Understanding the mechanisms that influence consumers’ decisions and their perception of the value of products and services is a subject of intense consideration in numerous fields of science, including economics, psychology, marketing, and management. Researchers and business practitioners are continuously trying to evaluate the determinants of the valuation of various goods by consumers and to find methods and models that help to appropriately establish prices.
In this paper, I examined the influence of selected psychological factors, called behavioural effects, on the valuation of private consumer goods. As such, I focussed on two well-known phenomena:
The results of the study may have both theoretical and practical implications. First, this paper will supplement and broaden the existing knowledge of the determinants of the valuation of consumer goods, as well as improve current research methods for eliciting the market prices. As using the framing effect (especially, the positive attribute framing) is remarkably common in advertising messages and slogans, the research will also facilitate recommendations within marketing sciences.
The first behavioural effect relevant to my study is the hypothetical bias. Individuals overstate their valuation in declarative research in comparison to their actual WTP, as confirmed by meta-analyses such as Murphy and Stevens (2004). In the most recent meta-analyses, covering 77 studies, Foster and Burrows (2017) ascertained that the median hypothetical WTPs exceed values observed in situations with real transactions (or incentivised trials) by as much as 39%.
In the literature, many different research methods were used to obtain participants’ WTP values. The methods used to elicit hypothetical values include choice experiments (Moser, Raffaelli, & Notaro, 2014), direct elicitation (Doyon, Saulais, Ruffieux, & Bweli, 2015), and declarative Vickrey auctions (List, 2001). To obtain the actual values, the authors largely used auctions (List, 2003) and the BDM (Becker, DeGroot, & Marschak, 1964) procedure (Boyce, Brown, McClelland, Peterson, & Schulze, 1989); however, some studies related to hypothetical bias only observed purchasing decisions instead of eliciting subjects’ specific WTPs (Blumenschein, Johannesson, Blomquist, Liljas, & O’Conor, 1998). The general conclusion from the extant literature is that the type of valuation method can moderate the hypothetical bias; choice-based elicitation methods, in particular, may reduce it (Murphy, Allen, Stevens, & Weatherhead, 2005).
Several studies have also indicated that a number of specific tools are able to reduce hypothetical bias. These include both cheap talk (Doyon, Saulais, Ruffieux, & Bweli, 2015; List, 2001) and real talk (Alfnes, Yue, & Jensen, 2010), as well as various calibration techniques. Some researchers have also found that the usage of student samples may contribute to the bias (Murphy, Allen, Stevens, & Weatherhead, 2005) and that hypothetical bias is weaker in experiments with private goods compared to those with public goods (List & Gallet, 2001).
The second of the selected behavioural effects analysed in the study is the framing effect (see Piñon & Gambara, 2005 for a review). This phenomenon refers to the dependence of the decision made on the formulation of the decision problem. This concept was introduced by Tversky and Kahneman (1981), who linked it to their prospect theory (perceiving the effects of decisions in terms of profits vs. losses). Levin et al. (1998) distinguished different types of framing effects: the risky choice framing effect, the attribute framing effect (associated with the presentation of the attributes of the good), and the goal framing effect (differentiating the decisions of the respondents depending on the way in which the effects of the action are presented).
From the viewpoint of the paper’s subject, the results of previous research on the attribute framing effect are of paramount importance. Among them is the well-known study of Levin and Gaeth (1988), in which the designation of beef with a
Several studies have also indicated that a number of respondent characteristics may affect the power of the framing effect: for example, Braun, Gaeth, and Levin (1997) demonstrated that women are more sensitive to attribute framing than men when the chosen attribute is the percentage of fat in chocolate (
Only a small fraction of the framing literature is directly related to eliciting WTP. Most studies concern either judgements or assessments. Moreover, it should be highlighted that they typically involve solely hypothetical choices.
There is some research on the framing effect comparing real outcomes with hypothetical ones; however, what should be stressed is that none of them are related to private consumer goods and their valuation. Typically, they concern gambling (Kühberger, Schulte-Mecklenbeck, & Perner, 2002; Levin, Chapman, & Johnson, 1988; Wiseman & Levin, 1996) or time allocation decisions (Paese, 1995; Wiseman & Levin, 1996); moreover, no studies have found any significant differences in participants’ choices.
I formulated the following research hypotheses:
This hypothesis is based on a broad literature. I expect that participants who make hypothetical, declarative decisions indicate higher WTP for a given product than those making decisions with actual financial consequences.
In order to verify this hypothesis, I will use one of the types of framing, which consists of differentiating the ways of presenting the product’s attributes. I expect that participants who have been exposed to positive attribute framing have a higher WTP than subjects who have been exposed to negative framing.
I assume that the framing effect will be stronger in the case of hypothetical decisions than in actual purchasing decisions. In other words, I expect that negative attribute framing will reduce hypothetical bias compared to positive framing.
To verify the hypotheses, I conducted a laboratory experiment. The participants were asked to reveal their WTP for a tube of mascara. I chose this good because it is a well-known everyday product that is diversified by price, model, and brand. The price for mascara in Poland ranges from 10 PLN to more than 200 PLN. I selected the Cover Girl Lash Blast Clump Crusher mascara because it is inaccessible in cosmetic shops in Poland (and is rarely found online); so I expected that participants would not know the market price (57.40 PLN or approximately 13.37 EUR in an online shop in November 2016). Post-experiment questionnaires confirmed my assumptions: only a very small number of participants were acquainted with the brand or with the model.
My experiment was paper and pencil. Four different treatments were used in this experiment in a 2 x 2 design: RealN (real transactions, negative attribute framing), RealP (real transactions, positive attribute framing), HypoN (hypothetical valuation, negative attribute framing), and HypoP (hypothetical valuation, positive attribute framing). Participants were randomly assigned to either a real or hypothetical condition at the session level, whereas negative or positive attribute framing was randomly assigned to each subject within a session.
In treatments with real transactions, at the beginning, I informed participants of the rules of the experiment (see Appendix). Next, they took part in a BDM procedure; this method is regarded to be correct in terms of incentive compatibility (Kagel, 1995). In this procedure, I asked participants to give the maximum price (
Participants were carefully informed of the principles of the procedure used, both orally and in writing. I subsequently showed them the mascara and presented the additional information about the product and framing sentence. Afterwards, I distributed the valuation questionnaires with the following request:
In treatments with a hypothetical valuation (HypoLow and HypoHi), the scheme was similar, but I used the direct declarative method of eliciting participants’ WTP for the product instead of the BDM procedure. I informed the subjects that their valuation was purely declarative. In valuation questionnaires, participants were asked:
In order to test the framing effect, I compared the positive and negative framing. In the HypoP and RealP treatments, the positive framing was formulated as follows: “As many as 71% of users would buy this product again (information from the makeupalley. com)”. Instead, the negative framing in the HypoN and RealN treatments was as follows: “Only 29% of users would not buy this product again (information from the
In the end, in all treatments, the participants were asked to complete the post-experimental questionnaire concerning their shopping habits and consumer preferences regarding cosmetics, as well as their sociodemographic characteristics.
It took about 15 minutes for each session to be conducted. Participants were given both oral and written instructions. The experiment was conducted at the Faculty of Economic Sciences of the University of Warsaw. In total, 167 female students took part in my experiment; their mean age was 20 years. A typical participant was an unemployed student in a good financial situation.
The mean WTP for the product in the overall sample was equal to 25.47 PLN, the median was 25 PLN, and the standard deviation was 16.46 PLN. I began my analysis by comparing the WTP values in each treatment. Table 1 and Figure 1 show the values of WTP by treatment.
Descriptive statistics (monetary values in PLN)
HypoN | HypoP | RealN | RealP | |
---|---|---|---|---|
42 | 38 | 44 | 43 | |
31.19 | 35.11 | 17.30 | 19.56 | |
30 | 35 | 15 | 15 | |
13.95 | 15.47 | 13.44 | 15.77 |
When analysing Figure 1, we are able to notice that the mean WTP is considerably lower in the real treatments than in the hypothetical ones, while the anchor made little difference. To verify these observations, I used non-parametric Mann–Whitney
In the next step, I estimated a simple ordinary least-squares (OLS) regression model to identify factors that impacted the valuation of the mascara. The dependent variable in this model is the participant’s WTP value. Most of the independent variables are extracted from the post-experimental questionnaire, and two of them represent experimental conditions (
Regression table: WTP values
Ml | M2 | M3 | M4 | |
---|---|---|---|---|
hypothetical | 11.197*** (4.970) | 11.923*** (4.033) | 13.547*** (4.906) | 12.341*** (6.022) |
framing_pos | 2.594 (1.189) | 3.320 (1.144) | 2.857 (1.033) | |
information_e | 11.539*** (5.254) | 11.649*** (5.243) | 9.653*** (4.447) | 9.462*** (4.422) |
needs_e | 2.423 (1.044) | 2.412 (1.036) | ||
finance_e | 3.480 (1.411) | 3.463 (1.399) | ||
using_mascara | 4.395 (1.283) | 4.299 (1.248) | ||
price_30–50 | 6.167** (2.529) | 6.171** (2.523) | 5.792** (2.546) | 5.826** (2.569) |
price>50 | 9.371*** (2.797) | 9.450*** (2.807) | 8.426*** (2.752) | 8.851*** (2.944) |
cosm_expenses | 5.714** (2.309) | 5.662** (2.278) | 5.705** (2.431) | 5.569** (2.388) |
the_same_masc | -3.358 (-1.401) | -3.469 (-1.432) | ||
self_assessment | -2.304 (-0.941) | -2.242 (-0.911) | ||
relationship | 4.397** (2.007) | 4.282* (1.931) | ||
finaneial_syt | -0.793 (-0.359) | -0.871 (-0.391) | ||
age | -1.083 (-0.869) | -1.080 (-0.863) | ||
hypoXfram_pos | -1.650 (-0.381) | -2.481 (-0.608) | ||
design_e | -5.826** (-2.390) | -5.902** (-2.434) | ||
constant | 24.056 (0.924) | 23.760 (0.909) | 8.486*** (3.329) | 9.951*** (4.732) |
N | 154 | 154 | 160 | 160 |
R-sgr | 0.4604 | 0.4610 | 0.4401 | 0.4361 |
F | 8.47 | 7.87 | 14.83 | 19.72 |
Prok>F | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
* p<.10, ** p<.05, *** p<.01
In the final form of the OLS model, we obtained six statistically significant variables:
hypothetical (1 – for declarative (hypothetical) valuation, 0 – for BDM procedure),
design_e (1– if the participant took the design of the product into consideration during the valuation process, 0 – in all other cases),
information_e (1 – if the participant took the enclosed information about the product into consideration in the valuation process, 0 – in all other cases),
price_30–50 (1 – if the participant usually buys mascara priced between 30–50 PLN, 0 – if the participant usually buys mascara priced lower than 30 PLN),
price>50 (1 – if the participant usually buys mascara priced above 50 PLN, 0 – if the participant usually buys mascara priced below 50 PLN), and
cosm_expenses (1 – if the participant spends more than 50 PLN a month on cosmetics, 0 – in all other cases).
It is worth mentioning that the interaction between the two most important variables,
The RESET test showed that the functional form of the model was correct (
To summarise, the results of the current study demonstrated that hypothetical bias influences the valuation of goods. Participants who valued mascara hypothetically had a higher WTP than the others by 12.34 PLN; however, we cannot observe the impact of the framing effect on WTP for the mascara, nor the interaction between hypothetical bias and the framing effect.
I confirmed Hypothesis 1, that hypothetical bias influences the valuation of the product. The study demonstrated that the hypothetical bias determines the perception of goods and influences their valuation. Participants overstated their actual WTPs in hypothetical situations. However, when using declarative methods, we are not able to discern participants’ actual preferences, only their hypothetical ones; thus functioning primarily in the symbolic sphere. The experimenter demand effect or social desirability bias may also be the source of the inflated WTP values.
I did not confirm Hypothesis 2, that the framing effect influences the valuation of the product. Similarly, I rejected Hypothesis 3, that hypothetical bias and the framing effect interact. Of course, the reason for the latter may be that the framing per se was too weak and so did not affect the valuation; this outcome may be a random incident because of the relatively small sample. The usage of a mild frame or a mismatch between the type/formulation of the framing information and the sample may also play a role.
Nevertheless, in light of these findings, we cannot conclude that hypothetical data are of lower quality than actual data. Hypothetical results are shifted towards higher values but remain internally consistent. In this sense, there is no reason to question the validity of declarative research methods. It is worth remembering, however, that I investigated only one manifestation of supposed low quality of hypothetical data, so further research should be pursued.