Adoption of Information Systems in the Electricity Sector: The Issue of Smart Metering

Given increases in electricity consumptions, coupled with finite resources and technological advances, ICT-enabled electrical networks such as smart grids are increasingly being deployed by energy companies. One part of smart grids is smart meters, which are digital electrical meters, having the potential to increase energy efficiency in both residential and industrial sectors. However, a challenge to smart meter implementation in residential settings is acceptability and adoption by the end-users (or consumers). Despite the acknowledged challenges in smart meter adoption, little academic research has been conducted on this topic. This study attempts to contribute towards that by developing a model of SMT adoption (drawing on existing literature on adoption behaviors and motivational psychology) and testing it using a survey of German consumers. Results highlight the important role played by factors such as internal and external locus of control (among others) on consumers’ intention to adopt SMT.

[1]  Lutz Kolbe,et al.  Smart Metering Information Management , 2011, Bus. Inf. Syst. Eng..

[2]  Ahmad Faruqui,et al.  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 , 2009 .

[3]  Detmar W. Straub,et al.  A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example , 2005, Commun. Assoc. Inf. Syst..

[4]  Viswanath Venkatesh,et al.  A Longitudinal Investigation of Personal Computers in Homes: Adoption Determinants and Emerging Challenges , 2001, MIS Q..

[5]  Edward L. Deci,et al.  Intrinsic Motivation and Self-Determination in Human Behavior , 1975, Perspectives in Social Psychology.

[6]  Paul A. Pavlou,et al.  Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003, Int. J. Electron. Commer..

[7]  John Hulland,et al.  Use of partial least squares (PLS) in strategic management research: a review of four recent studies , 1999 .

[8]  Daniel Veit,et al.  An Agent-Based Analysis of the German Electricity Market with Transmission Capacity Constraints , 2009 .

[9]  J. M. Hines,et al.  Analysis and synthesis of research on responsible environmental behavior: A meta-analysis. , 1987 .

[10]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[11]  J. Bettman.  Perceived Risk and Its Components: A Model and Empirical Test , 1973 .

[12]  S. Schwartz.  Normative Influences on Altruism , 1977 .

[13]  Paul A. Pavlou,et al.  Predicting E-Services Adoption: A Perceived Risk Facets Perspective , 2002, Int. J. Hum. Comput. Stud..

[14]  Patrick D. McDaniel,et al.  Security and Privacy Challenges in the Smart Grid , 2009, IEEE Security & Privacy.

[15]  E. Deci,et al.  Handbook of Self-Determination Research , 2002 .

[16]  Fred D. Davis,et al.  User Acceptance of Computer Technology: A Comparison of Two Theoretical Models , 1989 .

[17]  Viswanath Venkatesh,et al.  Model of Adoption and Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle , 2005, MIS Q..

[18]  I. Ajzen.  The theory of planned behavior , 1991 .

[19]  R. Bagozzi.  A Field Investigation of Causal Relations among Cognitions, Affect, Intentions, and Behavior , 1982 .

[20]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[21]  P. Stern.  New Environmental Theories: Toward a Coherent Theory of Environmentally Significant Behavior , 2000 .

[22]  Anol Bhattacherjee,et al.  Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model , 2006, MIS Q..

[23]  M. Csíkszentmihályi.  Flow: The Psychology of Optimal Experience , 1990 .

[24]  M. S. Cunningham.  The Major Dimensions of Perceived Risk , 1967 .

[25]  Izak Benbasat,et al.  Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation , 1991, Inf. Syst. Res..

[26]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[27]  D. F. Blankertz,et al.  Risk taking and information handling in consumer behavior , 1969 .

[28]  Fred D. Davis,et al.  Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1 , 1992 .

[29]  Susan A. Brown.  Household technology adoption, use, and impacts: Past, present, and future , 2008, Inf. Syst. Frontiers.

[30]  Philip H. Mirvis.  Flow: The Psychology of Optimal Experience , 1991 .

[31]  M. Csíkszentmihályi.  Flow. The Psychology of Optimal Experience. New York (HarperPerennial) 1990. , 1990 .

[32]  Robert J. Sternberg,et al.  The Theory of Successful Intelligence , 2000 .

[33]  Richard T. Watson,et al.  Information Systems and Environmentally Sustainable Development: Energy Informatics and New Directions for the IS Community , 2010, MIS Q..

[34]  P. Pavlou,et al.  Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model , 2003 .

[35]  R. Ryan,et al.  Perceived locus of causality and internalization: examining reasons for acting in two domains. , 1989, Journal of personality and social psychology.

[36]  C. Vlek,et al.  A review of intervention studies aimed at household energy conservation , 2005 .

[37]  Dennis F. Galletta,et al.  How Endogenous Motivations Influence User Intentions: Beyond the Dichotomy of Extrinsic and Intrinsic User Motivations , 2008, J. Manag. Inf. Syst..

[38]  Andrew B. Whinston,et al.  Research Commentary: Introducing a Third Dimension in Information Systems Design - The Case for Incentive Alignment , 2001, Inf. Syst. Res..

[39]  Aie.  Gadgets and Gigawatts : Policies for Energy Efficient Electronics , 2009 .

[40]  R. Staelin,et al.  A Model of Perceived Risk and Intended Risk-handling Activity , 1994 .