網路口碑對線上應用程式的購買意圖之影響-以Apple App Store為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：23

、訪客IP：3.147.60.62

姓名

殷健翔(Chien-hsiang Yin) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

網路口碑對線上應用程式的購買意圖之影響-以Apple App Store為例
(The Influence of eWOM on purchase Intention of Online Applications: An Empirical Study of Apple App Store)

相關論文

★ 網站設計探討—以國內外連鎖飯店集團為例	★ 智慧型手機使用者介面與消費者態度之關聯
★ 零售業全通路策略分析 -以Family Mart為例	★ 應用服務藍圖及顧客旅程優化汽車租賃業之服務流程: 以某汽車租賃業為例
★ 顧客對折扣促銷的評價－探討商店定價策略、促銷語意線索與折扣深度對消費者認知的影響	★ 網路拍賣中外部線索的一致性:在不同的資訊不對稱程度下，起始價格以及賣方聲譽對於拍賣結果影響之探討。
★ 影響金融服務業之多重通路使用者評估通路整合性的因素	★ 影響網路口碑行為意圖之因素分析
★ 在多通路環境中，實體通路的存在如何影響顧客決策	★ 混合情緒操控與間接體驗產品途徑對消費者之影響－以創新產品為主
★ 服務業不同廣告型式的效果如何受消費者性別及代言人類型而改變	★ 消費者於多通路環境下之跨通路搭便車行為之決定因素
★ 如何撰寫較好的網路口碑	★ 探討線上環境中互動與故事對廣告效果之影響
★ 不同決策特質與搜尋時間壓力下部落格設計與使用者滿意度之探討	★ 主購社群關係與主購經驗分享對網路合購行為因素影響之探討

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

隨著行動裝置的快速成長，消費者透過行動裝置購買應用軟體的情形也越來越普及。然而，在目前全球的線上軟體商店的龍頭—蘋果電腦的App Sotre裡，有成千上萬的軟體可供消費者下載和購買。擁有很多的選擇對消費者不一定是好事情，消費者有可能因此不知道哪個商品或服務才是他們想要的。消費者為了更了解他們想要的商品或服務，通常會採用其他人的意見做為決策制定的來源，也就是所謂的口碑。在網路的環境中，將口碑數位化為網路口碑。而網路口碑的形式有很多種，像是論壇、部落格、官方評論系統和電子郵件。
本研究主要在探索官方評論系統（評分人數、平均評分、評論）和購買意圖的關係，並且以蘋果電腦的評論系統為研究的對象。此外，本篇研究還採用應用軟體的類別作為調節變數，來驗證是否官方評論系統會因為不同的應用軟體類別，導致消費者對應用軟體的評估有所差異。本研究的主要結果有：第一，評分人數只有在消費者購買娛樂性的應用軟體時，才會跟軟體評估有相關性。第二，不論消費者在購買娛樂性或是實用性的應用軟體時，平均評分都會影響消費者的軟體評估。而且，在購買實用性產品的時候，消費者受到平均評分的影響會更嚴重。第三：不論消費者在購買娛樂性或是實用性的應用軟體時，評論都會影響消費者的軟體評估。最後，本研究提供給軟體開發者一些管理實務上的建議。

摘要(英)

Along with rapid growth of mobile devices, the phenomenon of consumers’ purchasing apps in online app store becomes more popular. There’re, however, thousands upon thousands of apps for downloading or buying in Apple App Store which is the biggest app store in the world. Having lots of choices is sometimes not a good thing for consumers because they may beset by that they don’t know which product or service is the one they really want. In order to get more familiar with the product or services which are really in need, people usually refer to others’ opinions as resources while making purchase decision, which is so-called word-of-mouth (WOM). Within online environment, it digitizes these personal opinions to electronic form which we call it electronic WOM (eWOM). There are many types of eWOM, such as online forum, blog, official review system, and email.
This research is focusing on examining the relationship between review system (Number of Ratings, Average Ratings, and Reviews) in Apple App Store and purchase intention; moreover, this research adopts types of apps (hedonic and utilitarian) as moderator to validate whether consumers’ evaluations are different due to moderating effect. The results of this research provide three contributions. First, the Number of Ratings has impact on consumers’ evaluation when consumers are buying hedonic apps. Second, consumers’ evaluation is influenced by Average Rating. Moreover, the influence is more critical when consumers are buying utilitarian apps than hedonic apps. Third, Reviews has influence on consumers’ evaluation when consumers are buying both hedonic and utilitarian apps. Finally, this research suggests some implications for application developers.

關鍵字(中)

★ 實用性
★ 娛樂性
★ 購買意圖
★ 網路口碑
★ Apple App Store

關鍵字(英)

★ Utilitarian
★ Hedonic
★ Apple App Store
★ Purchase Intention
★ eWOM

論文目次

1. Introduction 1
2. Literatures and Hypotheses 3
2.1 The business model of app store 3
2.2 eWOM 4
2.2.1 review system 4
2.2.2 Number of Ratings 5
2.2.3 Average Rating 6
2.2.4 Reviews 6
2.3 Hedonic versus Utilitarian 7
2.4 Purchase Intention 8
3. Method 10
3.1 Experiment Design and Stimuli 10
3.2 Item Measurement 11
3.3 Subjects 14
3.4 Experimental Procedure 14
4. Analysis and Results 16
4.1 Descriptive analysis 16
4.2 Reliability 17
4.3 Validity 17
4.4 Hypothesis testing 18
4.5 Moderator testing 19
4.6 Mediator testing 20
5. General Discussion 21
5.1 Conclusion 21
5.2 Managerial Implications 23
5.3 Limitations and Future Research 24
References 26
Appendix: Questionnaire 30

參考文獻

[1]. Addis, M., & Holbrook, M. B. (2001). On the conceptual link between mass customisation and experiential consumption: an explosion of subjectivity. Journal of Consumer Behavior, 1(1), 50-66.
[2]. Argo, J. J., Dahl, D. W., & Morales, A. C. (2008). Positive consumer contagion: responses to attractive others in a retail context. Journal of Marketing Research, 45(6), 690-701.
[3]. Awad, N., & Zhang, J. (2006). A framework for evaluating organizational involvement in online ratings communities. MWAIS 2006 Proceedings, 24.
[4]. Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: measuring hedonic and utilitarian shopping value. The Journal of Consumer Research, 20(4), 644-656.
[5]. Bass, F. M. (1969). A simultaneous equation regression study of advertising and sales of cigarettes. Journal of Marketing Research, 6(3), 291-300.
[6]. Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003). How critical are critical reviews? The box office effects of film critics, star power, and budgets. Journal of Marketing, 67(4), 103-117.
[7]. Burnkrant, R. E., & Cousineau, A. (1975). Informational and normative social influence in buyer behavior. The Journal of Consumer Research, 2(3), 206-215.
[8]. Chen, P. Y., Wu, S. Y., & Yoon, J. S. (2004). The impact of online recommendations and consumer feedback on sales. ICIS 2004 Proceedings, 58.
[9]. Chen, Y., Fay, S., & Wang, Q. (2003). Marketing implications of online consumer product reviews. Working paper, department of marketing, University of Florida.
[10]. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43(3), 345-354.
[11]. Combs, D., (2010). App store has 300,000 apps, but what does that really mean [Obviously tech by Dylan Combs]. Retrieved from http://www.lockergnome.com/dylancombs/2010/12/16/app-store-has-300000-apps-but-what-does-that-really-mean/ (available June 2011)
[12]. Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter?--An empirical investigation of panel data. Decision Support Systems, 45(4), 1007-1016.
[13]. Dellarocas, C. (2005). Reputation mechanism design in online trading environments with pure moral hazard. Information Systems Research, 16(2), 209-230.
[14]. Dhanasobhon, S., Chen, P. Y., Smith, M., & Chen, P. (2007). An analysis of the differential impact of reviews and reviewers at Amazon.com. ICIS 2007 Proceedings, 94.
[15]. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers' product evaluations. Journal of Marketing Research, 28(3), 307-319.
[16]. Engle, J. F., Blackwell, R. D., & Miniard, P. W. (1993). Consumer behavior. Chicago: Dryden.
[17]. Enter, R., Henderson, N., & Purcell, K. (2010). The rise of apps culture [PewInternet]. Retrieved from http://pewinternet.org/Reports/2010/The-Rise-of-Apps-Culture.aspx (available June 2011)
[18]. Farago, P. (2010). Madison avenue and the land of make believe [FLURRY]. Retrieved from http://blog.flurry.com/bid/48418/Madison-Avenue-and-the-Land-of-Make-Believe (available June 2011)
[19]. Granovetter, M., & Soong, R. (1988). Threshold models of diversity: Chinese restaurants, residential segregation, and the spiral of silence. Sociological Methodology, 18(6), 69-104.
[20]. Han, I., Lee, J., & Park, D. H. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341-352.
[21]. Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. The Journal of Consumer Research, 9(2), 132-140.
[22]. Jap, S. D., & Anderson, E. (2003). Safeguarding interorganizational performance and continuity under ex post opportunism. Management Science, 49(12), 1684-1701.
[23]. Kahnx, B., Ratner, R., & Kahneman, D. (1997). Patterns of hedonic consumption over time. Marketing Letters, 8(1), 85-96.
[24]. Kent, J. (2011). Apple maintains dominance of mobile application store market in 2010 [iSuppli Market Intelligence]. Retrieved from http://www.isuppli.com/Media-Research/News/Pages/Apple-Maintains-Dominance-of-Mobile-Application-Store-Market-in-2010.aspx (available June 2011)
[25]. Keller, K. L., & Staelin, R. (1987). Effects of quality and quantity of information on decision effectiveness. The Journal of Consumer Research, 14(2), 200-213.
[26]. Khare, A., Labrecque, L. I., & Asare, A. K. (2011). The assimilative and contrastive effects of word-of-mouth volume: an experimental examination of online consumer ratings. Journal of Retailing, 87(1), 111-126.
[27]. Kim, S., & Park, D. H. (2009). 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.
[28]. Kim, S., & Pysarchik, D. T. (2000). Predicting purchase intentions for uni-national and bi-national products. International Journal of Retail & Distribution Management, 28(6), 280-291.
[29]. Lascu, D., & Zinkhan, G. M. (1999). Consumer conformity: review and applications for marketing theory and practice. Journal of Marketing Theory and Practice, 7(3), 1-12.
[30]. Lee, J., & Lee, J. N. (2009). Understanding the product information inference process in electronic word-of-mouth: An objectivity-subjectivity dichotomy perspective. Information & Management, 46(5), 302-311.
[31]. Lin, T. M. Y., Huang, Y. K., & Yang, W. I. (2007). An experimental design approach to investigating the relationship between Internet book reviews and purchase intention. Library & Information Science Research, 29(3), 397-415.
[32]. Mahajan, V., Muller, E., & Bass, F. M. (1990). New product diffusion models in marketing: A review and directions for research. The Journal of Marketing, 54(1), 1-26.
[33]. Muller, T. & Bowcock, J. (2011). Apple’s app store downloads top 10 billion [Apple]. Retrieved from http://www.apple.com/pr/library/2011/01/22appstore.html (available June 2011)
[34]. Olshavsky, R. W., & Granbois, D. H. (1979). Consumer decision making-fact or fiction? The Journal of Consumer Research, 6(2), 93-100.
[35]. Resnick, P., & Zeckhauser, R. (2002). Trust among strangers in internet transactions: empirical analysis of eBay's reputation system. Advances in Applied Microeconomics: A Research Annual, 11, 127-157.
[36]. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854-856.
[37]. Salganik, M. J., & Watts, D. J. (2008). Leading the herd astray: An experimental study of self-fulfilling prophecies in an artificial cultural market. Social Psychology Quarterly, 71(4), 338-355.
[38]. Sawhney, M. S., & Eliashberg, J. (1996). A parsimonious model for forecasting gross box-office revenues of motion pictures. Marketing Science, 15(2), 113-131.
[39]. Schiffman, L. G., & Kanuk, L. L. (2000). Consumer behavior. New Jersey: Prentice-Hall.
[40]. Sternthal, B., Tybout, A. M., & Calder, B.J. (1994). Experimental design: generalization and theoretical explanation. Principles of Marketing Research, 195-223.
[41]. Torikka, M. (2011). App downloads will hit 44 billion in 2016 [MobileBeat]. Retrieved from http://venturebeat.com/2011/04/29/44-billion-apps-2016/ (available June 2011)
[42]. Yarow, J., & Angelova, K. (2011). Chart of the day: google is closing the gap on apple's app store [Business Insider]. Retrieved from http://www.businessinsider.com/chart-of-the-day-smartphone-apps-2011-3 (available June 2011)
[43]. Zou, P., Yu, B., & Hao, Y. (2011). Does the valence of online consumer reviews matter for consumer decision making? The moderating role of consumer expertise. Journal of Computers, 6(3), 484-488.

指導教授

謝依靜(Yi-ching Hsieh)

審核日期

2011-7-26

推文