Information Load and Consumer Decision Making

Malhotra, Naresh K.

doi:10.1086/208882

Cited by 604 publications

(383 citation statements)

References 44 publications

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“…The original information overload hypothesis states that the quality of a decision decreases with high amounts of information. Although this initial evidence was criticized on methodological and conceptual grounds, subsequent research confirmed that an overload of information may reduce decision quality (e.g., Malhotra, 1982;Van Herpen & Pieters, 2002). Notably, this research also took the distribution of attribute levels into account, which may appear as yet another aspect of the psychological concept of choice complexity suggested here.…”

Section: Discussionmentioning

confidence: 67%

Less may be more when choosing is difficult: Choice complexity and too much choice

2010

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Section: Discussionmentioning

confidence: 67%

Less may be more when choosing is difficult: Choice complexity and too much choice

2010

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“…Past studies have investigated the negative effects of information overload on consumers (Iyengar and Lepper 2000;Malhotra 1982;Scheibehenne, Greifeneder, and Todd 2010). This research finds more information can drive poor decision quality (Jacoby, Speller, and Kohn 1974) and less satisfied consumer decision making (Lee and Lee 2004).…”

Section: Discussionmentioning

confidence: 94%

Known Unknowns in Judgment and Choice

Walters¹

2014

PsycEXTRA Dataset

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This dissertation investigates how people make inferences about missing information.Whereas most prior literature focuses on how people process known information, I show that the extent to which people make inferences about missing information impacts judgments and choices. Specifically, I investigate how (1) awareness of known unknowns affects overconfidence in judgment in Chapter 1, (2) beliefs about the knowability of unknowns impacts investment strategies in Chapter 2, and (3) inferences about forgotten unknowns influence choices from memory in Chapter 3.Chapter 1 investigates how overconfidence can stem from neglecting to consider missing information. Most prior research has attributed overconfidence to people focusing disproportionately on evidence favoring the chosen hypothesis relative to its alternatives. In this iii chapter, I find that neglecting unknown evidence independently contributes to overconfidence. In a first study, respondents answered questions such as, "Which of these fast food items has more calories, a Subway sandwich, or a McDonald's cheeseburger? / How confident are you?" Using a process tracing technique, I found that participants who considered more missing evidence were less overconfident than those that thought about more known evidence. Meanwhile, participants who considered more unknown information answered the same number of questions correctly, resulting in better calibration. In two additional studies, I prompted participants to list unknowns before assessing confidence in their judgments. This "consider the unknowns" technique reduced overconfidence substantially, and was more effective than the de-biasing technique most often prescribed in the research literature ("consider the alternative"). Importantly, considering the unknowns was selective in its impact: it reduced confidence only in domains where participants were overconfident, but did not affect confidence in domains where participants were wellcalibrated or under-confident.Chapter 2 investigates how inferences about the knowability of missing information impacts investment choices. Recent research has found that people intuitively distinguish aleatory uncertainty that is inherently random or stochastic (e.g. What is the probability that a fair coin will land heads?) from epistemic uncertainty that is attributed to missing knowledge or information (e.g., Which company had a larger market capitalization at the end of 2015, Google or Apple?). In a series of surveys and experiments involving laypeople, experienced investors, and financial advisors, I found that investors who viewed stock market uncertainty as more epistemic/knowable searched for more stock information and were willing to pay more for financial advice, whereas investors who viewed stock market uncertainty as more aleatory/random diversify more. Similarly, when investors were primed to think about epistemic iv uncertainty they are more willing to pay for stock information whereas when they were primed to think about aleatory uncertainty they diversified mo...

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“…This leads to greater cognitive errors (Reason, 1990), which in turn have a detrimental effect on decision-making (e.g., Malhotra, 1982;Wright, 1975). In a betting market context, this suggests that bettors may turn to simpler criteria for selecting horses (e.g., in extremis, basing their selection on the horse's name) and may make more random errors in races involving greater complexity.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the effect should be most noticeable in races associated with extreme complexity (handicap races with many runners). Malhotra (1982), for example, noted particularly dysfunctional effects of complexity on decision-making when the number of alternatives exceeds 10 and Johnson and Bruce (1998) identified the multiplicative effects of alternative-and attribute-based complexity. However, the results displayed in Figure 1 and Table 4 show that…”

Section: Discussionmentioning

confidence: 99%

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Complexity as a guide to understanding decision bias: A contribution to the favorite‐longshot bias debate

Sung¹,

Johnson²,

Dror³

2008

Behavioral Decision Making

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This paper investigates the origins of a widespread decision bias in betting markets, the favorite-longshot bias (FLB); in particular, whether it is caused by cognitive errors on the part of bettors or by the pricing policies of bookmakers. The methodology is based on previous literature, which has suggested that: (i) races, as decision tasks for bettors, can be distinguished by their degree of complexity and their attractiveness to those with access to privileged information (insiders), (ii) cognitive errors increase as complexity increases, and (iii) bookmakers set odds in a manner to protect themselves from insiders. The degree of FLB was examined in races of varying complexity and attractiveness to insiders using a dataset of 8545 races drawn from the parallel bookmaker and pari-mutuel markets operating in the UK in 2004. The results, interpreted in the light of the cognitive error and complexity literature, suggest that neither bettors' nor bookmakers' cognitive errors are the main cause of the bias. Rather, bettors' preferences for risk and the deliberate pricing policies of bookmakers play key roles in influencing the bias in markets where bookmakers and pari-mutuel operators coexist.

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Information Load and Consumer Decision Making

Cited by 604 publications

References 44 publications

Less may be more when choosing is difficult: Choice complexity and too much choice

Less may be more when choosing is difficult: Choice complexity and too much choice

Known Unknowns in Judgment and Choice

Complexity as a guide to understanding decision bias: A contribution to the favorite‐longshot bias debate

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