Emotional priming for sustainable consumption? Differential effects on valuation of labelled chocolate

Our randomized controlled trial includes four information treatments about the coca sector and no control group. The format of the treatments are short videos which consist of several animated slides. We designed the videos in line with formats commonly used on social media like Instagram. Our four treatments are distinct because they differ in sustainability objective as well as emotional prime.

2022-05-06

2022-06-30

The most important outcome to test the stated expectations is the consumers’ marginal WTP for each one of the varying sustainability attributes. The marginal WTP corresponds to the marginal rate of substitution (MRS) between one attribute and price.

As we are using the random parameters multinomial logit (RPL) model, we cannot use a simple ratio to estimate the marginal WTP. Instead, we reparametrize the RPL to the WTP space by using maximum simulated likelihood like in Train and Weeks to derive marginal WTP of each attribute (or better marginal WTP for no certification or no claim to a certification or a claim).

We consider four other outcomes: utility, willingness-to-purchase, recall attention frequency of certified and uncertified sustainability claims on products, and emotions triggered by the treatments.

We will simulate the utlity of the chosen attributes using the RPL model.
A supplementary outcome is the willingness-to-purchase, a factor based on the self-reported likelihood or willingness of purchasing of one of the following four types of chocolate each: either with Fairtrade certification, Rainforest alliance certification, claim regarding social responsibility, or claim regarding environmental sustainability. We adapt the measure of self-stated likelihood or willingness to purchase Fairtrade chocolate in the future from Hansen et al. (see attached document). We also adjust the measure to a seven-point Likert-Scale. We choose to have an option of indifference despite a possible framing effect to ensure that we allow for all possible preferences of the participants. We will perform principal component analysis in order to narrow down the statement batteries to the core of the concept.
For the outcome of recall attention frequency we integrate a 14-day recall question by asking, how often they have paid attention to a number of claims or certifications in the last 14 days whilst shopping, e.g. the Nutri-Score and GMO-free labels. We include certifications and claims not related to our study so that we do not prime participants towards the certifications of interest. The categorical outcomes include seven levels of frequency.
Lastly, we capture how participants feel after watching the videos at the end of the questionnaire in the first wave and in a similar manner in the second wave. For that purpose, we employ the Discrete Emotions Questionnaire by Harmon-Jones et al. that measures anger, disgust, fear, anxiety, sadness, desire, relaxation and happiness via four items each. We will perform principal component analysis to establish continuous factors of eight groups of emotions that are based on each four components. Based on the findings in our pilot study, we will reduce the number of emotions asked for in the final survey.

To answer whether emotional priming in information provision increases consumers’ valuation of sustainable foods, we collect panel data of German consumers via online surveys. We randomly assign four information treatments that differ in sustainability aspect or addition of emotional primes (pictures). We employ a discrete choice experiment and answers related to self-reported consumption behavior to measure the consumers’ valuation of chocolate bars.

The data collection will be conducted by the panel provider respondi between May and July 2022. We conducted a pre-test with the same quota and the same panel provider in January 2022 for about one week.
The first survey wave will take place in May to June 2022 for about two weeks, and the second survey wave - about two weeks later - will take place in June to July for about three weeks.
For the discrete choice experiment, we offer choices with four varying sustainability attributes at two levels and four levels of the attribute price for a 100-g bar of chocolate with unknown brand and flavor. The sustainability attributes include the presence of the label Fairtrade, the label Rainforest Alliance, a claim related to social responsibility, or a claim related to environmental sustainability.
Since we are only interested in the main effects of the sustainability attributes, we use a fractional factorial design. Due to the large number of choice sets, we assign the participants randomly to one of seven blocks of choice sets that are fixed groups of choice sets. To counteract order effects, we vary the order of the choice sets and the order of the choices within each set. The participants are each exposed to four choice sets in which they are asked to make a choice between two chocolate bars. Respondents have the option to indicate that they would not buy any of the chocolate bars as a third option.

The participants will be randomly selected among the pool of available participants in a panel provider’s respondent pool. Invitations to participate will be sent out according to the quota settings for age, gender, income and education in order to match the population in Germany in these characteristics.
Once the participants have agreed to take part in the survey and match the quota, their assignment to the treatment occurs randomly. The software UniPark for designing the online questionnaire will randomize the treatments, choice set blocks, and choice set order.

The randomization of the treatment is on the individual level.

No

N/A

3,500 participants

875 participants in each of the four treatment arms

The sample size is terminated by the available funds, 3,500 participants, i.e. 875 participants in each of the four treatment arms. As our focus lies on the outcomes of the discrete choice experiment, we only concentrate on the power calculations for the utility outcomes. According to the rule of thumb by Johnson and Orme, this sample size should be a large enough sample to detect statistically significant main effects. In our case with c= 4 (largest number of levels of any attribute), t= 4 (number of choice tasks), and a=3 (number of alternatives), N needs to be larger than 167.