臺灣博碩士論文加值系統

隨著電子商務不斷發展加上疫情強烈催化作用，網紅直播帶貨新模式逐漸成為主流行銷模式。直播帶貨這樣的創新商務交易發展方式，同時也為既有的電商銷售模式帶來了威脅。近幾年，直播行業在經歷爆發式增長之後，發展漸趨成熟，就目前發展階段來說，直播行業不僅僅只是單純關注用戶數量和網紅熱度而已，而是側重產品變現和商業落地能力。不過，雖然抖音直播是近年來最佳典型代表，但究竟有哪些因素影響了消費者從原有電商平台轉換利用抖音直播來進行購買的意願，相關研究仍相對有限。因此，本研究之研究目的，係試圖透過「推拉繫住力模型」架構，探索影響消費者轉換至使用直播帶貨購買商品之意願程度如何。
本研究回顧並整理相關影響因素外，事前亦同步進行多次消費者訪談並歸納出相關推拉繫住力因素，後續則透過微信群及微信朋友圈等進行問卷發放。問卷共計回收306份，經信效度檢定及後續假說檢定後，本研究發現，「直播帶貨吸引程度」(拉力) 對消費者直播帶貨轉換意圖影響最大，「現有購買方式不滿意程度」(推力) 影響則次之，而「轉換成本」(繫住力) 影響則不顯著。此外「低互動性」對「現有購買方式不滿意程度」影響最大，「直播主魅力」也影響「直播帶貨吸引程度」最多，而「直播帶貨不熟悉程度」則對「轉換成本」影響最大。本研究後續也以「性別」及「職業」進行分群分析，從中也獲得許多相關研究發現。本研究後續針對上述研究發現進行討論後，並闡述相關研究與實務貢獻及意涵。期待透過消費者的角度並了解其轉換選擇網紅直播帶貨的動機因素，進而促使直播帶貨的直播族們可以更清楚瞭解並掌握消費者的真實需求，改進並優化直播帶貨的方式，為各位直播族提高消費者的留存率提供策略建議，最終增強直播帶貨族的核心競爭力是盼。

With the continuous development of e-commerce and the strong catalytic effect of the epidemic, the new model of live streaming ecommerce has gradually become the main popular sales model. The development of innovative business transactions such as live streaming ecommerce also poses a threat to the existing e-commerce sales model. In recent years, the live streaming industry has experienced explosive growth, and its development has gradually matured. As far as the current stage of development is concerned, the live streaming industry is not only concerned with the number of users and the popularity of Internet celebrities, but also focuses on abilities of product realization and commercial landing. Although，Tik Tok Live Streaming is the best representative in recent years，what factors affect consumers' willingness to buy from the original Tik Tok platform, and the related research is still relatively limited. Therefore, the purpose of this study is to explore the extent to which consumers' willingness to switch to live delivery to buy goods is affected through the framework of "push-pull tethering force model".
In addition to reviewing and sorting out the relevant influencing factors, this study also conducted a number of consumer interviews and summarized the relevant push-pull binding force factors in advance, and then distributed the questionnaire through wechat group and wechat circle of friends. In total, 306 questionnaires were recovered. After reliability and validity tests and subsequent hypothesis tests, this study found that the degree of attraction (pull) of live delivery has the greatest impact on consumers’ intention to switch to live streaming ecommerce, the degree of dissatisfaction (push) of existing purchase methods has the second impact, and the impact of switching cost (mooring force) is not significant. In addition, low interactivity has the greatest impact on dissatisfaction with existing purchase methods, and charisma also has the greatest impact on attraction of live broadcasting commerce, while unfamiliarity with live broadcasting commerce has the greatest impact on switching cost. In the follow-up of this study, cluster analysis was also carried out by gender and occupation, from which many related research findings were also obtained. After discussing the above findings, this study expounds the relevant research and practical contributions and implications. It is hoped that through the perspective of consumers and understanding the motivational factors for their conversion and selection of online Red live goods, so as to enable the live broadcast families with goods to better understand and master the real needs of consumers, improve and optimize the way of live goods, provide strategic suggestions for the live broadcast families to improve the retention rate of consumers, and finally enhance the core competitiveness of the live broadcast families.

目錄 I
圖目錄 III
表目錄 IV
第一章 緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究目的 3
1.4研究流程 4
第二章 文獻探討 5
2.1大陸電商發展概況及抖音直播帶貨發展 5
2.1.1 網站及社群電商 8
2.2轉換行爲意圖及PPM理論觀點介紹 11
2.3PPM理論觀點之過去相關研究 11
2.4 抖音直播帶貨購買轉換意圖之相關PPM驅力因素 13
2.4.1 推力 (Push Effect) 13
2.4.2 拉力 (Pull Effect) 15
2.4.3 繫助力 (Mooring Effect) 18
第三章 研究方法 21
3.1研究架構 21
3.2研究假說與推論 22
3.3操作型定義與衡量題項來源 27
3.4研究設計 34
3.4.1問卷設計 34
3.4.2研究對象 35
3.5 資料分析方法 35
3.5.1 敘述性統計分析 35
3.5.2 信效度分析 35
3.5.3 假說檢定 36
第四章 資料分析與實證結果 38
4.1 樣本結構分析 38
4.1.1 人口統計變數之敘述性統計分析 38
4.1.2 使用行為之敘述性統計分析 39
4.2 效度分析 41
4.2.1 收斂效度分析 41
4.2.2 區別效度分析 44
4.3 假說檢定 44
4.3.1 研究假說檢定 45
4.3.2 假說分析與討論 47
4.4.1依性別分群 51
4.4.2依職位分群 53
第五章 結論與建議 56
5.1研究結論 56
5.2研究貢獻與建議 57
5.3 管理意涵 59
5.4 研究限制與未來研究方向 60


 
圖目錄
圖1-1 研究流程圖 4
圖2-1 中國大陸電子商務產業發展歷程圖 6
圖2-2 抖音電商佈局發展歷程 18
圖3-1 研究架構 22
圖4-1 整體模式路徑分析圖 43
圖4-2 路徑分析圖 (男性) 50
圖4-3 路徑分析圖 (女性) 51
圖4-4 路徑分析圖 (學生) 52
圖4-5 路徑分析圖 (非學生) 53

表目錄
表3-1 研究假說彙整 26
表3-2 操作型定義 26
表3-3 對現有購買方式不滿意之題項整理 26
表3-4 直播帶貨的吸引程度之題項整理 26
表3-5 轉換成本之題項整理 26
表3-6 消費混亂程度之題項整理 26
表3-7 低互動性之題項整理 30
表3-8 魅力之題項整理 30
表3-9 產品知識之題項整理 31
表3-10 沉浸式體驗之題項整理 31
表3-11 直播帶貨不熟悉程度的題項整理 32
表3-12 個人習慣之題項整理整理 32
表3-13 轉換意圖的題項 32
表4-1 基本資料之樣本特性 36
表4-2 使用行為之樣本特性 38
表4-3 因素負荷量表 40
表4-4 組合信度與平均變異抽取量彙整表 41
表4-5 各構念間相關係數矩陣 42
表4-6 各研究假說之 t-value與路徑係數 44

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二、中文部分
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三、網頁部分
1.排行榜 (2020)：https://www.36kr.com/p/1025269536131075
2.中國互聯網路資訊中心 (2020):https://cbndata.com/report/2349/detail?isReading=report&page=4
3.极光大数据 (2017)：https://report.iimedia.cn/repo1-0/39000.html
4.中國電子商務報告 (2020)：http://www.gov.cn/xinwen/202007/02/5523479/files/0a2c57d8ba6d4e26b83d96cdd764d6f0.pdf
5.中國電商行業研究報告 (2020):file:///D:/%E7%A0%94%E7%A9%B6%E6%89%80%E6%AF%95%E4% B8%9A%E8%AE%BA%E6%96%87/%E6%96%87%E7%8C%AE/2020%E4%B8%AD%E5%9B%BD%E7%9B%B4%E6%92%AD%E7%94%B5%E5%95%86%E8%A1%8C%E4%B8%9A%E7%A0%94%E7%A9%B6%E6%8A%A5%E5%91%8A.pdf
6.商務部電子商務和信息化司 (2020)：http://www.gov.cn/xinwen/202007/02/5523479/files/0a2c57d8ba6d4e26b83d96cdd764d6f0.pdf
7.商務部電子商務和信息化司(2019):http://dzsws.mofcom.gov.cn/
8.全球移動通信系統協會(2020)：http://finance.people.com.cn/BIG5/n1/2021/0223/c1004-32034713.html