scholarly journals A multi-attribute data mining model for rule extraction and service operations benchmarking

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hannan Amoozad Mahdiraji ◽  
Madjid Tavana ◽  
Pouya Mahdiani ◽  
Ali Asghar Abbasi Kamardi
Keyword(s):  
Data Mining ◽  
Service Operations ◽  
Rule Extraction ◽  
Service Industries ◽  
Quality Analysis ◽  
Behavior Patterns ◽  
Content Type ◽  
Attribute Data ◽  
Data Mining Approach ◽  
Combining Data

PurposeCustomer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.Design/methodology/approachThe authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.FindingsAs a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.Originality/valueThe authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

Promoting knowledge sharing performance in a knowledge management system: do knowledge workers’ behavior patterns matter?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Navid Nezafati ◽  
Shokouh Razaghi ◽  
Hossein Moradi ◽  
Sajjad Shokouhyar ◽  
Sepideh Jafari
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Knowledge Workers ◽  
Reward System ◽  
Knowledge Management System ◽  
Behavior Patterns ◽  
Content Type ◽  
Organizational Variables ◽  
Top Managers ◽  
The Impact

Purpose This paper aims to identify the impact of demographical and organizational variables such as age, gender, experiences use of knowledge management system (KMS), education and job level on knowledge sharing (KS) performance of knowledge workers in knowledge activities of a KMS. Specifically, it seeks to explore that is there any relationship between the KS behavior patterns of high KS performance knowledge workers with their performance. Furthermore, this study using its conceptual attitude model aims to show that whether knowledge workers’ behavior patterns in sharing information and knowledge throughout a KMS have any specific effect or not. Design/methodology/approach This paper proposed a framework to mine knowledge workers’ raw data using data mining techniques such as clustering and association rules mining. Also, this research uses a case-based approach to a knowledge-intensive company in Iran that works in the field of information technology with 730 numbers of workers. Findings Findings suggest that demographical and organizational variables such as age, education and experience use of KMS have positive effects on knowledge worker’s KS behavior in KMSs. In fact, people who have lower age, higher education degrees and more experience use of KMS, have more participation in KS in KMS. Also, results depict that the experienced use of KMS has the most impact on the intention of KS in this KMS. Findings emphasize on the importance of the influence of the behavioral, organizational environments and psychological factors such as reward system, top management support, openness and trust, on KS performance of knowledge workers in the KMS. In fact, according to data, the KMS reward system caused to increasing participation of the users in KS, also in each knowledge activity that top managers participate in, the scores were higher. Practical implications This research helps top managers in designing policies and strategies to improve the participation of knowledge workers in KS and helps human resource managers to improve their membership policies. Also, assist Information Technology (IT) managers to enhance KMSs’ design to leverage with organization strategies in the field of improving KS and encourage people to participate in KMS. Originality/value This research has two key values. First, this paper applies a data mining framework to mining and analyzing data and this paper uses actual data of a KMS in a specialist company in Iran, with about 27,740 real data points. Second, this paper investigates the impact of demographical and organizational attributes on KS behavior, which little is empirically known about the impact of demographical variables on KS intention.

Measuring the variance of customer waiting time in service operations

2014 ◽  
Vol 52 (2) ◽  
pp. 296-312 ◽  
Author(s):  
Xiaofeng Zhao ◽  
Jianrong Hou ◽  
Kenneth Gilbert
Keyword(s):  
Standard Deviation ◽  
Waiting Time ◽  
Coefficient Of Variation ◽  
Service Operations ◽  
Exact Expression ◽  
Service Industries ◽  
Significant Feature ◽  
Content Type ◽  
Queuing Models ◽  
Spreadsheet Model

Purpose – Waiting lines and delays have become commonplace in service operations. As a result, customer waiting time guarantee is a widely used competition strategy in service industries. To implement waiting time guarantee strategy, managers need to not only know the average of waiting time, but also the variance around average waiting time. This paper aims to discuss these issues. Design/methodology/approach – This research provides a mathematically exact expression for the coefficient of variation of waiting time for Markov queues. It then applies the concept of isomorphism to approximate the variance of customer waiting time in a general queue. Simulation experiments are conducted to verify the accurate approximations. Findings – A significant feature of the approximation method is that it is mathematically tractable and can be implemented in a spreadsheet format. It provides a practical way to estimate the variance of customer waiting time in practice. The results demonstrate the usefulness of the queuing models in providing guidance on implementing appointment scheduling and waiting time guarantee strategy. Also, the spreadsheet can be used to conduct what-if analysis by inputting different parameters. Originality/value – This paper develops a simple, easy-to-use spreadsheet model to estimate the standard deviation of waiting time. The approximation requires only the mean and standard deviation or the coefficient of variation of the inter-arrival and service time distributions, and the number of servers. A spreadsheet model is specifically designed to analyze the variance of waiting time.

scholarly journals Network-based pricing for 3D printing services in two-sided manufacturing-as-a-service marketplace

2020 ◽  
Vol 26 (1) ◽  
pp. 82-88 ◽  
Author(s):  
Deepak Pahwa ◽  
Binil Starly
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
3D Printing ◽  
Ad Hoc ◽  
Activity Based Costing ◽  
Content Type ◽  
Data Mining Approach ◽  
Price Range ◽  
Competitive Prices ◽  
Profile Characteristics

Purpose This paper presents approaches to determine a network-based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The purpose of this study is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad hoc and subjective prices. Design/methodology/approach A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders and scale of operations, among others, to estimate a price range for suppliers’ services. Data were gathered from existing marketplace websites, which were then used to train and test the model. Findings The model demonstrates an accuracy of 65 per cent for US-based suppliers and 59 per cent for Europe-based suppliers to classify a supplier’s 3D printer listing in one of the seven price categories. The improvement over baseline accuracy of 25 per cent demonstrates that machine learning-based methods are promising for network-based pricing in manufacturing marketplaces Originality/value Conventional methodologies for pricing services through activity-based costing are inefficient in strategically priced 3-D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.

Using Twitter to engage with customers: a data mining approach

2015 ◽  
Vol 25 (3) ◽  
pp. 416-434 ◽  
Author(s):  
Shintaro Okazaki ◽  
Ana M. Díaz-Martín ◽  
Mercedes Rozano ◽  
Héctor David Menéndez-Benito
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Web 2.0 ◽  
Knowledge Sharing ◽  
Customer Engagement ◽  
Service Development ◽  
Content Type ◽  
Customer Complaints ◽  
Data Mining Approach ◽  
Robust Model

Purpose – The purpose of this paper is to explore customer engagement in Twitter via data mining. Design/methodology/approach – This study’s intended contributions are twofold: to find a clear connection among customer engagement, presumption, and Web 2.0 in a context of service-dominant (S-D) logic; and to identify social networks created by prosumers. To this end, the study employed data mining techniques. Tweets about IKEA were used as a sample. The resulting algorithm based on 300 tweets was applied to 4,000 tweets to identify the patterns of electronic word-of-mouth (eWOM). Findings – Social networks created in IKEA’s tweets consist of three forms of eWOM: objective statements, subjective statements, and knowledge sharing. Most objective statements are disseminated from satisfied or neutral customers, while subjective statements are disseminated from dissatisfied or neutral customers. Satisfied customers mainly carry out knowledge sharing, which seems to reflect presumption behavior. Research limitations/implications – This study provides partial evidence of customer engagement and presumption in IKEA’s tweets. The results indicate that there are three forms of eWOM in the networks: objective statements, subjective statements, and knowledge sharing. It seems that IKEA successfully engaged customers in knowledge sharing, while negative opinions were mainly disseminated in a limited circle. Practical implications – Firms should make more of an effort to identify prosumers via data mining, since these networks are hidden behind “self-proclaimed” followers. Prosumers differ from opinion leaders, since they actively participate in product development. Thus, firms should seek prosumers in order to more closely fit their products to consumer needs. As a practical strategy, firms could employ celebrities for promotional purposes and use them as a platform to convert their followers to prosumers. In addition, firms are encouraged to make public how they resolve problematic customer complaints so that customers can feel they are a part of firms’ service development. Originality/value – Theoretically, the study makes unique contributions by offering a synergic framework of S-D logic and Web 2.0. The conceptual framework collectively relates customer engagement, presumption, and Web 2.0 to social networks. In addition, the idea of examining social networks based on different forms of eWOM has seldom been touched in the literature. Methodologically, the study employed seven algorithms to choose the most robust model, which was later applied to 4,000 tweets.

Applying data mining for online CRM marketing strategy

2018 ◽  
Vol 120 (3) ◽  
pp. 665-675 ◽  
Author(s):  
Wen-Yu Chiang
Keyword(s):  
Data Mining ◽  
Marketing Strategy ◽  
Association Rules ◽  
Clustering Algorithm ◽  
Customer Relationship ◽  
Coffee Shop ◽  
Content Type ◽  
Fuzzy Association Rules ◽  
Data Mining Approach ◽  
Coffee Shops

Purpose The purpose of this paper is to propose a data mining approach for mining valuable markets for online customer relationship management (CRM) marketing strategy. The industry of coffee shops in Taiwan is employed as an empirical case study in this research. Design/methodology/approach Via a proposed data mining approach, the study used fuzzy clustering algorithm and Apriori algorithm to analyze customers for obtaining more marketing and purchasing knowledge of online CRM systems. Findings The research found three hard markets and one fuzzy market. Furthermore, the study discovered two association rules and two fuzzy association rules. Originality/value However, industry of coffee shops has been always a fast-growing and competitive business around the world. Thus, marketing strategy is important for this industry. The results and the proposed data mining approach of this research can be used in the industry of coffee shop or other retailers for their online CRM marketing systems.

Customer credit scoring using a hybrid data mining approach

Kybernetes ◽  
2016 ◽  
Vol 45 (10) ◽  
pp. 1576-1588 ◽  
Author(s):  
Mohammadali Abedini ◽  
Farzaneh Ahmadzadeh ◽  
Rassoul Noorossana
Keyword(s):  
Data Mining ◽  
Ensemble Learning ◽  
Majority Voting ◽  
Support Vector ◽  
Type I ◽  
Content Type ◽  
Data Mining Approach ◽  
Hybrid Data ◽  
Last Stage ◽  
Bagging Ensemble

Purpose A crucial decision in financial services is how to classify credit or loan applicants into good and bad applicants. The purpose of this paper is to propose a four-stage hybrid data mining approach to support the decision-making process. Design/methodology/approach The approach is inspired by the bagging ensemble learning method and proposes a new voting method, namely two-level majority voting in the last stage. First some training subsets are generated. Then some different base classifiers are tuned and afterward some ensemble methods are applied to strengthen tuned classifiers. Finally, two-level majority voting schemes help the approach to achieve more accuracy. Findings A comparison of results shows the proposed model outperforms powerful single classifiers such as multilayer perceptron (MLP), support vector machine, logistic regression (LR). In addition, it is more accurate than ensemble learning methods such as bagging-LR or rotation forest (RF)-MLP. The model outperforms single classifiers in terms of type I and II errors; it is close to some ensemble approaches such as bagging-LR and RF-MLP but fails to outperform them in terms of type I and II errors. Moreover, majority voting in the final stage provides more reliable results. Practical implications The study concludes the approach would be beneficial for banks, credit card companies and other credit provider organisations. Originality/value A novel four stages hybrid approach inspired by bagging ensemble method proposed. Moreover the two-level majority voting in two different schemes in the last stage provides more accuracy. An integrated evaluation criterion for classification errors provides an enhanced insight for error comparisons.

Assessing the impact of students’ activities in e-courses on learning outcomes: a data mining approach

2019 ◽  
Vol 16 (2) ◽  
pp. 117-129 ◽  
Author(s):  
Damijana Keržič ◽  
Aleksander Aristovnik ◽  
Nina Tomaževič ◽  
Lan Umek
Keyword(s):  
Data Mining ◽  
Learning Outcomes ◽  
Undergraduate Students ◽  
Research Question ◽  
Final Exam ◽  
Content Type ◽  
Data Mining Approach ◽  
Student’S Performance ◽  
The Impact ◽  
The Relationship

Purpose This paper aims to study the relationship between students’ activities in the e-classroom and grades for the final exam. The study was conducted at the Faculty of Administration, University of Ljubljana among first-year undergraduate students. In the e-classroom, students learn new content for individual self-study, and their knowledge is checked with quizzes. Design/methodology/approach In the empirical study, the relationship between performance in quizzes and at the final exam was studied from two perspectives. First, successful and unsuccessful students (in terms of quizzes) were compared. Second, the Orange data mining software was used for two predictive modelling tasks. The research question was based on a student’s quiz performances, is it possible to predict whether the student will pass an exam and will the student’s grade for the exam be good. Findings The empirical results indicate a very strong connection between a student’s performance in quizzes and their score for the final exam in the course. Significant differences in performance were found between students who had completed most quizzes and those who had not. Moreover, the results highlighted which quizzes, in other words topics, are most important for passing an exam or obtaining a better grade. Therefore, the quality of individual study in the e-classroom positively influences a student’s performance. Originality/value The paper is the first to assess the impact of students’ activities on learning outcomes in undergraduate public administration programmes by applying a data mining approach.

Routing Attribute Data Mining Based on Rough Set Theory

2008 ◽  
pp. 3033-3048 ◽  
Author(s):  
Yanbing Liu ◽  
Shixin Sun ◽  
Menghao Wang ◽  
Hong Tang
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Selection ◽  
Attribute Data ◽  
Data Mining Approach ◽  
New Information

QOSPF(Quality of Service Open Shortest Path First)based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision-making. This paper focuses on routing algorithms and their applications for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory of-fers a knowledge discovery approach to extracting routing-decisions from attribute set. The extracted rules can then be used to select significant routing-attributes and make routing-selections in routers. A case study is conducted to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algo-rithm based on data mining and rough set offers a promising approach to the attribute-selection problem in internet routing.

Routing Attribute Data Mining Based on Rough Set Theory

2011 ◽  
pp. 322-337
Author(s):  
Yanbing Liu ◽  
Menghao Wang ◽  
Jong Tang
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Selection ◽  
Attribute Data ◽  
Data Mining Approach ◽  
New Information

QOSPF (Quality of Service Open Shortest Path First) based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services on the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision making. This article focuses on routing algorithms and their applications for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory offers a knowledge discovery approach toextracting routing decisions from attribute set. The extracted rules then can be used to select significant routing attributes and to make routing selections in routers. A case study is conducted in order to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algorithm based on data mining and rough set offers a promising approach to the attribute selection problem in Internet routing.

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