臺灣博碩士論文加值系統

隨著網際網路發展與普及，O2O 商業模式已進入高速發展階段。繼線上購物之後，線上訂餐平台在近 年來正逐漸成為 O2O 商業模式的另一片熱土。線上訂餐不僅給予案牘勞形的民眾方便用餐，而且拓寬了傳 統餐飲行業的經營範圍。就目前而言，中國大陸地區線上訂餐的用戶規模高達 3 億，線上訂餐模式廣受大陸 地區消費者的喜愛。但仍有許多隨著網際網路的發展與普及，O2O商業模式已進入高速發展階段。繼線上購物之後，線上訂餐平台在近年來正逐漸成為O2O商業模式的另一片熱土。線上訂餐不僅給予案牘勞形的民眾方便用餐，而且拓寬了傳統餐飲行業的經營範圍。就目前而言，中國大陸地區線上訂餐的用戶規模高達3億，線上訂餐模式廣受大陸地區消費者的喜愛。但仍有許多人拒用此消費模式，到底是什麼原因致使消費者採用或拒用此模式，乃是本研究欲去加以探討的。
本研主要以「餓了麼」線上訂餐平台為例，應用科技接受模型探討線上服務品質、知覺風險與網路外部性對中國大陸地區消費者線上訂餐之使用意願。本研究針對回收的333份有效樣本，進行了敘述性統計分析、信度與效度分析、相關性分析及結構方程模型分析，結果有下列重要發現：
1. 線上訂餐之知覺有用性對消費者使用「餓了麼」的使用意願具有顯著正向影響。
2. 線上訂餐之知覺易用性對知覺有用性具有正向顯著影響。
3. 線上訂餐之線上服務品質與網路外部性對知覺有用性均有顯著影響。
4. 線上訂餐之線上服務品質、知覺風險與網路外部性對知覺易用性均有顯著影響。
5. 消費者人口統計變項中線上訂餐頻率對消費者之使用意願具有顯著差異。

With the continuous development of the Internet, O2O business model has being entered a stage of rapid development. After the online shopping, the platform for online reservation shows has gradually become a hot spot for O2O business model in recent years. Online repast reservation not only for people having meal more convenience, but also for traditional food industry broadening the scope of business. For the moment, there are 300 million online repast reservation users in Mainland China, and online repast reservation model is popular with consumers in China. However, there are many people still refuse to use this kind of consumption model, so this study intends to explore what causes Chinese consumer to adopt or reject this model.
The main purpose of this study takes the case of Eleme to apply the technology acceptance model to explore online service quality, perceived risk, network externalities and user intentions with online repast reservation in China. Based on the 333 valid samples, this study conducted descriptive statistical analysis, reliability and validity analysis, correlation analysis, and structural equations modeling analysis. Finally, the study has following important findings:
1. Perceived usefulness of online repast reservation has a significant positive impact on user intentions.
2. Perceived ease of use of online repast reservation has a significant positive impact on perceived usefulness.
3. Online service quality and network externalities of online repast reservation both have a significant impact on perceived usefulness.
4. Online service quality, perceived risk, and network externalities have a significant impact on perceived ease of use.
5. Online ordering frequency of consumer demographic variables has a significant impact on user intentions.

目錄 .......................................................................................... I
表次 .......................................................................................... III
圖次............................................................................................IV
第一章 緒論 ................................................................................1
第一節 研究背景與動機................................................................1
第二節 研究目的 .........................................................................5
第三節 研究流程 .........................................................................6
第二章 文獻探討..........................................................................7
第一節 線上訂餐之模式及發展現狀...............................................7
第二節 科技接受模型之內涵及應用..............................................10
第三節 研究變項之內涵與衡量 ....................................................13
第四節 各研究變項之關係............................................................19
第三章 研究方法.........................................................................25
第一節 研究架構 ........................................................................25
第二節 研究假說 .......................................................................26
第三節 變項之操作性定義與衡量 ................................................26
第四節 問卷調查對象及抽樣方式 .................................................31
第五節 資料分析方法..................................................................31
第四章 資料分析與結果...............................................................33
第一節 敘述性統計分析...............................................................33
第二節 信度與效度分析...............................................................36
第三節 T 檢定與變異數分析.........................................................39
第四節 相關性分析 .....................................................................45
第五節 結構方程模型...................................................................45
第六節 假說驗證結果彙整與討論 .................................................48
第五章 結論與建議......................................................................52
第一節 結論................................................................................52
第二節 管理意涵 .......................................................................54
第三節 研究限制 ........................................................................57
第四節 建議................................................................................57
參考文獻....................................................................................60
一、中文部分............................................................................60
二、英文部分.............................................................................61
附錄:問卷 ..................................................................................69

表次
表 2-1 線上服務品質常見量表......................................................15
表 3-1 本研究之假說內容 .......................................................................26
表 3-2 線上服務品質之衡量問項......................................................27
表 3-3 知覺風險之衡量問項 .............................................................28
表 3-4 網絡外部性之衡量問項.......................................................29
表 3-5 知覺有用性之衡量問項..........................................................29
表 3-6 知覺易用性之衡量問項...........................................................30
表 3-7 使用意願之衡量問項 ..............................................................31
表 4-1 受訪者基本資料頻數統計表.........................................................34
表 4-2 驗證性因素分析之模型擬合結果 .............................................36
表 4-3 信度與效度檢驗結果 ................................................................37
表 4-4 性別的差異檢驗結果 ............................................................39
表 4-5 年齡的差異檢驗結果 ...............................................................40
表 4-6 教育程度的差異檢驗結果........................................................41
表 4-7 職業的差異檢驗結果 .............................................................42
表 4-8 月薪的差異檢驗結果 ................................................................43
表 4-9 上網時間的差異檢驗結果........................................................44
表 4-10 線上訂餐頻率的差異檢驗結果 ..................................................45
表 4-11 各研究變項的平均數、標準差與相關係數表 ..............................45
表 4-12 結構方程模型的擬合結果..........................................................46
表 4-13 結構方程模型中的路徑係數情況 ...........................................47
表 4-14 本研究假說驗證彙整表............................................48


圖次
圖 1-1 研究流程.............................................................................6
圖 2-1 科技接受模型..............................................................................11
圖 3-1 研究架構.........................................................................................25
圖 4-1 本研究結構模型結果圖...........................................................46

一、中文部分
王樂鵬、李春麗、王穎(2014)。餐飲行業 O2O 的發脹路徑及趨勢研究。科技廣場，10，166-169。
吳芝新(2012)。簡析 O2O 電子商務模式。重慶科技學院學報:社會科學版，13，73-73。
肖敏淇(2017)。外賣平台經濟的研究。內江科技，(1)，130-131。
鄒悅(2013)。基於TAM 模型的 B2C 電子商務網站質量對消費者態度影響的研究:以大型綜合性購物網站為例。中國東華大學企業管理系碩士論文。
姜奇平(2011)。O2O 商業模式剖析。互聯網周刊，2011.10.5，20-23。
胡桂珍(2013)。O2O 模式在我國餐飲業中的應用研究。電子商務，3，128-129。
張郁芝(2013)。我國發展電子商務之現況與未來趨勢:以第三方支付為例。Economic Research，14，109-129。
張瑞琇、江睿盈(2017)。應用科技接受模式探討顧客價值、知覺風險及使用願之關係:以星巴克的行動支付為例。休閒事業研究，15(2)，36-54。
單聰、孫細明(2010)。網絡訂餐的發展現狀及對策研究。武漢工程大學管理學院，湖北武漢。
黃懿諄(2002)。網路訂餐消費行為之研究-由決策過程分析。國立東華大學觀光暨遊憩管理研究所碩士論文。
趙雪冉、孫永波(2017)。外賣 O2O 平台商業模式分析及發展對策。商業時代，(3)，114-116。
鄧朝華、魯耀斌、張金隆(2007)。基於 TAM 和網路外部性的移動服務使用行為研究。中國管理學報，4(2)，216-221。
謝宛儒(2001)。台灣地區消費者線上購買食品消費行為分析。東海大學食品科學研究所碩士論文。

二、英文部分
Agarwal, S., Kenneth Teas. R. (2001). Perceived value: Mediating role of perceived risk. Journal of Marketing Theory and Practice, 9, 1-14.
Agha, A. M. Q. (2017). E-service quality factors impacting customers purchase retention in e-retailing in Malaysia. The 1st International Conference on Information Technology, April, 10, 266-283.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Ajzen, I., Fishbein, M. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research: Reading, MA: Addison-Wesley.
Ajzen, I., Madden, T. J. (1986). Prediction of goal-directed behavior from attitudinal and normative variables. Journal of Experimental Social Psychology, 22, 453-474.
Alex Rampell. (2010). Why Online2Offline Commerce Is A Trillion Dollar Opportunity. TechCrunch, 2010, 8, 7.
Alex Rampell. (2012). Payment Data is More Valuable than Payment Fees. Tech Crunch, 2012, 8, 10.
Ariffin, Z. Z., Heng, K. T., Yaakop, A. Y., Mokhtar, N. F., and Mahadi, N. (2017). Conceptualizing gen Y online shopping behaviour: Integrating task-technology FIT(TTF) model and extended technology acceptance model(TAM). Proceeding of Icarbss 2017 Langkawi, Malaysia, (29th), 330-336.
Bashir, I., Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102.
Bauer, R. A. (1960). Consumer behavior as risk taking. In Risk Taking and Information
Handling in Consumer Behavior, Chicago: American Marketing Association.
Bemanian, M. R. Ghofrani, S. Bemanian, R. (2011) Evaluation of factors affecting the adoption of e-services using integrated information technology acceptance model (TAM) and theory of planned behavior (TPB) and trust, case study. Electronics offices in Tehran, urban management, 29, 150-132.
Blackwell, R. D., Miniard, P.W., and Engel, J. F. (2001). Consumer Behavior, 9th ed., Harcourt College Publishers, Fort Worth, TX. 282-288.
Blut, M. (2016). E-service quality: Development of a hierarchical model. Journal of Retailing, 92(4), 500-517.
Cheng, B., Yee, S. (2014). Factors influencing consumers’ online purchase intention: A study among university students in Malaysia. International Journal of Liberal Arts and Social Science, 2(8), 121-133.
Collier, J. E., Bienstock, C. C. (2006). Measuring service quality in e-retailing. Journal of Service Research, 8(3), 260-275.
Corbitt, B. J., Thanasankit, T., and Yi, H. (2003). Trust and e-commerce: A study of consumer perceptions. Electronic Commerce Research and Application, 2(3), 203- 215.
Cox, D. F. (1967). Risk handling in consumer behavior: An intensive study of two cases. Risk Taking and Information Handling in Consumer Behavior, Boston: Harvard University Press, 34-81.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., and Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8),982-1003.
Drennan, J., Sullivan, G., and Previte, J. (2006). Privacy, risk perception, and expert online behavior: An exploratory study of household end users. Journal of Organizational and End User Computing, 18(1), 22.
Dumpit, D. Z., Fernandez, C. J. (2017). Analysis of the use of social media in higher education institutions (HEIs) using the technology acceptance model. International Journal of Educational Technology in Higher Education, 14(1), 5.
Fassnacht, M., Koese, I. (2006). Quality of electronic services: Conceptualizing and testing a hierarchical model. Journal of service research, 9(1), 19-37.
Featherman, M. S., Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474.
Forsythe, S. M., Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research, 56(11), 867-875.
Friderici, A. V., Ravesteyn, P. and Benny, M. E. (2016). Integration of e-service quality, customer satisfaction and technology acceptance. International Conferences ICT, WBC, BIGDACI and TPMC 2016, 81-88.
Gefen, D., Karahanna, E., and Straub, D.W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-59.
Ghezelbash, S., Khodadadi, H. (2017). Evaluating the impact of promotion price, product quality, service quality, customer satisfaction and repeating purchase incentives (Case study: Amiran Chain Stores). The Journal of Internet Banking and Commerce, 22(1), 1-17.
Hamari, J., Hanner, N., and Koivisto, J. (2017). Service quality explains why people use freemium services but not if they go premium: An empirical study in free-to- play games. International Journal of Information Management, 37(1), 1449-1459.
Hansen, J. M., Saridakis, G., and Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 80, 197-206.
Hew, T. S., Leong, L. Y., Ooi, K. B., and Chong, A. Y. L. (2016). Predicting drivers of mobile entertainment adoption: A two-stage SEM-artificial-neural-network analysis. Journal of Computer Information Systems, 56(4), 352-370.
Hong, H., Cao, M., and Wang, G. A. (2017). The effects of network externalities and herding on user satisfaction with mobile social apps. Journal of Electronic Commerce Research, 18(1), 18-31.
Jacoby, J., Kaplan, L. B. (1972). The Components of Perceived Risk. Paper presented at the SV-Proceedings of the third annual conference of the association for consumer research.
Jarvenpaa, S. L., Todd, P. A. (1996). Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce, 1(2), 45-71.
Kalia, P., Arora, R., and Kumalo, S. (2016). E-service quality, consumer satisfaction and future purchase intentions in e-retail. E-Service Journal, 10(1), 24-41.
Katz, M. L., Shapiro, C. (1985). Network externalities, competition, and compatibility. The American Economic Review, 75(3), 424-440.
Katz, M. L., Shapiro, C. (1994). Systems competition and network effects. The Journal of Economic Perspectives, 8(2), 93-115.
Kauffman, R. J., Mcandrews, J., and Wang, Y. M. (2000). Opening the black box of network externalities in network adoption. Information Systems Research, 11(1), 61-82.
Kauffman, R. J., Wang, B. (2002). Bid together, buy together: On the efficacy of group- buying business models in internet-based selling. Handbook of Electronic Commerce in Business and Society, Boca Raton, FL: CRC Press.
Kesharwani, A., Singh Bisht, S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International Journal of Bank Marketing, 30(4), 303-322.
Kim, H., Song, J. (2010). The quality of word-of mouth in the online shopping mall. Journal of Research in Interactive Marketing, 4(4), 376-390.
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223.
Lai, E., and Wang, Z. (2012). An empirical research on factors affecting customer purchasing behavior tendency during online shopping. Institute of Electrical and Electronics Engineers, 583-586.
Lee, G. G., Lin, H. F. (2005). Customer perceptions of e-service quality in online shopping. International Journal of Retail and Distribution Management, 33(2), 161-176.
Lee, M.-C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130-141.
Li, Y. N., Tan, K. C. and Xie, M. (2002). Measuring web-based service quality. Total Quality Management, 13(5), 685-700.
Liat, C. B., Wuan, Y. S. (2014). Factors influencing consumers’ online purchase intention: A study among university students in Malaysia. International Journal of Liberal Arts and Social Science, 2(8), 121-133.
Lin, C-P., Bhattacherjee, A. (2008). Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13(1), 85-108.
Lin, J. C. C., Lu, H. (2000). Towards an understanding of the behavioural intention to use a web site. International Journal of Information Management, 20(3), 197-208.
Lin, K. Y., Lu, H. P. (2011). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152-1161.
Littler, D., Melanthiou, D. (2006). Consumer perceptions of risk and uncertainty and the implications for behaviour towards innovative retail services: The case of internet banking. Journal of Retailing and Consumer Services, 13(6), 431-443.
Loiacono, E. T., Watson, R. T., and Goodhue, D. L. (2002). WebQual: A measure of website quality. American Marketing Association, Conference Proceedings, 13, 432-438.
Masoud, E., AbuTaqa, H. (2017). Factors affecting customers'' adoption of e-banking services in Jordan. Information Resources Management Journal (IRMJ), 30(2), 44-60.
Money, W., Turner, A. (2004). Application of the technology acceptance model to a knowledge management system. In System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on (pp. 9-pp). IEEE.
Mwencha, P., Muathe, S. (2014). Effects of perceived attributes, perceived risk and perceived value on usage of online retailing services. Journal of Management Research, 6(2), 140-161.
Nunnally, J. C. (1978). Psychometric theory, 2nd ed., New York: McGraw-Hill. Qasim, H., Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact
of network externalities. Information Systems Frontiers, 18(5), 1021-1034.
Rahi, S., Ghani, M. (2016). Internet banking, customer perceived value and loyalty:
The role of switching costs. J Account Mark 5: 188.
Rahi, S., Ghani, M., and Alnaser, M. (2017). Predicting customer’s intentions to use internet banking: The role of technology acceptance model (TAM) in e-banking. Management Science Letters, 7, 513-524.
Roselius, T. (1971). Consumer rankings of risk reduction methods. Journal of Marketing, 35(1), 56-61.
Rowley, J. (2006). An analysis of the e-service literature: Towards a research agenda. Internet Research, 16(3), 339-359.
Santos, J. (2003). E-service quality: A model of virtual service quality dimensions. Managing Service Quality: An International Journal, 13(3), 233-246.
Savage, S. J., Waldman, D. M. (2015). Factors influencing Chinese tourists’ intentions to use the taiwan medical travel app. Economics Letters, 137,171-175.
Shih, H. (2004). An empirical study on predicting user acceptance of shopping on the web. Information and Management, 41, 351-368.
Stone, R. N., Grønhaug, K. (1993). Perceived risk: Further considerations for the marketing discipline. European Journal of Marketing, 27(3), 39-50.
Strader, T. J., Ramaswami, S. N., and Houle, P. A. (2007). Perceived network externalities and communication technology acceptance. European Journal of Information Systems, 16(1), 54-65.
Szymanski, D. M., Hise, R. T. (2000). E-satisfaction: An initial examination. Journal of Retailing, 76(3), 309-322.
Taylor, S. Todd, P. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.
Tseng, S. Y., Wang, C. N. (2016). Perceived risk influence on dual-route information adoption processes on travel websites. Journal of Business Research, 69, 2289- 2296.
Venkatesh, V., Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.
Venkatesh, V., Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V., Thong, J. Y., and Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
Wang, S. T., Lin, R. L. (2016). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behaviour and Information Technology, 2, 1-9.
Wolfinbarger, M., Gilly, M. C. (2003). ETailQ: Dimensionalizing, measuring, and predicting etail quality. Journal of Retailing, 79(3), 183-98.
Woodside, A. G. (1968). Social character, product use and advertising appeals. Journal of Advertising Research, 8(4), 31-35.
Yoo, Boonghee, Naveen Donthu. (2001). Developing a scale to mea-sure the perceived quality of an internet shopping site (sitequal). Quarterly Journal of Electronic Commerce, 2(1), 31-46.
Zeithaml, V. A., Berry, L. L., and Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.
Zeithaml, V. A., Parasuraman, A., and Malhotra, A. (2000). E-service quality: Definition, dimensions, and conceptual model. working paper, Marketing Science Institute, Cambridge, MA.
Zeithaml, V. A., Parasuraman, A., and Malhotra, A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362-375.
Zeithaml, V. A., Parasuraman, A., and Malhotra, A. (2005). E-S-QUAL: A multiple- item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213-233.
Zhou, T., Lu, Y. B. (2011). Examining mobile instant messaging user loyalty from the perspectives of network externalities and flow experience. Computers in Human Behavior, 27(2), 883-889.