跳到主要內容

臺灣博碩士論文加值系統

(44.221.73.157) 您好!臺灣時間:2024/06/20 12:09
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:魏子傑
研究生(外文):WEI, TZU-CHIEH
論文名稱:自助結賬系統使用意願影響因素之研究: 比較台灣與德國市場
論文名稱(外文):Factors Influencing Usage Intention of Self-Checkout System: A comparative study between Taiwan and Germany
指導教授:葉曉萍葉曉萍引用關係
指導教授(外文):YEH, HSIAOPING
口試委員:葉曉萍趙沛胡寬裕
口試委員(外文):YEH, HSIAOPINGCHAO, PEIHU, KUAN-YU
口試日期:2023-03-24
學位類別:碩士
校院名稱:國立高雄科技大學
系所名稱:國際管理碩士學位學程
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:英文
論文頁數:102
中文關鍵詞:創新理論擴散自助服務科技文化比較使用意願
外文關鍵詞:Diffusion of InnovationSelf-Service TechnologyCultural ComparisonUsage Intention
相關次數:
  • 被引用被引用:0
  • 點閱點閱:108
  • 評分評分:
  • 下載下載:23
  • 收藏至我的研究室書目清單書目收藏:0
隨著自助科技的快速成長,零售業在競爭環境下未來將逐步引進自助結帳系統,為消費者提供新的體驗和滿意度,因此,若消費者願意擴大這類科技的使用意願,其影響之前因是值得探討。
本研究採用創新擴散理論模式為基礎,探討可能影響台灣和德國消費者決定採用自助結帳系統的因素,這些因素包含了消費者對自助結帳的相對利益、相容性、複雜性、可試驗性、可觀察性及使用意願,並以人口統計變數及購物模式做為調節因素。本研究以網路問卷為媒介方式進行,共計有418份有效問卷,其中247來自台灣,171來自德國。本研究結果總結如下:
一、 消費者的個人特性與平時消費模式對使用自助結帳系統的意願具有差異性。例如教育程度高者或年輕的消費者比較願意接受自助結帳系統,但年長者或女性則比較抗拒。
二、 台灣和德國消費者均在「相對利益」、「相容性」、和「可試驗性」會正向顯著影響其「使用自助結帳系統意願」。
三、 台灣消費者在「複雜性」和「可觀察性」對於「使用自助結帳系統意願」都達到正向關聯,而德國消費者則為反向關聯。
四、 台灣消費者偏向自助結賬系統是否容易使用,而德國消費者偏向是否帶來效益。
關鍵詞:創新理論擴散、自助服務科技、文化比較、使用意願
With the rapid growth of self-service technology, retailers will gradually introduce self-checkout systems in the future in a competitive environment to provide consumers with new experiences and satisfaction. Therefore, if consumers are willing to expand their intention to use such technologies, its previous influencing factors will be worth exploring.
In this study, the diffusion of innovation theory model is used as a basis to investigate the factors that may influence consumers' decision to adopt self-checkout systems in Taiwan and Germany. The study was conducted using an online questionnaire. A total of 418 participants were included, of which 247 were from Taiwan and 171 were from Germany. Hypotheses in this study were tested using structural equation modeling. The results show that there are some differences between the two regions, which can be summarized as follows:
1. Consumers' personal data and shopping experience greatly impact their intention to use self-checkout systems.
2. Consumers' personal characteristics and shopping experiences have differences in their intention to use self-checkout systems. For example, educated or young consumers are more willing to accept self-checkout systems, but older people or women are more resistant.
3. For consumers in Taiwan and Germany, "relative advantage", "compatibility", and "trialability" will positively and significantly affect their "intention to use" self-checkout systems".
4. Most Taiwanese consumers have a significant positive correlation with " intension to use a self-checkout system" in terms of "complexity" and "observability", while German consumers have a negative correlation.
5. Taiwanese consumers prefer whether the self-checkout system is easy to use, while German consumers prefer whether it brings benefits.
Keywords: Diffusion of Innovation, Self-Service Technology, Cultural Comparison, Usage Intention
TABLE OF CONTENTS
摘要 .......................................................................................................................................... i
Abstract .................................................................................................................................... ii
Acknowledgment .................................................................................................................... iii
Table of contents ..................................................................................................................... iv
List of Tables ........................................................................................................................... vi
List of Figures ........................................................................................................................ viii
CHAPTER 1: INTRODUCTION ...................................................................................... 1-1
1.1 Research Background ................................................................................................ 1-1
1.2 Research Motivation and Purpose ............................................................................. 1-2
CHAPTER 2: LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT...... 2-1
2.1 Revolution of the Retail Industry .............................................................................. 2-1
2.2 Smart Retail ............................................................................................................... 2-2
2.3 Theoretical Models commonly adopted in Technology Use ..................................... 2-4
2.3.1 Theory of Reasoned Action (TRA) .................................................................. 2-4
2.3.2 Technology Acceptance Model (TAM) ........................................................... 2-5
2.3.3 Diffusion of Innovation Theory (DOI) ............................................................. 2-7
2.3.4 Application of Innovation Diffusion Theory to Self-Service Technology ....... 2-8
2.4 Intercultural Acceptance of Self-Service Technologies ............................................ 2-9
2.5 Research Hypotheses …………………………....................................................... 2-14
2.5.1 Relative Advantage ........................................................................................ 2-14
2.5.2 Compatibility .................................................................................................. 2-14
2.5.3 Complexity ..................................................................................................... 2-14
2.5.4 Observability .................................................................................................. 2-15
2.5.5 Trialability …….............................................................................................. 2-15
2.5.6 Demographics…….......................................................................................... 2-15
CHAPTER 3: RESEARCH METHODOLOGY .................................................................. 3-1
3.1 Research Framework ................................................................................................. 3-1
3.2 Variables Operational Definitions ............................................................................. 3-4
3.3 Variable Measurement .............................................................................................. 3-5
3.4 Research Targets and Data Collection ...................................................................... 3-8
3.5 Data Analysis Methods ............................................................................................. 3-8
3.5.1 Descriptive Statistical Analysis ........................................................................ 3-8
3.5.2 Reliability Analysis .......................................................................................... 3-9
3.5.3 Exploratory Factor Analysis (EFA) ................................................................ 3-10
3.5.4 Confirmatory Factor Analysis (CFA) ............................................................. 3-11
3.6 Structural Equation Model (SEM) ........................................................................... 3-12
3.6.1 Measuring Model Fit in SEM.......................................................................... 3-14
CHAPTER 4: DATA ANALYSIS AND RESULTS ......................................................... 4-1
4.1 Demographics ………………………....................................................................... 4-1
4.2 Purchasing Experience .............................................................................................. 4-2
4.3 Exploratory Factor Analysis ...................................................................................... 4-3
4.4 Homogeneity Test ..................................................................................................... 4-9
4.5 Reliability Analysis ................................................................................................. 4-11
4.6 Confirmatory Factor Analysis ................................................................................. 4-11
4.7 Causal Model …....................................................................................................... 4-13
4.7.1 Invariance Analysis between Taiwan and Germany……………………….... 4-15
4.8 Impact of Demographic Data on Causal Relationships ........................................... 4-17
4.8.1 Gender ............................................................................................................ 4-17
4.8.2 Age ................................................................................................................. 4-19
4.8.3 Educational ..................................................................................................... 4-22
4.9 Impact of Shopping Experience on Causal Relationships ................................. 4-24
4.9.1 Shopping Frequency ....................................................................................... 4-24
4.9.2 Monthly Average Spending ............................................................................ 4-26
4.9.3 Knowledge about SSTs .................................................................................. 4-28
4.9.4 Experience with SSTs ................................................................................... 4-30
4.10 Summary of the Findings ...................................................................................... 4-31
CHAPTER 5: CONCLUSIONS ......................................................................................... 5-1
5.1 Discussion of Research Findings .............................................................................. 5-1
5.2 Managerial Implications ............................................................................................ 5-2
5.2.1 Implications in Taiwan ..................................................................................... 5-3
5.2.2 Implications in Germany .................................................................................. 5-4
5.3 Conclusion ................................................................................................................. 5-5
5.4 Limitations and Suggestions for Future Research ..................................................... 5-5
References …....................................................................................................................... R-1
Appendices .......................................................................................................................... A-1
English Version of the Questionnaire ........................................................................... A-1
German Version of the Questionnaire ........................................................................... A-5
Mandarin Version of the Questionnaire ........................................................................ A-9


References
1.Amazon (2020), "Introducing Amazon Go and the world’s most advanced shopping technology", Sep. 2020.
https://www.youtube.com/watch?v=NrmMk1Myrxc
2.BehrTech Blog (2020), “Smart Retail: 8 Innovative Examples of IoT in Retail”, Behr Technologies Inc., 2020.
https://behrtech.com/blog/smart-retail-8-innovative-examples-of-iot-in-retail/
3.Bentler, P.M. (1995), “EQS structural equations program manual”, Encino, CA: Multivariate Software, 1995.
4.Browne, M.W. and Cudeck, R. (1993), “Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.)”, Testing structural equation models, pp. 136-162, Newbury Park, CA: Sage, 1993.
5.Blut, M., Wang, C. and Schoefer, K. (2016), “Factors Influencing the Acceptance of Self-Service Technologies: A Meta-Analysis”, Journal of Service Research, Vol. 19, Issue 4, Aug. 2016.
6.Cho, H. and Fiorito, S.S. (2010), “Self-Service Technology in Retailing. The Case of Retail Kiosks”, Symphonya Emerging Issues in Management (1), pp. 43-55, 2010.
7.Cuieford, J.P. (1965), “Fundamental Statistics in Psychology and Education, 4th ed.”, N.Y., McGraw-Hill, 1965.
8.Curran J.M., and Meuter, M.L. (2005), “Self‐service technology adoption: comparing three technologies”, Journal of Services Marketing, 19 (2), pp. 103–113, 2005.
9.Dabholkar, P.A. (1996), “Consumer Evaluations of New Technology-Based Self-Service Options: An Investigation of Alternative Models of Service Quality”, International Journal of Research in Marketing. Vol. 13, Issue 1, pp. 29-51. Feb. 1996.
10.Davis, F.D. (1989), “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology”, MIS Quarterly, Vol.13, No.3, pp. 319-339, Sep. 1989.
11.Fan, X., Thompson, B. and Wang, L. (1999), “Effects of sample size, estimation method, and model specification on structural equation modeling fit indexes”, Structural Equation Modeling, 6, pp. 56-83, 1999.
12.Fishbein, M., & Ajzen, I. (1975), “Belief, attitude, intention, and behavior: An introduction to theory and research”, Reading, Addison-Wesley Pub. Co., ISBN-13: ‎ 978-0201020892, May. 1975.
13.Fornell, C. and Larcker, D.F (1981)., “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, 18 (1), pp. 39-50, 1981.
14.Gaski, J.F., and Nevin, J.R. (1985), “The differential effects of exercised and unexercised power sources in a marketing channel”, Journal of Marketing Research, 22 (2), pp. 130-142, 1985.
15.Gorsuch, R.L (1983)., “Factor Analysis 2nd Edition”, N.Y., Psychology Press, ISBN: 9780203781098, Nov. 1983.
16.Guadagnoli, E., and Velicer (1988), W.F., “Relation of sample size to the stability of component patterns”, Psychological Bulletin, 103(2), pp. 265–275, 1988.
17.Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), “Multivariate data analysis (5th ed.)”, Prentice Hall, New Jersey, 1998.
18.Hew, J.J., Lee, V.H., Ooi K.B., and Wei, J. (2015), “What catalyses mobile apps usage intention: an empirical analysis”, Industrial Management & Data Systems, Vol. 115 No. 7, pp. 1269–1291, 2015.
19.Hofstede, G. and Bond, M.H. (1984), “Hofstede's culture dimensions: An independent validation using Rokeach's value survey”, Journal of Cross-Cultural Psychology, 1984.
20.Hu, L. and Bentler, P.M. (1999), “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives”, Structural Equation Modeling, 6(1), pp. 1-55, 1999.
21.IBM (2021), “KMO and Bartlett's Test”, SPSS Statistics V.28.0.0, Nov. 2021.
https://www.ibm.com/docs/en/spss-statistics/28.0.0?topic=detection-kmo-bartletts-test
22.Ibrahim, M.A. and Sohail, M.S. (2012), “Mobile Banking Adoption: Application of Diffusion of Innovation Theory”, Journal of Electronic Commerce Research, Vol. 13, No. 4, pp. 379-391, Nov. 2012.
23.Jöreskog, K.G. (1973), “A general method for estimating a linear structural equation system”, In A. S. Duncan, Structural equation models in the social sciences, pp. 85-112, New York: Seminar, 1973.
24.Kaiser, H.F. (1958), “The varimax criterion for analytic rotation in factor analysis”, Psychometrika. 23, pp. 187-200, Sep. 1958.
25.Lee, H.J., Cho, H.J., Xu W., and Fairhurst, A (2010), “The influence of consumer traits and demographics on intention to use retail self-service checkouts”, Marketing Intelligence & Planning Vol. 28 No. 1, pp. 46-58, 2010.
26.MacCallum, R.C. and Hong, S. (1997), “Power analysis in covariance structure modeling using GFI and AGFI”, Multivariate Behavioral Research, 32, pp.193-210, 1997.
27.Marsh, H.W. and Balla, J.R. (1994), “Goodness of fit in confirmatory factor analysis: The effect of sample size and model parsimony”, Quality & Quality, 28, pp. 185-217, 1994.
28.Marsh, H.W. and Hocevar, D. (1985), “Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups”, Psychological Bulletin, 97, pp. 562-582, 1985.
29.Masimba, F., Appiah M., and Zuva T. (2019), “A Review of Cultural Influence on Technology Acceptance”, 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Nov. 2019.
30.Mick, D.G. and Fournier, S. (1998), “Paradoxes of Technology: Consumer Cognizance, Emotions, and Coping Strategies”, Journal of Consumer Research, Volume 25, Issue 2, pp. 123–143, Sep. 1998.
31.Million Insights (2020), “Self-service Technology Market Analysis Report By Product, By Application, By Region And Segment Forecasts From 2020 To 2027”, Million Insights, Sep. 2020.
32.Mulaik, S.A., James, L.R., Altine, J.V., Bennett, N., Lind, S. and Stilwell, C.D. (1989), “Evaluation of goodness-of-fit indices for structural equation models”, Psychological Bulletin, 105(3), pp. 430-445, May. 1989.
33.National Retail Federation (2021), “Top 50 Global Retailers 2021”, Apr. 2021.
https://nrf.com/resources/top-retailers/top-50-global-retailers/top-50-global-retailers-2021
34.Ozbilen, P. (2017), “The Impact of Natural Culture on New Technology Adoption by Firms: A Country Level Analysis”, International Journal of Innovation, Management and Technology, 8(4), Aug. 2017.
35.Parasuraman A., and Grewal, D. (2000), “The Impact of Technology on the Quality-Value-Loyalty Chain: A Research Agenda”, Journal of the Academy of Marketing Science, 28, No.4, pp. 168-174, 2000.
36.QYResearch (2022), “Global Smart Retail Market Size, Status and Forecast 2022”, Jan. 2022.
https://reports.valuates.com/market-reports/QYRE-Othe-4D232/smart-retail
37.Robertson, T.S. and Gatignon, H. (1991), “How innovators thwart new entrants into their market”, Planning Review, Vol. 19 No. 5, pp. 4-11, May 1991.
38.Rogers, E.M. (1995), “Diffusion of innovation (4th Ed.)”, The Free Press, ISBN-13‏: ‎978-0028740744, Feb. 1995.
39.Rogers, E.M. and Murcott, S. (1995), “Attributes of innovations and their rate of adoption”, semanticscholar.org, 1995.
40.Schumacker, R.E. and Lomax, R.G. (2004), “A beginner’s guide to structural equation modeling (2nd ed.)”, Mahwah, NJ: Lawrence Erlbaum Associates, 2004.
41.Scott, J.E. (1994), “The measurement of information systems effectiveness: Evaluating a measuring instrument”, Proceedings of the Fifteenth International Conference on Information Systems, Vancouver: British Columbia, pp. 111-128, 1994.
42.Stepaniuk, D. (2021), “Smart Retail Solutions That Change Ecommerce”, Netguru, Sep. 2021.
https://www.netguru.com/blog/smart-retail-solutions
43.Spearman, C.E. (1927), “Two-factor Theory of Intelligence”, 1927.
44.Summit Research (2008), “Kiosks and Interactive Technology, Seventh Edition”, Summit Research Associates, 2008.
45.Ullman, J.B. (2013), “Structural equation modeling. In B.G. Tabachnick & L.S. Fidell (Eds.), Using multivariate statistics”, 6th ed., pp. 676–731, NewYork, NY: Allyn Bacon, 2013.
46.Venkatesh V., Morris M.G., Davis G.B. and Davis F.D. (2003), “User Acceptance of Information Technology: Toward a Unified View”, MIS Quarterly, Vol. 27, No. 3 (Sep. 2003), pp. 425-478.
47.Weijters, B., Rangarajan, D., Falk, T. and Schillewaert, N. (2007), “Determinants and Outcomes of Customers' Use of Self-Service Technology in a Retail Setting”, Journal of Service Research, Vol. 10, Issue 1, pp. 3–21, Aug. 2007.
48.Wikimedia Foundation, Inc. (2022), “Self-checkout”, Apr. 2022.
https://en.wikipedia.org/wiki/Self-checkout
49.Wikimedia Foundation, Inc. (2022), “Kaiser–Meyer–Olkin test”, Feb. 2022.
https://en.wikipedia.org/wiki/Kaise-Meye-Olkin_test
50.Advantech iCity Services (2021), “What is people tracking analysis?”, Jun. 2021.
研華智誠 (2021), “人流分析是什麼?”, Jun. 2021.
https://aics.advantech.com/zh/blogpage/customer-flow-analysis
51.Bnext Media (2021), “A picture to understand the future battlefield of retail in Taiwan!”, Apr. 2021.
Bnext Media (2021), “一張圖看懂台灣零售未來戰場!”, Apr. 2021.
https://www.bnext.com.tw/article/62512/taiwan-grocery-ec-industry-map
52.udn.com (2021), “Smart consumption and smart retail in the new retail era”, Dec. 2021.
聯合新聞網 (2021), “新零售時代下的智慧消費與智慧零售”, Dec. 2021.
https://udn.com/news/story/11726/5955666
53.Fang, S.M. (2021), “Retail technology transformation to open up the blue sea, AI assistant & 3D shopping in Family Mart”, www.CardU.com.tw, Dec. 2021.
范詩敏 (2021), “零售科技轉型闢藍海 全家AI小幫手3D購物”, 卡優新聞網, Dec. 2021.
https://www.cardu.com.tw/news/detail.php?44929
54.Huang, J. (2021), “What is OMO?”, 91APP, May. 2021.
https://www.91app.com/blog/what-is-omo/
55.Mirai Business Research Institute (2021), "[Key Data Diagram] Market Share Rankings for Taiwanese Retailers and E-commerce Companies ", Aug. 2021.
未來流通研究所 (2021), “【商業數據圖解】2020台灣「零售&電商」產業市佔率英雄榜”, Aug. 2021.
https://www.mirai.com.tw/2020-taiwan-retail-ec-market-share-analysis/
56.Shen, D. (2020), “Will AI become the creative director of the advertising industry?”, Future Commerce, Aug. 2020.
沈勤譽 (2020), “AI會成為廣告界的創意總監嗎?”, 未來商務, Aug. 2020.
https://fc.bnext.com.tw/articles/view/78?
57.Strikingly (2021), “What is SCM and why is it important for e-commerce companies to understand it?”, Sep. 2021.
Strikingly (2021), “什麼是SCM?為什麼電商必須了解SCM?”, Sep. 2021.
https://tw.strikingly.com/content/blog/supply-chain-management/
https://www.cardu.com.tw/news/detail.php?44929
58.Strikingly (2022), “What is the Internet of Things? Take a walk into the world of the Internet of Things !”, Feb. 2022.
Strikingly (2022), “什麼是物聯網?帶你走進物聯網世界!”, Feb. 2022.
https://tw.strikingly.com/content/blog/the-internet-of-thing/
59.Yu, M. (2018), “Deep dive into Amazon's first unmanned store in the world”, News, iThome, Jun. 2018.
余至浩 (2018), “深度直擊全球Amazon首家無人商店”, News, iThome, Jun. 2018.
https://www.ithome.com.tw/news/124133

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊