درخت حوزه‌های تخصصی

The purpose of the current study is to identify the aspects of cause-related marketing in the banking system and determine the outcomes of implementing cause-related marketing. Design, methodology, and approach: The research methodology is qualitative with in-depth interviews with 16 senior managers. Data were analyzed using open coding, axial coding, selective coding, and the software named MaxQDA12pro. The results revealed that the aspects of cause-related marketing were affected by internal and external factors as well as their categories. The emergence of a new marketing concept for audiences has made it difficult to carry out the research. The qualitative research approach was mainly implemented in the working field of the experts, with the resulting model created from their opinions. Banks must implement different strategies for different groups. Implementing cause-related marketing results in social, economic, marketing, and environmental outcomes for banks and the country, each of which affecting the banking activities. If cause-related marketing penetrates into consumer attitudes and behaviors, it will be a marketing tool required to improve organizational reputation, develop a brand image of the company, raise customer satisfaction and loyalty, increase sales, and in the meantime benefit the community. This qualitative research helps companies that consider new consumers understand their needs and demands, share their values with them, and remain in the current competitive environment. Hence, cause-related marketing can be a safe and distinguished tool in their success.

One of the most important applications of data envelopment analysis tech-nique is measuring the efficiency of bank branches. Performance measure-ment in the banking industry is important for several groups, including bank managers, customers, investors, and shareholders. The purpose of this study is to examine and design a mixed structure to measure the efficiency of branches of Iranian banks according to their policies. In order to obtain the efficiency of the structure divisions proposed in this study, a slack-based NDEA model was selected to solve its mathematical model. The study sam-ple consists of 31 branches of a large commercial bank in Iran. The ad-vantage of this research to previous studies is that the result will be more realistic considering the inputs and outputs consistent with Iran's banking conditions.

One of the main concerns of investors is the evaluation of the return on investment, which is conducted using various models such as the CAPM (single-factor model), Fama-French three/five-factor models, and Roy and Shijin’s six-factor model and other models known as multi-factor models. Despite the widespread use of these models, their major drawbacks include sensitivity to unexpected changes, sudden shocks, high turbulence of price bubble, and so on. To eliminate such negatives, the multi-factor model using the penalty function method is used, in which, instead of averaging, the optimization and avoidance of the effects of abnormal changes and other factors affecting the capital market are considered. In order to evaluate stock returns, it is possible to select effective factors, to simulate and develop a model appropriate to the conditions governing the capital market in Iran. In the present study, by forming portfolios of investments and identifying and refining effective factors, the classification and estimation of the hybrid model of penalty and multi-factor (P & PCA) functions were performed based on the functional data during 2007-2017. The results of this study indicated that the extensive use of the simulation algorithm for the penalty function in the form of P & PCA estimation method improves the efficiency of multi-factor methods in stock return evaluation, and that the use of the hybrid algorithm of penalty and multi-factor functions, compared to the exclusive use of multi-factor models, brings a higher accuracy in estimating stock returns.