Previous studies have convincingly shown that employees' family lives can affect their work outcomes. We investigate whether family-to-work conflict (FWC) experienced by the employee also affects the turnover intention of a co-worker. We predict that the employee's FWC has an effect on the co-worker's turnover intention through the crossover of positive and negative work attitudes. Using a sample of 154 co-worker dyads, we found that the employee FWC was positively related to co-worker turnover intention through the crossover of (reduced) work engagement. Results show that family matters at work, affecting employee. In addition, employee's job engagement was positively related to his (her) co-worker job engagement and it was negatively related co-worker turnover intention and employee's FWC was not positively related to co-worker turnover intention trough the crossover of (reduced) feelings of engagement.

This paper presents a systematic approach for evaluating the performance of a project based organization using a two level fuzzy data envelopment analysis (DEA) technique in project based organizations. In order to determine the required inputs and outputs, important indicators are selected using both expert judgments and statistical analysis and a two-level DEA model is adapted. In this model, by considering different inputs and outputs through a hierarchical process, a large number of sub indicators are provided and rolled up to a higher level. Since inputs and outputs are combinations of qualitative and quantitative indicators, fuzzy logic is also included through the modeling procedure. In addition, since the exact amount cannot be attributed to the indicators, the proposed model uses interval values for the project life cycle. Finally, some of the projects are evaluated throughout the approach proposed in this paper.

The purpose of this paper is to predict the S & P500 down moves with technical analysis indicators using learning vector quantization (LVQ) neural networks and probabilistic neural networks (PNN). In addition, entropy-based input selection technique is employed to improve the prediction accuracies. The out-of-sample simulations show that LVQ outperforms PNN. In addition, the Entropy-LVQ system achieved higher accuracy in comparison with the literature.

Decision making problem is the process of finding the best option from all of the feasible alternatives. One of the most important concepts in decision making process is to identify the weights of criteria. In real-world situation, because of incomplete or non-obtainable information, the data (attributes) are often not deterministic and can be treated in forms of fuzzy numbers. This paper investigates a method for deriving the weights of criteria from the pair-wise comparison matrix with fuzzy elements. Finding the weights of criteria has been one of the most important issues in the field of decision-making and the present method uses goal programming to solve the resulted model. In addition, using a ranking function we convert each obtained fuzzy weight to a crisp one, which makes it possible to compare the criteria. The proposed model of this paper is supported by several examples and a case study.

This paper presents the decentralized supply chain with two suppliers and two competing retailers. It also investigates the sourcing and pricing strategies of two retailers in a decentralized supply chain system under a supply disruption environment. These retailers face their individual stochastic demand markets; however, they compete with each other through a two-stage price and service operation. The interactive dynamics among retailers is characterized, including the existence and uniqueness of the Nash Equilibrium in service and price games demonstrated.

In today & apos; s competitive world, productivity- as a core source of production - is the most important target of the organization. Experimental studies in developed industrial countries prove that productivity improvements resulted from development of management systems play a more important role in production than physical increases in labor and capital factors. This paper, while focusing on productivity from a CRM perspective, employs a European Organizational Excellence Model framework to identify factors affecting productivity and the role of CRM systems. We perform an empirical study for a case study of home appliance manufacturing and using a questionnaire computed present status and compared with desired status of CRM components such as customer leadership, strategy, skill and motivation of labor work, effective use of information technology and process management.

This research investigates the human-behavioral dimension of technology acceptance in enterprises. It is evident that accepting a technology depends on the underlying circumstances of the environment. We have approached this issue from two different angles of social and technological architecture. The research tries to explore proper enterprise architecture for ERP system acceptance. Social Architecture (SA) is defined as the set of circumstances that makes people behave in a particular way. So behavior of persons (employees of an enterprise) can be a function of SA. Hence acceptance of a system can be dictated by SA and manipulating SA can result in desirable success for a technology system. We have achieved various variables of social architecture and have examined their relevance to system acceptance and success in related enterprises (research domain) beside technological architecture variables.
The results have indicated that a special form of social and technological architecture can lead to success for ERP system in the enterprises of the research domain. This gave us a model of architecture.

Distribution network design is an important issue in supply chain management and plays an important role in making new market development. Because of JIT philosophy, most of managers now have focused on designing appropriate distribution networks. Thus, categorizing distributors and selecting the best ones are crucial for companies. This paper provides a new method to categorize and select distributors. The fuzzy Adaptive Resonance Theory (ART) algorithm is utilized to categorize distributors according to their similarity. To improve the performance of the algorithm, we train the algorithm using the past data. Finally, a numerical example is illustrated to examine the validity of the proposed algorithm.

Railroad maintenance engineering plays an important role on availability of roads and reducing the cost of railroad incidents. Rail is of the most important parts of railroad industry, which needs regular maintenance since it covers a significant part of total maintenance cost. Any attempt on optimizing total cost of maintenance could substantially reduce the cost of railroad system and it can reduce total cost of the industry. The paper presents a new method to estimate the cost of rail failure using different cost components such as cost of inspection and cost of risk associated with possible accidents. The proposed model of this paper is used for a real-world case study of railroad transportation of Tehran region and the results have been analyzed.

Measuring the relative efficiency of similar units has been an important topic of research among many researchers. Data envelopment analysis has been one of the most important techniques for measuring the efficiency of different units. However, there are some limitations on using such technique and some people prefer to use other methods such as analytical hierarchy process to measure the relative efficiencies. Besides, uncertainty in the input data is another issue, which makes some misleading results. In this paper, we present an integrated robust DEA-AHP to measure the relative efficiency of similar units. The proposed model of this is believed to capable of presenting better results in terms of efficiency compared with exclusive usage of DEA or AHP. The implementation of the proposed model is demonstrated for a real-world case study of Airport industry and the results are analyzed.