Sra 2014 presentation engagement goals and engagement

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Scientists’ perceptions of online public engagement (and the need for theory!)

Anthony Dudo, Ph.D.Assistant ProfessorDept. of Advertising & PRTexas at Austin

John C. Besley, Ph.D.Associate Professor & Ellis N. Brandt ChairDept. of Advertising & PRMichigan State

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Broad context the three moments of science communication

What brings people to science? (focus on public)

What brings science to people? (focus on scientists)

How do gatekeepers contribute?(focus on media / PIOs / bloggers)

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More attention to PES on the ground

• More PES training

• Pedagogical shifts

• Scientist-to-scientist advice

• Popular books

• Third-party resources

• Active blogging community

• Risk communication is key underlying theme

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More research on PES

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This research …has provided a strong baseline understanding of scientists’ perceptions and activity related to PES

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Aim to examine the nature of PESthink about PES from the perspective of strategic communication: planned communication with a goal in mind

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When a scientist engages …what is she or he hoping to accomplish? what are scientists’ goals? what impact do these goals have?

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Communication theory?…theory focuses on communication effects … theory focuses on information seeking … theory predicting communication choices?

Communication strategy

as planned behavior?

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5 goals from the literature …

EducateDefend science

ExciteBuild trust

Frame debates

Strategic goals

Traditional goals

Research Questions

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2

X

What goals do scientists prioritize when communicating with the public?

Are these goals associated with willingness to engage

(Past research focused on predictors of goal selection)

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Method

Sample

• U.S.-based, university-based Ph.D.s who were AAAS members

2013 AAAS Scientist Survey

Distribution

• Online (Qualtrics), Tailored Design Method

• All requests sent from AAAS Membership Dept. (to protect privacy)

• Incentive: 1/200 chance to win $500 amazon.com gift card or donation to AAAS

Response Rate

• 390/5,000 = 8%!!! (not adjusted for undeliverable emails)

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Descriptive Results

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2013 Scientist Survey: Past Engagement“About how many total days … did you devote to … online engagement through websites, blogs and/or social networks (e.g., Facebook, Twitter) aimed at communicating science with ADULTS who are not scientists?”

0 days about 1 day about 2 days

about 3-4 days

about 5 days

6-10 Days 11+ days0

10

20

30

40

50

60

note: treated as continuous (using dummy variable made no difference; relationship is linear)

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2013 Scientist Survey: Willingness to engage (%)“How willing would you be to take part in … online engagement through websites, blogs and/or social networks (e.g., Facebook, Twitter) aimed at communicating science with ADULTS who are not scientists?”

not at all willing

very willing0

5

10

15

20

25

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2013 Scientist Survey: Goals

messaging goal average (r = .54)

describing … in ways that make them relevant

framing research … {to} resonate …

trust goals average (r = .54)

demonstrating … openness & transparency

hearing what others think …

getting people excited about science

knowledge goals average (r = .41)

ensuring that scientists … are part of …

ensuring that people are informed …

defensive goals average (r = .63)

defending science …

correcting scientific misinformation

1 2 3 4 5 6 7

4.96

5.34

4.59

5

5.22

4.76

5.59

5.88

5.72

6.04

5.96

5.79

6.14

Strate-gic

goals

“How much should each of the following be a priority for online public engagement?”

All questions had a range of 1-7 where 1 was the “lowest priority” and 7 was the “highest priority”

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2013 Scientist Survey: Goals

messaging goal average (r = .54)

describing … in ways that make them relevant

framing research … {to} resonate …

trust goals average (r = .54)

demonstrating … openness & transparency

hearing what others think …

getting people excited about science

knowledge goals average (r = .41)

ensuring that scientists … are part of …

ensuring that people are informed …

defensive goals average (r = .63)

defending science …

correcting scientific misinformation

1 2 3 4 5 6 7

4.96

5.34

4.59

5

5.22

4.76

5.59

5.88

5.72

6.04

5.96

5.79

6.14

Strate-gic

goals

“How much should each of the following be a priority for online public engagement?”

All questions had a range of 1-7 where 1 was the “lowest priority” and 7 was the “highest priority”

Paper in Revision– Predictors of goals: • Perceived goal ethicality• Goal-specific external efficacy• Goal-specific internal efficacy• Perceptions of colleagues goals

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Additional multivariate results:Willingness to engage online

RQ: If you prioritize a goal (any goal), does that mean you might be more willing to engage?

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Predictors of online engagement willingnessModel specification for hierarchical regressions (based on theory of planned behavior)

Engage!

Controls

Goals

Attitudes

Field and Funding

Efficacy and Norms

age, gender, ideology, productivity, science news online / offline, engagement experience, comm. training

biomedicine, chemistry, physics/astronomy, social science, DOD, NSF, NIH, private, other funding

fairness: respect, fairness: career outcome, personal enjoyment

external efficacy, internal efficacy, subjective norms, descriptive norms

defend, educate, excite, build trust, messaging

willingness to engage online

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Predictors of online engagement willingnessModel specification for hierarchical regressions(based on theory of planned behavior)

Engage!

Controls

Goals

Attitudes

Field and Funding

Efficacy and Norms

age, gender, ideology, productivity, science news online / offline, engagement experience, comm. training

biomedicine, chemistry, physics/astronomy, social science, DOD, NSF, NIH, private, other funding

fairness: respect, fairness: career outcome, personal enjoyment

external efficacy, internal efficacy, subjective norms, descriptive norms

defend, educate, excite, build trust, messaging

willingness to engage online

Additional multi-item measures

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Model specification for hierarchical regressions

fairness: external procedural, four items, alpha = .86(e.g., I would be treated rudely …)

fairness: external distributive, three items, alpha = .93

(e.g. I would see my research hurt …)

subjective norms, two items, r = .83(e.g. Scientists who engage online … well-regarded by … peers)

descriptive norms, two items, r = .61 (e.g. Most scientists do not take part in …)

efficacy – external impact of goals, seven items, alpha = .88(e.g. How effective … each of the following …)

efficacy – personal skill/ability toward goals, seven items, alpha = .90

(e.g. How effective do you think could be … each of the following …)

(All other items single-item measures)

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age

conservative-Liberal

amount of training

past online media use

chemical scientist

social scientist

NIH NIH funding

federal funding-other

funding-other

fairness (audience will treat with respect)

enjoyment of communication

descriptive norms

efficacy - external impact

defense of science goal

build trust goal

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50

Beta r

Adjusted r2 = .10

Regression results (betas)

Correlation coefficientsLight orange, p > .05Standardized betas(dark orange = p > .05)

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age

conservative-Liberal

amount of training

past online media use

chemical scientist

social scientist

NIH NIH funding

federal funding-other

funding-other

fairness (audience will treat with respect)

enjoyment of communication

descriptive norms

efficacy - external impact

defense of science goal

build trust goal

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50

Beta r

Older people less willing to engage online

Adjusted r2 = .10

Regression results (betas)

Correlation coefficientsLight orange, p > .05Standardized betas(dark orange = p > .05)

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Regression results (betas)

age

conservative-Liberal

amount of training

past online media use

chemical scientist

social scientist

NIH NIH funding

federal funding-other

funding-other

fairness (audience will treat with respect)

enjoyment of communication

descriptive norms

efficacy - external impact

defense of science goal

build trust goal

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50

Beta r

Experience predicts future willingness

Adjusted r2 = .26

Correlation coefficientsLight orange, p > .05Standardized betas(dark orange = p > .05)

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Regression results (betas)

age

conservative-Liberal

amount of training

past online media use

chemical scientist

social scientist

NIH NIH funding

federal funding-other

funding-other

fairness (audience will treat with respect)

enjoyment of communication

descriptive norms

efficacy - external impact

defense of science goal

build trust goal

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50

Beta r

Field and funding do not seem to matter

Correlation coefficientsLight orange, p > .05Standardized betas(dark orange = p > .05)

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Regression results (betas)

age

conservative-Liberal

amount of training

past online media use

chemical scientist

social scientist

NIH NIH funding

federal funding-other

funding-other

fairness (audience will treat with respect)

enjoyment of communication

descriptive norms

efficacy - external impact

defense of science goal

build trust goal

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50

Beta r

Some fear of hostile audience impact on career

Adjusted r2 = .27

Correlation coefficientsLight orange, p > .05Standardized betas(dark orange = p > .05)

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Regression results (betas)

age

conservative-Liberal

amount of training

past online media use

chemical scientist

social scientist

NIH NIH funding

federal funding-other

funding-other

fairness (audience will treat with respect)

enjoyment of communication

descriptive norms

efficacy - external impact

defense of science goal

build trust goal

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50

Beta r

Perceived skill may affect willingness

Adjusted r2 = .30

Correlation coefficientsLight orange, p > .05Standardized betas(dark orange = p > .05)

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Regression results (betas)

age

conservative-Liberal

amount of training

past online media use

chemical scientist

social scientist

NIH NIH funding

federal funding-other

funding-other

fairness (audience will treat with respect)

enjoyment of communication

descriptive norms

efficacy - external impact

defense of science goal

build trust goal

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50

Beta r

Goals do not seem to matter?

Correlation coefficientsLight orange, p > .05Standardized betas(dark orange = p > .05)

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Key findings

Scientists prioritize online public communication that is designed to defend science and educate

Scientists find the least value in the goals that are most likely to lead to positive engagement outcomes: building trust and tailoring messages

Scientists’ willingness to engage online a function of past experiences with engagement and social media, concern about impact, internal efficacy

Prioritizing specific goals has little impact on willingness to engage online

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What’s next? Long-term goal: help build a community focused on evidence-based science communication

Current project PES research needs

‣ 2-year NSF-AISL “Pathways” project that will enable …

‣ Qualitative interviews with science engagement trainers

‣ Surveys with members from 10+ major US scientific societies

‣ Experiments testing messages related to communication goals

‣ Identify most important goals

‣ Establish whether TPB is best

‣ Longitudinal/experimental data

‣ Operational consistency

‣ International data

‣ What role risk?

‣ How to maximize response rate?

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Contact usAnthony Dudodudo@utexas.edu

John C. Besleyjbesley@msu.edu

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Regression results

Predictors that cut across the goals

Standardized betas* p<.05, ** p<.01, ***p<.001

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Regression results

The education goal is the most different in terms of its predictors

Standardized betas* p<.05, ** p<.01, ***p<.001