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With the proliferation of health information online, scholars are increasingly turning their attention to the role of the Internet as a source of health information that influences individuals' health-related behaviors (e.g., Lee, Gray, & Lewis, 2010; Wu, Jiang, & Chen, 2016). Scholars have focused on how people being treated for illness or a health problem use the Internet effectively, given its distinctive features. For example, the Internet enables users to control the pace at which they receive information, to archive information they select, and to employ goal-oriented learning (Dutta-Bergman, 2004). A large amount of health information that facilitates an increase in users' health-related knowledge can serve as a foundation for people's choice of treatments and engagement in health-related behaviors (Bennett & Glasgow, 2009). In the context of cancer, van de Poll-Franse and van Eenbergen (2008) documented that most people being treated for cancer use the Internet as a reliable information source, which helps them to be well equipped with knowledge about their disease and treatment. Therefore, such information seeking on the Internet potentially leads them to engage in preventative behaviors. Although seeking cancer information online can directly influence cancer-preventative behaviors, through the current study we have also left open the possibility of its indirect effect on cancer-preventative behaviors, considering the mediating roles of two cognitive factors, fatalistic beliefs and self-efficacy.

Individuals with a high level of fatalism are likely to behave passively, as they believe that whatever happens to them is the result of uncontrollable powers, whereas those with a low level of fatalism tend to exhibit the motivation to change their current situation (Powe & Finnie, 2003). Self-efficacy refers to "people's belief about their capabilities to produce designated levels of performance that exercise influence over events that affect their lives" (Bandura, 1994, p. 2), and has been considered a significant factor in predicting individuals' engagement in certain behaviors. Several researchers (e.g., Luszczynska, Tryburcy, & Schwarzer, 2007) have demonstrated positive associations between self-efficacy and engagement in cancer-preventative behaviors. This is because those with a high level of self-efficacy believe that they can stay healthy if they devote sufficient effort to doing so. Thus, they are inspired to use diverse resources in effective ways.

Both fatalism and self-efficacy are related to information-seeking behaviors. Fatalism can be reduced through health information seeking, given that increased knowledge about diseases through information seeking reduces uncertainty, anxiety, and fatalism, and ultimately leads to informed decision making (Kim, Kim, & Lee, 2014; Miles, Voorwinden, Chapman, & Wardle, 2008). The value of online information seeking by cancer sufferers has been much emphasized, because the Internet now serves as a major source of cancer-related information. Indeed, Lee, Niederdeppe, and Freres (2012) have argued that the sheer amount of information available allows individuals to rectify any gaps in their knowledge. By accessing integrated information, individuals may reduce their fatalism and uncertainty about cancer.

Information seeking may reduce fatalistic belief, but it may strengthen self-efficacy. Findings reported in studies (e.g., Bass et al., 2006) have shown that the availability and accessibility of health information on the Internet led to patients' increased empowerment and sense of control which, in turn, enhanced patients' levels of self-efficacy. Results in several surveys (e.g., Selsky, Luta, Noone, Huerta, & Mandelblatt, 2013) have also shown that patients seeking health information on the Internet have feelings of greater empowerment and communicate well with physicians. Thus, reduced fatalistic belief and enhanced self-efficacy via online information seeking could ultimately lead those who have sought the information to increase their use of cancer-preventative behaviors.

It is yet to be established by researchers whether or not fatalistic beliefs and self-efficacy are related to each other, and, if they are related, how the interplay of these factors influences the relationship between online cancer-related information seeking and cancer-preventative behaviors of people with cancer.

Fatalistic beliefs and self-efficacy can be inherently related to each other if a fatalistic belief takes hold when an individual lacks a sense of control or confidence to overcome a given situation. In other words, the belief that events are controlled by external forces may weaken people's belief about their capability to influence the events that affect their life. On the other hand, if people are confident in pursuing certain behaviors to change the current situation, they are more likely to engage in those behaviors.

Regarding this relationship, some researchers (e.g., Niederdeppe & Levy, 2007) have demonstrated that having fatalistic beliefs about cancer prevention reduces self-efficacy. In particular, Straughan and Seow (2000) have conceptualized and identified fatalism as a source of low self-efficacy. They have demonstrated that women with fatalistic beliefs have low levels of self-efficacy, which leads to low participation in screening tests.

In sum, based on the preceding discussion, in this study we aimed to test not only the direct influence of online cancer information seeking on cancerpreventative behaviors, but also its indirect effect on cancer-preventative behaviors by way of fatalistic belief and self-efficacy. In addition, we proposed a relationship between fatalistic belief and self-efficacy. Thus, we developed the following hypotheses:

Hypothesis 1: Using the Internet for cancer-related information seeking will promote individuals' cancer-preventative behaviors.

Hypothesis 2: Using the Internet for cancer-related information seeking will reduce individuals' fatalistic belief, which, in turn, will increase their engagement in cancer-preventative behaviors.

Hypothesis 3: Using the Internet for cancer-related information seeking will increase individuals' self-efficacy, which, in turn, will increase their engagement in cancer-preventative behaviors.

Hypothesis 4: There will be a negative relationship between self-efficacy and fatalistic belief.


Participants and Procedure

The data used in the present study were obtained from the National Cancer Institute's (2013) Health Information National Trends Survey (2013), which was conducted with a national sample in the United States in 2012. Of the 3,630 respondents, 2,896 (79.8%) provided full responses and these data were analyzed in our study (men = 1,744, women = 1,152).


Information seeking on the Internet. To measure the extent of cancer-related information seeking on the Internet (ISIN), participants were asked "How much attention do you pay to information about cancer from each of the following sources: (a) the Internet, (b) the online newspaper?" By averaging the two items, a scale (M = 2.29, SD = .79, r = .33) was created.

Cancer fatalism. Respondents were asked to assess their cancer fatalism (CFAT) according to the following three Likert-type scale items: "There is not much you can do to lower your chances of getting cancer," "There are so many different recommendations about preventing cancer, it's hard to know which ones to follow," and "It seems like everything causes cancer." Of these items, we concluded that the first two accurately reflected an association between ISIN and CFAT, when considering the low level of reliability for all three items taken together (Cronbach's a = .60), which had been similarly reported in another study (Lee et al., 2012; M = 2.45, SD = .72, r = .36).

Self-efficacy. Two items were used to estimate participants' self-efficacy regarding their health (HSE): "Overall, how confident are you that you could get advice or information about cancer if you needed it?" and "Overall, how confident are you about your ability to take good care of your health?" We yielded a scale by averaging these items (M = 3.82, SD = .76, r = .29).

Cancer-preventative behaviors. We selected two items on cancer-preventative behaviors (CPB) by referring to previous studies (e.g., Lewis et al., 2012): "About how many cups of fruit (including 100% pure fruit juice) do you eat or drink each day?" and "About how many cups of vegetables (including 100% pure vegetable juice) do you eat or drink each day?" These items were measured on a 7-point scale ranging from 0 = none to 6 = four or more cups. We created a new scale by averaging the two items (M = 2.56, SD = 1.16, r = .51).

Control variables. Demographics were included as control variables, just as they had been in previous studies (e.g., Zhao & Cai, 2009). For age and education, we recoded items into a 5-point scale. Participants' attention to cancer information delivered via other mass media (television and newspaper) was also considered a control variable.

Data Analysis Plan

We tested the hypothesized research model using structural equation modeling (SEM) with estimation of the maximum likelihood estimation. To evaluate the model fit, the root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), and the comparative fit index (CFI) were examined. We developed a hypothesized cognitive mediation model that incorporated one predictor, two mediators, and one behavioral outcome. Online cancer-related ISIN and five covariates were used in the analysis as exogenous variables, with CFAT, HSE, and CPB included as endogenous variables. Age, gender, education, and attention to cancer-related information delivered via other mass media were included in the structural path model as control variables, from which pathways were drawn to all study variables.

To test the hypotheses regarding the mediation of the two cognitive predictors, in the present study we bootstrapped the indirect effects of these predictors on individuals' cancer-preventative behaviors by repeating the sampling cases 5,000 times with replacements from the data (Hayes, 2009).


Hypotheses Tests

We performed an SEM analysis using the maximum likelihood estimator to test the hypothesized path model. For this model, fit indices indicated a favorable model fit, [chi square](13) = 15.545, p < .001, CFI = .999, RMSEA = .008, 90% confidence interval (CI) [.000, .021]), SRMR = .0123. All parameters were tested twice with maximum likelihood estimation and bootstrapping. With the 95% bias-corrected confidence intervals for all variables of interest, we found that none of the results changed in terms of statistical significance when assessed with the bootstrap method. Thus, this model was used to test our hypotheses.

In Hypothesis 1 we posited that individuals' participation in cancer-related ISIN would promote their engagement in CPB. Contrary to our expectations, the analysis revealed no direct effect of cancer-related ISIN on individuals' engagement in CPB. Hence, Hypothesis 1 was not supported.

In Hypothesis 2 we hypothesized that fatalistic beliefs would mediate the association between cancer-related ISIN and engagement in CPB. The analysis showed that cancer-related ISIN was not related to respondents' level of CFAT; for ISIN [right arrow] CFAT, [beta] = -.008, SE = .019, ns, but respondents' level of CFAT was significantly associated with their engagement in CPB; for CFAT [right arrow] CPB, [beta] = -.168, SE = .030, p < .001. Thus, Hypothesis 2 was not supported.

In Hypothesis 3 we posited that individuals' level of HSE would mediate the association between the predictor (ISIN) and the outcome variable (CPB). As we predicted, individuals' engagement in cancer-related ISIN was significantly associated with their level ofHSE; for ISIN [right arrow] HSE, [beta] = .058, SE=.018, p < .01. Level of HSE, in turn, increased level of engagement in CPB; for HSE [right arrow] CPB, [beta] = .170, SE = .028, p < .001. The bootstrap test indicated that participants' cancer-related ISIN influenced their engagement in CPB by increasing their level of HSE; 95% CI for ISIN [right arrow] HSE [right arrow] CPB = [.001, .023]. Hence, Hypothesis 3 was supported.

Lastly, in Hypothesis 4 we predicted an association between mediators and the effects of CFAT on the association between individuals' cancer-related ISIN and their participation in CPB via HSE. Our analysis showed that lower levels of fatalistic beliefs led to higher levels of HSE; for CFAT [right arrow] HSE, [beta] = -.168, SE = .030, p < .001. Also, the result of the bootstrap test demonstrated that individuals' fatalistic beliefs caused a decrease of their engagement in CPB; 95% CI for CFAT -> HSE [right arrow] CPB = [-.014, -.035]. However, as already noted, there was no association between users' cancer-related ISIN and their CFAT. Therefore, Hypothesis 4 was only partially supported.


In this study we investigated the structural relationship among cancer-related ISIN, cognitive mediators (CFAT and HSE) and CPB, in search of a health cognitive mediation model. In our analysis we found that individuals' HSE mediated the association between cancer-related ISIN and CPB; however, contrary to our expectations, CFAT did not mediate the aforementioned relationship. Moreover, ISIN did not directly affect CPB. Although in previous studies (e.g., Lee et al., 2012) researchers have reported finding a significant effect of ISIN on CFAT and/or related behaviors, in the present study we found only a partial association between these variables.

One reason for this unexpected result might be that the Internet use alone did not influence individuals' engagement in cancer-related behaviors. Rather, as noted, the effect of ISIN enhancing CPB disappeared when we controled for the use of other mass media, which implies that the cancer-related ISIN was compounded with other media usage. In particular, we reasoned that Internet use seems somewhat related to newspaper reading, given that both the Internet and the newspaper are regarded as active media channels requiring higher levels of goal-oriented motivation, as well as involvement in locating information (Dutta-Bergman, 2004).

For another reason, ISIN may promote the diversification of individuals' media usage patterns, when considering the popularity of social media and smartphones. Thus, effects of information seeking on cognitive and behavioral outcomes might be contingent upon individuals' preferences in regard to accessing information channels. This possibility needs to be investigated in follow-up studies.

Further, despite the limited effects of Internet use on CPB, we believe it is noteworthy that we found a significant mediation effect of self-efficacy. This finding demonstrates the need for consideration of this cognitive mediator in explaining the effect of ISIN on CPB. Indeed, in established models the role of cognitive factors is emphasized. For example, the orientation 1-stimulus-orientation 2-response framework (e.g., Shah et al., 2007) explains the crucial relationship between the reception of messages and the subsequent responses, as well as the specific role of cognitive process represented by the orientation 2 stage of the framework. By drawing on this theoretical framework, a more elaborate model can be created for use in health communication studies, in which specific cognitive predictors mediate the association between media use and behavioral outcomes.

Another of our findings that we found interesting is that individuals' level of self-efficacy significantly mediated the association between cancer fatalism and engagement in preventative behaviors. Specifically, we found that individuals' level of fatalistic beliefs had a causal effect on their level of self-efficacy. Furthermore, consistent with findings reported in previous studies (e.g., Viswanath et al., 2006), we found that there was a correlation between demographics and individuals' engagement in health-related behaviors. Education was associated with health-related behavioral outcomes and related cognitive factors, which calls for attention by researchers as to how social disparities are associated with health-related cognitive processes and behavioral outcomes.

In sum, in this study we explored the cognitive mediation effects on the behavioral outcomes of users of health-related information about cancer on the Internet for cancer prevention, in search of a health-related cognitive mediation framework. Despite some limitations arising from the use of a secondary data set, the results of our analysis contribute to a deeper understanding of the role of cognitive indicators in mediating the relationship between cancer-related information seeking on the Internet and cancer-preventative behaviors, and the relationship between cognitive mediators. From these findings, future researchers could develop a cognitive mediation framework to investigate further the structure of health-related information processing.


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Western Illinois University


Hankuk University of Foreign Studies

Eun Go, Department of Broadcasting and Journalism, Western Illinois University; Kyung Han You, Minerva College, Hankuk University of Foreign Studies.

This work was supported by the Ministry of Education of the Republic of Korea, the National Research Foundation of Korea (2017S1A5A8022290), and the Hankuk University of Foreign Studies Research Fund.

Correspondence concerning this article should be addressed to Kyung Han You, Hanhwa Obelisk APT#426, Mapodaero 33, Mapogu, Seoul, Republic of Korea. Email:
Table 1. Parameter Estimates

                                       ISIN                CFAT

Control variables
  Gender                          -.067 (.025) (**)   -
  Age                             -.081 (.011) (**)
  Education                        .087 (.013) (**)   -.105 (.011) (**)
  Newspaper                                           -.090 (.033) (**)
  Television                      -                   -
Independent variables
  ISIN                            -
  CFAT                            -                   -
  HSE                             -                   -
Indirect effects
  Parameters                                          Estimate
CFAT [right arrow]
HSE [right arrow] CPB                                 -.023 (**)
ISIN [right arrow]
HSE [right arrow] CPB                                  .011 (*)
ISIN [right arrow] CFAT
[right arrow]
HSE [right arrow] CPB                                  .002

                                    HSE                     CPB

Control variables
  Gender                       -                       -.234 (.043) (**)
  Age                                                  -
  Education                     .085 (.012) (**)        .144 (.019) (**)
  Newspaper                    -                       -
  Television                   -                        .15 (.020)**
Independent variables
  ISIN                          .058 (.018) (**)
  CFAT                         -.137 (.020) (**)       -.168 (.030) (**)
  HSE                          -                       -.170 (.028) (**)
Indirect effects
  Parameters                   Lower limit             Upper limit
                               95% CI                  95% CI
CFAT [right arrow]
HSE [right arrow] CPB          -.014                   -.035
ISIN [right arrow]
HSE [right arrow] CPB           .001                    .023
ISIN [right arrow] CFAT
[right arrow]
HSE [right arrow] CPB          -.000                    .003

Note. Unstandardized coefficients are presented. The variables that
present a significant effect were only addressed for the purpose of
clarity. ISIN = information seeking on the Internet, CFAT = cancer
fatalism, HSE = health self-efficacy, CPB = cancer-preventative
(*) p < .05, (**) p < .01.
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Article Details
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Author:Go, Eun; You, Kyung Han
Publication:Social Behavior and Personality: An International Journal
Article Type:Report
Date:May 1, 2018

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