Self-Service Technology Use by Older Adults: Moderating Effects of Need for Interaction
DOI:
https://doi.org/10.18502/kss.v9i11.15802Abstract
Changing face-to-face services to technology-based self-service can pose several challenges. This study aims to analyze whether the characteristics of elderly consumers who like to interact directly can moderate intentions toward behavior using self-service technology. Data were collected using questionnaires distributed to 204 elderly respondents in two provinces in Indonesia. The results showed that perceived usefulness of self-service technology is more important to increase behavioral intention to use STT than perceived ease of use. In addition, the influence of behavioral intention on self-service use behavior will be more assertive in individuals who enjoy face-to-face interaction. This study emphasizes the moderating role of the need for interaction in the relationship between behavioral intention and the use of SST among older individuals in developing countries. Self-service technology can be an alternative for older adults in developing countries to get services without relying on human services. However, the usefulness of service technology must be conveyed to consumers as service users. This research was conducted during the transition period due to COVID-19. However, future research, conducted in more normal conditions, might yield different results.
Keywords: self-service technology, older adult, need for interaction, TAM, developing country
References
Hong EP, Ahn J. The role of autonomy, competence and relatedness in motivation to use self-service technology (SST) among customers with difficulties in SST. J Hosp Tour Technol. 2023. DOI: https://doi.org/10.1108/JHTT-09-2022-0265
Nam J, Kim S, Jung Y. Elderly Users’ emotional and behavioral responses to self-service technology in fast-food restaurants. Behav. Sci. 2023. DOI: https://doi.org/10.3390/bs13040284
Supriyatna I. Ini Alasan Mereka yang Purnabakti Semangat Ambil Langsung Uang Pensiun di Bank. https://money.kompas.com/read/2016/08/10/220036226/ ini.alasan.mereka.yang.purnabakti.semangat.ambil.langsung.uang.pensiun.di. bank?page=all. 2016.
Forman AM, Sriram V. The Depersonalization of Retailing: Its Impact on The “Lonely” Consumer. J Retail. 1991;67:226.
Lee HJ, Lyu J. Exploring factors which motivate older consumers’ self-service technologies (SSTs) adoption. Int Rev Retail Distrib Consum Res. 2019;29:218–239. DOI: https://doi.org/10.1080/09593969.2019.1575261
King WR, He J. A meta-analysis of the technology acceptance model. Inf Manage. 2006;43:740–755. DOI: https://doi.org/10.1016/j.im.2006.05.003
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q Manag Inf Syst. 1989;13:319–339. DOI: https://doi.org/10.2307/249008
Berger SC. Self-service technology for sales purposes in branch banking. Int J Bank Mark. 2009;27:488–505. DOI: https://doi.org/10.1108/02652320911002322
Wessels L, Drennan J. An investigation of consumer acceptance of M-banking. Int J Bank Mark. 2010;28:547–568. DOI: https://doi.org/10.1108/02652321011085194
Lallmahomed MZI, Lallmahomed N, Lallmahomed GM. Factors influencing the adoption of e-Government services in Mauritius. Telemat Inform. 2017;34:57–72. DOI: https://doi.org/10.1016/j.tele.2017.01.003
Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inf. 2017;101:75–84. DOI: https://doi.org/10.1016/j.ijmedinf.2017.02.002
Kamal SA, Shafiq M, Kakria P. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol Soc. 2020;60:101212. DOI: https://doi.org/10.1016/j.techsoc.2019.101212
Kattara HS, El-Said OA. Customers’ preferences for new technology-based selfservices versus human interaction services in hotels. Tour Hosp Res. 2014;13:67–82. DOI: https://doi.org/10.1177/1467358413519261
Arun TM, Singh S, Khan SJ, Akram MU, Chauhan C. Just one more episode: Exploring consumer motivations for adoption of streaming services. Asia Pac J Inf Syst. 2021;31:17–42. DOI: https://doi.org/10.14329/apjis.2021.31.1.17
Kaushik AK, Kumar V. Investigating consumers’ adoption of SSTs – a case study representing India’s hospitality industry. J Vacat Mark. 2018;24:275–90. DOI: https://doi.org/10.1177/1356766717725560
Evanschitzky H, Iyer GR, Pillai KG, Kenning P, Schütte R. Consumer trial, continuous use, and economic benefits of a retail service innovation: The case of the personal shopping assistant. J Prod Innov Manag. 2015;32:459–75. DOI: https://doi.org/10.1111/jpim.12241
Demoulin NTM, Djelassi S. An integrated model of self-service technology (SST) usage in a retail context. Int J Retail Distrib Manag. 2016;44:540–59. DOI: https://doi.org/10.1108/IJRDM-08-2015-0122
Kazancoglu I, Kursunluoglu Yarimoglu E. How food retailing changed in Turkey: spread of self-service technologies. Br Food J. 2018;120:290–308. DOI: https://doi.org/10.1108/BFJ-03-2017-0189
Kim JH, Park JW. The effect of airport self-service characteristics on passengers’ technology acceptance and behavioral intention. J Distrib Sci. 2019;17:29–37. DOI: https://doi.org/10.15722/jds.17.5.201905.29
Taufik N, Hanafiah MH. Airport passengers’ adoption behaviour towards self-checkin Kiosk Services: the roles of perceived ease of use, perceived usefulness and need for human interaction. Heliyon. 2019;5. DOI: https://doi.org/10.1016/j.heliyon.2019.e02960
Zhong Y, Oh S, Moon HC. Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technol Soc. 2021;64:101515. DOI: https://doi.org/10.1016/j.techsoc.2020.101515
Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Manag Sci. 1989;35:982–1003. DOI: https://doi.org/10.1287/mnsc.35.8.982
Gollwitzer PM, Sheeran P. Implementation intentions and goal achievement: A metaanalysis of effects and processes. Adv Exp Soc Psychol. 2006. p. 69–119. DOI: https://doi.org/10.1016/S0065-2601(06)38002-1
Webb TL, Sheeran P. Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol Bull. 2006;132:249– 68. DOI: https://doi.org/10.1037/0033-2909.132.2.249
Ferreira A, Silva GM, Dias ÁL. Determinants of continuance intention to use mobile self-scanning applications in retail. Int J Qual Reliab Manag. 2023;40:455–77. DOI: https://doi.org/10.1108/IJQRM-02-2021-0032
Kaushik AK, Rahman Z. An empirical investigation of tourist’s choice of service delivery options: SSTs vs service employees. Int J Contemp Hosp Manag. 2017;29:1892–913. DOI: https://doi.org/10.1108/IJCHM-08-2015-0438
Venkatesh V, Davis FD. A model of the antecedents of perceived ease of use: Development and test. Decis Sci. 1996;27:451–81. DOI: https://doi.org/10.1111/j.1540-5915.1996.tb01822.x
Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q. 2003;27:425–78. DOI: https://doi.org/10.2307/30036540
Venkatesh V, Thong JYL, Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012;36:157–78. DOI: https://doi.org/10.2307/41410412
Ajzen I. Attitudes, personality, and behavior (2nd ed.). Milton-Keynes: Open University Press/McGraw-Hill; 2005.
Hofstede G, Hofstede GJ, Minkov M. Cultures and organizations: Software of the Mind. Mc Graw Hill. 2010.
Dabholkar PA, Bagozzi RP. An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors. J Acad Mark Sci. 2002;30:184–201. DOI: https://doi.org/10.1177/00970302030003001
Dabholkar PA. Consumer evaluations of new technology-based self-service options: An investigation of alternative models of service quality. Int J Res Mark. 1996;13:29– 51. DOI: https://doi.org/10.1016/0167-8116(95)00027-5
Lee HJ. Personality determinants of need for interaction with a retail employee and its impact on self-service technology (SST) usage intentions. J Res Interact Mark. 2017;11:214–31. DOI: https://doi.org/10.1108/JRIM-04-2016-0036
Verhoef PC, Lemon KN, Parasuraman A, Roggeveen A, Tsiros M, Schlesinger LA. Customer experience creation: Determinants, dynamics and management strategies. J Retail. 2009;85:31–41. DOI: https://doi.org/10.1016/j.jretai.2008.11.001
Serener B. Testing the Homogeneity of Non-Adopters of Internet Banking. Bus Econ Res J. 2019;10:699–708. DOI: https://doi.org/10.20409/berj.2019.194
Arif I, Aslam W, Hwang Y. Barriers in adoption of internet banking: A structural equation modeling - Neural network approach. Technol Soc. 2020;61:101231. DOI: https://doi.org/10.1016/j.techsoc.2020.101231
Shim HS, Han SL, Ha J. The effects of consumer readiness on the adoption of selfservice technology: Moderating effects of consumer traits and situational factors. Sustain Switz. 2021;13:1–17. DOI: https://doi.org/10.3390/su13010095
Mason MC, Zamparo G, Pauluzzo R. Amidst technology, environment and human touch. Understanding elderly customers in the bank retail sector. Int J Bank Mark. 2023;41:572–600. DOI: https://doi.org/10.1108/IJBM-06-2022-0256
Hair JF, Hult GTM, Ringle CM, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks. Sage. 2017.
He C, Baranchenko Y, Lin Z, Szarucki M, Yukhanaev A. From Global Mindset To International Opportunities: the Internationalization of Chinese Smes. J Bus Econ Manag. 2020;21:967–86. DOI: https://doi.org/10.3846/jbem.2020.12673
Xu X, Wang L, Zhao K. Exploring determinants of consumers’ platform usage in “double eleven” shopping carnival in china: Cognition and emotion from an integrated perspective. Sustain Switz. 2020;12. DOI: https://doi.org/10.3390/su12072790
Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Cluster Analysis. Multivariate data analysis. Vol. 7th Ed. 2019.
Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015;43:115–35. DOI: https://doi.org/10.1007/s11747-014-0403-8
Li J, Ma Q, Chan AH, Man SS. Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Appl Ergon. 2019;75:162–9. DOI: https://doi.org/10.1016/j.apergo.2018.10.006
Miltgen CL, Popovič A, Oliveira T. Determinants of end-user acceptance of biometrics: Integrating the “big 3” of technology acceptance with privacy context. Decis Support Syst. 2013;56:103–14. DOI: https://doi.org/10.1016/j.dss.2013.05.010
Yap YY, Tan SH, Choon SW. Elderly’s intention to use technologies: A systematic literature review. Heliyon. 2022;8:e08765. DOI: https://doi.org/10.1016/j.heliyon.2022.e08765
National Research Council. When I’m 64. Committee on aging frontiers in social psychology, personality, and adult developmental psychology. Laura L. Carstensen and Christine R. Hartel, Editors. Washington, DC: The National Academies Press; 2006.