Skip to main content

Determinants of Trust in Smart Technologies

  • Chapter
  • First Online:
Human-Technology Interaction

Abstract

Smart technologies are ubiquitous. Yet, although investments are rising, their positive economic effects are empirically questionable. One main reason for this lack of efficiency improvement are human factors: Humans need to cooperate with technology and, therefore, trust it. In this paper, we analyze particular antecedents of trust in technology both theoretically and empirically. Our results reveal that immaterial, psychological benefits affect trust stronger than material benefits. Thus, addressing advantageous aspects of new technology that benefit users is essential. However, breaking the promise of immaterial benefits may easily lead to distrust.

This research and development project is funded by the German Federal Ministry of Education and Research (BMBF) within the “The Future of Value Creation—Research on Production, Services and Work” program (02L19C115) and managed by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 84.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ashleigh, M. J., & Nandhakumar, J. (2007). Trust in technologies: Implications for organizational work practices. Decision Support Systems, 42(2), 607–617.

    Article  Google Scholar 

  2. Carter, M., Thatcher, J. B., Clay, P. F., & Mc Knight, D. H. (2011). Trust in specific technology: An investigation of its components and measures. ACM Transactions on. Management Information Systems, 2(2), 1–25.

    Google Scholar 

  3. Susi, T., & Ziemke, T. (2001). Social cognition, artefacts, and stigmergy: A comparative analysis of theoretical frameworks for the understanding of artefact-mediated collaborative activity. Journal of Cognitive Systems Research, 2, 273–290.

    Article  Google Scholar 

  4. Taddeo, M. (2010). Trust in technology: A distinctive and a problematic relation. Knowledge, Technology & Policy, 23(3–4), 283–286.

    Article  Google Scholar 

  5. Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development, 53(4), 5–23.

    Article  Google Scholar 

  6. Xu, J., Le, K., Deitermann, A., & Montague, E. (2014). How different types of users develop trust in technology: A qualitative analysis of the antecedents of active and passive user trust in a shared technology. Applied Ergonomics, 45(6), 1495–1503.

    Article  Google Scholar 

  7. Lippert, S. K., & Davis, M. (2006). A conceptual model integrating trust into planned change activities to enhance technology adoption behavior. Journal of Science, 32(5), 434–448.

    Google Scholar 

  8. Hilty, L. M., Köhler, A., Von Scheele, F., Zah, R., & Rudy, T. (2006). Rebound effects of progress in information technology. Poiesis & Praxis, 4(1), 19–38.

    Article  Google Scholar 

  9. Boston Consulting Group. (2019). Winning with AI. Pioneers combine strategy, organizational behavior and technology. MIT Sloan Management Review.

    Google Scholar 

  10. Capgemini. (2020). Digital mastery. How organizations have progressed in their digital transformation over the past two years. Capgemini Research Institute. Accessed January 21, 2021, from https://www.capgemini.com/wp-content/uploads/2021/01/Digital-Mastery-Report-1.pdf

  11. PricewaterhouseCoopers. (2020). COVID-19. A digital technology agenda driving an accelerated transition to the new normal. Accessed January 21, 2021, from https://www.pwc.de/de/deals/covid-19-a-digital-technology-agenda-driving-an-accelerated-transition-to-the-new-normal.pdf

  12. Astebro, T. (2004). Sunk costs and the depth and probability of technology adoption. The Journal of Industrial Economics, 52(3), 381–399.

    Article  Google Scholar 

  13. Keil, M., Turex, D. P., & Mixon, R. (1995). The effects of sunk cost and project completion on information technology project escalation. IEEE Transactions of Engineering Management, 42(4), 372–381.

    Article  Google Scholar 

  14. Budnick, C. J., Rogers, A. P., & Barber, L. K. (2020). The fear of missing out at work: Examining costs and benefits to employee health and motivation. Computers in Human Behavior, 104, 106–161.

    Article  Google Scholar 

  15. Cave, S., & Dihal, K. (2019). Hopes and fears for intelligent machines in fiction and reality. Nature Machine Intelligence, 1, 74–78.

    Article  Google Scholar 

  16. Lucas, H. C., & Goh, J. M. (2009). Disruptive technology. How Kodak missed the digital photography revolution. The Journal of Strategic Information Systems, 18(1), 46–55.

    Article  Google Scholar 

  17. Bahmanziari, T., Pearson, J. M., & Crosby, L. (2003). Is trust important in technology adoption? A policy capturing approach. Journal of Computer Information System, 43(4), 46–54.

    Google Scholar 

  18. Siau, K., & Wang, W. (2018). Building trust in Artificial Intelligence, machine learning, and robotics. Cutter Business Journal, 31(1), 47–53.

    Google Scholar 

  19. Ashoori, M. & Weisz, J. D. (2019). In AI we trust? Factors that influence trustworthiness of AI-infused decision-making processes. Accessed January 21, 2021, from https://arxiv.org/abs/1912.02675

  20. Muir, B. M. (1987). Trust between humans and machines, and the design of decision aids. International Journal of Man-Machine Studies, 27(5–6), 527–539.

    Article  Google Scholar 

  21. Ejdys, J. (2018). Building trust in ICT application at a university. International Journal of Emerging Markets, 13(5), 980–996.

    Article  Google Scholar 

  22. Jeon, M. (2017). Emotions and affect in human factors and human-computer interaction: Taxonomy, theories, approaches and methods (pp. 3–26). Academic Press.

    Book  Google Scholar 

  23. Palmer, J. & Terry, N. (2016). Smart homes and saving energy. The REFIT project final report for industry and government. Accessed January 18, 2021, from https://www.refitsmarthomes.org/publications/

  24. Jacques, P. H., Garger, J., Brown, C. A., & Deale, C. S. (2009). Personality and virtual reality team candidates: The roles of personality traits, technology anxiety and trust as predictors of perceptions of virtual reality teams. Journal of Business and Management, 15(2), 143–158.

    Google Scholar 

  25. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information System Research, 11(4), 342–365.

    Article  Google Scholar 

  26. Lee, Z., & Sargeant, A. (2011). Dealing with social desirability bias: An application to charitable giving. European Journal of Marketing, 45(5), 703–719.

    Article  Google Scholar 

  27. McKnight, D. H., & Chervany, N. L. (2001). Trust and distrust definitions: One bite at a time. In R. Falcone, M. Singh, & Y.-H. Tan (Eds.), Trust in cyber-societies. Integrating the human and artificial perspectives (pp. 27–54). Springer.

    Chapter  MATH  Google Scholar 

  28. Andras, P., Esterle, L., Guckert, M., Han, T. A., Lewis, P. R., Milanovic, K., Payne, T., Perret, C., Pitt, J., Powers, S. T., Urquhart, N., & Wells, S. (2018). Trusting intelligent machines. Deepening trust within socio-technical systems. IEEE Technology and Society Magazine, 37(4), 76–83.

    Article  Google Scholar 

  29. Deutsch, M. (1958). Trust and suspicion. The Journal of Conflict Resolution, 2(4), 265–279.

    Article  Google Scholar 

  30. Worden, K., Bullough, W. A., & Haywood, J. (2003). Smart technologies. World Scientific Publishing.

    Book  Google Scholar 

  31. Akhilesh, K. B., & Möller, D. P. F. (2020). Smart technologies. Scope and Applications.

    Book  Google Scholar 

  32. Hernandez-de-Menendez, M., Diaz, C. A. E., & Morales-Menendez, R. (2020). Engineering education for smart 4.0 technology: A review. International Journal in Interactive Design and Manufacturing, 14(3), 789–803.

    Article  Google Scholar 

  33. Cook, D. J., & Das, S. K. (2005). Smart environments. Technologies, protocols, and applications. Wiley Interscience.

    Google Scholar 

  34. Preuveneers, D., Tsingenopoulos, I., & Joosen, W. (2020). Resource usage and performance trade-offs for machine learning models in smart environments. Sensors, 20(4), 1–27.

    Article  Google Scholar 

  35. Yu, K., Berkovsky, S., Taib, R., Zhou, J., & Chen, F. (2019). Do i trust my machine teammate? An investigation from perception to decision. Intelligent User Interfaces 2019: Proceedings of the 24th international conference on Intelligent User Interfaces.

    Google Scholar 

  36. Madhavan, P., & Wiegmann, D. A. (2004). A new look at the dynamic of human-automation trust: Is trust in humans compareable to trust in machines? Human Factors and Ergonomics Society Annual Meeting, 48(3), 581–585.

    Article  Google Scholar 

  37. Lee, J. E. R., & Nass, C. I. (2010). Trust in computers: The computers-are-social-actors (CASA) paradigm and trustworthiness perception in human-computer communication. In Trust and technology in a ubiquitous modern environment: Theoretical and methodological perspectives (pp. 1–15). IGI Global.

    Google Scholar 

  38. Nass, C., Takayama, L., & Brave, S. (2006). Social consistency: From technical homogeneity to human epitome. In P. Zhang & D. Galletta (Eds.), Human-computer interaction in management information systems: Foundations (pp. 373–391). M. E. Sharpe.

    Google Scholar 

  39. Hoff, K. A., & Bashir, M. (2015). Trust in automation. Integrating empirical evidence on factors that influence trust. Human Factors and Ergonomics Society, 57(3), 407–434.

    Article  Google Scholar 

  40. Atkinson, D., Hancock, P., Hoffman, R. R., Lee, J. D., Rovira, E., Stokes, C., & Wagner, A. R. (2012). Trust in computers and robots: The use and boundaries of the analogy of interpersonal trust. Human Factors and Ergonomics Society 56th Annual Meeting, 56(1), 303–307.

    Article  Google Scholar 

  41. Hoffmann, R. R., Bradshaw, J. M., & Johnson, M. (2013). Trust in automation. IEEE Intelligent Systems, 28(1), 84–88.

    Article  Google Scholar 

  42. Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623.

    Article  Google Scholar 

  43. Rempel, J. K., Holmes, J. G., & Zanna, M. P. (1985). Trust in close relationships. Journal of Personality and Social Psychology, 49, 95–112.

    Article  Google Scholar 

  44. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. The Journal of Human Factors and Ergonomics Society, 46(1), 50–80.

    Article  MathSciNet  Google Scholar 

  45. Hoehle, H., Huff, S., & Godde, S. (2012). The role of continuous trust in information systems continuance. Journal of Computer Information Systems, 52(4), 1–9.

    Google Scholar 

  46. Li, X., Hess, T. J., & Valacich, J. S. (2008). Why do we trust new technology? A study of initial trust formation with organizational information systems. The Journal of Strategic Information Systems, 17(1), 39–71.

    Article  Google Scholar 

  47. Borum, R. (2010). The science of interpersonal trust. Mental Health Law & Policy, 574, 1–80.

    Google Scholar 

  48. Suresh, S., & Sruthi, P. V. (2015). A review on smart home technology. In Online International Conference on Green Engineering and Technologies (IC-GET) (pp. 1–3). IEEE.

    Google Scholar 

  49. European Commission. (2015). Towards an integrated strategic energy technology (SET) plan: Accelerating the European Energy System Transformation. Accessed June 16, 2021, from https://ec.europa.eu/energy/sites/default/files/documents/1_EN_ACT_part1_v8_0.pdf

  50. European Commission. (2019). The strategic energy technology (SET) plan: At the heart of energy research and innovation in Europe. Accessed June 16, 2021, from https://op.europa.eu/en/publication-detail/-/publication/064a025d-0703-11e8-b8f5-01aa75ed71a1

  51. Harper, R. (2003). Inside the smart home. Springer.

    Book  Google Scholar 

  52. Möller, D. P. F., & Vakilzadian, H. (2014). Ubiquitous networks: Power line communication and Internet of things in smart home environments. In IEEE International Conference on Electro/Information Technology (pp. 596–601). IEEE.

    Chapter  Google Scholar 

  53. Bregman, D. (2010). Smart home intelligence – The eHome that learns. International Journal of Smart Home, 4(4), 35–46.

    MathSciNet  Google Scholar 

  54. Kabir, M. H., Hoque, M. R., Seo, H., & Yang, S.-H. (2015). Machine learning based adaptive context-aware system for smart home environment. International Journal of Smart Home, 9(11), 55–62.

    Article  Google Scholar 

  55. Lin, Y. (2015). Study of smart home system based on cloud computing and the key technologies. In International conference on computational intelligence and communication networks (pp. 968–972). IEEE.

    Google Scholar 

  56. Solaimani, S., Keijzer-Broers, W., & Bouwman, H. (2015). What we do—and don’t—know about the smart home: An analysis of the smart home literature. Indoor and Built Environment, 24(3), 370–383.

    Article  Google Scholar 

  57. Office for National Statistics. (2020). Population estimates for the UK, England and Wales, Scotland and Northern Ireland: Mid-2019. Accessed June 17, 2021, from https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/bulletins/annualmidyearpopulationestimates/mid2019estimates

  58. Ullman, J. B., & Bentler, P. M. (2003). Structural equation modeling. In J. A. Schinka, W. F. Velicer, & I. B. J. Weiner (Eds.), Handbook of psychology (Vol. 2, pp. 607–634). Wiley.

    Google Scholar 

  59. Weston, R., & Gore, P. A. (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5), 719–751.

    Article  Google Scholar 

  60. Pohlmann, J. T. (2004). Use and interpretation of factor analysis in The Journal of Educational Research: 1992-2002. The Journal of Educational Research, 98(1), 14–23.

    Article  Google Scholar 

  61. Mair, P. (2018). Modern psychometrics with R. Springer.

    Book  MATH  Google Scholar 

  62. Taber, K. S. (2018). The use of Cronbach’s Alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296.

    Article  Google Scholar 

  63. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s Alpha. International Journal of Medical Education, 2, 53–55.

    Article  Google Scholar 

  64. Osborne, J. W. (2015). What is rotating in exploratory factor analysis. Practical Assessment, Research, and Evaluation, 20(2), 1–8.

    Google Scholar 

  65. Bowen, N. K., & Guo, S. (2012). Structural equation modeling. Oxford University Press.

    Google Scholar 

  66. Jackson, D. L., & Gillaspy, J. A. (2009). Reporting practices in confirmatory factor analysis: An overview and some recommendations. Psychological Methods, 14(1), 6–23.

    Article  Google Scholar 

  67. Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34.

    Article  Google Scholar 

  68. Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2010). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323–338.

    Article  Google Scholar 

  69. Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods, 51, 409–428.

    Article  Google Scholar 

  70. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

    Article  Google Scholar 

  71. Bollen, K. A. (1987). Total, direct and indirect effects in structural equation models. Sociological Methodology, 17, 37–69.

    Article  Google Scholar 

  72. Holbert, R. L., & Stephenson, M. T. (2003). The importance of indirect effects in media effects research: Testing for mediation in structural equation modeling. Journal of Broadcasting and Electronic Media, 47(4), 556–572.

    Article  Google Scholar 

  73. Hox, J., & Bechger, T. (1998). An introduction to structural equation modeling. Family Science Review, 11, 354–373.

    Google Scholar 

  74. Baron, R. M., & Kenny, D. A. (1986). The moderator – mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

    Article  Google Scholar 

  75. Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420.

    Article  MathSciNet  Google Scholar 

  76. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83–104.

    Article  Google Scholar 

  77. Rucker, D. D., Preacher, K. J., Tormala, Z. L., & Petty, R. E. (2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5(4), 359–371.

    Article  Google Scholar 

  78. Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(1), 197–206.

    Article  Google Scholar 

  79. Cobb, M. D., & Macoubrie, J. (2004). Public perceptions about nanotechnology: Risks, benefits and trust. Journal of Nanoparticle Research, 6, 395–405.

    Article  Google Scholar 

  80. Nguyen, H. M., & Khoa, B. T. (2019). The relationship between the perceived mental benefits, online trust, and personal information disclosure in online shopping. Journal of Asian Finance, Economics and Business, 6(4), 261–270.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jörg Papenkordt .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Papenkordt, J., Thommes, K. (2023). Determinants of Trust in Smart Technologies. In: Röcker, C., Büttner, S. (eds) Human-Technology Interaction. Springer, Cham. https://doi.org/10.1007/978-3-030-99235-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-99235-4_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99234-7

  • Online ISBN: 978-3-030-99235-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics