Discussion
The main goal of the present study was to elucidate how price framing influences purchase decision-making and its neural underpinnings. The behavioral results indicated that ZP led to a higher purchase rate and reduced RT than NP. Moreover, the ERP results showed larger LPP amplitude elicited by ZP in contrast to NP, providing neurophysiological evidence for the moderating effect of price framing on information processing and purchase decision making.
A remarkable price framing effect was discovered: people showed higher purchase rate when they were presented with bundles that contained a free component than when presented with bundles in which each component was offered at a normal price. Such a finding might be due to the positive affect induced by the zero-priced component (Shampanier et al., 2007; Nicolau, 2012; Nicolau and Sellers, 2012; Votinov et al., 2016). Previous studies have demonstrated that when people have to choose between two products, they tend to switch their preference from the preferred more expensive product to the less preferable but cheaper alternative when the cheaper option is offered for free (Shampanier et al., 2007; Votinov et al., 2016). A free offer could invoke a stronger positive affect and become extraordinary attractive since the zero price not only symbolizes no-cost but also implies extra benefit. This positive affect is used by consumers as a central input for decision making so that they're inclined toward the free option (Shampanier et al., 2007; Hüttel et al., in press). The zero price effect is not only confined to single products but also applies in multi-component contexts when one of the components becomes free (Nicolau and Sellers, 2012; Baumbach, 2016). In this study, stronger positive affect was evoked by the tie-in product when it was offered free rather than when offered at a normal price. This affect could extend to the evaluation of the bundle and made the bundle in ZP ostensibly more attractive. As a matter of fact, if consumers were rational persons, they would buy the same amount of bundles under different price frames since the total price of a bundle remained the same across different frames. We argue that people do not always act as rational economic models predict but instead they make decisions based substantially upon bounded rationality (Simon, 1956; Gigerenzer and Gaissmaier, 2011). For a purchase decision based on price information, affect may play a key role in the decision-making processes (Nicolau and Sellers, 2012; Somervuori and Ravaja, 2013), which give rise to the probability of non-rational economic behavior. When an individual's attention is focused on the positive aspect of a bundle (i.e., the zero-priced component), favorable associations could be evoked between the free component and its cost/benefit, leading to a higher purchase likelihood.
Moreover, people made purchase decisions faster in ZP rather than NP. It is proposed that RT is correlated with task difficulty and cognitive load (Wang et al., 2016). A shorter RT is generally suggestive of lower task difficulty and cognitive load (Cheng et al., 2014; Jin et al., 2017). In Jin et al. (2017)'s study, they asked participants to make purchase decisions in different attribute framing conditions (positive vs. negative), and found that the positive framing condition led to reduced RT relative to the negative framing condition, indicating that the stronger desirability of positive framing messages made purchase decisions easier. In the current study, the RT differentiation implicates that the task difficulty of ZP is lower than that of NP, and it entails less cognitive effort to make purchase decisions in ZP vs. NP. In line with Jin et al. (2017), ZP was more desirable to participants' expectation than NP, which might make purchase decision-making easier. A free component may lead people to feel more interested, elicit stronger positive affect, and accordingly capture a lot of attention. However, such an interpretation should be taken with caution since the lower difficulty of calculating a total price in ZP vs. NP could also contribute significantly to the shorter RT for ZP.
With regard to the ERPs component, we observed an effect of price framing on LPP in the 400–600 ms time window, with a topographical distribution across centro-parietal sites. LPP may be indicative of overt, post-perceptive deliberative cognitive processing related to stimulus significance (Olofsson et al., 2008). In consonance with the behavioral results, the neurophysiological results of this study showed larger LPP amplitude for ZP compared to NP, suggesting enhanced motivational engagement toward bundles with a free component, which increased resource allocation and facilitated sustained attentive processing (Schupp et al., 2004). A large number of studies have demonstrated that motivationally significant stimuli such as emotional stimuli, in contrast to neutral stimuli, lead to enlarged LPP amplitude (Schupp et al., 2004; Ferrari et al., 2011; Leite et al., 2012). In recent years, researchers have gained increasing interest in exploring the neural underpinnings of consumer emotion, attitude, and purchase intention (Pozharliev et al., 2015; Zhao et al., 2015; Bosshard et al., 2016; Goto et al., 2017; Wang et al., 2017). As Goto et al. (2017) noted, evaluating motivationally relevant consumer goods is quite similar to processing emotional stimuli in that they are usually associated with motivated attention. Zhao et al. (2015) reported that services with a high emotional value triggered a greater LPP amplitude, indicating that these services may motivate more positive emotions during purchase decision making. Pozharliev et al. (2015) examined the neural processes underlying passive viewing of luxury vs. basic branded goods, and showed increased LPP for luxury goods than for basic branded goods when the participants were together with another person, reflecting enhanced activation of motivational system in the brain for stimuli with higher emotional value. Furthermore, Goto et al. (2017) categorized ERP waveforms based on participants' preferences for a large variety of products and noted a positive relationship between LPP amplitude and subjective preferences, suggesting that subjective preferences were built on more elaborative and conscious cognitive processes. In a recent fMRI study, Votinov et al. (2016) engaged participants in a binary preference choice task with differentially priced products, which demonstrated a positive relationship between the activation of medial prefrontal cortex and the subjective happiness of obtaining free products and confirmed the role of affective evaluation in zero-price effect. As aforementioned in the current study, ZP might induce a stronger positive affect than NP because the former option contained a free component, which seemingly connoted no cost but extra value added to the bundle and made the offer highly attractive (Shampanier et al., 2007; Nicolau and Sellers, 2012; Votinov et al., 2016). Thereby, consistent with previous studies, the increased LPP amplitude for ZP vs. NP implies that ZP is motivationally more significant and is selected by the brain for heightened attentive processing, which to a large extent facilitates consumer purchase decision making, as evidenced by the higher purchase rate for ZP vs. NP.
It was worth noting that there were statistically significant differences at neither behavioral nor neural level between ZP and LP, as well as between NP and LP. The contrast between ZP and LP was of particular interest to this study. As Mao (2016) noted, in the context of price promotions offering product upgrades, it generated greater sales when the upgrades were offered at a low token price (e.g., buy a Canon camera and upgrade its memory capacity from 16G to 32G for ¥0.1) rather than for free (e.g., buy a Canon camera and upgrade its memory capacity from 16G to 32G for free). He suggested that when an upgrade was offered at a low price, its perceived attractiveness would be enhanced due to that the consumers tended to compare the token price with the upgrade's normative value and found the token price disproportionally small relative to the retail price; whereas when an upgrade was offered free, consumers were prone to evaluate it with the amount of required purchase. However, a token-priced upgrade would be no more favorable when consumers were asked to consider deal savings before evaluating the deal, which suppressed relative thinking (Mao, 2016). Thereby, we surmise that two reasons may account for the undifferentiated responses toward ZP and LP. Firstly, participants were exposed to different price frames in the current study, rendering it rather difficult to change their mindset rapidly, which implied that participants were inclined to resort to a sole criteria (e.g., perceived absolute savings) for decision making. Additionally, a number of products were used as stimuli in this study, which made it impossible for participants to estimate the normative value of the tie-in products (thought they were familiar with the products per se) and compare it with the token price within a limited time.
Based upon the above findings and discussion, this study also has practical implications for marketers and retailers. Bundling is a constant strategy in retailing in pursuit of not only more sales per order but also developing customer loyalty. The advance in e-commerce (including mobile e-commerce) has boosted the application of bundling strategy. Understanding the impact of the price of each component on consumer response to the bundle may prompt managers to make effective pricing decisions, especially in nowadays when e-commerce enables consumers to organize bundles by themselves. Given a fixed total price, setting a zero price for the tie-in product could evoke stronger positive affect than setting a normal discounted price for each component in the bundle, and lead the consumers more likely to make “buy” decisions. In other words, the free component in a bundle may act as a bait that draws attention from consumers and makes them more willing to give the bundle a try (Nicolau and Sellers, 2012).
However there are several limitations of our study which have to be acknowledged. First, we didn't measure positive/negative emotion directly via subjective ratings and the inference about the involvement of affective/emotional processes in price framing effect relied largely on observed electrophysiological activities during the task. This kind of reasoning is called reverse inference. Though reverse inference is extremely prevalent in cognitive neuroscience and neuromarketing, its validity has been regarded by some researchers as limited (e.g., Lee et al., 2017). Yet some researchers asserted that reverse inference was not intrinsically weak when applied with caution (e.g., Hutzler, 2014). Future studies are needed to replicate our findings by taking subjective measures of emotion into account, which allow a direct comparison between behavioral and neural results and draw conclusions in a more comprehensive way. Second, the difficulty of calculating the total price was not strictly controlled across different experimental conditions. It might be relatively easier to calculate the total price in ZP vs. NP, since the former condition contained a zero-priced component. Thus it could be argued that the differences in RT, LPP amplitude and purchase rate between ZP and NP might be partly due to the differentiated cognitive demand induced by calculating the total price. It was difficult to rule out the influence of task difficulty in the current research paradigm. However, we conjecture that the higher purchase rate in ZP vs. NP could not be simply attributed to the lower task difficulty since task difficulty has been found to be associated more often with cognitive and behavioral efficiency (as reflected in RT and accuracy) but less often with purchase decision outcome. In addition, contrary to the present study, higher task difficulty and cognitive load could also be accompanied by higher purchase rate (Wang et al., 2016).