Skip to main content
SpringerLink
Log in
Book cover

International Conference on Business Informatics Research

BIR 2020: Perspectives in Business Informatics Research pp 3–18Cite as

The Acceptance of Smart Home Technology

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 398))

Abstract

The market for smart home technology (SHT) has increased rapidly and is said to do so during the next years. In particular, comfort and security features are the main focus of vendors. This paper aims to examine the different influencing factors that have an impact on the adoption decision of consumers. For this, a survey was conducted among 327 German consumers. Results show that perceived security and comfort are significant influencing factors. In particular, control functions play an important role. In contrast, neither usability of SHT nor costs show a noteworthy impact on the adoption decision, although costs are expected to be high.

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

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Splendid Research: Smart Home Monitor 2019 (2019). https://www.splendid-research.com/de/smarthome.html. Accessed 18 Feb 2020

  2. Splendid Research. Smart Home Monitor 2016 (2016). https://www.splendid-research.com/de/statistiken/item/studie-smart-home.html. Accessed 18 Feb 2020

  3. Statista: Smart Home - Deutschland (2019a). https://de.statista.com/outlook/279/137/smart-home/deutschland. Accessed 18 Feb 2020

  4. Balta-Ozkan, N., Davidson, R., Bicket, M., Whitmarsh, L.: Social barriers to the adoption of smart homes. Energy Policy 63, 363–374 (2013). https://doi.org/10.1016/j.enpol.2013.08.043

    Article  Google Scholar 

  5. Mittal, Y., Toshniwal, P., Sharma, S., Singhal, D., Gupta, R., Mittal, V.K.: A voice-controlled multi-functional smart home automation system. In: 2015 Annual IEEE India Conference (INDICON), pp. 1–6. IEEE (2015)

    Google Scholar 

  6. Hubert, M., Blut, M., Brock, C., Zhang, R., Koch, V., Riedl, R.: The influence of acceptance and adoption drivers on smart home usage. Eur. J. Mark. 53(6), 1073–1098 (2019). https://doi.org/10.1108/ejm-12-2016-0794

    Article  Google Scholar 

  7. Statista: Number of Digital Voice Assistants in Use Worldwide from 2019 to 2023 (in Billions)* (2019b). https://www.statista.com/statistics/973815/worldwide-digital-voice-assistant-in-use/. Accessed 19 Feb 2020

  8. Statista: Security - Worldwide (2019c). https://www.statista.com/outlook/281/100/security/worldwide. Accessed 19 Feb 2020

  9. Statista: Gebäudesicherheit (2019d). https://de.statista.com/outlook/281/137/gebaeudesicherheit/deutschland. Accessed 19 Feb 2020

  10. Demiris, G., Hensel, B.K.: Technologies for an aging society: a systematic review of ‘smart home’ applications. Yearb. Med. Inform. 17(01), 33–40 (2008). https://doi.org/10.1055/s-0038-1638580

    Article  Google Scholar 

  11. BDEW Bundesverband der Energie- und Wasserwirtschaft e.V.: Digitalisierung Aus Kundensicht (2017). https://www.bdew.de/energie/digitalisierung/der-digitale-kunde/. Accessed 10 Feb 2020

  12. Sponselee, A., Schouten, B., Bouwhuis, D., Willems, C.: Smart home technology for the elderly: perceptions of multidisciplinary stakeholders. In: Mühlhäuser, M., Ferscha, A., Aitenbichler, E. (eds.) AmI 2007. CCIS, vol. 11, pp. 314–326. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85379-4_37

    Chapter  Google Scholar 

  13. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989). https://doi.org/10.1055/s-0038-1638580

    Article  Google Scholar 

  14. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. JSTOR, 425–478 (2003). https://doi.org/10.2307/30036540

  15. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46(2), 186–204 (2000). https://doi.org/10.1287/mnsc.46.2.186.11926

    Article  Google Scholar 

  16. Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991). https://doi.org/10.1016/0749-5978(91)90020-t

    Article  Google Scholar 

  17. Jin Noh, M., Seong Kim, J.: Factors influencing the user acceptance of digital home services. Telecommun. Policy 34(11), 672–682 (2010)

    Article  Google Scholar 

  18. Gaul, S., Ziefle, M.: Smart home technologies: insights into generation-specific acceptance motives. In: Holzinger, A., Miesenberger, K. (eds.) USAB 2009. LNCS, vol. 5889, pp. 312–332. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10308-7_22

    Chapter  Google Scholar 

  19. Nguyen, T.-H.-L., Duong, M.-H., Nguyen, H.-D., Vo, D.-H., Vu, D.-P.-T., Le, H.-S.: Key determinants for users intention to use smarthome devices in Vietnam. J. Multimed. Inf. Syst. 5(4), 283–290 (2018). https://doi.org/10.9717/jmis.2018.5.4.283

    Article  Google Scholar 

  20. Yang, H., Lee, H., Zo, H.: User Acceptance of Smart Home Services: An Extension of the Theory of Planned Behavior. Industrial Management & Data Systems. Emerald Publishing Limited, Bingley (2017)

    Google Scholar 

  21. Park, E., Kim, S., Kim, Y.S., Kwon, S.J.: Smart home services as the next mainstream of the ICT industry: determinants of the adoption of smart home services. Univers. Access Inf. Soc. 17(1), 175–190 (2017). https://doi.org/10.1007/s10209-017-0533-0

    Article  Google Scholar 

  22. Shih, T.-Y.: Determinates of consumer adoption attitudes: an empirical study of smart home services. Int. J. E-Adopt. (IJEA) 5(2), 40–56 (2013). https://doi.org/10.4018/jea.2013040104

    Article  Google Scholar 

  23. Bao, H., Chong, A.Y., Ooi, K., Lin, B.: Are Chinese consumers ready to adopt mobile smart home? An empirical analysis. Int. J. Mob. Commun. 12(5), 496–511 (2014). https://doi.org/10.1504/ijmc.2014.064595

    Article  Google Scholar 

  24. Rogers, E.M.: Diffusion of Innovations, 5th edn. Free Press, New York (2003). Social Science

    Google Scholar 

  25. Featherman, M.S., Pavlou, P.A.: Predicting E-services adoption: a perceived risk facets perspective. Int. J. Hum Comput Stud. 59(4), 451–474 (2003). https://doi.org/10.1016/s1071-5819(03)00111-3

    Article  Google Scholar 

  26. Kjellén, U., Sklet, S.: Integrating analyses of the risk of occupational accidents into the design process Part I: a review of types of acceptance criteria and risk analysis methods. Saf. Sci. 18(3), 215–227 (1995). https://doi.org/10.1016/0925-7535(94)00053-6

    Article  Google Scholar 

  27. Wirtz, J., Chew, P., Lovelock, C.: Essentials of Services Marketing, 2nd edn. (2012)

    Google Scholar 

  28. Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results, Massachusetts Institute of Technology (1985)

    Google Scholar 

  29. Chau, P., Hu, P.: Examining a model of information technology acceptance by individual professionals: an exploratory study. J. Manag. Inf. Syst. 18, 191–229 (2002)

    Article  Google Scholar 

  30. Ma, Q., Liu, L.: The technology acceptance model: a meta-analysis of empirical findings. JOEUC 16, 59–72 (2004). https://doi.org/10.4018/978-1-59140-474-3.ch006

    Article  Google Scholar 

  31. Lee, Y., Kozar, K.A., Larsen, K.R.T.: The technology acceptance model: past, present, and future. Commun. Assoc. Inf. Syst. 12(1), 50 (2003). https://doi.org/10.17705/1cais.01250

    Article  Google Scholar 

  32. Gentry, L., Calantone, R.: A comparison of three models to explain shop-bot use on the web. Psychol. Mark. 19(11), 945–956 (2002). https://doi.org/10.1002/mar.10045

    Article  Google Scholar 

  33. Mathieson, K., Peacock, E., Chin, W.W.: Extending the technology acceptance model: the influence of perceived user resources. ACM SigMIS Database 32(3), 86–112 (2001). https://doi.org/10.1145/506724.506730

    Article  Google Scholar 

  34. Wong, J.K.W., Leung, J., Skitmore, M., Buys, L.: Technical requirements of age-friendly smart home technologies in high-rise residential buildings: a system intelligence analytical approach. Autom. Constr. 73, 12–19 (2017). https://doi.org/10.1016/j.autcon.2016.10.007

  35. Hair Jr., J.F., Hult, G.T.M., Ringle, C., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage publications, Thousand Oaks (2016)

    Google Scholar 

  36. Chin, W., Newsted, P.: Structural equation modeling analysis with small samples using partial least square. Stat. Strat. Small Sample Res. 1, 307–341 (1999)

    Google Scholar 

  37. Reinartz, W., Haenlein, M., Henseler, J.: An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int. J. Res. Mark. 26, 332–344 (2009). https://doi.org/10.1016/j.ijresmar.2009.08.001

    Article  Google Scholar 

  38. Ringle, C.M., Wende, S., Becker, J.-M.: SmartPLS 3. SmartPLS GmbH, Boenningstedt (2015)

    Google Scholar 

  39. Lowry, P.B., Gaskin, J.: Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: when to choose it and how to use it. IEEE Trans. Prof. Commun. 57(2), 123–146 (2014)

    Article  Google Scholar 

  40. Jarvis, C.B., MacKenzie, S.B., Podsakoff, P.M.: A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consum. Res. 30(2), 199–218 (2003). https://doi.org/10.1086/376806

    Article  Google Scholar 

  41. Chin, W.W.: The partial least squares approach to structural equation modeling. Mod. Methods Bus. Res. 295(2), 295–336 (1998)

    Google Scholar 

  42. Hair Jr., J.F., Sarstedt, M., Hopkins, L., Kuppelwieser, V.: Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research. Eur. Bus. Rev. 26(2), 106–121 (2014). https://doi.org/10.1108/ebr-10-2013-0128

    Article  Google Scholar 

  43. Nunnally, J.C., Bernstein, I.H.: Psychometric Theory, United States (1994)

    Google Scholar 

  44. Henseler, J.: Partial least squares path modeling. In: Leeflang, P.S.H., Wieringa, J.E., Bijmolt, T.H.A., Pauwels, K.H. (eds.) Advanced Methods for Modeling Markets. ISQM, pp. 361–381. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53469-5_12

    Chapter  Google Scholar 

  45. Henseler, J., Ringle, C.M., Sarstedt, M.: A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43(1), 115–135 (2014). https://doi.org/10.1007/s11747-014-0403-8

    Article  Google Scholar 

  46. Sarstedt, M., Ringle, C.M., Smith, D., Reams, R., Hair Jr., J.F.: Partial least squares structural equation modeling (PLS-SEM): a useful tool for family business researchers. J. Fam. Bus. Strat. 5(1), 105–115 (2014). https://doi.org/10.1016/j.jfbs.2014.01.002

  47. Kock, N.: Common method bias in PLS-SEM: a full collinearity assessment approach. Int. J. E-Collab. 11, 1–10 (2015). https://doi.org/10.4018/ijec.2015100101

    Article  Google Scholar 

  48. Herzberg, F., Mausner, B., Snyderman, B.: Work and motivation, behaviour science concepts and management application. Stud. Pers. Policy (216) (1959)

    Google Scholar 

  49. Turner, M., Kitchenham, B., Brereton, P., Charters, S., Budgen, D.: Does the technology acceptance model predict actual use? A systematic literature review. Inf. Softw. Technol. 463–479 (2010). https://doi.org/10.1016/j.infsof.2009.11.005

  50. Badica, C., Brezovan, M., Badica, A.: An overview of smart home environments: architectures, technologies and applications. In: Georgiadis, C.K., Kefalas, P., Stamatis, D. (eds.) BCI (Local), p. 78. CEUR-WS.org (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TU Dortmund, Otto-Hahn-Str. 12, 44227, Dortmund, Germany

    Christina Gross, Markus Siepermann & Richard Lackes

Authors
  1. Christina Gross
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Markus Siepermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Richard Lackes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christina Gross .

Editor information

Editors and Affiliations

  1. Babeș-Bolyai University, Cluj Napoca, Romania

    Robert Andrei Buchmann

  2. University of Camerino, Camerino, Italy

    Andrea Polini

  3. Linköping University, Linköping, Sweden

    Björn Johansson

  4. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gross, C., Siepermann, M., Lackes, R. (2020). The Acceptance of Smart Home Technology. In: Buchmann, R.A., Polini, A., Johansson, B., Karagiannis, D. (eds) Perspectives in Business Informatics Research. BIR 2020. Lecture Notes in Business Information Processing, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-030-61140-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61140-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61139-2

  • Online ISBN: 978-3-030-61140-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation