Previous behavioral research has shown that consumers overreact to zero-price offers of goods such as candies or chocolates (Shampanier, Mazar, Ariely (2007)). In these experiments, researchers offered participants two goods. One – a high-value more expensive good, the other one – a low-value cheaper good. Some participants saw both goods offered at a positive price, e.g., 15 and 1 Cent. Other participants saw the high-value good offered at 14 and the low-value good at 0 Cents. Researchers observed that in the group which was offered the low-value good at a zero-price, the demand for the low-value good dramatically increased and for the high-quality good decreased as compared to the group which was offered the goods for 15 and 1 Cent. This effect was surprising, because when the price of the high-value good decreases we should also observe an increase in demand for it, regardless of the price of the low-value good. This combination of an increase in demand for a zero-price low-value good coupled with a decrease in demand for a high-quality good which price decreased by the same amount as the price of a low-value good is called a zero-price effect.
Later studies have replicated this effect with multicomponent tourism products (i.e., hotel with breakfast; Nicolau and Sellers (2011)), investigated its neural mechanisms (Votinov, Aso, Fukuyama, & Mima, 2016) and tested it with different types of products (i.e., utilitarian v hedonic; Hossain and Saini (2015)). Recent research by Hüttel, Schumann, Mende, Scott, and Wagner (2018) suggests that the zero-price effect might also be observed when zero-price digital content involves non-monetary costs. Using hypothetical scenarios, Hüttel and colleagues showed that the zero-price effect is present also in case of zero-price online services involving non-monetary costs in the form of exposure to advertisements. Importantly, they demonstrated that the lack of a price leads to both – overvaluation of benefits and undervaluation of non-monetary costs.
There are two crucial limitations of these studies. First, they involve goods which may not be necessarily beneficial to consumers. Some consumers may perceive a chocolate as having no or even negative utility to them (e.g., when someone is on a diet). Second, later studies including the one by Hüttel and co-authors relied on hypothetical scenarios, i.e., participants were presented with scenarios describing the details of an offer and were asked to imagine what they would do if they had seen such an offer in reality. Such studies provide valuable knowledge as to consumers’ attitudes or beliefs, yet they do raise a question as to whether consumers’ choices in such hypothetical scenarios reflect their actual decisions when real money is at stake.
The first study I conducted in collaboration with Caroline Goukens from the Maastricht University School of Business and Economics was designed to address these two limitations. In the experiment, participants performed a real-effort task. They were shown a series of matrices with ‘d’ and ‘b’ letters. Their task was to check the boxes next to all letters ‘d’. They were not allowed to check any ‘b’ letter by mistake. They received a bonus for each correctly solved matrix. After performing the task in trial rounds, participants chose whether to use one of the tools offered to them. The tools could help them solve a real-effort task that they performed in the experiment and, thus, earn more money. One tool was cheaper and had only basic features (Basic tool), the other tool was more expensive but also offered additional features (Premium tool). To some participants (Paid treatment), Premium tool was offered for 15 Pence and Basic tool for 1 Pence. To other participants (Free treatment), Premium tool was offered for 14 Pence and Basic tool – for free. This means that in Free treatment both tools were cheaper by 1 Pence compared to the prices of the tools in Paid treatment and one of them (i.e., Basic tool) was offered for free.
The results of the first study showed that the share of participants deciding for a Premium tool was lower in Free than in Paid treatments, although its price decreased (17% vs 8%). At the same time, the demand for a Basic tool dramatically increased between the Paid and Free treatments (from 28% to 48%).
In the second study, we wanted to test if this effect is robust. Would we observe it with different prices? In addition, we wanted to exclude a straightforward explanation of the results of the first study, i.e., that the decrease in price from 15 to 14 Cents seem smaller to consumers than a decrease from 1 to 0 Cents (concave utility of money). We conducted an experiment in which we assigned participants to four groups. Each group saw a different combination of the prices of Basic and Premium tool.
| Treatment | Premium tool | Basic tool |
| 15_2 | 15 Pence | 2 Pence |
| 14_1 | 14 Pence | 1 Pence |
| 13_0 | 13 Pence | Free |
| 10_0 | 10 Pence | Free |
The results showed that the share of participants deciding for the Basic tool again dramatically increased when its price dropped to zero. Yet, differently from the first study the decrease in demand for the Premium tool was very small an statistically non-significant comparing participants who were offered the Premium tool for 13 Pence with participants who were offered this tool for 14 or 15 Pence. The share of participants offered with the Premium tool for 10 Pence increased suggesting that indeed a zero-price effect observed in the first study can be explained by consumers perceiving a drop in price from 1 to 0 Pence as a bigger decrease than a drop from 15 to 14 or to 13 Pence.
Both studies (hypotheses, design and planned analyses) were pre-registered on Open Science Framework. There, you can also find a more detailed report of the results.
Monika Leszczyńska 