Self-Service Technology Use by Older Adults: Moderating Effects of Need for Interaction

Abstract

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
[1] 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.

[2] Nam J, Kim S, Jung Y. Elderly Users’ emotional and behavioral responses to self-service technology in fast-food restaurants. Behav. Sci. 2023.

[3] 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.

[4] Forman AM, Sriram V. The Depersonalization of Retailing: Its Impact on The “Lonely” Consumer. J Retail. 1991;67:226.

[5] 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.

[6] King WR, He J. A meta-analysis of the technology acceptance model. Inf Manage. 2006;43:740–755.

[7] Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q Manag Inf Syst. 1989;13:319–339.

[8] Berger SC. Self-service technology for sales purposes in branch banking. Int J Bank Mark. 2009;27:488–505.

[9] Wessels L, Drennan J. An investigation of consumer acceptance of M-banking. Int J Bank Mark. 2010;28:547–568.

[10] Lallmahomed MZI, Lallmahomed N, Lallmahomed GM. Factors influencing the adoption of e-Government services in Mauritius. Telemat Inform. 2017;34:57–72.

[11] 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.

[12] Kamal SA, Shafiq M, Kakria P. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol Soc. 2020;60:101212.

[13] 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.

[14] 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.

[15] Kaushik AK, Kumar V. Investigating consumers’ adoption of SSTs – a case study representing India’s hospitality industry. J Vacat Mark. 2018;24:275–90.

[16] 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.

[17] 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.

[18] Kazancoglu I, Kursunluoglu Yarimoglu E. How food retailing changed in Turkey: spread of self-service technologies. Br Food J. 2018;120:290–308.

[19] 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.

[20] 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.

[21] 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.

[22] Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Manag Sci. 1989;35:982–1003.

[23] Gollwitzer PM, Sheeran P. Implementation intentions and goal achievement: A metaanalysis of effects and processes. Adv Exp Soc Psychol. 2006. p. 69–119.

[24] Webb TL, Sheeran P. Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychol Bull. 2006;132:249– 68.

[25] 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.

[26] 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.

[27] Venkatesh V, Davis FD. A model of the antecedents of perceived ease of use: Development and test. Decis Sci. 1996;27:451–81.

[28] Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q. 2003;27:425–78.

[29] 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.

[30] Ajzen I. Attitudes, personality, and behavior (2nd ed.). Milton-Keynes: Open University Press/McGraw-Hill; 2005.

[31] Hofstede G, Hofstede GJ, Minkov M. Cultures and organizations: Software of the Mind. Mc Graw Hill. 2010.

[32] 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.

[33] 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.

[34] 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.

[35] 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.

[36] Serener B. Testing the Homogeneity of Non-Adopters of Internet Banking. Bus Econ Res J. 2019;10:699–708.

[37] Arif I, Aslam W, Hwang Y. Barriers in adoption of internet banking: A structural equation modeling - Neural network approach. Technol Soc. 2020;61:101231.

[38] 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.

[39] 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.

[40] Hair JF, Hult GTM, Ringle CM, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks. Sage. 2017.

[41] 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.

[42] 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.

[43] Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Cluster Analysis. Multivariate data analysis. Vol. 7th Ed. 2019.

[44] 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.

[45] 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.

[46] 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.

[47] Yap YY, Tan SH, Choon SW. Elderly’s intention to use technologies: A systematic literature review. Heliyon. 2022;8:e08765.

[48] 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.