Antecedents and Consequences of Unsolicited vs Antecedents and Consequences of Unsolicited vs. Explicitly Solicited Advice Alyssa Mitchell Gibbons Janet A. Sniezek University of Illinois at Urbana-Champaign Reeshad S. Dalal Purdue University SJDM 2003

In memory of Janet A. Sniezek 1951-2003

Background Judge Advisor System (JAS) – decision making group with role differentiation (Sniezek & Buckley, 1995) Judge – responsible for making the final decision Advisor(s) – provides information to Judge (information, recommendations, explanations, expressions of confidence, etc.) Previous JAS research focused mostly on the Judge: How Judges reconcile external and internal information and conflicting advice (Sniezek & Buckley, 1995) Judges’ motivations for using advice (Harvey & Fischer, 1997) How well Judges assess advice vs. how well they use it (Harvey, Harries, & Fischer, 2000)

Advice is usually provided on every decision. (e. g Advice is usually provided on every decision. (e.g., Birnbaum & Stegner, 1979; Harvey & Fischer, 1997; Harvey, Harries, & Fischer, 2000; Koestner et al., 1999) In the “real world,” however, advice is neither automatically sought nor automatically provided for every decision. Research has not (yet) focused on the factors that facilitate advice …when it is explicitly solicited by the Judge …when it is provided unsolicited by the Advisor

Design Participants randomly paired and assigned to be either Judge or Advisor. Advisor in Control (AC): 101 dyads Judge in Control (JC): 40 dyads Answered 20 moderately difficult 2-choice items about social psychology. Worked at networked computers in separate rooms – advice was given by means of a videoconferencing link. Dyads had opportunity to win cash reward based on the Judge’s final answers. Items thought to be difficult, but not impossible, for introductory psychology students – they would not have expertise in this domain, but they should have some familiarity with the terms, etc. on which they could base their reasoning.

Judge in Control (advice is solicited) On each decision… Advising interaction occurs Yes Judge and Advisor provide initial opinions and confidence estimates Judge and Advisor provide final opinions and confidence estimates No Does Judge solicit advice?

Advisor in Control (unsolicited advice) On each decision… Advising interaction occurs Yes Judge and Advisor provide initial opinions and confidence estimates Judge and Advisor provide final opinions and confidence estimates No Does Advisor volunteer advice? Advisor becomes aware of Judge’s initial opinion and confidence estimate

Variables of Interest Outcomes Variables Offering unsolicited advice or seeking advice Response to advice once given Answer change Agreement with advisor Final decision accuracy (judge) Variables Accuracy – choosing the correct answer Confidence – self-report of confidence in own choice, from .50 to 1.00 Conflict – difference in Judge and Advisor initial choices # of items answered not necessarily the same for all participants – time was limited but didn’t rush them Initial and final measures of confidence and accuracy

Results: Initial Comparisons AC JC Judge Initial Mean Accuracy .59 .60 Judge Initial Mean Confidence .66 .69 Advisor Initial Mean Accuracy .58 .61 Advisor Initial Mean Confidence .67 Within each condition, Judges and Advisors were not significantly different from each other in their initial answers. Participants in the two conditions were not initially significantly different from each other. Calibration – correlation between accuracy and confidence – was poor: highest correlation in any group = .095.

Results: Advice and Consequences AC JC Proportion of Advice Sought or Given .22 .52 Judge Final Mean Accuracy .60 .64 Judge Final Mean Confidence .67 .71 Advisor Final Mean Accuracy .61 .62 Advisor Final Mean Confidence .66 Based significance test on arcsin transformed data , but reported interpretable proportion Judges appear to want more advice than advisors want to give Judges appear to have better outcomes when the advice is on their terms However, we don’t know from these data that the advice enhances outcomes – it could be that simply having control makes Judges more comfortable, which allows them to make more accurate decisions, etc… Need to look at the item level to determine the antecedents and consequences of advice. Judges asked for significantly more advice than Advisors offered unsolicited. Judges who were in control of advice were significantly more accurate and confident.

Predicting Advice Giving and Seeking

Predicting Advice: Regression Models Judge in Control (solicited advice) : Model R2 = .149 Advisor in Control (unsolicited advice) : Model R2 = .279 All beta values reported were significant, p<.05 Variable Beta (std) Judge Initial Confidence -.379 Variable Beta (std) Advisor Initial Confidence .300 Judge Initial Confidence -.164 Conflict 1.037 Conflict x Advisor Initial Confidence 1.019 Conflict x Judge Initial Confidence -.433 Interpretation: As expected, only judge confidence predicts explicit advice seeking.

Confidence and Unsolicited Advice No difference between high & low confidence with no conflict for either When conflict exists, advice is more likely in general, plus high advisor conf -> more advice, but higher judge conf -> less advice

Advice Response

Advice Response: Regression Models Judge in Control (solicited advice) : Model R2 = .149 Advisor in Control (unsolicited advice) : Model R2 = .279 Variable Beta (std) Conflict x Advisor Initial Confidence .597 Conflict x Judge Initial Confidence -.933 Interpretation: As expected, only judge confidence predicts explicit advice seeking. Variable Beta (std) Conflict x Advisor Initial Confidence .544 Conflict x Judge Initial Confidence -.541

Answer Change After Advice High & Low Conf are NS different for both studies, regardless of whether or not conflict was present. However, the studies are sig diff, and conflict is always higher than no conflict.

Answer Change After Advice Under No Conflict, Low/High cells are NS different from each other. All corresponding cells are sig different across studies. Conflict is different from no conflict in both studies. When conflict, low conf is sig diff from high conf in both studies.

Conclusions Advisors weighed more factors in deciding to give unsolicited advice than Judges in deciding to seek advice. Conflict Confidence – Advisor high or Judge low Somewhat intuitive – Advisors had more information than Judges – but they did make use of this information.

Conclusions Answer change was more likely when Judge sought advice than when advice was unsolicited. Confidence an important determinant of answer change. Limitations – Novice advisors No performance feedback

Questions?

Final Accuracy

Final Decision Accuracy (Advisor in Control) Model R2 = .659 Variable Beta (std) Judge Initial Accuracy .678 Advisor Initial Accuracy .196 Advice -.131 Advice x Advisor Initial Accuracy .242 Advice x Judge Initial Accuracy -.451 Interpretation:

Final Decision Accuracy (Judge in Control) Model R2 = .619 Variable Beta (std) Judge Initial Accuracy .563 Advice x Advisor Initial Accuracy .390 Advice x Judge Initial Accuracy -.430 Advisor Initial Accuracy X Judge Initial Confidence -.445 Advisor Initial Confidence X -.498 Judge Initial Accuracy X Judge Initial Confidence .647 Interpretation:

Results Summary: Both Studies  indicates positive effect on Judge Final Accuracy  indicates negative effect on Judge Final Accuracy = indicates no effect General results: Effect of: Is: When: Judge Correct  (all cases) Advice Judge Incorrect  Advisor Incorrect Advisor Correct

Results Summary JUDGE IN CONTROL Effect of: Is: When: Advisor Correct  Judge Confidence = Low = Judge Confidence = High Advisor Confidence Judge Incorrect Judge Correct Judge Confidence  ADVISOR IN CONTROL Effect of: Is: When: Advisor Correct  (all cases) Advice 

Data Considerations - Dependency Item level data are doubly dependent: Within-persons Within-items Not interested in item or person factors as such, but in the situational factors that induce advice giving or taking over and above these general tendencies. Strategy: Double-mean-center data to eliminate effects of person & item: Advice = (person mean) + (item mean) + (situation effects) Advice - person mean - item mean = situation effects