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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/100600
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dc.contributor.advisor魏志平zh_TW
dc.contributor.advisorChih-Ping Weien
dc.contributor.author杜沛慈zh_TW
dc.contributor.authorPei-Cih Tuen
dc.date.accessioned2025-10-08T16:04:19Z-
dc.date.available2025-10-09-
dc.date.copyright2025-10-08-
dc.date.issued2025-
dc.date.submitted2025-08-06-
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Dženopoljac, V., Ognjanović, J., Dženopoljac, A., & Kraus, S. (2023). Exploring the impact of employer brand attributes on financial performance: An intellectual capital perspective. Journal of Intellectual Capital, 24(7), 31-54.
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Hu, Y., Xu, A., Hong, Y., Gal, D., Sinha, V., & Akkiraju, R. (2019). Generating business intelligence through social media analytics: Measuring brand personality with consumer-, employee-, and firm-generated content. Journal of Management Information Systems, 36(3), 893-930.
HR Vision (n.d.). Real-Life Examples of Effective Employer Branding – and How to Leverage Them. Retrieved July 25, 2025, from https://www.hrvisionevent.com/content-hub/real-life-examples-of-effective-employer-branding-and-how-to-leverage-them/#
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Kashyap, V., & Verma, N. (2018). Linking dimensions of employer branding and turnover intentions. International Journal of Organizational Analysis, 26(2), 282-295.
Khera, S. N., & Divya. (2018). Predictive modelling of employee turnover in Indian IT industry using machine learning techniques. Vision, 23(1), 12-21.
Landers, R. N., Brusso, R. C., & Auer, E. M. (2019). Crowdsourcing job satisfaction data: Examining the construct validity of Glassdoor. com ratings. Personnel Assessment and Decisions, 5(3), 6.
Lee, C. C., Kao, R. H., & Lin, C. J. (2018). A study on the factors to measure employer brand: the case of undergraduate senior students. Chinese Management Studies, 12(4), 812-832.
Li, Q., Lourie, B., Nekrasov, A., & Shevlin, T. (2022). Employee turnover and firm performance: Large-sample archival evidence. Management Science, 68(8), 5667-5683.
Lin, H., Zhu, H., Wu, J., Zuo, Y., Zhu, C., & Xiong, H. (2020). Enhancing employer brand evaluation with collaborative topic regression models. ACM Transactions on Information Systems (TOIS), 38(4), 1-33.
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Lo, Y. L. (2024). CAR-TFP: Company-aware RNN-based modeling for talent flow prediction. Unpublished Master’s Thesis, Department of Information Management, National Taiwan University, Taipei, Taiwan, ROC.
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Zhu, F., Wang, Z., Yu, Q., Hu, T., Wen, Y., & Liu, Y. (2014). Reconsidering the dimensionality and measurement of employer brand in the Chinese context. Social Behavior and Personality: an international journal, 42(6), 933-948.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/100600-
dc.description.abstract雇主品牌在當前高度競爭的人力市場中至關重要,是企業建立人力資本的有效策略。然而,過往研究對於雇主品牌資訊的取得主要仰賴問卷方法,因而受到資料規模、即時性不足、員工沉默等限制;僅有少數研究對線上員工評論進行有限運用,而未能深入捕捉評論文本中蘊含的雇主品牌洞察。
本研究提出一個基於線上員工評論的雇主品牌萃取方法,該方法結合了預訓練語言模型、注意力機制及多種分類架構,充分利用雇主品牌與員工評論間的細緻語意線索,並考量不同雇主品牌面向間的交互影響。此外,本研究進一步將萃取結果應用於企業層級的員工流失率及員工滿意度分析。實驗結果顯示,我們的雇主品牌萃取方法具有良好表現,亦能有效揭示雇主品牌與兩項關鍵人力資本指標間的關聯。
本研究不僅提供了一種新穎的雇主品牌萃取方式,該方法更可作為企業雇主品牌管理與人力資源策略的重要依據,總體而言拓展了雇主品牌的應用場景,並具備高度實務價值。
zh_TW
dc.description.abstractEmployer brand plays a critical role in today’s highly competitive labor market and serves as an effective strategy for organizations to build human capital. However, previous research has primarily relied on survey-based methods to obtain employer brand information, which are limited in terms of data scale, timeliness, and employee silence. Only a few studies have made limited use of online employee reviews and have not adequately captured the in-depth employer brand insights embedded in their textual content.
This study proposes an employer brand extraction method based on online employee reviews. The method integrates a pretrained language model, an attention mechanism, and diverse classification architectures, fully leveraging the nuanced semantic cues between employer brand dimensions and employee reviews, while also accounting for interdependencies among the dimensions. Furthermore, the extracted results are applied to company-level analyses of turnover rate and employee satisfaction. Experimental results demonstrate that our employer brand extraction method yields promising performance and effectively reveals the relationships between employer brand and the two key human capital indicators.
This study not only introduces a novel approach to employer brand extraction but also provides important guidance for corporate employer brand management and human resource strategies. Overall, this research broadens the application scope of employer brand and offers substantial practical value.
en
dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-10-08T16:04:19Z
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dc.description.provenanceMade available in DSpace on 2025-10-08T16:04:19Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontents致謝 i
摘要 ii
Abstract iii
Table of Contents v
List of Figures viii
List of Tables ix
Chapter 1 Introduction 1
1.1 Background 1
1.2 Research Motivation 3
1.3 Research Objectives 5
Chapter 2 Literature Review 6
2.1 Measurement of Employer Brand Dimensions 6
2.2 Prior Research on Employer Brand 9
2.3 Organizational-level Turnover Rate Prediction 12
2.4 Organizational-level Employee Satisfaction Prediction 13
2.5 Summary 14
Chapter 3 Methodology 16
3.1 Study 1: Our Proposed Employer Brand Extraction Method 17
3.1.1 Problem Formulation 17
3.1.2 Model Architecture 18
3.1.3 Employer Brand Description / Employee Review Sentence Encoder 19
3.1.4 Employer Brand-Aware Attention Mechanism 20
3.1.5 Classifier Layer 21
3.1.6 Loss Weighting Strategy 25
3.2 Employer Brand Score Aggregation 26
3.3 Study 2: Company-level Turnover Rate Prediction 27
3.4 Study 3: Company-level Employee Satisfaction Prediction 28
Chapter 4 Data 29
4.1 Phase 1 30
4.1.1 Digital Resumes Collection and Cleaning 30
4.1.2 Position Grouping 31
4.1.3 Company Filtering 31
4.2 Phase 2 33
4.2.1 Employee Reviews Collection and Cleaning 33
4.2.2 Position Grouping 34
4.2.3 Employer Brand Annotation 34
Chapter 5 Evaluation of Our Employer Brand Extraction Method 36
5.1 Baseline Methods 36
5.2 Evaluation Procedure and Hyperparameters 36
5.3 Evaluation Metrics 37
5.4 Evaluation Results 38
5.5 Robustness Test 40
Chapter 6 Evaluation of Two Application Tasks 42
6.1 Data Preparation 42
6.1.1 Data Compilation and Time Frame 42
6.1.2 Feature Construction 42
6.1.3 Sample Selection 43
6.2 Evaluation Metrics 44
6.3 Evaluation Results 45
6.3.1 Results of Company-level Turnover Rate Prediction 45
6.3.2 Results of Company-level Employee Satisfaction Prediction 48
Chapter 7 Conclusion 51
7.1 Conclusion 51
7.2 Limitations and Future Works 52
References 54
Appendix 62
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dc.language.isoen-
dc.subject雇主品牌,雇主品牌萃取,線上員工評論探勘,深度學習,員工流失率,員工滿意度,zh_TW
dc.subjectEmployer Brand,Employer Brand Extraction,Online Employee Review Mining,Deep Learning,Employee Turnover Rate,Employee Satisfaction,en
dc.title從線上員工評論萃取雇主品牌:萃取方法開發及其應用zh_TW
dc.titleEmployer Brand Extraction from Online Employee Reviews: Extraction Method Development and Its Applicationsen
dc.typeThesis-
dc.date.schoolyear113-2-
dc.description.degree碩士-
dc.contributor.oralexamcommittee向倩儀;楊錦生zh_TW
dc.contributor.oralexamcommitteeChien-Yi Hsiang;Chin-Sheng Yangen
dc.relation.page63-
dc.identifier.doi10.6342/NTU202504051-
dc.rights.note未授權-
dc.date.accepted2025-08-12-
dc.contributor.author-college管理學院-
dc.contributor.author-dept資訊管理學系-
dc.date.embargo-liftN/A-
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