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The costs of retaining an existing customer are one fifth of the costs of acquiring a new customer for a firm. This statement is considered as a predominant notion in marketing. According to this notion, existing customers switching of a firm leads to create lots of costs for that firm. Therefore, the present study has been conducted with the aim of finding the causes of the switching intentions and influential factors on the customers switching intentions in Iran Banking Industry.
This study is a descriptive-survey research carried out on Iran Banking Industry. 397 customers from five selected banks in Tehran were chosen for this research. In order to examine and analyze the [R1] data, Structural Equation Modeling (SEM), Confirmatory Factor Analysis (CFA) and one-way Analysis of Variance (ANOVA) have been used.
“Satisfaction”, “trust”, “loyalty” and “switching barriers” are considered as the main factors weakening “switching intention”. The findings confirmed that the variables of satisfaction, trust and loyalty have significant negative impacts on the switching intention, but the impact of the switching barriers on the switching intention in Iran banking was not significant. The present research has been conducted only on banking services industry and only in Tehran which reduces the generalizing effect of the study. Moreover, quantitative analysis methods were used in order to evaluate the subjective factors such as customer switching behavior.
 
 



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One of the most fundamental economic issues for holding companies is capital allocation. Typically, investors in selecting investment alternatives follow conflicting preferences and goals simultaneously. Therefore, developing a model based on available information can help decision makers to identify the most important competitive factors and focus their attention on the improvement of performance. However, several techniques have been introduced to determine the most important components. Analytical hierarchy process (AHP), a branch of multi criteria decision making (MCDM) methods, is a powerful tool for ranking a set of elements. Nevertheless, the AHP is disable to take into account the uncertainty involved in the process of decision making. On the other hand, intuitionistic fuzzy sets (IFS) are capable of handling the vagueness and ambiguity by using the scale of the pairwise comparisons represented by the IFS. The IFS-AHP (a combination of the IFS and AHP method) can lead to more precise description of the problem under consideration since the IFS is robust in describing complexity and uncertainty. Therefore, the IFS-AHP technique has much more advantages in comparison with the conventional AHP or fuzzy AHP. To demonstrate the potential application of the proposed approach, a real case study on ranking the critical factors influencing the Investment Options in the Holding Companies is illustrated. The results show that criterion C6 (Risk) with value of 0.1451 is the most important factor in Holding Companies.