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Evaluation of Online Services Using the MCDM Approach: User Evaluation and Expert Evaluation : 다기준 의사결정 방법론을 활용한 온라인 서비스의 평가: 사용자 평가와 전문가 평가
DC Field | Value | Language |
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dc.contributor.advisor | 박용태 | - |
dc.contributor.author | 강대국 | - |
dc.date.accessioned | 2017-07-13T06:06:16Z | - |
dc.date.available | 2017-07-13T06:06:16Z | - |
dc.date.issued | 2015-08 | - |
dc.identifier.other | 000000066729 | - |
dc.identifier.uri | https://hdl.handle.net/10371/118272 | - |
dc.description | 학위논문 (박사)-- 서울대학교 대학원 : 산업·조선공학부, 2015. 8. 박용태. | - |
dc.description.abstract | The recent development of the Internet technology has drawn attention toward online services. In response, many kinds of online services have been developed. These online services have some distinct characteristics compared with those of traditional offline services. First, online services can provide more service functions than offline services, because there is no restriction of time and space in terms of the service provision. Second, customers often have deficient information about online service providers, because online service provision generally takes place between parties who have never transacted with each other before. Third, online services have difficulty providing the spontaneous responses that can be offered in offline services through face-to-face contact. Given these circumstances, the evaluation of online services through continuous monitoring to reliably provide various service functions has been emphasized. In response, much attention has been paid to the evaluation of online services. Many studies have utilized multiple criteria decision-making (MCDM) approaches for evaluating online services by considering various evaluation criteria simultaneously. Although previous studies contributed to improving our initial understanding of the evaluation of online services, they were limited, as they did not consider the characteristics of evaluators and different types of evaluation data.
Accordingly, the focus of this study is to suggest an evaluation framework of online services that considers characteristics of evaluators and types of data in the process of evaluation. The study is composed of two modules and three themes. First, module 1 focuses on the issue of user evaluation of online services using MCDM. Specifically, user evaluation approaches can be divided into two types in terms of the data that can be collected from evaluators: evaluations based on customer reviews and evaluations based on surveys. Therefore, different frameworks for user evaluation in terms of data are provided as different themes. Second, module 2 deals with the issue of expert evaluation of online services using MCDM. This module includes the third theme. The objectives of the three themes are specified as follows. Theme #1 aims to suggest a framework for user evaluation of online services using customer reviews. Customer reviews have been recognized as valuable information sources for monitoring and enhancing customer satisfaction levels, and they provide the real voices of users. Each customer review has concise core content focused on important criteria in comparison with survey data, which contains information about evaluation scores for alternatives in terms of various criteria. It can be useful in situations in which we cannot collect survey data. To analyze a large number of customer reviews effectively and evaluate online services, this study combines VIKOR (in Serbian: ViseKriterijumsa Optimizacija I Kompromisno Resenje) and sentiment analysis. The suggested framework of this study mainly consists of two stages: (1) data collection and preprocessing and (2) customer satisfaction measurement. In the first stage, data collection and preprocessing, text mining is utilized to compile customer-review-based dictionaries of attributes and sentiment words. Then, using sentiment analysis, sentiment scores for attributes are calculated for each mobile service. In the second stage, levels of customer satisfaction are measured using VIKOR. For the purpose of illustration, an empirical case study was conducted on customer reviews of mobile application services. Theme #1 contributes to propose a systematic way of utilizing customer reviews in the evaluation process Theme #2 presents a tailored framework for user evaluation of online services using survey data. In this study, fuzzy hierarchical TOPSIS based on E-SERVQUAL (E-S-QUAL) is suggested for the effective evaluation of online services using information gathered from surveys. To put it more concretely, with E-S-QUAL, which is the extended version of SERVQUAL for the measurement of electronic service quality, information about the gap between the expected and perceived service level collected from users can be extracted effectively. In addition, with fuzzy hierarchical TOPSIS, the hierarchical structure of the evaluation criteria between the main dimensions and their sub-criteria can be considered. With this approach, based on the preservation of the core concept of E-S-QUAL, the hierarchy between the main dimensions and their sub-criteria can be captured. Theme #3 focuses on the suggestion of an integrated model consisting of fuzzy DEMATEL and fuzzy VIKOR as a stepwise method of expert evaluation of online services. This theme mainly focuses on the effective approach for making the best use of knowledge of expert opinions collected from surveys. For expert evaluations, more detailed information (e.g., interrelationships among evaluation criteria) is collected. The evaluation process proceeds as follows. First, several online services providing similar functions are selected as alternatives, and service-based and product-based criteria are defined based on a literature review and expert opinions. Second, interrelationships and importance weights of criteria are calculated using fuzzy DEMATEL. Third, the online service is evaluated using fuzzy VIKOR. In this theme, the inevitable and often conflicting relationships among service-based criteria and product-based criteria can be effectively captured to reflect the evaluation of online services. | - |
dc.description.tableofcontents | Contents
Chapter 1. Introduction 1 1.1. Background and motivation 1 1.2. Purpose 3 1.3. Scope and framework 5 1.4. Thesis outline 6 Chapter 2. Background 8 2.1. Theoretical background 8 2.1.1. Evaluation of online services 8 2.1.2. MCDM approach 10 2.2. Methodological background 11 2.2.1. Sentiment analysis 12 2.2.2. VIKOR 13 2.2.3. E-S-QUAL method 15 2.2.4. Fuzzy set theory 17 2.2.5. Fuzzy hierarchical TOPSIS 21 2.2.6. Fuzzy DEMATEL method 22 Chapter 3. User evaluation of online services using MCDM 23 3.1. User evaluation based on customer reviews 23 3.1.1. Introduction 23 3.1.2. Proposed approach 26 3.1.3. Empirical case study 33 3.1.4. Conclusions 41 3.2. User evaluation based on surveys 43 3.2.1. Introduction 43 3.2.2. Proposed approach 47 3.2.3. Empirical case study 57 3.2.4. Conclusions 73 Chapter 4. Expert evaluation of online services using MCDM 75 4.1. Expert evaluation based on surveys 75 4.1.1. Introduction 75 4.1.2. Proposed approach 77 4.1.3. Empirical case study 85 4.1.4. Conclusions 98 Chapter 5. Conclusions and discussions 99 5.1. Summary and contributions 99 5.2. Limitations and future research 101 Bibilography 103 초록 116 List of Tables Table 2 1 Perspectives on evaluation of online services 9 Table 2-2 Methodologies utilized in this study 12 Table 2 3 Descriptions of four dimensions of E-S-QUAL 16 Table 2 4 TFNs of linguistic variables for rating of alternatives 19 Table 2 5 TFNs of linguistic variables for importance weight of criteria 20 Table 3 1 Number of customer reviews in each category of mobile application service 34 Table 3 2 Criteria for measurement of customer satisfaction with mobile application service in category of social networking service 35 Table 3 3 Calculated scores of attributes in each mobile application service 36 Table 3 4 Normalized scores of attributes and weights of attributes 37 Table 3 5 Positive ideal solutions and negative ideal solutions 37 Table 3 6 Scores of 38 Table 3 7 values and ranking 39 Table 3 8 Evaluation criteria of four dimensions and sub-criteria of E-S-QUAL 58 Table 3 9 TFN of linguistic variables for rating of alternatives 60 Table 3 10 Aggregated expected fuzzy values and the perceived fuzzy values 61 Table 3 11 Defuzzified matrix based on calculated BNP values 62 Table 3 12 Aggregated normalized decision matrix 64 Table 3 13 Importance weights of main dimensions of E-S-QUAL 65 Table 3 14 Importance weights of sub-criteria of E-S-QUAL 66 Table 3 15 Final importance weights of sub-criteria of E-S-QUAL 67 Table 3 16 Weighted normalized decision matrix 68 Table 3 17 Final weighted normalized decision matrix by addition principle 69 Table 3 18 PIS and NIS for each alternative 69 Table 3 19 Distance from PIS and NIS for each alternative 70 Table 3 20 Closeness coefficients ( ) of six alternatives and ranking order 71 Table 3 21 Results of the sensitivity analysis 72 Table 4 1 Criteria for mobile service evaluation within navigation category 86 Table 4 2 Initial direct-influence fuzzy matrix as assessed by evaluators 87 Table 4 3 Aggregated initial direct-influence defuzzified matrix 88 Table 4 4 Total influence matrix 89 Table 4 5 Linguistic variables for rating of alternatives 92 Table 4 6 Aggregated defuzzified ratings of alternatives 93 Table 4 7 Positive ideal solutions and negative ideal solutions 93 Table 4 8 Scores of 94 Table 4 9 values and ranking 95 List of Figures Figure 1-1 Purposes of two modules 3 Figure 1-2 Type of evaluation in this study 5 Figure 1-3 Scope of study 6 Figure 1-4 Overall structure of study 7 Figure 2 1 Membership functions of linguistic variables for rating 19 Figure 2 2 Membership functions of linguistic variables representing importance weight of criteria 19 Figure 3 1 Overall research framework 27 Figure 3 2 Example of construction of keyword vector 30 Figure 3 3 Examples of normalized polarity scores for attributes 32 Figure 3 4 Dictionary of sentiment words 35 Figure 3 5 Sensitivity analysis 40 Figure 3 6 Research framework for evaluation of B2C e-commerce websites 47 Figure 3 7 Results of the sensitivity analysis 73 Figure 4 1 Overall framework 78 Figure 4 2 Influence relation map (IRM) 90 Figure 4 3 Sensitivity analysis 97 | - |
dc.format | application/pdf | - |
dc.format.extent | 33882553 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | Online service evaluation | - |
dc.subject | user evaluation | - |
dc.subject | expert evaluation | - |
dc.subject | multiple criteria decision-making (MCDM) | - |
dc.subject | sentiment analysis | - |
dc.subject.ddc | 623 | - |
dc.title | Evaluation of Online Services Using the MCDM Approach: User Evaluation and Expert Evaluation | - |
dc.title.alternative | 다기준 의사결정 방법론을 활용한 온라인 서비스의 평가: 사용자 평가와 전문가 평가 | - |
dc.type | Thesis | - |
dc.contributor.AlternativeAuthor | Daekook Kang | - |
dc.description.degree | Doctor | - |
dc.citation.pages | viii, 118 | - |
dc.contributor.affiliation | 공과대학 산업·조선공학부 | - |
dc.date.awarded | 2015-08 | - |
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