Three Classes of Behavioral Biases

In this section, we introduce and briefly explain behavioral biases, which are widely covered in behavioral economics and marketing. We follow the framework of DellaVigna (2009) in classifying deviations (i.e., behavioral biases) of individual behavior from the standard economic model (Rabin 2002), which serves as a benchmark. The modern field of (micro) economics has already adopted many ideas from behavioral economics and currently has a far richer understanding of human behavior than the standard economic model suggests. Contemporary economists distinguish the following three classes of deviations from this model (DellaVigna 2009): nonstandard preferences, nonstandard beliefs, and nonstandard decision- making5. We follow this classification due to the following advantages. First, these classes are theoretically substantiated and widely used in the field of economics; however, these classes are also related to marketing and consumer psychology. Second, using the three classes, we can categorize a large set of individual biases based on economic theory and facilitate the uncovering of relationships among biases (i.e., aggregating insights). Third, using this framework is helpful because the standard economic model provides a well-defined benchmark.

2.2.2.1

Nonstandard Preferences

The first class of behavioral biases, nonstandard preferences, includes the follow- ing deviations from the standard economic model regarding the utility function: time-inconsistent preferences, reference-dependent utility, and social preferences. Regarding time-inconsistent preferences, empirical evidence suggests that individu- als can have a “present bias” or “declining impatience”, which is consistent with (quasi)-hyperbolic discounting (Laibson 1997; Loewenstein and Prelec 1992), that can capture consumers’ problems of self-control. For example, a person signs up for a gym membership to force their future self to exercise. As the future approaches, the person must decide whether to exercise, and the future utility is discounted more steeply. Thus, the person tends to procrastinate and postpone exercising (DellaVigna and Malmendier 2006). Alternative theories, e.g., temporal construal

level theory, can also help explain such behavior (Trope and Liberman 2000).

5_{The Appendix summarizes examples of each bias dimension from seminal papers, including a}

Furthermore, by assuming reference-dependent utility, loss aversion, and a nonlin- ear probability weighting function, prospect theory (Kahneman and Tversky 1979) addresses several issues supported by empirical evidence, such as that individuals 1) focus on relative versus absolute trade-offs and think in terms of gains and losses rather than overall wealth, 2) are more sensitive to losses than gains, and 3) over/under weigh small/large probabilities. Finally, vast empirical evidence suggests that people have social preferences and are not purely self-interested but are also concerned with social welfare and fairness, e.g., in ultimatum or dictator games (Camerer and Thaler 1995), or engage in charitable giving (DellaVigna et al. 2012).

2.2.2.2

Nonstandard Beliefs

The second class of deviations considered are nonstandard beliefs, which emerge in the presence of uncertain factors in decision-making. Under uncertainty, decision- makers must form beliefs regarding potential outcomes or “states of the world”. The standard economic model predicts that, on average, people correctly evaluate the distribution of these states and update their beliefs using Bayes’ rule for incoming information. However, empirical evidence suggests that consumers often form systematically incorrect beliefs and do not act as Bayesian information processors (DellaVigna 2009; Rabin 2002). Three main dimensions related to this context can

be distinguished.

First, belief-based biases comprise overconfidence, which involves overestimating one’s actual ability, performance, level of control, or chance of success; considering one’s abilities to be better-than-average (overplacement); or being too confident of one’s knowledge, e.g., overprecision (see, e.g., Moore and Healy 2008). Similarly, individuals may be overly positive about the prospect of a desirable outcome that is unrelated to their abilities or knowledge (overoptimism). As a result, a wide range of irrational behavior may occur, such as clinging to one’s beliefs despite contradictory evidence, disregarding other prospects and opportunities, or underestimating risks (Windschitl and Stuart 2015).

Second, consumers might be affected by projection bias, i.e., individuals project their current state into the future, such as when ordering food in a hungry state (Read and van Leeuwen 1998) or ordering winter clothing on a cold day (Conlin et al.

2007). Third, the misconception that small random samples are as representative as large samples, which is known as the law of small numbers (Tversky and Kahneman 1971), might lead to false generalizations as people tend to ignore base-rate frequency (prior probability) and sample size when making inferences (Tversky and Kahneman 1974). Examples of the law of small numbers include the “gambler’s fallacy” (Tversky 1974) and the related “hot hand fallacy” (Gilovich

2.2. CONCEPTUAL FRAMEWORK AND RESEARCH PROCESS 19 et al. 1985), which describe individuals’ beliefs that respective negative or positive correlations exist in random processes.

2.2.2.3

Nonstandard Decision-Making

This class of deviations addresses observations of non-utility-maximizing behavior due to violations of the following assumptions: individuals are full and perfect information processors, who consider the incentives of information sources and make context-invariant choices; moreover, they are emotionless and deliberate.

The notion that choices are constructed (Bettman et al. 1998) and that the particular choice architecture (framing/context) affects the choices people make is generally accepted (Simonson 2008). For example, framing effects may emerge due to the reference-dependent utility function, which render some characteristics more salient or implicitly manipulate goals (see Levin et al. 1998). This pattern is also observed in an intertemporal choice context, such as “temporal frames” (Loewenstein 1988), which describes the same option (with the same time interval) as a delayed or expedited decision and can lead to different discount rates. Moreover, choices might be affected by context effects that emerge due to a choice set composition. In particular, a product may attract a larger share in settings in which it is a middle rather than an extreme option, which is referred to as the compromise effect. Relatedly, a locally inferior product can be introduced, resulting in the so-called attraction effect. Tversky (1972) further distinguishes the similarity effect, which implies that an alternative loses choice share relative to a more similar alternative.

Furthermore, the premise of individuals’ limited cognitive abilities and limited attention has been addressed since Simon (1955). People tend to pay more attention to salient factors or ignore some available information. Considerations of inattention have given rise to many alternative decision rules of utility maximization, including elimination-by-aspect (Tversky 1972), lexicographic rules (Tversky 1969), and satisficing (Simon 1959).

Subsequently, studies investigating persuasion effects have largely disputed the assumption that rational agents are aware of the incentives of information providers (e.g., firms or politicians) and that they consider them when making decisions.

Moreover, individuals’ attitudes and behaviors might be subjected to social pressure (DellaVigna et al. 2012) or social influence, i.e., pressure from their reference group (e.g., peers or family).

Finally, emotions are largely neglected by standard economic theory. However, emotions, including visceral influences, e.g., hunger or thirst (Loewenstein 1996), anticipatory emotions, e.g., anxiety or fear, and anticipated emotions, e.g., regret (Loewenstein et al. 2001), have been shown to drive consumer behavior.