| 000 | 00000nam c2200205 c 4500 | |
| 001 | 000046145354 | |
| 005 | 20230524091835 | |
| 006 | m d | |
| 007 | cr | |
| 008 | 221222s2023 ulka obmAC 000c eng | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
| 085 | 0 | ▼a 0510 ▼2 KDCP |
| 090 | ▼a 0510 ▼b 6B2 ▼c 2336 | |
| 100 | 1 | ▼a Xu, Zili |
| 245 | 1 0 | ▼a Unifying model of conjoint analysis and MCDM ▼h [전자자료] : ▼b focused on music streaming services / ▼d Zili Xu |
| 260 | ▼a Seoul : ▼b Graduate School, Korea University, ▼c 2023 | |
| 300 | ▼a 전자책 1책(35 p.) : ▼b 천연색삽화 | |
| 500 | ▼a 지도교수: 임호순 | |
| 500 | ▼a 본표제는 표제면 이미지의 표제임 | |
| 502 | 0 | ▼a 학위논문(석사)-- ▼b 고려대학교 대학원, ▼c 경영학과, ▼d 2023. 2 |
| 504 | ▼a 참고문헌 수록 | |
| 653 | ▼a Music streaming ▼a MCDM ▼a conjoint analysis | |
| 900 | 1 0 | ▼a 임호순, ▼g 任浩淳, ▼d 1963-, ▼e 지도교수 ▼0 AUTH(211009)38996 |
| 900 | 1 0 | ▼a Rhim, Hosun, ▼e 지도교수 |
| 945 | ▼a ITMT | |
| 991 | ▼a E-Book(학위논문) ▼w (DCOLL211009)000000270935 |
Electronic Information
| No. | Title | Service |
|---|---|---|
| 1 | Unifying model of conjoint analysis and MCDM [전자자료] : focused on music streaming services (10회 열람) |
View PDF Abstract Table of Contents |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Main Library/e-Book Collection/ | Call Number CT 0510 6B2 2336 | Accession No. E13000119 | Availability Loan can not(reference room) | Due Date | Make a Reservation | Service |
Contents information
Abstract
In this study, we present a model unifying conjoint analysis and MCDM. Attributes of conjoint analysis are configured using the text mining method. We analyzed the web texts through the natural language processing algorithm to extract attributes of conjoint models from online user comments. Profiles of the conjoint analysis model are compared to obtain multiple objective for decision making. Subjective weights are assigned through a survey method to form a unified model. We generate the market share through market simulation in conjoint analysis, and further improve the best alternative by fine-tuning the criteria to arrive at the final decision.
Table of Contents
Abstract 1 1. Introduction 3 2. Literature review 5 2.1 Text mining 5 2.2 Conjoint analysis 6 2.3 MCDM 7 3. The model 8 4. Example setting 14 5. Discussion 31 References 33
