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Conflict of interest perceptions and risk-related research partnerships
John C. Besley, Aaron M. McCright, Kevin C. Elliott, Nagwan Zahry (graduate assistant), Tsuyoshi Oshita (graduate assistant)
Business University Government NGO
SRA 2015
A philosopher A sociologist An historian
A scientist A …Another scientist
Our interdisciplinary research group …
We want to do researchResearch costs money
Industry has moneyPeople worry about industry money
There are some ways to mitigate real problems (!)Are there ways to mitigate perceptual problems?
A problem…
What do we know so far…
• Clear evidence that groups such as doctors (and others) question validity of industry funded research
What do we know so far…
• Some evidence that people use perceptions of procedural fairness to assess whether decisions taken in situations of uncertainty (such as scientific research) are valid
What do we know so far…
• Suggestion that procedural fairness perceptions can be used to operationalize perceptions of …
“conflict of interest”
… a conflict of interest can be understood as a situation where an individual or organization has a decision-making role that might allow them to improperly benefit from the decisions the individual might make*
*See: Davis, M. (2001). Introduction. In M. Davis & A. Stark (Eds.), Conflict of Interest in the Professions (pp. 3-19). New York, NY: Oxford University Press.
2 Experiments(And maybe a bonus experiment)
Study 1: Does partner choice affect fairness and legitimacy perceptions?
Design• Four partners (University, Industry, Government, NGO)• 15 combinations (2 x 2 x 2 x 2 [- 1])
• All 4 partners, all 3 of 4 partner combos, etc. • Pre-test used to identity partners with
high positive and low negative perceptions
• Context: Regulation of low levels of transfats• Sample: 626 US-based mTurkers
• Multiple attention/manipulation checks (data excluded)
Study 1: What it looked like …
Partner manipulation(also repeated in question stems)
Study 1: What it looked like …
• Six statements for procedural fairness DV as bias control + voice• Cronbach’s alpha = .93
• Highly correlated with direct measure of conflict of interest
Study 1: What it looked like …
• Four statements for legitimacy DV as willingness to use results• Cronbach’s alpha = .79
• MLE factor analysis suggests itemsdifferent from fairness items, r = .64
Study 1: Fairness for Partnership
Industry = Relative Low Fairness
Shared letter = Not significantly different based on a post-hoc test
Kelloggs
(a)
Kelloggs
+ CDC (ab)
Kelloggs
+ UCS (ab)
Purdue +
Kelloggs +
CDC (abc)
Purdue + Kello
ggs (
abc)
Kelloggs
+ CDC + UCS (abcd
)
Purdue + Kello
ggs + CDC + UCS (
bcd)
Purdue + Kello
ggs +
UCS (bcd
)
Purdue + CDC (b
cd)
CDC (bcd
)
Purdue (b
cd)
Purdue + CDC + UCS (
cd)
UCS (d)
CDC + UCS (d
)
Purdue +
UCS (d)
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Study 1: Fairness for Partnership
Some benefit to adding partners
Shared letter = Not significantly different based on a post-hoc test
Kelloggs
(a)
Kelloggs
+ CDC (ab)
Kelloggs
+ UCS (ab)
Purdue +
Kelloggs +
CDC (abc)
Purdue + Kello
ggs (
abc)
Kelloggs
+ CDC + UCS (abcd
)
Purdue + Kello
ggs + CDC + UCS (
bcd)
Purdue + Kello
ggs +
UCS (bcd
)
Purdue + CDC (b
cd)
CDC (bcd
)
Purdue (b
cd)
Purdue + CDC + UCS (
cd)
UCS (d)
CDC + UCS (d
)
Purdue +
UCS (d)
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Study 1: Same pattern for legitimacy …
Shared letter = Not significantly different based on a post-hoc test
Kelloggs
+ UCS (a)
Purdue + Kello
ggs +
CDC (ab)
Kelloggs
+ CDC (abc)
Purdue + Kello
ggs (abc)
Kelloggs
(abc)
Kelloggs
+ CDC + UCS (abc)
UCS (abc)
Purdue + CDC (a
bc)
Purdue +
Kelloggs +
UCS (abc)
CDC + UCS (b
c)
Purdue (b
c)
Purdue + Kello
ggs +
CDC + UCS (bc)
CDC (bc)
Purdue + CDC + UCS (
bc)
Purdue + UCS (c
)1.00
2.00
3.00
4.00
5.00
6.00
7.00
Study 1: As a mediation model in PROCESS …
Shared letter = Not significantly different based on a post-hoc test
B SE Sig. B SE Sig.Outcome: Perceived Fairness Direct and Indirect Effects(Constant) 4.88 .15 .00 Direct effects of Kellogg's -.07 .11 .51Partnership includes Kellogg's -.92 .11 .00 Indirect effects of Kellogg's -.61 .08 [-.79, -.46]Partnership includes Purdue .21 .12 .07Partnership includes CDC -.02 .11 .87 Direct effects of Purdue -.02 .10 .81Partnership includes UCS .41 .12 .00 Indirect effects of Purdue .14 .07 [-.01, .30]
r2 .15Outcome: Perceived Legitimacy Direct effects of CDC .28 .10 .00(Constant) 1.17 .23 .00 Indirect effects of CDC -.01 .08 [-.16, .14]Perceived fairness of partnership .66 .04 .00Partnership includes Kellogg’s -.07 .11 .51 Direct effects of UCS .06 .10 .54Partnership includes Purdue -.02 .10 .81 Indirect effects of UCS .27 .07 [.12, .41]Partnership includes CDC .28 .10 .00Partnership includes UCS .06 .10 .54
r2 .42• Industry hurts fairness• NGO/university(?) helps fairness• Fairness mediates the
relationship with legitimacy• Government has direct effect
Study 2: Replicate study in context of GMO partnership
Design• Same 4-partner design• DVs: Fairness and Legitimacy• New context: GMO safety testing• Sample: 627 US-based mTurkers, with attention test
Study 1: Fairness for Partnership
Shared letter = Not significantly different based on a post-hoc test
Purdue +
Kelloggs +
CDC (a)
Kelloggs
(a)
Kelloggs
+ UCS (ab)
Kelloggs
+ CDC (abc)
Purdue + Kello
ggs (
abc)
Kelloggs
+ CDC + UCS (abcd
)
Purdue + Kello
ggs +
UCS (abcd
)
CDC + UCS (a
bcd)
Purdue + Kello
ggs +
CDC + UCS (abcd
)
Purdue + CDC (a
bcd)
CDC (bcd
)
Purdue + CDC + UCS (
cd)
Purdue (c
d)
UCS (cd)
Purdue +
UCS (d)
1.00
2.00
3.00
4.00
5.00
6.00
7.00Industry = Relative Low Fairness (again)
• A similar pattern again for legitimacy
Study 1: Transfats Study 2: GMOs B SE Sig. B SE Sig.Outcome: Perceived Fairness(Constant) 4.88 .15 .00 4.77 .14 .00Partnership includes Kellogg's -.92 .11 .00 -.73 .11 .00Partnership includes Purdue .21 .12 .07 .18 .11 .09Partnership includes CDC -.02 .11 .87 -.03 .11 .77Partnership includes UCS .41 .12 .00 .27 .11 .01
r2 .15 .09Outcome: Perceived Legitimacy(Constant) 1.17 .23 .00 1.81 .20 .00Perceived fairness of partnership .66 .04 .00 .60 .03 .00Partnership includes Kelloggs -.07 .11 .51 -.09 .09 .34Partnership includes Purdue -.02 .10 .81 .14 .09 .12Partnership includes CDC .28 .10 .00 .12 .09 .17Partnership includes UCS .06 .10 .54 -.01 .09 .90
r2 .42 .61Direct and Indirect EffectsDirect effects of Kelloggs -.07 .11 .51 -.09 .09 .34Indirect effects of Kellogg's -.61 .08 [-.79, -.46] -.44 .07 [-.61, -.32]
Direct effects of Purdue -.02 .10 .81 .14 .09 .12Indirect effects of Purdue .14 .07 [-.01, .30] .11 .07 [-.03, .24]
Direct effects of CDC .28 .10 .00 .12 .09 .17Indirect effects of CDC -.01 .08 [-.16, .14] -.02 .06 [-.15, .11]
Direct effects of UCS .06 .10 .54 -.02 .09 .90Indirect effects of UCS .27 .07 [.12, .41] .16 .07 [.03, 30]
• Industry still a problem• NGO still helps• University still a (weak) positive• No direct effect for government
Bonus Study: Beyond Partners to ProceduresDesign• 4 partner combos x 4 procedures• Same 3-partners (no government), between subjects
• Four types of procedures to protect research• No procedure• Transparency• Third party review• Arm’s length agreement
• Context: Back to transfats• DV: Fairness (also legitimacy) • Sample: 962 US-based mTurkers, with attention test
Bonus Study: Beyond Partners to Procedures
Procedural manipulation
Partner combo manipulation
Fairness by partnership and type of procedure
Any Kelloggs
Involvement
Kelloggs
+ UCS
Purdue + Kello
ggs
Purdue + Kello
ggs + UCS
Purdue + UCS
1.00
2.00
3.00
4.00
5.00
6.00
7.00
No procedureTransparency3rd partyArm's Length
ab
abb ab
b
[F(3, 958) = 6.24, p = .00]
Fairness by procedure and type of partnership
No procedure Transparency 3rd party Arm's Length0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Any Kelloggs InvolvementKelloggs + UCSPurdue + KelloggsPurdue + Kelloggs + UCSPurdue + UCS
GLM Parameter Estimates for Fairness B SE Sig. Part-
Eta2Intercept 5.33 .23 .00 .30Purdue .31 .10 .00 .01UCS .06 .10 .56 .00Kelloggs -1.31 .18 .00 .04
Arm's Length .17 .22 .45 .00Kelloggs*Arm's Length .34 .25 .17 .00
Transparency -.22 .21 .28 .00Kellogg's*Transparency .52 .24 .03 .00
3rd Party -.13 .20 .53 .00Kellogg's*3rd Party .53 .23 .02 .00
• University partner good for fairness
• Arm’s length policy n.s.• Transparency procedure
helpful with industry partner• 3rd party oversight help
with industry partner• Arm’s length (b = .42)
and 3rd party procedures (b = .28) are significant without interaction terms
Adjusted r2 = .13
Discussion …
When it comes to perceptions of fairness (and legitimacy)1. An industry partner (even a nice one) hurts perceptions2. Adding additional partners doesn’t help much3. Adding procedural safeguards doesn’t help much
Should academic scientists never accept industry collaboration?
Discussion …
What if we were to add multiple procedures?What about other issues, including non-risk issues?Is it all collaboration or just ones involving funding?
Does it really matter in the ‘real’ world?