The Nexus Between Service Quality and Self-service Technology on Customer Satisfaction and Continuance Intention: A Survey on Trans-Java Toll Road Users

Authors

  • Muhamad Fajar Subkhan Department of Business Administration, University of Brawijaya, Malang
  • Endang Siti Astuti Department of Business Administration, University of Brawijaya, Malang
  • Andriani Kusumawati Department of Business Administration, University of Brawijaya, Malang
  • ‎ Sunarti Department of Business Administration, University of Brawijaya, Malang

DOI:

https://doi.org/10.18502/kss.v10i13.18975

Keywords:

continuance intention, customer satisfaction, self-service technology, service quality, social cognitive theory

Abstract

Drawing upon Social Cognitive Theory as its theoretical foundation, this study examines how service quality and self-service technology (SST) interconnect to shape customer satisfaction and continuance intention. Utilizing a quantitative approach, the study employs Partial Least Square (PLS) analysis to uncover relationships within the data. The investigation reveals that while service quality and SST demonstrate substantial positive impacts on user satisfaction, neither factor directly influences continuance intention. The findings indicate a complex relationship wherein both service quality and SST enhance customer satisfaction, though supplementary elements appear to shape long-term loyalty decisions. Notably, user satisfaction emerges as the critical full mediator between service quality/SST and continuance intention, functioning as the essential channel through which these factors influence users’ ongoing engagement. This discovery suggests that elevating service quality or SST independently proves insufficient for maintaining customer commitment without prioritizing user satisfaction. The research yields meaningful theoretical and practical implications, emphasizing how a strategic focus on user satisfaction mediates between service quality and self-service technologies to foster customer loalty and sustained engagement.

References

[1] Parasuraman A, Zeithaml VA, Berry LL. A conceptual model of service quality and its implications for future research. J Mark. 1985;49(4):41–50. DOI: https://doi.org/10.1177/002224298504900403

[2] Lai WT, Chen CF. Lai W-T, Chen C-F. Behavioral intentions of public transit passengers—the roles of service quality, perceived value, satisfaction and involvement. Transp Policy. 2011;18(2):318–25. DOI: https://doi.org/10.1016/j.tranpol.2010.09.003

[3] Bitner MJ, Zeithaml VA, Gremler DD. Handbook of service science. Maglio PP, Kieliszewski CA, Spohrer JC, editors. Boston, MA: Springer US; 2010. Technology’s Impact on the Gaps Model of Service Quality; p. 197-218. DOI: https://doi.org/10.1007/978-1-4419-1628-0_10

[4] Henderson H, Leong TW. Lessons learned: A study on user difficulties with parking meters. Proceedings of the 29th Australian Conference on Computer-Human Interaction. 2017: 533-537. DOI: https://doi.org/10.1145/3152771.3156172

[5] Henderson H, Grace K, Gulbransen-Diaz N, Klaassens B, Leong TW, Tomitsch M. From parking meters to vending machines: A study of usability issues in self-service technologies. Int J Hum Comput Interact. 2024;40(16):4365–79. DOI: https://doi.org/10.1080/10447318.2023.2212228

[6] Bandura A. Entertainment-education and social change. Arvind S, Michael JC, Everett MR, Miguel S, editors. 1st ed. New York: Routledge; 2003. Social cognitive theory for personal and social change by enabling media; p. 97-118. DOI: https://doi.org/10.4324/9781410609595-11

[7] Bandura A. Media effects - Advances in theory and research. Bryant J, Oliver MB, editors. 3rd ed. New York: Routledge; 2008. Social cognitive theory of mass communication; p. 110-140. DOI: https://doi.org/10.4324/9780203877111-12

[8] Polacco A, Backes K. The amazon go concept: Implications, applications, and sustainability. J Bus Manag. 2018;24(1):79–92. DOI: https://doi.org/10.1504/JBM.2018.141263

[9] Abdelaziz SG, Hegazy AA, Elabbassy A. Study of airport self-service technology within experimental research of check-in techniques case study and concept [IJCSI]. International Journal of Computer Science Issues. 2010;7(3):30.

[10] Sung HJ, Jeon HM. Untact: Customer’s Acceptance Intention toward Robot Barista in Coffee Shop. Sustainability (Basel). 2020;12(20):8598. DOI: https://doi.org/10.3390/su12208598

[11] Razak FZ, Mokhtar AE, Rahman AA, Abidin MZ. Service quality, satisfaction and users’ continuance intention to use e-campus: A mediation analysis. J Phys Conf Ser. 2021;1793(1):012019. DOI: https://doi.org/10.1088/1742-6596/1793/1/012019

[12] De Leon MV, Atienza RP, Susilo D. Influence of self-service technology (SST) service quality dimensions as a second-order factor on perceived value and customer satisfaction in a mobile banking application. Cogent Bus Manag. 2020;7(1):1794241. DOI: https://doi.org/10.1080/23311975.2020.1794241

[13] Park E, Lee S, Kwon SJ, Del Pobil AP. Determinants of behavioral intention to use South Korean airline services: effects of service quality and corporate social responsibility. Sustainability (Basel). 2015;7(9):12106–21. DOI: https://doi.org/10.3390/su70912106

[14] Vasić N, Kilibarda M, Kaurin T, Vasić N, Kilibarda M, Kaurin T. The influence of online shopping determinants on customer satisfaction in the Serbian market. J Theor Appl Electron Commer Res. 2019;14(2):70–89. DOI: https://doi.org/10.4067/S0718-18762019000200107

[15] Kim MJ, Chung N, Lee CK, Preis MW. Motivations and use context in mobile tourism shopping: applying contingency and task–technology fit theories. Int J Tour Res. 2015;17(1):13–24. DOI: https://doi.org/10.1002/jtr.1957

[16] Bandura A. Entertainment-education and social change. Arvind S, Michael JC, Everett MR, Miguel S, editors. 1st ed. New York: Routledge; 1986. Social cognitive theory for personal and social change by enabling media; p. 97-118.

[17] Quinnell R, Thompson R, LeBard RJ. It’s not maths; It’s science: exploring thinking dispositions, learning thresholds and mindfulness in science learning. Int J Math Educ Sci Technol. 2013;44(6):808–16. DOI: https://doi.org/10.1080/0020739X.2013.800598

[18] Grönroos C. A service quality model and its marketing implications. Eur J Mark. 1984;18(4):36–44. DOI: https://doi.org/10.1108/EUM0000000004784

[19] Rust RT, Oliver RL. Service quality: New directions in theory and practice. USA: SAGE Publications, Inc.; 1994. Chapter 1, Service quality: Insights and managerial implications from the frontier; p. 1-20. DOI: https://doi.org/10.4135/9781452229102.n1

[20] Dabholkar PA, Thorpe DI, Rentz JO. A measure of service quality for retail stores: scale development and validation. J Acad Mark Sci. 1996;24(1):3–16. DOI: https://doi.org/10.1007/BF02893933

[21] Zhu Z, Nakata C, Sivakumar K, Grewal D. Self-service technology effectiveness: the role of design features and individual traits. J Acad Mark Sci. 2007;35(4):492–506. DOI: https://doi.org/10.1007/s11747-007-0019-3

[22] Hilton T, Hughes T, Little E, Marandi E. Adopting self-service technology to do more with less. J Serv Mark. 2013;27(1):3–12. DOI: https://doi.org/10.1108/08876041311296338

[23] Fitzsimmons JA. Is self-service the future of services? Manag Serv Qual. 2003;13(6):443–4. DOI: https://doi.org/10.1108/09604520310506496

[24] Walker RH, Johnson LW. Why consumers use and do not use technology-enabled services. J Serv Mark. 2006;20(2):125–35. DOI: https://doi.org/10.1108/08876040610657057

[25] Meuter ML, Ostrom AL, Roundtree RI, Bitner MJ. Self-service technologies: understanding customer satisfaction with technology-based service encounters. J Mark. 2000;64(3):50–64. DOI: https://doi.org/10.1509/jmkg.64.3.50.18024

[26] Engel JF, Blackwell RD, Miniard PW. Consumer behavior. UK: Dryden Press; 1990.

[27] Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: An introduction to theory and research. Reading (MA): Addison-Wesley; 1975.

[28] Mouakket S, Al-hawari MA. Examining the antecedents of e-loyalty intention in an online reservation environment. J High Technol Manage Res. 2012;23(1):46–57. DOI: https://doi.org/10.1016/j.hitech.2012.03.005

[29] Teo T, Zhou M. Explaining the intention to use technology among university students: A structural equation modeling approach. J Comput High Educ. 2014;26(2):124–42. DOI: https://doi.org/10.1007/s12528-014-9080-3

[30] Bhattacherjee A. Understanding information systems continuance: an expectationconfirmation model. Manage Inf Syst Q. 2001;25(3):351–70. DOI: https://doi.org/10.2307/3250921

[31] Creswell JW, Creswell JD. Research design: Qualitative, quantitative, and mixed methods approaches. USA: Sage Publications; 2017.

[32] Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis: Pearson College division. 7th ed. London, UK: Person; 2010.

[33] Hair JF, Sarstedt M, Ringle CM. Rethinking some of the rethinking of partial least squares. Eur J Mark. 2019;53(4):566–84. DOI: https://doi.org/10.1108/EJM-10-2018-0665

[34] Roscoe JT. Fundamental research statistics for the behavioral scinces. New York (NY): Holt, Rinehart and Winston; 1975.

[35] Zuna HT, Hadiwardoyo SP, Rahadian H. Atribut pelayanan jalan tol dalam peningkatan kualitas berkendara (Studi kasus: Jalan tol Makassar) [Toll road service attributes in improving driving quality (Case study: Makassar toll road)]. Proceeding Konferensi Regional Teknik Jalan ke-13, Makassar. 2014:1-13. Indonesian

[36] Ayodeji Y, Rjoub H. Investigation into waiting time, self-service technology, and customer loyalty: the mediating role of waiting time in satisfaction. Hum Factors Ergon Manuf. 2021;31(1):27–41. DOI: https://doi.org/10.1002/hfm.20867

[37] Iniesta-Bonillo MA, Sánchez-Fernández R, Jiménez-Castillo D. Sustainability, value, and satisfaction: model testing and cross-validation in tourist destinations. J Bus Res. 2016;69(11):5002–7. DOI: https://doi.org/10.1016/j.jbusres.2016.04.071

[38] Shao Z, Li X, Guo Y, Zhang L. Influence of service quality in sharing economy: understanding customers’ continuance intention of bicycle sharing. Electron Commerce Res Appl. 2020;40:100944. DOI: https://doi.org/10.1016/j.elerap.2020.100944

[39] Shah B, Singh G. Can collaborative buffering strategies reduce distribution costs while improving product returns?: A case of an Asian e-retailer. Benchmarking (Bradf). 2021;28(9):2808–34. DOI: https://doi.org/10.1108/BIJ-09-2020-0478

[40] Chin WW. Modern methods for business research. Marcoulides GA, editor. Mahwah: Lawrence Erlbaum; 1998. The partial least squares approach to structural equation modeling; p. 295-336.

[41] 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(1):115–35. DOI: https://doi.org/10.1007/s11747-014-0403-8

[42] Dash G, Kiefer K, Paul J. Marketing-to-millennials: Marketing 4.0, customer satisfaction and purchase intention. J Bus Res. 2021;122:608–20. DOI: https://doi.org/10.1016/j.jbusres.2020.10.016

[43] Hair JJ, Hult GT, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). USA: SAGE; 2017.

[44] Le HT, Carrel AL, Li M. How much dissatisfaction is too much for transit? Linking transit user satisfaction and loyalty using panel data. Travel Behav Soc. 2020;20:144–54. DOI: https://doi.org/10.1016/j.tbs.2020.03.007

[45] Tjiptono F, Chandra G. Service, quality satisfaction. Yogyakarta: Andi Offset; 2012.

[46] Lewis RC. Emerging Perspectives on Service Marketing. Berry LL, Shostack G, Upah G, editors. Chicago, Illinois: American Marketing Association; 1983. The marketing aspects of service quality; p. 99-107.

[47] Djelassi S, Diallo MF, Zielke S. How self-service technology experience evaluation affects waiting time and customer satisfaction? A moderated mediation model. Decis Support Syst. 2018;111:38–47. DOI: https://doi.org/10.1016/j.dss.2018.04.004

[48] Iqbal MS, Hassan MU, Habibah U. Impact of self-service technology (SST) service quality on customer loyalty and behavioral intention: the mediating role of customer satisfaction. Cogent Bus Manag. 2018;5(1):1. DOI: https://doi.org/10.1080/23311975.2018.1423770

[49] Wu R, Wu Z, Wen J, Cai Y, Li Y. Extrinsic and intrinsic motivations as predictors of bicycle sharing usage intention: an empirical study for Tianjin, China. J Clean Prod. 2019;225:451–8. DOI: https://doi.org/10.1016/j.jclepro.2019.04.016

[50] Chuang SS, Lai HM. Knowledge management in organizations. Uden L, Ting IH, Corchado JM, editors. 1027. Cham: Springer International Publishing; 2019. Understanding consumers’ continuance intention toward self-service stores: An integrated model of the theory of planned behavior and push-pull-mooring theory; p. 149-164. DOI: https://doi.org/10.1007/978-3-030-21451-7_13

[51] Mugion RG, Toni M, Raharjo H, Di Pietro L, Sebathu SP. Does the service quality of urban public transport enhance sustainable mobility? J Clean Prod. 2018;174:1566– 87. DOI: https://doi.org/10.1016/j.jclepro.2017.11.052

[52] Nguyen-Phuoc DQ, Su DN, Tran PT, Le DT, Johnson LW. Factors influencing customer’s loyalty towards ride-hailing taxi services – A case study of Vietnam. Transp Res Part A Policy Pract. 2020;134:96–112. DOI: https://doi.org/10.1016/j.tra.2020.02.008

Downloads

Published

2025-06-25

How to Cite

Subkhan, M. F., Astuti, E. S., Kusumawati, A., & Sunarti, ‎. (2025). The Nexus Between Service Quality and Self-service Technology on Customer Satisfaction and Continuance Intention: A Survey on Trans-Java Toll Road Users. KnE Social Sciences, 10(13), 396–415. https://doi.org/10.18502/kss.v10i13.18975