博碩士論文 107423042 詳細資訊




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姓名 宋安喬(An-Qiao Sung)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱
(Predicting eWOM’s Influence on Purchase Intention Based on Helpfulness, Credibility, Information Quality and Professionalism.)
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摘要(中) 由許多網路評論家共同撰寫的產品評論使企業有參考依據去改善其業務策略,並賦予口碑新的價值。其中企業積極地想了解的即是對於購買意圖的影響力,過去文獻多是以評論的相關面向去討論間接影響力,因此我們將直搗核心,補足過去並未深入探討的這部分。本研究將預測評論的影響力評估視為分類問題,並採用四項重要的理論構面作為擷取變數的基礎,分別為幫助度、可信度、資訊質量以及專業度,除了分析單一變數相關性,我們也運用屬性篩選算法去檢視各種變數組合,並提出一項集成式學習架構,用以預測產品評論的購買意圖影響程度,此外與其他著名的幾項分類演算法相比,我們提出的模型表現皆為最佳。最後,我們證實了結合評論的四個重要構面,才能達到較完整影響力的預測。
摘要(英) Product reviews, co-authored by many Internet reviewers, can help consumers make purchasing decisions and give businesses a basis for improving their business strategies. Among them, the most important thing for companies to find out actively is the influence on purchase intention. In the past, most of the literature discussed the indirect influence based on the relevant aspects of the review. Thence, we home in on the core of issue and complement the part of the past literature that has not been explored in depth. This study treats the influence evaluation of predictive reviews as a classification issue, and use four important theoretical aspects as the basis framework for extracting variables. Which are helpfulness, credibility, information quality, and professionalism. In addition to analyzing the correlation of a single variable, we also use attribute filtering algorithms to examine various combinations of variables. Besides, we propose an ensemble learning architecture to predict the degree of purchase intention influence of product reviews. Furthermore, compared with other well-known classification algorithms, our proposed model performs best. In the end, we confirmed the four important facets of the review in order to reach a more complete influence forecast.
關鍵字(中) ★ 電子口碑
★ 購買意圖
★ 幫助度
★ 資訊質量
★ 可信度
★ 專業度
★ 屬性篩選
★ 集成式學習模型
關鍵字(英) ★ eWOM
★ Purchase Intention
★ Helpfulness
★ Information Quality
★ Credibility
★ Professionalism
★ Feature Selection
★ Ensemble model
論文目次 1. INTRODUCTION 1
2. LITERATURE REVIEW 6
2.1 Usefulness / Helpfulness 6
2.2 Information Quality 9
2.3 Credibility 12
2.4 Professionalism 15
3. FRAMEWORK AND METHOD 18
3.1 Data Collection 19
3.2 Features Extraction 20
3.2.1 Reviews Content 23
3.2.2 Reviews Action 30
3.2.3 Product Data 32
3.2.4 Author of a Review 32
3.3 Dataset Labeling 36
3.4 Feature Scaling 39
4. FEATURE ANALYSIS AND SELECTION 40
4.1 Correlation Analysis 40
4.2 Feature Evaluation 41
4.2.1 CfsSubsetEval 42
4.2.2 InfoGainAttributeEval 42
4.2.4 ReliefFAttributeEval 44
4.2.5 OneRAttributeEval 45
5. ENSEMBLE MODEL 46
5.1 Ensemble Model 46
5.1.1 RandomForest 48
5.1.2 Multilayer Perceptron 48
5.1.3 Logistic Regression 50
5.1.4 Support Vector Machines (SVM) 51
5.1.5 REPTree 51
6. EXPERIMENT AND EVALUATION 53
6.1 Model Evaluation 54
6.2 Experiments 55
6.2.1 Study 1 55
6.2.2 Study 2 56
6.2.3 Study 3 76
7. CONCLUSION 78
7.1 Discussion 78
7.2 Future Work 79
8. REFERENCE 81
參考文獻 [1] Abd-Elaziz, M. E., et al. (2015). "Determinants of Electronic word of mouth (EWOM) influence on hotel customers′ purchasing decision." International Journal of Heritage, Tourism, and Hospitality 9(2/2).
[2] Al-Tashi, Q., et al. (2020). A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification. Evolutionary Machine Learning Techniques, Springer: 273-286.
[3] Amaral, F., et al. (2014). "User-generated content: tourists’ profiles on Tripadvisor." International Journal of Strategic Innovative Marketing 1(3): 137-145.
[4] Amblee, N. and T. Bui (2007). "Freeware downloads: An empirical investigation into the impact of expert and user reviews on demand for digital goods." AMCIS 2007 Proceedings: 21.
[5] Ashraf, M., et al. (2013). "Feature selection techniques on thyroid, hepatitis, and breast cancer datasets." International Journal on Data Mining and Intelligent Information Technology Applications 3(1): 1.
[6] Atika, A., et al. (2018). "The effect of electronic word of mouth, message source credibility, information quality on brand image and purchase intention." EKUITAS (Jurnal Ekonomi dan Keuangan) 20(1): 94-108.
[7] Belouch, M., et al. (2017). "A two-stage classifier approach using reptree algorithm for network intrusion detection." International Journal of Advanced Computer Science and Applications 8(6): 389-394.
[8] Bian, J., et al. (2009). Learning to recognize reliable users and content in social media with coupled mutual reinforcement. Proceedings of the 18th international conference on World wide web.
[9] Boswell, D. (2002). "Introduction to support vector machines." Departement of Computer Science and Engineering University of California San Diego.
[10] Bowen, J., et al. (2015). "Social media practices applied by city hotels: a comparative case study from Turkey." Worldwide Hospitality and Tourism Themes.
[11] Breiman, L. (2001). "Random forests." Machine learning 45(1): 5-32.
[12] Breiman, L., et al. (1984). Classification and regression trees, CRC press.
[13] Brock, T. C. (1965). "Communicator-recipient similarity and decision change." Journal of Personality and Social Psychology 1(6): 650.

[14] Chen, C. C. and Y.-D. Tseng (2011). "Quality evaluation of product reviews using an information quality framework." Decision Support Systems 50(4): 755-768.
[15] Cheung, C. M., et al. (2008). "The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities." Internet Research: Electronic Networking Applications and Policy 18(3): 229-247.
[16] Cheung, C. M.-Y., et al. (2012). "Is this review believable? A study of factors affecting the credibility of online consumer reviews from an ELM perspective." Journal of the Association for Information Systems 13(8): 618.
[17] Cheung, M. Y., et al. (2009). "Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations." International journal of electronic commerce 13(4): 9-38.
[18] Chevalier, J. A. and D. Mayzlin (2006). "The effect of word of mouth on sales: Online book reviews." Journal of marketing research 43(3): 345-354.
[19] Chua, A. Y. and S. Banerjee (2013). Reliability of reviews on the Internet: The case of Tripadvisor. World Congress on Engineering & Computer Science, York.
[20] Chua, A. Y. and S. Banerjee (2016). "Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality." Computers in Human Behavior 54: 547-554.
[21] Chua, A. Y. K. and S. Banerjee (2014). "Developing a theory of diagnostic for online reviews."
[22] Collins, M. (2002). Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, Association for Computational Linguistics.
[23] Cortes, C. and V. Vapnik (1995). "Support-vector networks." Machine learning 20(3): 273-297.
[24] Cox, J. and D. Kaimann (2015). "How do reviews from professional critics interact with other signals of product quality? Evidence from the video game industry." Journal of Consumer Behaviour 14(6): 366-377.
[25] Dalvi, P. T. and N. Vernekar (2016). Anemia detection using ensemble learning techniques and statistical models. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE.
[26] De Bruyn, A. and G. L. Lilien (2008). "A multi-stage model of word-of-mouth influence through viral marketing." International journal of research in marketing 25(3): 151-163.

[27] Deng, X., et al. (2019). "Feature selection for text classification: A review." Multimedia Tools and Applications 78(3): 3797-3816.
[28] Deutsch, M. and H. B. Gerard (1955). "A study of normative and informational social influences upon individual judgment." The journal of abnormal and social psychology 51(3): 629.
[29] Džeroski, S. and B. Ženko (2004). "Is combining classifiers with stacking better than selecting the best one?" Machine learning 54(3): 255-273.
[30] Filieri, R., et al. (2018). "What makes information in online consumer reviews diagnostic over time? The role of review relevancy, factuality, currency, source credibility and ranking score." Computers in Human Behavior 80: 122-131.
[31] Flesch, R. (1948). "A new readability yardstick." Journal of Applied Psychology 32(3): 221.
[32] Forman, C., et al. (2008). "Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets." Information systems research 19(3): 291-313.
[33] Freund, Y. and R. E. Schapire (1999). "Large margin classification using the perceptron algorithm." Machine learning 37(3): 277-296.
[34] Gardner, M. W. and S. Dorling (1998). "Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences." Atmospheric Environment 32(14-15): 2627-2636.
[35] Ghorbani, A. A. and K. Owrangh (2001). Stacked generalization in neural networks: generalization on statistically neutral problems. IJCNN′01. International Joint Conference on Neural Networks. Proceedings (Cat. No. 01CH37222), IEEE.
[36] Ghose, A. and P. G. Ipeirotis (2010). "Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics." IEEE transactions on knowledge and data engineering 23(10): 1498-1512.
[37] Gretzel, U. and K. H. Yoo (2008). "Use and impact of online travel reviews." Information and communication technologies in tourism 2008: 35-46.
[38] Gunawan, D. D. and K.-H. Huarng (2015). "Viral effects of social network and media on consumers’ purchase intention." Journal of Business Research 68(11): 2237-2241.
[39] Gupta, P. and J. Harris (2010). "How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective." Journal of Business Research 63(9-10): 1041-1049.
[40] Gupta, S., et al. (2017). Predictive analytics of sensor data based on supervised machine learning algorithms. 2017 International Conference on Next Generation Computing and Information Systems (ICNGCIS), IEEE.
[41] Hall, M. A. and L. A. Smith (1998). "Practical feature subset selection for machine learning."
[42] Hall, S. R. (1921). The Advertising Handbook: A Reference Work Covering the Principles and Practices of Advertising, McGraw-Hill Book Company, Incorporated.
[43] Hennig-Thurau, T., et al. (2003). "Electronic word-of-mouth: Motives for and consequences of reading customer articulations on the Internet." International journal of electronic commerce 8(2): 51-74.
[44] Hodžić, A., et al. (2016). Comparison of machine learning techniques in phishing website classification. International Conference on Economic and Social Studies (ICESoS′16).
[45] Holte, R. C. (1993). "Very simple classification rules perform well on most commonly used datasets." Machine learning 11(1): 63-90.
[46] Hu, M. and B. Liu (2004). Mining opinion features in customer reviews. AAAI.
[47] Hu, N., et al. (2008). "Do online reviews affect product sales? The role of reviewer characteristics and temporal effects." Information Technology and management 9(3): 201-214.
[48] Hu, Y.-H. and K. Chen (2016). "Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings." International Journal of Information Management 36(6): 929-944.
[49] Huang, J. H. and Y. F. Chen (2006). "Herding in online product choice." Psychology & Marketing 23(5): 413-428.
[50] Jameson, D. A. (2001). "Narrative discourse and management action." The Journal of Business Communication (1973) 38(4): 476-511.
[51] Jensen, M. L., et al. (2013). "Credibility of anonymous online product reviews: A language expectancy perspective." Journal of management information systems 30(1): 293-324.
[52] Jiang, Z. and I. Benbasat (2007). "The effects of presentation formats and task complexity on online consumers′ product understanding." MIS quarterly: 475-500.
[53] Juran, J. M., et al. (1974). Quality control handbook, McGraw-Hill New York.
[54] Kincaid, J. P., et al. (1975). "Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel."
[55] Kira, K. and L. A. Rendell (1992). A practical approach to feature selection. Machine Learning Proceedings 1992, Elsevier: 249-256.
[56] Kononenko, I. (1994). Estimating attributes: analysis and extensions of RELIEF. European conference on machine learning, Springer.
[57] Kononenko, I., et al. (1997). "Overcoming the myopia of inductive learning algorithms with RELIEFF." Applied Intelligence 7(1): 39-55.
[58] Korfiatis, N., et al. (2012). "Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content." Electronic Commerce Research and Applications 11(3): 205-217.
[59] Kusumasondjaja, S., et al. (2012). "Credibility of online reviews and initial trust: The roles of reviewer’s identity and review valence." Journal of Vacation Marketing 18(3): 185-195.
[60] Lawer, C. and S. Knox (2006). "Customer advocacy and brand development." Journal of Product & Brand Management.
[61] Lee, E.-J. and S. Y. Shin (2014). "When do consumers buy online product reviews? Effects of review quality, product type, and reviewer’s photo." Computers in Human Behavior 31: 356-366
[62] Lee, H. A., et al. (2011). "Helpful reviewers in TripAdvisor, an online travel community." Journal of Travel & Tourism Marketing 28(7): 675-688.
[63] Lee, I.-H., et al. (2011). "A filter-based feature selection approach for identifying potential biomarkers for lung cancer." Journal of Clinical Bioinformatics 1(1): 11.
[64] Lee, J., et al. (2011). "The different effects of online consumer reviews on consumers′ purchase intentions depending on trust in online shopping malls." Internet research.
[65] Li, M., et al. (2013). "Helpfulness of online product reviews as seen by consumers: Source and content features." International journal of electronic commerce 17(4): 101-136.
[66] Lin, C., et al. (2013). "Electronic word-of-mouth: The moderating roles of product involvement and brand image." TIIM 2013 Proceedings: 39-47.
[67] Liu, Z. and S. Park (2015). "What makes a useful online review? Implication for travel product websites." Tourism Management 47: 140-151.
[68] Mackiewicz, J. (2010). "Assertions of expertise in online product reviews." Journal of Business and Technical Communication 24(1): 3-28.
[69] Martin, L. and P. Pu (2014). Prediction of helpful reviews using emotions extraction. Twenty-Eighth AAAI conference on artificial intelligence.
[70] Mathwick, C. and J. Mosteller (2017). "Online reviewer engagement: A typology based on reviewer motivations." Journal of Service Research 20(2): 204-218.
[71] Mauri, A. G. and R. Minazzi (2013). "Web reviews influence on expectations and purchasing intentions of hotel potential customers." International Journal of Hospitality Management 34: 99-107.
[72] Mc Laughlin, G. H. (1969). "SMOG grading-a new readability formula." Journal of reading 12(8): 639-646.
[73] McDonald, R., et al. (2010). Distributed training strategies for the structured perceptron. Human language technologies: The 2010 annual conference of the North American chapter of the association for computational linguistics, Association for Computational Linguistics.
[74] Mudambi, S. M. and D. Schuff (2010). "Research note: What makes a helpful online review? A study of customer reviews on Amazon. com." MIS quarterly: 185-200
[75] O′Connor, R. E., et al. (2002). "Teaching reading to poor readers in the intermediate grades: A comparison of text difficulty." Journal of Educational Psychology 94(3): 474.
[76] Paek, T., et al. (2010). Predicting the importance of newsfeed posts and social network friends. Twenty-Fourth AAAI Conference on Artificial Intelligence.
[77] Panchendrarajan, R., et al. (2016). Implicit aspect detection in restaurant reviews using cooccurence of words. Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis.
[78] Park, D.-H. and S. Kim (2008). "The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews." Electronic Commerce Research and Applications 7(4): 399-410.
[79] Park, Y.-J. (2018). "Predicting the helpfulness of online customer reviews across different product types." Sustainability 10(6): 1735.
[80] Racherla, P. and W. Friske (2012). "Perceived ‘usefulness’ of online consumer reviews: An exploratory investigation across three services categories." Electronic Commerce Research and Applications 11(6): 548-559.
[81] Racherla, P., et al. (2012). "Factors affecting consumers′ trust in online product reviews." Journal of Consumer Behaviour 11(2): 94-104.
[82] Rendell, L. and R. Seshu (1990). "Learning hard concepts through constructive induction: Framework and rationale." Computational Intelligence 6(4): 247-270.
[83] Rieh, S. Y. (2002). "Judgment of information quality and cognitive authority in the Web." Journal of the American Society for Information Science and Technology 53(2): 145-161.
[84] Rieh, S. Y. and N. Belkin (2000). Interaction on the Web: Scholars′ judgement of information quality and cognitive authority. Proceedings of the annual meeting-american society for information science, Information Today; 1998.
[85] Roed, J. (2003). "Language learner behaviour in a virtual environment." Computer assisted language learning 16(2-3): 155-172.
[86] Ruiterkamp, L. (2013). Electronic word-of-mouth, University of Twente.
[87] Russo, J. E., Meloy, M. G., & Medvec, V. H. (1998). Predecisional distortion of product information. Journal of Marketing Research, 35(4), 438-452.
[88] Salehan, M. and D. J. Kim (2016). "Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics." Decision Support Systems 81: 30-40.
[89] Sarangi, S. and A. Clarke (2002). "Zones of expertise and the management of uncertainty in genetics risk communication." Research on language and social interaction 35(2): 139-171.
[90] Savolainen, R. (2011). "Judging the quality and credibility of information in Internet discussion forums." Journal of the American Society for Information Science and Technology 62(7): 1243-1256.
[91] Sipos, R., et al. (2014). Was this review helpful to You? It depends! Context and voting patterns in online content. Proceedings of the 23rd international conference on World wide web.
[92] Smith, E. A. and J. P. Kincaid (1970). "Derivation and validation of the automated readability index for use with technical materials." Human Factors 12(5): 457-564.
[93] Smith, R. E. and C. A. Vogt (1995). "The effects of integrating advertising and negative word‐of‐mouth communications on message processing and response." Journal of Consumer Psychology 4(2): 133-151.
[94] Tellis, G. J. and J. Johnson (2007). "The value of quality." Marketing Science 26(6): 758-773.
[95] Tormala, Z. (2011). Experts are more persuasive when they′re less certain, HARVARD BUSINESS SCHOOL PUBLISHING CORPORATION 300 NORTH BEACON STREET ….
[96] Tsao, W.-C. and M.-T. Hsieh (2015). "eWOM persuasiveness: do eWOM platforms and product type matter?" Electronic Commerce Research 15(4): 509-541.
[97] Tuominen, P. (2011). "The influence of TripAdvisor consumer-generated travel reviews on hotel performance."
[98] Van Eck, P. S., et al. (2011). "Opinion leaders′ role in innovation diffusion: A simulation study." Journal of Product Innovation Management 28(2): 187-203.
[99] Vásquez, C. (2012). "Narrativity and involvement in online consumer reviews: The case of TripAdvisor." Narrative Inquiry 22(1): 105-121.
[100] Wang, R. Y. and D. M. Strong (1996). "Beyond accuracy: What data quality means to data consumers." Journal of management information systems 12(4): 5-33.
[101] Wee, C. H., et al. (1995). "Word‐of‐mouth Communication in Singapore: With Focus on Effects of Message‐sidedness, Source and User‐type." Asia Pacific Journal of Marketing and Logistics.
[102] Weiner, B. (2000). "Attributional thoughts about consumer behavior." Journal of Consumer research 27(3): 382-387.
[103] Wolpert, D. H. (1992). "Stacked generalization." Neural networks 5(2): 241-259.
[104] Xie, H. J., et al. (2011). "Consumers’ responses to ambivalent online hotel reviews: The role of perceived source credibility and pre-decisional disposition." International Journal of Hospitality Management 30(1): 178-183.
[105] Yildirim, P. (2015). "Filter based feature selection methods for prediction of risks in hepatitis disease." International Journal of Machine Learning and Computing 5(4): 258.
[106] Yin, D., et al. (2014). "Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews." MIS quarterly 38(2): 539-560.
[107] Zhang, W. and S. Watts (2003). "Knowledge adoption in online communities of practice." ICIS 2003 Proceedings: 9.
[108] Zhao, Y., et al. (2013). "Modeling consumer learning from online product reviews." Marketing Science 32(1): 153-169.
[109] Zheng, X., et al. (2013). "Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach." Decision Support Systems 56: 211-222.
[110] Zhou, W. and W. Duan (2016). "Do professional reviews affect online user choices through user reviews? An empirical study." Journal of management information systems 33(1): 202-228.
指導教授 陳彥良(Yen-Liang Chen) 審核日期 2020-6-24
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