博碩士論文 106421605 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:61 、訪客IP:3.145.14.30
姓名 許倪(NI XU)  查詢紙本館藏   畢業系所 企業管理學系
論文名稱
(Factors and traits that drive customers to shop at unmanned stores)
相關論文
★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究★ 太陽能光電產業經營績效評估-應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 結合人格特質與海報主色以類神經網路推薦電影之研究★ 資料視覺化圖表與議題之關聯
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 由於信息和通信技術的快速發展,大量無人商店陸續開設。這些無人商店具有明顯的優勢,例如快速的結賬流程和較低的人工成本。無人商店行業有望快速發展,但事實上一些無人商店已經倒閉。本研究努力建立一個集成模型來回答以下問題:(a)什麼類型的顧客傾向於訪問無人商店?,(b)在全自動無人商店環境中,什麼物聯網 (IoT)情境因素是否可以激發客戶的購物意願?(c) 哪些物聯網情境因素可以激勵客戶傳播他們的積極體驗?
這項研究整合了情境因素和技術準備。本研究使用社會環境、時間視角、先行狀態和任務定義來描述物聯網服務的情境因素。由於無人店廣泛部署物聯網設備,顧客長期採用技術的傾向對無人店現狀的接受度起著關鍵作用。在本研究中, TR( technology readiness )被視為一種長期先行狀態。TR被分為PTR( positive TR )和NTR (negative TR ),兩者都被視為二階結構。為了了解客戶參與營銷的意願,推薦意圖被整合到本研究提出的模型中。
首先,結果顯示適應無人商店的情境因素可以提高購物意願。其次,雖然大多數檢查情境因素的研究都傾向於忽視客戶的心理狀態而側重於環境因素,但本研究表明,技術準備在影響用戶對身體狀況的評估方面起著關鍵作用。 第三,結果表明積極的情境因素也鼓勵顧客推廣無人商店。
據我們所知,本研究首次提出並驗證長期前因狀態可用於探索影響客戶使用無人商店甚至 self-service technology (SST) 的意向的因素。 由於客戶採用技術的傾向在技術接受中起著關鍵作用(Kim & Chiu, 2018; C.-H. Lin et al., 2007; Walczuch et al., 2007),本研究將TR作為長期先行狀態。其次,雖然一些研究(Collier et al., 2015; Gelderman et al., 2011; Kazancoglu & Kursunluoglu Yarimoglu, 2018)也檢查了一個或兩個情境因素作為吸引客戶使用SST的主要決定因素,但沒有一項研究同時採用所有五個因素(特別是先行狀態)。 Collier 等人強調了這個問題(Collier et al., 2015),他認為研究人員沒有意識到 SST 研究應該採用的情境因素。 商店級別的SST使用四種情境因素進行描述,這些因素被視為購物意圖和 推薦意圖的主要決定因素。
摘要(英) The recent rapid development of information and communication technologies have led to the opening of numerous unmanned stores worldwide. These stores have clear advantages, such as fast checkout processes and low personnel cost. The unmanned store industry was expected to grow quickly, but some unmanned stores have gone out of business. To identify suitable customers and beneficial shopping situations, this study strives to establish an integrated model that answers the following questions: (a) What types of customers are inclined to visit unmanned stores? (b) In a fully automated unmanned store environment, what are the Internet of Things (IoT)-enabled situations that can arouse the shopping intentions of customers? (c) What are the IoT-enabled situations that can motivate customers to spread their positive experiences?
This study integrates situational theory and technology readiness. To describe the crucial role of the unique services provided by unmanned stores, the present study used the concept of social surroundings, temporal perspectives, and task definitions to describe the IoT-enabled services. Because unmanned stores extensively deploy IoT devices, the long-term tendency of customers to adopt technology plays a key role in their acceptance of the situations present in unmanned stores. In the present study, technology readiness (TR) was treated as a long-term antecedent state that is present when customers evaluate the situations created through IoT in unmanned stores. TR was divided into positive TR (PTR) and negative TR (NTR), both of which were treated as second-order constructs. To understand the willingness of customers to participate in viral marketing, intention to recommend is integrated into the model proposed in the present study.
First, the results reveal that the situational factors adapted for the application of unmanned stores can enhance shopping intention. Second, although most studies examining situational factors have tended to ignore the psychological status of customers and focused on environmental factors, the present study demonstrates that technology readiness plays a key role in influencing user evaluation of physical situations. Third, the results indicate that positive situational factors also encourage customers to promote unmanned stores.
To the best of our knowledge, the present study is the first to posit and verify that long-term antecedent states can be utilized to explore the factors that affect the intention of customers to use unmanned stores and even self-service technology (SST). Because the tendency of customers to adopt technology plays a key role in technology acceptance (Kim & Chiu, 2018; C.-H. Lin et al., 2007; Walczuch et al., 2007), the present study incorporated TR as a long-term antecedent. Second, although several studies (Collier et al., 2015; Gelderman et al., 2011; Kazancoglu & Kursunluoglu Yarimoglu, 2018) have also examined one or two situational factors as the main determinants for attracting customers to use SST, none of them have adopted all five factors simultaneously (particularly antecedent states). This problem was highlighted by Collier et al. (Collier et al., 2015), who argued that researchers are unaware of the situational factors that should be adopted for SST research. Store level SST factors are described using four situational factors, which are treated as the main determinants of shopping intention and intention to recommend.
關鍵字(中) ★ 無人商店
★ 技術準備
★ 情境因素
★ 推薦意圖
★ 用戶意圖
關鍵字(英) ★ Unmanned store
★ Technology readiness index
★ Situational factors
★ Intention to recommend
★ User intention
論文目次 摘要 i
Abstract iii
致謝 vi
List of Figures ix
List of Tables x
Chapter I Introduction 1
1-1 Background 1
1-2 Research motivation 2
1-3 Research gap 2
1-4 Research objective 3
Chapter II Literature Review 6
2-1 User behavior in unmanned stores 6
2-2 Intention to utilize SST 7
2-3 Role of situational factors in SST 9
2-4 Role of TR 10
2-5 Intention to recommend 11
Chapter III Proposed Methodology 13
3-1 Hypotheses 16
Chapter IV Research methodology 22
4-1 Data collection 22
4-2 Questionnaire and measurements 22
4-3 Data analysis 24
4-3-1 Analysis of the measurement model 25
4-3-2 Analysis of structural model 28
Chapter V Discussion and Implications 35
5-1 Discussion 35
5-2 Implications 36
5-2-1 Implications for research 36
5-2-2 Implications for practitioners 39
Chapter VI Conclusions and limitations 41
6-1 Conclusions 41
6-2 Limitations 42
References 44
Appendix 65
參考文獻 Amblee, N., & Bui, T. (2011). Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts. International Journal of Electronic Commerce, 16(2), 91–114. https://doi.org/10.2753/JEC1086-4415160205
Atulkar, S., & Kesari, B. (2018). Role of consumer traits and situational factors on impulse buying: Does gender matter? International Journal of Retail & Distribution Management, 46(4), 386–405. https://doi.org/10.1108/IJRDM-12-2016-0239
Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value. Journal of Consumer Research, 20(4), 644–656. https://doi.org/10.1086/209376
Badgaiyan, A. J., & Verma, A. (2015). Does urge to buy impulsively differ from impulsive buying behaviour? Assessing the impact of situational factors. Journal of Retailing and Consumer Services, 22, 145–157. https://doi.org/10.1016/j.jretconser.2014.10.002
Balakrishnan, V., & Shuib, N. L. M. (2021). Drivers and inhibitors for digital payment adoption using the Cashless Society Readiness-Adoption model in Malaysia. Technology in Society, 65, 101554. https://doi.org/10.1016/j.techsoc.2021.101554
Belk, R. (1975). Situation Variables and Consumer Behavior. Journal of Consumer Research, 2, 157–164. https://doi.org/10.1086/208627
Ben Zur, H., & Breznitz, S. J. (1981). The effect of time pressure on risky choice behavior. Acta Psychologica, 47(2), 89–104. https://doi.org/10.1016/0001-6918(81)90001-9
BingoBox. (2020). BingoBox. https://www.bingobox.com/news/content.html?id=7andindex=5
Braxton, D. F. (2019). Consumer Responses to the Use of Technology-Based Self-Service: A Self-Determination Theory Perspective [Ph.D.]. https://www.proquest.com/docview/2300204517/abstract/6B37C092055A4C36PQ/1
BUSINESS WIRE. (2020, May 11). Insights into the Worldwide Unmanned Convenience Store Industry to 2027—Featuring Amazon, F5 Future Store & BingoBox Among Others—ResearchAndMarkets.com. https://www.businesswire.com/news/home/20200511005615/en/Insights-into-the-Worldwide-Unmanned-Convenience-Store-Industry-to-2027---Featuring-Amazon-F5-Future-Store-BingoBox-Among-Others---ResearchAndMarkets.com
Chang, Y.-W., & Chen, J. (2021). What motivates customers to shop in smart shops? The impacts of smart technology and technology readiness. Journal of Retailing and Consumer Services, 58, 102325. https://doi.org/10.1016/j.jretconser.2020.102325
Chen, T., Guo, W., Gao, X., & Liang, Z. (2021). AI-based self-service technology in public service delivery: User experience and influencing factors. Government Information Quarterly, 38(4), 101520. https://doi.org/10.1016/j.giq.2020.101520
Chiu, W., & Cho, H. (2020). The role of technology readiness in individuals’ intention to use health and fitness applications: A comparison between users and non-users. Asia Pacific Journal of Marketing and Logistics, 33(3), 807–825. https://doi.org/10.1108/APJML-09-2019-0534
Chuawatcharin, R., & Gerdsri, N. (2019). Factors influencing the attitudes and behavioural intentions to use just walk out technology among Bangkok consumers. International Journal of Public Sector Performance Management, 5(2), 146–163. https://doi.org/10.1504/IJPSPM.2019.099091
Chung, N., Han, H., & Joun, Y. (2015). Tourists’ intention to visit a destination: The role of augmented reality (AR) application for a heritage site. Computers in Human Behavior, 50, 588–599. https://doi.org/10.1016/j.chb.2015.02.068
Clemons, E. K., Gao, G. G., & Hitt, L. M. (2006). When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry. Journal of Management Information Systems, 23(2), 149–171. https://doi.org/10.2753/MIS0742-1222230207
Collier, J. E., Moore, R. S., Horky, A., & Moore, M. L. (2015). Why the little things matter: Exploring situational influences on customers’ self-service technology decisions. Journal of Business Research, 68(3), 703–710. https://doi.org/10.1016/j.jbusres.2014.08.001
Compernolle, M. van, Buyle, R., Mannens, E., Vanlishout, Z., Vlassenroot, E., & Mechant, P. (2018). “Technology readiness and acceptance model” as a predictor for the use intention of data standards in smart cities. Media and Communication, 6(4), 127–139. https://doi.org/10.17645/mac.v6i4.1679
Curren, M. T., & Harich, K. R. (1994). Consumers’ mood states: The mitigating influence of personal relevance on product evaluations. Psychology & Marketing, 11(2), 91–107. https://doi.org/10.1002/mar.4220110202
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, 13(1), 29–51. https://doi.org/10.1016/0167-8116(95)00027-5
D’Ammassa, A. (2021, July 30). Like it or not, retail is embracing self-checkout, as are shoppers—Las Vegas Sun Newspaper. https://lasvegassun.com/news/2021/jul/30/like-it-or-not-retail-is-embracing-self-checkout-a/
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dellarocas, C., Zhang, X. (Michael), & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21(4), 23–45. https://doi.org/10.1002/dir.20087
Demoulin, N. T. M., & Djelassi, S. (2016). An integrated model of self-service technology (SST) usage in a retail context. International Journal of Retail & Distribution Management, 44(5), 540–559. https://doi.org/10.1108/IJRDM-08-2015-0122
Dhar, R., & Nowlis, S. M. (1999). The Effect of Time Pressure on Consumer Choice Deferral. Journal of Consumer Research, 25(4), 369–384. https://doi.org/10.1086/209545
Dominici, A., Boncinelli, F., Gerini, F., & Marone, E. (2021). Determinants of online food purchasing: The impact of socio-demographic and situational factors. Journal of Retailing and Consumer Services, 60, 102473. https://doi.org/10.1016/j.jretconser.2021.102473
ET Net. (2020). High costs and rapid expansion invite their own demise? China’s unmanned stores are closing down! ET Net. https://www.etnet.com.hk/www/tc/lifestyle/digitalnewage/larryleung/60139
Fernandes, T., & Pedroso, R. (2017). The effect of self-checkout quality on customer satisfaction and repatronage in a retail context. Service Business, 11(1), 69–92. https://doi.org/10.1007/s11628-016-0302-9
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural Equation Modeling and Regression: Guidelines for Research Practice. Communications of the Association for Information Systems, 4(1). https://doi.org/10.17705/1CAIS.00407
Gelderman, C. J., Ghijsen, P. W. Th., & van Diemen, R. (2011). Choosing self-service technologies or interpersonal services—The impact of situational factors and technology-related attitudes. Journal of Retailing and Consumer Services, 18(5), 414–421. https://doi.org/10.1016/j.jretconser.2011.06.003
Glynn Mangold, W., Miller, F., & Brockway, G. R. (1999). Word‐of‐mouth communication in the service marketplace. Journal of Services Marketing, 13(1), 73–89. https://doi.org/10.1108/08876049910256186
Guan, X., Xie, L., Shen, W.-G., & Huan, T.-C. (2021). Are you a tech-savvy person? Exploring factors influencing customers using self-service technology. Technology in Society, 65, 101564. https://doi.org/10.1016/j.techsoc.2021.101564
Gures, N., Inan, H., & Arslan, S. (2018). Assessing the self-service technology usage of Y-Generation in airline services. Journal of Air Transport Management, 71, 215–219. https://doi.org/10.1016/j.jairtraman.2018.04.008
Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research, 62(5), 565–571. https://doi.org/10.1016/j.jbusres.2008.06.016
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Richter, N. F., & Hauff, S. (2017). Partial Least Squares Strukturgleichungsmodellierung. Verlag C.H.Beck. https://doi.org/10.15358/9783800653614
Hair, Jr., J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I – method. European Business Review, 28(1), 63–76. https://doi.org/10.1108/EBR-09-2015-0094
Hauser, M., Günther, S. A., Flath, C. M., & Thiesse, F. (2019). Towards Digital Transformation in Fashion Retailing: A Design-Oriented IS Research Study of Automated Checkout Systems. Business & Information Systems Engineering, 61(1), 51–66. https://doi.org/10.1007/s12599-018-0566-9
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic Consumption: Emerging Concepts, Methods and Propositions. Journal of Marketing, 46(3), 92–101. https://doi.org/10.1177/002224298204600314
Horwitz, J. (2018). Amazon Go: China is both ahead of and behind Amazon in cashier-less stores. https://qz.com/1185081/amazon-go-china-is-both-ahead-of-and-behind-amazon-in-cashier-less-stores
Hult, G. T. M., Hair, J. F., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018). Addressing Endogeneity in International Marketing Applications of Partial Least Squares Structural Equation Modeling. Journal of International Marketing, 26(3), 1–21. https://doi.org/10.1509/jim.17.0151
iResearch. (2017). 2017 China Unmanned Retail Industry Research Report.
Jayasankaraprasad, C. (2010). Effect of Situational Factors on Store Format Choice Behaviour in Food and Grocery Retailing in India—A Multiple Discriminant Analysis. IBSU Scientific Journal, 4(2), Article 2.
Jin, C. (2013). The perspective of a revised TRAM on social capital building: The case of Facebook usage. Information & Management, 50(4), 162–168. https://doi.org/10.1016/j.im.2013.03.002
Jin, C.-H. (2020). Predicting the Use of Brand Application Based on a TRAM. International Journal of Human–Computer Interaction, 36(2), 156–171. https://doi.org/10.1080/10447318.2019.1609227
Kazancoglu, I., & Kursunluoglu Yarimoglu, E. (2018). How food retailing changed in Turkey: Spread of self-service technologies. British Food Journal, 120(2), 290–308. https://doi.org/10.1108/BFJ-03-2017-0189
Khan, N., Hui, L., Tan, B. C., & Hong, Y. H. (2015). Impulse Buying Behaviour of Generation Y in Fashion Retail. International Journal of Business and Management, 11, 144. https://doi.org/10.5539/ijbm.v11n1p144
Khayer, A., Talukder, M. S., Bao, Y., & Hossain, M. N. (2023). Application-based mobile payment systems: Continuance intention and intention to recommend. International Journal of Mobile Communications, 21(1), 19–53. https://doi.org/10.1504/IJMC.2023.127374
Kim, T., & Chiu, W. (2018). Consumer acceptance of sports wearable technology: The role of technology readiness. International Journal of Sports Marketing and Sponsorship, 20(1), 109–126. https://doi.org/10.1108/IJSMS-06-2017-0050
kknews. (2018). Amazon, Tencent, and Alibaba have opened unmanned stores one after another. What if someone runs away with it? https://kknews.cc/tech/o8lgnap.html
Kline, R. B. (2005). Principles and Practice of Structural Equation Modeling.
Knowles, P. A., Grove, S. J., & Burroughs, W. J. (1993). An Experimental Examination of Mood Effects on Retrieval and Evaluation of Advertisement and Brand Information. Journal of the Academy of Marketing Science, 21(2), 135–142. https://doi.org/10.1177/009207039302100205
Kokkinou, A., & Cranage, D. A. (2013). Using self-service technology to reduce customer waiting times. International Journal of Hospitality Management, 33, 435–445. https://doi.org/10.1016/j.ijhm.2012.11.003
Kumar, P., Kalwani, M. U., & Dada, M. (1997). The Impact of Waiting Time Guarantees on Customers’ Waiting Experiences. Marketing Science, 16(4), 295–314. https://doi.org/10.1287/mksc.16.4.295
Kuo, K.-M., Liu, C.-F., & Ma, C.-C. (2013). An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems. BMC Medical Informatics and Decision Making, 13(1), 88. https://doi.org/10.1186/1472-6947-13-88
Kwak, Y.-A., & Cho, Y.-S. (2019). Unmanned Store, Retailtech and Digital Divide in South Korea. Journal of Distribution Science, 17(9), 47–56. https://doi.org/10.15722/jds.17.9.201909.47
Lam, T., Cho, V., & Qu, H. (2007). A study of hotel employee behavioral intentions towards adoption of information technology. International Journal of Hospitality Management, 26(1), 49–65. https://doi.org/10.1016/j.ijhm.2005.09.002
Lancelot Miltgen, C., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context. Decision Support Systems, 56, 103–114. https://doi.org/10.1016/j.dss.2013.05.010
Lee, J.-Y., Son, I.-S., & Lee, D.-W. (2012). Does Online Social Network Contribute to WOM Effect on Product Sales? Journal of Intelligence and Information Systems, 18(2), 85–105. https://doi.org/10.13088/jiis.2012.18.2.085
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. https://doi.org/10.1016/S0378-7206(01)00143-4
Leung, L. S. K. (2019). What Are Basic Human Needs? A Challenge to the Self-Determination Theory in the SST Context. Psychology, 10(7), Article 7. https://doi.org/10.4236/psych.2019.107063
Li, Q., & Luo, Y. (2020). No one is asking for the unmanned retail? Behind the closure is the shuffle or demise? http://sc.people.com.cn/BIG5/n2/2020/0106/c346366-33691694.html
Liao, C.-H., & Tsou, C.-W. (2009). User acceptance of computer-mediated communication: The SkypeOut case. Expert Systems with Applications, 36(3, Part 1), 4595–4603. https://doi.org/10.1016/j.eswa.2008.05.015
Liljander, V., Gillberg, F., Gummerus, J., & van Riel, A. (2006). Technology readiness and the evaluation and adoption of self-service technologies. Journal of Retailing and Consumer Services, 13(3), 177–191. https://doi.org/10.1016/j.jretconser.2005.08.004
Lin, C.-H., Shih, H.-Y., & Sher, P. J. (2007). Integrating technology readiness into technology acceptance: The TRAM model. Psychology & Marketing, 24(7), 641–657. https://doi.org/10.1002/mar.20177
Lin, J. C., & Chang, H. (2011). The role of technology readiness in self‐service technology acceptance. Managing Service Quality: An International Journal, 21(4), 424–444. https://doi.org/10.1108/09604521111146289
Liu, Y. (2006). Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue. Journal of Marketing, 70(3), 74–89. https://doi.org/10.1509/jmkg.70.3.074
Lo, C.-H., & Wang, Y.-W. (2019). Constructing an Evaluation Model for User Experience in an Unmanned Store. Sustainability, 11(18), Article 18. https://doi.org/10.3390/su11184965
Mallat, N. (2007). Exploring consumer adoption of mobile payments – A qualitative study. The Journal of Strategic Information Systems, 16(4), 413–432. https://doi.org/10.1016/j.jsis.2007.08.001
MarketsandMarkets. (2020). Smart Retail Market Size Share Global forecast to 2021-2030. MarketsandMarkets. https://www.marketsandmarkets.com/Market-Reports/smart-retail-market-78791828.html
Maxham, J. G. (2001). Service recovery’s influence on consumer satisfaction, positive word-of-mouth, and purchase intentions. Journal of Business Research, 54(1), 11–24. https://doi.org/10.1016/S0148-2963(00)00114-4
McDonald, H., & Alpert, F. (2000). A Broadened Situational Framework: Transient Versus Enduring Situational Factors.
Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-Service Technologies: Understanding Customer Satisfaction with Technology-Based Service Encounters. Journal of Marketing, 64(3), 50–64. https://doi.org/10.1509/jmkg.64.3.50.18024
Mullen, M. R. (1995). Diagnosing Measurement Equivalence in Cross-National Research. Journal of International Business Studies, 26(3), 573–596. https://doi.org/10.1057/palgrave.jibs.8490187
Nicholls, J. A. F., Roslow, S., Dublish, S., & Comer, L. B. (1996). Relationship between situational variables and purchasing in India and the USA. International Marketing Review, 13(6), 6–21. https://doi.org/10.1108/02651339610151890
Nilsson, E., Pers, J., & Grubbström, L. (2021). Self-Service Technology in Casual Dining Restaurants. Services Marketing Quarterly, 42(1–2), 57–73. https://doi.org/10.1080/15332969.2021.1947085
Oh, J. C., Yoon, S. J., & Chung, N. (2014). The role of technology readiness in consumers’ adoption of mobile internet services between South Korea and China. International Journal of Mobile Communications, 12(3), 229–248. https://doi.org/10.1504/IJMC.2014.061460
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030
Pan, Y., & Siemens, J. C. (2011). The differential effects of retail density: An investigation of goods versus service settings. Journal of Business Research, 64(2), 105–112. https://doi.org/10.1016/j.jbusres.2010.02.011
Parasuraman, A. (2000). Technology Readiness Index (Tri): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies. Journal of Service Research, 2(4), 307–320. https://doi.org/10.1177/109467050024001
Parasuraman, A., & Colby, C. L. (2015). An Updated and Streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59–74. https://doi.org/10.1177/1094670514539730
Park, C. W., Iyer, E. S., & Smith, D. C. (1989). The Effects of Situational Factors on In-Store Grocery Shopping Behavior: The Role of Store Environment and Time Available for Shopping. Journal of Consumer Research, 15(4), 422–433. https://doi.org/10.1086/209182
Park, J.-S., Ha, S., & Jeong, S. (2020). Consumer acceptance of self-service technologies in fashion retail stores. Journal of Fashion Marketing and Management: An International Journal, ahead-of-print. https://doi.org/10.1108/JFMM-09-2019-0221
Park, S., & Gupta, S. (2012). Handling Endogenous Regressors by Joint Estimation Using Copulas. Marketing Science, 31(4), 567–586. https://doi.org/10.1287/mksc.1120.0718
Petter, S., Straub, D., & Rai, A. (2007). Specifying Formative Constructs in Information Systems Research. MIS Quarterly, 31(4), 623–656. https://doi.org/10.2307/25148814
Pillai, R., Sivathanu, B., & Dwivedi, Y. K. (2020). Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services, 57, 102207. https://doi.org/10.1016/j.jretconser.2020.102207
Podsakoff, P. M., & Organ, D. W. (1986). Self-Reports in Organizational Research: Problems and Prospects. Journal of Management, 12(4), 531–544. https://doi.org/10.1177/014920638601200408
Ramsey, J. B. (1969). Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis. Journal of the Royal Statistical Society: Series B (Methodological), 31(2), 350–371. https://doi.org/10.1111/j.2517-6161.1969.tb00796.x
Riegner, C. (2007). Word of Mouth on the Web: The Impact of Web 2.0 on Consumer Purchase Decisions. Journal of Advertising Research, 47(4), 436–447. https://doi.org/10.2501/S0021849907070456
Rinta-Kahila, T., Penttinen, E., Kumar, A., & Janakiraman, R. (2021). Customer reactions to self-checkout discontinuance. Journal of Retailing and Consumer Services, 61, 102498. https://doi.org/10.1016/j.jretconser.2021.102498
Rogers, E. M. (2010). Diffusion of Innovations, 4th Edition. Simon and Schuster.
Roy, S. K., Balaji, M. S., Quazi, A., & Quaddus, M. (2018). Predictors of customer acceptance of and resistance to smart technologies in the retail sector. Journal of Retailing and Consumer Services, 42, 147–160. https://doi.org/10.1016/j.jretconser.2018.02.005
Ryu, K., Han, H., & Jang, S. (Shawn). (2010). Relationships among hedonic and utilitarian values, satisfaction and behavioral intentions in the fast‐casual restaurant industry. International Journal of Contemporary Hospitality Management, 22(3), 416–432. https://doi.org/10.1108/09596111011035981
San-Martín, S., Jiménez, N., & Liébana-Cabanillas, F. (2020). Tourism value VS barriers to booking trips online. Journal of Retailing and Consumer Services, 53, 101957. https://doi.org/10.1016/j.jretconser.2019.101957
Sarstedt, M., Becker, J.-M., Ringle, C. M., & Schwaiger, M. (2011). Uncovering and Treating Unobserved Heterogeneity with FIMIX-PLS: Which Model Selection Criterion Provides an Appropriate Number of Segments? Schmalenbach Business Review, 63(1), 34–62. https://doi.org/10.1007/BF03396886
Sarstedt, M., & Mooi, E. (2019). A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics. Springer. https://doi.org/10.1007/978-3-662-56707-4
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating Unobserved Heterogeneity in PLS-SEM: A Multi-method Approach. In H. Latan & R. Noonan (Eds.), Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (pp. 197–217). Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_9
Seiders, K., Voss, G. B., Godfrey, A. L., & Grewal, D. (2007). SERVCON: Development and validation of a multidimensional service convenience scale. Journal of the Academy of Marketing Science, 35(1), 144–156. https://doi.org/10.1007/s11747-006-0001-5
Shim, H.-S., Han, S.-L., & Ha, J. (2021). The Effects of Consumer Readiness on the Adoption of Self-Service Technology: Moderating Effects of Consumer Traits and Situational Factors. Sustainability, 13(1), Article 1. https://doi.org/10.3390/su13010095
Shin, H., & Dai, B. (2022). The efficacy of customer’s voluntary use of self-service technology (SST): A dual-study approach. Journal of Strategic Marketing, 30(8), 723–745. https://doi.org/10.1080/0965254X.2020.1841269
Silva, R., Bido, Ringle, C., Silva, D., & Bido, D. (2014). STRUCTURAL EQUATION MODELING WITH THE SMARTPLS. Revista Brasileira de Marketing, 13, 56–73.
Sirgy, M. J., Grewal, D., & Mangleburg, T. (2000). Retail Environment, Self-Congruity, and Retail Patronage: An Integrative Model and a Research Agenda. Journal of Business Research, 49(2), 127–138. https://doi.org/10.1016/S0148-2963(99)00009-0
Sivathanu, B. (2019). An Empirical Study on the Intention to Use Open Banking in India. Information Resources Management Journal (IRMJ), 32(3), 27–47. https://doi.org/10.4018/IRMJ.2019070102
Stanton, J. L., & Bonner, P. G. (1980). An Investigation of the Differential Impact of Purchase Situation on Levels of Consumer Choice Behavior. ACR North American Advances, NA-07. https://www.acrwebsite.org/volumes/9756/volumes/v07/NA-07/full
Svensson, G., Ferro, C., Høgevold, N., Padin, C., Carlos Sosa Varela, J., & Sarstedt, M. (2018). Framing the triple bottom line approach: Direct and mediation effects between economic, social and environmental elements. Journal of Cleaner Production, 197, 972–991. https://doi.org/10.1016/j.jclepro.2018.06.226
Talukder, M. S., Chiong, R., Bao, Y., & Hayat Malik, B. (2018). Acceptance and use predictors of fitness wearable technology and intention to recommend: An empirical study. Industrial Management & Data Systems, 119(1), 170–188. https://doi.org/10.1108/IMDS-01-2018-0009
Teo, A.-C., Tan, G. W.-H., Ooi, K.-B., Hew, T.-S., & Yew, K.-T. (2015). The effects of convenience and speed in m-payment. Industrial Management & Data Systems, 115(2), 311–331. https://doi.org/10.1108/IMDS-08-2014-0231
Van Kenhove, P., De Wulf, K., & Van Waterschoot, W. (1999). The impact of task definition on store-attribute saliences and store choice. Journal of Retailing, 75(1), 125–137. https://doi.org/10.1016/S0022-4359(99)80007-4
Velázquez, B. M., Blasco, M. F., & Gil Saura, I. (2015). ICT adoption in hotels and electronic word-of-mouth. Academia Revista Latinoamericana de Administración, 28(2), 227–250. https://doi.org/10.1108/ARLA-10-2013-0164
Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer Experience Creation: Determinants, Dynamics and Management Strategies. Journal of Retailing, 85(1), 31–41. https://doi.org/10.1016/j.jretai.2008.11.001
Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206–215. https://doi.org/10.1016/j.im.2006.12.005
Wang, C., Harris, J., & Patterson, P. G. (2012). Customer choice of self‐service technology: The roles of situational influences and past experience. Journal of Service Management, 23(1), 54–78. https://doi.org/10.1108/09564231211208970
Wei, W., Torres, E. N., & Hua, N. (2017). The power of self-service technologies in creating transcendent service experiences: The paradox of extrinsic attributes. International Journal of Contemporary Hospitality Management, 29(6), 1599–1618. https://doi.org/10.1108/IJCHM-01-2016-0029
Wilkie, W. L. (1990). Consumer Behavior. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=CAlI30gAAAAJ&citation_for_view=CAlI30gAAAAJ:P5F9QuxV20EC
wp-support. (2018, February 19). Are we ready for amazon go? Shorr Packaging. https://www.shorr.com/resources/blog/are-we-ready-for-amazon-go/
Wu, H.-C., Ai, C.-H., & Cheng, C.-C. (2019). Experiential quality, experiential psychological states and experiential outcomes in an unmanned convenience store. Journal of Retailing and Consumer Services, 51, 409–420. https://doi.org/10.1016/j.jretconser.2019.07.003
Yan, L.-Y., Tan, G. W.-H., Loh, X.-M., Hew, J.-J., & Ooi, K.-B. (2021). QR code and mobile payment: The disruptive forces in retail. Journal of Retailing and Consumer Services, 58, 102300. https://doi.org/10.1016/j.jretconser.2020.102300
Yoon, C., & Choi, B. (2020). Role of Situational Dependence in the Use of Self-Service Technology. Sustainability, 12(11), Article 11. https://doi.org/10.3390/su12114653
Yoon Kin Tong, D., Piew Lai, K., & Fa Tong, X. (2012). Ladies’ purchase intention during retail shoes sales promotions. International Journal of Retail & Distribution Management, 40(2), 90–108. https://doi.org/10.1108/09590551211201856
Zhao, X., Hou, J., & Gilbert, K. (2014). Measuring the variance of customer waiting time in service operations. Management Decision, 52(2), 296–312. https://doi.org/10.1108/MD-01-2013-0012
Zhuang, G., Tsang, A. S. L., Zhou, N., Li, F., & Nicholls, J. A. F. (2006). Impacts of situational factors on buying decisions in shopping malls: An empirical study with multinational data. European Journal of Marketing, 40(1/2), 17–43. https://doi.org/10.1108/03090560610637293
Zhuang, G., Zhou, N., & Li, F. (2002). The Impact of Situational Factors on Chinese Mall Shoppers’ Buying Decisions. ACR Asia-Pacific Advances, AP-05. https://www.acrwebsite.org/volumes/11835/volumes/ap05/AP-05/full
指導教授 許秉瑜(Ping-Yu Hsu) 審核日期 2023-6-20
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明