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컨조인트 분석을 이용한 온라인 제품 평가점수의 해부: 환대산업의 선택속성을 중심으로
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 강주영 | - |
| dc.contributor.author | Rhee, Hosung Timothy | - |
| dc.date.accessioned | 2018-11-08T08:11:59Z | - |
| dc.date.available | 2018-11-08T08:11:59Z | - |
| dc.date.issued | 2016-08 | - |
| dc.identifier.other | 23247 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/11577 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :경영정보학과,2016. 8 | - |
| dc.description.tableofcontents | 1. INTRODUCTION 1 1.1 Overview 1 1.2 Hotel and Restaurant Attributes and Conjoint Analysis 3 1.2.1. Attributes Manifested in Online Product Reviews 3 1.2.2. Conjoint Analysis Implementation 3 1.3. Overall Scope of the Dissertation 3 1.4. Theoretical Foundations 5 1.4.1. Theory based on Information Systems 6 1.4.2. Theories based on Cognition 8 1.5. Highlights of the Results 12 1.5.1. Essays 1, 2, and 3: Overall Travelers Group Study 13 1.5.2. Essays 1 and 2: Attribute Combinations TPWV Study 15 1.5.3. Essays 1 and 3: Country-of-residence Study 17 2. ESSAY 1: How Does Hotel Attribute Importance Vary among Different Travelers An Exploratory Case Study based on a Conjoint Analysis Approach 22 2.1. Introduction 22 2.2. Literature Review 23 2.2.1. Hotel Research Trend 23 2.2.2. Hotel Research Using Conjoint Analysis 24 2.2.3. Hotel Attributes 25 2.3. Methodology 31 2.3.1. Research Design 31 2.3.2. Data Collection 32 2.4. Results 34 2.4.1. Study 1: Overall Travelers Group 34 2.4.2. Study 2: Different Trip-Purpose Groups 36 2.4.3. Study 3: Different Country-of-residence Groups 38 2.5. Discussion and Propositions 39 2.6. Conclusion 45 2.6.1. Managerial Implications 45 2.6.2. Theoretical Implications 46 2.6.3. Limitations and Future Research 47 3. Essay 2: Does Hotel Attribute Importance Differ by Hotels Focusing on Hotel Star-classifications and Customers Overall Ratings 49 3.1. Introduction 49 3.2. Literature Review 50 3.2.1. Hotel Attributes 50 3.2.2. Hotel Star-classifications 51 3.2.3. Hotel Attribute Importance, Hotel Star-classifications, and Customers Overall Ratings 52 3.3. Research Methodology 57 3.3.1. Research Design 57 3.3.2. Data Collection 59 3.4. Results 61 3.4.1. Study 1: Hotel Attribute Importance at Aggregated level 61 3.4.2. Study 2: Comparison between 2-star and 4-star Hotel Groups 62 3.4.3. Study 3: Comparison between Low- and High-rated Hotel Groups 64 3.4.4. Study 4: Comparison among Different Combinations of Six Attributes 66 3.5. Discussion and Propositions 68 3.6. Conclusion 72 4. Essay 3: Exploring the Comparative Salience of Restaurant Attributes: a Conjoint Analysis Approach 76 4.1. Introduction 76 4.2. Literature Review 78 4.2.1. Restaurant Research Employing Conjoint Analysis 78 4.2.2. Restaurant Attributes 82 4.2.2.1. Value 82 4.2.2.2. Service 83 4.2.2.3. Atmosphere 84 4.2.2.4. Food 84 4.3. Research Methodology 85 4.3.1. Research Sample and Design 85 4.3.2. Data Collection 87 4.4. Results 88 4.4.1. Study 1: Overall Customer (Domestic and Foreign travelers) Group across Three Restaurants (SS, BJ, and TSP) 88 4.4.2. Study 2: Comparison between Domestic and Foreign Travelers for Three Restaurants (SS, BJ, and TSP) 90 4.4.3. Study 3: Overall Restaurant Customer Group (Domestic and Foreign Travelers) for Each Restaurant 91 4.4.4. Study 4: Domestic Versus Foreign Traveler Groups for Each Restaurant 93 4.4.4.1. Shake Shack 93 4.4.4.2. Burger Joint 94 4.4.4.3. The Spotted Pig 95 4.5. Discussion and Propositions 96 4.6. Conclusion and Implications 102 4.6.1. Findings 102 4.6.2. Managerial Implications 103 4.6.3. Theoretical Implications 103 4.6.4. Limitations and Future Research 104 5. Conclusion 105 6. References 107 7. Appendix 115 Abstract in Korean Language 132 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 컨조인트 분석을 이용한 온라인 제품 평가점수의 해부: 환대산업의 선택속성을 중심으로 | - |
| dc.title.alternative | Essays on Deconstructing Online Product Reviews’ Numerical Rating | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 경영정보학과 | - |
| dc.date.awarded | 2016. 8 | - |
| dc.description.degree | Doctoral | - |
| dc.identifier.localId | 758564 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000023247 | - |
| dc.subject.keyword | online product reviews | - |
| dc.subject.keyword | hotels attributes | - |
| dc.subject.keyword | restaurant attributes | - |
| dc.subject.keyword | attribute importance value | - |
| dc.subject.keyword | part-worth values | - |
| dc.subject.keyword | conjoint analysis | - |
| dc.description.alternativeAbstract | Internet technology has introduced an online business platform where the collaboration among product/service providers, third-party website providers, and consumers leads to a new value creation for society. One of the important aspects of this platform is the relationship between user generated contents (UGSs) in the form of online product reviews (OPRs) and e-WOM (electronic word-of-mouth). This dissertation explores the dynamics of the comparative importance of critical attributes of hospitality industry (specifically, hotels and restaurants) by deconstructing the numerical rating of OPRs. The application of conjoint analysis to the Tripadvisor.com data enables the interpretation of six hotel attributes (i.e., value, service, rooms, sleep quality, location, and cleanliness) and four restaurant attributes (i.e., value, service, food, and atmosphere) in two particular ways: attribute importance value and attribute part-worth value (PWV). This explorative case study consists of three independent essays that investigate a myriad of granular information regarding travelers’ assessment on the attributes. Travelers are divided into domestic and foreign residents, and also into distinctive trip purpose groups (i.e., business, families, couples, solo, and friends). In addition to examining the effects of hotel’s star rating (4-star versus 2-star) and overall rating (high-rated versus low-rated) on travelers' demand, the impact of each attribute’s PWV on the total part-worth value (TPWV) is analyzed. Furthermore, how the type of restaurant information, by itself or combined with the travelers' residency information, influences travelers' behavior is scrutinized. From the pool of numerous meaningful findings from the study, some of the emerged facts are as follows: (1) Whether staying at hotels or dining at restaurants, travelers consider the value and the ‘industry-specific attribute (i.e., rooms for hotels and food for restaurants)’ as the most important attributes. (2) A group of families or friends staying at hotels place a higher degree of importance on value than a group with other purposes (i.e., business, couples, and solo). (3) Whereas domestic travelers indicate a greater degree of importance on rooms than foreign travelers, foreign travelers convey a greater degree of importance on location than domestic travelers. (4) The 2-star hotels amass a greater degree of importance on location from travelers than the 4-star hotels. (5) The management of high-rated hotels should closely monitor the service attribute, but not cleanliness and location attributes. (6) If two kinds of travelers were to eat at restaurants, foreign travelers’ emphasis on food and atmosphere is greater than domestic travelers'. (7) If indoor restaurants keep the price low, foreign travelers’ emphasis on value and service is greater than domestic travelers’ while domestic travelers’ emphasis on food and atmosphere is greater than its counterpart. The management of hospitality industry may unearth ‘hidden’ information that could be transformed into the knowledge of ‘jewels’ by integrating OPR data with conjoint analysis. This novel method can assist the management to strategically prioritize important attributes in the middle of changing circumstances. Moreover, an efficient resource deployment may be achieved by knowing the trade-off implications among different attributes, and their impact on the overall rating. Lastly, the decomposition of data into multi-dimensions of sub-levels may pave a way to developing a new theory that is suitable for big data interpretation. | - |
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- Graduate School of Ajou University > Department of Management Information Systems > 4. Theses(Ph.D)
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