Responsive image
博碩士論文 etd-0803116-202527 詳細資訊
Title page for etd-0803116-202527
論文名稱
Title
電子商務中商品推薦效果的腦神經科學研究
A Neural Science Study on the Effect of Product Recommendation in Electronic Commerce
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
87
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2016-07-26
繳交日期
Date of Submission
2016-09-03
關鍵字
Keywords
眼動研究、腦波研究、神經資訊系統、推薦系統
Brainwave, Electroencephalogram (EEG), Eyetracking, Recommendation systems, Neural information systems
統計
Statistics
本論文已被瀏覽 5896 次,被下載 857
The thesis/dissertation has been browsed 5896 times, has been downloaded 857 times.
中文摘要
隨著近日電子商務的蓬勃發展,消費者所面臨的不再是資訊量的不足,而是資訊的超載。因此,為了解決這樣的困境,有學者提出推薦系統來降低消費者的搜尋成本。目前,推薦系統已成為網路商店帶給消費者良好購物體驗不可或缺的功能。
近年來有許多探討推薦商品推薦效果的研究,主要目的都是為了找出它如何影響消費者對推薦商品的態度及購買意圖。然而,大部分的研究只以傳統的問卷來衡量消費者的感受,可能受到受測者回答真實性的影響。因此,本研究結合問卷和人類的生理反應以比對兩者的結果。本研究首先根據過往的文獻找出可能影響商品推薦效果的3個因素,再將推敲可能性模式與腦波數值結合,藉此觀察消費者在評估商品時他們採取的思考方式。另外,本研究也以消費者的凝視時間,去探討消費者在不同因素下,其注意力的變化來推論推薦的效果。
問卷研究的結果顯示商品類型及對興趣的高低會影響消費者本身對推薦商品的態度,而在腦波數值方面,發現消費者在觀察不同類型商品或是興趣程度不同的商品時,其所採取的思考路徑可能並不相同,而其凝視時間也有所不同。
本研究的研究成果支持消費者的生理反應與其所填寫的問卷結果一致,能提供學者後續想要進行商品推薦效果與人類生理反應研究時的參考。而網路商店的管理者未來也能依據消費者的生理反應,找出更適合推薦給消費者的商品。
Abstract
The rapid development of e-commerce, the problem facing consumers is not insufficient information but information overload. Therefore, recommendation systems have been used to reduce the search cost and they have become an essential function for most websites to enhance consumer’s shopping experience.
Many previous studies have investigated the effect of product recommendation with the goal of finding factors that influence consumer’s attitude and purchase intention. However, most of them used traditional questionnaires to measure collect subjective data, which may be biased and subject to the common method bias. The purpose of this study is to collect objective physiological responses of the subjects using electroencephalogram (EEG) and eye-tracking techniques and compare the result with behavioral data collected from questionnaires.
We further used the neural science data to examine the elimination likelihood data for exploring possible mechanisms in the decision process. Our findings indicate that product type and consumer’s interest will affect the attitude toward the product in the behavioral study. Consumer’s attention levels vary in different product types and consumer’s interest levels based on the EEG and fixation times observed from the eye-tracker.
目次 Table of Contents
電子商務中商品推薦效果的腦神經科學研究+2
摘要+2
Abstract+3
目錄+4
圖次+5
表次+6
第一章 緒論+7
第一節 研究背景+7
第二節 研究動機與目的+8
第三節 研究流程 +9
第二章 文獻探討 +11
第一節 推薦系統 +11
第二節 消費者態度的形成與改變+16
第三節 腦波與神經資訊學+19
第四節 眼球運動與注意力+25
第三章 研究架構與方法+27
第一節 研究架構 +27
第二節 研究假說 +29
第三節 變數的操作型定義與測量+34
第四節 實驗設計 +38
第四章 資料分析與討論+44
第一節 樣本基本資料描述+44
第二節 信效度分析+47
第三節 腦波與眼動資料分析+49
第四節 敘述統計分析+50
第五節 假說檢定的討論+52
第五章 結論與建議+65
第一節 研究結論 +65
第二節 研究貢獻 +67
第三節 研究限制與未來建議+68
參考文獻+69
附錄一 受測者基本資料調查問卷+76
附錄二 態度與購買意圖調查問卷+77
附錄三 消費者對於網路商品的認知(前測一問卷)+78
參考文獻 References
Adomavicius, G. and Tuzhilin A. (2005), "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions", IEEE Transcations on Knowledge and Data Engineering, Vol. 17, No. 6, pp. 734-749.
Ajzen, I., and Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior , Englewood Cliffs, NJ: Prentice-Hall.
Alspector, J., Kolcz, A., and Karunanithi, N.(1998) "Comparing Feature-based and Clique-based User Models for Movie Selection," in Proceedings of the Third ACM Conference on Digital Libraries, Pittsburgh, PA, 1998, pp. 11-18.
Balabanović, M. and Shoham, Y. (1997), ‘Fab: Content-based, collaborative recommendation’, Communications of the ACM, Vol. 40, No. 3, pp. 66-72.
Berger H. (1929). “Über das Elektrenkephalogramm des Menschen (On the EEG in European Archives of Psychiatry and humans),” Journal of Clinical Neuroscience , Vol. 87, No. 1, pp527-570.
Blair, A. (2010), ‘Information Overload, Then and Now’, The Chronicle Review, available at http://chronicle.com/article/Information-Overload-Then-and/125479/ (accessed 14 October 2014).
Brewer, M. B., and Crano, W. D. (1994), Social Psychology, Minnearpolis/St. Paul, MN:West Publishers, 1994
Brush, T.H. and Artz, K.W. (1999) “Toward a Contingent Resource-Based Theory: The Impact of Information Asymmetry on the Value of Capabilities in Veterinary Medicine”, Strategic Management Journal , 20(3), 223-250.
Chaiken, S. and Eagly, A.H. (1983), “Communication Modality as a Determinant of Persuasion: The Role of Communicator Salience,” Journal of Personality and Social Psychology, 45(2), pp.241-256.
Chaiken, S. (1980) “Heuristic Versus Systematic Information Processing and the Use of Source Versus Message Cues in Persuasion,” Journal of Personality and Social Psychological, 39:5, pp. 752-766.
Chiang, K.P. and Dholakia, R.R. (2003) “Factors Driving Consumer Intention to Shop Online: An Empirical Investigation,” Journal of Consumer Psychology (13:1-2), pp. 177-183.
Chen, D.N., Hu, P.J., Kuo, Y.R., and Liang, T.P. (2010), "A Web-based Personalized Recommendation System for Mobile Phone Selection: Design, Implementation, and Evaluation," Expert Systems with Applications , Vol. 37, pp. 8201-8210.
Cheung, K. W., Tsui, K. C., and Liu, J. (2004), "Extended latent class models for collaborative recommendation," IEEE Transcations on Systems, Vol. 34, No. 1, pp. 143-148.
Cho, YH and Kim, JK (2004), "Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce," Expert Systems with Applications , Vol. 26, pp. 233-246.
Cooper, DR, and Schindler. (2001), PS Business Research Methods , McGraw-Hill.
Copeland, MT. (1923) “Relation of Consumers'Buying Habits of Marketing Methods,” Harvard Business Review (1), pp. 282-289.
Crano, WD, and Messé, LA. (1982) Social Psychology: Principles and Themes of Interpersonal Behavior , Homewood, IL: Dorsey.
Darby, MR and Karni, E.(1973), “Free competition and the optimal amount of fraud”, Journal of Law and Economics , Vol. 6, April, pp. 67-88.
Dimoka, A., Pavlou, P. and Davis, F. (2007), ‘Neuro-IS: the potential of cognitive neuroscience for information systems research’, Proceedings of the International Conferenceon Information Systems(ICIS), Montreal, Canada, December 11-14, pp. 122.
Doomen, J. (2009), ‘Information Inflation’, Journal of Information Ethics, Vol. 18, No. 2, pp. 27-37.
Fishbein, M., and Ajzen, I. (1975) Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research , Addison-Wesley, Reading, MA.
Frazier, M. (2006), “How can your package standout, eye tracking looks hard for answers” Advertising Age, 77(42), pp.14-14.
Gerrard, P. and Malcolm, R. (2007), ‘Mechanisms of modafinil: a review of current research’, Neuropsychiatric Disease and Treatment, Vol. 3, No. 3, pp. 349-364.
Gregor, S., Lin, A.C., Gedeon, T., Riaz, A. and Zhu, D. (2014), ‘Neuroscience and a nomologicalnetwork for the understanding and assessment of emotions in information systems research’, Journal of Management Information Systems,
Vol.30, No. 4, pp. 13-48.
Gupta, A., Su, BC, and Walter, Z. (2004), “An Empirical Study of Consumer Switching from Traditional to Electronic Channels: A Purchase-Decision Process Perspective,” International Journal of Electronic Commerce (8:3), pp. 131-161.
Hassib, M. (2012). Mental task classification using single-electrode brain computer interfaces. Germany, Universit t tutt art, Masterarbeit.
Herlocker, J. L., Konstan, J. A., and Riedl, J. (1999), "An Alogrithmic Framework for Performing Collaborative Filltering," in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, PP. 230-237.
Herlocker, J. L., Konstan, J. A., and Riedl, J. (2000), "Explaining Collaborative Filtering Recommendations," in Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, December.
Hoffamn, J. E., & Subramaniam, B. (1995), The Role of Visual Attention in Saccadic Eye Movements. Perception & Psychophysics, 57, 787-795.
Huerta, E., and Ryan, T. (2003), “The Credibility of Online Information,” in Proceedings of Ninth Americas Conference on Information Systems, pp. 2162-2170.
Hughes, J.R. (2008), ‘Gamma, fast, and ultrafast waves of the brain: their relationships with epilepsy and behavior’, Epilepsy & Behavior, Vol. 13, No. 1, pp. 25-31.
Javal, E. (1878). Essai sur la physiologie de la lecture. Ann. Oculist, 79(97-117),240-274.
Kiang, MY, TS Raghu, and KH Shang. (2000), “Marketing on the Internet – Who Can Benefit from an Online Marketing Approach?“ Decision Support System , 27(4), 383-393.
Kuan, K.K.Y., Zhong, Y. and Chau, P.Y.K. (2014), ‘Informational and normative social influence ingroup-buying: evidence from self-reported and EEG data’, Journal of Management Information Systems, Vol. 30, No. 4, pp. 151-178.
Laurent, G. and Kapferer, JN. (1985) “Measuring Consumer Involvement Profiles,” Journal of Marketing Research (12),pp. 41-53.
Lee, YH, Hu, JH, Cheng, TH, and Hsieh, YF. (2012), "A cost-sensitive technique for positive-example learning supporting content-based product recommendations in B-to-C e-commerce," Decision Support Systems , Vol. 53, pp. 245-256.
Liang, T.P. (2012), ‘Cognitive Neuroscience in Information Systems Research’, Pacific Asia Journal of the Association for Information Systems, Vol. 4, No. 1, pp. 1-3.
Liang, T.-P., et al. (2007). "Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings." J. Manage. Inf. Syst. 23(3): 45-70.
Liebermann, Y. and A. Flint-Goor. (1996), “Message Strategy by Product-Class Type: A Matching Model”, International Journal of Research in Marketing , 13 (3),pp. 237-249
Lin, W. (2000), Association rule mining for collaborative recommender systems. Unpublished master's thesis, Worcester Polytechnic Institute, Worcester.
Middleton, SE, Shadbolt, NR, and De-Roure, DC. (2004), "Ontological user profiling in recommender system," ACM Transactions on Information Systems (TOIS) , Vol. 22, No. 1, pp. 54-88.
Minas, R.K., Dennis, A.R. and Potter, R.F., Bartelt, V. and Bae, S. (2014), ‘Putting on the thinking cap: Using NeuroIS tounderstand information processing biases in virtual teams’, Journal of Management Information Systems, Vol. 30, No. 4, pp. 49-82.
Munch, JM, Boller, GW, and Swasy, JL. (1993), “The Effects of Argument Structure and Affective Tagging on Product Attitude Formation,” Journal of Consumer Research (20) , pp. 294-302.
de Guinea, A.O., Titah, R. and Léger, P.M. (2014), ‘Explicit and implicit antecedents of users’ behavioral beliefs in information systems: A neuropsychological investigation’, Journal of Management Information Systems, Vol. 30, No. 4, pp. 179-210.
Nelson, P. (1970), “Information and Consumer Behavior,” Journal of Political Economy (78:2), pp. 311-329.
NeuroSky Inc. (2009), NeuroSky Mindset Instruction Manual. 6-8. Noachtar, S., Binnie, C., Ebersole, J., Mauguiere, F., Sakamoto, A., & Westmoreland, B. (1998). A glossary of terms most commonly used by clinical electroencephalographers and proposal for the report form for the EEG findings. The International Federation of Clinical Neurophysiology.Electroencephalography and clinical neurophysiology. Supplement, 52, 21-41.
Norton, Seth W. and Will Norton Jr. (1988), “An Economic Perspective on the Information Content of Magazine Advertisements”, International Journal of Advertising , 7 (2), 138-148.
Novak, T. P., Hoffman, D. L., & Yung, Y. F. (2000), Measuring the customer experience in online environments: A structural modeling approach. Marketing science, 19(1), 22-44.
Petty, RE, and Cacioppo, JT. (1979), “Issue Involvement Can Increase or Decrease Persuasion by Enhancing Message-Relevant Cognitive Responses,” Journal of Personality and Social Psychology (37), pp. 1915-1926.
Petty, RE, and Cacioppo, JT. (1986), “The Elaboration Likelihood Model of Persuasion,” in L. Berkowitz (ed.), Advances in Experimental Social sychology (19), Orlando: Academic Press, pp. 123-205.
Petty, RE, and Cacioppo. (1996), JT Attitudes and Persuasion: Classic and Contemporary Approaches , Westview Press.
Petty, RE, Cacioppo, JT, and Goldman, R. (1981), “Personal Involvement as a Determinant of Argument-Based Persuasion,” Journal of Personality and Social Psychology (41), pp. 847-855.
Petty, RE, Cacioppo, JT, and Schumann, D. (1995), “Central and Peripheral Routes to Advertising Effectiveness: The Moderating Role of Involvement,” Journal of Consumer Research (10), 1983, pp. 135-146. Pitt, LF, Watson, RT, and Kavan, CB “Service Quality: A Measure of Information Systems Effectiveness,” MIS Quarterly (19:2), pp. 173-187.
Pizzagalli, D.A. (2007), ‘Electroencephalography and high density electrophysiological source localization’, in Cacioppo, J.T., Tassinary, L.G. and Berntson, G. (Eds.), Handbook of Psychophysiology, Cambridge University Press, New York, pp. 56-84.
Rangaswamy, M., Porjesz, B., Chorlian, D.B., Wang, K., Jones, K.A., Bauer, L.O., Rohrbaugh, J., O’Connor, S.J., Kuperman, S., Reich, T. and Begleiter, H. (2002), 'Beta power in the EEG of alcoholics', Biol Psychiatry, Vol. 52, No. 8,
pp. 831-842.
Reinstein, DA, and Snyder, CM. (2005), “The Influence of Expert Reviews on Consumer Demand for Experience Goods: A Case Study of Movie Critics,” The Journal of Industrial Economics (53:1), pp. 27-51.
Riedl, R., Banker, R.D., Benbasat, I., Davis, F.D., Dennis, A.R., Dimoka, A., Gefen, D., Gupta, A., Ischebeck, A., Kenning, P., Müller-Putz, G., Pavlou, P.A., Straub, D.W., vomBrocke, J. and Weber, B. (2010), ‘On the Foundations of NeuroIS: Reflections
on the Gmunden Retreat 2009’, Communications of the Association for Information Systems, Vol. 27, No. 1, pp. 243-264.
Riedl, R., Hubert, M. and Kenning, P. (2010), ‘Are there neural gender differences in online trust? An fMRI study on the perceived trustworthiness of eBay offers’, MIS Quarterly, Vol. 34, No. 2, pp. 397-428.
Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2000), "Analysis of recommendation algorithms for e-commerce," Proceedings of the 2nd ACM conference on Electronic commerce , pp. 158-167.
Schafer,J. B., Konstan, J.A., and Riedl, J.(2000), "E-commerce recommendation applications." Journal of Data Mining and Knowledge Discovery, Vol. 5, No. 1, pp. 115-152.
Shardanand, U. and Maes, P. (1995), "Social information filtering: algorithms for automating word of mouth," Proceedings of the SIGCHI Conference on Human Factors in Comparing Systems, pp. 210-217.
Shih, YY and Liu DD (2008), "Product recommendation approaches: collaborative filtering via customer lifetime value and customer demands, " Expert Systems with Applications , Vol. 35, pp. 350-360.
Singh, SN, and Dalal, NP. (1999), “Web Home Pages as Advertisements,” Communications of the ACM (42:8), pp. 91-98.
Sinha, R., and Swearingen, K. (2002), “The Role of Transparency in Recommender Systems,” in Proceedings of the ACM Conference on Human Factors in Computing Systems ( CHI'02 ) , Minneapolis, April.
Smith, ER, and Mackie, DM. (2000), Social Psychology (2nd ed.), PA: Psychology Press, New York: Philadelphia.
Schein,A.I., Popescul,A.,Ungar,L.H., & Pennock,D.M.(2002).Methods and Metrics for Cold-Start Recommendations.ACM 1-58113-561-0/02/0008.
Vom Brocke, J. and Liang, T.P. (2014), ‘Guidelines for Neuroscience Studies inInformation Systems Research’, Journal of Management Information Systems, Vol. 30, No. 4, pp. 211-234.
Wang, X.J. (2010), ‘Neurophysiological and computational principles of cortical rhythms in cognition’, Physiol reviews, Vol. 90, No. 3, pp. 1195-1268.
Watts Sussman, S., and Siegal, WS. (2003), “Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption,” Information Systems Research (14:1), pp. 47-65.
Yang, W., Wang, Z., and You, M. (2004), "An improved collaborative filtering method for recommendations generation," Proceedings of the 2004 IEEE International Conference on Systems , Vol. 5, pp. 4135-4139.
Zeithaml, VA. (1988), “Consumer Perceptions of Price, Quality, and Value: A Mean Send Model and Synthesis of Evidence”, Journal of Marketing , 52 (July) , 2-22.
Zhang, W., and Watts, S. (2003), “Knowledge Adoption in Online Communities of Practice,” in Proceedings of Twenty-Fourth International Conference on Information Systems , Seattle, Washington, USA, pp. 96-109.
(二)中文文獻
王凱,超媒體電腦媒介環境中消費者涉入與廣告效果研究 —廣告情境與導引策略的調節影響,國立中央大學資訊管理學系未出版博士論文,2001。
陳灯能、蘇柏銘(2015),『結合腦波分析與內容導向過濾為基礎的文章推薦系統』,中華民國資訊管理學報,第二十二卷,第二期,頁 141-170。
顧宜錚,影響網路商店推薦商品購買意願之因素,國立中山大學資訊管理學系博士論文,2005。
陳思帆,網站體驗之沉浸經驗與腦波分析,國立政治大學資訊管理學系碩士學位論文,2014。
王任輝,電子商務網站RWD介面設計法則之使用者經驗與眼動行為研究,國立中山大學資訊管理學系碩士學位論文,2015。
唐大崙、徐明景和許國基 (2006)。 人眼具有備好的伽瑪值嗎? 人因工程學刊,8(1), 1-8。
別蓮蒂,雙軌溝通說服模式的腦神經研究觀點:功能性核磁共振影像在推敲可能性模式與系統簡則考模式的應用,2014。
黎佩芬、賴建都,「網路重度使用者對網頁訊息認知與瀏覽模式之研究-以台灣購物網站商品訊息呈現為例」,電子商務學報,第十三卷第三期,2011年09月,pp.517-554。
陳韋婷,以眼動研究情緒對誘餌效應的影響,國立中山大學資訊管理學系碩士學位論文,2013。
侯承志,以眼動儀探討商品圖片之情境對於電子商務網站視覺吸引力和使用者購買意願之影響,2015。
邱尉庭、陳彥勛,睡眠、學習、意識──人類的腦波,網路文獻
http://www.shs.edu.tw/works/essay/2009/11/2009110518425739.pdf。
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code