Due to the phenomenon of aging, a new consumer segment known as the “new-silver generation” is emerging. Unlike the previous silver generation, this generation possesses significant economic power and consuming willingness, attracting attention from consumer goods companies. However, both the new-silver generation and the elderly face challenges in adopting contactless or self-service technologies such as self-order kiosks, resulting in negative reactions. Therefore, this study aims to investigate the attitude and response of the new-silver generation towards kiosks, as well as the factors influencing their resistance to such technology. By applying theoretical perspectives from the innovation resistance model, technostress theory, and the value-based model, this study identifies influencing factors for innovation resistance among the new-silver generation when using contactless technologies implemented in fast-food restaurants. The findings indicate that a lower awareness of new technologies and services corresponds to decreased adoption resistance, while a higher perceived value leads to more positive behaviors and attitudes among the new-silver generation utilizing kiosks at fast-food restaurants.
AlfaddaH. A., & MahdiH. S. (2021). Measuring students’ use of Zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50(4), 883–900.
2.
AyyagariR.GroverV., & PurvisR. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831–858.
3.
BagozziR. P.YiY., & PhillipsL. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421–458.
4.
BanduraA. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359–373.
5.
BloomA. J., & HautaluomaJ. E. (1990). Anxiety management training as a strategy for enhancing computer user performance. Computers in Human Behavior, 6(4), 337–349.
6.
BrodC. (1984). Technostress: The human cost of the computer revolution.Reading: Addison-Wesley.
7.
ByunM. Y. (2017). The fashion design development for launching fast fashion brand of new silver generation. Journal of the Korean Society of Design Culture, 23(3), 421–431.
8.
CardinP., & OlsonP. (1994). Chaotic thermal convection in a rapidly rotating spherical shell: Consequences for flow in the outer core. Physics of the Earth and Planetary Interiors, 82(3–4), 235–259.
9.
CarrollJ. M., & ThomasJ. C. (1988). Fun. ACM SIGCHI Bulletin, 19(3), 21–24.
ChangY. H., & ParkJ. G. (2010). An exploratory study on barriers of the diffusion of smartphone: Focusing on the determinants of postponers’ innovation resistance. Studies of Broadcasting Culture, 22(2), 37–62.
12.
ChenZ., & DubinskyA. J. (2003). A conceptual model of perceived customer value in e-commerce: A preliminary investigation. Psychology & Marketing, 20(4), 323–347.
13.
ChengA.KooC., & YoonH. (2021). The use of travel-related WeChat mini-programs in China: An affordance theory perspective. Journal of Smart Tourism, 1(2), 37–46.
ChoiH. C.JoJ. E., & HahmS. P. (2009). Self-service check-in kiosk use behaviour: An application of technology acceptance model. Journal of Tourism and Leisure Research, 21(4), 295–315.
16.
CoxT., & MacKayC. (1982). Psychosocial factors and psychophysiological mechanisms in the aetiology and development of cancers. Social Science & Medicine, 16(4), 381–396.
17.
DabholkarP. A., & BagozziR. P. (2002). An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184–201.
18.
DavisF. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
19.
DavisF. D.BagozziR. P., & WarshawP. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
20.
De Lapparent, M. (2010). Attitude toward risk of time loss in travel activity and air route choices. Journal of Intelligent Transportation Systems, 14(3), 166–178.
21.
DowlingG. R., & StaelinR. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119–134.
22.
EllenP. S.WienerJ. L., & Cobb-WalgrenC. (1991). The role of perceived consumer effectiveness in motivating environmentally conscious behaviors. Journal of Public Policy & Marketing, 10(2), 102–117.
23.
FeathermanM. S., & PavlouP. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474.
24.
FornellC., & LarckerD. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.
25.
GutmanJ. (1982). A means-end chain model based on consumer categorization processes. Journal of Marketing, 46(2), 60–72.
26.
HanS. J. (2019). A study on the cultural capital and taste of the new silver generation in Korea.Ph.D. dissertation, Seoul National University, Seoul.
27.
HanS. J., & HaJ. S. (2017). Lifestyles and fashion styles of new silver generation women. Journal of Fashion Design, 17(3), 67–81.
28.
HirschheimR., & NewmanM. (1988). Information systems and user resistance: Theory and practice. The Computer Journal, 31(5), 398–408.
29.
HuhW. W.HongS. K.HanS. W., & KimW. S. (2012). A study about emotional value factors of new silver generation. Design Convergence Study, 11(11), 3–12.
30.
HwangS. W., & KimH. S. (2019). A study on the user experience of unmanned order payment kiosk in fast food stores. Journal of Digital Contents Society, 20(8), 1491–1501.
31.
HyunH.ParkJ. K, & RenT. (2017). The role of perceived multi-dimensions of risk and product knowledge on purchasing Chinese product. Journal of Product Research, 35(6), 85–91.
JeonH.KimK. A., & KimS. H. (2018). The effects of customer's relational benefits on continuous usage intention of technology-based self-service: Focusing on types of technology-based self-service. Journal of Marketing Management Research, 23(1), 27–49.
JinS. H.LeeD. G., & LeeS. J. (2012). The influence of technostress and anti-smart on smartphones. Journal of Digital Convergence, 10(10), 187–195.
36.
JoI. J.KimS. K., & YangS. B. (2015). A study on influencing factors on user's adoption resistance to personal cloud computing service. Knowledge Management Research, 16(1), 117–142.
37.
JungM. H.RhoH. O., & RyuI. (2013). The impacts of technostress on IS strain and performance expectancy among information system users. Asia Pacific Journal of Small Business, 35(4), 121–142.
38.
KimG.KimS., & Cha. Y. (2017). Entry into an aged society and opportunities for senior business. Seoul: Samjong KPMG Economic Research Institute.
39.
KimH. W.ChanH. C., & GuptaS. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126.
40.
KimJ.ChristodoulidouN., & ChooY. (2013). Factors influencing customer acceptance of kiosks at quick service restaurants. Journal of Hospitality and Tourism Technology, 4(1), 40–63.
41.
KimJ. E., & Yeo. J. S. (2007). Digital stress of consumers in using digital goods. Journal of Korean Home Management Association, 25(3), 119–135.
KimY., & Moon. Y. (2018). An empirical research on psychological resistance factors of omni channel's unmanned order payment system. The Journal of Internet Electronic Commerce Research, 18(6), 1–19.
44.
KramerT. (2007). The effect of measurement task transparency on preference construction and evaluations of personalized recommendations. Journal of Marketing Research, 44(2), 224–233.
45.
LeeM. S., & ParkY. J. (2020). A comparative study of domestic travel patterns and determinant factors affecting satisfaction by generations. Information Systems Review, 22(2), 137–166.
46.
LeeS. B.LeeS. C., & SuhY. H. (2016). Technostress from mobile communication and its impact on quality of life and productivity. Total Quality Management & Business Excellence, 27(7–8), 775–790.
47.
LeeS. H. (2018). The effects of dietary types of new silver generation on satisfaction with dining out, select Properties. Journal of Tourism and Leisure Research, 30(4), 399–412.
48.
LeeS. M. (2019). A study on the factors affecting innovation resistance of unattended order payment system: Focused on fastfood store. Journal of Communication Design, 67, 170–179.
49.
LimN. (2003). Consumers’ perceived risk: Sources versus consequences. Electronic Commerce Research and Applications, 2(3), 216–228.
50.
MandelN. (2003). Shifting selves and decision making: The effects of self construal priming on consumer risk taking. Journal of Consumer Research, 30(1), 30–40.
51.
MeuterM. L.OstromA. L.RoundtreeR. I., & BitnerM. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50– 64.
52.
MillerR. S. (1992). The nature and severity of self-reported embarrassing circumstances. Personality and Social Psychology Bulletin, 18(2), 190– 198.
53.
MinB. K., & ParkJ. Y. (2010). Determinants of users acceptance of technology-based self-service. Journal of Korea Technology Innovation Society, 13(1), 204–230.
54.
MooreG. C., & BenbasatI. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.
55.
MorrisM. R., & HorvitzE. (2007, October). Search together: An interface for collaborative web search. Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, New York, NY, USA, 3–12. https://doi.org/10.1145/1294211.1294215
NhoH. O.KimY. H., & Hong. S. J. (2015). A study on technostress of information communication technology user. Journal of the Korea Convergence Society, 6(4), 41–46.
58.
NohM., & Lee. K., (2012). The effects of the perceived risk in the users’ acceptance of the social commerce: Moderating effects of the collectivism. Korean Management Review, 41(1), 57–87.
59.
NunnallyJ. C., & BernsteinI. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.
60.
OsbergT. M., & ShraugerJ. S. (1986). Self-prediction: Exploring the parameters of accuracy. Journal of Personality and Social Psychology, 51(5), 1044–1057.
61.
ParasuramanA. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.
62.
ParkH., & KimS. (2017). Impacts of perceived risks and technical traits of mobile easy payment service on use conflict and acceptance resistance. The Journal of Internet Electronic Commerce Research, 17(4), 119–138.
63.
ParkH. H., & JeonJ. (2013). The effect of web characteristics of social commerce on satisfaction and reuse intention: Moderating role of perception of discount price fairness. The Journal of Internet Electronic Commerce Research, 13(3), 111–136.
64.
ParkJ. S. (2018). A study on difference of consumer knowledge and underlying technology perception on introduction of blockchain based transaction authentication technology: Focused on innovation resistance model.Ph.D. dissertation, Chung-Ang University, Seoul.
65.
PatilP.TamilmaniK.RanaN. P., & RaghavanV. (2020). Understanding consumer adoption of mobile payment in India: Extending meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144.
66.
PeresR.MullerE., & MahajanV. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing, 27(2), 91–106.
67.
Ragu-NathanT. S.TarafdarM.Ragu-NathanB. S., & TuQ. (2008). The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Information Systems Research, 19(4), 417–433.
68.
RamS. (1987). A model of innovation resistance. Advances in Consumer Research, 14, 208–212.
69.
RamS., & JungH. S. (1991). “Forced” adoption of innovations in organizations: Consequences and implications. Journal of Product Innovation Management, 8(2), 117–126.
70.
RamS., & ShethJ. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5–14.
71.
ReindersM. J.DabholkarP. A., & FrambachR. T. (2008). Consequences of forcing consumers to use technology-based self-service. Journal of Service Research, 11(2), 107–123.
72.
RogersE. M. (1995). Diffusion of innovations: Modifications of a model for telecommunications. In StoetzerM. W. & MahlerA. (Eds.), Die Diffusion von Innovationen in der Telekommunikation (pp. 25–38). Berlin, Heidelberg: Springer.
73.
SamiL. K., & PangannaiahN. B. (2006). “Technostress” a literature survey on the effect of information technology on library users. Library Review, 55(7), 429–439.
74.
Sánchez-FernándezR. &. Iniesta-Bonillo, M. Á. (2007). The concept of perceived value: A systematic review of the research. Marketing Theory, 7(4), 427–451.
75.
SchiffmanL. G., & KanukL. L. (1991). Communication and consumer behavior. Consumer Behavior, 2, 268–306.
76.
SelyeH. (1956). The stress of life.New York: McGraw-Hill Book Company.
77.
ShethJ. N. (1981). An integrative theory of patronage preference and behavior (pp. 9–28). Urbana, IL, USA: College of Commerce and Business Administration, Bureau of Economic and Business Research, University of Illinois, Urbana-Champaign.
ShinK. M.YuJ. W., & SeoJ. (2015). The effect of perceived risk for travel agencies web-sites on trust and loyalty. Journal of Tourism Studies, 27(1), 99–118.
80.
ShuQ.TuQ., & WangK. (2011). The impact of computer self-efficacy and technology dependence on computer-related technostress: A social cognitive theory perspective. International Journal of Human-Computer Interaction, 27(10), 923–939.
81.
SongJ. H. (2018). A study on HMR purchasing behavior according to new silver generation lifestyle. Master's Dissertation, Kyonggi University, Suwon.
82.
SongJ. W. (2019). A study on usage intention of technology-based self-service (TBSS) of food service companies: Focus on innovation resistance model. Journal of Hospitality and Tourism Studies, 21(3), 176–188.
83.
SongS. B.KangJ. Y., & LeeS. G. (2013). Analyzing impact factors of user resistance to accepting paid mobile application. The Journal of the Korea Contents Association, 13(4), 361–375.
84.
UmT., & ChungN. (2022). Attitude change towards self-service technology adoption using latent growth modeling. Journal of Smart Tourism, 2(3), 5–15.
85.
Van der Heijden, H. (2004). User acceptance of hedonic information systems. Management Information Systems Quarterly, 28(4), 695–704.
86.
VenkateshV.ThongJ. Y., & XuX. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology, MIS Quarterly, 36(1), 157–178.
87.
YangS. B. (2013). An empirical study on the factors influencing perceived risks and intention to use online bookstores. Journal of Information Technology Services, 12(4), 267–287.
YunS. (2016). Efficient communication support plan for new silver generation utilizing virtual assistant contents. A Journal of Brand Design Association of Korea, 14(4), 167–178.
90.
ZeithamlV. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.
