臺灣博碩士論文加值系統

在高度虛實整合，消費者更主動掌握訊息，以及移動技術快速發展下，提供了嶄新的購物消費體驗，而讓零售通路經營的挑戰不斷提高，然而善用大數據瞭解消費者行為，並透過分析找出顧客的決策，並進行行銷預測。本研究利用質性研究與量化研究，質性研究透過美妝零售社群網站Fashion Guide之消費者評論，進行評論蒐集，計算評論值與情緒值，運用內容分析，透過消費者使用經驗發現影響消費者情緒與評論之相關字詞。量化研究，首先，問卷設計與問卷發放，所採用的分析方法包括信度分析、因素分析、集群分析、決策樹分析。
本研究探討零售社群口碑與顧客決策分析，研究結果得知質性研究，影響消費者情緒之關鍵字詞為「喜歡」產品、「擔心」產品適合性等情緒詞，評論之關鍵字詞為使用效果「清爽」、產品品質「不錯」等評論字詞。量化研究，透過市場區隔決策樹分析，購買動機目標變數發現女性消費者，年齡二十六至三十五族群，在選購保養品時會依據核心功能而進行選購，消費者每年平均購買次數四到五次，購買動機為風格象徵導向，針對此族群的女性消費者進行風格象徵廣告投放。購買次數目標變數，消費者每年平均購買次數八次以上，花費金額為四千以上，針對此消費族群可以在先在百貨公司、藥妝店、直銷方式、大型量販店等門口進行廣告文宣傳單發放，針對此族群進行相關商品廣告投放在社群網站針對風格象徵或享受樂趣的廣告。

With a high degree of virtual reality integration, consumers are more active in mastering information, and with the rapid development of mobile technology, it provides a brand-new shopping consumption experience, which makes the retail channel operation more challenging. However, we should make good use of big data to understand consumer behavior, find out customers'' decisions through analysis, and make marketing forecasts. In this study, qualitative research and quantitative research are used. Through consumer reviews of fashion guide, a beauty retail community website, reviews are collected, comments and emotions are calculated. Content analysis is used to find out the relevant words that affect consumer sentiment and evaluation through consumer experience. Quantitative research, first, questionnaire design and questionnaire distribution, the analysis methods used include reliability analysis, factor analysis, cluster analysis, decision tree analysis.
This study explores the relationship between word-of-mouth and customer decision-making in retail communities. The results show that the key words influencing consumers'' emotions are "like" products and "worried" about the suitability of products, and the key words of reviews are "refreshing" and "good quality". Through the analysis of market segmentation decision tree, we find that female consumers, aged from 26 to 35, choose maintenance products according to their core functions. The average number of purchases per year is four to five times. The purchase motivation is style symbol oriented, and the female consumers of this group carry out style symbol advertising. The target variable of purchase times: the average number of purchases per year is more than eight times, and the cost is more than 4000. For this group of consumers, advertising can be carried out at the door of department stores, cosmetology stores, direct sales methods, large-scale mass stores, etc. for this group of consumers, advertising related products should be put on the community website aiming at style symbols or enjoying fun.

摘要 i
ABSTRACT ii
致謝 iv
目次 v
表目次 viii
圖目次 x
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 8
1.3 研究範圍與對象 9
1.4 研究流程圖 10
第2章 文獻探討 11
2.1 零售業業種業態 11
2.2 零售業大數據之趨勢與應用現況 12
2.3 大數據的定義、分析、應用 15
2.3.1 大數據定義 15
2.3.2 大數據分析技術與工具 17
2.3.3 大數據在實務上的應用 20
2.3.4 大數據的挑戰與解決方法 21
2.4 社群媒體定義、分類、分析 22
2.4.1 社群媒體定義 23
2.4.2 社群媒體的分類 23
2.4.3 社群媒體分析 25
2.5 顧客市場區隔與決策 26
2.5.1 市場區隔變數 26
2.5.2 顧客口碑與評價 27
2.5.3 顧客行為決策因素 29
2.6 情緒分析與斷詞系統 35
2.6.1 情緒分析 35
2.6.2 情感分析相關詞庫 37
2.6.3 分類技術 41
2.6.4 斷詞系統 42
2.7決策樹定義與應用、分類演算方法 42
2.7.1 決策樹的定義 42
2.7.2 決策樹的應用 43
2.7.3 決策樹的分類演算方法 44
第3章 研究方法 47
3.1方法一 47
3.1.1 研究架構 47
3.1.2 消費者評論蒐集 48
3.1.3 前置處理 50
3.1.4 情緒詞庫的蒐集與建立 51
3.1.5 情緒值計算方式 54
3.1.6 資料分析方法 56
3.2方法二 57
3.2.1 研究架構 57
3.2.2 問卷設計 58
3.2.3 問卷問項概念性定義、操作性定義 58
3.2.4 資料分析方法 62
第4章 資料分析與研究結果 64
4.1方法一分析結果 64
4.1.1 樣本結構 64
4.1.2 內容編碼 64
4.1.3 意見詞彙整 66
4.1.4意見詞分析 71
4.2方法二分析結果 77
4.2.1 樣本結構 77
4.2.2 因素分析 79
4.2.3 集群分析 81
4.2.4 決策樹分析 82
第5章 結論與建議 97
5.1 研究結論 97
5.2 理論意涵 100
5.3 管理意涵 101
5.4 研究限制 103
參考文獻 105
附錄1 113
附錄2 119

A lHanai T,Ghassemi M M. (2017). Predicting latent narrative mood using audio and physiological data. AAAI.
A. Labrinidis and H. V. Jagadish. (2012). Challenges and opportunities with big data. Proc. VLDB Endowment, pp. 2032-2033.
A. Vera-Baquero, R. Colomo-Palacios, O. Molloy. (2014). Towards a process to guide big data based decision support systems for business processes, Procedia Technol. 11–21.
Al Hanai T,Ghassemi M M. (2017). Predicting Latentnarrativemood using audioand physiologic data. AAAI .
Alaoui, Gahi. (2019). The Impact of Big Data Quality on Sentiment Analysis Approaches. Procedia Computer Science 160, (pp. 803-810).
Amir Gandomi, Murtaza Haider. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, (pp. 137–144).
Anderson, Eric T. and Duncan Simester. (2003). “Effects of $9 Price Endingson Retail Sales: Evidence from Field Experiments,”. Quantitative Marketingand Economics, pp. 93–110.
Anderson, J. L. , Jolly, L. D. , & Fairhurst, A. E. (2007). Customer relationship manage- ment in retailing: A content analysis of retail trade journals. Journal of Retailing and Consumer Services, 394–399.
Andreas M. Kaplan , Michael Haenlein. (2010). Users of the world, unite! The challenges and. Business Horizons , 53, pp. 59—68.
Avita Katal, Mohammad Wazid, R. H. Gouder. (n.d.). Big Data: Issues, Challenges, Tools and Good Practices,. 13(1).
B. Brown, M. Chui, J. Manyika. (2011). Are you ready for the era of ‘big data’. McKinsey Q. 4, 24–35.
B. Fang, P. Zhang. (2016). Big data in finance, Big Data Concepts, Theories, and Applications. Springer, 391–412.
Bagozzi RP,Gopinath,NyaPU. (1999). The roleofemotionsin marketing. Acad.Mark.Sci., 27, 184-206.
Bagozzi RP,GopinathM,NyerPU. (1999,). The roleofemotionsin marketing. J. Acad.Mark.Sci., 184-20.
Barbier, G., & Liu, H. (2011). Data mining in social media. Social network data analytics, (pp. 327–352).
Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J.,. (1984). Classification and Regression Trees. , . Monterey:Wadsworth and Brooks/Cole.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics:Form big data to big impact. MIS Quarterly, pp. 1165–1188.
Chung, W. (2014). Biz Pro: Extracting and categorizing business intelligence fac-tors from textual news articles. International Journal of Information Management, 272–284.
Cowie R, Douglas-CowieE, Savvidou, McMahonE, SaweyM, Schro¨ der M. (2000). FEELTRACE:Aninstrumentforrecording perceived emotioninrealtime. ISCA TutorialandResearch Workshop (ITRW)onSpeechandEmotion.
Cowie R,Douglas-CowieE,Savvidou,McMahon E,Sawey M. (2000). Aninstrumentforrecording perceived emotion realtime. ISCA Tutorial and Research Workshop (ITRW)on Speech and Emotion.
Daft, R. L., & Lengel, R. H. (1986). Organizational information. Management Science, 35(5), pp. 554-571.
Davenport, T. H. (2012). Enterprise analytics: Optimize performance, process, and deci-sions through big data. Upper Saddle River, New Jersey: FT Press OperationsManagement.
Dekimpe, Marnik G. and Dominique M. Hanssens. (2000). “Time-series Modelsin Marketing: Past, Present and Future”. International Journal of Researchin Marketing, 17(2), pp. 183–93.
Dietrich, B. L., Plachy, E. C., & Norton, M. F. (n.d.). Analytics across the enterprise: How IBM realizes business value from big data and analytics. Financial Times Prentice. Hall, IBM press.
Faasse, R. E. M., Helms, R. W., & Spruit, M. R. (2011). Web 2.0 in the CRM domain: Defining social CRM. International Journal of Electronic Customer Relationship Management,, 5(1), 1–22.
Fan, S., Lau, R. Y. K., & Zhao, J. L. (2015). Demystifying Big Data analytics for business intelligence through the lens of marketing mix. Retrieved from Big Data Research.: http://doi.org/10.1016/j.bdr.2015.02.006.
Fosso Wamba, S. A. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal. International Journal of Production Economics, 165, 234–246.
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246. .
Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.
Gartner. (2016, 10 31). Gartner Says 6.4 Billion Connected Things WillBe in Use in 2016, Up 30 Percent From 2015. Retrieved from Gartner PressRelease: http://www.gartner.com/newsroom/id/3165317
GemaBello-Orgaz,JasonJ.Jung,∗,DavidCamacho. (2016). Socialbigdata:Recentachievementsandnewchallenges. Information Fusion, (pp. 45–59).
Goffman, E. (1959). The presentation of self in everyday life. New York: Doubleday Anchor Books.
Goldsmith, R., Horowitz, D. (2006). Measuring motivations for online opinion seeking. Interact. Advert, 6(2), 1–16.
Greenberg, P. (2010). The impact of CRM 2.0 on customer insight. Journal of Business & Industrial Marketing, 25(6), 410–419.
Gregory T.Bradley,Elizabeth K.LaFleur. (2016). Toward the development of hedonic-utilitarian measures of retail service. Journal of Retailing and Consumer Services.
Gupta, Sunil, Dominique Hanssens, Bruce Hardie, Wiliam Kahn, V. Kumar,Nathaniel Lin, Nalini Ravishanker and S. Sriram. (2006). Modeling Cus-tomer Lifetime Value. Journal of Service Research, 9(2), pp. 139–55.
H.KimG.J.,Koehler. (1995). Theory and practice of decision tree induction. Omega.
Hauser JR,UrbanGL,LiberaliG. (2009). BraunM: Website morphing. Mark. Sci.(28), 202-223.
Hirsh JB,KangSK,BodenhausenGV. (2012). Personalized persuasion: tailoring persuasive appealstorecipients’personality traits. Psychol. Sci., 578-581.
Hirsh JB,KangSK,BodenhausenGV. (2012). Personalized persuasion: tailoring persuasiveappealstorecipients’personalitytraits. Psychol. Sci, 23:578-581.
Holsti. (1969). Content Analysis for the Social Sciences and Humanities. . MA.
Hui, Sam K., Peter S. Fader and Eric T. Bradlow. (2009). “Path Data in Mar-keting: An Integrative Framework and Prospectus for Model Building,”. Marketing Science, 28(2), pp. 320–35.
I.A.T.Hashema, I.Yaqoob,N.B.Anuara,S.Mokhtara,A.Gania,S.U.Khanh. (2015). There is eof bigdata and cloud computing : review and open research issues,. 47, pp. 98–115.
I.A.T.Hashema,I.Yaqooba,N.B.Anuara,S.Mokhtara,A.Gania,S.U.Khanb,. (2015). Ther ise of big data on cloud computing : review and open research issues. Inf .Syst., 98–115.
Javier Cuesta, MD, Paula Antuña, MD, César Jiménez, MD, Fernando Rivero, MD, Teresa Bastante, MD,. (2019). Can Plaque Erosion Be Visualized by High-Definition Intravascular Ultrasound? IMAGES IN INTERVENTION.
JL, A. (1997). In Dimensions Of Brand Personality.. (pp. 34:347-356). J. Mark.Res.
JL, A. (1999). In The malleableself:theroleofselfexpressionin persuasion (Vol. 36, pp. 45-57.). J. Mark.Res.
K. Venkatram, M.A. Geetha. (2017). Review on big data & analytics–concepts, philosophy, process and applications, Cybernet. Inform. Technol, 3-27.
Kaplan, A.M. and Haenlein, M. (2010). Users of the world unite! The challenges and opportunities of SM. Science direct, 53(59-68).
Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data., (pp. 119-127). Applied Statistics.
Kassarjian, H. H. (1977). Content analysis in Consumer Research. Journal of Consumer.
Kenrick DT, FunderDC. (988). Profiting fromcontroversy:lessons from theperson-situationdebate. Psychol, 43:23.
Kenrick DT,FunderDC. (1988). Profiting fromcontroversy:lessons from theperson-situationdebate. Am. Psychol, 43:23.
Kopalle, P. K. (2012). The Impact of Household Level Heterogeneity in Reference Price Effectson Optimal Retailer Pricing Policies”. Journal of Retailing, 88(1), pp. 102–14.
Kosinski M, M. S. (2015). Still well D: Facebook as are search tool for the social sciences. Am. Psycho, 543-556.
Kosinski M, Matz S C, Gosling S D, Popov V, Still well D. (2015). Facebook as are search tool for the social sciences. In Psychol. (Vol. 70, pp. 543-556).
Kotler, P. J. (1991). Marketing Management,Seventh Edition. NJ: Prentice-Hall.
KotlerPhilip. (2014). 行銷3.0 ：與消費者心靈共鳴. 天下雜誌.
KotlerPhilip. (2017). 行銷4.0：新虛實融合時代贏得顧客的全思維. 天下雜誌.
Kowalczyk, M. , & Buxmann, P. (2015). n ambidextrous perspective on business in- telligence and analytics support in decision processes: Insights from a multiple case study. Decision Support Systems, 80, pp. 1–13 .
Kumar, G. (2015). An encyclopedic overview of “big data” analytics. International Journal of Applied Engineering Research, 10(3), 5681–5705.
Kumar, S., & Rishi, R. (2015). Data collection and analytics strategies of social networking websites. In Green Computing and Internet of Things (ICGCIoT), 643-648.
Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Va- riety, Technical. Retrieved from Data-Management-Controlling-Data-Volume-Velocity-and- Variety: http://blogs.gartner.com/doug-laney/files/ 2012/01/ad949-3D
Lazer D, Pentland A S, Adamic L, Aral S,Barabasi A L, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M. (2009). Life in the network: the oming age of computation alsocial science. New York, NY, 323:721.
Li Kam Wa R, Liu Y, LaneND, ZhongL. (2013). Moodscope:buildinga mood sensorfromsmartphoneusagepatterns. . Proceeding of the 11thannualinternationalconferenceonMobilesystems, applications, andservices, (pp. 389-402).
Li Kam Wa R,Liu Y,Lane N D,Zhong L. (2013). Moodscope:building a mood sensor from smartphone usage patterns. Proceeding of the 11th annual international conference on Mobile Systems, applications, and services (pp. 389-402). ACT.
LiKam Wa R, Liu Y, Lane N D, Zhong L. (2013). building a mood sensor from smartphone usage patterns. Proceeding of the 11thannualinternationalconferenceonMobilesystems, applications, andservices (pp. 389-402.). ACM.
Liu, B. (2010). Sentiment analysis and subjectivity. Handbook Nat. Lang. Process, 5 (1), pp. 1–38.
M.A. Beyer, D. Laney. (2012). The Importance of ‘Big Data’: A Definition. Stamford: Gartner.
M.R. Bendre, V.R. Thool. (2016). Analytics, challenges and applications in big data environment:a survey. J. Manage. Analyt, 206–239.
M.-T. Martin-Valdivia, E. Martinez-Cámara, J.-M. Perea-Ortega, L.A. (2012). Sentiment polarity detection in Spanish reviews combining. supervised and unsupervised approaches, Expert Syst.
Marcus B,MachilekF,Schu¨ tz. (2006). Personality in cyberspace: personal Websites Asmedia For person ality expressions and impressions. J. Pers.Soc.Psychol., 90, 1014-1031.
Mark J.Arnold,Kristy E.Reynolds. (2003). Hedonic shopping motivations. Journal of Retailing.
Marshall, A., Mueck, S., & Shockley, R. (2015). How leading organizations use big data and analytics to innovate. Strategy & Leadership, 43(5), 32–39.
Martin. (2016, 4 3). Best use of big data in marketing. Retrieved from http://www.cleverism.com/best-uses-big-data marketing/
Matz S,KosinskiM,NaveG,StillwellD. (n.d.). Personality-matching increases theeffectivenessofdigitaladvertising. Working Paper.
McAfee, Andrew, Erik Brynjolfsson, Thomas H. Davenport, D.J. Patil and Dominic Barton. (2012). Big Data: The Management Revolution. Harvard Business Review, 90 (10), 61–70.
Mille, G. (1995). WordNet: a lexical database for English. Commun. ACM, 38, 39-41.
Mohan, S., Choi, E., & Min, D. (2008). Conceptual modeling of enterprise application system using social networking and web 2.0 “Social CRM system”. 2008 International conference on convergence and hybrid information technology, (pp. pp. 237–244).
Moharm, K. (2019). State of the art in big data applications in microgrid: A review. Advanced Engineering Informatics 42, (p. 100945).
Muhammad Anshari, Mohammad Nabil Almunawar , Syamimi Ariff Lim , Abdullah Al-Mudimigh. (2019). Customer relationship management and big data enabled:Personalization & customization of services. Applied Computing and Informatics 15, (pp. 94–101).
Ohbyung Kwona,, Namyeon Lee, Bongsik Shin,. (2014). Data quality management, data usage experience and acquisitionintention of big data analytics. International Journal of Information Management 34, (pp. 387–394).
Orenga-Roglá, S., & Chalmeta, R. (2016). Social customer relationship management: Taking advantage of Web 2.0 and Big Data technologies. SpringerPlus, 5(1462), 1–17.
Panigrahi, B. K., Abraham, A. (2010). Computational intelligence in powerengineering. Springer.
Park G,SchwartzH,EichstaedtJ,KernML,KosinskiM,StillwellD, Ungar LH,. (2014). SeligmanMEP: Automaticpersonalityassessment through socialmedialanguage. J. Pers.Soc.Psychol, 180, 934-952.
Paulus, P. (2009). Mental health backbone of the soul. Health Education, 109(4), 289-298.
Pierluigi Zerbino, Davide Aloini, Riccardo Dulmin, Valeria Mininno. (2018). Big Data-enabled Customer Relationship Management: A holistic. Information Processing and Management 54, (pp. 818–846).
Planalp, S. (1989). Relational communication and cognition, Rethinking Commun. 269–277.
Plotkina, D., Munzel, A. (2016). Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new. Retail. Consum. Serv., (pp. 1-11).
Quercia D,KosinskiM,StillwellD,CrowcroftJ. (2011). Our Twitter profiles, ourselves:predictingpersonalitywith Twitter. IEEE International Conference on Social Computing, (pp. 18:180-185).
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning.
Quinlan., J. R. ( 1983). Learning efficient classification procedures and their application to chess end-games. Machine Learning, pp. Vol. 1, pages 463-482.
R.K. Srivastava, T.A. Shervani, L. Fahey. (1998). Market-based assets and shareholder value: a framework for analysis. J. Marketing, 2-18.
R.L. Villars, C.W. Olofson, M. Eastwood. (2011). Big data: what it is and why you should care. White Paper, IDC.
R.L.Villars,C.W.Olofson,M.Eastwood. (2011). Bigdata:whatitisandwhy you shouldcare. WhitePaper.
Reinartz, W., Krafft, M., & Hoyer, W. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing, 41(3), 293–305. Retrieved from http://doi.org/10.1509/jmkr.41.3.293.35991.
Rossi, Peter E., Robert E. McCulloch and Greg M. Allenby. (1996). “The Valueof Purchase History Data in Target Marketing”. Marketing Science, 15(4), pp. 321–40.
RW, B. (1975). Situational variables consumer behavior. Consum., 157-164.
RW, B. (1975, 2:157-164.). Situational variablesandconsumerbehavior. J. Consum.
RW, B. (1975,). Situational variables and consumer behavior. J. Consum. Res.
S. Baccianella, A. Esuli, F. Sebastiani. (2010). SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the Seventh International Conference on Language (pp. 2200–2204). in: N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, D. Tapias (Eds.).
S.C. Deerwester, S.T. Dumais, T.K. Landauer, G.W. Furnas, R.A. (1990). Indexing by latent semantic analysis (Vol. 41). J. Am. Soc.
Sandra C Matz , Oded Netzer. (n.d.). Behavioral Sciences. Using Big data as a window into consumer’s psychology. 18(1), 2352-1546.
Schau, H. J., & Gilly, M. C. (2003). We are what we post? Selfpresentation in personal web space. Journal of Consumer Research, 30(3), 385-404.
Schectman, J. (2012). Netflix uses big data to improve streaming video. Retrieved from Wall Street J: http://blogs.wsj.com/cio/2012/10/26/netflix-usesbig- data-to-improve-streaming-video
Shandong Mou , David J. Robb , Nicole DeHoratius. (2018). Retail store operations: Literature review and research directions. European Journal of Operational Research, (pp. 399-422).
Smith, W. (1956). Product differentiation and market segmentation as alternative marketing strategies. The Journal of Marketing, 3-8.
Sundsøy, P., Bjelland, J., Iqbal, A. M., Pentland, A., & De Montjoye, Y. A. (2014). Big data-driven marketing: How machine learning outperforms marketers’ gut-feeling. Retrieved from Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics): http://doi.org/10.1007/978-3-319-05579-4-45
T, Y. (2010). Personalityin100,000words:alarge-scaleanalysis of personalityandworduseamongbloggers. 44:363-373.
Teixeira T, Wedel M, PietersR. (n.d.). Emotion-induced engagementin internet videoadvertisements. 49, 144-159.
Teixeira T,WedelM,PietersR. (2012). Emotion-induced engagementin internet videoadvertisements. J. Mark.Res.
Torre-Bastida, A. I., Villar-Rodriguez, E., Gil-Lopez, S., & Del Ser, J. (2015). Design and implementation of an extended corporate CRM database system with big data analytical functionalities. Journal of Universal Computer Science, 21(6), 757–776.
Vyncke, P. (2002). Lifestyle SegmentationFrom Attitudes, Interests and Opinions, to Values, Aesthetic Styles, Life Visions and Media Preferences. European Journal of Communication.
W. Zhang, H. Xu, W. Wan. (2012). Weakness finder: find product weakness from Chinese reviews by using aspects based sentiment analysis. In Expert Syst. (Vol. 39 (11), pp. 10283–10291).
Walker, S. J. (2014). Big data: A revolution that will transform how we live, work, and think.
Wan-YuLiu,Chun-ChengLin,Yang SunLee,Der-JiunnDeng. (2013). On gender differences in consumer behavior for online financial transaction of cosmetics. Mathematical and Computer Modelling.
Watson D, C. T. (1988). Developmentandvalidationof brief measuresofpositiveandnegativeaffect:thePANAS scales. Soc.Psychol, (pp. 1063-1070).
Watson D,ClarkLa,Tellegena. (1988). Development And Validation Of brief measure of positive negative affect:the PANAS scales. J. Pers.Soc.Psychol., 54:1063-1070.
Watson, H. (2014). Tutorial: big data analytics: concepts, technologies, and applications. Commun. Assoc. Inf. Syst, 1247–1268.
Weisstein, F.L., Song, L., Andersen, P., Zhu, Y. (2017). Examining impacts of negative reviews and purchase goals on consumer purchase decision. Retail. Consum. Serv, (pp. 201–207).
Wirawan Dony Dahana,Yukihiro Miwa,Makoto Morisada. (2019). Linking lifestyle to customer lifetime value: An exploratory study in an online fashion retail market. Journal of Business Research.
X. Ge, J. Jackson. (2014). The big data application strategy for cost reduction in automotive industry. SAE Int. J. Commer. Veh. 7, 588–598.
YanSong,ShuGuoaMingZhang. (2019). Assessing customers'' perceived value of the anti-haze cosmetics under haze pollution. Science of The Total Environment.
Yuexiang Huang,Chun-Hsien Chen,Li Pheng Khoo. (2012). Products classification in emotional design using a basic-emotion based semantic differential method. International Journal of Industrial Ergonomics.
Zeng Z,PanticM,RoismanGI,HuangTS. (2009). A surveyofaffect recognition methods:audio,visual,andspontaneous expressions. IEEE Trans, 31:39-58.
Zeng Z,PanticM,RoismanGI,HuangTS. (2009). A surveyofaffect recognition methods:audio,visual,andspontaneous expressions. IEEE Trans.PatternAnal.Mach.Intell., (pp. 39-58).
王素灣. (2018). 零售業發展現況與展望. 產業暸望, 頁 115-120.
任立中， 楊泮池， 陳厚銘，賈景光，謝邦昌，陳傑豪，梁錦琳，吳師豪. (2016). 大數據戰略4.0. 新北市: 傅國彰.
行銷別再區分線上和線下！一張表教你國際品牌都在做的「全通路」經營. (2018年04月16日). 擷取自 數位時代: https://www.bnext.com.tw/article/48806/omnichannel
李欣宜. (2015年04月01日). 一次搞懂大數據（上）. 擷取自 數位時代: https://www.bnext.com.tw/article/35807/bn-2015-03-31-151014-36
林津瑩. (2006). 以資源基礎理論探討流通業物流能力、企業策略與經營績效之關係.
范淑雯. (2011). 零售商店顧客經驗管理:商店環境、購物動機之觀點. 國立臺中科技大學流通管理系碩士論文. 台中市, 台灣: 范淑雯.
徐欽盛. (2019). 大數據分析 市場行銷得力助手. 擷取自 品牌志: https://www.expbravo.com/7584/%E5%A4%A7%E6%95%B8%E6%93%9A%E5%88%86%E6%9E%90-%E5%B8%82%E5%A0%B4%E8%A1%8C%E9%8A%B7%E5%BE%97%E5%8A%9B%E5%8A%A9%E6%89%8B.html
高端訓. (2019). 大數據行銷預測. 台北市: 趙政岷.
彭夢竺. (2018年8月27日). 台灣零售業營業額屢創新高　超商、超市占比增加. 擷取自 今日新聞: https://www.nownews.com/news/20180827/2806463/
曾靉. (2016年10月30日). 從線下到線上！Walmart用數據抓住消費者的心. 擷取自 數位時代: https://www.bnext.com.tw/article/41482/walmart-offline-to-online-big-data-e-commerce
勤業眾信聯合會計師事務所. (2019). 2019年零售力量與趨勢展望論壇－數位轉型時代下新零售產業之決勝關鍵. 勤業眾信聯合會計師事務所.
楊爵宇. (2012年01月18日). 如何善用社群行銷(Social Media Marketing)與目標族群對話－台灣企業實例分享. 擷取自 知識管理中心: http://mymkc.com/article/content/21288
葉子維. (2017). 顧客消費行為分析及行動銀行使用預測-決策樹、隨機森林與判別分析之比較.
資策會. (2018). 2018 台灣網路報告. 資策會.
劉雅方. (2019). 運用決策樹預測產品不良因素之研究-以半導體封裝廠為例.
蕭豐達. (2004). 應用模糊理論與決策樹法於空載影像建築物分類之探討.
謝家德. (2017年06月06日). 網路社群媒體零售轉型. 擷取自 FashionExpress: https://www.fashionexpress.org.tw/focus/paper/5357118299
顏理謙. (2016年05月03日). 新零售時代來了！5大趨勢重新定義消費場景. 擷取自 數位時代: https://www.bnext.com.tw/article/39438/bn-2016-05-03-185757-178