Prioritizing critical success factors of knowledge management using FAHP: A case study in Refah Bank branches of Iran

Therefore, this research has explained the critical success factors of knowledge management, for this purpose following previous research and literature review, research prototype was developed. In the next stage, the prototype was given to experts and after implementing amendments based on a consensus of experts opinion the components and model parameters of Li Huang’s model (2012) was the basis of the research. He has introduced the critical success factors in six categories, including cultural factors, environmental factors, organizational characteristics, individual characteristics, information technology infrastructures and knowledge management features. On the other hand, due to the influence of various factors on the process of knowledge management implementing, the multi-criteria decision making techniques were used and based on this the decision making issue was structured in three hierarchical levels. And linguistic words with their meanings were replaced in the form of triangular fuzzy numbers by gaining the knowledge and information of decision makers in the form of linguistic words through related paired comparisons questionnaire. Then the obtained data was arranged in matrix form in Excel and finally prioritization was done based on fuzzy hierarchy analysis using code writing in Excel and with the help of Super decision software. Results of this research could be used as a guide of way and a supplement for implementation of knowledge management so that the organization devote its resources such as financial and time resources to achieve and improve critical success factors of knowledge management according to the priorities of the key success factor of knowledge management

An Intelligent Customer Relationship Management (I-CRM) Framework and its Analytical Approaches to the Logistics Industry

This thesis develops a new Intelligent Customer Relationship Management (i-CRM) framework, incorporating an i-CRM analytical methodology including text-mining, type mapping, liner, non-liner and neuron-fuzzy approaches to handle customer complaints, identify key customers in the context of business values, define problem significance and issues impact factors, coupled with i-CRM recommendations to help organizations to achieve customer satisfaction through transformation of the customer complaints to organizational opportunities and business development strategies

How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation

What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent than statistics-focused metrics. However, extant studies rarely use domain experts in evaluating the outcomes. Out of the 169 studies that provided details about hyperparameters, more than four out of five used segment size as their only hyperparameter. Typically, studies generate four segments, although the maximum number rarely exceeds twenty, and in most cases, is less than ten. Based on these findings, we propose seven key goals and three practical implications to enhance customer segmentation research and application.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed