Modelling the factors that influence the adoption intention of a residential energy management information system (REIMS)

This research investigates the intention of residential energy users to adopt (accept and use) a Residential Energy Management Information Systems (REMIS) for better energy management, as well as the factors contributing to this intention. REMIS refer to a class of information systems (IS) designed to monitor, analyse, and benchmark residential energy consumption, in order to instigate behavioral change towards efficient energy use.<br><br>The study was motivated by three interrelated reasons. First, notwithstanding improved appliance energy efficiency and advances in Smart Home Technologies (SHTs), residential energy consumption is still a concern, due to lifestyle choices and behavioral issues associated with using more appliances and digital devices which lead to energy intensification. Second, while information systems (IS) can contribute to energy savings, major implementations of REMIS in the world (by Microsoft and Google) had failed because of lack of acceptance. Furthermore, resistance to smart meter roll-outs in Canada and Australia and a 10,000 respondent 2016 Gartner survey that indicated a low 9% REMIS adoption rate amongst owners of SHTs (in which REMIS is a component) highlight the need for investigating the likelihood of REMIS adoption. Third, existing studies on technological adoption do not draw from the entire spectrum of technological, economic, consumer and sustainability factors, and applies more to the work-place. Based on these issues, the research was set out to address four questions: (1) What is REMIS and its functional affordances?; (2) How likely are residents to adopt REMIS?; (3) What are the belief factors that affect intention to adopt REMIS?; and (4) What factors contribute to the formation of these beliefs?<br><br>Theoretically, the study draws from the theory of planned behavior (TPB), a framework of factors categorised as the Technology, Economic, Consumer, and Sustainability (TECS) factors, as well as four moderators (Household Age, Presence of Children, Household Size, and Apartment Size), underpinning a conceptual model used in a quantitative study. Envisaged by TPB, BI is a partial mediator of the actual behaviour of adopting REMIS, which would not otherwise be measurable directly when REMIS is not fully diffused. This is useful when the aim is to predict its adoption before introducing it or further diffusing it.<br><br>Empirically, under a positivistic paradigm, the study followed a sequential mixed method approach comprising of a first phase interview-based qualitative study and a second phase survey-based quantitative study. Thematic analysis of the interview data from the first phase exploratory study reinforced the choice of theory and the TECS framework, and together with a literature review of related research provided inputs for the development of the conceptual model and hypotheses. The second phase survey-based quantitative study validated the conceptual model and tested the hypotheses, addressing the four research questions. Items generated for the survey instrument were judged via Q-sorting by expert judges with a pilot run to further refine it. Data for the survey was collected from 209 apartment residents in Singapore in a governmental pilot REMIS trial. Examination and preparation of the data established the statistical basis for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to establish validity and reliability of the measurement and for further structural model testing.<br><br>The findings indicate that REMIS provides the ability to monitor, analyse and compare energy consumption. Attitude towards REMIS adoption (AT), subjective norm concerning REMIS adoption (SN) and perceived behavioural control over REMIS adoption (PBC) were found to positively influence intention to adopt REMIS (BI). Presence of children was found to moderate the relationship between AT and BI and between SN and BI while apartment size moderates the relationship between PBC and BI.<br>REMIS' affordability, perceived usefulness, perceived ease of use and supportive policies were found to contribute to the formation of attitude towards its adoption (AT). Compatibility and trialability of REMIS is positively associated to residents' perception of control over adopting REMIS (PBC). Residents' sense of environmental altruism and their perception of sustainability advertising were found to influence the development of normative beliefs to adopt REMIS (SN).<br><br>The conceptual model developed for the research explains 63% of the variance (R2) in BI and is among the highest variance explained in similar empirical research with R2 ranging from 32% to 76.9%.<br><br>A key contribution to theory from this research was the exploitation of a theoretical gap in existing REMIS adoption research that did not fully take advantage of adoption/diffusion theories such as the theory of planned behaviour (TPB), technology acceptance model (TAM), and diffusion of innovation (DOI) theory, and various related knowledge domains in environmentally significant behaviour, energy use behaviour, and Green IT/IS. This gap was filled by leveraging on TPB as the core theory, and by collating the full range of independent factors (of which some are new to technology adoption studies), collectively known as the TECS (Technology, Economy, Consumer, Sustainability) factors, as antecedent to TPB's beliefs constructs.<br><br>Insights drawn from implications to REMIS design, to policy/programs encouraging REMIS adoption, and to building an energy efficient community provided valuable inputs for improved REMIS design, efficient energy use education, and crafting of policies/programs to further diffuse REMIS. The research also uncovered opportunities for, and challenges to a green innovation diffusion. These contributions to practice benefit the stakeholder groups of energy regulators, energy service providers, town planners, housing developers, REMIS technology providers/marketers, and the residential energy users themselves. <br><br>Generalisability of the findings is limited due to the selected sample frame being a once-off study confined to a specific population, but this could be addressed via further cross-sectional studies. Proposals for further studies address these limitations, provide further research agenda incorporating emerging technologies, and look into unfulfilled energy informatics (EI) and Green IT/IS research agenda.

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