What Is the Best Way to Present Savings Information Produced by “Efficient” Actions?
November 4, 2013
By Adrian Camilleri, Marie Komori, Gabriel Goffman, Shajuti Hossain
Our team, which is guided by the project title “Goals and Collective Efficacy: Routes to Energy Saving”, is developing a number of exciting experiments that are described below. The general theme for all of our work is how to present information about energy savings from engaging in efficient actions, such as shutting down your computer each night.
The question that I hope to answer in my research is how to best present efficiency savings information to people in such a way that motivates them to make more pro-environmental decisions that reduce energy consumption. In order to do this, I describe potential savings to people for a range of activities – for example, turning off computers each night, or turning down thermostats in the winter – by aggregating over different periods of time (1 day vs. 1 year) and different group sizes (1 person vs. 1 group). I try to link this work to existing theories of cognitive and social psychology. Current experiments are being conducted online but we are working towards a collaboration with Sustainable Duke to send out information to Duke students and employees to save energy. In answering this research question, I will reveal both the underlying psychological mechanisms by which people make decisions and also the optimal formats by which to present efficiency saving information that saves energy.
The questions I seek to answer through my current research are (1) Do people react more positively to one or to multiple reasons for taking an action? (2) Is it more effective for multiple reasons to be in same domains or different domains? In order to answer these questions, I present people with either one or two reasons in the same or different domains for adopting a range of pro-environmental behaviors (e.g., engaging in a meat-free diet, or buying CFL light bulbs). By measuring the behavioral intent of people, I am hoping to see differences between the different message types and combinations. I expect that groups with two reasons in the same domain will result in higher behavioral intent and groups with just one reason will have the least behavioral intent. Through my research findings, I aim to gain insight into ways marketers can most effectively give reasons for using their product or service that can be used to encourage pro-environmental decision-making.
The questions I hope to answer through my research are erwhether people are more receptive to the framing of different sorts of behavioral types (for example, “not eat meat” vs. “be vegetarian”). I also want to test whether people are more persuaded by large numbers or scales when describing the benefits of vegetarian diet when discussing greenhouse gas emissions and global warming eg. (1 day vs 1000 days) and (1 person vs 1000 people). In order to answer these questions, I created groups that present the vegetarian diet framed either explicitly as “be vegetarian” or implicitly as “to not eat meat” then I discuss the greenhouse gas reduction made possible by this diet if were to not eat meat for a specific time frame and if a certain number of people were to participate. I made the time frame value and the participant value the same (1000 days or people) so that the scale difference would be the same. I am hoping to see if frame size across time and number of people is the same or differs. I expect that the larger frame size that shows that over longer amounts of time or more people will lead to the most responses in being vegetarian. I expect the not explicitly vegetarian frame will lead to higher response rates for non-vegetarian respondents. This research will allow us to see how the large number affects decisions making in the environmental sphere. This will also explore the effects of the term vegetarian and if it has negative connotations to meat eaters.
The main question I am trying to answer through my research is how do people perceive the amount of greenhouse gas emissions from certain appliances and foods. To answer this question, I split participants into three groups. One group will be given emissions from a 100-watt incandescent light bulb used for one hour as a reference point, another will be given the production of one medium-sized tomato as a reference point, and the last group will be given both. In the survey questions, the emissions from these reference points are set to 100 units, and participants are asked to estimate the units of emissions from using other appliances and producing other foods compared to the reference point. For example, if they think the use of a certain appliance or production of a certain food emits more greenhouse gasses than a light bulb or a tomato, they will enter a number larger than 100. I expect people to underestimate the greenhouse gas emissions from appliances and food, especially for the larger appliances, meats, and fish. Understanding how people perceive emissions from appliances they use and foods they eat on a daily basis will help determine how we can better educate the public on how to reduce their greenhouse gas emissions. Once people realize which of their behaviors are emitting the most greenhouse gases and consuming the most energy, we expect that they can adjust their behavior in the right places accordingly.