Human Communication Research ISSN 0360-3989
ORIGINAL ARTICLE
Talking Health With a Machine: How Does
Message Interactivity Affect Attitudes
and Cognitions?
Saraswathi Bellur1 & S. Shyam Sundar2
1 Department of Communication, University of Connecticut, Storrs, CT 06269, USA
2 College of Communications, The Pennsylvania State University, University Park, PA 16802, USA
By affording interactive communication and natural, human-like conversations, can media
tools affect the way we engage with content in human–machine interactions and influence
our attitudes toward that content? A between-subjects experiment (N = 172) examined the
effects of two communication variables: (a) message-interactivity and (b) conversational
tone, in an online health information (Q&A) tool. Findings suggest that informal conversational tone lowers perceptions of relative susceptibility to health risks. Perceived contingency
positively mediates the influence of message interactivity on individuals’ health attitudes
and behavioral intentions whereas perceived interactivity negatively mediates the relationships between these variables. These contrasting mediation effects are further explored via
a phantom model analysis that tests two theoretically distinct paths, with implications for
both theory and practice.
Keywords: Interactivity, Contingency, Turn-Taking, E-Health, Online Health, Interactive
Health Technologies.
doi:10.1111/hcre.12094
A poll by the National Public Radio, the Robert Wood Johnson Foundation, and
the Harvard School of Public Health (2012) revealed that a majority of individuals
(61%) are dissatisfied with the amount of time that doctors spend talking with their
patients. In order to address it, many individuals are turning to online health resources
(Fox & Jones, 2009; Rice, 2006), such as interactive quizzes and risk assessment tools.
However, very little is known about their impact. This study explores how interactive
tools affect users’ understanding of health risk information, attitudes, and behavioral
intentions.
Corresponding author: Saraswathi Bellur; e-mail: [email protected]
This article was accepted for publication under the editorship of Dr. John Courtright.
[Correction added on 9/20/2016, after initial online publication: “Acknowledgments section
added and typo on page 17 fixed (asystematic to a systematic).”]
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Knowing how online health tools are designed could help us assess their impact.
A common characteristic is their interactive nature, that is, they provide information based on each user’s unique input into the system. Conceptually, this aspect of
interactivity is known as the contingency principle: the capacity of a system (or an
interaction partner) to maintain highly interdependent message exchanges (Burgoon,
Bonito, Bengtsson, Ramirez, et al., 2000; Rafaeli, 1988; Rafaeli & Sudweeks, 1997; Sundar, Kalyanaraman, & Brown, 2003).
However, while contingent exchange of information constitutes one facet of interactive media, it does not capture another important aspect—namely, the tone of the
communication. As Brennan (1998) pointed out, individuals are preoccupied not only
with what they tell each other but also how they say it. If a communication scenario
was to involve strictly task-oriented exchanges (e.g., ATM machine dispensing cash),
the display of affiliation behaviors, such as a friendly conversational tone, may not
be important. However, as studies in patient-physician communication have shown,
healthcare interactions are different in nature. They not only feature informational
messages but also a good deal of psychosocial discourse (Roter & Hall, 2004) characterized by factors such as empathy, active listening, and humor. Thus, examining
the role of conversational tone can help us translate effective strategies from offline
patient-physician communication to online user interactions with a system.
Therefore, the goals of this study are to understand how (a) the variable of message
interactivity, manipulated as the degree of contingency in an interactive health tool,
and (b) the conversational tone, manipulated as part of the content emerging from the
tool, can influence users’ attitudes, behavioral intentions, and health risk perceptions.
Literature review
Message interactivity and contingency
Historically, one of the defining features of interactivity has been the dependency, or
the “contingency” of current messages upon prior messages and past actions. Rafaeli
(1988) formally defined interactivity as “an expression of the extent that in a given
series of communication exchanges, any third (or later) transmission (or message)
is related to the degree to which previous exchanges referred to even earlier transmissions” (p. 111). Since this type of interactivity focuses on the manner in which
messages are exchanged, it is labeled “message interactivity” in the literature (Sundar,
2007).
In prior work, Rafaeli’s conceptualizations of contingency were explored in
group-based computer-mediated communication (CMC) settings such as bulletin
boards and chat rooms. More recently, the role of contingency has been extended to
examine user-system interaction as it occurs in human–computer interaction (HCI)
contexts as well. For instance, Sundar et al. (2003) operationalized message interactivity in the form of structural hyperlinks that allowed information to be accessed
via user-determined paths. The study found that the greater the number of layers of
hierarchical hyperlinks, the higher the perception of interactivity, which influenced
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the manner in which users processed information on a website. Sundar and Kim
(2005) extended this “contingency view” by altering the number of information
layers that users had to access in interactive marketing units. The study demonstrated
the ability of message interactivity to significantly influence attitudes toward the ad.
High-interactive ads (with three or more layers) generated a significantly higher
degree of product involvement than ads with low (no layers) or medium (two layers)
levels of interactivity.
Thorson and Rodgers (2006) investigated the affordance of being able to offer
publicly visible feedback to a political candidate’s blog with a hyperlink function.
They found that this interactive feature not only made blog readers develop favorable
attitudes toward the candidate but also enhanced their voting intentions. Wise, Hamman, and Thorson (2006) found similar results, where higher levels of interactivity
enhanced individuals’ intention to participate in an online community.
In essence, the contingency aspect of interactivity appears to enhance user involvement with the message, leading to positive attitudes and behavioral intentions—not
only toward the messages delivered interactively, but also toward the venue (website or
online community) that features interactivity. That is, interactivity in a site can potentially enhance users’ behavioral intentions to return to the site, spend more time in
it, recommend it to others, and so on. We extend these effects of contingency-based
operationalization of interactivity to the domain of health communication by testing
the following hypotheses:
H1: Greater levels of message interactivity will lead to more positive attitudes toward
the website (H1a) and toward the content (H1b); and more positive behavioral
intentions toward the website (H1c) and its content (H1d).
The fundamental theoretical requirement for these outcomes is the effective realization of contingency in the minds of the user as they interact with the system. Burgoon, Bonito, Bengtsson, Cederberg et al. (2000) tested the contingency principle
by manipulating whether study confederates either ignored remarks that were not
associated with the study task (minimally contingent) or took into account even the
task-irrelevant comments made by participants. The findings showed that contingent
face-to-face interaction promoted a sense of mutuality and connectedness with the
communication partner.
Sundar, Bellur, Oh, Jia, and Kim (2016) adopted a visual approach of graphically
displaying the interaction between the user and the system, labeled interaction history,
in order to operationalize contingency on a movie search engine. The results of the
study indicated that the presence of interaction history did indeed promote greater
perceptions of contingency in users’ minds, which in turn influenced users’ attitudes
and engagement toward a website.
Therefore, message interactivity, when operationalized via user-system interaction
history (a form of contingency), can boost perceptions of contingency. This is possible
mainly because of its ability to document unique actions of each user and also showcase these actions as users’ “idiosyncratic path” (Sundar, 2007) to a task goal. Thus,
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this study seeks to promote the subjective perception of contingency by operationalizing message interactivity in the form of visual display of interaction history. While
message interactivity could trigger various perceptions, we focus on one of the chief
subjective experiences that prior studies have revealed, notably perceived contingency
or the perception of interdependency among messages exchanged:
H2: Perceived contingency will positively mediate the effect of message interactivity on
participants’ attitudes toward the website (H2a) and its content (H2b); and behavioral
intentions toward the website (H2c) and its content (H2d).
Apart from broad persuasion outcomes, the study expects message interactivity
to promote individuals’ processing of health risk messages (both directly and indirectly via perceived contingency). This is because the information delivered to each
individual user would be contingent upon the user’s own inputs into the system and,
hence, unique to their individual health status. And, assuming that most study participants would likely fall short on the recommended guidelines for healthy behaviors,
we propose:
H3: Greater levels of message interactivity will lead to increased risk perceptions as
reflected in the extent of perceived susceptibility (H3a) and perceived severity (H3b).
H4: Perceived contingency will positively mediate the effect of message interactivity on
risk perceptions as reflected in the levels of perceived susceptibility (H4a) and perceived
severity (H4b).
This mediation is expected to occur because the contingency in message exchanges
serves to engage users more closely with the content of the messages. In Sundar’s
(2007) interactivity effects model, user engagement is a critical mediator of the effects
of interactivity on psychological outcomes. Oh, Bellur, and Sundar (2015) conceptualized user engagement as having two key dimensions: psychological and behavioral.
As a psychological state, user engagement is marked by users’ initial assessment of a
medium’s interface, followed by subsequent immersion or absorption with the content. This state is characterized by increased attentional focus toward and involvement
with the content of the interaction (Webster & Ho, 1997). Studies operationalizing
user engagement as the extent of self-reported absorption and immersion in the interaction (Agarwal & Karahanna, 2000) have empirically demonstrated that it mediates
the effects of message interactivity (Sundar, Bellur, Oh, Jia, et al., 2016). Engagement,
as mental elaboration, is also known to mediate the effect of message interactivity
on persuasiveness of health advocacy messages (Oh & Sundar, 2015). Therefore, we
propose the following two-step mediation path:
H5: Level of perceived contingency (M1) and the degree of user engagement (M2) will
positively mediate the effect of message interactivity, on attitudes toward the website
(H5a) and toward the content (H5b); on behavioral intentions toward the website
(H5c) and its content (H5d).
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Role of perceived interactivity
Perceptions of contingency hinge on users’ realization of the relatedness of message
inputs and outputs. Often, this involves a minimum of three message exchanges
to operationalize. However, this technical notion of message interactivity may go
unnoticed by users. Typically, users tend to globally assess the broad concept of
interactivity based on simple notions of feedback and two-way communication.
Scholars have labeled this assessment as “perceived interactivity” (e.g., Wu, 2005)
and shown that it often has a more significant influence on final outcomes, when
compared to the effect of manipulated or “actual interactivity” based on technical
parameters such as provision of contingency (e.g., Kalyanaraman & Sundar, 2006;
Tao & Bucy, 2007; Thorson & Rodgers, 2006). Hence, we consider the effects of both
manipulated interactivity (namely, message interactivity as hypothesized in H1 and
H3), and measured perceived interactivity (as a psychological state) in the same study
model, via the next set of hypotheses:
H6: Perceived interactivity is likely to positively mediate the effect of manipulated
message interactivity on attitudes toward the website (H6a) and toward the content
(H6b); on behavioral intentions toward the website (H6c) and its content (H6d); on
perceived susceptibility (H6e) and perceived severity (H6f).
Role of perceived relevance
The perception of interactive exchange suggests to the user that the system is responding to them as an individual, that is, tailoring the message to them, rather than engaging in mass communication. Studies in health communication have long shown that
individuals consider tailored material as being more relevant than “generic” or nontailored material (Kreuter, Strecher, & Glassman, 1999; Kreuter & Wray, 2003). Tailoring
makes messages more personally salient for the individual and enhances their elaboration on the topic (Brinol & Petty, 2006; Petty & Cacioppo, 1986). This has been well
documented with both print-based and web-based tailoring interventions such as the
Comprehensive Health Enhancement Support Systems (Hawkins et al., 1997; Kroeze,
Werkman, & Brug, 2006; Noar, Benac, & Harris, 2007).
However, message tailoring in such interventions is based on a user profile (often
drawn from demographics and prior online behaviors). Unlike tailoring, which is
user-centered, message interactivity is an attribute of the medium (display of interaction history) and also the message (i.e., references to prior responses in Q&A content).
Therefore, the interaction has the quality of an interpersonal, rather than mass, communication, where the system is being responsive to users at the level of individual
messages instead of the user as a whole.
Oh and Sundar (2015) recently demonstrated that a website featuring high levels
of message interactivity was more likely to lead to mental elaboration of related
thoughts that are personally relevant. Considering that cognitive elaboration strategies, such as question-asking, content of questions asked, and their format (closedor open-ended), form an important part of patient–physician interaction (Davis,
Koutantji, & Vincent, 2008; Oermann & Pasma, 2001; Roter & Hall, 2004), message
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interactivity can aid such strategies by enhancing user perceptions of personal
relevance of health content. Therefore, we propose the following hypotheses:
H7: Message interactivity will be positively related to perceived relevance.
H8: Perceived relevance is likely to positively mediate the relationship between message
interactivity and cognitive outcomes.
Role of power usage
Although interactivity affordances can lead to various outcomes on their own, there
is growing recognition that user characteristics can also moderate the outcomes of
interactivity. One such variable is power usage, which has been studied as the extent
to which users report confidence and competence in interacting with and learning
new technologies (Sundar & Marathe, 2010). Studies have shown that power users
report greater engagement with some types of interactive features (e.g., mouseover)
over others (e.g., 3D carousel). This appears to influence how they evaluate likeability
and credibility of content (Sundar, Bellur, Oh, Xu, & Jia, 2014).
Power users also report feeling more “in control” when they are able to customize
their media to provide personally relevant content (Sundar & Marathe, 2010). Thus,
the degree to which users feel skillful during their interactions with media technologies has implications for various cognitive, attitudinal, and behavioral outcomes.
Recent scholarship has accounted for such individual differences by measuring
Internet Self-Efficacy (Bucy & Tao, 2007) or eHealth literacy (Norman & Skinner,
2006). Further, the effect of self-efficacy, in general, has been studied widely in the
domains of communication, psychology, and health (Bandura, 2004). We would
expect that efficacy with technology would also have a role in communication outcomes. With this in mind, we explore the moderating role of power usage by posing
the following research question:
RQ1: Will the degree of power usage moderate the effects of message interactivity on
participants’ attitudes, behavioral intentions, and risk perceptions?
Conversational tone
Although message interactivity shows considerable promise in imitating the
back-and-forth that takes place between a doctor and a patient, it cannot, by itself,
approximate the social exchange that occurs naturally in a human–human interaction at the doctor’s office. For that, we will need a system that incorporates turn-taking
cues and back-channel behaviors (Yngve, 1970), which can imbue an informal conversational tone to the interaction. Bickmore and Cassell (2005) distinguished between
two types of information: One is “propositional information” (p. 27) that referred
to the actual content of the conversation. The second type, called “interactional
information,” consisted of turn-taking cues that can help monitor the conversation.
Interactional information comprise both nonverbal cues, such as head nods, as well
as verbal cues in the form of regulatory speech patterns, for example, “huh?,” “mm
hmm,” “do go on,” and other similar paraverbals (Cassell et al., 1999, p. 522).
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Studies have shown that such short utterances and “small talk” meet socialoriented goals of the interaction, a dimension of great importance in patient–
physician interactions (Fitzgerald & Leudar, 2010; Roter & Hall, 2004; Roter
& Larson, 2002). Studies of psychotherapy (Fitzgerald & Leudar, 2010) have also
adopted these verbal turn-taking cues in the form of acknowledgement tokens or
“continuers.” These seemingly “empty vocalizations” (p. 3188) demonstrate active
listening and sustain provider-patient interaction. Hence, apart from the main
function of building rapport and trust, verbal turn-taking behaviors signify an
informal conversational tone. An informal tone promotes a sense of friendliness and
camaraderie, which we explore in the form of “perceived warmth” in the interaction.
H9: Presence of an informal conversational tone will lead to a perception of increased
warmth toward the user-system interaction.
H10: Presence of an informal conversational tone will lead to more positive attitudes
toward the website (H10a) and its content (H10b), as well as more positive behavioral
intentions toward the website (H10c) and its content (H10d).
The underlying principle governing the effect of verbal turn-taking cues could be
attributed to the communication system taking on the role of an encouraging coach
or a collaborator (Hawkins et al., 1997; Walther, Pingree, Hawkins, & Buller, 2005).
For example, Bickmore, Caruso, and Clough-Gorr (2005) found that users looked
up more health information, and expressed greater overall satisfaction, liking, and
trust toward an online health coach. We explore whether an interactive health assessment tool can afford a similar virtual coaching function and affect risk perceptions
via informal user-system exchanges. Again, as in H3 and H4, we make the assumption that study participants will fall short on recommended guidelines and therefore
receive messages from the interactive system that are designed to heighten their risk
perceptions.
RQ2: Will the presence of an informal conversational tone lead to greater risk
perceptions as seen in the level of perceived susceptibility and perceived severity?
Because users’ level of power usage could influence the extent to which they are
responsive to interactional conversations with computer systems, we ask:
RQ3: Will the degree of power usage moderate the effects of conversational tone on
participants’ attitudes, behavioral intentions, and risk perceptions?
In sum, the study tests the effects of two independent variables—levels of message
interactivity and the type of conversational tone—on participants’ cognitive, attitudinal, behavioral intention, and risk perception outcomes in a tool designed to provide
users with health risk information.
Method
In order to explore the research questions and test the hypotheses stated above, a
3 (Level of Interactivity: low, medium, high) × 2 (Conversational Tone: presence or
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absence of verbal turn-taking cues) between-subjects factorial experiment was conducted with six versions of a health risk assessment website designed especially for
this study. Because the purpose of the study was to adopt a “Question and Answer”
(Q&A) interaction format that is found in several existing interactive health assessment tools on the Internet, the website was created to look like an instant messaging
(IM) interface.
A set of health topics about diet, exercise, drug, alcohol, HIV- and AIDS-related
risk factors were included in the Health Q&A interaction based on a pretest among
college-aged adults (N = 54), representative of the larger sample in the main study.
Health information from several online health resources and the local student health
center were modified and adapted to suit the needs and design requirements of the
main study. There were a total of 25 questions in the Q&A. The Institutional Review
Board for protection of human subjects at the second author’s university approved the
study design and procedure.
Participants
The study sample consisted of 172 undergraduate students (142 females and 30
males) recruited from several communications classes at a large public university in
the United States. The average age of the sample was 20.5 years (SD = 1.19, N = 149,
23 missing data). A majority of the participants reported being Caucasian or White
(70.93%). The remainder of the sample consisted of 6.4% African Americans,
4.65% Hispanic, and 16.3% Asians. About 1.7% reported belonging to other ethnic
categories.
Message interactivity
The first independent variable examined in the study is the level of message interactivity, operationalized on the basis of three levels of contingency: two-way, reactive, and
interactive (Rafaeli, 1988). In the low-interactivity (two-way) condition (Figure 1),
participants engaged in a simple back-and-forth exchange. This involved the system
asking questions, participants picking an answer option, and then the system providing a tailored response based on the answer option selected by the participant.
These tailored responses took the form of brief recommendation messages that gave
basic information on safety and preventive health behaviors. The website in the
low-interactivity condition did not display any signs or visual cues of the ongoing
interaction between the system and the user.
In the medium-interactivity (reactive) condition, participants took part in the
same Q&A task but after each question, whenever participants chose a response
option (e.g., if they chose “Sometimes” for the question on how often they eat out in
restaurants), the site would take this response option into account. Further, it would
also display this information visually in a box that said “Your response: Sometimes.”
This simple textual cue serves two functions: (a) it conveys to the user that the
system’s health messages and recommendations are contingent upon the particular
answer chosen by the user; (b) it acts as evidence of interaction.
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Figure 1 Question and answer interaction in the low (left), medium (center), and high (right)
interactivity conditions.
In the high-interactivity condition, two additional features were designed to
convey a higher degree of contingency. First, the system displayed the entire Q&A
interaction history. Second, the site factored in participants’ responses to previous
questions; for example, “Previously, you mentioned … ” or “Earlier, you reported … ”
and so on.
Conversational tone
The second independent variable examined in this study is the conversational tone,
operationalized as the presence or absence of verbal turn-taking cues. The presence
condition involved several short sentences conveyed by the system (inserted between
questions), such as “Let’s move on to the next question”; “OK, let’s talk about exercise”;
“All right, let’s look at the next question now”; and so on. The sentences were brief,
and they did not add any extra information about the health content contained in
the Q&A.
Moderator, mediator, and covariates
The study design examined the role of power usage as a moderator. The scale for measuring power usage contained 12 items adapted from Sundar and Marathe (2010)
and Sundar, Xu, Bellur, Oh, and Jia (2011). Four variables—perceived contingency
(Sundar, Bellur, Oh, Jia, et al., 2016), perceived interactivity (Liu, 2003; McMillan &
Hwang, 2002), perceived warmth (Kim & Sundar, 2012; Powers & Kiesler, 2006), and
perceived relevance (Thorson & Zhao, 1997; Wells, 1989)—were hypothesized as the
mediating variables in this study. Prior to participants’ exposure to the study stimulus,
a pretest questionnaire gathered information on demographic variables and power
usage. The pretest also gathered data on participants’ social extraversion (Bendig,
1962), general health beliefs (Ajzen & Timko, 1986; Becker, 1974), and preference
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for online social interaction (POSI; Caplan, 2003). These helped to control for innate
personality factors, prior health attitudes and tendency to choose online interactions
over nonmediated exchanges, respectively.
Dependent variables
Cognitive responses
Participants were instructed to create a list of questions that they would like to ask
their doctors (Davis et al., 2008; Oermann & Pasma, 2001). Open-ended responses to
this task were used as a measure of participants’ cognitive response. These responses
were counted for the number of health related issues that participants mentioned and
were categorized into thoughts or questions related to four topics: diet, exercise, HIV,
and other sexually transmitted infections. Other health issues that were not discussed
as part of health Q & A interaction in the site were also coded.1 Responses from all
the four coding categories of cognitive responses were summed. The measure had a
mean of M = 1.62 and SD = 0.99.
Website attitudes
This included two factors, the extent to which participants thought the website was
appealing and exciting.
Content attitudes
Attitude toward the health Q&A content was measured with three factors—content
quality, content enjoyment, and information value. Both sets of attitudinal measures
were adapted from Sundar (2000) and Sundar et al. (2011), Sundar, Bellur, Oh, Jia,
et al. (2016).
Behavioral intentions
Participant’s intention to perform preventive health behaviors were measured with
two scales of future health behavior (FHB-1: safer sex and alcohol consumption and
FHB-2: diet and exercise) adapted from Armitage and Conner (1999); Rimal and
Real (2003). Apart from likelihood measures, the extent to which participants would
want to know more about the health topics discussed in the Q&A, and the extent to
which they would discuss these health issues with their friends was measured as a way
to capture potential health information exchange (HIE) behaviors. There were seven
items (adapted from Rimal & Real, 2003) that comprised two factors. The first factor
(HIE-F1) pertained to diet and exercise. The second factor (HIE-F2) contained items
on the topic of safer sex and sexually transmitted infections. Additionally, behavioral
intentions in the form of return and repeat visits and sharing of the website with
others were measured with a scale of Website behavior intention (Web BI) adapted
from Hu and Sundar (2010).
Risk perception
The following item measured susceptibility likelihood, labeled percentage susceptibility: “Out of 100%, what do you think are your chances of being diagnosed with
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the following health conditions (obesity, diabetes, HIV, etc)?” (Lerman et al., 1995).
Further, participants’ perception of relative susceptibility (health risks compared to
similar others) and perceived severity of the health risks was also gathered as part of
the overall risk perception measures (Rimal & Real, 2003).
User engagement
The variable of user-engagement comprised three factors: fun and enjoyment, immersion, and amount of control (Agarwal & Karahanna, 2000). An appendix including a
list of sample measures used in the study, along with reliability estimates of the scales,
can be found next to the electronic version of this article.
Procedure
The study was conducted in a laboratory setting. After participants arrived at the lab
and completed the informed consent procedure, they completed an online pretest
questionnaire. They were then instructed to browse the website containing the health
message Q&A interaction, which took approximately 15 to 20 minutes. After this
interaction, participants completed a posttest questionnaire online. All participants
were thanked and compensated (with course credit) for their participation.
Results
General linear model (GLM) analyses were conducted with the two manipulated independent variables—(a) level of interactivity (low, medium, high) and (b) conversational tone (presence or absence of verbal turn-taking cues)—and level of power
usage (a continuous, measured variable) as predictors. Three covariates—POSI, level
of extraversion, and general health beliefs—were also included in the analysis model.
Message interactivity manipulation check
A scale of eight items (alpha = .79) was used to check if the display of interaction history manipulation was psychologically apparent. Items included statements such as,
“The site remembered my actions,” “The actions I performed were clearly evident on
the site,” and “The site was transparent in showing the actions I performed.” A general linear model revealed a significant main effect for the level of message interactivity
F(2, 169) = 12.44, p < .01, ηp 2 = .13. The high-interactivity (M = 8.15, SE = 0.14) condition evoked a greater perception of the participants’ actions and interaction history
being displayed by the system, when compared to the low- (M = 7.28, SE = 0.14) and
the medium- (M = 7.29, SE = 0.14) interactivity conditions. The Tukey HSD post hoc
test showed that the high-interactivity condition differed significantly from both the
medium and the low conditions, but the latter two were not significantly different
from each other.
Conversational tone manipulation check
A single-item, 9-point semantic differential scale, with formal–informal as the
anchors, was used to assess participants’ perceived interaction with the site. There
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was a significant one-tailed t-test t (170) = 1.86, p < .05, d = .29 showing that participants perceived the interaction with the site to be more informal when verbal
turn-taking cues were was present (M = 6.03, SE = 0.24), compared to the absence of
turn-taking cues in the user system interaction (M = 5.41, SE = 0.24).
Main effects
Message interactivity manipulation had a significant main effect on perceived
contingency, F(2, 165) = 6.55, p < .05, ηp 2 = .07, and perceived interactivity, F(2,
165) = 4.51, p < .05, ηp 2 = .05. Analysis of covariance revealed that participants in
the high condition (M = 7.49A , SE = 0.22) perceived the site to be the most contingent, when compared to either medium (M = 6.50B , SE = 0.21) or low (M = 6.55B ,
SE = 0.21). Participants considered the low condition (M = 6.20A , SE = 0.18) to be the
most interactive, compared to the medium (M = 5.59B , SE = 0.18) or high conditions
(M = 5.49B , SE = 0.18).
There were no significant main effects for either of the two independent variables
on any of the outcome variables, with one exception: A significant main effect was
found, but only for the conversational tone variable, on relative susceptibility to three
health issues: obesity, diabetes, and heart disease, t (1, 170) = 2.19, p < .05, d = .34.
Those who received verbal turn-taking cues (M = 3.40, SE = 0.24) reported feeling
significantly less susceptible than those who did not receive these cues (M = 4.14,
SE = 0.24). Therefore, most of the main-effect hypotheses—H1 (a–d), H3 (a and
b), H7, H9, and H10 (a–d)—were unsupported, but their indirect effects were
significant, as described below.
Mediation effects
To test the five mediation hypotheses (H2, H4, H5, H6, and H8), we used the bootstrapping approach with indicator coding (Hayes & Preacher, 2014; Preacher & Hayes,
2004). The low-interactivity condition was considered the baseline from which two
sets of comparisons were made: (a) high-to-low interactivity and (b) medium-to-low
interactivity. In each instance, the low-interactivity condition was coded as 0, and the
medium and high interactivity conditions were coded as 1. While running the mediation analysis, for the high-to-low comparison, medium condition was excluded. Similarly, while running the medium-to-low comparison, the high condition was excluded
from the analysis. The SPSS macro used to run this procedure (PROCESS Model 4)
employed 5,000 bootstrap samples and a 95% bootstrap percentile confidence interval
(Hayes, 2013). As hypothesized in H2 (a–d) and H4 (a and b), perceived contingency
did significantly mediate the relationship between levels of message-interactivity and
proposed outcomes, but only for the high-to-low interactivity comparison (Table 1).
Further, hypothesis H5 (a–d) was tested via PROCESS macro (Model 6) with
5,000 resamples. This analysis provided support to the two-step mediation of the message interactivity path (IV) via perceived contingency (M1) and user engagement
(M2), as delineated in the interactivity effects model (Sundar, 2007). These findings
were significant only for the high-to-low interactivity comparison (Table 1).
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Table 1 Indirect Effects via Perceived Contingency and User Engagement
Indirect Effect
95% Confidence Intervals
Indirect Effects via
Perceived Contingency
Website appealing
Website exciting
Content quality
Content enjoyment
Information value
Future health behavior (FHB F2)
Perceived severity
Two-Step Mediation via
Perceived Contingency and
User Engagement
Website appealing
Website exciting
Content quality
Content enjoyment
Information value
Web BI
Future health behavior (FHB F1)
Health info exchange (HIE F1)
Health info exchange (HIE F2)
Indirect Effect
Bootstrap Estimate
LLCI
ULCI
.35 (.13)
.27 (.07)
.21 (.11)
.19 (.06)
.22 (.08)
.27 (.10)
.15 (.10)
.109
.051
.079
.041
.071
.079
.043
.670
.623
.387
.446
.446
.682
.319
Indirect Effect
95% Confidence Intervals
Indirect Effect
Bootstrap Estimate
(M1 and M2)
LLCI
ULCI
.12 (.05)
.25 (.07)
.04 (.02)
.18 (.06)
.05 (.02)
.29 (.06)
.08 (.02)
.11 (.03)
.17 (.04)
.037
.076
.008
.054
.007
.091
.013
.034
.053
.290
.538
.121
.413
.168
.636
.240
.283
.421
Note: FHB F2 = future health behavior (factor 2: diet and exercise). Web BI = behavioral intentions toward the website. HIE F1 = health information exchange (factor 1: diet and exercise);
HIE F2 = health information exchange (factor 2: safer sex and STIs). Low interactivity condition was coded as 0, medium was excluded, and high was coded as 1. Mediation effects were
significant at p < .05 for high-to-low interactivity comparison. Standardized estimates (beta)
are included in parentheses. Indirect effect confidence intervals apply to unstandardized estimates. LLCI & ULCI = lower level and upper level confidence intervals, respectively.
With perceived interactivity as the mediator (H6a to H6f), there were significant indirect effects for both comparisons (Table 2), but in the negative direction.
The lesser the visual display of user-system interaction (i.e., low message interactivity condition), the greater the perceptions of interactivity, which in turn,
led to more positive outcomes. Neither H7 (linear effect of message interactivity
on perceived relevance) nor H8 (indirect effect via perceived relevance) was
supported.
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Table 2 Indirect Effects via Perceived Interactivity
Medium-to-Low Interactivity
Comparison
Website appealing
Website exciting
Content quality
Content enjoyment
Information value
Web BI
Future health behavior (FHB F2)
Perceived severity
High-to-Low Interactivity
Comparison
Website exciting
Website appealing
Content quality
Content enjoyment
Information value
Web BI
Future health behavior (FHB F2)
Perceived severity
Indirect Effect
Bootstrap Estimate
−.37 (−.14)
−.33 (−.09)
−.11 (−.06)
−.28 (−.09)
−.17 (−.06)
−.46 (−.09)
−.10 (−.04)
−.09 (−.06)
Indirect Effect
95% Confidence Intervals
LLCI
ULCI
−.681
−.671
−.234
−.591
−.368
−.953
−.359
−.209
−.087
−.103
−.023
−.065
−.029
−.130
−.001
−.017
Indirect Effect
95% Confidence Intervals
Indirect Effect
Bootstrap Estimate
LLCI
ULCI
−.39 (−.15)
−.32 (−.09)
−.14 (−.07)
−.32 (−.10)
−.19 (−.07)
−.45 (−.09)
−.16 (−.06)
−.11 (−.07)
−.667
−.644
−.310
−.645
−.444
−.891
−.449
−.232
−.115
−.098
−.040
−.090
−.041
−.142
−.033
−.036
Note: FHB F2 = future health behavior (factor 2: diet and exercise). Mediation effects were significant at p < .05 for both medium-to-low and high-to-low interactivity comparisons. Low
interactivity condition was coded as 0, while the medium and high interactivity conditions
were coded as 1 in the respective analyses. Standardized estimates (beta) are included in parentheses. Indirect effect confidence intervals apply to unstandardized estimates.
Role of power usage
Two research questions in the study explored whether the degree of power usage
is likely to moderate the effects of message interactivity (RQ1) and conversational
tone (RQ3). An interaction effect F(1, 157) = 5.82, p < .05, ηp 2 = .04 between power
usage and conversational tone (RQ3) was found on future health behaviors (FHB-F1),
pertaining to safer sex and alcohol consumption. Participants scoring higher on the
power usage scale showed more positive behavioral intentions in the absence of verbal
turn-taking cues. But, in the presence of those cues, power usage did not significantly
predict the likelihood of performing healthy behaviors. Power usage did not moderate
the effects of message interactivity on outcomes proposed in RQ1.
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Figure 2 Phantom model testing mediation effects via two theoretical paths.
Phantom model analysis to simultaneously calculate multiple indirect effects
The mediating mechanisms via perceived contingency and perceived interactivity
seemed to follow an intriguing pattern, with the former leading to positive indirect
effects and the latter resulting in negative indirect effects. Hypotheses H2 (a to d), H4
(a and b), and H6 (a to f) tested these mediating mechanisms one at a time. Phantom
model analysis (Macho & Ledermann, 2011; Rindskopf, 1984) using AMOS software
was employed to test how these two mediation paths (Figure 2) would act together
(5,000 resamples and 95% bias-corrected confidence interval).2 The phantom model
analysis (Table 3) clarifies the differences between the two mediating paths, with four
mediators operating in tandem.
The path consisting of perceived contingency and user engagement as mediators (Path 1) supports the theoretical mechanism proposed in the interactivity effects
model. Here, the high-interactivity condition (with its greater display of structural
contingency) resulted in significant indirect effects on both website-related attitudes
and one content attitude factor (i.e., content enjoyment). This shows not only that
participants were sensitive and responsive to the contingency manipulation but it also
encouraged user engagement with the website.
In Path 2, comprising perceived interactivity and perceived relevance as mediators, both high- and medium-interactivity conditions are perceived as being less interactive (than the low-interactivity condition). This, in turn, has a negative impact on
perceived relevance and subsequent outcomes (e.g., health information exchange).
Both paths had a significant impact on behavioral intentions toward the website.
The phantom model analysis clarified the pattern of contrasting indirect effects
in two ways: (a) the model suggested that it is pivotal for subjective perceptions of
contingency (followed by user engagement) to intervene, if the effects of high message interactivity are to be discerned. This finding becomes even more relevant, given
the absence of any main effects of message interactivity. Further, (b) the phantom
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Table 3 Summary of Significant Indirect Effects from the Phantom Model Analysis
Confidence Intervalb
Mediation Path
Path 1: High-to-low message interactivity
Message interactivity → perceived contingency →
user engagement → website appealing
Message interactivity → perceived contingency →
user engagement → website exciting
Message interactivity → perceived contingency →
user engagement → content enjoyment
Message interactivity → perceived contingency →
user engagement → Web BI
Message interactivity → perceived contingency →
user engagement → HIE F2
Path 2: High-to-low message interactivity
Message interactivity → perceived interactivity →
perceived relevance → content enjoyment
Message interactivity → perceived interactivity →
perceived relevance → information value
Message interactivity → perceived interactivity →
perceived relevance → FHB F1
Message interactivity → perceived interactivity →
perceived relevance → HIE F1
Message interactivity → perceived interactivity →
perceived relevance → HIE F2
Message interactivity → perceived interactivity →
perceived relevance → Web BI
Message interactivity → perceived interactivity →
perceived relevance → cognitive responses
Path 2: Medium-to-low message interactivity
Message interactivity → perceived interactivity →
perceived relevance → website appealing
Message interactivity → perceived interactivity →
perceived relevance → content enjoyment
Message interactivity → perceived interactivity →
perceived relevance → HIE F1
Message interactivity → perceived interactivity →
perceived relevance → HIE F2
Message interactivity → perceived interactivity →
perceived relevance → cognitive responses
Ba
SE
LLCI CI
ULCI CI
.06 (.03)
.002
.012
.176
.17 (.05)
.002
.032
.434
.08 (.03)
.002
.018
.220
.17 (.04)
.003
.034
.426
.05 (.01)
.004
.003
.192
−.11 (−.03)
.002
−.260
−.031
−.10 (−.04)
.002
−.249
−.028
−.13 (−.04)
.003
−.335
−.034
−.15 (−.04)
.003
−.384
−.038
−.19 (−.04)
.004
−.446
−.057
−.09 (−.02)
.003
−.265
−.024
−.04 (−.02)
.002
−.131
−.008
−.03 (−.01)
.001
−.126
−.002
−.05 (−.02)
.002
−.165
−.008
−.08 (−.02)
.004
−.270
−.014
−.11 (−.02)
.004
−.279
−.024
−.03 (−.02)
.002
−.113
−.005
Notes: FHB F1 = future health behavior (factor 1: safer sex and alcohol); HIE F1 = health information
exchange (factor 1: diet and exercise); HIE F2 = health information exchange (factor 2: safer sex and STIs);
Web BI = website behavioral intentions.
a
Unstandardized path coefficient, followed by beta in the parentheses significant at p < .05.
b Bias-corrected and accelerated 95% confidence interval apply to unstandardized estimates.
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model also showed that regardless of the ontological manipulation of the interactivity
variable, if perceived interactivity is low, it can adversely impact perceived relevance
of the content conveyed in the interaction. This can further diminish other outcome
variables (e.g., attitudes, behavioral intentions). Conversely, if perceived interactivity
is high, it can lead to favorable outcomes overall.
The phantom model analysis showed that with the high-to-low message interactivity comparison, perceived contingency-led mediation effects (Path 1) are positive,
suggesting that users preferred very high levels of contingency displayed on the website, as opposed to none. In contrast, perceived interactivity-led mediation (Path 2)
was negative. Post hoc examinations did not lend support to the possibility that perceived contingency could be suppressing the effect of the manipulation on perceived
interactivity, although perceived contingency and perceived interactivity were positively correlated (r = .31, p < .001).3 Even in the absence of perceived contingency in
the model, the effect on perceived interactivity was negative and significant. With the
medium-to-low message interactivity comparison, there were no significant indirect
effects for Path 1. Both sets of high-to-low and medium-to-low interactivity comparisons in Path 2 led to negative indirect effects that demonstrate a clear preference for
the low-interactivity condition.
These findings point toward important conceptual differences between the two
variables (i.e., contingency and interactivity), which have hitherto been examined as
identical concepts. What our data suggest is that, based on how these two concepts
are operationalized and measured, they can result in divergent subjective perceptions.
Hence, parsing them out in order to obtain a more nuanced understanding of the
larger construct of interactivity becomes imperative. In the discussion section, we
elaborate on the implications of this finding.
Summary of results
Manipulation of message interactivity had a significant effect on perceived contingency, one of the chief mediators in the study. As expected, we found that
high-interactivity condition led to greater perceptions of contingency. In contrast,
the same message interactivity manipulation resulted in the low-interactivity condition being perceived as highly interactive. Further, tests of mediation showed that
perceived contingency turned out to be a significant mediator of message interactivity
effects on website and content attitudes, users’ behavioral intentions, and risk perception. In contrast, the variable of perceived interactivity also showed significant indirect
effects but in a direction opposite to that of perceived contingency. Path 1 in the phantom model supported the message interactivity path as proposed in the interactivity
effects model. Path 2 revealed the conjoint mediating effects of perceived interactivity
and perceived relevance on cognitive responses and health related outcomes. Finally,
the presence of verbal turn-taking cues significantly reduced participants’ relative
susceptibility to health issues such as diabetes, obesity, and heart disease.
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Discussion
While the rapid proliferation of interactive health tools and apps in the marketplace
appears to be driven by a conviction that interactivity is desirable for promoting
user engagement with health, our study has attempted to empirically assess the
effects of message interactivity in a theoretically grounded fashion. One of the key
contributions of the study lies in demonstrating that a systematic manipulation
of message interactivity (as a structural affordance of the health Q&A tool) not
only has a significant influence on overall user perceptions and evaluations of the
interactive system, but it can also alter users’ perceptions about the content that is
being conveyed via that system.
Empirical importance of contingency
Among researchers who study face-to-face communication, interactivity is often considered a “native state” (Walther, Gay, & Hancock, 2005, p. 640). The challenge lies in
effectively recreating such a state in human-computer interactions. Our study offered
a technique to accomplish this by conceptualizing (and operationalizing) message
interactivity not only as a structural property of the medium, but also as a part of
the user-system interaction. A disproportionate number of studies on the interactivity variable has examined the effects of modality-related features (i.e., addition of
multimedia). Even though previous research has examined the concept of contingency (Burgoon, Bonito, Bengtsson, Ramirez, et al., 2000; Sundar et al., 2003; Wise
et al., 2006), empirical data shedding light on the underlying theoretical link was still
lacking (Walther, Gay, et al., 2005). Our study has addressed this gap by not only operationalizing the contingency principle at the level of the interface (visual display of
interaction history), but also embedding contingency as part of the user-system interaction.
Recent empirical evidence (Sundar, Bellur, Oh, Jia, et al., 2016) has demonstrated
the validity of operationalizing the contingency variable via cumulative display of
interaction history. The current study extends this operationalization by displaying
contingency to users not just at the visual or structural level, but also at the level of
interaction content. Simple semantic cues such as “Previously, you mentioned … ”
or “Earlier, you reported … ” are sufficient to imbue a sense of back and forth in the
minds of the users, which in turn promotes greater user engagement with the system.
Our data show that displaying only a small amount of contingency (e.g., medium condition) without fully delivering on it is as good as not having any contingency at all.
On the contrary, affording a high degree of contingency (complete interaction history,
plus references to prior messages) is bound to positively impact user engagement, and
subsequent outcomes.
In sum, our study demonstrates that we need to comprehensively account for
different dimensions of the message-interactivity (i.e., contingency) variable: (a)
at a structural (ontological) level; (b) at the level of interaction content; and (c)
as a subjective psychological state. Doing so helps us investigate the effects of
message-interactivity by grounding it in the core concept of contingency rather
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than in a blanket fashion that is based on overall user perceptions of interactivity, as
discussed below.
Interactivity versus perceived interactivity
Although we successfully operationalized message interactivity via the contingency
principle, an important empirical question is whether it translates into higher
perceptions of interactivity in the minds of the users. Based on the manipulation of
the threaded back-and-forth interaction, those in the high-interactivity condition
perceived the site to be the most contingent. However, contrary to expectations,
they rated the low-interactivity condition as the most interactive, with no significant
differences between the medium and the high conditions. In terms of design considerations, it is likely that the lesser visual display of the interaction (i.e., less “clutter”)
in the low-interactivity condition may have led users to form richer perceptions of
interactivity. As Walther (1992) noted, even very lean forms of CMC are often powerful enough to evoke rich perceptions of communication. With its instant message or
texting-like interface, the simple Q&A interaction in the low-interactivity condition
possibly appealed to the avid smartphone users who comprised the study sample.
Based on this, one could argue that perceptions of interactivity rest mainly on the
imagination of the user. However, theorizing solely on such subjective perceptions
of interactivity can be misleading, and, further still, unhelpful in aiding design and
product development goals. For interactivity researchers, this counterintuitive finding
calls into question the wisdom of using “perceived interactivity” as a proxy for actual
interactivity, and indeed emphasizes the need to distinguish between the effects of
actual interactivity from those that are due to subjective perceptions of interactivity.
Further, our phantom model analysis illustrates how the specific indirect paths
led by these two mediators significantly differed from each other even though they
are intended to be indicators of the same concept, that is, message interactivity. In
sum, these findings advance our understanding of the interactivity variable. Based on
how the variable is operationalized and measured, the specific effects of interactivity
can vary vastly from a more global understanding of the term in users’ minds (Sundar,
Bellur, Oh, Jia, et al., 2016).
Implications for health communication
The role of perceived relevance has been central in understanding the effects of
message tailoring on health communication outcomes (Brinol & Petty, 2006). Our
study offers another unique approach toward enhancing personal relevance in online
health tools by means of enhancing perceived interactivity. Our findings show that
if the online user-to-system interaction is perceived as being interactive, it not only
contributes to greater perceived relevance but also encourages cognitive responses.
This finding could extend the models of persuasion that adopt the tailoring approach
(Kreuter et al., 1999) specifically by adding the variable of perceived interactivity
to create effective web-based interventions. This interpretation requires a caveat:
Researchers and designers may build a highly interactive website. However, if users
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do not deem it interactive, the effects could boomerang. This reiterates the need to
separate the effects of actual versus perceived interactivity, as discussed above.
Further, the interaction effect with the conversational tone manipulation indicates
that informal conversational tone aids those who are less proficient with technology
use. Power users preferred not to have any turn-taking cues. However, those who
scored low on the power usage scale seemed to benefit from the presence of verbal
turn-taking cues. Perhaps turn-taking cues coming from a system serve to challenge
perceptions of control that is important for high-power users (Sundar & Marathe,
2010).
Regardless of power usage differences, the presence of turn-taking cues seemed to
significantly reduce perceptions of relative susceptibility. Thus, the presence of even
minimal turn-taking cues serves an important “hand-holding function.” This finding offers practical implications for the design of online health tools, in the form of
attentive and empathetic virtual coaches, with risk reduction goals.
Limitations
Given the student sample, this study covered a relatively narrow range of health topics
(diet, exercise, alcohol consumption, drugs), but preventive health behaviors extend
to many other chronic health conditions (e.g., diabetes, cancer, heart diseases), which
may benefit from interactive health tools. Furthermore, our data showed that general health beliefs in our sample was very positive (M = 7.83 on a 9-point scale and
SD = 0.75), leading to a lower number of risk messages by the system, thus diminishing the variance on measures related to risk perceptions. Studying a more diverse
sample, and focusing on health behavior outcomes (instead of perceptions alone),
could show more variability on risk perceptions. Future research could also explore
the impact of voice modality (in the Q&A tool) on the conversational tone variable.
Conclusion
A key contribution of the study lies in operationalizing the concept of message interactivity in terms of contingency at both structural and content levels of a health risk
assessment tool. We discovered that perceptions of contingency, not perceived interactivity, were predictive of user engagement with the health tool, thereby emphasizing the need for theory-driven design of interactivity. Message interactivity not only
impacted users’ attitudes, engagement, and behavioral intentions toward the tool but
it also shaped users’ responses toward the health content delivered by the tool. Even
though health information was kept constant across all the six experimental conditions, the data showed that subtle changes in the design of message interactivity
affordances can serve a persuasion function.
This ability to draw users into preventive health—even as they casually interact
with a health assessment tool—is what makes the use of interactive health tools
a promising strategy for pursuing preventive clinical and behavior-change interventions. More generally, message interactivity, when operationalized in terms of
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contingency, can be a powerful communication tool in its own right, aiding strategic
communications in a number of content domains.
Acknowledgments
This research is supported by the U.S. National Science Foundation (NSF) via Standard Grant No. IIS-0916944; the Korea Science and Engineering Foundation under
the WCU (World Class University) program funded through the Ministry of Education, Science and Technology, S. Korea (Grant No. R31-2008-000-10062-0); and
the Ministry of Education, Korea, under the Brain Korea 21 Plus Project (Grant No.
10Z20130000013).
Notes
1 In order to establish intercoder reliability, a random number table was used to select a set
of 15 responses, which was coded by two coders, independently. Reliability formula by
Cohen’s Kappa (1960) method revealed an intercoder reliability of .91.
2 The purpose of the phantom model analysis is to investigate two or more pathways
simultaneously by isolating their relative individual contributions, rather than test the fit of
the whole model (Card & Little, 2007). In Figure 2, the ovals to the left of the message
interactivity (IV) variable represent latent variables. The path coefficient for A1 in the main
model (right of the IV) is matched with the path coefficient (also labeled A1) in the
phantom model (left of the IV). That is, their path coefficients are constrained to be the
same. This was done to all paths in the latent and the main model. Details of the
conventions for constructing and reporting phantom model analyses can be found in
Macho and Ledermann (2011).
3 Zero-order correlations among all the measured variables in the study are reported in the
Appendix section.
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49
50
Anchors
9-point strongly disagree to
strongly agree
9-point describes poorly to
describes very well
9-point semantic differential
9-point describes poorly to
describes very well
Open-ended
9-point describes poorly to
describes very well
9-point describes poorly to
describes very well
9-point strongly describes
poorly to describes very well
9-point strongly describes
poorly to describes very well
Variable
1. Mediator: Perceived contingency
(4 items)
2. Mediator: Perceived
interactivity(4 items)
3. Mediator: Perceived warmth (4
items)
4. Mediator: Perceived relevance (6
items)
5. Dependent measure: Cognitive
responses
6. Dependent measure: Website
appealing (7 items)
7. Dependent measure: Website
exciting (6 items)
8. Dependent measure: Content
Quality (3 items)
9. Dependent measure: Content
enjoyment (4 items)
Sundar (2000), Sundar et al. (2011),
Sundar, Bellur, Oh, Jia, et al.,
(2016)
Sundar (2000), Sundar et al. (2011),
Sundar, Bellur, Oh, Jia, et al.,
(2016)
Sundar (2000); Sundar et al. (2011),
Sundar, Bellur, Oh, Jia, et al.,
(2016)
Sundar (2000), Sundar et al. (2011),
Sundar, Bellur, Oh, Jia, et al.,
(2016)
Davis et al. (2008), Oermann and
Pasma (2001)
Thorson and Zhao (1997), Wells
(1989)
Kim and Sundar (2012), Powers and
Kiesler (2006)
Liu (2003), McMillan and Hwang
(2002)
Sundar, Bellur, Oh, Jia, et al. (2016)
Sources
Table A1 List of Measures Along with Respective Reliability Estimates
Appendix
Boring (reversed), enjoyable, lively, and
interesting
Believable, accurate, precise
Fun, interesting, imaginative, and so on
“The website took into account my previous
interactions with it”/“The website’s
responses were related to my earlier
input”
“The site enabled simultaneous
communication”/“The site was effective
in gathering my feedback”
“Unfriendly–friendly,”/“Cold–
warm,”/“Impersonal–
personal,” and “Unsocial–social”
“The messages conveyed by the site are
important to me.”/“Interacting with the
site was meaningful for me.”
“The next time you go for a general physical
check-up, what are some questions that
you would like to ask your Doctor? Please
list these questions in the space below”
Useful, positive, likable, and so on
Sample Items
Cronbach’s alpha = .80
Cronbach’s alpha = .91
Cronbach’s alpha = .93
Cronbach’s alpha = .93
Cohen’s Kappa = .91
Cronbach’s alpha = .87
Cronbach’s alpha = .82
Cronbach’s alpha = .63
Cronbach’s alpha = .87
Reliability Estimates
Interactivity as Conversation
S. Bellur & S. S. Sundar
Human Communication Research 43 (2017) 25–53 © 2016 International Communication Association
Rimal and Real (2003)
Rimal and Real (2003)
Hu and Sundar (2010)
Lerman et al. (1995)
9-point strongly disagree to
strongly agree
9-point strongly disagree to
strongly agree
9-point extremely unlikely
to extremely likely
0–100%
9-point extremely low to
extremely high
9-point strongly disagree to
strongly agree
17. Dependent measure: Relative
susceptibility (9 items)
18. Dependent measure: Perceived
severity (10 items)
Human Communication Research 43 (2017) 25–53 © 2016 International Communication Association
Rimal and Real (2003)
Rimal and Real (2003)
Armitage and Conner (1999);
Rimal and Real (2003)
9-point very unlikely to
very likely
11. Dependent measure: Future
health behavior (FHB-1: safer sex
and alcohol, 4 items)
12. Dependent measure: Future
health behavior (FHB-2: diet and
exercise, 3 items)
13. Dependent measure: Health
information exchange (HIE-F1:
diet and exercise, 4 items)
14. Dependent measure: Health
information exchange (HIE-F2:
safer sex and STIs, 4 items)
15. Dependent measure: Website
behavioral intentions (Web BI, 6
items)
16. Dependent measure: Percentage
susceptibility (9 items)
Sundar (2000), Sundar et al. (2011),
Sundar, Bellur, Oh, Jia, et al.,
(2016)
Armitage and Conner (1999),
Rimal and Real (2003)
9-point strongly describes
poorly to describes very
well
9-point very unlikely to
very likely
10. Dependent measure: Content
information value (2 items)
Sources
Anchors
Variable
Table A1 Continued.
“How likely are you to discuss HIV status with
your partner?”/“How likely are you to
practice safer sex?”
“How likely are you to eat more fruits and
vegetables?”/“How likely are you to exercise
regularly?”
“I would like to know more about the topic of
diet and nutrition”/“I would discuss the topic
of nutrition and exercise with my friends”
“I would like to know more on the topic of safer
sex practices”/“I would discuss the topic of
HIV and AIDS with my friends”
“I would bookmark this website for future
use”/“I would recommend this website to
others”
“Out of 100%, what do you think are your
chances of being diagnosed with the
following health conditions?”
Compared to most people my age, I understand
that my risk of being diagnosed with the
medical conditions below, are” ________
(obesity, heart disease, HIV, etc)
“Obesity can be more deadly than most people
realize”/“HIV infection is more serious than
most people realize”
Insightful, informative
Sample Items
Cronbach’s alpha = .66
Cronbach’s alpha = .87
Cronbach’s alpha = .67
Cronbach’s alpha = .97
Cronbach’s alpha = .88
Cronbach’s alpha = .89
Cronbach’s alpha = .77
Cronbach’s alpha = .78
Pearson’s r: .62, p < .05
Reliability Estimates
S. Bellur & S. S. Sundar
Interactivity as Conversation
51
52
Sundar and Marathe
(2010), Sundar, Bellur,
Oh, Jia, et al., (2016)
9-point strongly disagree to
strongly agree
9-point strongly disagree to
strongly agree
9-point strongly disagree to
strongly agree
9-point strongly disagree to
strongly agree
23. Covariate: Preference for online
social interaction (6 items)
24. Covariate: Social extraversion (8
items)
25. Covariate: General health beliefs
(10 items)
Ajzen and Timko (1986),
Becker (1974)
Bendig (1962)
Caplan (2003)
Agarwal and Karahanna
(2000)
9-point strongly disagree to
strongly agree
Agarwal and Karahanna
(2000)
9-point strongly disagree to
strongly agree
21. Dependent measure: User
engagement (amount of control,
2 items)
22. Moderator: Power usage (12
items)
Agarwal and Karahanna
(2000)
9-point strongly disagree to
strongly agree
19. Dependent measure: User
engagement (fun and enjoyment,
6 items)
20. Dependent measure: User
engagement (immersion, 4 items)
Sources
Anchors
Variable
Table A1 Continued.
“I lost track of time when I was interacting with
the site”/“While I was interacting with the
site, I was able to block out most other
distractions”
“I felt in control while I was browsing the
site”/“I felt that I had no control over my
interaction with the site (reversed)”
“I make good use of most of the features
available in any technological device”/“I love
exploring all the features that any
technological gadget has to offer.”
“I prefer communicating with other people
online rather than face-to-face”/“My
relationships online are more important to
me than many of my face-to-face
relationships”
“I usually take initiative in making new
friends”/“I am inclined to keep in the
background on social occasions (reversed)”
“Maintaining good health is important to
me”/“I think it is worthwhile to keep track of
my exercise behavior”
“I had fun interacting with the site”/“Interacting
with the site provided me a lot of enjoyment”
Sample Items
Cronbach’s alpha = .68
Cronbach’s alpha = .81
Cronbach’s alpha = .78
Cronbach’s alpha = .79
Pearson’s r = .5, p < .001
Cronbach’s alpha = .88
Cronbach’s alpha = .94
Reliability Estimates
Interactivity as Conversation
S. Bellur & S. S. Sundar
Human Communication Research 43 (2017) 25–53 © 2016 International Communication Association
S. Bellur & S. S. Sundar
Interactivity as Conversation
Table A2 Zero-order Correlations Among All Measured Variables
1
2
3
4
5
6
7
8
9
10
11
1 Perceived
1
contingency
2 Perceived
.31** 1
interactivity
3 Perceived warmth
.37** .38** 1
4 Perceived relevance .27** .46** .43** 1
5 Website appealing
.46** .59** .68** .59** 1
6 Website exciting
.31** .37** .49** .47** .72** 1
7 Content quality
.34** .31** .42** .33** .52** .29** 1
8 Content enjoyment .23* .44** .55** .62** .63** .65** .37** 1
9 Information value
.26** .31** .48** .41** .53** .32** .55** .54** 1
10 Fut Health Bhr FHB .05
.14
.28** .30** .29** .24* .23* .36** .30** 1
F1
11 Fut Health Bhr FHB .31** .18* .10
.19* .26** .22* .20* .17* .13
.16* 1
F2
12 Health Info Exc HIE .22* .22* .19* .49** .21** .27** .14
.36** .11
.22** .27**
F1
13 Health Info Exc HIE .07
.21* .23* .44** .15* .28** −.01
.39** .05
.36** .14
F2
14 Web BI
.17* .41** .36** .54** .58** .71** .20* .70** .28** .32** .17*
15 Percentage
−.14 −.08 −.01 −.02 −.06 −.03 −.04 −.05 −.11 −.08 −.19*
susceptibility
16 Relative
−.06 −.002 −.03 −.01 −.02 −.01 −.03
.04
.03 −.01 −.08
susceptibility
1
17Perceived severity
18Engagement (enjoyment)
19Engagement (immersion)
20Engagement (control)
21Cognitive responses
2
.35**
.27**
.17*
.37**
.02
12
12
13
14
15
16
17
18
19
20
21
3
.21*
.38**
.23*
.41**
.10
13
4
.31**
.47**
.34**
.33**
.08
14
5
.17*
.55**
.41**
.35**
.25*
6
.27**
.63**
.52**
.52**
.01
15
Health Info Exc HIE F1 1
Health Info Exc HIE F2
.55** 1
Web BI
.38** .40** 1
Percentage susceptibility −.06
.05 −.004 1
Relative susceptibility
−.08 −.05
.02
.39**
Perceived severity
.12
.10
.20** −.09
Engagement (enjoyment) .39** .38** .72** .02
Engagement (immersion) .23** .20** .48** −.03
Engagement (control)
.13
.01
.37** −.17
Cognitive responses
.22** .08
.22** .14
7
.22*
.81**
.59**
.43**
.12
16
1
−.03
−.07
−.13
.04
.004
8
.34**
.27**
.28**
.29**
.03
17
1
.18*
.11
.11
.09
9
.19*
.68**
.43**
.39**
.08
18
10
.28**
.26**
.23*
.23*
.03
19
11
.11
.24**
.18**
.15**
.003
.29**
.12
.11
.28**
.01
20 21
1
.67** 1
.42** .31** 1
.19*
.09
.12 1
Note: Correlations significant at *p < .05 and **p < .001.
Human Communication Research 43 (2017) 25–53 © 2016 International Communication Association
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