Bridging the gap between sustainable technology adoption and protecting natural resources: Predicting intentions to adopt energy management technologies in California

Abstract To achieve energy savings, emerging energy management technologies and programs require customer adoption. Although a variety of models can be used to explain the adoption of energy management technologies and programs, they overlook the seemingly unconventional element of level of affiliation with nature. In fact, connectedness to nature has been identified as an important driver of many pro-environmental behaviors, but its role in pro-environmental technology adoption is also not well understood. Can affiliation with nature help to bridge the apparent gap—and complex chain of events—between sustainable technology adoption and protecting natural resources? Based on survey data from 856 southern California residents, this study investigated the influence of connectedness to nature and other factors on intentions to adopt five energy management technologies and programs: using three platforms to monitor home energy use (website, mobile phone application, in-home display); signing up for a time-of-use pricing plan; and participating in demand response events. Regression results showed that nature connectedness was the strongest predictor of all outcomes such that higher nature connectedness predicted greater likelihood of technology and program adoption. These findings suggest that connectedness to nature may facilitate “bridging the logic gap” between sustainable innovation adoption and environmental protection.

[1]  Mohamed M. Mostafa,et al.  Gender Differences in Egyptian Consumers Green Purchase Behaviour: The Effects of Environmental Knowledge, Concern and Attitude , 2007 .

[2]  Thomas C. Kinnear,et al.  Exploring the Consumer Decision Process in the Adoption of Solar Energy Systems , 1981 .

[3]  Susan Lutzenhiser.  Beyond the Price Effect in Time-of-Use Programs: Results from a Municipal Utility Pilot, 2007-2008 , 2009 .

[4]  R. Winett,et al.  Effects of self-monitoring and feedback on residential electricity consumption. , 1979, Journal of applied behavior analysis.

[5]  E. Gosling,et al.  Connectedness to nature, place attachment and conservation behaviour: Testing connectedness theory among farmers , 2010 .

[6]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

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

[8]  Rebecca Ford,et al.  Graphical Displays in Eco-Feedback: A Cognitive Approach , 2013, HCI.

[9]  Sudha Ram,et al.  “Forced” adoption of innovations in organizations: Consequences and implications , 1991 .

[10]  V. Venkatesh,et al.  AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE , 2000 .

[11]  Andrew K Przybylski,et al.  Can Nature Make Us More Caring? Effects of Immersion in Nature on Intrinsic Aspirations and Generosity , 2009, Personality & social psychology bulletin.

[12]  F. Mayer,et al.  Why Is Nature Beneficial? , 2009 .

[13]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[14]  A. E. Luloff,et al.  Connectivity With Nature as a Measure of Environmental Values , 2007 .

[15]  A. McCright,et al.  Politics and Environment in America: Partisan and Ideological Cleavages in Public Support for Environmentalism , 2001 .

[16]  P. Sheeran.  Intention—Behavior Relations: A Conceptual and Empirical Review , 2002 .

[17]  Viswanath Venkatesh,et al.  Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model , 2000, Inf. Syst. Res..

[18]  W. Abrahamse,et al.  How do socio-demographic and psychological factors relate to households' direct and indirect energy use and savings? , 2009 .

[19]  R. Kelly Rainer,et al.  The Influence of Individual Differences on Skill in End-User Computing , 1992, J. Manag. Inf. Syst..

[20]  Neveen Hamza,et al.  Renewable energy adoption in an ageing population: Heterogeneity in preferences for micro-generation technology adoption , 2011 .

[21]  D. Medin,et al.  Cultural mosaics and mental models of nature , 2007, Proceedings of the National Academy of Sciences.

[22]  Nicole D. Sintov,et al.  Unlocking the potential of smart grid technologies with behavioral science , 2015, Front. Psychol..

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

[24]  Daniel A. Mazmanian,et al.  California's Climate Change Policy , 2008 .

[25]  G. T. Gardner,et al.  Household actions can provide a behavioral wedge to rapidly reduce US carbon emissions , 2009, Proceedings of the National Academy of Sciences.

[26]  Tiffany Holmes,et al.  Eco-visualization: combining art and technology to reduce energy consumption , 2007, C&C '07.

[27]  Lilliard E. Richardson,et al.  Environmental Policy Attitudes: Issues, Geographical Scale, and Political Trust , 2008 .

[28]  B. Skinner,et al.  Science and human behavior , 1953 .

[29]  John E. Taylor,et al.  Investigating the impact eco-feedback information representation has on building occupant energy consumption behavior and savings , 2013 .

[30]  C. Vlek,et al.  Measurement and Determinants of Environmentally Significant Consumer Behavior , 2002 .

[31]  A. M. Lokhorst,et al.  There is an I in nature: The crucial role of the self in nature conservation , 2014 .

[32]  L. Steg,et al.  Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework , 2012 .

[33]  Sinnott Murphy,et al.  Analysis of High‐Resolution Utility Data for Understanding Energy Use in Urban Systems: The Case of Los Angeles, California , 2016 .

[34]  A. McCright,et al.  The Politicization of Climate Change and Polarization in the American Public's Views of Global Warming, 2001–2010 , 2011 .

[35]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[36]  Naresh K. Malhotra,et al.  Predicting System Usage from Intention and Past Use: Scale Issues in the Predictors , 2005, Decis. Sci..

[37]  Fred D. Davis.  Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[38]  S. Barr,et al.  The household energy gap: examining the divide between habitual- and purchase-related conservation behaviours , 2005 .

[39]  W. Abrahamse,et al.  Factors Related to Household Energy Use and Intention to Reduce It : The Role of Psychological and Socio-Demographic Variables , 2011 .

[40]  Chenyang Xiao,et al.  Gender differences in environmental behaviors in China , 2010 .

[41]  H. Friedman,et al.  Connectedness and Environmental Behavior: Sense of Interconnectedness and Pro-Environmental Behavior , 2011 .

[42]  W. Abrahamse,et al.  Social influence approaches to encourage resource conservation: A meta-analysis , 2013 .

[43]  D. Sperling,et al.  California's climate and energy policy for transportation , 2014 .

[44]  S. Bamberg,et al.  Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour , 2007 .

[45]  F. Mayer,et al.  The connectedness to nature scale: A measure of individuals’ feeling in community with nature ☆ , 2004 .

[46]  Aaron Johnson,et al.  Cross‐National Gender Variation in Environmental Behaviors , 2004 .

[47]  Miriam Fischlein,et al.  Information Strategies and Energy Conservation Behavior: A Meta-Analysis of Experimental Studies from 1975 to 2012 , 2013 .

[48]  David L. Kaserman,et al.  HOUSING MARKET CAPITALIZATION OF ENERGY‐SAVING DURABLE GOOD INVESTMENTS , 1983 .

[49]  P. Stern,et al.  The New Ecological Paradigm in Social-Psychological Context , 1995 .

[50]  B. Sovacool.  What Are We Doing Here? Analyzing Fifteen Years of Energy Scholarship and Proposing a Social Science Research Agenda , 2014 .

[51]  C. Vlek,et al.  Encouraging pro-environmental behaviour : An integrative review and research agenda , 2009 .

[52]  Ernst H. Noppers,et al.  The adoption of sustainable innovations: The role of instrumental, environmental, and symbolic attributes for earlier and later adopters , 2015 .

[53]  Chien-fei Chen,et al.  Energy at work: Social psychological factors affecting energy conservation intentions within Chinese electric power companies , 2014 .

[54]  Douglas L. Medin,et al.  Folkbiology of freshwater fish , 2006, Cognition.

[55]  James M. Walker.  Energy demand behavior in a master-metered apartment complex: An experimental analysis. , 1979 .

[56]  Leif Gustavsson,et al.  An adopter-centric approach to analyze the diffusion patterns of innovative residential heating systems in Sweden , 2008 .

[57]  Loren Lutzenhiser,et al.  Better home energy audit modelling: incorporating inhabitant behaviours , 2014 .

[58]  Jennifer J. Tabanico,et al.  Implicit connections with nature , 2004 .

[59]  N. Selwyn,et al.  Older adults' use of information and communications technology in everyday life , 2003, Ageing and Society.

[60]  C. Midden,et al.  Using feedback, reinforcement and information to reduce energy consumption in households: A field-experiment , 1983 .

[61]  E. Rogers.  Diffusion of Innovations , 1962 .