Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data

We focus on predicting the adoption time probabilities of photo-voltaic solar panels by households using discrete choice experiments and an innovation diffusion model. The primary objective of this research is cohesively mapping the theory of disruptive innovation into diffusion of innovations to aid policy makers by linking two critical uncertainties of new technology: (1) whether households prefer the new attributes of the new technology and how these preferences vary by market segments? and (2) when are they going to adopt (if at all)? Our study uses recent developments of discrete choice experiments and establishes a causal link between the attributes of the technology, attitudinal constructs and socio-demographics, and adoption time probabilities using the Bass diffusion model. The data was collected from Ontario, a province of Canada. The innovation diffusion model allows us to compute the cumulative probability of adoption over time per household. Technology awareness and energy cost saving have a significant effect on the adoption probability, reinforcing the need for effective education. These findings also suggest that campaigns should explain more about investment criteria, feed-in tariffs and environmental attributes. This study findings call for a need to use seeding strategies to accelerate exogenous Word-of-Mouth (WOM) for this new technology.

[1]  M. Pentecost,et al.  The Innovator's Solution: Warum manche Unternehmen erfolgreicher wachsen als andere , 2018 .

[2]  Qiang Liu,et al.  A Micro-level Diffusion Model for New Drug Adoption , 2011 .

[3]  David A. Huettner,et al.  The Feasibility of Changing Electricity Consumption Patterns , 1981 .

[4]  Vijay Mahajan,et al.  Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance , 1982 .

[5]  Clayton M. Christensen Seeing What's Next , 2004 .

[6]  N. Meade,et al.  Modelling the dependence between the times to international adoption of two related technologies , 2003 .

[7]  P. Stern,et al.  Personal and contextual influences on househould energy adaptations. , 1985 .

[8]  V. Govindarajan,et al.  The Usefulness of Measuring Disruptiveness of Innovations Ex Post in Making Ex Ante Predictions* , 2006 .

[9]  Thomas A. Heberlein,et al.  The influence of price and attitude on shifting residential electricity consumption from on- to off-peak periods , 1983 .

[10]  Glen M. Schmidt LOW-END AND HIGH-END ENCROACHMENT STRATEGIES FOR NEW PRODUCTS , 2004 .

[11]  Vicki G. Morwitz,et al.  Using Segmentation to Improve Sales Forecasts Based on Purchase Intent: Which “Intenders” Actually Buy?: , 1992 .

[12]  Viswanath Venkatesh,et al.  Role of time in self-prediction of behavior , 2006 .

[13]  Nigel Meade,et al.  Forecasting the Development of the Market for Business Telephones in the UK , 1996 .

[14]  J. Hauser,et al.  Dynamic Analysis of Consumer Response to Marketing Strategies , 1982 .

[15]  Denzil G. Fiebig,et al.  Modelling multinational telecommunications demand with limited data , 2002 .

[16]  Towhidul Islam,et al.  Conceptual Relations Between Expanded Rank Data and Models of the Unexpanded Rank Data , 2012 .

[17]  J. Louviere,et al.  Determining the Appropriate Response to Evidence of Public Concern: The Case of Food Safety , 1992 .

[18]  M. Olsen,et al.  Consumers' Attitudes Toward Energy Conservation , 1981 .

[19]  P. Green Iteratively reweighted least squares for maximum likelihood estimation , 1984 .

[20]  N. Meade,et al.  The impact of attribute preferences on adoption timing: The case of photo-voltaic (PV) solar cells for household electricity generation , 2013 .

[21]  Nigel Meade,et al.  Using copulas to model repeat purchase behaviour - An exploratory analysis via a case study , 2010, Eur. J. Oper. Res..

[22]  Tax Credits as a Means of Influencing Consumer Behavior , 1981 .

[23]  Y. Trope,et al.  The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. , 1998 .

[24]  Shimon Awerbuch,et al.  Investing in photovoltaics: risk, accounting and the value of new technology , 2000 .

[25]  F. Bass A new product growth model for consumer durables , 1976 .

[26]  G. Loewenstein,et al.  Mixing virtue and vice: combining the immediacy effect and the diversification heuristic , 1999 .

[27]  L. G. Tornatzky,et al.  Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings , 1982, IEEE Transactions on Engineering Management.

[28]  Frank M. Bass,et al.  A New Product Growth for Model Consumer Durables , 2004, Manag. Sci..

[29]  Cheryl T. Druehl,et al.  When Is a Disruptive Innovation Disruptive , 2008 .

[30]  Willy C. Shih,et al.  Innovation killers: how financial tools destroy your capacity to do new things. , 2010, Harvard business review.

[31]  Koert van Ittersum,et al.  Cumulative Timed Intent: A New Predictive Tool for Technology Adoption , 2010 .

[32]  Ashish Sood,et al.  Demystifying Disruption: A New Model for Understanding and Predicting Disruptive Technologies , 2010, Mark. Sci..

[33]  Wayne S. DeSarbo,et al.  The Stochastic Modeling of Purchase Intentions and Behavior , 1998 .

[34]  Lyman E. Ostlund Perceived Innovation Attributes as Predictors of Innovativeness , 1974 .

[35]  Erwin Danneels Disruptive Technology Reconsidered: A Critique and Research Agenda , 2004 .

[36]  Denzil G. Fiebig,et al.  Modelling the development of supply-restricted telecommunications markets , 2001 .

[37]  Jordan J. Louviere,et al.  An introduction to the application of (case 1) best–worst scaling in marketing research , 2013 .

[38]  Evert Nieuwlaar,et al.  Energy viability of photovoltaic systems , 2000 .

[39]  James E. Long,et al.  An econometric analysis of residential expenditures on energy conservation and renewable energy sources , 1993 .

[40]  Staffan Jacobsson,et al.  The politics and policy of energy system transformation—explaining the German diffusion of renewable energy technology , 2006 .

[41]  Michael J. Walsh Energy tax credits and housing improvement , 1989 .

[42]  Eugene Borgida,et al.  The Differential Impact of Abstract vs. Concrete Information on Decisions , 1977 .

[43]  Adam Faiers,et al.  Consumer attitudes towards domestic solar power systems , 2006 .

[44]  Nigel Meade,et al.  The impact of competition, and economic globalization on the multinational diffusion of 3G mobile phones , 2012 .

[45]  P. Stern What psychology knows about energy conservation. , 1992 .

[46]  N. Meade,et al.  Modelling and forecasting the diffusion of innovation – A 25-year review , 2006 .

[47]  A. Jäger-Waldau,et al.  Photovoltaics and renewable energies in Europe , 2007 .

[48]  Frank M. Bass,et al.  Market Segmentation: Group versus Individual Behavior , 1968 .

[49]  G. Urban,et al.  Pre-Test-Market Evaluation of New Packaged Goods: A Model and Measurement Methodology , 1978 .

[50]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

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

[52]  J. David Tàbara,et al.  Harmonization of renewable electricity feed-in laws in the European Union , 2007 .

[53]  Clayton M. Christensen The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail , 2013 .

[54]  W. Poortinga,et al.  Household preferences for energy-saving measures: A conjoint analysis , 2003 .

[55]  Martin A. Green,et al.  Photovoltaics: technology overview , 2000 .

[56]  Jordan J. Louviere,et al.  Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information , 2008 .

[57]  S. Satchell,et al.  Retirement Investor Risk Tolerance in Tranquil and Crisis Periods: Experimental Survey Evidence , 2010 .

[58]  M. Held,et al.  Social impacts of energy conservation , 1983 .

[59]  E. Koukios,et al.  Simulation of acid-catalysed organosolv fractionation of wheat straw. , 2004, Bioresource technology.

[60]  Peter E. Rossi,et al.  A Bayesian Approach to Estimating Household Parameters , 1993 .

[61]  Wander Jager,et al.  Stimulating the diffusion of photovoltaic systems: A behavioural perspective , 2006 .

[62]  Don A. Dillman,et al.  Lifestyle and home energy conservation in the United States: the poor accept lifestyle cutbacks while the wealthy invest in conservation , 1983 .

[63]  Jim Watson,et al.  Strategies for the deployment of micro-generation: Implications for social acceptance , 2007 .

[64]  J. Louviere,et al.  The Role of the Scale Parameter in the Estimation and Comparison of Multinomial Logit Models , 1993 .

[65]  James Keirstead,et al.  Behavioural responses to photovoltaic systems in the UK domestic sector , 2007 .

[66]  J. Louviere,et al.  Some probabilistic models of best, worst, and best–worst choices , 2005 .

[67]  Geoffrey P. Hammond,et al.  Prospects for and barriers to domestic micro-generation: A United Kingdom perspective , 2008 .

[68]  M. Guidolin,et al.  Cross-country diffusion of photovoltaic systems: Modelling choices and forecasts for national adoption patterns , 2010 .

[69]  Sang-Hoon Kim,et al.  A Conjoint-Hazard Model of the Timing of Buyers' Upgrading to Improved Versions of High Technology Products , 2006 .

[70]  Donald R. Lehmann,et al.  A Meta-Analysis of Applications of Diffusion Models , 1990 .