Copyright by Jisoo Ahn 2018
The Dissertation Committee for Jisoo Ahn Certifies that this is the approved version of the following Dissertation:
The Effects of Modality Interactivity and Health Literacy on User Engagement and Processing of Public Health Information
Committee:
Michael Mackert, Supervisor
Lee Ann Kahlor
Lucy Atkinson
Brad Love
Erin Donovan
The Effects of Modality Interactivity and Health Literacy on User Engagement and Processing of Public Health Information
by
Jisoo Ahn
Dissertation Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy
The University of Texas at Austin May 2018
Dedication
I would like to acknowledge my advisor, Dr. Michael Mackert for his assistance during my entire doctoral program. Working with him on projects and this dissertation, I have learned how to make good progress by working step-by-step. Also, his positive strategies for resolving conflicts and emotional support were very helpful to me to reach this stage.
I would also like to acknowledge the expert guidance of Drs. Lee Ann Kahlor,
Lucy Atkinson, Erin Donovan, and Brad Love from helping me build my research idea to refining this dissertation, leading me to finalize this work. All their suggestions and compliments helped to move me forward during this painful but valuable journey.
I am also thankful to Dr. Jeeyun Oh who provided overall assistance on interactivity research and advice on my data analysis.
Lastly, I would like to dedicate this dissertation to my family. Their endless love and support has helped me make it to where I am today. Needless to say, thank you also to my colleagues and friends in the United States and Korea!
Abstract
The Effects of Modality Interactivity and Health Literacy on User Engagement and Processing of Public Health Information
Jisoo Ahn, Ph.D.
The University of Texas at Austin, 2018
Supervisor: Michael Mackert
Considering the importance of learning new information in the emerging disease context, this study examines how individuals cognitively process information with an interactivity feature on a website. Modality interactivity, which was operationalized as a slider, was used to identify its effects on user engagement (i.e., cognitive absorption: the degree to absorb the content) and attitudes and behavioral intentions serially. In addition, whether an individual characteristic, such as health literacy (i.e., the degree to read and understand health information), varies the effects was asked.
A single factor (modality interactivity: slider vs. control) experiment was conducted on a health website which provides information about a new fictitious infectious disease, Logi. With 350 participants, the results revealed that modality interactivity increased cognitive absorption and in turn, enhanced favorable attitudes toward the website, the message, and the agency. The indirect effects of modality
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interactivity also positively influenced the intentions of users to revisit the website, follow the recommendations in the message, and seek further information from the agency about the disease. These effects did not depend on health literacy; that is, participants at all levels of health literacy had similar effects of modality interactivity on attitudes and intentions through cognitive absorption. Discussion of theoretical and practical implications is presented.
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Table of Contents
List of Tables ...... x
List of Figures ...... xi
Chapter 1: Introduction ...... 1
Chapter 2: Literature Review ...... 5
Heuristic-Systematic Model ...... 5
Interactivity and Information Processing ...... 8
Explication of Interactivity ...... 9
Theory of Interactive Media Effects: An Explanation of Interactivity Effects by Dual-Processing ...... 11
A Mediating Role of Cognitive Absorption ...... 14
Health Literacy and Information Processing ...... 18
Chapter 3: Method ...... 26
Overview ...... 26
Participants...... 27
Procedure ...... 28
Stimulus ...... 30
Measurement ...... 35
Moderating Variable ...... 35
Control Variables ...... 38
Mediating Variable ...... 39
Dependent Variables ...... 41
Demographics ...... 43
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Analysis ...... 43
Chapter 4: Results ...... 45
Manipulation Check ...... 45
Simple Mediation Analysis ...... 50
Attitudes toward the Website/Message/Agency/Disease ...... 51
Intentions regarding the Website/Message/Agency/Disease ...... 54
Moderation of the Effect of Modality Interactivity on Absorption by Health Literacy ...... 56
Moderated Mediation Analysis ...... 57
Attitudes toward the Website/Message/Agency/Disease ...... 58
Intentions regarding the Website/Message/Agency/Disease ...... 60
Unexpected Findings ...... 62
Attitudes toward the Website ...... 63
Intentions regarding the Disease ...... 65
Chapter 5: Discussion ...... 67
Interpretation of Findings ...... 67
Indirect Effects of Modality Interactivity on Attitudes through Cognitive Absorption ...... 67
Indirect Effects of Modality Interactivity on Intentions through Cognitive Absorption ...... 69
Conditional Indirect Effects of Modality Interactivity on Attitudes/Intentions through Cognitive Absorption by Health Literacy ...... 69
Theoretical Implications ...... 72
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The Role of Modality Interactivity to Shift the Modes of Information Processing ...... 72
The Importance of Engagement in the Learning Process ...... 72
Reification of Attitudes and Intensions ...... 74
Preservation of All Health Literacy Levels with Johnson-Neyman Technique...... 74
Practical Implications ...... 75
Effects of Modality Interactivity for Public Health Education ...... 75
Effects of Modality Interactivity for the Agency's Communication and Web Design Strategies ...... 76
Future Research ...... 77
Two Sides of Interactivity...... 77
Tyes of Device Used for Web Browsing ...... 77
Health Literacy and Information Delivery...... 78
Presentation Style of Health Information ...... 79
Tyes of Topics for Application ...... 79
Limitations ...... 80
Weakness of Measurement ...... 80
Potential of Perceived Risk of Threat as a mediator ...... 81
Consideration of Emotional Components ...... 81
Need of More Suitable Population for Generalization ...... 82
Chapter 6: Conclusion ...... 84
References ...... 85
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List of Tables
Table 1: Baseline Characteristics of the Sample ...... 28
Table 2: Descriptive Statistics for Variables Used in the Analysis ...... 37
Table 3: Zero-order Correlations of All Variables ...... 44
Table 4: Ordinary Least Squares Regression Model Coefficients ...... 46
Table 5: Conditional Indirect Effects of Modality Interactivity on Attitudes and
Intentions through Absorption at Levels of Health Literacy ...... 48
x
List of Figures
Figure 1: TIME Model ...... 12
Figure 2: The Conceptual Diagrams of Moderated Mediated Model ...... 24
Figure 3: Proposed Model ...... 25
Figure 4: A sample page from the CDC ...... 31
Figure 5: The Format of the Stimulus Website ...... 31
Figure 6: Static Images of Each Category in the Control Condition ...... 33
Figure 7: Morphing Images of Each Category in the Interactivity Condition ...... 34
Figure 8: The Newest Vital Sign ...... 36
Figure 9: Images of Interactive Features ...... 39
Figure 10: H1s ...... 50
Figure 11: Paths Coefficients for H1s ...... 52
Figure 12: Findings from H1s ...... 53
Figure 13: Path Coefficients for H2s ...... 55
Figure 14: Findings from H2s ...... 56
Figure 15: Findings from RQ1s ...... 60
Figure 16: Findings from RQ2s ...... 62
Figure 17: The Conceptual Diagrams of Model 8 in PROCESS Representing a
Moderated Mediation Model ...... 63
Figure 18: Conditional Direct Effect of Modality Interactivty on Website Attitude
by Health Literacy...... 65
Figure 19: Conditional Direct Effect of Modality Interactivty on Seeking Intention about the Disease by Health Literacy ...... 66
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Chapter 1: Introduction
Delivering information about an unfamiliar disease and promoting the relevant preventive behaviors to the public are not easy when people do not fully understand the disease. The reasons are not being aware of the seriousness of the disease and the lack of need to get to know about the disease. The problem of not understanding the information correctly when the disease is new is that misinformation about the disease can cause unexpected consequences and correcting misinformation after the information about the disease has been disseminated is more difficult. In fact, during the Ebola outbreak in
2014, there was a concerted effort to convey correct information about this emerging disease and reduce communities’ fear and stigma, although the risk of contracting the disease was very low in the United States (Santibañez, Siegel, O'Sullivan, Lacson, &
Jorstad, 2015). To prevent the extra effort required to communicate with the public after misinformation is formed, it is necessary to find ways to get the public interested in learning about what they are not familiar with, changing their behaviors, and compelling them to look further for more information from specific sources; this public education work is not only for the government but also websites of private organizations such as
WebMD.com (Freimuth, Linnan, & Potter, 2000) because the primary purpose of those information providers and websites is to educate patients and potential patients to enable them to be aware of, prevent, and control diseases (Keselman, Logan, Smith, Leroy, &
Zeng-Treitler, 2008).
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One strategy of informing and entertaining the public is to help them engage in learning. For example, website users successfully improve their engagement with interactive features during web browsing and become active users (Cole, 2004); users are provided opportunities to customize content (Revere & Dunbar, 2001) and utilize interactive features such as zooming-in and out, panning, and rotating images (Lu, Kim,
Dou, & Kumar, 2014) from eHealth interventions using interactivity. Through playing with the interactive features and having an interest in the content, activated users are more likely to seek and use health information (Nijman et al., 2014). Moreover, the use of technology is found to be a powerful cognitive tool to lead individuals to extensive processing, for example, building new knowledge and analyzing information about unfamiliar topics such as science (Wang, Hsu, Reeves, & Coster, 2014). Therefore, technology can be expected to be applicable to the health area and be effective in showing its effects on cognitive processing by presenting information with multiple types of interactivity. This dissertation intends to examine individuals’ processing of health information from using interactivity from website features; how do technology features other than text affect user engagement and consequential cognitive outcomes such as attitudes and intentions?
Besides having the public not pay attention to information about a new disease, the ineffective delivery of health information results in only some of the public deriving benefits from the information; for others, health information may not be appropriately understood because of a lack of comprehension of the information (Longo & Patrick,
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2001; Nijman, Hendriks, Brabers, De Jong, & Rademakers, 2014). Those with limited capacity of “obtaining, processing, understanding, and communicating about health- related information” (Berkman, Davis, & McCormack, 2010, p. 16), i.e., health literacy, is an example of this disadvantaged population. Many reasons have been identified in terms of why people have difficulties in gaining benefits from the dissemination of health information: the reasons can include their inability to gain and understand health information, accessing too few sources to obtain health information (Suka et al., 2015) and not being knowledgeable of health conditions (Kalichman et al., 2000; Williams,
Baker, Honig, Lee, & Nowlan, 1998). Accordingly, adverse health outcomes such as being exposed to risk behaviors, not engaging in healthy behaviors (Suka et al., 2015), and high mortality rates (Peterson et al., 2011) occur.
More importantly, low health literacy is related with not only lower knowledge gain but also lower motivation (see Muller et al., 2017). As simply providing information does not necessarily inform or engage health consumers in health behaviors (Faber,
Bosch, Wollersheim, Leatherman, & Grol, 2009; Hibbard, Peters, Dixon, & Tusler,
2007), diverse presentation methods have been recommended in website design to stimulate different senses for understanding the relevant information: displaying text with pictures, including an audiovisual format, and applying consistent navigation (Campbell,
Honess-Morreale, Farrell, Carbone, & Brasure, 1999; Muller et al., 2017). Although these strategies suggest key factors for increasing positive health outcomes, it is appropriate to
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question why and how modality influences these results and whether the reason is dependent on health literacy levels.
In sum, this dissertation asks two research questions: 1) how a website feature
(i.e., modality interactivity) motivates cognitive processing (i.e., user engagement) and the related outcomes (i.e., attitudes and intentions) serially, and 2) whether health literacy makes a difference in this process. For this, a dual-process model will be introduced in the next section to understand the mechanism of information processing via more vs. less effortful routes. Following this theoretical framework, the concept of interactivity will be explicated. Then, the hypotheses and research questions, methods, results, and discussion will follow.
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Chapter 2: Literature Review
Briefly mapping this section, the heuristic-systematic model (HSM) will be explained to introduce how individuals process information using two separate modes and sometimes with those modes at the same time. After understanding the principles of information processing and the applied HSM research, the following sections will discuss what interactivity is and how it is conceptualized and operationalized in this dissertation by reviewing relevant previous studies. Then, the theory of interactive media effects
(TIME), which is based on HSM, will describe the role of interactivity on dual- processing and focus on one of the mediators, cognitive absorption, engages individuals in elaborative information processing. Following the explanation of the effects of interactivity on attitudes and intentions through absorption, two conflicting perspectives of explaining the relationship between health literacy and information processing will be presented. At the end, the moderating role of health literacy on the indirect effects of interactivity will be questioned.
Heuristic-systematic model (HSM)
The heuristic-systematic model (HSM) is a useful framework based on the proposition that individuals process information via two routes: heuristic and systematic processing. Two information processing routes occur within two large principles:
Basically, individuals tend to exert the least amount of effort on information processing, for example, by relying on a judgmental cue such as source credibility (Chaiken, 1980,
1987; Chaiken, Liberman, & Eagly, 1989; Chen & Chaiken, 1999; Eagly & Chaiken,
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1993), “available” or currently held information and a “like” issue (Wilson, Evans,
Leppard, & Syrette, 2004). Similar to the peripheral route in the elaboration likelihood model (ELM; Petty & Cacioppo, 1986), heuristic processing does not require much cognitive ability. If there needs to be more cognition for decision making, which is the second principle of HSM, it leads to other processing, called systematic processing in which individuals engage in deeper processing to satisfy the “sufficiency principle”
(Chaiken et al., 1989; Chen & Chaiken, 1999). In this processing, individuals make a decision based on relevant information such as the content of the message itself (Koh &
Sundar, 2010) and exert more effort analyzing given information until reaching a sufficient degree of confidence in the accuracy of their judgment. For this, more motivation and a greater ability to analyze the information are required (Chaiken, 1980,
1987; Chaiken et al., 1989; Chen & Chaiken, 1999; Eagly & Chaiken, 1993). As this processing is slower and requires more comprehensive thinking than heuristic processing
(Chen & Chaiken, 1999; Fiske & Taylor, 1991), more permanent and stronger attitudes are produced than those resulting from heuristic processing (Griffin, Neuwirth, Giese, &
Dunwoody, 2002).
The second principle of HSM is applied to other theories and models regarding information processing. For example, the risk information seeking and processing (RISP;
Griffin, Dunwoody, & Neuwirth, 1999) model focuses on the psychological need for information (i.e., information insufficiency), which is a gap between perceived current knowledge and perceived amount of information needed for decision making. This
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concept is compatible with “judgment confidence” in HSM in terms of the motivation of being accurate in accounting for a decision; because individuals avoid revealing that their decisions are incorrect (Bohner, Moskowitz, & Chaiken, 1995), they elaborate information until they feel confident about their decisions or perceive that their information is sufficient to make decisions.
To date, HSM research has been based on several types of heuristics that the original HSM exemplifies. For example, in the health and risk context, risk information including negative emotions (Slovic, Finucane, Peters, & MacGregor, 2007) and narratives using different source perspectives (Nan, Dahlstrom, Richards, & Rangarajan,
2015), were viewed heuristically and with increased risk perception. Nan et al.’s (2015) study found that individuals perceived greater risk of getting HPV and were more likely to express intention to get a free vaccine when exposed to first-person narratives rather than third-person narratives. Recently, the number of posts and followers have been considered as heuristic cues in the social media environment (Castillo, Mendoza, &
Poblete, 2011; Zhang, Peng, Zhang, Wang, & Zhu, 2014), which helps users make decisions from information overload.
Applying various types of heuristic cues in HSM research, some repeated patterns were found: co-occurrence of heuristic and systematic processing. The difference between HSM and ELM is the acknowledgement of the interdependent relationship of the two information processing routes; heuristic and systematic processing modes occur in parallel, not in competition (Zhang et al., 2014). For example, individuals had both
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heuristic and systematic thoughts about the risk of and attitudes toward tanning beds rather than processing solely heuristically or systematically (Nazione, 2016). In addition, when individuals use microblogging sites, they not only examine the topic and emotional degree of a post but also rely on information regarding the author (e.g., degree of activeness and experience) (Zhang et al., 2014).
One of the hypotheses that explains these co-occurrence of two systems is the bias hypothesis (Chaiken et al., 1989). When message content is ambiguous and amenable to different interpretations, heuristic cues play a role in disambiguating message content and motivating recipients to process the message systematically. The underlying mechanism of this hypothesis is that “heuristic cues influence people’s expectations about the probable validity of persuasive messages or the probable nature of attitude objects and issues” (Chaiken & Maheswaran, 1994, p. 461) so that cognitive and motivational factors (i.e., heuristic cues) can bias perceptions and evaluations. In other words, heuristic cues can work to evoke immediate decision-making without reflection as well as stimulate more effort on elaboration by using themselves.
Interactivity and information processing
The explanation of the two-sided role of heuristic cues can be exampled with interactivity which motivates heuristic and also systematic processing. This dissertation focuses on how interactivity as a heuristic cue absorbs individuals’ attention and leads to their cognitive thinking and the related outcomes.
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Explication of interactivity. Interactivity is commonly defined as a term implying interaction and activity (Sundar, 2008). However, the definition of this concept varies depending on with whom one interacts. Liu and Shrum (2002) suggested three perspectives of interaction: user-machine interaction, user-user interaction, and user- message interaction. From a user-machine perspective, interactivity refers to a computer system’s responsiveness, but as technology advances, the importance of the concept has declined (Liu & Shrum, 2002); user-user interaction views interactivity as a face-to-face and real-time interaction such as interpersonal communication, but this is also less acceptable these days because people can interact without seeing each other as well as not communicating simultaneously, as Internet and computer-mediated communication develop (Ha & James, 1998). Lastly, in user-message interaction perspective (Cho &
Leckenby, 1997), interactivity is defined as users’ controllability and modifiability of a message (Steuer, 1992).
In this dissertation focusing on the online environment, interactivity will be bounded to action possibilities (Jensen, 1998; Liu & Shrum, 2009; Lombard & Synder-
Dutch, 2001) on the web. Applying definitions of interactivity from the different perspectives into this boundary, three characteristics of interactivity can be summarized:
1) two-way communication (Liu & Shrum, 2002) or reciprocal communication (Ha &
James, 1998), 2) active control (Liu & Shrum, 2002) or user control (Coyle & Thorson,
2001; McMillan & Hwang, 2002; Steuer, 1992), and 3) synchronicity (Liu & Shrum,
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2002) or responsiveness (Rafaeli, 1988). In other words, users communicate with and control a system and its content via technologies.
These dimensions of interactivity are embedded in previous literature that examines its consequences and is shown in different conceptualizations. In the area of studying media effects, interactivity is conceptualized as “affordance” (Sundar & Bellur,
2010), which is a technological and non-message attribute of a medium that changes the process of communication and its outcomes: the presence of it serves as a cue to evoke perceptual consequences, while the affordance is also used to boost deeper processing to form outcomes more stable than perceptions, such as attitudes and intentions (Sundar, Jia,
Waddell, & Huang, 2015). Besides the characteristics of the affordance, —functionality
(i.e., eliciting a contingent response from user actions) (Rafaeli, 1988) and real-time modifiability (i.e., using and adjusting the interface and content) (Steuer, 1992), this interactive affordance, on the other hand, is considered as an attribute of users and explained with its psychological effects. Thus, some scholars offered perceived interactivity, which results from users’ interaction with a system from the perspective of the uses and gratifications paradigm (Liu & Shrum, 2002; McMillan & Huang, 2002;
Rubin, 1993; Sohn, 2011).
Among diverse conceptualizations of interactivity, this dissertation will focus on interactivity as a technological attribute of a medium; the functions by manipulatable tools on an interface would be called interactivity, which let users experience interactions
(Sundar, Xu, Bellur, Oh, & Jia, 2010). This type of interactivity is labeled modality
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(medium) interactivity (Sundar, 2007). The different methods of interaction (i.e., modality interactivity) have been manipulated by the interactive media literature such as clicking, zooming in and out, scrolling, mouse-over, and sliding (e.g., Sundar, Bellur, Oh,
Xu, & Jia, 2014). As users manually play with various modalities, they can encode information differently according to each modality (Sundar, Xu, & Bellur, 2010). To illustrate, a mouse-over highlights a point, and scrolling is able to control the speed of access to information, as compared to a non-interactive, static form of information. The advantage of these types of interactivity is to enhance users’ mental representation and expand their sensory exploring the interface (Steuer, 1992; Sundar et al., 2015).
Theory of interactive media effects: An explanation of interactivity effects by dual-processing. The theory of interactive media effects (TIME; Sundar et al., 2015) explicates interactivity’s effects on the process of communication (Figure 1). The gist of the theory is that modality interactivity induces several steps that shape cognition, attitudes, or behavior via two different routes; it describes how affordance affects distinctive outcomes through two systems of processing based on HSM. For example, the presence of the feature itself (i.e., modality) can work as a cue in heuristic processing
(upper arrows and a series of diagrams in Figure 1), or the use of the feature leads to more engagement in systematic processing (lower arrows and a series of diagrams in
Figure 1). At the end, each processing has different outcomes.
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Figure 1. TIME model (Sundar et al., 2015).
Heuristic processing provided by the modality is somewhat simple; users evaluate media content by identifying the affordances (i.e., modality), resulting in outcomes of source/interface/content perception (Sundar, 2008; Sundar et al., 2015). In other words, the affordances act as a cue to assess the quality and credibility of the content (e.g., Oh,
Robinson, & Lee, 2013). In fact, users liked a website when they discovered the presence of interactivity as a cue (Liu & Shrum, 2009). The presence of the affordances can affect behavioral outcomes as well (not displayed in Figure 1; Sundar et al., 2015). For example 12
in online advertising and marketing, modality interactivity influences attitude and behavior through cognitive heuristics such as being-there and distraction (Sundar, Xu, &
Dou, 2012). When products are presented by 3D visualization, brand attitudes and purchase intentions improved because customers thought the modality was authentic (Li,
Daugherty, & Biocca, 2002; Grigorovici & Constantin, 2004). Although modality can draw attention, it can also be obtrusive or distracting, thus negatively affect behavioral intentions (e.g., Stavrositu & Sundar, 2004; Sundar & Kalyanaraman, 2004).
TIME also assumes that affordances boost intrinsic motivation and consequently enhance user engagement (Sundar, Bellur, & Jia, 2012). That is, affordances work as not only a cue but also a stimulator of systematic processing. In other words, although individuals process information superficially as a default mode of heuristic processing, they can be involved in deeper processing triggered by interactivity features. The heuristic cues can aid systematic processing, which has been supported by dual-process research (e.g., Chen & Chaiken, 1999; Meyers-Levy & Peracchio, 1995; Shavitt, Swan,
Lowrey, & Wänke, 1994). Sundar (2008) also noted that using heuristics is not always interpreted as heuristic processing but as a tool for analytic processing. This means that the external factor, modality interactivity, is able to evoke motivations to fulfill the confidence for judgment through more elaborative processing.
When it comes to systematic processing, more steps exist to explain the mediating roles between affordances and outcomes. When compared to non-interactive and static graphics, affordances require users’ actions such as clicking and dragging, which enhance
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mental representation in terms of the speed, range, and vividness of information (Sunder et al., 2015). In other words, “perceptual bandwidth” (Reeves & Nass, 2000), which is the assessment of the interface, is expanded so that users can interpret the information more widely and deeply. The improvement of the perception brings about more engagement and consequently knowledge, attitudes, and behaviors (Stavrositu & Sundar, 2004;
Sundar & Kalyanaraman, 2004).
One type of engagement that explains how the interface features activate deeper processing is cognitive absorption, as TIME suggests. In this dissertation, the ways individuals transition their mode by using a heuristic cue is explicated: the least effort for making a decision to more elaboration on the message from the cue. In other words, how a heuristic cue (e.g., interface features) lets individuals absorb into the interactivity and shows the connection between these two opposite-sided but compatible modes.
A mediating role of cognitive absorption. Engagement or user engagement in computer-human interaction is a concept for understanding user experience in physical, cognitive, and affective aspects (O’Brien & Toms, 2008). The concept comprises components from various theories: focused attention, control, and motivation from flow theory (Csikszentmihalyi, 1990), pleasure, intrinsic motivation, attention, and curiosity from aesthetic theory (Beardsley, 1982), feedback, motivation, and challenge from play theory (Stephenson, 1967), and connectivity between attributes of user, system, and contexts from information interaction (Toms, 2002). Therefore, engagement can be
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referred to as the stimulation of sensual, emotional, spatiotemporal aspects from the user experience of a system (O’Brien & Toms, 2008).
Engagement has been measured with the inclusion of those components above.
For example, cognitive absorption is one type of engagement that users can experience during interaction with a site (Agarwal & Karahanna, 2000), which TIME also proposes.
Absorption is constructed with dimensions of temporal dissociation (i.e., transformation of time), focused immersion (i.e., attention), heightened enjoyment (i.e., pleasure and enjoyment), control (i.e., perception of regulating the interaction), and curiosity (Agarwal
& Karahanna, 2000). Not all these dimensions were used in one study; in other words, the fitness or the relevance of each aspect of absorption depends on the context and/or the purpose of the study. Therefore, the instrument of engagement or absorption is modified to be optimized for a study (e.g., Wiebe, Lamb, Hardy, & Sharek, 2014).
The previous research shows how absorption has been applied to examine its effects including as the role of mediator in persuasion and information delivery. When users use modality interactivity and determine that the interface is easy to use or intuitive, this makes users more absorbed in the content and more likely to form favorable attitudes regarding the interface and the content (Sundar et al., 2015). For example, rotating, zooming, and hovering interface functions to see images of products on a shopping website were effective in magnifying users’ feeling of fun and control and, in turn, increased positive attitudes toward the website and the products (Xu & Sundar, 2014).
The slider function also encouraged users to absorb information while browsing website
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sections and to have favorable attitudes toward an antismoking website and its messages
(Oh & Sundar, 2015). Also, improved enjoyment and user engagement by modality caused a greater level of trust and satisfaction (Dou & Sundar, 2014). Not only modality, a structure affordance (e.g., the presence of live chat, the presentation of interaction history) positively and serially influenced perceived contingency, user engagement (i.e., absorption), attitudes toward the website and intentions to recommend and know about the website (Sundar, Bellur, Oh, Jia, & Kim, 2014).
Risk communicators are concerned with how to enhance audience interest in a risk because a greater level of interest is likely to lead to systematic processing (Kahlor et al., 2003) and make the user be aware of the risk. The answer can be found in the effect of interactivity, which enables the audience to pay attention to the risk information.
Although ELM assumes that highly involved individuals tend to process information sophisticatedly, it was found that even if the information is not related to the users, their involvement increases during the processing of information with modality (e.g., Guillory
& Sundar, 2014).
Given that the main purpose of a public service announcement in emergency situations is making the public aware of new information involving the public, active information processing is critical, especially during the outbreak of an infectious disease.
Therefore, this dissertation proposes hypotheses predicting the effects of modality interactivity—operationalized as the presence of a slider—on attitudes and intentions through cognitive absorption. Developed from previous literature, attitudes and intentions
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are more specific according to the context. For example, attitudes toward the website, the message, the agency, and the disease are posed. To be specific, while using the interactive features and information on a website, the users evaluate several aspects of what they experienced: the quality and impression. They may want to assess whether they are impressed positively or negatively on the website and disease (i.e., attitudes toward the website; attitudes toward the disease; e.g., Spears & Singh, 2004), whether the message about the disease is useful (i.e., attitudes toward the message; e.g., Watts,
Shankaranarayanan, & Even, 2009), and whether the information provider or the website owner has a high source credibility (i.e., attitudes toward the agency; e.g., Büttner &
Göritz, 2008)
Being matched with the attitudes, the intentions are specified. As many cognitive models and research verified the relationship between attitudes and intentions, it is assumed that individuals have intentions to engage in certain actions if they think about the favorableness of the related aspects. For example, if they evaluate that the website is appealing, they would like to revisit the website. Therefore, explicating the types of intentions, intentions to revisit the website, is to understand the website users’ likelihood of keeping a website in mind and using it again in the future (Dou, 2013); intentions to follow the recommendation refers to the likelihood of accepting the suggestions from the message; intentions to seek more information from the agency is the likelihood of using the information source for future research about the health information (Dou, 2013); and intentions to seek more information about the disease is the likelihood of looking for
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information about the disease in the future to satisfy their knowledge insufficiency (Yang,
2012).
Based on the literature and the purpose of this dissertation, the following hypotheses are proposed:
H1: Cognitive absorption will mediate the effects of modality interactivity on attitudes toward the website (H1a), the message (H1b), the agency (H1c), and the disease
(H1d).
H2: Cognitive absorption will mediate the effects of modality interactivity on intentions to revisit the website (H2a), follow the message (H2b), seek information from the agency (H2c), and seek information about the disease (H2d).
Health literacy and information processing
The type of processing individuals devote their attention to is dependent on not only message factors but also individual factors (Petty & Cacioppo, 1984). HSM proposes that individuals adopt one of two ways of processing or move from heuristic processing to systematic processing according to their capacity and motivation to process the given information (Eagly & Chaiken, 1993; Griffin et al., 1999). While motivation has been studied extensively in dual-process research, identifying the different forms of processing due to capacity is scarce. In the context of interpreting health or disease information, health literacy, which refers to “the degree to which individuals can obtain, process, understand, and communicate about health-related information needed to make informed health decisions” (Berkman, Davis, & McCormack, 2010, p. 16), is an essential
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individual factor that determines whether individuals put more or less cognitive effort into evaluating the information.
A systematic review of health literacy literature (Sørensen et al., 2012) reported that situational and personal determinants influence individuals’ ability or skills to comprehend and process health information; for example, socioeconomic status, income,
English proficiency, age, and peer and parental influences. Even though are many demographic and social factors that cause individuals to struggle with their processing of health information, Griffin and colleagues (2002) focused on capacity as a primary factor that effects information processing and outcomes and delineated the concept; although capacity for critical thinking is what HSM usually mentions, it also includes 1) perceptions that individuals are able to acquire information and that the information is useful and credible, and 2) the knowledge structure they already have. These qualifications lead to systematic processing.
RISP research often focuses on perceptions, especially perceived ability to gather information (i.e., “perceived information gathering capacity [PIGC]” in the model). PIGC was found to be negatively related with heuristic processing; in other words, people who believe they can process information well tend not to rely on heuristic cues for decision making (Kahlor et al., 2003). However, Yang and colleagues (2014) observed the marginal role of PIGC in a society of information overflow because individuals do not need to labor in seeking and processing information these days. This makes for mixed results in the relationship between PIGC and information processing paths. Therefore,
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individuals’ actual status rather than their perception is more appropriate for understanding their pattern of information processing.
When health literacy is considered as a factor operationalizing “capacity,” it can cover individuals’ actual ability of seeking as well as interpreting and applying information. This factor places additional value on the PIGC, which focuses on
“perceived” capacity of “gathering” information. It is difficult to find research that examines the influence of health literacy on information processing, but a possible idea of using different routes depending on health literacy has been proposed. Chiang and
Jackson (2013) suggested that high health literacy would be helpful to use the central route whereas low health literacy would be more likely to be assisted by peripheral cues
(e.g., visuals).
The different route of processing depending on the level of health literacy is explainable by cognitive overload; cognitive capacity is required in message processing, but if health literacy, which is an ability to understand health information, is limited, cognitive overload will be experienced (Wilson & Wolf, 2009). In this case, individuals cannot help using heuristics to process information, for example, in evaluating the quality of information (Mackert, Kahlor, Tyler, & Gustafson, 2009).
Another reason that low health-literate individuals use heuristic cues first is because they skip a lot of text (Colter & Summers, 2014). The information that is not considered as relevant is not encoded and recalled (Lang, 2000, 2006). Therefore, the remaining information from the given text is not sufficient to make decisions so that, for
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that population, heuristic processing occurs. However, even though cognitive heuristics are considered as efficacious (e.g., Richard & David, 2001), the mere use of heuristics creates a risk of informal fallacies (Cummings, 2014) in health decision-making, and therefore, heuristics should be appropriately used or guidance should be provided for appropriate use.
To prevent low health-literate individuals’ selective information processing and to improve the usability of online information, various modes of information presentation are suggested: text with graphics or video/audio, interactive features, options for navigating information, etc. (U.S. Department of Health and Human Services & Office of
Disease Prevention and Health Promotion, 2010). These options were found to be effective in helping users recall information and form favorable attitudes toward health behavior (e.g., cancer screening) as well as drawing attention and triggering the motivation of that population (Meppelink & Bol, 2015; Meppelink, Smit, Buurman, & van Weert, 2015; Meppelink, Van Weert, Haven, & Smit, 2015). This strategy of tailoring information also facilitated elaborative processing and promoted attractiveness and comprehensibility of a website for those who have limited ability and motivation such as older adults (Nguyen, Smets, Bol, Loos, & Van Weert, 2018)
However, using multiple modalities may not be welcomed according to the limited capacity model of motivated mediated message processing (LC4MP; Lang,
2000). The basic premise of the theory is that when individuals encode, store, and retrieve information, they use a limited amount of resources, and if the information requires more
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resources than those available, the aversive (avoidance) system rather than the appetitive
(approach) system is automatically turned on (Lang, Bradley, & Cuthbert, 1997). In other words, when resources are insufficient to perform information processing, cognitive overload occurs. For example, older adults who suffer from age-related limitations took longer than younger adults to choose which mode of information was delivered on the website (e.g., text, illustrations, or video) (Nguyen et al., 2017).
Likewise, people with low health literacy may struggle from processing information given multiple modalities. Usually a website that has the purpose of information delivery contains text plus visuals or videos. Controlling different modalities displayed on the website, users are exposed to multitasking, which demands users switch quickly from one task (e.g., reading and interpreting the information) to another (e.g., watching and interpreting the information) (Taatgen & Lee, 2003). Therefore, low health- literate people may fail in encoding information from various modalities on a website because they need to use most of their cognitive resources to understand the text-based information. Perhaps, the worst case in the web browsing of this population is that all content is discarded because of the lack of ability to allocate a similar amount of their cognitive resources and their motivation to process information to different modalities
(Elbert, Dijkstra, & Oenema, 2016).
In sum, more than one type of modality can be helpful in motivating people to use online information and stimulating deliberate information processing by drawing the attention of users who are not easily able to construe health information via text.
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However, this method brings up a conflicting idea that low health-literate users use extra effort distributing their limited resources on other modalities in addition to text. Users with greater health literacy are expected not to be vulnerable to the method information conveyance. Research regarding different modalities and health literacy shows that high health-literate people were not influenced by the types of modality on the recall and attitudes toward the message (e.g., Meppelink, van Weert et al., 2015). This finding may make sense in terms of the ability to understand health information. While low health- literate people can get help from modality interactivity or visuals to supplement their lack of understanding of the text information, or their information processing may be interrupted by multiple modes of information presentation, high health-literate people may not be dependent on the type or the amount of modality to obtain the information.
Therefore, the literature regarding the essential factors of information processing for people with different capacities of communicating with information (i.e., health literacy) prompts exploration of how modality interactivity promotes systematic processing for both low and high health-literate users. Operationalizing modality interactivity as the presence of a slider in this dissertation, two research questions are posed to see whether the effects of modality interactivity on attitudes and behavioral intentions through cognitive processing (i.e., absorption) is conditional by health literacy:
RQ1: Will health literacy moderate the indirect effect of modality interactivity on attitudes toward the website (RQ1a), the message (RQ1b), the agency (RQ1c), and the disease (RQ1d) through absorption?
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RQ2: Will health literacy moderate the indirect effect of modality interactivity on intentions to revisit the website (RQ2a), follow the message (RQ2b), seek information from the agency (RQ2c), and seek information about the disease (RQ2d) through absorption?
Figure 2 and 3 summarize the hypothesized and questioned relationships.
Figure 2. The conceptual diagrams of moderated mediation model
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Figure 3. Proposed model.
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Chapter 3: Method
Overview
A single-factor experimental design (modality interactivity condition vs. control) was used to examine the indirect and conditional indirect effects of modality interactivity on a variety of attitudes and intentions. Modality interactivity was manipulated by a slider function. Two versions of a website provided information about a fictitious infectious disease, Logi. An existing disease was not used because the study explores the pure effects of website features on understanding and evaluating new information when individuals do not depend on prior knowledge. This study used an online experiment providing a post-test survey questionnaire after participants browsed a website (i.e., stimulus). Thus, the study includes three parts: first, questions for filtering out an inappropriate population for this study and pre-questionnaire for controlling in the analysis. The second part is the task of browsing the website, and the last part is to fill out the questionnaire related to the web browsing.
Participants’ activities during the website-browsing were recorded to identify whether participants used the interactive feature and clicked on more than one category to read different types of information about the disease. This process of making a history from login to logout is to remove those who did not do any relevant actions for this study.
The answers on the rest of survey in addition to the web-browsing result was also recorded to match the participants’ use of interactivity (from the web record) and its effect (from the survey).
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Participants
Participants of this study were recruited from the general population from
Amazon MTurk. Initially, 350 participants were collected. The first step for sample selection was to compare the data sets: one is the data from using the website and another is from the Qualtrics survey. When participants browsed the website, they entered an ID randomly provided by Qualtrics. After the rest of the survey was finished, they entered their ID again. Therefore, if they successfully performed the web browsing and completed the survey, they should have their IDs in both the log data and the survey data.
Comparing the two sets of data, if the same user ID was not found in either of the data sets, those cases were excluded.
The next step was to rule out the data by the following criterion: the website data included all interactivities participants performed, for example, the number of clicks on the menu and the number of drags of the slider. Therefore, if there were any participants who did not use any interactivity such as using the interactive function (i.e., slider) in the interactivity condition, those cases were not considered for analysis.
After cleaning the data, the final number of participants was 319. Baseline characteristics of the sample including gender, age, educational level, and race were reported to understand the study population (Table 1). The final participant sample included 197 females (61.8%) and 122 males (38.2%) from 18 to 74, with an average age of 38.13 (SE = 11.48). Three-quarters of the participants were Caucasian (n = 242), followed by African American (n = 33), Asian (n = 28), Hispanic (n = 13), and other (n =
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3). Most participants were educated at the college level (n = 234), some were more highly educated with graduate degrees (n = 52), and some had lower levels, such as a high school education (n = 33).
Table 1 Baseline Characteristics of the Sample (N = 319) Variables n or M % or SD Gender (female) 197 61.8 Age 38.13 (18 to 74) 11.48 Educational level Low education (high school/GED) 33 10.3 Intermediate education (college degree or 234 73.3 university bachelor’s degree) High education (graduate degree) 52 16.3 Race Caucasian 242 75.9 African American 33 10.3 Asian 28 8.8 Hispanic 13 4.1 Other 3 9
Procedure
Participants were asked to read the consent form and instructions of the study, which explained the three parts of the survey flow, the expected time they would spend on each part, and informed them that their web browsing activity would be captured.
After agreement, they were asked several filter questions such as the type of web browser
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and their familiarity with Logi. This is because the website was designed to work on a laptop, not a tablet PC or a mobile phone, because of the display characteristics. The appropriate devices were explained in the instructions before the survey. In addition, since Logi is a fictitious disease, if any participants indicated that they had heard of this disease, they were automatically redirected to the end of the survey. Next, a pre- questionnaire was given, asking about participants’ usual usage of interactive structures on websites and that of interactive websites even though they do not use the interactive functions. Moreover, their health literacy level was measured by the Newest Vital Sign
(NVS; Weiss et al., 2005), which requires the interpretation of an ice cream label. At the end of this part, demographics including age, sex, race, education level, and their first language were recorded. If the participant was not a Native English speaker, the survey was terminated, because understanding health information and one’s first language is related.
The second part asked the participants to browse a health website designed for this study. They were given a four-digit number and asked to enter this number when logging into the website. Randomly being assigned to one of two conditions—with or without a slider function—participants were instructed about their tasks on the website.
The instruction included learning opportunities from different types of information on a menu. Participants could use the website until they wanted to stop and logged out.
Average browsing time participants spent on the website was 294.16 seconds (SD =
173.09, Min = 181.05 seconds, Max = 1797.15 seconds).
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When they logged out of the website, the window of the website automatically closed so that participants were able to come back to the Qualtrics survey window. They were asked to continue filling out the rest of the questionnaire, Part 3, which included outcome measures regarding attitudes and intentions. All measures can be seen in the
Measurement section.
Stimulus
The stimulus was a fictitious website called the “Office of Infectious Diseases,” which provided health information including a topic regarding a fictitious disease. The disease was one that all general people are susceptible to and is severe for all people, not a specific population (e.g., Zika is dangerous for only pregnant women). This disease was named Logi, and it was transmitted via bacteria in soil, rivers, and reservoirs.
This fictitious website had a similar information presentation and display format as health information websites such as cdc.gov (Figure 4) and provided a variety of information through a menu list that was located at the left side of the website (see Figure
5). Specifically, the list included an overview, symptoms, consequences, and treatment of the disease, and recent outbreaks of the disease.
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Figure 4. A sample page from the CDC.
Figure 5. The format of the stimulus website.
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Participants could click on the list in the menu and see corresponding content.
Regardless of condition, the website was comprised of the name of the website at the left top corner, a logout button at the right top corner, a menu list on the left side, a picture in the middle, information about the picture on the right side of the website, and the dates of information reviewed and updated at the left bottom corner.
Manipulation of modality interactivity. The stimuli comprised two versions of the website with the same settings explained above. In detail, all information, features, and format were the same, but only the presence/absence of sliders, as the operationalization of modality interactivity, was different.
In the control condition, two static pictures were provided for each category
(Figure 6). The “About Logi” category included basic information about Logi such as the source of infection, at-risk population, and its transmission route. Two images comparing the inside of a river with and without bacteria were placed next to the text. In the
“Symptoms, Consequences, & Treatment” category, signs and symptoms of the illness and test results from labs treating the disease were presented. An image of a woman’s body was designed to show the difference between a half of her body infected and a half in normal status. The “Recent Outbreaks” category showed the number of observed cases and deaths in October 2017 and November 2017. Two maps, including Wisconsin and surrounding areas, indicating where the disease affected people, were exhibited to depict the spread of the disease.
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Figure 6. Static images of each category in the control condition.
In the interactivity condition, a slider button was placed in the middle of two images to drag and move side by side, horizontally. The website in the interactivity condition displayed an additional introduction stating, “Switch your view by swiping the arrow button below.” Therefore, participants could easily learn how to use the button on the image and see the changes with morphed images by performing this activity (Figure
7). Specifically, the slider movement showed gradual changes of a river, the human body, and areas at risk before and after infection.
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Figure 7. Morphing images of each category in the interactivity condition.
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Measurement
Moderating variable.
Health literacy. Among various ways to measure health literacy, the Newest Vital
Sign (NVS; Weiss et al., 2005), which measures individuals’ understanding of numeracy, was applied. The advantages of this measure with six items are a) an estimate of actual ability to read and interpret health information different from other measurements that measure perceived health literacy (e.g., Norman & Skinner, 2006), and b) great internal consistency reliability, α = .76. Participants were given an ice cream label and asked questions about details on the label (see Figure 8). The questions were as follows: “If you eat the entire container, how many calories will you eat?” “If you are allowed to eat 60g of carbohydrates as a snack, how much ice cream could you have?” “Your doctor advises you to reduce the amount of saturated fat in your diet. You usually have 42g of saturated fat each day, which includes 1 serving of ice cream. If you stop eating ice cream, how many grams of saturated fat would you be consuming each day?” “If you usually eat
2500 calories in a day, what percentage of your daily value of calories will you be eating if you eat one serving?” “Pretend that you are allergic to the following substances: penicillin, peanuts, latex gloves, and bee stings. Is it safe for you to eat this ice cream?” and “(Ask only if the participant responds “no” to question 5) Why not?” One point for each correct answer was added up, and the sum of correct answers was the health literacy score (M = 4.69, SD = 1.48, Min = 0, Max = 6; see Table 2). The scores were divided into three groups (i.e., low, medium, and high health literacy) by calculating the sample mean
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and standard deviation. In addition, the scores were used as a continuous variable for further analysis not to miss any information during the analysis.
Figure 8. The Newest Vital Sign (NVS; Weiss et al., 2005)
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Table 2 Descriptive statistics for variables used in the analysis
Variables M SD MIN MAX Skew. Kurt. α
Moderator Health literacy 4.69 1.48 0.00 6.00 -1.08 .52 Control variables Frequency of using interactivity functions 4.67 1.37 1.00 7.00 -0.32 -0.31 .76 Frequency of using interactive websites 5.46 1.34 1.00 7.00 -0.86 0.37 Mediator Absorption 5.19 1.29 1.20 7.00 -0.76 0.21 .89 Dependent variables Attitudes toward the website 5.48 1.33 1.00 7.00 -1.00 0.88 .97 Attitudes toward the message 5.82 1.17 1.00 7.00 -1.34 2.18 .96 Attitudes toward the agency 5.56 1.25 1.00 7.00 -0.92 0.60 .96 Attitudes toward the disease 1.38 1.07 1.00 7.00 3.91 15.69 .98 Intention to revisit the website 4.56 1.76 1.00 7.00 -0.45 -0.75 .92 Intention to follow the message 5.37 1.39 1.00 7.00 -0.88 0.78 Intention to seek information from the 5.16 1.57 1.00 7.00 -0.77 -0.11 .97 agency Intention to seek information about the 5.00 1.53 1.00 7.00 -0.77 0.10 .94 disease
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Control variables. Since using interactivity functions and gaining information from a website are the primary performances and influential factors on outcome variables in this study, frequency of using interactive features on online websites and frequency of using interactive online websites were controlled. Because of familiarity with online website features and that of skills to look for health information online can disrupt true interactivity effects, those variables had to be measured. The frequency of using interactive features was measured by 4 items of “How frequently do you use this following interactive feature?: drag and drop (Figure 9 left top)/drag and swipe (Figure 9 right top)/mouse-over (Figure 9 left bottom)/zoom (Figure 9 right bottom)?” on a 7-point
Likert scale (1: Never, 7: A lot) (M = 4.67, SD = 1.37, Pearson’s r = .76). For understanding these features, each question had a related image (Figure 9). Different from the frequency of using interactive features, the measure of the frequency of using interactive online websites asked about usage of online websites including those with the aforementioned features even though they were not used: “How frequently do you use online websites with interactive features including drag and drop/drag and swipe/mouseover/zoom, even though you do not use those features?” (M = 5.46, SD =
1.34).
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Figure 9. Images of interactive features.
Manipulation check. Two items were used to measure perceived interactivity, which show perceptions of the website and its interactive features (Kalyanaraman &
Sundar, 2006): “The content of the website was interactive,” and “The structure of the website was interactive.” These items were highly correlated (Pearson’s r = .82) A 9- point Likert scale (1: strongly disagree, 9: strongly agree) were applied for this measure
(M = 4.87, SD = 1.76).
Mediating variables. Cognitive absorption was defined as the degree to which individuals are temporally dissociated and immersed, have curiosity, and perceive controllability during interaction (Agarwal & Karahanna, 2000). Agarwal and
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Karahanna’s instruments of cognitive absorption have commonly been adopted in interactivity research. This measure had 20 items including five dimensions: temporal dissociation, focused immersion, heightened enjoyment, control, and curiosity. However, after a factor analysis with principle component extraction based on an eigenvalues greater than 1, two factors were obtained. The first factor included a dimension of
“temporal dissociation”: “Time appeared to go by very quickly when I was using the
Web,” “Sometimes I lost track of time when I was using the Web,” “When I got on to the
Web, I ended up spending more time than I had planned,” and “I spent more time on the
Web than I had intended” (M = 5.19, SD = 1.29, Cronbach’s α = .86). The second factor contained both “focused immersion” and “heightened enjoyment.”: “While using the
Web, I was absorbed in what I was doing,” “While on the Web, I was immersed in the task I was performing,” “When on the Web, I got distracted by other attentions very easily,” “While on the Web, my attention did not get diverted very easily,” “I had fun interacting with the Web,” “I enjoyed using the Web,” and “Using the Web bored me
(reverse-coded)” (M = 3.95, SD = 1.57, Cronbach’s α = .89). Considering the weight in the relevancy among those five dimensions within this dissertation, the second factor with focused immersion and heightened enjoyment is more suitable for the purpose of using cognitive absorption to explain the use of an interactive feature on a website. Not necessarily having all dimensions of absorption makes sense as referring to other studies
(e.g., Oh & Sundar, 2015) which also included a specific dimension related with their studies. Therefore, this dissertation only used the second factor as a cognitive absorption.
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Dependent variables. Systematic processing outcomes included various types of attitudes and behavioral intentions.
Attitude toward the website was measured using five items adopted from Spears and Singh (2004): “The website is … (unappealing/appealing, bad/good, unfavorable/favorable, unpleasant/pleasant, and unlikable/likable)” (M = 5.48, SD = 1.33,
Cronbach’s α = .97). A 7-point semantic differential scale was used. Attitudes toward the message had four items adapted from Watts et al. (2009), which were originally a measure for assessing data quality: “The message about Logi is …
(helpful/useful/valuable/informative)” (M = 5.82, SD = 1.17, Cronbach’s α = .96).
Attitude toward the agency of the website was measured by selected items from Büttner and Göritz’s (2008) study that are appropriate in terms of the topic of this study: “One can expect good advice from this provider,” This provider is very competent,” “This provider is able to fully satisfy its users,” “This provider is genuinely interested in its users’ health,” “You can believe the statements of this provider,” and “I would rely on advice from this provider.” 7-point Likert scales (1: strongly disagree, 7: strongly agree) were used for attitudes toward the agency (M = 5.56, SD = 1.25, Cronbach’s α = .96).
Attitude toward the disease was measured by the items adapted from Spears and Singh
(2004): “This disease is … (unappealing/appealing, bad/good, unpleasant/pleasant, unlikable/likable, and unfavorable/favorable).” (M = 1.38, SD = 1.07, Cronbach’s α
= .98).
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Each behavioral intention was paired with each attitude. For example, attitudes toward the website, the message, the agency, and the disease were referred to create intentions to revisit the website, follow the message, and seek information from the agency and about the disease. All measures except agency-related intentions used a 7- point, Likert-type scale (1: strongly disagree, 7: strongly agree).
Behavioral intention to revisit the website was measured by three items selected from Dou (2013): “I would bookmark this website for future use,” “I would visit this website again in the future,” and “I would like to know more about this website” (M =
4.56, SD = 1.76, Cronbach’s α = .92). Behavioral intention to follow the message from the website was measured by a single item, “I will follow the recommendation from the website to prevent the disease” (M = 5.37, SD = 1.39). Behavioral intention to seek health information from the agency was measured by four items using adjusted items from Dou
(2013) according to the topic: “Please indicate the likelihood of seeking health information from the agency of the website in the future (unlikely/likely, improbable/probable, uncertain/certain, definitely not/definitely)” (M = 5.16 SD = 1.57,
Cronbach’s α = .97). This measure used a 7-point semantic differential scale. Behavioral intention to seek further information about the disease was measured by two items borrowed from Yang (2012): “I would like to search for information about Logi” and “I would look for information about Logi to understand it better” (M = 5.00, SD = 1.53,
Pearson’s r = .94).
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Demographics. Participants’ age, gender, race, and education level were asked at the end of Part 1 in the survey.
Analysis
Bivariate correlations of main and control variables were tested (Table 3). A linear regression analysis and further analysis using standard path-analytic approaches
(Preacher & Hayes, 2008; Hayes, 2013) were used to show the direct and indirect effects of modality interactivity on attitudes and intentions through absorption. Next, the moderated mediation approach (Preacher, Rucker, & Hayes, 2007; Hayes, 2013) described the conditional indirect effects of modality interactivity on attitudes and intentions through absorption as a function of health literacy. Finally, conditional direct effects of modality interactivity on specific attitudes and intentions were analyzed for unexpected findings. In those analyses, the frequency of using interactive websites and interactive functions on websites were controlled.
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Table 3 Zero-order correlations of all variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. Modal interactivity 1.00
2. Health literacy -.03 1
3. Frequency of interactive structure usage .06 .10 1
4. Frequency of interactive website usage -.02 .11* .66** 1
5. Cognitive absorption .20** -.07 .18** .19** 1
6. Perceived interactivity .50** -.07 .12* .12* .49** 1
7. Attitudes toward the website .00 -.12* .14* .15** .56** .37** 1
8. Attitudes toward the message .01 -.11* .21** .22** .50** .33** .68** 1
9. Attitudes toward the agency .03 -.13* .11* .16** .48** .30** .67** .79** 1
10. Attitudes toward the disease .06 -.22** -.03 -.14* .01 .09 .07 -.02 -.01 1
11. Website revisit intention .09 -.16** .10 .09 .59** .35** .63** .64** .63** .11 1
12. Message advocacy intention .02 -.12* .10 .13* .43** .28** .47** .60** .68** .01 .61** 1
13. Information seeking intention from the agency .002 -.16** .13* .12* .51** .30** .67** .65** .77** .05 .76** .65** 1
14. Information seeking intention about the -.01 -.12* .21** .10 .44** .16** .32** .35** .29** .03 .49** .34** .44** 1 disease * p < .05. ** p < .01.
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Chapter 4: Results
The results section is organized by the following parts: first, manipulation check of modality interactivity is presented. Then, indirect effects of modality interactivity on attitudes and intentions through cognitive absorption, as proposed by a series of H1s and
H2s, are analyzed. Next, conditional indirect effects of modality interactivity on attitudes and intentions through cognitive absorption depending on health literacy are analyzed to answer a series of RQ1s and RQ2s. Tables 4 and 5 show the overview of the results section and the significant findings. Finally, the analysis of unexpected findings regarding combinatory effects of modality interactivity and health literacy on attitudes and intentions is reported.
Manipulation check
The manipulation of modality interactivity was checked by analyzing participants’ perceptions of interactivity functions on the website. The result showed that participants in the interactivity condition (M = 5.83, SD = 1.23) perceived that the content and the structure of the website were more interactive than those in the control condition (M =
4.07, SD = 1.74), t (317) = 10.22, p < .001.
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Table 4 Ordinary Least Squares Regression Model Coefficients (Standard Errors in Parentheses; N = 319) Absorption Attitude 1 Attitude 2 Attitude 3 Attitude 4 Outcome → Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10 4.06*** 4.64*** 2.83*** 1.87*** 1.84*** Constant 3.88*** (.32) 4.56*** (.33) 2.32*** (.34) 4.64*** 2.75*** (.34) (.31) (.29) (.31) (.27) (.33) Modality .51*** (.14) .51*** (.14) .23 (.15) -.07 (.13) .02 (.13) -.21+ (.12) .08 (.14) -.16 (.13) .10 (.12) .09 (.12) interactivity Health literacy -.08+ (.05) (HL) HL × Modality -.03 (.10) Int. Interactivity .06 (.07) .06 (.07) .05 (.07) .02 (.06) .09 (.06) .06 (.06) .01 (.07) -.02 (.06) .09 (.06) .09 (.06) frequency Website .15* (.07) .16* (.07) .11 (.07) .03 (.06) .14* (.07) .07 (.06) .14* (.07) .07 (.06) -.17** (.06) -.17 ** (.06) frequency Absorption .57*** (.05) .44*** (.05) .47*** (.05) .02 (.05) R2 0.08*** 0.09*** 0.03 0.32*** 0.06 .27*** 0.03 0.24*** 0.03 0.03+ Note. Attitude 1 = attitudes toward website. Attitude 2 = attitudes toward message. Attitude 3 = attitudes toward agency. Attitude 4 = attitudes toward disease. + p ≤ .10. * p ≤ .05. ** p ≤ .01. *** p ≤ .001.
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Table 4 (Continued) Intention 1 Intention 2 Intention 3 Intention 4 Outcome → Model 11 Model 12 Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Constant 3.75*** (.45) .53 (.44) 4.47*** (.35) 2.57*** (.39) 4.28*** (.40) 1.63*** (.42) 4.15*** (.38) 1.94*** (.42) Modality .29 (.20) -.12 (.17) .08 (.16) -.16 (.14) -.01 (.18) -.34* (.16) -.08 (.17) -.35* (.16) interactivity Health literacy
(HL) HL × Modality
Int. Interactivity .08 (.10) .03 (.08) .03 (.08) -.002 (.07) .11 (.09) .07 (.07) .29** (.08) .26*** (.07) frequency Website .06 (.10) -.06 (.08) .12 (.08) .05 (.07) .07 (.09) -.03 (.08) -.08 (.09) -.16* (.08) frequency Absorption .81*** (.06) .47*** (.06) .64*** (.06) .53*** (.06) R2 0.02 0.35*** 0.02 0.20*** 0.02 0.27*** 0.05 0.23*** Note. Intention 1 = intention to revisit the website. Intention 2 = intention to follow the message. Intention 3 = intention to seek information from the agency. Intention 4 = intention to seek information about the disease. + p ≤ .10. * p ≤ .05. ** p ≤ .01. *** p ≤ .001.
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Table 5 Conditional Indirect Effects of Modality Interactivity on Attitudes and Intentions through Absorption at Levels of Health literacy (Standard Errors in Parentheses; N = 319) Attitude 1 Attitude 2 Attitude 3 Attitude 4 95% Bias- 95% Bias- 95% Bias- 95% Bias- corrected corrected corrected corrected bootstrap bootstrap bootstrap bootstrap Health Point confidence Point confidence Point confidence Point confidence literacy estimate interval estimate interval estimate interval estimate interval Low (-1.48; 3.21 before 0.31 (0.10) 0.11 to 0.51 0.24 (0.08) 0.08 to 0.41 0.26 (0.09) 0.09 to 0.43 0.01 (0.02) -0.03 to 0.05 centering) Moderate (0; 4.69 before 0.29 (0.08) 0.13 to 0.46 0.22 (0.06) 0.10 to 0.35 0.24 (0.07) 0.11 to 0.37 0.01 (0.02) -0.03 to 0.05 centering) High (1.48; 6.00 before 0.27 (0.11) 0.05 to 0.50 0.21 (0.08) 0.05 to 0.38 0.22 (0.09) 0.04 to 0.41 0.01 (0.02) -0.03 to 0.05 centering)
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Table 5 (Continued) Intention 1 Intention 2 Intention 3 Intention 4 95% Bias- 95% Bias- 95% Bias- 95% Bias- corrected corrected corrected corrected bootstrap bootstrap bootstrap bootstrap Health Point confidence Point confidence Point confidence Point confidence literacy estimate interval estimate interval estimate interval estimate interval Low (-1.48; 3.21 before 0.44 (0.15) 0.18 to 0.77 0.26 (0.09) 0.09 to 0.45 0.35 (0.12) 0.13 to 0.60 0.29 (0.10) 0.11 to 0.49 centering) Moderate (0; 4.69 before 0.41 (0.12) 0.20 to 0.66 0.24 (0.07) 0.11 to 0.40 0.32 (0.09) 0.16 to 0.53 0.27 (0.08) 0.13 to 0.43 centering) High (1.48; 6.00 before 0.38 (0.16) 0.09 to 0.70 0.22 (0.09) 0.05 to 0.42 0.30 (0.13) 0.07 to 0.57 0.25 (0.10) 0.06 to 0.47 centering)
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Simple mediation analysis
Two hypotheses imply that the increased absorption caused by modality interactivity explains why a slider function is related to attitudes and intentions.
Specifically, H1 (H1a to H1d) predicted that modality interactivity (i.e., a slider) would enhance cognitive absorption and, in turn, attitudes toward the website, the message, the agency, and the disease (Figure 10). For the analyses, ordinary least-squares regression and a bootstrapping approach (Hayes, 2013) with 5,000 bootstrap samples and 95% bias- corrected confidence intervals were used. The indirect effects were examined by entering modality interactivity as an independent variable, absorption as a mediator, various types of attitudes as dependent variables, and the frequency of using interactive functions and websites as control variables.
Figure 10. H1s.
As hypothesized, most of the indirect effects of modality interactivity on attitudes were significant (Table 4). To be specific, modality interactivity was associated with absorption (Model 1 in Table 4, path a in Figures 11), which was related with attitudes 50
except disease-related attitudes (Models 4, 6, and 8 in Table 4, path b in Figures 11). As can be compared R2 in models (Model 3 and 4; Model 5 and 6; Model 7 and 8; Model 9 and 10) in Table 4, models including the mediating variable of absorption had statistically significant explanatory powers compared to models without the variable.
Attitudes toward the website. H1a hypothesized that cognitive absorption would mediate the effect of modality interactivity on attitudes toward the website. The mediation result had a significant indirect effect (B = .29, SE = .08, 95% C.I. from 0.14 to
0.47). Thus, H1a was supported. Participants were more absorbed from the modality interactivity, which subsequently increased the attitudes toward the website, thinking that the website was appealing, good, favorable, pleasant, and likeable.
Attitudes toward the message. As hypothesized in H1b, the analysis showed a significant finding (B = .23, SE = .06, 95% C.I. from 0.14 to 0.37) that modality interactivity had an effect on attitudes toward the message through absorption. Thus, H1b was supported, indicating that increased absorption from the modality interactivity led to favorable attitudes toward the message; the information about the disease on the website was perceived as helpful, useful, valuable, and informative.
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Figure 11. Path coefficients for simple mediation analyses on attitudes toward the website, the message, the agency, and the disease in order. Note: A dotted line implies the direct effects of modality interactivity on attitudes when absorption was not entered as a mediator. The frequency of using interactive functions and websites were considered as covariates. + p < .10. * p < .05. ** p < .01. *** p < .001. 52
Attitudes toward the agency. H1c predicted the effects of modality interactivity on attitudes toward the agency through absorption. The result revealed a significant indirect effect (B = .24, SE = .07, 95% C.I. from 0.11 to 0.39), supporting H1c. In other words, higher absorption occurred from using the slider, and the participants formed more positive attitudes toward the agency such as competence, satisfaction, and believability.
Attitudes toward the disease. H1d hypothesized the indirect effect of modality interactivity on attitudes toward the disease through absorption. However, there was no significant finding with respect to this hypothesis (B = .01, SE = .02, 95% C.I. from -0.02 to 0.06). Even though participants compared infected and non-infected images morphed by a slider and were absorbed in their interactivity, this procedure did not affect their evaluation on the disease. Therefore, H1d was not supported.
The summary of significant findings is presented in Figure 12.
Figure 12. Findings from H1s. Note: a regular line denotes significant and a dotted line nonsignificant.
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Same as the analyses of attitudes models, different types of intentions were included as dependent variables and analyzed using the mediating procedure. As can be seen in Table 4, all indirect effects of modality interactivity on intentions were significant. Specifically, absorption was associated with modality interactivity (Model 1 in Table 4, path a in Figures 13), which was also related with intentions (Models 12, 14,
16, and 18 in Table 4, path b in Figures 13). The increased coefficients in the mediation models compared with simple regression models showed that cognitive absorption has a significant mediating role (Table 4).
Intention to revisit the website. As hypothesized, absorption mediated the effects of modality interactivity on revisit intention (B = .42, SE = .12, 95% C.I. from
0.20 to 0.66). As participants experienced absorption in what they were doing with the slider, they were more likely to bookmark the website for future use and indicate that they would visit the website again in the future. Thus, H2a was supported.
Intention to follow the message. Consistent with H2b, the significant indirect effect of modality interactivity on message advocacy intention was found (B = .24, SE
= .07, 95% C.I. from 0.11 to 0.40). The result indicates that enhanced absorption by modality interactivity led participants to have willingness to follow the recommendations from the website for disease prevention. Therefore, H2b was supported.
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Figure 13. Path coefficients for simple mediation analyses on behavioral intentions to revisit the website, follow the message, seek information from the agency, and seek information about the disease in order. Note: A dotted line implies the direct effects of modality interactivity on intentions when absorption was not entered as a mediator. The frequency of using interactive functions and websites were considered as covariates. + p < .10. * p < .05. ** p < .01. *** p < .001. 55
Intention to seek information from the agency. H2c hypothesized that modality interactivity would increase seeking intention from the agency via absorption. The result supported H3b in that participants who were more absorbed in their activity with the slider expressed a higher likelihood of seeking information from the agency (B = .33, SE
= .09, 95% C.I. from 0.15 to 0.52).
Intention to seek information about the disease. As predicted by H2d, the indirect effect of modality interactivity on disease seeking intention was significant (B
= .27, SE = 08, 95% C.I. from 0.13 to 0.44). Participants wanted to look for additional information about the disease, Logi, to understand it better. Thus, H2d was supported.
The significant findings were summarized in Figure 14.
Figure 14. Findings from H2s.
Moderation of the Effect of Modality Interactivity on Absorption by Health Literacy
The moderation of the effects of modality interactivity on cognitive absorption by health literacy was tested before analyzing the moderated mediation models. The purpose of this analysis is to separate groups by health literacy level and examine the moderation 56
effects on the mediator, absorption. For this, OLS regression was used, including modality interactivity, health literacy, and their products as independent variables, absorption as a dependent variable, and the frequency of interactive functions and websites as controls.
The analysis applied the “pick-a-point” approach (Preacher, Curran, & Bauer,
2006; Hayes & Matthes, 2009) to divide groups of a continuous variable. Typically, three groups are created by using one standard deviation above and below the sample mean:
“high” (M + 1 SD), “moderate” (M), and “low” (M – 1 SD). The result found that the effect of modality interactivity on absorption did not differ by health literacy (Model 2 in
Table 4). The mean of each group was 3.21 (low), 4.69 (moderate). 6.00 (high).
Moderated Mediation Analysis
The next step is to combine the simple mediation analysis and the moderation analysis to answer two research questions (Figure 2). In other words, the indirect effect of modality interactivity on attitudes and intentions through absorption will be analyzed with the conditional effect of modality interactivity on absorption depending on health literacy. Therefore, we can see how the indirect effect of modality interactivity on attitudes and intentions depends on health literacy levels. For this analysis, conditional indirect effects using a bootstrapping procedure (Preacher et al., 2007) in PROCESS
(Hayes, 2013) was employed. The foundation of this analysis is to combine the conditional effect of modality interactivity on absorption as a function of health literacy
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and the effect of absorption on attitudes and intentions (respectively) controlling for modality interactivity.
The first part of the following section is about answers to RQ1, from RQ1a to
RQ1d, regarding a variety of attitudes.
Attitudes toward the website. RQ1a asked whether health literacy moderates the indirect effect of modality interactivity on attitudes toward the website. The analysis revealed that modality interactivity was significantly and positively related to attitudes toward the website through absorption at all three points (Table 5). The conditional effects for low (B = .31, SE = .10, 95% C.I. from 0.12 to 0.52), moderate (B = .29, SE
= .08, 95% C.I. from 0.14 to 0.46), and high (B = .27, SE = .11, 95% C.I. from 0.05 to
0.50) health literacy groups were found. The result means that although participants with low, moderate, and high levels of health literacy were more absorbed when modality interactivity was present and subsequently considered the website more favorably, there was no difference between the groups in terms of the indirect effect of modality interactivity.
Attitudes toward the message. The same approach was used to answer RQ1b regarding the conditional indirect effect of modality interactivity on attitudes toward the message. This dependent variable also produced significant and positive results at all levels of health literacy [low (B = .24, SE = .08, 95% C.I. from 0.08 to 0.42), moderate (B
= .22, SE = .07, 95% C.I. from 0.10 to 0.36), and high (B = .21, SE = .09, 95% C.I. from
0.04 to 0.38) values of health literacy] (Table 5). Modality interactivity was effective on
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increasing absorption and favorable attitudes to the message, but this effect was not differentiated by health literacy levels.
Attitudes toward the agency. RQ1c posited about the effect of modality interactivity on attitudes toward the agency through absorption as a function of health literacy. The results showed that low, moderate, and high health-literate participants had more absorption from the interactivity condition and subsequently had more favorable attitudes toward the agency [(B = .26, SE = .09, 95% C.I. from 0.09 to 0.44), (B = .24, SE
= .07, 95% C.I. from 0.11 to 0.39), (B = .22, SE = .09, 95% C.I. from 0.05 to 0.41), respectively; Table 5]. No difference in the indirect effect was observed among the groups.
Attitudes toward the disease. In order to answer RQ1d about the conditional effect of modality interactivity on attitudes toward the disease, the values of the indirect effect in each group were estimated. The indirect effect of modality interactivity on attitudes toward the disease by health literacy was not significant, as can be seen from the
95% bias-corrected bootstrap confidence interval that included zero (Table 5). The result that all groups did not have any significant findings indicates that modality interactivity worked on enhancing favorable attitudes toward the disease through absorption, but its effect was ignorable (point estimates were 0.01 for all levels of health literacy; Table 5).
All findings are visualized in Figure 15.
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Figure 15. Findings from RQ1s.
The next part is to answer RQ2s about various types of intentions as dependent variables.
Intention to revisit the website. RQ2a asked whether health literacy differs regarding the indirect effect of modality interactivity on intention to revisit the website.
Table 5 shows the significant indirect effects at all levels of health literacy; participants with low (B = .44, SE = .15, 95% C.I. from 0.18 to 0.77), moderate (B = .41, SE = .12,
95% C.I. from 0.20 to 0.66), and high (B = .38, SE = .16, 95% C.I. from 0.09 to 0.70) health literacy were absorbed in their activity on the website and were more likely to visit the website again in the future when they used a slider. However, all groups have similar values of the effect, meaning that health literacy did not affect the indirect effect of modality interactivity.
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Intention to follow the message. RQ2b asked about the conditional indirect effect of modality interactivity on intention to follow the message. The results found that there were significant and positive effects among all health literacy groups (Table 5).
Modality interactivity effectively enhanced absorption and, in turn, willingness to support and follow the information on the website for participants with low (B = .26, SE = .09,
95% C.I. from 0.09 to 0.45), moderate (B = .24, SE = .07, 95% C.I. from 0.11 to 0.40), and high (B = .22, SE = .09, 95% C.I. from 0.05 to 0.42) health literacy; but no difference among the groups was found.
Intention to seek information from the agency. RQ2c asked whether there is any difference between health literacy groups in terms of modality interactivity effects on information seeking intention from the agency. Again, the effects were significant and positive at all health literacy groups (Table 5): low (B = .35, SE = .12, 95% C.I. from 0.13 to 0.60), moderate (B = .32, SE = .09, 95% C.I. from 0.16 to 0.53), and high (B = .30, SE
= .13, 95% C.I. from 0.07 to 0.57) levels of health literacy. Participants intended to seek further information from the agency who provided the disease information on the website they used, regardless of health literacy, with the effects of absorption caused by modality interactivity.
Intention to seek information about the disease. RQ2d considered the moderation effect of health literacy in the mediating effect of absorption between modality interactivity and information seeking intention about the disease. Participants in all levels of health literacy had significant indirect effects (Table 5): low (B = .29, SE
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= .10, 95% C.I. from 0.11 to 0.49), moderate (B = .27, SE = .08, 95% C.I. from 0.13 to
0.43), and high (B = .25, SE = .10, 95% C.I. from 0.06 to 0.47) health literacy. This result means that modality interactivity had an impact on absorption and eventually, information-seeking intention about the disease, irrespective of health literacy levels.
The findings of conditional indirect effects on intentions are summarized in
Figure 16.
Figure 16. Findings from RQ2s.
Unexpected Findings
The analyses were based on the hypotheses and research questions assuming that there would be mediating effects of absorption between the interaction of modality interactivity and health literacy on attitudes and intentions. That means the results show whether the indirect effects of X (i.e., modality interactivity) are conditional on W (i.e., health literacy). Therefore, PROCESS model 7 was used for these analyses. However, it is possible to think that the direct effect of X on Y can be different in such a
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circumstance. PROCESS model 8 depicts what the conceptual model of conditional direct effect of X on Y looks like (Figure 17).
Figure 17. Conceptual diagram of conditional indirect and direct effects.
The analysis revealed that significant conditional direct effects of modality interactivity on some types of attitudes and intentions, attitudes toward the website, and intentions to seek information about the disease, were found.
Attitudes toward the website. When the health literacy of participants was divided into three groups applying one plus and minus from the sample mean, there was no difference between groups regarding the direct effect of modality interactivity on
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attitudes toward the website. However, the Johnson-Neyman technique (Bauer & Curran,
2005; Hayes & Matthes, 2009; Johnson & Fay, 1950) delivers more detailed information rather than the “pick-a-point” approach when the moderator is continuous. The advantages of this technique are to be able to probe a region of significance for the conditional effect of modality interactivity, meaning that we can calculate where the moderator makes the simple slope of a focal predictor statistically different from zero
(Miller, Stromeyer, & Schwieterman, 2013). In other words, instead of missing information with arbitrary values, the technique determines the exact values of the moderator in the direct effect of X on Y. In addition, the point estimate of the simple slope can be precisely indicated by confidence bands.
Figure 18 demonstrates where health literacy scores made significant differences between modality interactivity on attitudes toward the website. To be specific, when health literacy is less than -2.11 units, a significant negative impact of modality interactivity on attitudes toward the website is expected; for participants with low health literacy, the slider function decreased their favorable attitudes toward the website.
However, for those with higher health literacy, the impact of modality interactivity on attitudes toward the website was nonsignificant.
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1
0.5
0 -4.8 -3.8 -2.8 -1.8 -0.8 0.2 1.2 Simple Slope -0.5 Lower 95% CI Upper 95% CI -1
AttitudesToward the Website -1.5 Simple Simple Slope Modalityof interactivityon
-2 Health Literacy
Figure 18. Johnson-Neyman plot of the region of significance for the simple slope of modality interactivity on attitudes toward the website (m < -2.11).
Intention to seek information about the disease. As can be seen in Figure 19, a negative and significant impact is expected that modality interactivity decreases intention to seek information about the disease when health literacy is less than 0.24. Participants who had lower health literacy scores were likely to seek information about the disease after using the slider function. However, the higher health-literate status of participants did not have a significant impact on intentions.
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0.5
0 -4.8 -3.8 -2.8 -1.8 -0.8 0.2 1.2
-0.5
Simple Slope -1
Lower 95% CI disease Upper 95% CI -1.5
-2
Intentions seek to information aboutthe Simple Simple Slope Modalityof interactivityon
-2.5 Health Literacy
Figure 19. Johnson-Neyman plot of the region of significance for the simple slope of modality interactivity on intentions to seek information about the disease (m < 0.24).
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Chapter 5: Discussion
This study aims to examine how a health website interface affects individuals’ cognitive information processing in an emerging infectious disease context and explore whether this processing varies by health literacy levels. For this, a health website was created that provided information about a fictitious disease and opportunities for users to use a slider function.
Interpretation of findings
Indirect effects of modality interactivity on attitudes through cognitive absorption. Modality interactivity, operationalized as the presence/absence of a slider on the website, positively influenced a variety of attitudes through increased absorption.
First, in the case of attitudes toward the website, the slider generates concentration on the content as well as interest in web browsing. From this effect, users found the website pleasant and likeable. The positive relationship between the slider and absorption affects the relationship between the slider and attitudes toward the message; users are immersed in the content when using the slider and think that the message is useful and informative.
The slider effect also has positive impacts on the agency that delivers information and manages the website. In the process of absorbing the information via a slider, users believe that the source (i.e., agency) cares for users’ satisfaction with the website or the message and has concern for their health. The findings of attitudes toward the website, message, and agency are consistent with the findings from HSM research regarding the role of website features as a heuristic cue in determining source credibility and message
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quality/content credibility (Metzger & Flanagin, 2013; Flanagin & Metzger, 2007; Kim,
2011) and the relationship between source perception and attitudes (Chaiken &
Maheswaran, 1994; Tormala, Briñol, & Petty, 2006). In addition, the study results confirm the conceptual explanation of TIME (Sundar et al., 2015) with respect to the cognitive process from affordance to outcomes via engagement.
Alternatively, the absorbing process from the slider did not influence attitudes toward the disease. This may be because absorption is rarely correlated with disease attitudes (r = .01). Even though users gain information enjoyably by comparing images from the slider, this might not be influential on the evaluation of the disease. In a shopping website study, which is in a different context, image interactivity technology increased shopping enjoyment and reduced perceived risk at the same time (Lee, Kim, &
Fiore, 2010). In the health context, interactivity induced pleasure when users got information about the disease, but if it also decreased perceived risk of the disease, attitudes toward the disease might not clearly come out because these two conflicting components combined.
In sum, the effects of a slider, specifically provoking focused immersion and heightened enjoyment, was effective in that individuals had favorable attitudes toward most of the variables except disease. The fact that cognitive absorption fully explains the relationship between modality interactivity and attitudes, as a mediator, means that enhancing absorption is important in forming users’ positive attitudes.
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Indirect effects of modality interactivity on intentions through cognitive absorption. Interpreting the results of mediating effects of cognitive absorption between modality interactivity and behavioral intentions in the same way of attitudes, each matching attitude and intention had similar results. As there were positive attitudes toward the website, message, and agency, the related intentions also came out positive; users wanted to revisit the website, follow the recommendation in the message, and look for health information from the agency again. Interestingly, any significant findings of disease attitudes did not occur even though users enjoyed the slider actions, whereas, they desired learning more about the disease. It is possible that the amusing learning process from the slider is not very meaningful for evaluating the disease, but the method of learning intrigues learning motivation. For example, the presentation style of showing the changes of infected areas would be attractive to website users so that they expect and want to see other changes of the disease spread after a month. Compared to the direct effects of a slider on attitudes/intentions, which did not have any significant findings, a slider may be more useful for making people aware of the severity of a disease and learn about the disease from being motivated to get interested about the disease. Afterwards, people desired to search for information about the disease in the future.
Conditional indirect effects of modality interactivity on attitudes/intentions through cognitive absorption by health literacy. Research questions asked how the mediating effects of absorption would be applied to people with different health literacy levels. As a result, consistent patterns appeared regardless of health literacy level; users
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with all levels of health literacy were absorbed and thought favorably of the website, the message, and the agency when they were exposed to the slider function. In contrast, attitudes toward the disease were not affected by the process. This is not surprising because the indirect effect of modality interactivity on attitudes toward the disease was not significant.
Intentions had similar findings. Health literacy did not differentiate the indirect effects of modality interactivity; significant and positive relationships between the slider, absorption, and intentions were found at all levels of health literacy.
The purpose of investigating conditional indirect effects is to see whether the magnitude of the indirect effects are dependent on health literacy levels (Figure 16), and if there are significant differences, the results can say “mediation is moderated” (Hayes,
2013). However, the results showed that indirect effects are significant at all health literacy levels, which means health literacy does not play a role as a moderator in a slider’s indirect effects on outcome variables. We can infer the reason from the results of conditional direct effects, in terms of the power of the mediator. Even though significant findings were only from two dependent variables, attitudes toward the website and intention to seek further information about the disease, those showed different effects depending on health literacy levels. For users with lower health literacy, slider usage leads to a negative relationship with the attitudes, whereas the slider does not have impacts for those with higher health literacy. It is interesting that indirect effects had all positive relationships, but direct effects showed some negative relationships. In other
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words, since the role of mediator, absorption, is more powerful than health literacy, it may overwhelm the effects of the moderator; when controlling for absorption, the effects of a slider are distinguished by health literacy, while when considering absorption, the relationships between the slider and the attitudes, which are not changed by health literacy, are explained by a stronger focus on the role of absorption.
Second, the results of the conditional direct and indirect effects can be construed in the perspective of the type of information processing route. First, considering that two separate heuristic and systematic processing routes exist as dual-process theories urge, this study predicted that a slider as a heuristic cue brings up systematic processing and its outcomes (i.e., attitudes and intentions) and found the accordant results. On the other hand, the results of conditional direct effects of this cue caused less favorable attitudes toward the website and higher intention of seeking information about the disease for lower health-literate users whereas it was not impactful for more health-literate users, which can be interpreted with heuristic processing. A slider only worked as a cue activating heuristic processing and had direct effects on attitudes and intentions. The aspect of heuristic processing that a simple feature leads to automatic cognitive processing without a conscious operation process (Kahneman, 2003; Thaler & Sunstein,
2008) demonstrates the results well. To illustrate, the result that only clicking “next” and
“previous” buttons on a shopping website made users select more healthful snacks (Lee,
Kiesler, & Forlizzi, 2011) shows that even though a heuristic cue does not include the message-related judgmental rule as HSM describes, it can reach systematic-processing
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led outcomes” without elaborative processing. Reflecting this explanation, a slider introduces automatic processing in a desirable way (more intentions to seek information about the disease) or not (negative attitudes toward the website) for individuals with a lower level of health literacy.
Contrary to less stable outcomes from heuristic processing, consistent and positive outcomes come out when going through absorption. In other words, a slider works on promoting systematic processing, showing mediating effects regardless of health literacy levels, which indicates critical impact of a mediator itself.
Theoretical implications
The role of modality interactivity to shift the modes of information processing. This study presented how a simple interface feature, a slider (i.e., modality interactivity), develops elaborative processing. Rather than independent relationships of heuristic and systematic routes, the study results showed that two routes are interdependent as the bias hypothesis; by using an affordance as a salient cue, users are involved in deliberate processing. In other words, systematic processing mediates the indirect effect of the heuristic cue (Koh & Sundar, 2010). In addition, the findings of this study confirmed the effects of interactivity with respect to cognitive processing that the framework of modality interactivity (TIME; Sundar et al., 2015) conceptually proposes.
The importance of engagement in the learning process. To be significantly considered, the type of cognitive absorption can be different by topic and purpose. For example, in the topic of anti-smoking for persuasion (Oh & Sundar, 2015), “heightened
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enjoyment” and “curiosity” aspects were employed as measures of absorption. In this study, “focused immersion” and “heightened enjoyment” aspects were chosen. This may be because the dimensions of absorption, which make people focused on and interested in gaining new information, would be important when they have no prior knowledge so that learning about the topic is necessary. Those aspects of absorption in this study seem reasonable in the process of achieving new information because users feel satisfaction and pleasure from interactivity that they can control (Liu & Shrum, 2002; Sicilia, Ruiz, &
Munuera, 2005). However, among the aspects of cognitive absorption that Agarwal and
Karahanna (2000) suggest, temporal dissociation was not selected in the study. Since temporal dissociation is more related to learning programs such as mobile training program of a license exam (e.g., Reychav & Wu, 2015) or a video game, including virtual reality (e.g., Wiebe, Lamb, Hardy, & Sharek, 2014), which include scenarios and stories and/or have a purpose of fun, this aspect may not be appropriate for a study that required acknowledging the change of images from the slider as well as the information from text.
Moreover, absorption was found to be a powerful mediator to explain systematic processing. The study verified that modality interactivity and attitudes and intentions are not associated without the mediator by proving that there are no direct effects between those variables. In addition, this mediating effect was not changed by condition of health literacy, meaning that the effect is less prone to individual differences. In other words, user engagement has a pivotal role in acquiring information on the web and predicting future informative behaviors, the effects of which are promoted by slider. The findings
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imply that engagement is important not only in well-known topics or persuasive settings, but also in new topics or in the process of discovering very new information.
Reification of attitudes and intentions. The study examined various types of attitudes and intentions possibly occurring from modality interactivity. TIME (Sundar et al., 2015) describes examples of attitudes such as interface, content, and relational attitudes. Starting from this theory, the study reified the targets of attitudes: a website that provides an interactivity feature, information (i.e., message itself) and topic (i.e., disease) that users can learn from the feature, and further, from agency who delivers the website and the message. Intentions were also constructed by matching the attitudes: whether users revisit the website, follow the content in the message, and look for more information about the disease and from the agency. Developed from HSM research, which simply has focused on source perception and message quality, and interactivity research, which has examined website and content attitudes, the current study has the benefit of taking concrete shape to the effects of an interactivity features on the web by investigating different types of attitudes and intentions.
Preservation of all health literacy levels with Johnson-Neyman technique.
Methodologically, health literacy level was not split by median or mean, and instead was analyzed as a continuous variable so that all scores of health literacy were meaningfully used in the analysis. Even though a low/high grouping may lose much information, many studies have employed this method. However, this study adopted a technique that minimizes the risk; the Johnson-Neyman technique from the new Hayes’ PROCESS
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model version 3 was used to clearly indicate the exact score point of health literacy in having differences in a graph, which the original version could run only for models 1, 4, and 5.
Practical implications
Effects of modality interactivity for public health education. A slider improves enjoyment and user focus on the information and in turn motivates users or health consumers to acquire more information. The potential of a slider is to lead to cognitive processing no matter the health literacy level, as a method of learning. Low health-literate people tend to rely on traditional media outlets because they have difficulties in gaining health information with information technology (Manganello et al., 2017), but if the interface feature is simple and easy to use, it will be helpful to that population. The feature also will diminish resistance to new information. The finding that higher health- literate people had fun using the slider and were likely to want to know more about the disease compared to accessing online health information via only text indicates that interactivity is successfully applicable to health materials for this population. Finally, the reason that interactivity works well in the learning context is that users are engaged in the activity of learning information by themselves and feel a higher sense of agency, a perception of control (Oh, Ahn, Lim, & Kim, 2017). Therefore, if the environment is where the positive aspect of interactivity can apply, it will be beneficial regardless of health literacy.
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Effects of modality interactivity for the agency’s communication and web design strategies. For an agency that needs to communicate with the public, more diverse implications can be discussed. First, the relationship between the public and the agency may be improved through a simple interface feature, such as a slider. The findings that ‘fun’ and ‘immersion’ factors of a slider favorably affect agency itself, its website, and its message is good news for the agency. In addition, since these effects are not only for attitudes but also intentions—website revisits, message advocacy, and information seeking about the disease and from the agency—it is necessary to reconsider how to develop modality interactivity in web design. This slider effect will enable the agency to provide information and form and maintain relations, by presuming users to be those who can use information through interactive media in eHealth contexts rather than those who receive information (Jacobs et al., 2016). Moreover, as the study results indicated, no difference in the indirect effects by health literacy suggests no need to create separate messages related to the presence/absence of interactivity for different target audiences with different levels of health literacy, which reduces the cost for the agency. However, when designing interactive features, the number of features needs to be of concern.
According to Oh (2017), if features are displayed a lot, users cannot perceive the value of the features in gaining knowledge by using them, rather, curvilinear effects of interactivity happen. Thus, an appropriate number of interactivity features would provide the benefits of transmitting health information to the public and forging a good relationship with them.
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Future research
Two sides of interactivity. Interactivity has been studied as an enhancer of information processing, as it has in this dissertation. Individuals can feel absorbed from interactivity functions on websites and it will promote favorable attitudes and intended behavioral intentions. Although this perspective emphasizing the positive aspects of interactivity has been accepted in the area of interactivity research, this pleasurable experience of interactivity may also reduce risk perception (see Lee et al., 2010) which cannot be neglected in processing health information. As explained in many risk information seeking models, perceived risk contributes systematic information processing and enhances attitudes and intentions. Therefore, perceived less risk from interactive actions should be of concern in terms of how to minimize this negative aspects of interactivity.
Types of device used for web browsing. This study used a laptop version of a website to deliver health information and did not allow participants to use any other devices such as a mobile phone because of the screen size. However, it is possible to consider that the use of other types of devices would make a difference in the results. For example, a mobile phone has distinct functions to read health information on a website; it requires scrolling, swiping, zooming in and out, etc. In other words, this type of device, which includes navigation via a touchscreen display, already presents users with interactive functions. The curiosity of website users’ to focus on the intended interactive
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functions related to health information can be an idea to examine the effects of different types of devices for web browsing.
Health literacy and information delivery. This dissertation found that modality interactivity was effective for individuals across health literacy levels. In addition to the interactivity effect, ways to absorb or comprehend health information while using interactivity is necessary to be considered when information processing on a website is studied with a linkage to health literacy. In other words, even though in the current stage, interactivity is found to be useful to make website users attentive to the content, it needs to be studied whether engaging in interactivity functions and understanding the text at the same time works for individuals having different health literacy levels. For this, the effect of interactivity is suggested to be investigated with different amounts of health information displayed on a health website. Framed with TIME (Sundar et al., 2015) and
LC4MP (Lang, 2000), there would be interesting research which shows different results of the interactivity effects depending on the amount of information and ability to process the information (i.e., health literacy).
Another aspect of health literacy to explore is to identify the relationship between interactivity and health literacy. As seen in the results, health literacy does not moderate the effects of interactivity on absorption, which resulted in nonsignificant moderated mediation. Therefore, it is necessary to identify factors more closely related to health literacy. For example, whether lower or higher health literate people desire to think about the given health information thoroughly, in other words, need for cognition is different by
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health literacy may be studied. Then, the findings from this study can interpret why health literacy did not have an impact on the effects of interactivity.
Presentation style of health information. Health information was provided by an official website in this dissertation, which means that participants were exposed to fact-focused information, as used on health websites. If the same information is presented differently, the interaction between modality interactivity and health literacy may have different results. For example, in a journalistic context, health information is delivered with examples or narratives. Reflecting the narrative literature that narratives absorb individuals in a story and are better for persuading individuals than factual information
(Green, 2006), interplaying this type of information with interactivity may provide another method for processing health information for people with different health literacy levels.
Types of topics for application. The findings of the dissertation is for a new disease because the purpose of this study is to find the mechanism used for information processing about a disease that people have no previous knowledge of. To generalize the findings, future research is recommended to study other diseases or topics for which people need to learn new information such as new diseases, reoccurring diseases with modification, or issues in other areas, such as the environment,. If research using other topics produce the same results as this study, modality interactivity can be seen as useful to explain engagement in gaining new information theoretically and be applicable in health and other contexts where the target audience needs to gain knowledge.
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Limitations
Weakness of measurement. The current study probed discrete attitudes and intentions but did not find the established measures suitable for the variables.
Specifically, for attitudes toward the disease, the measurement of message attitude
(Spears & Singh, 2004) was borrowed. This measure is about the attractiveness of the message such as “unappealing/appealing” and “unfavorable/favorable.” This variable was considered because of the expectation that vivid images by slider may promote negative attitudes toward the disease. However, recognizing that the study examined attitudes and accordant intentions, more relevant measures related to information seeking about the disease should have been considered. For example, it would have been better to ask about
‘concerns of the disease’ or ‘the relevance of the disease’ for the attitudes toward the disease. Then, even though a linear relationship of attitude and intention was not identified, there could be more natural connection that modality interactivity enhances absorption—increase the interest of the disease or recognize the relevance of the disease—and subsequently lead to a desire to seek more information about the disease.
For the measurement of health literacy, only one aspect (i.e., numeracy) focused on evaluating individuals’ skill to understand health information. This weakness is not only present in this dissertation but also in other existing health literacy measures
(Pleasant, McKinney, & Rikard, 2011). Therefore, there is a need to develop a health literacy measurement that encompasses whole dimensions in the conceptual definition of health literacy (Haun, Valerio, McCormack, Sørensen, & Paasche-Orlow, 2014).
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Potential of perceived risk or threat as a mediator. Using absorption as a mediator was to identify and extend the effect of an interface feature based on the existing theory of interactive media (TIME; Sundar et al., 2015). Yet, as long as the background of this study is to learn information about a new disease, other mediators can be reflected. Perceived risk or threat can be suggested from information processing models such as PRISM (Kahlor, 2010) and EPPM (Witte, 1994), which constitute cognitive aspects, to explain possible slider effects. For instance, if the potential variables are incorporated in TIME and a new model (slider → perceived vividness → perceived risk/threat → attitudes/intentions) is created, another facet of modality interactivity may be discerned when individuals obtain new information. In a high threat situation, the positive relationship between vividness of information and attitudes toward the recommendation (Blondé & Girandola, 2018) may support verifying the new model in the high threat context in which an emerging infectious disease spreads.
Consideration of emotional components. This study focused on cognitive aspects of information processing proposed in TIME. However, as numerous message and information processing models describe, individuals make decisions by emotion as well. The decisions of people with low health literacy may be especially vulnerable to affective responses from a cue rather than cognitive processes by message. Additionally, in terms of the factors influencing low health literacy, it is valuable to think about what causes individuals to have difficulties with managing health information. Even though health literacy is evaluated by its measurement, other factors cannot be neglected on
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information processing, such as emotions when individuals get horrible health news, are shocked about a diagnosis, and are anxious about their family or friends in relation to a disease. For this reason, it is possible to explore emotions—fear and/or anxiety—which can be evoked by seeing the changes of an infected body using the slider. If the combinatory effects of a slider and health literacy influence affective responses and, in turn, attitudes and intentions is determined, both cognitive and affective processing from the slider are recognizable. Here, one interesting question can be posed: How do positive effects of a slider affect negative emotions concerning a disease? For example, future research can investigate whether the fun aspects of using a slider suppresses fear, or if the vividness of a slider promotes fear. It is not easy to predict the direction because of a lack of related research. If this relationship is identified, the result regarding attitudes toward the disease in this study may be interpretable. Therefore, I would suggest examining how a slider impacts individuals’ emotional processing, specifically whether the morphing images through engagement would be helpful to the population, who find it difficult to understand text-based information, to reduce their negative affective responses.
Need of more suitable population for generalization. The participants in this study are highly educated: 90% of them had more than college education. Even though scores under 4 are considered as low health literacy (Weiss et al., 2005) and the low health literacy group had 3.21 out of 6 in their health literacy test, this may not reflect the real “low” health literacy population. Since one characteristic of low health-literate people is the limited access to online health information (Jensen, King, Davis, &
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Guntzviller, 2010), using the online survey system users may not include this population very well. As the importance of searching and using health information online is growing, research of interactive media effects with the relevant target audience is needed.
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Chapter 6: Conclusion
As emerging diseases occur and the public’s need for information increases, learning new information in an interesting way must be emphasized. This study revealed the importance of cognitive absorption in explaining the effect of technological affordance on a health website on evaluations of the website, the message, and the source as well as individuals’ future behavioral intentions to use the website and the source, and to seek more information about the disease. The findings about cognitive processing of information and its application for all levels of health literacy have implications for theoretical mechanisms and practice. Even when no previous knowledge exists, the strategy of using interactive features on a website is effective for learning new health information and motivating the public to look for more information by engaging in activities. Follow-up studies are necessary to confirm the indirect effects of interactivity in different contexts and populations.
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References
Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665- 694.
Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivariate Behavioral Research, 40(3), 373-400.
Berkman, N. D., Davis, T. C., & McCormack, L. (2010). Health literacy: What is it? Journal of Health Communication, 15, 9-19.
Blondé, J., & Girandola, F. (2018). Are Vivid (Vs. Pallid) Threats Persuasive? Examining the Effects of Threat Vividness in Health Communications. Basic and Applied Social Psychology, 40(1), 36-48.
Bohner, G., Moskowitz, G. B., & Chaiken, S. (1995). The interplay of heuristic and systematic processing of social information. European Review of Social Psychology, 6(1), 33-68.
Büttner, O. B., & Göritz, A. S. (2008). Perceived trustworthiness of online shops. Journal of Consumer Behaviour, 7(1), 35-50.
Campbell, M. K., Honess-Morreale, L., Farrell, D., Carbone, E., & Brasure, M. (1999). A tailored multimedia nutrition education pilot program for low-income women receiving food assistance. Health Education Research, 14(2), 257-267.
Castillo, C., Mendoza, M., & Poblete, B. (2011, March). Information credibility on twitter. In Proceedings of the 20th international conference on World Wide Web (pp. 675-684). ACM.
Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752-766.
Chaiken, S. (1987). The heuristic model of persuasion. In M. P. Zanna, J. M. Olson, & C. P. Herman (Eds.), Social influence: The Ontario Symposium (Vol. 5, pp. 3-39). Hillsdale, NJ: Erlbaum.
85
Chaiken, S., Liberman, A., & Eagly, A. H. (1989). Heuristic and systematic information processing within and beyond the persuasion context. In J. S.s Uleman & J. A. Bargh (Eds.), Unintended thought (pp. 212-252). New York: Guilford Press.
Chaiken, S., & Maheswaran, D. (1994). Heuristic processing can bias systematic processing: Effects of source credibility, argument ambiguity, and task importance on attitude judgment. Journal of Personality and Social Psychology, 66(3), 460- 473.
Chen, S., & Chaiken, S. (1999). The heuristic-systematic model in its broader context. In S. Chaiken & Y. Trope (Eds.), Dual-process theories in social psychology (pp. 73- 96). New York: Guilford Press.
Chiang, K. P., & Jackson, A. (2013). Health literacy and its outcomes: application and extension of elaboration likelihood model. International Journal of Healthcare Management, 6(3), 152-157.
Cho, C. H., & Leckenby, J. D. (1997). Internet-related programming technology and advertising. In Proceedings of the Conference-American Academy of Advertising (pp. 69-79). American Academy of Advertising.
Cole, J. (2004). Now is the time to start studying the Internet age. Chronicles of Higher Education, 50(3), 1-4.
Colter, A., & Summers, K. (2014). Low literacy users. In J. Romano Bergstrom & A. J. Schall (Eds.), Eye tracking in user experience design (pp. 331-348). Waltham, MA: Morgan Kaufmann.
Coyle, J. R., & Thorson, E. (2001). The effects of progressive levels of interactivity and vividness in web marketing sites. Journal of Advertising, 30(3), 65-77.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper-Row.
Cummings, L. (2014). The “trust” heuristic: Arguments from authority in public health. Health Communication, 29(10), 1043-1056.
Dou, X. (2013). User experience of mobile interactivity: How do mobile websites affect attitudes and relational outcomes? (Doctoral dissertation, The Pennsylvania State University).
86
Dou, X., & Sundar, S. S. (2014, March). Cultivating relationships through a mobile website: The importance of modality interactivity and message interactivity. In 17 Th International Public Relations Research Conference (p. 207).
Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Orland, FL: Harcourt Brace Jovanovich.
Elbert, S. P., Dijkstra, A., & Oenema, A. (2016). A mobile phone app intervention targeting fruit and vegetable consumption: the efficacy of textual and auditory tailored health information tested in a randomized controlled trial. Journal of Medical Internet research, 18(6).
Faber, M., Bosch, M., Wollersheim, H., Leatherman, S., & Grol, R. (2009). Public reporting in health care: How do consumers use quality-of-care information?: A systematic review. Medical Care, 47(1), 1-8.
Flanagin, A. J., & Metzger, M. J. (2007). The role of site features, user attitudes, and information verification behaviors on the perceived credibility of web-based information. New Media & Society, 9(2), 319-342.
Fiske, S. T., & Taylor, S. E. (1991). Social cognition (2nd ed.). New York: McGraw Hill.
Freimuth, V., Linnan, H. W., & Potter, P. (2000). Communicating the threat of emerging infections to the public. Emerging Infectious Diseases, 6(4), 337.
Green, M. C. (2006). Narratives and cancer communication. Journal of Communication, 56(s1), S163-S183.
Griffin, R. J., Dunwoody, S., & Neuwirth, K. (1999). Proposed model of the relationship of risk information seeking and processing to the development of preventive behaviors. Environmental Research, 80(2), S230-S245.
Griffin, R. J., Neuwirth, K., Giese, J., & Dunwoody, S. (2002). Linking the heuristic- systematic model and depth of processing. Communication Research, 29(6), 705- 732.
Grigorovici, D. M., & Constantin, C. D. (2004). Experiencing interactive advertising beyond rich media: Impacts of ad type and presence on brand effectiveness in 3D gaming immersive virtual environments. Journal of Interactive Advertising, 5(1), 22-36.
87
Guillory, J. E., & Sundar, S. S. (2014). How does web site interactivity affect our perceptions of an organization?. Journal of Public Relations Research, 26(1), 44- 61.
Ha, L., & James, E. L. (1998). Interactivity reexamined: A baseline analysis of early business web sites. Journal of Broadcasting & Electronic Media, 42(4), 457-474.
Haun, J. N., Valerio, M. A., McCormack, L. A., Sørensen, K., & Paasche-Orlow, M. K. (2014). Health literacy measurement: an inventory and descriptive summary of 51 instruments. Journal of Health Communication, 19(sup2), 302-333.
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, NY: Guilford Press.
Hayes, A. F., & Matthes, J. (2009). Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations. Behavior Research Methods, 41(3), 924-936.
Hibbard, J. H., Peters, E., Dixon, A., & Tusler, M. (2007). Consumer competencies and the use of comparative quality information it isn't just about literacy. Medical Care Research and Review, 64(4), 379-394.
Jacobs, R. J., Lou, J. Q., Ownby, R. L., & Caballero, J. (2016). A systematic review of eHealth interventions to improve health literacy. Health Informatics Journal, 22(2), 81-98.
Jensen, J. F. (1998). Interactivity: Tracking a new concept in media and communication studies. Nordicom Review, 1, 185-204.
Johnson, P. O., & Fay, L. C. (1950). The Johnson-Neyman technique, its theory and application. Psychometrika, 15(4), 349-367.
Kahlor, L. (2010). PRISM: A planned risk information seeking model. Health Communication, 25(4), 345-356.
Kahlor, L., Dunwoody, S., Griffin, R. J., Neuwirth, K., & Giese, J. (2003). Studying heuristic‐systematic processing of risk communication. Risk Analysis, 23(2), 355- 368.
Kalichman, S. C., Benotsch, E., Suarez, T., Catz, S., Miller, J., & Rompa, D. (2000). Health literacy and health-related knowledge among persons living with HIV/AIDS. American Journal of Preventive Medicine, 18(4), 325-331. 88
Kahneman, D. (2003). A perspective on judgement and choice. Mapping bounded rationality. American Psychologist, 58(9), 697-720.
Kalyanaraman, S., & Sundar, S. S. (2006). The psychological appeal of personalized content in web portals: does customization affect attitudes and behavior?. Journal of Communication, 56(1), 110-132.
Keselman, A., Logan, R., Smith, C. A., Leroy, G., & Zeng-Treitler, Q. (2008). Developing informatics tools and strategies for consumer-centered health communication. Journal of the American Medical Informatics Association, 15(4), 473-483.
Kim, H. (2011). Pharmaceutical companies as a source of health information: A pilot study of the effects of source, web site interactivity, and involvement. Health Marketing Quarterly, 28(1), 57-85.
Koh, Y. J., & Sundar, S. S. (2010). Heuristic versus systematic processing of specialist versus generalist sources in online media. Human Communication Research, 36(2), 103-124.
Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46-70.
Lang, A. (2006). Using the limited capacity model of motivated mediated message processing to design effective cancer communication messages. Journal of Communication, 56(s1), S57-S80.
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1997). Motivated attention: Affect, activation, and action. In P. J. Lang, R. F. Simons, & M. T. Balaban (Eds.), Attention and orienting: Sensory and motivational processes. Hillsdale, NJ: Erlbaum.
Lee, M. K., Kiesler, S., & Forlizzi, J. (2011). Mining behavioral economics to design persuasive technology for healthy choices. Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, 325-334.
Lee, H. H., Kim, J., & Fiore, A. M. (2010). Affective and cognitive online shopping experience: Effects of image interactivity technology and experimenting with appearance. Clothing and Textiles Research Journal, 28(2), 140-154.
89
Li, H., Daugherty, T., & Biocca, F. (2002). Impact of 3-D advertising on product knowledge, brand attitude, and purchase intention: The mediating role of presence. Journal of Advertising, 31(3), 43-57.
Liu, Y., & Shrum, L. J. (2002). What is interactivity and is it always such a good thing? Implications of definition, person, and situation for the influence of interactivity on advertising effectiveness. Journal of Advertising, 31(4), 53-64.
Liu, Y., & Shrum, L. J. (2009). A dual-process model of interactivity effects. Journal of Advertising, 38(2), 53-68.
Longo, D. R., & Patrick, T. B. (2001). Consumer reports and health care information: a call for research. Journal of Health Care Finance, 28(1), 92-96.
Lu, Y., Kim, Y., Dou, X. Y., & Kumar, S. (2014). Promote physical activity among college students: Using media richness and interactivity in web design. Computers in Human Behavior, 41, 40-50.
Mackert, M., Kahlor, L., Tyler, D., & Gustafson, J. (2009). Designing e-health interventions for low-health-literate culturally diverse parents: Addressing the obesity epidemic. Telemedicine and e-Health, 15(7), 672-677.
Manganello, J., Gerstner, G., Pergolino, K., Graham, Y., Falisi, A., & Strogatz, D. (2017). The relationship of health literacy with use of digital technology for health information: implications for public health practice. Journal of Public Health Management and Practice, 23(4), 380-387.
Meyers-Levy, J., & Peracchio, L. A. (1995). Understanding the effects of color: How the correspondence between available and required resources affects attitudes. Journal of Consumer Research, 22(2), 121-138.
McMillan, S. J., & Hwang, J. S. (2002). Measures of perceived interactivity: An exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. Journal of Advertising, 31(3), 29-42.
Muller, I., Rowsell, A., Stuart, B., Hayter, V., Little, P., Ganahl, K., ... & Nutbeam, D. (2017). Effects on engagement and health literacy outcomes of web-based materials promoting physical activity in people with diabetes: an international randomized trial. Journal of Medical Internet research, 19(1), e21.
90
Meppelink, C. S., & Bol, N. (2015). Exploring the role of health literacy on attention to and recall of text-illustrated health information: An eye-tracking study. Computers in Human Behavior, 48, 87-93.
Meppelink, C. S., Smit, E. G., Buurman, B. M., & van Weert, J. C. (2015). Should we be afraid of simple messages? The effects of text difficulty and illustrations in people with low or high health literacy. Health Communication, 30(12), 1181-1189.
Meppelink, C. S., van Weert, J. C., Haven, C. J., & Smit, E. G. (2015). The effectiveness of health animations in audiences with different health literacy levels: An experimental study. Journal of Medical Internet Research, 17(1), e11.
Metzger, M. J., & Flanagin, A. J. (2013). Credibility and trust of information in online environments: The use of cognitive heuristics. Journal of Pragmatics, 59, 210-220.
Meyers-Levy, J., & Peracchio, L. A. (1995). Understanding the effects of color: How the correspondence between available and required resources affects attitudes. Journal of Consumer Research, 22(2), 121-138.
Miller, J. W., Stromeyer, W. R., & Schwieterman, M. A. (2013). Extensions of the Johnson-Neyman technique to linear models with curvilinear effects: Derivations and analytical tools. Multivariate Behavioral Research, 48(2), 267-300.
Muller, I., Rowsell, A., Stuart, B., Hayter, V., Little, P., Ganahl, K., ... & Nutbeam, D. (2017). Effects on engagement and health literacy outcomes of web-based materials promoting physical activity in people with diabetes: an international randomized trial. Journal of Medical Internet Research, 19(1), e21.
Nan, X., Dahlstrom, M. F., Richards, A., & Rangarajan, S. (2015). Influence of evidence type and narrative type on HPV risk perception and intention to obtain the HPV vaccine. Health communication, 30(3), 301-308.
Nazione, S. (2016). An investigation of first-versus third-person risk narrative processing through the lens of the heuristic-systematic model. Communication Research Reports, 33(2), 145-151.
Nguyen, M. H., van Weert, J. C., Bol, N., Loos, E. F., Tytgat, K. M., van de Ven, A. W., & Smets, E. M. (2017). Tailoring the mode of information presentation: effects on younger and older adults' attention and recall of online information. Human Communication Research, 43(1), 102-126.
91
Nguyen, M. H., Smets, E. M., Bol, N., Loos, E. F., & Van Weert, J. C. (2018). How Tailoring the Mode of Information Presentation Influences Younger and Older Adults’ Satisfaction with Health Websites. Journal of Health Communication, 1-11.
Nijman, J., Hendriks, M., Brabers, A., de Jong, J., & Rademakers, J. (2014). Patient activation and health literacy as predictors of health information use in a general sample of Dutch health care consumers. Journal of Health Communication, 19(8), 955-969.
Norman, C. D., & Skinner, H. A. (2006). eHealth literacy: essential skills for consumer health in a networked world. Journal of Medical Internet Research, 8(2), e9.
O’Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the Association for Information Science and Technology, 59(6), 938-955.
Oh, J. (2017). The effect of interactivity on smokers' intention to quit: A linear or curvilinear relationship?. Computers in Human Behavior, 75, 845-854.
Oh, J., Ahn, J., Lim, H. Y., & Kim, S. (2017). I want my voice to be heard: The effects of social media affordances in an anti-smoking website on sense of agency and intention to quit. In Proceedings of the 2017 National Communication Association. Dallas, TX.
Oh, J., Robinson, H. R., & Lee, J. Y. (2013). Page flipping vs. clicking: The impact of naturally mapped interaction technique on user learning and attitudes. Computers in Human Behavior, 29(4), 1334-1341.
Oh, J., & Sundar, S. S. (2015). How does interactivity persuade? An experimental test of interactivity on cognitive absorption, elaboration, and attitudes. Journal of Communication, 65(2), 213-236.
Peterson, P. N., Shetterly, S. M., Clarke, C. L., Bekelman, D. B., Chan, P. S., Allen, L. A., ... & Masoudi, F. A. (2011). Health literacy and outcomes among patients with heart failure. Jama, 305(16), 1695-1701.
Petty, R. E., & Cacioppo, J. T. (1984). The elaboration likelihood model of persuasion. Advances in Consumer Research, 11(1), 673-675.
Petty, R. E., & Cacioppo, J. T. (1986). Communication and persuasion: Central and peripheral routes to attitude change. New York: Springer-Verlag.
92
Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 31(4), 437-448.
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891.
Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42(1), 185-227.
Rafaeli, S. (1988). Interactivity: From new media to communication. In R. P. Hawkins, J. M. Wiemann & S. Pingree (Eds.), Advancing communication science: Merging mass and interpersonal processes (pp. 110-134). Newbury Park, CA: Sage.
Reeves, B., & Nass, C. (2000). Perceptual user interfaces: Perceptual bandwidth. Communications of the ACM, 43(3), 65-70.
Revere, D., & Dunbar, P. J. (2001). Review of computer-generated outpatient health behavior interventions. Journal of the American Medical Informatics Association, 8(1), 62-79.
Reychav, I., & Wu, D. (2015). Mobile collaborative learning: the role of individual learning in groups through text and video content delivery in tablets. Computers in Human Behavior, 50, 520-534.
Lau, R. R., & Redlawsk, D. P. (2001). Advantages and disadvantages of cognitive heuristics in political decision making. American Journal of Political Science, 951- 971.
Rubin, A. M. (1993). Audience activity and media use. Communication Monographs, 60, 98–103.
Santibañez, S., Siegel, V., O'Sullivan, M., Lacson, R., & Jorstad, C. (2015). Health communications and community mobilization during an Ebola response: partnerships with community and faith-based organizations. Public Health Reports, 130(2), 128-133.
Shavitt, S., Swan, S., Lowrey, T. M., & Wänke, M. (1994). The interaction of endorser attractiveness and involvement in persuasion depends on the goal that guides message processing. Journal of Consumer Psychology, 3(2), 137-162. 93
Sicilia, M., Ruiz, S., & Munuera, J. L. (2005). Effects of interactivity in a web site: The moderating effect of need for cognition. Journal of Advertising, 34(3), 31-44.
Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2007). The affect heuristic. European Journal of Operational Research, 177(3), 1333-1352.
Sohn, D. (2011). Anatomy of interaction experience: Distinguishing sensory, semantic, and behavioral dimensions of interactivity. New Media & Society, 13, 1320-1335.
Sørensen, K., Van den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., & Brand, H. (2012). Health literacy and public health: a systematic review and integration of definitions and models. BMC public health, 12(1), 80.
Spears, N., & Singh, S. N. (2004). Measuring attitude toward the brand and purchase intentions. Journal of Current Issues & Research in Advertising, 26(2), 53-66.
Stavrositu, C., & Sundar, S. S. (2004, May). Interstitials and their relevance to Website content: Influence on Website credibility. Paper presented in the 54th annual conference of the International Communication Association, New Orleans, LA.
Stephenson, W. (1967). The play theory of mass communication. Chicago: University of Chicago Press, pp. 45–65.
Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication, 42(4), 73-93.
Suka, M., Odajima, T., Okamoto, M., Sumitani, M., Igarashi, A., Ishikawa, H., ... & Sugimori, H. (2015). Relationship between health literacy, health information access, health behavior, and health status in Japanese people. Patient Education and Counseling, 98(5), 660-668.
Sundar, S. S. (2007). Social psychology of interactivity in human-website interaction. In A. N. Joinson, K. Y. A. McKenna, T. Postmes & U-D. Reips (Eds.), The Oxford Handbook of Internet Psychology (pp. 89-104). Oxford, UK: Oxford University Press.
Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. In M. J. Metzger & A. J. Flanagin (Eds.,), Digital media, youth, and credibility (pp. 72-100). Cambridge, MA: The MIT Press.
94
Sundar, S. S., & Bellur, S. (2010). Concept explication in the internet age: The case of interactivity. In E. P. Bucy & R. L. Holbert (Eds.), Sourcebook for political communication research: Methods, measures, and analytical techniques (pp. 485- 500). New York, NY: Routledge.
Sundar, S. S., Bellur, S., & Jia, H. (2012, June). Motivational technologies: a theoretical framework for designing preventive health applications. In M. Bang & E. L. Ragnemalm (Eds.), Proceedings of the 7th International Conference on Persuasive Technology (pp. 112-122). Berlin, Germany: Springer.
Sundar, S. S., Bellur, S., Oh, J., Jia, H., & Kim, H. S. (2016). Theoretical importance of contingency in human-computer interaction: Effects of message interactivity on user engagement. Communication Research, 43(5), 595-625.
Sundar, S. S., Bellur, S., Oh, J., Xu, Q., & Jia, H. (2014). User experience of on-screen interaction techniques: An experimental investigation of clicking, sliding, zooming, hovering, dragging, and flipping. Human–Computer Interaction, 29(2), 109-152.
Sundar, S. S., Jia, H., Waddell, T. F., & Huang, Y. (2015). Toward a theory of interactive media effect (TIME). In S. S. Sundar (Ed.), The handbook of the psychology of communication technology (pp. 47-86). John Wiley & Sons, Inc.
Sundar, S. S., & Kalyanaraman, S. (2004). Arousal, memory, and impression-formation effects of animation speed in web advertising. Journal of Advertising, 33(1), 7-17.
Sundar, S. S., Xu, Q., & Bellur, S. (2010, April). Designing interactivity in media interfaces: A communications perspective. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2247-2256). ACM.
Sundar, S. S., Xu, Q., Bellur, S., Oh, J., & Jia, H. (2011). Beyond pointing and clicking: How do newer interaction modalities affect user engagement? Proceedings of the 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems (CJO EA’11) (pp. 1477-1482). New York, NY: Association for Computing Machinery.
Sundar, S. S., Xu, Q., & Dou, S. (2012). Role of technology in online persuasion: A MAIN model perspective. In S. Rodgers & E. Thorson (Eds.), Advertising theory (pp. 355-372). New York, NY: Routledge.
Taatgen, N. A., & Lee, F. J. (2003). Production compilation: A simple mechanism to model complex skill acquisition. Human Factors, 45(1), 61-76.
95
Thaler, R. H., & Sunstein, C. R. (2008). Nudge. Improving decisions about health, wealth, and happiness. New Haven & London: Yale University Press.
Toms, E.G. (2002). Information interaction: Providing a framework for information architecture. Journal of the American Society for Information Science and Technology, 53(10), 855–862.
Tormala, Z. L., Briñol, P., & Petty, R. E. (2006). When credibility attacks: The reverse impact of source credibility on persuasion. Journal of Experimental Social Psychology, 42(5), 684-691.
US Department of Health and Human Services, & Office of Disease Prevention and Health Promotion. (2010). Quick guide to health literacy. Retrieved from https://health.gov/communication/literacy/quickguide/quickguide.pdf.
Wang, S. K., Hsu, H. Y., Reeves, T. C., & Coster, D. C. (2014). Professional development to enhance teachers' practices in using information and communication technologies (ICTs) as cognitive tools: Lessons learned from a design-based research study. Computers & Education, 79, 101-115.
Watts, S., Shankaranarayanan, G., & Even, A. (2009). Data quality assessment in context: A cognitive perspective. Decision Support Systems, 48(1), 202-211.
Weiss, B. D., Mays, M. Z., Martz, W., Castro, K. M., DeWalt, D. A., Pignone, M. P., ... & Hale, F. A. (2005). Quick assessment of literacy in primary care: the newest vital sign. The Annals of Family Medicine, 3(6), 514-522.
Wiebe, E. N., Lamb, A., Hardy, M., & Sharek, D. (2014). Measuring engagement in video game-based environments: Investigation of the User Engagement Scale. Computers in Human Behavior, 32, 123-132.
Williams, M. V., Baker, D. W., Honig, E. G., Lee, T. M., & Nowlan, A. (1998). Inadequate literacy is a barrier to asthma knowledge and self-care. Chest, 114(4), 1008-1015.
Wilson, C., Evans, G., Leppard, P., & Syrette, J. (2004). Reactions to genetically modified food crops and how perception of risks and benefits influences consumers' information gathering. Risk Analysis, 24(5), 1311-1321.
Wilson, E. A., & Wolf, M. S. (2009). Working memory and the design of health materials: a cognitive factors perspective. Patient Education and Counseling, 74(3), 318-322. 96
Witte, K. (1994). Fear control and danger control: A test of the extended parallel process model (EPPM). Communications Monographs, 61(2), 113-134.
Wu, G. (2005). The mediating role of perceived interactivity in the effect of actual interactivity on attitude toward the website. Journal of Interactive Advertising, 5(2), 29-39.
Xu, Q., & Sundar, S. S. (2014). Lights, camera, music, interaction! Interactive persuasion in e-commerce. Communication Research, 41(2), 282-308.
Yang, Z. J. (2012). Too scared or too capable? Why do college students stay away from the H1N1 vaccine?. Risk Analysis, 32(10), 1703-1716.
Yang, Z. J., Aloe, A. M., & Feeley, T. H. (2014). Risk information seeking and processing model: A meta‐analysis. Journal of Communication, 64(1), 20-41.
Zhang, L., Peng, T. Q., Zhang, Y. P., Wang, X. H., & Zhu, J. J. (2014). Content or context: Which matters more in information processing on microblogging sites. Computers in Human Behavior, 31, 242-249.
97