The Role of Perceived Control, Enjoyment, Cost, Sustainability and Trust on Intention to Use Smart Meters: An Empirical Study Using SEM-PLS

  • Ahmed Shuhaiber
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


Smart Meters are capable of collecting, storing, and analyzing electricity consumption data in real-time and of electronically transmitting data between the electricity provider and the electricity end user. Despite its potential, smart meter technology is in its early adoption stage in many developing countries, and little is known about residents’ acceptance and usage of smart meters in those countries. Thus, this research aimed to fill this gap by studying the important factors that influence residents’ intentions to use smart meters in Jordan. A quantitative approach was followed by obtaining 242 survey responses and statistically testing the associated hypotheses using SEM-PLS. Results revealed that perceived control, perceived enjoyment, sustainability and trust can significantly and positively influence residents’ intentions to use smart meters. However, perceived cost was not found to have a significant negative influence on intention to use. Theoretical and practical implications are indicated, and directions of future research are specified afterwards.


Energy consumption Intention to use Smartgrids Smart meters 


  1. Bertoldo, R., Poumadère, M., Rodrigues Jr., L.C.: When meters start to talk: the public’s encounter with smart meters in France. Energy Res. Soc. Sci. 9, 146–156 (2015)CrossRefGoogle Scholar
  2. Fornell, C., Larcker, D.: Structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981)CrossRefGoogle Scholar
  3. Chin, W.W.: The partial least squares approach for structural equation modeling. In: Marcoulides, G.A. (ed.) Modern Methods for Business Research, pp. 295–336. Lawrence Erlbaum Associates Publishers, Mahwah (1998)Google Scholar
  4. Chou, J., Yutami, I.G.A.N.: Smart meter adoption and deployment strategy for residential buildings in Indonesia. Appl. Energy 128, 336–349 (2014)CrossRefGoogle Scholar
  5. Chou, J.S., Kim, C., Ung, T.K., Yutami, I.G.A.N., Lin, G.T., Son, H.: Cross-country review of smart grid adoption in residential buildings. Renew. Sustain. Energy Rev. 48, 192–213 (2015)CrossRefGoogle Scholar
  6. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35(8), 982–1003 (1989)CrossRefGoogle Scholar
  7. Dobelt, S., Jung, M., Busch, M., Tscheligi, M.: Consumers’ privacy concerns and implications for a privacy preserving smart grid architecture—results of an Austrian study. Energy Res. Soc. Sci. 9, 137–145 (2015)CrossRefGoogle Scholar
  8. Gerpott, T.J., Paukert, M.: Determinants of willingness to pay for smart meters: an empirical analysis of household customers in Germany. Energy Policy 61, 483–495 (2013)CrossRefGoogle Scholar
  9. Huang, C., Kao, Y.: UTAUT2 based predictions of factors influencing the technology acceptance of phablets by DNP. Math. Probl. Eng. 2015, 1–23 (2015)Google Scholar
  10. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling. Sage, Thousand Oaks (2014)zbMATHGoogle Scholar
  11. Kranz, J., Gallenkamp, J.V., Picot, A.: Exploring the role of control-smart meter acceptance of residential consumers. In: AMCIS, p. 315, August 2010Google Scholar
  12. Krishnamurti, T., Schwartz, D., Davis, A., Fischhoff, B., de Bruin, W., Lave, L., Wang, J.: Preparing for smart grid technologies: a behavioral decision research approach to understanding consumer expectations about smart meters. Energy Policy 41, 790–797 (2012)CrossRefGoogle Scholar
  13. Mah, D., van der Vleuten, J., Hills, P., Tao, J.: Consumer perceptions of smart grid development: results of a Hong Kong survey and policy implications. Energy Policy 49, 204–216 (2012)CrossRefGoogle Scholar
  14. Neuman, W.L.: Social Research Methods: Quantitative and Qualitative Approaches, 6th edn. Allyn & Bacon, Boston (2005)Google Scholar
  15. Pickard, A.J.: Research methods in information: facet (2007)Google Scholar
  16. Pratt, R.G., Balducci, P.J., Gerkensmeyer, C., Katipamula, S., Kintner-Meyer, M.C., Sanquist, T.F., Secrest, T.J.: The smart grid: an estimation of the energy and CO2 benefits: Pacific Northwest National Laboratory (PNNL), Richland, WA (US) (2010)Google Scholar
  17. Shuhaiber, A., Lehmann, H.: Exploring customer trust in B2C mobile payments–a qualitative study. World Acad. Sci. Eng. Technol. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 7(5), 1128–1135 (2013)Google Scholar
  18. Shuhaiber, A., Lehmann, H., Hooper, T.: Positing a factorial model for consumer trust in mobile payments. In: Information System Development, pp. 397–408. Springer, Cham (2014)Google Scholar
  19. Wissner, M.: The smart grid – a saucerful of secrets? Appl. Energy 88(7), 2509–2518 (2011)CrossRefGoogle Scholar
  20. Zhou, S., Brown, M.: Smart meter deployment in Europe: a comparative case study on the impacts of national policy schemes. J. Clean. Prod. 144, 22–32 (2017)CrossRefGoogle Scholar
  21. Zikmund, W.G., Babin, B.J.: Exploring Marketing Research. Thomson/South-Western, Mason (2007)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Management and MIS Department, College of BusinessAl Ain University of Science and TechnologyAbu DhabiUnited Arab Emirates

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