Search results

The purpose of this paper is to identify the attributes that predict customer attrition behavior in the brokerage and investment banking sectors.

The authors analyze the complete stock trading records and customer profiles of 458,098 retail customers from a Korean brokerage house. The authors develop customer attrition prediction models and further explore the practicality of these models using statistical classification techniques.

The results from three different binary selection models indicate that customer transaction patterns effectively explain the attrition of active retail customers in subsequent periods. The study results demonstrate that monetary value variables are the most critical for predicting customer attrition in the securities industry.

This study contributes to the customer attrition literature by documenting the first large-scale field-based evidence that confirms the practicality of the canonical recency, frequency and monetary (RFM) framework in the investment banking and brokerage industry. The findings advance previous survey-based studies in the financial services industry by identifying the attributes that predict customer attrition behaviors in the securities industry.

The outcomes can be easily operationalized for attrition prediction by practitioners in financial service firms. Moreover, the ex post density of inactive customers in the top 10 percent most-likely-to-churn group is estimated to be five to six times the ex ante unconditional attrition ratio, which ascertains that the attributes recognized in this study work well for the purpose of target marketing.

While the securities industry is regarded as one of the most information-intensive industries, detailed empirical investigation into customer attrition in the field has lagged behind partly due to the lack of suitable securities transaction data and demographic information at the customer level. The current research fills this gap in the literature by taking advantage of a large-scale field data set and offers a starting point for more elaborate studies on the drivers of customer attrition in the financial services sector.

The purpose of this paper is to make an overview of 474 publications and 512 patents of FTL from 1987 to 2020 in order to provide a conclusive and comprehensive analysis for researchers in this field, as well as a preliminary knowledge of FTL for interested researchers.

Firstly, the FTL algorithms are classified and its functions are introduced in detail. Secondly, the structures of the publications are analyzed in terms of the fundamental information and the publication of the most productive countries/regions, institutions and authors. After that, co-citation networks of institutions, authors and papers illustrated by VOS Viewer are given to show the relationship among those and the most influential of them is further analyzed. Then, the characteristics of the patent are analyzed based on the basic information and classification of the patent and the most productive inventors. In order to obtain research hotspots and trends in this field, the time-line review and citation burst detection of keywords carried out by Cite Space are made to be visual. Finally, based on the above analysis, it draws some other important conclusions and the development trend of this field.

The research on FTL algorithm is still the top priority in the future, and how to improve the performance of SSD in the era of big data is one of the research hotspots.

This paper makes a comprehensive analysis of FTL with the method of bibliometrics, and it is valuable for researchers can quickly grasp the hotspots in this area.

This article draws the structural characteristics of the publications in this field and summarizes the research hotspots and trends in this field in recent years, aiming to inspire new ideas for researchers.

Knowledge is a main resource of any organization. Knowledge management (KM) is identified by four processes: creating, capturing, distributing and sharing of knowledge. Technology can enable successful KM. The purpose of this paper is to propose a technology knowledge management (TKM) taxonomy, which lists popular electronic tools that can enhance KM processes and shows which tool can contribute to which processes.

The taxonomy was developed by an extensive literature review of electronic KM tools and a three-year extensive analysis of different knowledge sources at the Jeddah Municipality (JM) in Saudi Arabia.

The taxonomy can be used by practitioners developing an organizational KM system to guide them to choose a sufficient subset of tools that covers all four processes in order to ensure that no process is overlooked.

The result of using the TKM taxonomy and its effect on KM success is an interesting area for further research. However, the current value underlies in it offering practitioners a rough roadmap to an electronic KM system and aids in giving at least a starting point.

The TKM taxonomy can be used by large scale organizations to guide in developing a KM system effectively and more efficiently. Furthermore, the JM KC is a good model for similar organizations to use, with all the tools explained in the paper.

The paper addresses some of the social elements related to successful KM in organizations. However, it is more technically targeted.

Researchers have investigated either the holistic effect of IT on KM or described certain tools. The types of IT tools and their effect on KM have not been investigated. Furthermore, limited research addresses the design of effective KM systems and no tools exist to guide designers. The TKM taxonomy is a tool that can help KM practitioners and strategists to design effective KM systems efficiently, by guiding them in choosing tools that are suitable for certain KM processes. The paper also describes the JM Knowledge Center as a KMS model for organizations which addresses all four KM processes.