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Nchcmm presentation 10 variables_the research_p_keller_8-2011

Building a Better Message: The 10 Variables That Really Matter (The Research)
Punam Keller, PhD, MBA
Tuck School of Business at Dartmouth College, Hanover, NH
Dr. Keller explores extensive meta-analysis of the main and interaction effects of message tactics and individual
characteristics on intentions to comply with health recommendations. Based on her research, Dr. Keller discusses
the empirical model on which the Message Development Tool is based and the 10 variables that are significant
predictors for stated intentions and behavior when socioeconomic, social influence, beliefs and attitudes, number
of ads, and exposure frequency are accounted for.

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Nchcmm presentation 10 variables_the research_p_keller_8-2011

  1. 1. Punam Anand Keller, MBA, PhD <ul><li>Charles Henry Jones Third Century Professor of Management </li></ul><ul><li>Tuck School of Business at Dartmouth </li></ul><ul><li>National Conference on Health Communication, Marketing, and Media </li></ul><ul><li>August 9-11, 2011 </li></ul>Building a Better Message: The 10 Variables That Really Matter The Research <ul><li>Division of Cancer Prevention and Control </li></ul><ul><li>National Center for Chronic Disease Prevention and Health Promotion </li></ul>
  2. 2. <ul><li>The Problem </li></ul><ul><li>Four barriers prevent the application of research to improve the effectiveness of public health communication campaigns. </li></ul><ul><li>The focus on one or two message tactics makes it difficult to generalize the results to situations where the audience is faced with a wide variety of message tactics in the same or different health campaigns </li></ul><ul><li>Most health communication studies do not provide guidelines for tailoring since they do not examine how message formats interact with measurable individual differences such as psychographics. </li></ul><ul><li>Small sample sizes in most studies raise concerns about whether findings can be replicated in the field. </li></ul><ul><li>There is no evidence that message formats determine health intentions when other factors such as peer influence are taken into account. </li></ul>BACKGROUND METHODS AND RESULTS CONCLUSIONS IMPLICATIONS FOR PRACTICE Division of Cancer Prevention and Control
  3. 3. <ul><li>To address these barriers, Drs. Keller and Lehmann systematically examined the role of message tactics and individual differences on intentions to comply with health recommendations. A model, Advisor for Risk Communication (ARC), was the outcome. </li></ul><ul><li>A meta-analysis of 60 experimental studies, involving 584 health message conditions and 22,500 participants (Keller and Lehmann, 2008). </li></ul><ul><li>main and interaction effects on intentions to comply with health recommendations </li></ul><ul><li>22 message tactics (e.g. gain/loss framing, vividness, self/other referencing, emotion) </li></ul><ul><li>six individual characteristics (e.g. gender, age, race, involvement) </li></ul><ul><li>two approaches to identify matches between message tactics and audience characteristics: a full and a reduced regression model. </li></ul>METHODS AND RESULTS BACKGROUND CONCLUSIONS IMPLICATIONS FOR PRACTICE Division of Cancer Prevention and Control
  4. 4. METHODS AND RESULTS BACKGROUND CONCLUSIONS IMPLICATIONS FOR PRACTICE Division of Cancer Prevention and Control Table 1: Main Effects from the Keller and Lehmann Advisor for Risk Communication Model Main Effects - Individual Characteristics <ul><li>Age </li></ul><ul><li>Gender </li></ul><ul><li>Race </li></ul>Older adults, women, whites have higher health intentions. <ul><li>Regulatory Focus </li></ul>Those with either a promotion or a prevention focus have lower health intentions. Main Effects – Message Tactics <ul><li>Health Goal: Discouraging Behavior and Detection Behavior </li></ul>Messages on detection behaviors enhance health intentions. Discouraging unhealthful behaviors enhanced health. <ul><li>Gain/Loss Framing </li></ul>Loss framing undermined health intentions and should not be used. <ul><li>Physical vs. Social Consequences </li></ul>Emphasizing social consequences may be more effective than emphasizing physical consequences because they arouse less fear <ul><li>Emotions </li></ul>Emotional messages may not be more persuasive then unemotional messages and are not advisable. <ul><li>Individual vs. Other-Referencing </li></ul>Health communications in which consequences of nonadherence are directed at others are more effective than when the consequences are directed at the individual. <ul><li>Vividness and Base/Case Effects </li></ul>Vivid presentations (e.g., pictures, examples of specific cases/stories) are more persuasive than non-vivid formats (e.g., text only, base-rate estimates.).
  5. 5. METHODS AND RESULTS BACKGROUND CONCLUSIONS IMPLICATIONS FOR PRACTICE Division of Cancer Prevention and Control Table 2: Effective Matches between Message Tactics and Audience Characteristics from the Keller and Lehmann Advisor for Risk Communication Model All ages respond to messages advocating detection behaviors Nonwhites seem to care more about vivid messages that emphasize the effect of health consequences on loved ones Women respond to emotional messages with social consequences for themselves or health consequences to near and dear ones Men are more influenced by unemotional messages that emphasize personal physical health consequences Contrary to popular use, framed health messages (loss or gain frames) are not advisable without knowledge of target audience goals (promotion vs. prevention)
  6. 6. Behavioral Intentions Behavioral Intentions White Males = .13 White Females = .30 Non-White Males = .52 Non-White Females = .30
  7. 7. Behavioral Intentions Behavioral Intentions White Males = .28 White Females = .14 Non-White Males = .62 Non-White Females = .39
  8. 9. 2004 2005 2006 Lifeguard Dribbling Bike Race Venus Williams Donovan McNabb Landon Donovan Runaway Cell Sun Emma Roberts
  9. 10. <ul><li>Results were further validated through application to the CDC Verb campaign (2004-2006), a process which involved: </li></ul><ul><li>1). coding CDC Verb campaign advertisements; </li></ul><ul><li>2). using the model to calculate intention and behavior estimates; and </li></ul><ul><li>3). comparing the model estimates to extensive evaluation data collected on outcomes of the Verb campaign.  </li></ul><ul><li>The CDC Verb campaign validation research found that the ARC predictions and stated intentions are closely correlated when socioeconomic status, social influence, beliefs and attitudes, number of ads, and exposure frequency are accounted for. </li></ul>METHODS AND RESULTS BACKGROUND CONCLUSIONS IMPLICATIONS FOR PRACTICE Division of Cancer Prevention and Control
  10. 13. Ad Exposures Commercial Pattern 1 Pattern 2 Pattern 3 Pattern 4 Bike Race 1 1 1 1 Dribbling 0 1 1 1 Life Guard 0 0 1 1 Venus Williams 0 0 0 1 Exponent of Sum Rule .77 .81 .86 .93 Intention Max Rule .77 .77 .77 .78 Sample Size 184 309 572 104
  11. 14. <ul><li>Keller and Lehmann's research suggests an empirical model to tailor health communications for different target audiences. </li></ul><ul><li>Keller and Lehmann's empirical model provides 10 variables that are significant predictors for stated intentions and behavior when socio-economic, social influence, beliefs and attitudes, number of ads, and exposure frequency are accounted for. </li></ul><ul><li>Intention and behavior predictions are approximately equally sensitive to family and social influence, parent education, and recall of message exposures, and in general have less impact than the child variables or model predictions. </li></ul>CONCLUSIONS METHODS AND RESULTS BACKGROUND IMPLICATIONS FOR PRACTICE Division of Cancer Prevention and Control
  12. 15. <ul><li>Results show there is a significant opportunity to tailor health communications and even market public health more efficiently to different market segments. </li></ul><ul><li>Keller and Lehmann's (2008) model formed the basis for CDC DCPC's Message Development Tool (MDT). </li></ul><ul><li>http://mba.tuck.dartmouth.edu/pages/faculty/punam.keller/docs/Designing%20Effective.pdf </li></ul>IMPLICATIONS FOR PRACTICE METHODS AND RESULTS CONCLUSIONS BACKGROUND Division of Cancer Prevention and Control
  13. 16. <ul><li>Four main features of the model: </li></ul><ul><li>If you have a message , the model can predict how to improve it. </li></ul><ul><li>If you have a message , the model can predict which audiences will respond better </li></ul><ul><li>If you have multiple messages , the model can help you choose one or predict which message should be sent to different audiences </li></ul><ul><li>If you don’t have a message, the model can provide guidelines for a health message </li></ul>
  14. 17. <ul><li>Division of Cancer Prevention and Control </li></ul><ul><li>National Center for Chronic Disease Prevention and Health Promotion </li></ul>

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