Interdisciplinary Journal of Management Studies
Year:2018 | Volume:11 | Issue:2 |Papers Count:8

The importance of supplier selection is nowadays highlighted more than ever as companies have realized that efficient supplier selection can significantly improve the performance of their supply chain. In this paper, an integrated model that applies Data Envelopment Analysis (DEA) and Support Vector Machine (SVM) is developed to select efficient suppliers based on their predicted efficiency scores. In the first step, fuzzy linguistic variables are changed to crisp data as initial dataset for DEA. Actual efficiency scores are then calculated for each Decision Making Unit (DMU) using CCR-DEA model. Afterwards, suppliers’ performance-related data are used for training SVM-DEA model. A numerical example representing an actual case is provided to indicate the applicability of the model.

This study used PGSI to measure the motives of online stock exchange gamblers according to their responses about their online gambling. The main aim of the current study is to holistically explore the impact of motivational factors that motivate more usage of online gambling in Pakistan and behavioral factors that investigate the level of implementation of responsible gambling practices on PGSI in Pakistan’s online stock exchange gamblers. We collected data through questionnaires and for analysis we used SEM, multiple regression and multinomial logistic regression. Results indicated that motivational factors that significantly impact PGSI are excitement, financial motivation, escape and relaxation and in terms of responsible gambling practices, game design and transparent terms and conditions are the key elements of behavioral factors while self-exclusion and selfhelp (SE and SH) are not considered as significant factors.

This paper develops a two-stage model to consider a franchise/franchisee environment in which supply chains are simultaneously entering the untapped market to produce either identical or highly substitutable products and give franchise to franchisees. Customer demand is elastic, price dependent and customer utility function is based on Huff gravity rule model. The supply chains, in the first stage, shape their networks and set the market prices based on dynamic games. The franchisees, in the second stage, specify their attractiveness levels and set the locations of their retailers in simultaneous games. Possibility theory was also applied to cope with uncertainty. Finally, we applied our model to a real world problem, discussed the results, conducted some sensitivity analyses, and gained some managerial insights.

In many experiments, such as biology and quality control problems, sample size cannot always be considered as a constant value. Therefore, the problem of predicting future data when the sample size is an integer-valued random variable can be an important issue. This paper describes the prediction problem of future order statistics based on upper and lower records. Two different cases for the size of the future sample is considered as fixed and random cases. To do this, we first derive a general formula for the coverage probability of the prediction interval for each case.For the case that the sample size is a random variable, we consider two different distributions for the sample size, such as binomial and Poisson distributions and we study further details. The numerical computations are also given in this paper.Another purpose of this paper is to determine the optimal prediction interval for each case. Finally, the application of the proposed prediction interval is illustrated by analyzing the data in a real-world case study.

Personality traits and their relation with emotional intelligence and psychological ownership (PO) have to be considered in public-sector organizations, because employees who work in the public sector may have fewer mechanisms to increase their feelings of PO toward their organization. Thus, with regard to conditions of public organizations, more attention should be paid to structural and organizational contexts while investigating the relations between personality traits, emotional intelligence and PO. The aim of the research is to probe the effect of personality traits on PO in a public organization with regard to the mediating role of emotional intelligence. SPSS and Smart PLS software applications were used to test the research hypotheses. Data were collected from 384 participants, engaged in a public organization administrative department. The results revealed that traits, including extroversion, agreeableness, conscientiousness, openness and neuroticism influence emotional intelligence; emotional intelligence, in turn, is positively associated with PO; these personality traits are directly associated with PO and influence PO indirectly through emotional intelligence.

This study aimed at providing a systematic method to analyze the characteristics of customers’ purchasing behavior in order to improve the performance of customer relationship management system. For this purpose, the improved model of LRFM (including Length, Recency, Frequency, and Monetary indices) was utilized which is now a more common model than the basic RFM model apt for analyzing the customer lifetime value. Since the RFM model does not take the customers’ loyalty into consideration, the LRFM model has instead been applied for making amendments. Contrary to most of the past studies in which the statistical clustering techniques were used besides the RFM or LRFM model, the current study has provided the possibility of clustering analysis by importing the LRFM indices into the framework of a fuzzy inference system. The results obtained for a wholesale firm based on the proposed approach indicated that there was a significant difference between clusters in terms of the four indices of LRFM. Therefore, this approach can be well utilized for clustering the customers and for studying their characteristics.The strong point of this approach compared to the older ones is its high flexibility, because in which it is not needed to re-cluster the customers and to reformulate the strategies when the number of customers is increased or decreased. Finally, after analyzing the attributes of each cluster, some suggestions on marketing strategies were made to be compatible with clusters, and totally, to improve the performance of customer relationship management system.

This paper studies a series production system through the integration of the decisions associated with Maintenance Management (MM) and Statistical Process Control (SPC). Hence, the primary question of the paper can be stated as follows: In a series production system, how can the decisions of MM and SPC be coordinated.To this end, an integrated mathematical model of MM and SPC is developed. Using a method of factorial design, sensitivity analyses are performed. According to a stand-alone maintenance model, the effectiveness of the integrated model is assessed. The series production system investigated consists of identical units. Each unit has two operational states including an in-control state and an out-of-control state. The system is in-control if both units of the system operate in the in-control state. On the other hand, the system is out-of-control, if at least one of the units operates in the out-of-control state. The failure mechanism of each unit is based on a random variable with a continuous distribution. The results of analyses clarify that the integrated model improves the profit of the system.

The present paper measures the risk perception of the bank employees in respect of investment in mutual fund and to identify the factors affecting risk perception. The paper also attempts to find out the impact of these factors on overall risk perception.The study is based on primary data collected by using questionnaire from the bank employees in Tripura state of India. For the analysis of data, Cronbach’s alpha, factor analysis, binary logistic regression, mean and standard deviation, and etcetera are used. It is found that bank employees’ overall level of risk perception is moderate. There are three factors that affect the overall risk perception namely fear psychosis, lack of knowledge, and lack of confidence and these three factors have impact on the investment decision employees are making with regard to investment in mutual fund. The study is the first of its kind and hence original in nature.