Advisors want their advice to be used – but not too much: An interpersonal perspective on advice taking

Ache, Fabian; Rader, Christina A.; Hütter, Mandy

doi:10.1016/j.jesp.2020.103979

Cited by 20 publications

(25 citation statements)

References 53 publications

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“…The prevailing demand for inferential conclusions anyway challenges the existence of factual differences in model(ing) complexity between ours and the more established approach. In the latter, researchers have to rely on a two-step procedure: Merely descriptive ROD-WOAs from step one are utilized as dependent variables in statistical testing procedures (e.g., multilevel modeling: Ache et al, 2020;Hütter & Fiedler, 2019;Minson & Mueller, 2012;Schultze et al, 2015) on step two. Therefore, those and other important merits and limitations of the two methods will be critically evaluated in the following.…”

Section: Discussionmentioning

confidence: 99%

Mixed-Effects Regression Weights for Advice Taking and Related Phenomena of Information Sampling and Utilization

Rebholz¹,

Biella²,

Hütter³

2023

Preprint

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Advice taking and related research are dominated by deterministic weighting indices such as ratio-of-differences-based formulas for investigating informational influence. They are intuitively simple but entail various measurement problems and restrict research to a certain paradigmatic approach. As a solution, we propose process-consistent mixed-effects regression modeling for specifying how strongly peoples’ judgment is influenced by external information. Our formal derivation of the proposed weighting measures is accompanied by a detailed elaboration on their most important technical and statistical subtleties. Essentially, the approach differentiates between components of endogenous (i.e., final judgment) and exogenous (e.g., initial judgment and advice) nature by relying on accordingly specified multilevel models. Corresponding mixed-effects regression coefficients of various exogenous sources of information hence also reflect individual weighting but are based on a conceptually consistent representation of the endogenous judgment process. We use this modeling approach to revisit empirical findings from sequential collaboration and advice taking paradigms. Specifically, whereas we do not obtain evidence for systematic order effects in sequential collaboration, we document recency effects in the weighting of sequentially sampled advice. We argue that process-consistent modeling of information sampling and utilization has the potential to increase the replicability of our science and opens up new avenues for innovative research. Moreover, the proposed method is relevant beyond sequential collaboration and advice taking. Mixed-effects regression weights can also inform research on related cognitive phenomena such as multidimensional belief updating, anchoring effects, hindsight biases, or attitude change.

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Section: Discussionmentioning

confidence: 99%

Mixed-Effects Regression Weights for Advice Taking and Related Phenomena of Information Sampling and Utilization

Rebholz¹,

Biella²,

Hütter³

2023

Preprint

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“…The opportunity to opt out of answering may help to reduce cognitive bias and judgment errors by preventing erroneous judgments of participants who do not feel sufficiently confident to answer a given question. This notion is supported by studies showing that advisors do not always want their advise to be fully adopted by advisees, especially for difficult tasks (Ache et al, 2020). Sequential collaboration provides an efficient way to express this uncertainty because contributors who are uncertain can opt out of providing a revised judgment.…”

Section: Unique Features Of Sequential Collaborationmentioning

confidence: 97%

Sequential Collaboration: The Accuracy of Dependent, Incremental Judgments

Mayer¹,

Dw²

2021

Preprint

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On the internet, people often collaborate to generate extensive knowledge bases such as Wikipedia for semantic information or OpenStreetMap for geographic information. When contributing to such online projects, individual judgments follow a sequential process in which one contributor creates an entry and other contributors have the possibility to modify, extend, and correct the entry by making incremental changes. We refer to this way of working together as sequential collaboration because it is characterized by dependent judgments that are based on the latest judgment available. Since the process of correcting each other in sequential collaboration has not yet been studied systematically, we compare the accuracy of sequential collaboration and wisdom of crowds, the aggregation of a set of independent judgments. In three experiments with groups of four or six individuals, accuracy for answering general knowledge questions increased within sequences of judgments in which participants had the possibility to correct the judgment of the previous contributor. Moreover, the ﬁnal individual judgments in sequential collaboration were slightly more accurate than the averaged judgments in wisdom of crowds. This shows that collaboration can beneﬁt from the dependency of individual judgments, thus explaining why large collaborative online projects often provide data of high quality.

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“…In the dominant experimental paradigm of advice taking research, the judge-advisor system (JAS) of Sniezek and Buckley (1995), participants first judge a set of items on a certain dimension without external help. For instance, they are asked to estimate the caloric content of food (Hütter & Ache, 2016;Schultze et al, 2015;Yaniv et al, 2009), airline distances between cities (Ache et al, 2020;Schultze et al, 2012Schultze et al, , 2017, dates of historic events (Gino, 2008;Hütter & Fiedler, 2019;Yaniv, 2004a), or carbon footprints of products (Rebholz & Hütter, 2022). In most of these examples, participants can revise their initial judgments after having been given access to a single piece of advice from another person.…”

Section: Seekingmentioning

confidence: 99%

Bayesian Advice Taking: Adaptive Strategy Selection in Sequential Advice Seeking

Rebholz¹,

Hütter²,

Voss³

2023

Preprint

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In sampling approaches to advice taking, participants can sequentially sample multiple pieces of advice before making a final judgment. To contribute to the understanding of active advice seeking, we develop and compare different strategies for information integration from external sources, including Bayesian belief updating. In a reanalysis of empirical data, we find that participants most frequently compromise between their initial beliefs and the distributions of multiple pieces of advice sampled from others. Moreover, across all participants, compromising predicts their final beliefs better than choosing one of the two sources of information. However, participants’ willingness to integrate external opinions is relatively higher for multiple pieces of reasonably distant as compared to close advice. Nevertheless, egocentrism is as pronounced as in the traditional paradigm where only a single piece of external evidence is provided. Crucially, there are large inter- and intra-individual differences in strategy selection for sequential advice taking. On the one hand, some participants choose their own or others’ judgments more often, and other participants are better described as compromisers between internal and external sources of information. On the other hand, virtually all participants apply different advice taking strategies for different items and trials. Our findings constitute initial evidence of the adaptive utilization of multiple, sequentially sampled external opinions.

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Advisors want their advice to be used – but not too much: An interpersonal perspective on advice taking

Cited by 20 publications

References 53 publications

Mixed-Effects Regression Weights for Advice Taking and Related Phenomena of Information Sampling and Utilization

Mixed-Effects Regression Weights for Advice Taking and Related Phenomena of Information Sampling and Utilization

Sequential Collaboration: The Accuracy of Dependent, Incremental Judgments

Bayesian Advice Taking: Adaptive Strategy Selection in Sequential Advice Seeking

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