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.

SCImago Journal Rank (SJR) is one indicator of a journal's reputation.  The value is calculated based on several published journals, such as scholarly journals' scientific impact, representing the number of quotes sent to a journal and the relevance or reputation of journals from which the quotations originate. A high SJR value means that the corresponding journal has a high reputation. This study aims to approach the SJR classification by implementing a machine learning approach. A simple yet powerful method Naïve Bayes Classifier (NBC), is selected. NBC utilizes probability calculations based on Bayes' theorem. However, NBC has an assumption that the attribute values do not depend on each other. This method is optimized using Particle Swarm Optimization (PSO) to overcome this weakness. This study used SJR data of the computer science domain from 2014 to 2017. Publication without Q rank is filtered for better performance. As a result, the accuracy of the proposed method is higher than the baseline. The use of PSO  significantly improves the NBC performance based on the performed T-test. The PSO-NBC selects four of eight features: H index, Cites/ Doc (2 Years), and Ref. / Doc. Overall results show that using PSO-NBC is closer to SJR rather than using mere NBC.