21P. Customer-centric Model for Performance Management in Banking Industry Using Soft System Methodology

This research uses soft system methodology in exploring a real world problem in managing the performance of banks’ branches. In the first step, a rich picture is drawn based on the semi-structured interviews with experienced personnel and managers of Iranian commercial banks. Extracting a rich picture about the problem situation and roots of the problem, and based on literature review and well-known theories including resource-based view of the firm and service-profit chain, the paper proposes a conceptual model for customer-centric performance management system (PMS). The proposed model suggests an integration of customer relationship management system and PMS using customer lifetime value metric in managing the bank’s performance. The paper also discusses the benefits of this metric. In practice, the model has a potential to provide more strategic use of information system (IS) by increasing the use of managerial knowledge and strategy making being extracted from IS

RFL-based customer segmentation using K-means algorithm

Customer segmentation has become crucial for the company’s survival and growth due to the rapid development of information technology (IT) and state-of-the-art databases that have facilitated the collection of customer data. Financial firms, particularly insurance companies, need to analyze these data using data mining techniques in order to identify the risk levels of their customer segments and revise the unproductive groups while retaining valuable ones. In this regard, firms have utilized clustering algorithms in conjunction with customer behavior-focused approaches, the most popular of which is RFM (recency, frequency, and monetary value). The shortcoming of the traditional RFM is that it provides a one-dimensional evaluation of customers that neglects the risk factor. Using data from 2586 insurance customers, we suggest a novel risk-adjusted RFM called RFL, where R stands for recency of policy renewal/purchase, F for frequency of policy renewal/purchase, and L for the loss ratio, which is the ratio of total incurred loss to the total earned premiums. Accordingly, customers are grouped based on the RFL variables employing the CRISP-DM and K-means clustering algorithm. In addition, further analyses, such as ANOVA as well as Duncan’s post hoc tests, are performed to ensure the quality of the results. According to the findings, the RFL performs better than the original RFM in customer differentiation, demonstrating the significant role of the risk factor in customer behavior evaluation and clustering in sectors that have to deal with customer risk

The Effect of Blockchain on Customer-To-Customer Electronic Commerce

The popularity of the Internet and e-business has led more people to online shopping, especially customer-to-customer (C2C) electronic commerce (e-commerce). However, C2C e-commerce faces challenges such as security, product and vendor trust, and more. One of the most important solutions to such problems is the use of decentralized and distributed technologies such as blockchain, which play an important role in identifying, verifying, and validating data, developing information security, and creating transparency and trust in a business. Therefore, this study uses a qualitative method to investigate the effect of blockchain on C2C e-commerce. After conducting the conceptual study, the experiences of experts in this field were used through semi-structured interviews. The data from the interviews were then coded and analyzed. Finally, 12 conceptual codes were extracted. Due to the high frequency of the transparency code, other concepts were studied around this axis and based on it. According to the results, strategies such as transparency, trust, and legal barriers were more important in using blockchain in C2C e-commerce, respectively

Stock Price Prediction Modeling Using Artificial Neural Network Approach and Imperialist Competitive Algorithm Based On Chaos Theory

Stock market is one of the options available to invest in liquidity. Investors in this area used a variety of approaches to predict stock prices. But due to the nonlinear relationship between variables affecting stock prices, Artificial Neural Networks are one of the most suitable approaches for this work. These networks, through different search optimization algorithms, try to identify the relationships between these variables. The higher the algorithms used, the higher the efficiency of the algorithms, the more accurate the identification of the relationships between the variables. In this paper, an attempt has been made to combine chaotic maps and colonial competition algorithms with the reform movement angle to the colonial colonies so that we can deal with the possibility of being trapped in local optimum to reduce as much as possible. Therefore, using this approach, it is tried to predict the stock price of Iran Khodro Company. To evaluate the performance of the proposed approach to other conventional approaches of neural network education, three perspectives: the degree of accuracy of prediction, the amount of memory used and the time of execution were used. The results show that the proposed approach has a better performance than other approaches

Identifying and Ranking the Effective Factors on Successful Implementation of Social Commerce in Iran, Using AHP Fuzzy

Social commerce has been introduced as a new approach to increase sales, number of customers and reduce marketing expenditures. This approach is a combination of business, communication between people, as well as communicative and informative technologies based on web 2.0 Its achievement originated from different factors relied on business, individuals, culture, and technology. These factors have been primarily identified on the basis of library researches and classified into six infrastructural groups including:  technical, economical and human resources, cultural, rules governing the countries, style of management, and business. Then, it identified priority of the factors by using the fuzzy analytic hierarchy process (AHP). Innovation of this research was to extract a comprehensive list of factors and to prioritize them based on specific conditions in Iran