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題名 外送使用行為的影響因素調查
Investigating the factors influencing the online food delivery service作者 廖緯霖
Liao, Wei-Lin貢獻者 洪叔民<br>白佩玉
Horng, Shwu-Min<br>Pai, Pei-Yu
廖緯霖
Liao, Wei-Lin關鍵詞 美食外送平台
共享經濟
服務品質
科技接受模型
food delivery platform
sharing economy
service quality
TAM日期 2020 上傳時間 3-Aug-2020 18:43:37 (UTC+8) 摘要 從 2018 年到 2019 年，台灣餐飲業呈現小幅成長，同時美食外送市場在整體的營業額佔比也逐漸提升，可見消費者對於美食外送服務的接受度逐漸提高。在眾多美食外送服務模式中，本研究以符合共享經濟概念的美食外送平台作為探討對象，這類平台不需自行開設餐廳且外送員上線時間不受平台管束，平台僅是資訊的串聯者，因此必須提供誘因吸引餐廳及外送員加入平台來為用戶提供服務。本研究探討平台服務品質、科技接受模型、個人因素對於用戶使用行為的影響，並以當前環境 COVID-19 疫情影響當作干擾變數，探討在疫情影響下用戶的使用行為，並提供平台業者在產品及服務模式的建議。本研究共收集 299 份有效問卷，透過多元迴歸統計方式驗證假說。研究結果發現，科技接受模型的「認知有用性與易用性」對於使用行為有正向顯著影響，其餘變數（平台可信度、服務提供者、社交互動、傾向節省價格、傾向節省時間）則無顯著影響；干擾效果中，「社交互動、認知有用性」存在負向的干擾效果、「認知易用性」存在正向的干擾效果。認知有用性與易用性是影響使用行為的主要因素，但目前服務範圍集中於商業區與使用者多為年輕族群，建議美食外送平台可以從兩個面向擴大市場；其他變數都無顯著影響，美食外送平台仍須提供相對應的服務，如：將消費者的資料妥善保管、對外送員定期提供訓練與更新規範，以讓外送員提供良好服務。在干擾效果中，建議美食外送平台在重大公衛事件發生時，用戶會為了減少干擾機會而避免出門，因此要想辦法減少外送員與用戶之間的接觸機會，以提高用戶的使用行為。
From 2018 to 2019, Taiwanese catering industry showed a slight growth. At the same time, the share of the food delivery market accounted for the overall catering industry was gradually increasing. Obviously, consumers are more willing to use food delivery services. Among all of food delivery services, this study selects those food delivery platforms which under the concept of the sharing economy as the object of discussion. Those platforms don’t need to own any restaurants and the delivery partners are not fully organized by them. They are only a tandem of information, so they must provide incentives to attract restaurants and delivery partners to join the platform then provide services to users. This research explores the relationships among platform service quality, the factors in technology acceptance model and the personal factors, to better understand user behaviors in food delivery platform. Considering the current environment of COVID-19 epidemic, this research also adds this situation as a moderating variable to explore how the epidemic will affect the usage behavior.This research collected 299 valid questionnaires and the hypotheses were verified through multiple regression statistics. The study finds that “perceived usefulness and ease of use” in the technology acceptance model have a positive and significant impact on usage behavior. The remaining variables (legal protection and trustworthiness, peer service supplier, social interaction, price saving orientation, time saving orientation) have no significant impact on usage behavior.Among the moderation effects, “social interaction” and “perceived usefulness” have negative moderation effect and “perceived ease of use” has a positive moderation effect on usage behavior.Based on the result, “perceived usefulness” and “perceived ease of use” are the factors influence usage behavior. However, the current service area is still limited to business areas and users are mostly younger. This study recommends the platforms can expand their markets from these two perspectives. Though other factors have no significant impact on usage behavior, this study still suggests the platforms should perform on an average level, like well-organized users’ information, offer training and update guideline to the delivery partners then they will provide good services. In the moderation effect, this study recommends that the food delivery platform should avoid the interaction between users and delivery partners during the time of epidemic outbreak. Because everyone is afraid of getting affection, it will properly increase users’ behavior.參考文獻 一、 中文文獻Kantar Insights Division 凱度洞察台灣 (2019)， 外食市場 10 年間翻倍成長 美食外送服務會成為台灣人的新習慣嗎？。http://www.tns-global.com.tw/News_detail.php?nid=51Kantar Insights Division 凱度洞察台灣 (2020)，Kantar凱度台灣 COVID-19疫情特輯：疫情帶來餐飲寒冬，美食外送平台逆勢成長。http://www.tns-global.com.tw/News_detail.php?nid=67王一芝 (2020)，拒絕Uber Eats 50次、砸錢建串接系統 獨家直擊鼎泰豐外送5大心法，天下雜誌。https://www.cw.com.tw/article/article.action?from=search&id=5100666&template=transformers林珮萱、蕭玉品 (2019)，業者不加入就賺不到錢？全家、星巴克、小吃店紛紛搶上外送平台，天下雜誌。https://www.gvm.com.tw/article/70019吳元熙 (2020)，外送國家隊成軍挺1.1萬間餐廳，有哪些業者加入？影響是什麼？，數位時代。https://www.bnext.com.tw/article/57284/taiwan-food-delivery-team陳威珞 (2018)，餐飲業發展趨勢，台灣趨勢研究股份有限公司，https://www.twtrend.com/share_cont.php?id=63經濟部統計處 (2019)，108年批發、零售及餐飲業經營實況調查報告。https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=28&html=1&menu_id=16959&bull_id=6384經濟部統計處 (2020)，產業經濟統計簡訊。https://www.moea.gov.tw/MNS/populace/news/News.aspx?kind=1&menu_id=40&news_id=89173二、 英文文獻Agarwal, R., and Venkatesh, V. (2002). Assessing a firm`s Web presence: A heuristic evaluation procedure for the measurement of usability. Information Systems Research, 13(2), 168–186.Bai, J., So, K. C., Tang, C. S., Chen, X. M., & Wang, H. (2019). Time-Based Payout Ratio for Coordinating Supply and Demand on an On-Demand Service Platform. In Sharing Economy (pp. 115-136). Springer, Cham.Barnes, S. J., and Mattsson, J. (2016). Understanding current and future issues in collaborative consumption: A four-stage Delphi study. Technological Forecasting and Social Change, 104, 200-211.Benjaafar, S., Kong, G., Li, X., and Courcoubetis, C. (2019). Peer-to-peer product sharing. In Sharing Economy (pp. 11-36). Springer, Cham.Browne, M., and Mels, G. (1990). Ramona user’s guide, columbus: Department of psychology’. Ohio State University.Browne, M. W., and Arminger, G. (1995). Specification and estimation of mean-and covariance-structure models. In Handbook of statistical modeling for the social and behavioral sciences (pp. 185-249). Springer, Boston, MA.Cachon, G. P., Daniels, K. M., and Lobel, R. (2017). The role of surge pricing on a service platform with self-scheduling capacity. Manufacturing & Service Operations Management, 19(3), 368-384.Cheng, X., Fu, S., and de Vreede, G. J. (2018). A mixed method investigation of sharing economy driven car-hailing services: Online and offline perspectives. International Journal of Information Management, 41, 57–64.Chiu, C. M., Wang, E. T., Fang, Y. H., and Huang, H. Y. (2014). Understanding customers` repeat purchase intentions in B2C e‐commerce: the roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24(1), 85-114.Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.De Stefano, V. (2015). The rise of the just-in-time workforce: On-demand work, crowdwork, and labor protection in the gig-economy. Comp. Lab. L. & Pol`y J., 37, 471.Ertz, M., Durif, F., and Arcand, M. (2016). Collaborative consumption: Conceptual snapshot at a buzzword. Journal of Entrepreneurship Education, 19(2), 1-23.Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.Hu, M. (Ed.). (2019). Sharing economy: making supply meet demand (Vol. 6). Springer.Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.Jöreskog, K. G. (1970). A general method for analysis of covariance structures. Biometrika, 57(2), 239-251.Jöreskog, K. G., and Sörbom, D. (1989). LISREL 7: A guide to the program and applications. Spss.Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business horizons, 54(3), 241-251.Laroche, M., Yang, Z., McDougall, G.H.G., Bergeron, J., 2005. Internet versus bricksand-mortar retailers: an investigation into intangibility and its consequences. J. Retail. 81 (4), 251 versLiao, S. H. (2007). Electronic commerce technologies and applications-a decade review from 1995 to 2005. 電子商務學報, 9(2), 399-434.Marimon, F., Llach, J., Alonso-Almeida, M., & Mas-Machuca, M. (2019). CC-Qual: A holistic scale to assess customer perceptions of service quality of collaborative consumption services. International Journal of Information Management, 49, 130-141.McDonald, R. P., and Marsh, H. W. (1990). Choosing a multivariate model: Noncentrality and goodness of fit. Psychological bulletin, 107(2), 247.Monroe, K. B., and Lee, A. Y. (1999). Remembering versus knowing: Issues in buyers’ processing of price information. Journal of the Academy of Marketing Science, 27(2), 207-225.Parasuraman, A., Zeithaml, V. A., and Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of marketing, 49(4), 41-50.Tunca, T. I. (2019). Operational Factors in the Sharing Economy: A Framework. In Sharing Economy (pp. 55-71). Springer, Cham.Ullman, J. B., and Bentler, P. M. (2003). Structural equation modeling. Handbook of psychology, 607-634Yaraghi, N., and Ravi, S. (2017). The current and future state of the sharing economy. Available at SSRN 3041207.Yeo, V. C. S., Goh, S. K., and Rezaei, S. (2017). Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, 35, 150-162.Zhang, J., Chen, H., and Zhang, W. (2016). How to coordinate supply chain under O2O business model when demand deviation and retailer`s sales cost deviation happen. Piscataway: The Institute of Electrical and Electronics Engineers, Inc. (IEEE).Zwick, A. (2018). Welcome to the Gig Economy: Neoliberal industrial relations and the case of Uber. GeoJournal, 83(4), 679-691. 描述 碩士
國立政治大學
企業管理研究所(MBA學位學程)
107363077資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107363077 資料類型 thesis dc.contributor.advisor 洪叔民<br>白佩玉 zh_TW dc.contributor.advisor Horng, Shwu-Min<br>Pai, Pei-Yu en_US dc.contributor.author (Authors) 廖緯霖 zh_TW dc.contributor.author (Authors) Liao, Wei-Lin en_US dc.creator (作者) 廖緯霖 zh_TW dc.creator (作者) Liao, Wei-Lin en_US dc.date (日期) 2020 en_US dc.date.accessioned 3-Aug-2020 18:43:37 (UTC+8) - dc.date.available 3-Aug-2020 18:43:37 (UTC+8) - dc.date.issued (上傳時間) 3-Aug-2020 18:43:37 (UTC+8) - dc.identifier (Other Identifiers) G0107363077 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131355 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 企業管理研究所(MBA學位學程) zh_TW dc.description (描述) 107363077 zh_TW dc.description.abstract (摘要) 從 2018 年到 2019 年，台灣餐飲業呈現小幅成長，同時美食外送市場在整體的營業額佔比也逐漸提升，可見消費者對於美食外送服務的接受度逐漸提高。在眾多美食外送服務模式中，本研究以符合共享經濟概念的美食外送平台作為探討對象，這類平台不需自行開設餐廳且外送員上線時間不受平台管束，平台僅是資訊的串聯者，因此必須提供誘因吸引餐廳及外送員加入平台來為用戶提供服務。本研究探討平台服務品質、科技接受模型、個人因素對於用戶使用行為的影響，並以當前環境 COVID-19 疫情影響當作干擾變數，探討在疫情影響下用戶的使用行為，並提供平台業者在產品及服務模式的建議。本研究共收集 299 份有效問卷，透過多元迴歸統計方式驗證假說。研究結果發現，科技接受模型的「認知有用性與易用性」對於使用行為有正向顯著影響，其餘變數（平台可信度、服務提供者、社交互動、傾向節省價格、傾向節省時間）則無顯著影響；干擾效果中，「社交互動、認知有用性」存在負向的干擾效果、「認知易用性」存在正向的干擾效果。認知有用性與易用性是影響使用行為的主要因素，但目前服務範圍集中於商業區與使用者多為年輕族群，建議美食外送平台可以從兩個面向擴大市場；其他變數都無顯著影響，美食外送平台仍須提供相對應的服務，如：將消費者的資料妥善保管、對外送員定期提供訓練與更新規範，以讓外送員提供良好服務。在干擾效果中，建議美食外送平台在重大公衛事件發生時，用戶會為了減少干擾機會而避免出門，因此要想辦法減少外送員與用戶之間的接觸機會，以提高用戶的使用行為。 zh_TW dc.description.abstract (摘要) From 2018 to 2019, Taiwanese catering industry showed a slight growth. At the same time, the share of the food delivery market accounted for the overall catering industry was gradually increasing. Obviously, consumers are more willing to use food delivery services. Among all of food delivery services, this study selects those food delivery platforms which under the concept of the sharing economy as the object of discussion. Those platforms don’t need to own any restaurants and the delivery partners are not fully organized by them. They are only a tandem of information, so they must provide incentives to attract restaurants and delivery partners to join the platform then provide services to users. This research explores the relationships among platform service quality, the factors in technology acceptance model and the personal factors, to better understand user behaviors in food delivery platform. Considering the current environment of COVID-19 epidemic, this research also adds this situation as a moderating variable to explore how the epidemic will affect the usage behavior.This research collected 299 valid questionnaires and the hypotheses were verified through multiple regression statistics. The study finds that “perceived usefulness and ease of use” in the technology acceptance model have a positive and significant impact on usage behavior. The remaining variables (legal protection and trustworthiness, peer service supplier, social interaction, price saving orientation, time saving orientation) have no significant impact on usage behavior.Among the moderation effects, “social interaction” and “perceived usefulness” have negative moderation effect and “perceived ease of use” has a positive moderation effect on usage behavior.Based on the result, “perceived usefulness” and “perceived ease of use” are the factors influence usage behavior. However, the current service area is still limited to business areas and users are mostly younger. This study recommends the platforms can expand their markets from these two perspectives. Though other factors have no significant impact on usage behavior, this study still suggests the platforms should perform on an average level, like well-organized users’ information, offer training and update guideline to the delivery partners then they will provide good services. In the moderation effect, this study recommends that the food delivery platform should avoid the interaction between users and delivery partners during the time of epidemic outbreak. Because everyone is afraid of getting affection, it will properly increase users’ behavior. en_US dc.description.tableofcontents 第一章 緒論-1第一節 研究背景-1第二節 研究問題與目的-5第三節 研究流程-6第二章 文獻探討-8第一節 行動裝置與行動電子商務-8第二節 共享經濟-9第三節 零工經濟-11第四節 美食外送平台-12第五節 使用行為-14第三章 研究方法-19第一節 研究架構與假說-19第二節 研究對象與方法-24第三節 問卷設計-25第四節 統計方法-27第四章 分析結果-28第一節 敘述統計-28第二節 信效度結果-30第三節 研究架構及假說修正-36第四節 迴歸分析-38第五章 結論與建議-43第一節 研究結論-43第二節 管理意涵-44第三節 研究限制-46參考文獻-48附錄-54 zh_TW dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107363077 en_US dc.subject (關鍵詞) 美食外送平台 zh_TW dc.subject (關鍵詞) 共享經濟 zh_TW dc.subject (關鍵詞) 服務品質 zh_TW dc.subject (關鍵詞) 科技接受模型 zh_TW dc.subject (關鍵詞) food delivery platform en_US dc.subject (關鍵詞) sharing economy en_US dc.subject (關鍵詞) service quality en_US dc.subject (關鍵詞) TAM en_US dc.title (題名) 外送使用行為的影響因素調查 zh_TW dc.title (題名) Investigating the factors influencing the online food delivery service en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、 中文文獻Kantar Insights Division 凱度洞察台灣 (2019)， 外食市場 10 年間翻倍成長 美食外送服務會成為台灣人的新習慣嗎？。http://www.tns-global.com.tw/News_detail.php?nid=51Kantar Insights Division 凱度洞察台灣 (2020)，Kantar凱度台灣 COVID-19疫情特輯：疫情帶來餐飲寒冬，美食外送平台逆勢成長。http://www.tns-global.com.tw/News_detail.php?nid=67王一芝 (2020)，拒絕Uber Eats 50次、砸錢建串接系統 獨家直擊鼎泰豐外送5大心法，天下雜誌。https://www.cw.com.tw/article/article.action?from=search&id=5100666&template=transformers林珮萱、蕭玉品 (2019)，業者不加入就賺不到錢？全家、星巴克、小吃店紛紛搶上外送平台，天下雜誌。https://www.gvm.com.tw/article/70019吳元熙 (2020)，外送國家隊成軍挺1.1萬間餐廳，有哪些業者加入？影響是什麼？，數位時代。https://www.bnext.com.tw/article/57284/taiwan-food-delivery-team陳威珞 (2018)，餐飲業發展趨勢，台灣趨勢研究股份有限公司，https://www.twtrend.com/share_cont.php?id=63經濟部統計處 (2019)，108年批發、零售及餐飲業經營實況調查報告。https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=28&html=1&menu_id=16959&bull_id=6384經濟部統計處 (2020)，產業經濟統計簡訊。https://www.moea.gov.tw/MNS/populace/news/News.aspx?kind=1&menu_id=40&news_id=89173二、 英文文獻Agarwal, R., and Venkatesh, V. (2002). Assessing a firm`s Web presence: A heuristic evaluation procedure for the measurement of usability. Information Systems Research, 13(2), 168–186.Bai, J., So, K. C., Tang, C. S., Chen, X. M., & Wang, H. (2019). Time-Based Payout Ratio for Coordinating Supply and Demand on an On-Demand Service Platform. In Sharing Economy (pp. 115-136). Springer, Cham.Barnes, S. J., and Mattsson, J. (2016). Understanding current and future issues in collaborative consumption: A four-stage Delphi study. Technological Forecasting and Social Change, 104, 200-211.Benjaafar, S., Kong, G., Li, X., and Courcoubetis, C. (2019). Peer-to-peer product sharing. In Sharing Economy (pp. 11-36). Springer, Cham.Browne, M., and Mels, G. (1990). Ramona user’s guide, columbus: Department of psychology’. Ohio State University.Browne, M. W., and Arminger, G. (1995). Specification and estimation of mean-and covariance-structure models. In Handbook of statistical modeling for the social and behavioral sciences (pp. 185-249). Springer, Boston, MA.Cachon, G. P., Daniels, K. M., and Lobel, R. (2017). The role of surge pricing on a service platform with self-scheduling capacity. Manufacturing & Service Operations Management, 19(3), 368-384.Cheng, X., Fu, S., and de Vreede, G. J. (2018). A mixed method investigation of sharing economy driven car-hailing services: Online and offline perspectives. International Journal of Information Management, 41, 57–64.Chiu, C. M., Wang, E. T., Fang, Y. H., and Huang, H. Y. (2014). Understanding customers` repeat purchase intentions in B2C e‐commerce: the roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal, 24(1), 85-114.Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.De Stefano, V. (2015). 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