Project Background

Relationships with End-Users

Oversight of the project is guided by a Reference Panel which has representation from four major potential groups of end-users: the Government (EECA), energy retailers (Mercury Energy), local authorities (Dunedin City Council), and consumer advice agencies (The Citizens Advice Bureau). The Reference Panel meets bi-monthly throughout the project to provide guidance and information exchange throughout the research. 



Our methodology is based on the integrated application of several social science techniques developed to study human behaviour in order to obtain a comprehensive understanding of consumer energy cultures. The team implements these techniques through a single, integrated objective in order to ensure each approach reliably and effectively informs the others.

Energy Cultures uses a laddering technique to identify householder values (1). This is a semistructured qualitative method of inquiry that uses in-depth interviews to probe deeply for the reasons for behaviour. It constructs means-end chains that display the links from behaviours to important product attributes to attitudes, and ultimately to underlying personal values. It requires formal text analysis methods such as those that are possible with analytical software such as NVIVO (2).

The project also uses choice modelling to quantitatively estimate the willingness of householders to make tradeoffs across the values identified with laddering. Choice modelling requires a householder to work through a series of hypothetical comparisons of product attributes, each of which requires the householder to make a trade-off. The choices made allow estimation of the relative value, or “utility,” the householder places on each attribute of the choices made. Choice modelling has been demonstrated to provide more reliable predictions of behavioural responses to actual changes in the respondent’s situation than conventional surveying methods (3).

Our choice modelling will utilise the unique 1000Minds software developed at Otago University. 1000Minds offers the advantages of simplified research designs, a webbased interface, and more detailed estimates at the individual level (4).

The choice modelling research will occur in tandem with household surveys. These will follow a format similar to surveys already trialed by research team members in two separate research exercises in Auckland and Dunedin. A variety of information will be collected, including house and householder characteristics, space and water heating, appliances and insulation, heating behaviours, recent changes in technologies or behaviours, and the locations from which householders source their energy-related information. This data will be used to help build the picture of ‘energy cultures’ in combination with the values and choice-modelling results.

The interviews and surveys will reveal information at the level of the individual household. Each of these households operates in a broader legal and social context that influences its behaviour. Energy Cultures investigates these broader influences in two ways:

  1. Professor Barton is leading an analysis of the influence of laws, regulation and public policies on household energy use for space and water heating using comparative techniques, and drawing from his extensive knowledge of international policy/institutional settings (5,6). The result will be a systematic appraisal of the ability of existing policy settings to achieve household energy efficiency targets.
  2. A broad understanding of how drivers of behaviour are experienced at a societal level will be achieved using ‘soft systems’ methodology (SSM) (7), an approach well tailored to the investigation of complex societal situations, and widely used internationally. Particular strengths of SSM are its ability to identity relationships among social attributes and potentially productive intervention points. SSM has so far had limited application within New Zealand, although it is currently being applied by Synergia to investigate complex health issues, such as obesity and mental well-being (the latter funded by HRC). Synergia is an Auckland-based consultancy with a track record in applying systems thinking and organizational learning theory to the analysis of complex social issues. Synergia will be subcontracted to work with our SSM research stream.

Analysis of the information collected will reveal the factors that appear to most constrain uptake of energy technologies and behaviours for different ‘energy cultures.’ Fundamental values may be the driving force for some households, while market, financial, or even regulatory constraints may be the critical factors for others.

To take account of these wider issues, Energy Cultures will conduct a comprehensive evaluation of the energy-relevant policy and regulatory environment in New Zealand. This review uses conventional desk and library research methods employed in law and policy studies.

While these methods are designed to provide a broad and integrated examination of the factors around energy decisions and behaviours, the constraints of a short programme require that we select particular behaviours as a focus for our study. In consultation with our end users, we have chosen to focus on two areas of energy behaviour: space heating and water heating. These have been selected because:

  1. Together they account for around 60% of potential household savings from greater energy efficiencies;
  2. New technologies are being developed that could offer major opportunities for change, such as hot-water heat pumps;
  3. They offer a wide variety of options for both investing in new technologies and changing behaviours; and
  4. They differ with respect to the ‘rebound effect.’

The ‘rebound effect’ occurs because an increase in the energy-efficiency of an appliance reduces the cost per unit of the service the appliance supplies. For example, replacing standard resistance heaters with a heat pump reduces the cost of a given amount of heat. Consumers sensibly respond to the lower cost by heating more of their house to higher temperatures for longer time periods. Consequently, the demand for energy falls by less than suggested by the increase in energy efficiency. Research in North America and Western Europe indicates rebound effects in residential space heating of 10 to 30% (8,9).

The rebound effect in space heating is likely to be higher in New Zealand because a large proportion of the housing stock is poorly insulated and inefficiently heated. In a sample of inefficiently heated New Zealand houses, installation of a more energy-efficient heating appliance (pellet stove or heat pump) resulted in an average rebound closer to 100%. Households on average maintained expenditure levels to achieve warmer and drier indoor environments (10). Energy conservation for space heating in New Zealand is unlikely to reach North American and European levels until average household temperatures increase overall. In contrast, because most houses have water cylinders that meet basic international standards, increased efficiencies in water heating are likely to result in greater energy conservation.

This combination of qualitative and quantitative approaches enables us to both research particular case studies in depth, and to obtain national data on some aspects of the project. While we cannot target every possible geographic region for the qualitative studies, we are concentrating the laddering, household surveys, and soft-systems work in three contrasting communities. These are:

  1. North East Valley in Dunedin. This is an area of poorly insulated older housing with diverse population characteristics. The area has been targeted by the local council for the implementation of specific upgrades to improve heating efficiency and the quality of the home environment.
  2. Eastern Auckland. This is an ethnically and economically diverse area with generally newer housing stock than our Dunedin location. One key reason for selecting this area is the availability of households with smart meters. These meters allow monitoring of electricity consumption at up to half hourly intervals. This potentially enables investigation of the importance of timely information about electricity usage on consumer behaviour.
  3. Cambridge. This is a rural service town. This town will contrast in demographics, income, and housing stock with our two urban locations.

For the quantitative studies, Energy Cultures uses a dual sampling method that allows for sufficient responses from each community to nationally generalise our findings. The sample sizes for the choice modelling and the household survey is 300 households from each of the study areas and 1,000 for the remaining national sample.

The sample size for the laddering is determined not by estimated error rates and variance but by saturation: the sample is augmented as long as new values and means/end structures are identified. Existing research suggests that for any one behaviour, 10-12 interviews are usually sufficient (11). Covering more than one specific behaviour in each interview, and a range of behaviour variations for efficiencies in hot water and space heating, we need approximately 50 in-depth interviews in each of the three study areas.

In the SSM applications, we use structured group discussions to model how people within a particular community think about and use energy in their households.

The differences between existing models and a desired future state are contrasted, and barriers to change are identified. Interventions are devised with the help of the respondents so that their objectives can be reconciled with the imperatives for securing gains in energy efficiency. At least two groups will be established in each of the three study areas, each of which will be brought together on two occasions to model the existing system and to evaluate possible changes required to meet the desired state.

Energy Cultures will complete all inquiries mentioned above during the first two years of the study. On the basis of an understanding of behavioural drivers and how they interact, the characteristics of effective interventions will be defined. These interventions may require regulatory or policy change to implement, or may involve information or other means of influencing household behaviour. The third year will focus on the design of an intervention strategy based on our findings, and piloting of at least one of these interventions. We will work directly with our end users, including through staff exchanges, to design a range of interventions that could encourage specific improvements in household energy efficient behaviours. We will identify key barriers to household behaviour change where interventions are likely to be most successful within and across a variety of ‘energy cultures,’ and how these interventions might be implemented through law, policy, and practice (12). We plan to implement at least one of these interventions to test its effects on behaviour. If successful to this ‘proof-of concept’ point, we would seek direct funding from the appropriate partner(s) to pursue the testing and roll-out of the wider range of interventions.



1. Gutman, J., 1999, Means-end chains as goal hierarchies. Psychology and Marketing 14(6): 545-560.

2. Richards, L., 1999, Using Nvivo in Qualitiative Research. Sage, London.

3. Haggett, C., 2004, Tilting at Windmills? The attitude-behaviour gap in renewable energy conflicts. Final report. ESRC Environment and Human Behaviour: New Opportunity Programme. Available at


5. Barton J. et al., eds, 2006, Regulating Energy and Natural Resources, Oxford University Press.

6. Geller, H. and S. Attali, 2004, The Experience with Energy Efficiency Policies and Programmes in IEA Countries, Paris: International Energy Agency.

7. Midgley, G. (ed), 2003, Systems Thinking. Sage, London; Checkland P. and S. Holwell, 1998, Action research: its nature and validity. Systemic Practice and Action research 11(1):9-21; Sterman, J.D., 1994, Learning in and about Complex Systems, System Dynamics Review 10(2-3): 291-330; Checkland P. & and S. Holwell, 1998, Information, Systems and Information Systems. John Wiley, Chichester.

8. Geller, H., 2005. The experience with energy efficiency policies and programmes in IEA countries: learning from the critics. International Energy Agency Information Paper.

9. Greening, L., Greene, D., Difiglio, C., 2000. Energy efficiency and consumption – the rebound effect – a survey. Energy Policy, 28:389-401.

10. Vujcich, H. 2008. Valuing Warm Homes – Exploring New Zealanders’ home and home heating choices. Master’s thesis, School of Geography, Environment and Earth Sciences, Victoria University of Wellington.

11. Christensen, G.L. and Olsen, J.C. 2002 Mapping Consumers’ Mental Models with ZMET. Psychology and Marketing, 19(6) 477-501.

12. Geller, H., P. Harrington, A. Rosenfeld, S. Tanishima, and F. Unander, 2006, “Policies for Increasing Energy Efficiency: Thirty Years of Experience in OECD Countries” Energy Policy 34:556.