fuzzy number. Since the proposed mathematical model is NP-hard, Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is presented. To validate the performance of MOPSO, we applied non-dominated Sorting Genetic Algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms. A practical case study in an agricultural industry is shown to demonstrate that the proposed algorithm can be applied to the real-world problems. The results are analyzed using quantitative criteria, performing parametric, and non-parametric statistical analyses.

In past recent years, by increasing in the considerations on the significance of data science many studies have been developed concerning the big data structured problems. Along with the information science, in the field of decision science, multi-attribute decision-making (MADM) approaches have been considerably applied in research studies. One of the most important procedures in supply chain management is selecting the optimal supplier to maintain the long-term productivity of the supply chain. There has been a vast amount of research which utilized MADM approaches to tackle the supplier selection problems, but only a few of these research considered big data structured problems. The current study presents a comprehensive novel approach for improving Multiple Criteria Decision Analysis (MCDA) based on cluster analysis considering crisp big data structure input which is called CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) algorithm. The proposed method is based on consolidating a data mining technique i.e. -means clustering method and a MADM approach which is MULTIMOORA method. CLUS-MCDA method is a fast and practical approach which has been developed in this research which is implied in a supplier selection problem considering crisp big data structured input. A real-world case study in MAMUT multi-national corporation has been presented to show the validity and practicality of the CLUS-MCDA approach which calculated considering the business areas and criteria based on expert comments of mentioned organizations and previuos literature on supplier selection problem.

No abstract