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The effect of life-cycle cost disclosure on consumer behavior: evidence from a field experiment with cooling appliances

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Abstract

Theory suggests that providing consumers with an estimated life-cycle cost (LCC) may make them buy more energy-efficient household appliances in cases where energy efficiency is cost effective. This article evaluates the link between the provision of LCC and consumer behavior by using an online field experiment for cooling appliances. Internet users arriving at a commercially operating price comparison website were randomly assigned to two experimental groups, and the groups were exposed to different visual stimuli. The control group received regular product price information, whereas the treatment group was offered additional information about estimated operating cost and total LCC. Consumers’ click behavior was evaluated with multiple regression controlling for several product characteristics (n = 1,969 clicks). We find that LCC disclosure reduces the mean specific energy use of chosen cooling appliances by 2.5% (p < 0.01), making it a potentially interesting approach for environmental policy regarding the market transformation toward more energy-efficient household appliances. However, LCC disclosure also decreases the number of clicks from the price comparison website to final retailers by about 23% (p < 0.01), which makes it—in the format chosen here—undesirable from a business perspective. Therefore, future research should clarify under what (if any) conditions can monetary energy cost disclosure be associated with more positive effects for price comparison websites.

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Notes

  1. A shopbot facilitates comparisons between different products with respect to prices and other characteristics, e.g., size, energy use, etc., by displaying items and associated retailers in list or matrix format. A shopbot differs from an online retailer in that it only acts as an intermediary and that it does not actually sell appliances itself. Evaluating shopbot data allows for tracking how consumers choose from different product options.

  2. See du Pont (1998) for a comprehensive study on energy label design and consumer decision-making with respect to energy labeling.

  3. Kaenzig and Wüstenhagen (2010) present a conceptual model of the effect of LCC disclosure on consumer investment decisions.

  4. See Deutsch (2009) for a detailed comparison and discussion.

  5. The concept of life-cycle costing is practically used in energy economics (McMahon et al. 2005) and can be understood as an application of investment theory to consumer behavior (Stern 1978). Consumers are expected to minimize the sum of all costs that accrue from buying a durable good and from using it over several time periods. Since life-cycle costing involves trading off initial purchase price and future operating costs, it requires some kind of explicit or implicit discounting on the part of the consumer (Liebermann and Ungar 2002).

  6. This does not mean that the actual physical lifetime of a given appliance cannot be longer than the specified time horizon. A reduction in the reference time horizon simply changes the measuring rod regarding the relative cost-effectiveness for a list of appliances.

  7. This procedure has been approved by the Institutional Review Board of the University mentioned in the acknowledgments.

  8. The exact period of time cannot be given due to proprietary information concerns.

  9. Specification tests show that a negative binomial model is more appropriate than a Poisson regression model here, given the values of the over-dispersion coefficient alpha (see Table 7).

  10. In each respective category, users see more than 300 different appliances from more than 25 brands sold by 30 different retailers.

  11. pp are the percentage points, covering a 95% confidence interval.

  12. This finding is fairly robust. As described in the “Measures” section above, we compared two sets of observations—firstly, all click-throughs and secondly, each user’s final click-through—and found that the treatment effects were very similar. See Deutsch (2007) for a comprehensive discussion of validity issues regarding the experimental findings.

  13. This finding needs a clarification. Comparing LCC estimates of the two groups necessarily involves comparing data of different quality: firstly, (1) LCC data directly gathered in the experiment with, secondly, (2) simulated LCC figures. (1) Users in the treatment group receive LCC information, they partly adjust the underlying assumptions, and they directly provide experimental LCC data to be evaluated. (2) Users in the control group do not receive LCC information during the experiment. Therefore, we have to simulate LCC figures for the control group based on appliances’ specific energy use and common default assumptions (see Table 2). Consequently, the LCC estimates in the two groups are asymmetric, based on user-adjusted assumptions (1) versus non-adjusted default assumptions (2). However, the adjustment of LCC assumptions occurs in so few cases that the outcome is not sensitive to the assumptions used (default versus user-adjusted) in estimating LCC.

  14. Answering such a process-oriented question, however, would require a different form of research design, including, e.g., consumer interviews.

  15. If LCC disclosure did actually decrease total appliance sales, this would have important consequences for the overall, economy-wide stock of appliances in use. Lower sales of modern appliances would decrease the average efficiency of the appliance stock. Moreover, lower sales would slow down the growth in cumulative production, implying a slower learning of manufacturers and a slower associated decline in production cost.

  16. We derive this discount rate by using a net present value equation and calculating the point of indifference between two appliances that only differ in purchase price and operating cost (Deutsch 2007; Liebermann and Ungar 1983).

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Acknowledgments

The research for this article was conducted during my stay at the University of Maryland, College Park. My dissertation committee and Matthias Ruth in particular provided valuable comments on an earlier version of this article. I also thank the anonymous referees for their helpful suggestions for improvement. Funding for data collection was graciously provided by the German Federal Environmental Foundation (DBU), Osnabrück. Personally, I was on a grant from the German National Merit Foundation. I thank the team from MENTASYS, Karlsruhe—Tim Stracke, Michael Krokoska, and especially Philipp Knüchel—for technically implementing the experiment, and I also thank WEB.DE, Karlsruhe for hosting the experimental shopbot.

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Appendix

Appendix

Derivation of an equivalent time horizon for indirect discounting

For the purpose of indirect discounting, we derive a reduced, equivalent time horizon from a given implicit discount rate. The default implicit discount rate we chose as a starting point equals about 18% and is based on a German survey concerning hypothetical refrigerator purchase decisions (Kuckartz and Rheingans-Heintze 2004).Footnote 16

Given an implicit discount rate of 18% and the known average service life of household appliances in Germany, one can calculate a reduced time horizon, which can substitute for direct discounting. In other words, one can determine the operating cost for an ETH that must be equal to the conventionally discounted operating cost. The general condition is given as

$$ \sum\limits_{t = 1}^T {{C_t}{{\left( {1 + r} \right)}^{ - t}}} \mathop { = }\limits^! \sum\limits_{t = 1}^{\rm{ETH}} {{C_t}} $$
(7)

where C t is the annual operating cost in year t, T is the known average service life of a given household appliance, r is the discount rate, and ETH is the equivalent time horizon. For constant C t (as assumed here), this expression can be reduced to

$$ \sum\limits_{t = 1}^T {{{\left( {1 + r} \right)}^{ - t}}} \mathop { = }\limits^! \sum\limits_{t = 1}^{\rm{ETH}} { = {\hbox{ETH}}} $$
(8)

Given an exogenous implicit discount rate of 18% and a known average service life of 14.4 years for refrigerators, the equivalent time horizon equals about 5 years. Practically, in the experiment, these 5 years are presented to consumers as a default value without making any explicit reference to the concept of discounting.

When consumers adjust the time horizon to their personal needs, they implicitly change the discount rate. The resulting overall equation for LCC is thus given as

$$ {\hbox{LCC}} = P + {\hbox{ETH}} \times {P_{\rm{E}}} \times {C_{\rm{E}}} $$
(9)

where P is the appliance purchase price [€], ETH is the equivalent time horizon [years], P E is the price of electricity [€/kWh], and C E is the specific energy consumption [kWh/year].

One problem in setting up the experiment is the difference in the known service life (see Table 2) between refrigerators (14.4 years) and freezers (18 years), which would have required differentiated time horizons for each appliance type. Consumers, however, were able to switch between appliance types and may have assumed that the estimation of operating cost remains the same for all appliances. Therefore, we applied one common service life (14.4 years) and one equivalent time horizon of 5 years to all cooling appliances.

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Deutsch, M. The effect of life-cycle cost disclosure on consumer behavior: evidence from a field experiment with cooling appliances. Energy Efficiency 3, 303–315 (2010). https://doi.org/10.1007/s12053-010-9076-4

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