在台灣民主開放的風氣下，資訊網路的普及讓民眾們都能在網路平台上進行發言，而網友對於產品的言論及評價往往能夠影響潛在消費者的購買意願，進而左右其購買行為，尤其是零售商品更加明顯，故本研究欲探討網路上的討論看板流量變化對於銷售數量的實質影響。
本研究以Nintendo Switch主機為例，資料取自台灣某網路電商平台之逐筆銷售資料，時間橫跨2017年12月11日至2020年12月31日，將為期3年多的資料搭配兩個台灣指標性線上論壇(PTT實業坊、巴哈姆特電玩資訊站)的網路流量加入探討，並把網路聲量之影響時間向後遞延數期，再加上時間及縣市地區的固定效果與其他控制變數，如距離最近遊戲大作發行時間差、各縣市年輕人口比率、促銷與否、COVID19疫情發生與否等，透過主要效果模型和交互作用模型來驗證其中之影響關係。
研究結果發現將網路聲量向後遞延四週的交互作用模型最具解釋能力，而加上各縣市網路年輕人口比率的交叉效果後，每項網路聲量變數對於Nintendo Switch銷售量在不同的縣市地區會產生不一樣的影響效果，另外研究也發現雖有些相同的網路聲量變數，在兩大論壇中卻會分別產生相反方向的影響。

As the development of information networks and freedom of speech in Taiwan, people can easily speak and publish their opinion on the internet, and Netizens’ comments and evaluations of the merchandises may affect consumers’ impression of the products, which in turn to influence their purchasing behavior, and the phenomenon is especially obvious in E-commerce, so this research intends to explore how does the changes of EWOM will impact the authentic sales amount.
The research takes Nintendo Switch console for instance, and the data is collected from one of Taiwan’s largest E-commerce, which time spans from December, 2017 to December, 2020. We take the two Taiwan’s iconic online forums: PTT and Baha Gaming forum, as the EWOM index. The study defers the impact of EWOM for few periods, and takes the several factors into consideration, it includes time & regions fixed effects and other control variables, such as the time interval of the nearest game masterpiece, the proportion of young people for each city, whether promotion and COVID19 epidemic occurs, etc. We use the main effect model and interaction model to verified the relationship of these factors.
The results of the study found that the interaction model which defers the internet voice for four weeks is the most explanatory. With the cross effect of the ratio of young population in each city, it shows that there are few EWOM variables has significant impact on the sales amount of Nintendo Switch, and the influence are different in respective city. We also notice that the same variable may have opposite effect in the two forums.

學位論文審定書 i
摘要 ii
Abstract iii
目錄 v
圖次 viii
表次 ix

目錄
第一章、 緒論 1
第一節、研究背景 1
(一) 電商平台的購物趨勢 1
(二) Nintendo Switch遊戲機的銷售 2
(三) 資訊網路的普及帶來的影響 3
第二節、 研究動機與目的 4
第三節、研究問題 5
第四節、研究步驟與流程 5
第二章、 文獻回顧 6
第一節、傳統口碑 vs電子口碑 6
(一) 傳統口碑 (Word of Mouth) 6
(二) 電子口碑 (Electronic Word-of-Mouth) 8
(三) 口碑行銷帶來的影響 10
第二節、 電子口碑對於不同族群的影響 11
第三節、 主機平台中的遊戲 12
(一) 第一方開發商 12
(二) 第二方開發商 13
(三) 第三方開發商 13
第四節、 遊戲發售所帶來的效果 14
(一) 領先效果 14
(二) 落後效果 15
(三) 其他周邊效果 16
第五節、遊戲主機的季節性趨勢以及玩家的習性 16
第三章、研究方法 17
第一節、網路電商資料介紹 17
第二節、資料清理流程 18
第三節、外部資料蒐集 22
(一) 距前後相鄰遊戲大作之發行時間差 22
(二) 網路聲量 24
(三) 台灣各縣市的年輕人口比率 27
(四) COVID-19的發生 28
第四節、研究資料欄位介紹 29
第五節、模型方式 32
(一) 多元回歸模型建立 32
(二) 固定效果模型(Fixed-Effect Model) 33
(三) 網路聲量之遞延效果 34
(四) 網路聲量與年輕人口比率之交互作用 35
第四章、研究結果 36
第一節、資料探索與敘述性統計 36
第二節、實證模型之選擇 40
(一) 主要效果模型 40
(二) 交互作用模型 44
第三節、模型結果 50
第四節、實證結果討論 53
(一) 電子口碑效果 53
(二) 其他效果 54
第五章、結論與建議 56
第一節、研究結論 56
第二節、研究建議與未來方向 57
參考文獻 58
一、英文文獻 58
二、相關網站 61
 
圖次
圖1-1 101至109年非店面零售業營業額統計............................................................2
圖1-2 全球Nintendo Switch銷售折線圖..................................................................3
圖3-1 綁售資料示意圖 (一)....................................................................................19
圖3-2 綁售資料示意圖 (二)....................................................................................19
圖3-3 綁售資料示意圖 (三)....................................................................................19
圖3-4 綁售資料示意圖 (四)....................................................................................21
圖3-5 巴哈姆特電玩資訊站Nintendo Switch版討論板…....................................24
圖3-6 PTT實業坊NSwitch版 文章列表....................................................................25
圖3-7 PTT實業坊NSwitch版 文章留言回覆............................................................26
圖3-8 Google Trend搜尋熱度..................................................................................27
圖4-1 Nintendo Switch主機銷售月柱狀圖............................................................36
圖4-2 電商Nintendo Switch總銷售量折線圖.....................................................37
圖4-3 電商Nintendo Switch各年度銷售量柱狀圖.............................................37
圖4-4 Switch銷售量vs PTT熱門時段與否 機率密度圖....................................38
圖4-5 Switch銷售量vs 促銷與否 機率密度圖...................................................38 

表次
表3-1 原始資料欄位變數介紹..................................................................................17
表3-2 Nintendo Switch 遊戲大作列表..................................................................23
表3-3 研究資料欄位變數介紹..................................................................................29
表4-1 各縣市16至34歲人口比率..............................................................................39
表4-2 主要效果模型結果..........................................................................................41
表4-3 交互作用模型結果..........................................................................................44
表4-4 模型8 固定效果結果表....................................................................................48
表4-5 交互作用模型偏微分結果..............................................................................50
表4-6 年輕人口比率帶入偏微分結果......................................................................51

一、英文文獻
Arndt, J. (1967). Role of Product-Related Conversations in the Diffusion of a New
Product, Journal of Marketing Research, 4 (3), 291–295.

Alexandrov, A., Lilly, B., & Babakus, E. (2013). The effects of social- and self-
motives on the intentions to share positive and negative word of mouth. Journal
of the Academy of Marketing Science, 41(5), 531–546.

Palomba, A. (2019). Digital Seasons: How time of the year may shift video game play
habits.

All Answers Ltd. (2018). Sony and Microsoft Games Console Marketing Strategies.

Askitas, H. & K.F. Zimmermann. (2009) . Google Econometrics and Unemployment
Forecasting. IZA Discussion Papers No. 4201.

Alexandrov, A., Lilly, B. & Babakus, E. (2013). The effects of social-and selfmotives
on the intentions to share positive and negative word of mouth. Journal of the
Academy of Marketing Science, 41(5), 531- 546.

Rajagopalan, B. & Subramani, M. R. (2003). Knowledge-sharing and influence in
online social networks via viral marketing.

Buttle, F. A. (1998). Word of mouth: Understanding and managing referral marketing.
Journal of Strategic Marketing, 6(3), 241–254.

Bughin, J., Doogan, J. & Vetvik, O. J. (2010). A New Way to Measure Word-of-
Mouth Marketing. McKinsey Quarterly, 2, 113-116.

Bickart, B. & Schindler, R. M. (2001). Internet forums as influential source of
consumer information.

Anastasiei, B. & Dospinescu, N. (2019) . ElectronicWord-of-Mouth for Online
Retailers: Predictors of Volume and Valence.

Bei, L. T., Chen, E. Y.I. & Widdows. R. (2004). Consumers’ Online Information
Search Behavior and the Phenomenon of Search and Experience Products,
Journal of Family and Economic Issues (25:4), p. 449-467.

Babb, J. & Terry, N. (2013). Comparing Video Game Sales by Gaming Platform.
Southwestern Economic Review, 40(1), 25-46.

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.

Nasif, C. (2016). The Impact of Electronic Word-of-Mouth on. Consumers'
Purchase Intentions.

Choi, H. & H. Varian. (2009). Predicting the Present with Google Trends.
Technical report, Google Inc.

Dellarocas, C. (2006). Strategic Manipulation of Internet Opinion Forums:
Implications for Consumers and Firms. Management Science, 52, 153-169.

D’Amuri, F. & J. Marcucci. (2009). Google it! Forecasting the US unemployment
rate with a Google Job search index. FEEM Working Paper No. 31. 2010.

Dittmar, H., Long, K. & Meek, R. (2004). Buying on the Internet: Gender
Differences in On-Line and Conventional Buying Motivations, in: Sex Roles, 50
(516), 423–444.

D. Horowitz & Goldsmith, R. E. (2006). Measuring Motivations for Online Opinion
Seeking.

Ismagilova, E., Slade, E. L. & Rana, N.P. et al. (2020). The Effect of Electronic Word
of Mouth Communications on Intention to Buy: A Meta-Analysis.

Harrison-Walker, L. J. (2001). The Measurement of Word-of-Mouth Communication
and an Investigation of Service Quality and Customer Commitment As Potential
Antecedents. Journal of Service Research, 4(1), 60–75.

Bertrand, J. L., Hershey, L. & Parnaudeau, M. (2016). Measuring and Managing
Weather Variability: Protecting Businesses from Weather Risks.
Babb, J., Terry, N. & Dana, K. (2013). The Impact Of Platform On Global Video
Game Sales.

Chiou, J. S. (2014) .Whose online reviews have the most influences on
consumers in cultural offerings? Professional vs consumer commentators.

Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y. & Traci Freling. (2014). How Online
Product Reviews Affect Retail Sales: A Meta-analysis.Journal of Retailing 90,2,
217–232.

Khan, K. & Ali, M. (2017). Impact of electronic word of mouth on consumer
purchase intention in footwear industry of Pakistan. Arab. J. Bus. Manag. Rev,
52–63.

Abdullah, L. (2019). The effect of EWOM on consumer trust and purchasing
intention online.

López, M. & Sicilia, M. (2011). The Impact of e-WOM: Determinants of influence.

Park, C. & Lee, T. (2009). Antecedents of Online Reviews' Usage and Purchase
Influence: An Empirical Comparison of U.S. and Korean Consumers, Journal of
Interactive Marketing, Elsevier, vol. 23(4), pages 332-340.

Susan M. Mudambi. (2010) .What Makes a Helpful Online Review? A Study of
Customer Reviews on Amazon.com.

Yan, Q., Wu, S., Wang, L., Wu, P., Chen, H. & Wei, G. (2015). E-WOM from e-
commerce websites and social media: Which will consumers adopt?
 
二、相關網站
內政部戶政司全球資訊網(各月人口資料) - 04 縣市人口按單齡(9701)
https://www.ris.gov.tw/app/portal/346

維基百科: 暢銷任天堂Switch遊戲列表
https://zh.wikipedia.org/wiki/%E6%9A%A2%E9%8A%B7%E4%BB%BB%E5%A4%A9%E5%A0%82Switch%E6%B8%B8%E6%88%B2%E5%88%97%E8%A1%A8

PTT實業坊 NSwitch板
https://www.ptt.cc/bbs/NSwitch/index.html

巴哈姆特 NS/Nintendo Switch哈拉板
https://forum.gamer.com.tw/A.php?bsn=31587

資策會MIC產業情報研究所，台灣有81%消費者在購物前搜尋口碑訊息，2014
https://mic.iii.org.tw/news.aspx?id=366

尼爾森Nielsen，口碑，最佳的廣告形式，2015
https://www.nielsen.com/tw/zh/press-releases/2015/news-taiwan-trust-in-ad-2015-ch/

武漢肺炎：疫情如何衝擊零售業？網路電商危機變轉機，2020
https://www.91app.com/blog/coronavirus-retail-impact/

中華民國經濟部，零接觸商機爆發，109年其他非店面零售業營業額可望雙位數成長，2021
https://www.moea.gov.tw/MNS/populace/news/News.aspx?kind=1&menu_id=40&news_id=92902

液晶面板恐供應不足？宅需求夯、全球電視需求活絡
https://www.moneydj.com/kmdj/news/newsviewer.aspx?a=6069303b-81c3-420e-b265-92ea936e6403

Google Trend
https://trends.google.com.tw/trends/?geo=TW


VGChartz
https://www.vgchartz.com/

Metacritic
https://www.metacritic.com/

IGN
https://www.ign.com/