Skip to main content

Advertisement

SpringerLink
Log in

Implications of customer value perceptions for the design of electricity efficiency services in times of smart metering

  • Original Article
  • Published:
Information Systems and e-Business Management Aims and scope Submit manuscript

Abstract

Smart meters are the backbone of modern electricity metering and an important enabler of reaching energy efficiency targets. The implementation of new metering infrastructure is, however, making little progress and is often focused on technical aspects only. Additionally, existing smart metering information systems do not yet exploit the possibilities to optimally support customers in their electricity savings activities. Knowing customer preferences is absolutely essential for the effectiveness of energy efficiency measures and, as a consequence, for realizing the economic value of smart metering technology. The presented research contributes to the field by identifying customer value perceptions concerning new smart meter services in the retail electricity market in Switzerland. Founded on a choice-based conjoint analysis with a data sample of more than 1500 respondents from three Swiss regions, five customer segments with different preferences are identified. With the exception of the comfort-oriented customer segment, the other four segments are comprised of customers who are willing (1) to pay for smart meter services and (2) to change their behavior to save electricity. Based on the identified customer value perceptions, implications for the design of smart meter-based energy efficiency services are elaborated.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. The amount of the basic fee per month differs significantly between the attributes, while the different tariff options themselves sum always up to the same amount for a standard load profile customer. A customer can therefore only be better off with a given tariff model, if he is willing to change his behavior significantly compared to the standard load profile of a customer. We do therefore not have two pricing attributes in the same questionnaire but one pricing attribute and one tariff attribute that allows to check the willingness to change to a new tariff model. The utility changing costs can therefore be directly used to identify the customers’ willingness to pay (calculated in comparison to the basic fee).

References

  • Baeriswyl M, Müller A, Rigassi R, Rissi C, Solenthaler S, Staake T, Weisskopf T (2012) Folgeabschätzung einer Einführung von « Smart Metering » im Zusammenhang mit « Smart Grids » in der Schweiz. http://www.news.admin.ch/NSBSubscriber/message/attachments/27519.pdf. Accessed 10 April 2013

  • Becker D, Wichmann J, Bader N (2012) Effizienzmodell für Schweizer Stromlieferanten. http://www.bfe.admin.ch/php/modules/publikationen/stream.php?extlang=de&name=de_250779416.pdf. Accessed 6 Oct 2014

  • Colmenar-Santos A, Campíñez-Romero S, Pérez-Molina C, Castro-Gil M (2012) Profitability analysis of grid-connected photovoltaic facilities for household electricity self-sufficienc. Energy Policy 51(2012):749–764

    Article  Google Scholar 

  • Darby S (2006) The effectiveness of feedback on energy consumption. Oxford

  • Dellaert B, Borgers A, Timmermans H (1996) Conjoint choice models of joint participation and activity choice. Int J Res Mark 13(3):251–264

    Article  Google Scholar 

  • Directive/27/Eu (2012) Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on energy efficiency, amending directives 2009/125/EC and 2010/30/EU and repealing directives 2004/8/EC and 2006/32/EC text with EEA relevance

  • Ehrhardt-Martinez K, Donnelly KA, Laitner JA (2010) Advanced metering initiatives and residential feedback programs: a meta-review for household electricity-saving opportunities. Americal Council for an Energy-Efficient Economy, Washington

    Google Scholar 

  • European Commission (2011) MEMO/11/440. Brussels

  • Faruqui A, George S (2005) Quantifying customer response to dynamic pricing. Electr J 18(4):53–63

    Article  Google Scholar 

  • Faruqui A, Sergici S (2008) The power of experimentation. San Francisco

  • Faruqui A, Hledik R, Sergici S (2009) Piloting the smart grid. Electr J 22(7):55–69

    Article  Google Scholar 

  • Faruqui A, Harris D, Hledik R (2010) Unlocking the €53 billion savings from smart meters in the EU: how increasing the adoption of dynamic tariffs could make or break the EU’s smart grid investment. Energy Policy 38(10):6222–6231

    Article  Google Scholar 

  • Fischer C (2008) Feedback on household electricity consumption: a tool for saving energy? Energ Effic 1(1):79–104

    Article  Google Scholar 

  • Flath C, Nicolay D, Conte T, van Dinther C, Filipova-Neumann L (2012) Cluster analysis of smart metering data. WIRTSCHAFTSINFORMATIK 54(1):33–42

    Article  Google Scholar 

  • Goebel C, Jacobsen H-A, del Razo V, Doblander C, Rivera J, Ilg J, Lässig J (2014) Energy informatics—current and future research directions. Bus Inform Syst Eng 6(1):25–31

    Article  Google Scholar 

  • Green PE, Srinivasan V (1978) Conjoint analysis in consumer research: issues and outlook. J Consum Res 5(2):103–123

    Article  Google Scholar 

  • Green PE, Srinivasan V (1990) Conjoint analysis in marketing: new developments with implications for research and practice. J Market 54(4):3–19

    Article  Google Scholar 

  • Hair JF, Black WC, Babin BJ, Anderson RE (2009) Multivariate data analysis, 7th edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Jasperson J, Carter PE, Zmud RW (2005) A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work Systems. MIS Q 29(3):525–557

    Google Scholar 

  • Kaufmann S, Künzel K, Loock M (2013) Customer value of smart metering: explorative evidence from a choice-based conjoint study in Switzerland. Energy Policy 53:229–239

    Article  Google Scholar 

  • Kim H-W, Chan HC, Gupta S (2007) Value-based adoption of mobile internet: an empirical investigation. Decis Support Syst 43(1):111–126

    Article  Google Scholar 

  • Kuan-Yu L, Hsi-Peng L (2011) Why people use social networking sites: an empirical study integrating network externalities and motivation theory. Comput Hum Behav 27(3):1152–1161

    Article  Google Scholar 

  • Kuo Y-F, Wu C-M, Deng W-J (2009) The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Comput Hum Behav 25(4):887–896

    Article  Google Scholar 

  • Larsen TJ, Sørebø AM, Sørebø Ø (2009) The role of task-technology fit as users’ motivation to continue information system use. Comput Hum Behav 25(3):778–784

    Article  Google Scholar 

  • Loock M, Staake T, Thiesse F (2013) Motivating energy-efficient behavior with green IS: an investigation of goal setting and the role of defaults. MIS Q 37(4):1313–1332

    Article  Google Scholar 

  • Louviere JJ (1988) Analyzing decision making: metric conjoint analysis

  • Mack B (2007) Energiesparen fürdern durch psychologische intervention. Waxmann Verlag, New York

    Google Scholar 

  • Magidson J, Eagle T, Vermunt JK (2003) New developments in latent class choice models. In: Sawtooth Software Conference Proceedings

  • Malhotra A, Melville NP, Watson RT (2013) Special issue: spurring impactful research on information systems for environmental sustainability. MIS Q Manag Inform Syst 37(4):1265–1274

    Article  Google Scholar 

  • Messerschmidt CM, Lilienthal M, Skiera B (2010) The utility of TAM-perceptions: integration of technology perceptions into choice-based conjoint analysis. ACIS, Thiruvananthapuram

    Google Scholar 

  • Muench S, Thuss S, Guenther E (2014) What hampers energy system transformations? The case of smart grids. Energy Policy 73:80–92

    Article  Google Scholar 

  • Nachreiner M, Mack B, Matthies E, Tampe-Mai K (2015) An analysis of smart metering information systems: a psychological model of self-regulated behavioural change (to be published). Energy Res Soc Sci 9:85–97

    Article  Google Scholar 

  • Owen HH (2008) Open space technology: a user’s guide, 3rd edn. ReadHowYouWant, San Francisco

    Google Scholar 

  • Römer B, Reichhart P, Kranz J, Picot A (2012) The role of smart metering and decentralized electricity storage for smart grids: the importance of positive externalities. Energy Policy 50:486–495

    Article  Google Scholar 

  • Sawtooth Software Inc (2007) Latent class v4. http://www.sawtoothsoftware.com/download/ssiweb/LClass_Manual.pdf. Accessed 16 Nov 2012

  • vom Brocke J, Simons A, Niehaves B, Riemer K, Plattfaut R, Cleven A (2009) Reconstructing the giant: on the importance of rigour in documenting the literature search process. In: Paper presented at the 17th European conference on information systems, Verona

Download references

Acknowledgments

This project is partially supported by the Swiss Commission for Technology and Innovation (CTI).

Author information

Authors and Affiliations

  1. Institute of Information Management, University of St.Gallen, Unterer Graben 21, 9000, St.Gallen, Switzerland

    Antonia Albani, Yannic Domigall & Robert Winter

Authors
  1. Antonia Albani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yannic Domigall
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert Winter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antonia Albani.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Albani, A., Domigall, Y. & Winter, R. Implications of customer value perceptions for the design of electricity efficiency services in times of smart metering. Inf Syst E-Bus Manage 15, 825–844 (2017). https://doi.org/10.1007/s10257-016-0332-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10257-016-0332-9

Keywords

Search

Navigation