Week 10 Interpersonal Influence Questions Response Read the articles and answer the questions. Please answer carefully with appropriate examples. It doesn’

Week 10 Interpersonal Influence Questions Response Read the articles and answer the questions. Please answer carefully with appropriate examples. It doesn’t have to be as formal as an essay. No citation required. 1. How are we influenced by everyday interactions with others? Parents? Children?
Friends? Partners?
2. Are persuasion & conflict two separate communication contexts? Or the same?
3. In what ways could we teach or train people to use person-centered messages?
What are some challenges that arise in using person-centered messages when
you disagree with the other person?
4. Why do validating messages work better than invalidating messages? And what
might be an example of each?
5. What types of influence strategies are you seeing on social media? Do they
seem effective/ineffective? Does it matter what the goal is? ALSO – please use a
content warning for messages that need it (example: CW: Violence). Think very
carefully about any images you might share. If you feel this thread might
introduce more trauma than you are currently willing to deal with, please feel
free to keep your participation to the other questions.
Health Communication, 27: 399–407, 2012
Copyright © Taylor & Francis Group, LLC
ISSN: 1041-0236 print / 1532-7027 online
DOI: 10.1080/10410236.2011.595771
Employing Interpersonal Influence to Promote Multivitamin Use
Franklin J. Boster
Department of Communication
Michigan State University
Christopher J. Carpenter
Department of Communication
Western Illinois University
Kyle R. Andrews
Department of Communication
Northern Illinois University
Paul A. Mongeau
Department of Communication
Arizona State University
Boster, Kotowski, Andrews, and Serota (2011) proposed that superdiffusers are well connected, persuasive, and a maven in a content area. They proposed that superdiffusers, if
recruited, could promote the adoption of health practices. In this article a model of this process is presented, and an intervention designed to test the efficacy of this influence strategy is
introduced. Specifically, superdiffusers were recruited to persuade their peers to take a daily
multivitamin. Evidence was found consistent with the intervention’s effectiveness.
Scholars examining the diffusion of information and behavior have long examined the influence of media and interpersonal message channels. Recent research on communication
campaigns has highlighted the complex interplay between
both aspects of influence, and has called attention to the need
for additional theoretical research to specify the relationship between communication campaigns and interpersonal
talk (Southwell & Yzer, 2007, 2009). The empirical research
conducted thus far demonstrates that interpersonal interaction is often related to the primary dependent variables
targeted by campaign designers. For example, media exposure on science topics was found to predict perceived topic
understanding, which in turn predicted an increased prevalence in relevant conversations (Southwell & Torres, 2006).
Such media-motivated interpersonal talk has been found to
affect cognitive and behavioral outcomes, such as attempts
Correspondence should be addressed to Franklin J. Boster, Department
of Communication, Michigan State University, 567 Communication Arts
and Sciences, East Lansing, MI 48824. E-mail: boster@msu.edu
to quit smoking (Hafstad & Aaro, 1997; Hafstad, Aaro, &
Langmark, 1996; Dunlop, Wakefield, & Kashima, 2008),
perceptions of personal risk (Morton & Duck, 2006), and
political participation and knowledge (Cho, Shah, McLeod,
McLeod, Scholl, & Gotlieb, 2009).
It has long been known that some people are more influential within social networks than others (Rogers, 2003).
Designing campaigns that can induce influential people to
engage in interpersonal talk, then, has the potential to be
a particularly promising strategy. Originally, those studying
the diffusion of innovations identified the influential members of particular networks after the fact (Katz & Lazarsfeld,
1955). More recently scholars have begun to identify a priori
the influential members of a network and seek their assistance in spreading ideas and behavior (Kelley et al., 1997;
Miller, Klotz, & Eckholdt, 1998; Schuster et al., 2006; see
Bertrand, 2004, for a review). Interventions such as that
of Kelley et al. often rely on insiders nominating opinion
leaders of small, well-defined communities. Although these
interventions have had some success, applying the opinion
400 BOSTER ET AL.
leader concept to larger, more heterogeneous communities
requires a more systematic method of identifying opinion
leaders.
IDENTIFYING THE EXTREMELY INFLUENTIAL
Traditional measures of opinion leadership (e.g., Rogers
& Cartano, 1962) do not include key aspects of what
makes an opinion leader influential (Baumgarten, 1975;
Chan & Misra, 1990; Fitzgerald, Ferlie, Wood, & Hawkins,
2002; Tschoertner, Jers, & Schenk, 2006). In order to build
upon the traditional concept of an opinion leader, Boster,
Kotowski, Andrews, and Serota (2011) developed the concept of the superdiffuser, someone able to influence many
others to accept new ideas or engage in novel action. Based
on their review of opinion leadership, they concluded that a
superdiffuser must be highly connected, highly persuasive,
and highly expert (i.e., a maven) in some domain.
According to Boster et al. (2011), someone connected
highly must be sociable and enjoy meeting new people.
Such persons are often a bridge or a liaison between disparate groups. Their ability to access different groups of
people gives connectors the ability to target a large number
of people when they choose to attempt to diffuse an idea or
practice.
A superdiffuser must also be highly persuasive (Boster
et al., 2011). Such persons are highly skilled at arguing for a
position (see Carpenter et al., 2009). They are able to frame
arguments effectively, tailoring them to have maximum suasory impact on particular people or groups of people. They
particularly enjoy argumentation, and will seek out opportunities to persuade others. These superior persuasion skills
enable them to convince many of those with whom they
interact.
Finally, a superdiffuser must be a maven in a particular domain (Boster et al., 2011). Although the attributes of
connection and persuasion are not domain specific, mavens
are extremely knowledgeable about a particular content area,
such as healthy lifestyles, politics, or popular music. They
enjoy giving people advice in their area of expertise and
people often seek their advice in the maven’s domain of
expertise. This attribute is pivotal to the diffusion process
because the maven’s combination of content specific credibility and knowledge provides the factual foundation that
enables superdiffusers to craft effective arguments promoting ideas or action. The original Boster et al. validation
study examined mavens within a health context; subsequent
research has found evidence of political mavens as well
(Serota, Carpenter, Andrews, & Boster, 2009).
Boster et al. (2011) developed items to assess each of
these three attributes, found them to fit the hypothesized
three-factor measurement model, and provided construct
validation evidence for each of the three attributes. They
defined a superdiffuser as someone high on each of the three
attributes, with “high” being given the statistical definition
of scoring at or above the 75th percentile. In a subsequent
experiment they found that their measures were able to
capture aspects of opinion leadership that previous scales
failed to include. For example, Carpenter et al. (2009)
demonstrated that superdiffusers produced more complex
arguments and were better able to adapt their arguments to
the characteristics of their target audience.
The Boster et al. (2011) measures of the attributes of a
superdiffuser permit highly influential people in large, diffuse communities to be identified. If those superdiffusers
could be recruited to advocate for a change in health behavior, a positive change in the behavior of the members of
that community would be predicted to result. The manner in which such an intervention is expected to operate is
explicated subsequently.
The Dynamics of Superdiffuser Effects
Some proportion, α, of a target population, P, may have
adopted the advocated health practice prior to the implementation of the intervention. Using this notation the number of
preexperiment adopters is given by αP, where 0 ≤ α ≤ 1.
Suppose that prior to the implementation of the intervention a survey designed to identify those high on the
connector, persuader, and maven scales is administered to
the target population. Furthermore, suppose that all persons
high on these three measures are identified, with .05 being
approximately the proportion of these superdiffusers in the
population estimated from prior research (Boster et al.,
2011). Further suppose that these .05P persons agree both
to adopt the advocated health practice and to attempt to
diffuse the advocated health practice to persons to whom
they are connected. Denoting as A0 those who adopt the
advocated health practice prior to the implementation of the
intervention,
A0 = f (those who have adopted preexperiment, number of
superdiffusers)
= those who have adopted + number of superdiffusers
= αP + .05P
(1)
Expression (1) presumes that the set of superdiffusers and
the set of early adopters do not overlap. Although this
assumption is likely false, α is likely to be so small in
applications of interest that violating it will not reduce the
predictive power of the model substantially.
To the extent that the intervention is effective,
superdiffusers would convince other members of the target population to adopt the advocated health practice. After
the implementation of the intervention the number of persons in P now practicing the advocated behavior would
EMPLOYING INTERPERSONAL INFLUENCE
include those who adopted the practice prior to the intervention plus those who had been convinced during the time
that the intervention was implemented. Denoting the number of persons having adopted the advocated health practice
postintervention as A1 ,
A1 = f (those who have adopted preexperiment, number of
markedly) k, M k , is substituted, then the estimate of A1 that
emerges remains reasonably accurate. This estimate of k also
presumes that there is no overlap in the contacts for each
I.2
Let ϕ denote the proportion of those k contacts that each
I convinces. Then, if X is the number of persons each I
convinces,
superdiffusers, number influenced during the
Xi = ϕk = ϕλP , where 0 < ϕ ≤ 1 implementation of the intervention) I = those who have adopted preexperiment and, presuming that neither the preintervention adopters nor the superdiffusers cease engaging in the advocated health practice, or that the number that does so equals the number of persons who might adopt the advocated health practice in the absence of superdiffuser influence, the change in adoption is given by A = A1 – A0 = those influenced during the intervention. The number of population members who are able to be influenced during the intervention is not equivalent to population size. Instead, because the early adopters and the superdiffusers engage in the advocated health practice prior to the intervention, these sets of persons are no longer able to be convinced to change in the direction of adopting the advocated health practice.1 The reduced population, (3) then denotes the members of P that could possibly change in the manner prescribed by the intervention. If superdiffusers are denoted as I, then each I has a given number of interpersonal contacts that are members of P . Denoting the number of these contacts as k, this figure can be expressed as a proportion of P . Specifically, (4) where λ is the proportion of P contacted by each superdiffuser. This expression presumes that k is a constant, which is almost certainly false. Nevertheless, if the mean or median (in cases in which the distribution of k is skewed 1 This ϕk = i=1 I ϕλP = IϕλP (6) i=1 A1 = f (number convinced, number of preexperiment = αP + .05P + those influenced during the intervention (2) k = λP , where 0 < λ ≤ 1 I (5) is the number of persons convinced. Hence, +those influenced during the intervention P = P − (αP + .05P) Xi = i=1 +number of superdiffusers 401 point does not discount the possibilities that messages might be sent to them that serve to strengthen their behavioral choices, that might make these choices less vulnerable to counterpersuasion efforts, or that might make them opt to become an advocate of the advocated health practice. adopters, I) = f (Xi + A0 ) (7) = I Xi + αP + .05P = IϕλP + αP + .05P i=1 = IϕλP + (α + .05)P provides an estimate of the number of persons predicted to adopt the advocated health practice after the duration of the intervention. The parameters in Eq. (7) must be estimated in order to estimate A1 a priori. This predicted figure could then be compared with an obtained figure generated from an experimental test of the model. The difficulty with this approach is the challenges that arise when attempting to generate estimates of these parameters. An estimate of P might be obtained from population statistics. An empirical estimate of I can be generated by noting the number of superdiffusers participating in the intervention. The parameter α can be estimated by conducting a pretest survey and obtaining an estimate of the proportion of those respondents who report having already adopted the advocated health practice. P can be estimated by assessing in the same pretest survey the number of persons who reported already having adopted the advocated health practice, and subtracting that figure from the total population. Presuming that each superdiffuser contacts each k, the parameter λ can be estimated by asking the participating superdiffusers to report the number of contacts they made during the intervention, calculating the mean of 2 This assumption can be relaxed, but at this juncture it can be noted that contacts who receive messages from more than one superdiffuser have a higher likelihood of adopting the recommended health practice (Harkins & Petty, 1981). Thus, when overlap in contacts produces an overestimate of k, the influence parameter, ϕ, introduced subsequently is underestimated. It is presumed that these errors cancel to within a small margin of error. 402 BOSTER ET AL. this variable across all superdiffusers, and dividing by the size of the population. Estimating ϕ presents the greatest challenge. Optimally, each superdiffuser would report the name of each contact, all contacts would be queried as to whether or not they had adopted the advocated health practice, and the ratio of the number of adopters to total contacts would be calculated. This labor-intensive method is impractical, likely to result in large amounts of missing data (see Katz & Lazarsfeld, 1955), and likely to produce unstable estimates. Alternatively, given that A1 can be measured, and given that the other parameters can be estimated, an estimate of ϕ can be obtained by presuming the model to be correct. If accurate, such an estimate is of substantial importance because it would provide a measure of the success of superdiffusers in influencing their peers. An Intervention The empirical test of this intervention proceeded in stages. First, the target community was surveyed using the superdiffuser scales developed by Boster et al. (2011). The health behavior of the target community and a control community were also surveyed prior to the intervention. In the next stage, those whose scores on the superdiffuser scales indicated that they were high on connection, persuasion, and domain expertise (mavens) were contacted and asked to help influence others to adopt a particular health innovation. Finally, after the superdiffusers had been given time to influence their peers,3 the target community and the control community were again surveyed concerning the target health behavior. The behavioral changes in the target community were predicted to exceed those of the control community. METHOD Locating Superdiffusers Sample. In order to determine which students on the target campus were superdiffusers, each undergraduate student (more than 36,000 students) was e-mailed a request to complete an online survey. In total, 2,958 undergraduate students (8.1%: 1,925 female, 1,026 male, 7 did not indicate their sex) filled out the survey. Their mean age was 20.51, SD = 2.40, and ranged from 18 to 54 years. 3 It might be noticed that there is no model of second-order effects, or even higher order effects. That is, the possibility that those influenced by superdiffusers might influence others is not considered. The model was not expanded in this manner for this project because the duration between pretest and posttest was deemed insufficient to produce measurable higher order effects. Procedures. Subjects were e-mailed a request to participate in the study in exchange for a chance to win a cash prize. Those who clicked on the link provided were taken to the online consent form. After they indicated their consent, the webpage took them to the survey. They were first asked for their age and sex. Then the connector, persuader, and health maven (CPM) scales followed. Finally, they were asked for their names and e-mail addresses so that they could be contacted if they won a prize or if they qualified to participate in a future study. Those who were chosen randomly to win a cash prize were contacted and informed how they could obtain their prize. Instrumentation. The CPM scales were taken from Boster et al. (2011). Responses to the items were made on 5-point Likert response scales anchored by the phrases “disagree strongly” and “agree strongly.” The maven items focused on health so as to identify those knowledgeable about healthy lifestyles. The mean of the responses to each of the three measures was employed to form a connector score, a persuader score, and a health maven score for each participant. There were five connector items. Example items included “I’m often the link between friends in different groups” and “I try to bring people I know together when I think they would find each other interesting.” The scores were somewhat skewed negatively with α = .85, M = 3.60, and SD = .76. There were five persuader items. Example items included “I am good at thinking of multiple ways to explain my position on an issue” and “More often than not, I am able to convince others of my position during an argument.” The scores were skewed negatively and leptokurtic with α = .86, M = 3.89, and SD = .66. There were five health maven items. Example items included “When I know something about a healthy lifestyle topic, I feel it is important to share that information with others” and “Being knowledgeable enough about healthy lifestyles so that I could teach someone else is important to me.” The scores approximated the normal distribution with α = .88, M = 3.51, and SD = .84. Subjects were considered a superdiffuser if they scored at or above the 75th percentile on all three CPM scales. In this sample, the cutoff points were at least 4.2 on the connector scale, 4.4 on the persuader scale, and 4.0 on the maven scale. Using this criterion, there were 146 superdiffusers in the sample (4.9%). Recruiting Superdiffusers Subjects who met the superdiffuser inclusion criteria received a second e-mail message.4 In this message, they were told that their scores on the initial survey made them eligible to participate in the second portion of the study. The 4 The recruitment message can be obtained by contacting the first author. EMPLOYING INTERPERSONAL INFLUENCE payment structure for continued participation was discussed, as were the broad parameters of what they would be asked to do. Next, subjects were asked to attend one of two training sessions. The training sessions began with an overview of the project, how the subjects were selected to be a part o... 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