Research Article Published 17 January 2022

Factors Affecting Generation Z’s Intention to Use Self-Service Technology (SST)

How to Cite

How to Cite

Chow , M. M., Yeow , J. A., & See , C. K. (2022). Factors Affecting Generation Z’s Intention to Use Self-Service Technology (SST). Journal of Business Management and Accounting, 12(1), 81-96. https://doi.org/10.32890/jbma2022.12.1.4
Mei Min Chow
Faculty of Business, Multimedia University, Melaka, Malaysia
Jian Ai Yeow
Faculty of Business, Multimedia University, Melaka, Malaysia
Chun Kent See
Faculty of Business, Multimedia University, Melaka, Malaysia
Full text
PDF View full text 712 KB · 16 pages XML JATS full text 56 KB · machine readable HTML Read online 33 KB
DOI 10.32890/jbma2022.12.1.4 Cite this article
Share this article
Citations
6 +1 · 2026
OpenAlex, highest of the sources checked
Reads
901 +1
Abstract + full text
Downloads
952 +1
PDF and XML
Percentile
90th
By citations, works of the same year

Article

Abstract

As the world moves towards a technological era, various innovations have been introduced across different industries in order to meet customers’ expectations and enhance service quality. Self-service Technology (SST) is one innovation that can be commonly found across industries, and has gained high popularity among users. The main objective of this study is to examine the factors that affect customer intention in using SST, specifically among Generation Z. It examines the relationship between the independent variables of perceived ease of use, perceived usefulness, need of interaction, and technology anxiety and the dependent variable of customer intention in using SST. A questionnaire survey adopted in this study secured the required information from 152 target respondents. Analyses of responses explained 65.5% variances in customer intention of using SST. The research findings established through data analysed with SPSS version 27 application, suggest that perceived ease of use and perceived usefulness have positive significant relationships towards customer intention of using SST, but no significant relationship was found on need of interaction and technology anxiety in customer intention of using SST. Therefore, in order to encourage and attract potential and future users to utilise the service of SST, the system and function of SST need to be upgraded consistently to ensure it brings benefits and usefulness to users. Besides, a company can also educate users on ways to operate the SST in order to enhance the level of ease of use, and to increase the awareness of the usefulness of SST among users. The limitations and recommendations of future study are discussed at the end of the paper.

Keywords
Self-service Technology (SST) technology anxiety perceived usefulness perceived ease of use need for interaction

Metrics & impact

Citations by source

Harvested 07-09-2026

Counts differ by index because each source crawls a different corpus. Crossref counts only registered reference lists; Google Scholar includes theses, preprints and grey literature.

OpenAlex
6
Open corpus, updated daily View →
OpenCitations
5
Open references only View →
Semantic Scholar
4
S2AG corpus View →
Crossref
4
Deposited reference lists only View →
Scopus
0 Not indexed
Indexed titles only View →
Google Scholar
Publishes no citation count that can be read automatically.
Includes theses and preprints Search →
Dimensions
Publishes no citation count that can be read automatically.
Includes policy documents Search →

Citations over time

New citations recorded in each year, as reported by OpenAlex.

4
1
0
1
2023 2024 2025 2026

Reads & downloads

Abstract views 901
PDF downloads 952
XML downloads 0
Full-text HTML 0
Counted under COUNTER Release 5 rules; known robots excluded.

Attention beyond citation

Citation percentile 90th
Field-weighted citation impact 2.14
Works this article cites 36

From OpenAlex. A field-weighted impact of 1.00 is the world average for work of the same field, type and year. No Altmetric key is configured, so news, policy and social attention are not shown.

Identifiers
DOI · 10.32890/jbma2022.12.1.4
OpenAlex · W4210891633
Semantic Scholar · CorpusID 246666269

References

References 44
Deposited to Crossref with the article DOI
  1. Bobbitt, M. L. & Dabholkar, P. A. (2001). Integrating attitudinal theories to understand and predict use of technology-based self-service. International Journal of Service Industry Management, 12(5), 423-450.
  2. Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E. & Karaali, D. (2014). Predicting the intention to use a web-based learning system: perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531.
  3. Caramba-Coker, F. O. (2009). Consumer attitudes and behavioural intention towards self-service kiosks in hotels: A cultural perspective. The degree of Master of Science, University of Delaware.
  4. Chai, K. F. (2008). Self-Service Technology and Internet Banking: An Investigation of Consumers Trial Decision, Master’s thesis, University of Malaya, Malaysia.
  5. Chang, M. (2015). Perceived Factors Influencing the Acceptance and Adoption of Self-service Technology. Master dissertation/ thesis, University Tunku Abdul Rahman (UTAR).
  6. Chatzoglou, P.D., Sarigiannidis, L., Vraimaki, E. & Diamantidis, A. (2009). Investigating Greek employees’ intention to use web-based training. Computers & Education, 53(3), 877-889.
  7. Cho, S. (2011). Self-service technology: An investigation of the potential for adoption in apparel retail settings. The degree of Doctor of Philosophy, The University of North Carolina at Greensboro.
  8. Chuttur M. Y. (2009). Overview of the Technology Acceptance Model: Origins, Developments and Future Directions. Sprouts: Working Papers on Information Systems, 9(37).
  9. Collier, J. E. (2006). Examining customers’ intentions to use self-service technology through utilitarian and hedonic value judgments. Retrieved 12 May, 2021 from http://search proquest.co /docview /304910318?accountid=5027.
  10. Curran, J. & Meuter, M. (2005). Self-service technology adoption: Comparing three technologies. Journal of Services Marketing, 19(2), 103-113.
  11. 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.
  12. Daneji, A. A., Khambari, M. N. Md. & Mohd Ayub, A. F. (2018). Influence of students’ perceived ease of use, perceived usefulness and time spent towards students’ continuance intention using MOOC among public university students. ICCE 2018-26th International Conference on Computers in Education, Workshop Proceedings, 576-581.
  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 3(3), 319-340.
  14. Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
  15. Elliott, K., Meng, G. & Hall, M (2012). The influence of technology readiness on the evaluation of self-service technology attributes and resulting attitude toward technology usage. Services Marketing Quarterly, 33(4), 311-329.
  16. Esman, F., Embi. R., & Jusoh, R. (2010). Usage of self-service technologies (SSTs): A case study of e-Debit system at Bursary UiTM Shah Alam. International Journal of Instructional Technology and Distance Learning, 7(9).
  17. Fu, J. R., Farn, C. K., & Chao, W. P. (2006). Acceptance of electronic tax filing: A study of taxpayer intentions. Information & Management, 43(1), 109-126.
  18. Godoe, P. & Johansen, T. S. (2012). Understanding adoption of new technologies: Technology readiness and technology acceptance as an integrated concept. Journal of European Psychology Students, 3(1), 38–52.
  19. Gunawardana, M. R. S. S., Kulathunga, D. & Perera, W. L. M. V. (2015). Impact of self-service technology quality on customer satisfaction: A case of retail banks in western province in Sri Lanka, Gadjah Mada. International Journal of Business, 17(1), 1-24.
  20. Hornik, J. (1992). Subjective vs. objective time measures: A note on the perception of time in consumer behaviour. Journal of Consumer Research, 11(3), 615-618.
  21. Horovitz, B. (2012). After Gen X, Millennials, what should next generation be? USA Today. Retrieved May 15, 2021, from http://usatoday30.usatoday.com/money/advertising/ story/2012-05-03/naming-the-next-generation/54737518/1.
  22. Iqbal, M. S., Hassan, M. U. & Habibah, U. (2017). Impact of self-service technology (SST) service quality on customer loyalty and behavioral intention: The mediating role of customer satisfaction. Cogent Business & Management, 5, 1-23.
  23. Kazancoglu, I. & Kursunluoglu Yarimoglu, E. (2018). How food retailing changed in Turkey: Spread of self-service technologies. British Food Journal, 120(2), 290-308.
  24. Khairat, G. (2014). Obstacles to the Adoption of Self-Service Technology at Cairo International Airport. Retrieved August 25, 2021, from https://www.semanticscholar.org/paper/ Obstacles-to-the-Adoption-of-Self-Service-at-Cairo-Khairat/5 8a75c78b3159148a00de2e7418e4faf44d9ee09
  25. Koch, W. H., & Pratarelli, M. E. (2004). Effects of intro/extraversion and sex on social internet use. North American Journal of Psychology, 6(3).
  26. Krbová, P. K. (2016). Generation Y attitudes towards shopping: A comparison of the Czech Republic and Slovakia. Journal of Competitiveness, 8(1), 38-54.
  27. Lee, K., Kim, J. & Noh, M. (2017). Influence Factors on Intention to use Mobile Banking. International Journal of Management and Applied Science, 3(3),11-15.
  28. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191-204.
  29. Lin,C. J. S. & Hsieh, P. L. (2011). Assessing the self-service technology encounters: Development and validation of SSTQUAL Scale. Journal of Retailing, 87(2), 196-206.
  30. Liu, S., Huang, L., & Chiou, Y. (2012). An integrated attitude model of self-service technologies: Evidence from online stock trading systems brokers. Service Industries Journal, 32(11), 1823-1835.
  31. Meuter M. L, Ostrom A. L., Roundtree R. I. & Bitner M. J. (2000). Self-service technologies: Understanding consumer satisfaction with technology-based service encounters. Journal of Marketing, 64, 3 (July), 50.
  32. Meuter, M. L., Ostrom, A. L., Bitner, M. J. & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899-906.
  33. Mohd Suki, N., Ramayah, T. & Mohd Suki, N. (2008). Internet shopping acceptance: Examining the influence of intrinsic versus extrinsic motivations. Direct Marketing: An International Journal, 2(2), 97-110.
  34. Muk, A. & Chung, C. (2015). Applying the technology acceptance model in a two-country study of SMS advertising. Journal of Business Research, 68(1), 1-6.
  35. Pallant, J. (2011). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS (4th ed.). Australia: Allen & Unwin.
  36. 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.
  37. Phongkusolchit, K. (2008). Improving self-service technologies utilization: The potential impacts of technology anxiety, need for interaction with service employees, and expected service quality. Retrieved May 12, 2021 from http://search.proquest. com/docview/304474695?accountid=50207.
  38. Prensky, M. (2001). Digital Natives, Digital Immigrants Part 1. On the Horizon, 9(5), 1-6.
  39. Sánchez-Prieto, J. C., Olmos-Migueláñez, S. & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519-528.
  40. Setiawan, R. A., Setyohadi, D., & Pranowo, P. (2018). Understanding customers’ intention to use social network sites as complaint channel: An analysis of young customers’ perspectives. E3S Web of Conferences, 31.
  41. Tanduklangi, A. (2017). Determinants of user intention in using e-learning technology in Indonesian context: An empirical study. Mediterranean Journal of Social Sciences, 8(3), 69-77.
  42. Van Raaij, E. M. & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838-852.
  43. Venkatesh, V. & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.
  44. Weijters, B., Rangarajan, D., Falk, T., & Schillewaert, N. (2007). Determinants and outcomes of customers’ use of self-service technology in a retail setting. Journal of Service Research. 10(1), 3-21.