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We  deal with the relationship termination problem in the context of individual-level customer relationship management (CRM) and use a Markov decision process to determine the most appropriate occasion for termination of the relationship with a seemingly unprofitable customer. As a particular case, the beta-geometric/beta-binomial model is considered as the basis to define customer behavior and it is explained how to compute customer lifetime value when one needs to take account of the firm’s choice as to whether to continue or terminate relationship with unprofitable customers. By numerical examples provided by simulation, it is shown how a stochastic dynamic programming approach can be adopted in order to obtain a more precise estimation of the customer lifetime value as a key criterion for resource allocation in CRM.     

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In traditional data envelopment analysis (DEA) the uncertainty of inputs and outputs is not considered when evaluating the performance of a unit. In other words, effects of uncertainty on optimality and feasibility of models are ignored. This paper introduces a new model for measuring the efficiency of decision making units (DMUs) having interval inputs and outputs. The proposed model is based on interval DEA (IDEA) in which the inputs and outputs are limited to be within uncertainty bounds. In this model, the inputs and outputs take fixed values for each DMU such that the sum of efficiencies is maximized. The DMUs are evaluated by the same production possibility set (PPS). The efficiency is measured based on the proposed conservatism level for each input and output. Indeed, the inputs and outputs are defined by the presented conservatism level. The proposed model is integrated measuring all the DMUs efficiencies simultaneously. These efficiency scores lie between the optimistic and pessimistic cases introduced by Despotis and Similar (2002) [11].

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The main objective of this study is to identify and rank foreign investment risk factors and determine their impact on attracting foreign investment in the upstream oil industries. In terms of nature and method, it is descriptive and, in terms of relationships, it is inferential and correlational. The statistical population of the research includes managers and experts in the upstream oil industries, and the sample size was estimated to be 103 people using random sampling. The collected data was analyzed using SPSS, Expert Choice, and Smart PLS software. The results showed that according to the experts in the statistical population, economic risk is the most important factor in foreign investment. Also, in the structural equation modeling method, the correlation between foreign investment risk and attraction factors was significant, with political risk having the greatest impact on foreign investment risk, followed by economic and financial risks and 87.4% of the changes in foreign investment attraction factors could be predicted by foreign investment risk, and the overall fit of the proposed model showed a GOF value of 0.447, indicating a high fit of the research model.

 

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The main objective of the research is to rank the risk factors of foreign investment and present a model of their impact on the risk of foreign investment in upstream oil industries. It is descriptive in nature and method, and in terms of relationships, inferential and correlational. The statistical population of the research includes managers and experts in the oil industry, and the sample size was estimated at 90 people by random sampling method. The data collected with questionnaires were analyzed using SPSS and Matlab software. The results showed that according to the experts of the statistical population, political risk is in the first rank of mportance in creating foreign investment risk. Also, in the fuzzy regression method, the correlation between foreign investment risk factors and foreign investment risk is completely significant, and political risk has the greatest impact on foreign investment risk, and economic, social and non-commercial risks are in the next ranks. By examining the overall fit of the proposed model, it was determined that the appropriate power of fit of the proposed model has been able to determine the relationship between the independent and dependent variables of the research well.