Impact of Targeted Recommendations on Consumer Choice
Recommendation algorithms, informed by large datasets, make consumer choices easier and efficient but undermine the consumers’ sense of autonomy. Targeted practices lower cognitive effort and decision-making costs. However, consumers’ alienation from choice and inability to control the recommendation algorithm lead to dissatisfaction.
Consumers display confidence in the existence of free will when choosing, perhaps due to a belief in the causation of personal actions and the attribution of favorable outcomes to their actions. They derive hedonistic pleasure from causing an event that is motivated by the psychological needs of competence and autonomy.
People make hundreds of decisions every day and spontaneously describe only a few as choices and fewer as generating an experience of autonomy. The conscious awareness of the act of choosing and not being restricted in one’s decision-making is vital in making the experience of autonomy salient.
People like having a sense of control. Resisting temptation and choosing the virtuous option may provide positive reinforcement to consumers of their willpower and sense of virtuousness, enhancing the utility of the chosen option, whereas giving in to temptation signals a lack of willpower and reduces the utility of the chosen option.
The “IKEA Effect” illustrates that consumers derive more pleasure from making certain products themselves than from purchasing them and that this also enhances evaluations of the products. The experience of freely choosing leads consumers to bolster the features of the preferred option and to minimize the attractiveness of the non-preferred option. People derive more satisfaction from consuming hedonic products when they made the choices, not the experts.
A heightened sense of control in one’s life can have far-reaching implications for physical health and other physiological outcomes. In an experiment, nursing home residents who were told that they were responsible for their own well-being, and were assigned more responsibility — given control over the care of a plant — showed significant improvement in alertness, active participation, and overall well-being.
Choices often consist of trying to pick the best option from a set: as a first step, consumers review and compare attributes of the different options. This task is relatively easy if a dominant option emerges from the choice set, one that is clearly superior to the others. In contrast, when no such option exists, this process of comparison is cognitively taxing and requires the consumer to trade off and sacrifice some benefits in return for others. This can result in a less satisfying consumption experience than if the same product had been consumed without choosing it from other options.
The rationalizing self-justification to support a choice triggers an “option attachment”, which induces a sense of loss once an option is foregone for another. This is exacerbated when the outcome of the selected option is suboptimal. The same processes that allow consumers to derive pride and satisfaction from self-determined choices can lead to guilt and dissatisfaction when the outcome of a choice is negative.
The act of choosing may also negatively impact consumers’ motivation. When options are plentiful, the act of choosing may become effortful, and consumers might be discouraged from choosing altogether. In a reputed study, a tasting booth for jams attracted more people when it offered 24 different options than when it offered 6, but there were more purchases from the set with 6 options than from the set with 24 options.
People overestimate the benefits of choice: in deciding how much time they should devote to selecting a better option, they do not incorporate the temporal, cognitive, and emotional costs of searching and of thinking. Choice is influenced by the cost of the decision.
Data-driven marketing is inherently reductive in its description of consumer behavior. Data-driven marketing mostly focuses on behavior, at the expense of higher-order psychological processes such as preferences, emotions, and moral judgments. A machine that analyzes consumer preferences from Google searches or browsing history on Amazon may ignore mental processes that lead to individual behavior.
Such recommendations can be negligent of aspirational preferences. A smoker trying to quit may have an immediate preference for cigarettes but an aspirational preference for quitting. This inherent tension between hedonism and autonomy, or between who the person is now and the ideal version of them isn’t well-considered by recommendation algorithms.
An algorithm presenting automated curation based on past preferences to best predict consumers’ current taste would encourage repetition of past behavioral patterns, and make exposure to unusual, serendipitous content less likely. This deprives consumers of their ability to evolve over time, or at the very least reduce the likelihood of radical changes in their tastes. The system reinforces sustained existence in information bubbles.
Data-driven marketing is simultaneously bolstering some aspects of autonomy and threatening others. The effects of algorithmic nudging are difficult to be labeled discriminatory or predatory because it is impossible to determine the intent of the algorithm.