Health Marketing Quarterly, 32:96–112, 2015 Copyright # Taylor & Francis Group, LLC ISSN: 0735-9683 print=1545-0864 online DOI: 10.1080/07359683.2015.1000758

Health Care Information Seeking and Seniors: Determinants of Internet Use XIAOJING SHENG and PENNY M. SIMPSON College of Business Administration, The University of Texas–Pan American, Edinburg, Texas

While seniors are the most likely population segment to have chronic diseases, they are the least likely to seek information about health and diseases on the Internet. An understanding of factors that impact seniors’ usage of the Internet for health care information may provide them with tools needed to improve health. This research examined some of these factors as identified in the comprehensive model of information seeking to find that demographics, trust in health information websites, perceived usefulness of the Internet, and internal locus of control each significantly impact seniors’ use of the Internet to seek health information. KEYWORDS information seniors, trust

seeking,

demographics,

Internet,

The Internet is increasingly becoming a primary channel for health care organizations and public health service agencies to disseminate health information and to provide health services (e.g., Cline & Haynes, 2001). Using the Internet for health and medical information offers a variety of benefits, such as instant access to a vast array of information, enhanced communication between patients and doctors, and support for interpersonal interactions and social support (e.g., Rice, 2006; Taha, Sharit, & Czaja, 2009). These benefits carry even greater importance for seniors because health concerns and health problems tend to increase with age and therefore, seniors tend to have an increased need for health as well as medical information (e.g., McMillan & Macias, 2008).

Address correspondence to Penny M. Simpson, College of Business Administration, The University of Texas–Pan American, 1201 West University Boulevard, Edinburg, TX 78539. E-mail: [email protected] 96

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Growing evidence suggests that the Internet can be useful in assisting seniors to become more active about their health, lead independent and social lives, and age successfully (e.g., Adams, Stubbs, & Woods, 2005; Mellor, Firth, & Moore, 2008). However, these potential benefits cannot be realized unless seniors actually utilize the Internet and capitalize on the opportunities provided by the Internet to better manage their health needs. So, why do seniors use or not use the Internet for health purposes? What are the factors that facilitate or prevent seniors from using the Internet for health information? The present article intends to answer these questions by drawing upon the theoretical framework of the comprehensive model of information seeking (CMIS; Johnson, 1983; Johnson & Meischke, 1993; Johnson, Donohue, Atkin, & Johnson, 1995) and by examining consumer characteristics variables and variables related to perceptions of the Internet as an information carrier as predictors of seniors’ using the Internet for health information. Researchers have made a good start in studying the influencing factors of consumers’ online health information seeking behavior. But the focus has been either on profiling consumers who use the Internet for health information on the basis of demographics (e.g., Lorence & Park, 2006a, 2006b; Rains, 2008; Renahy, Parizot, & Chauvin, 2008; Rice, 2006; Weaver et al., 2009; Ybarra & Suman, 2006) or on the design elements of health-related websites that facilitate or prevent consumers from using those websites (e.g., Adams et al., 2005; Hardt & Hollis-Sawyer, 2007). The CMIS provides a more holistic view as it takes into account both factors that relate to consumers as information seekers (e.g., demographic and other consumer characteristics variables) and those that relate to the Internet as an information carrier. As such, using the CMIS to examine seniors’ online health information-seeking behavior will present a fuller picture of why seniors use or do not use the Internet for health information. This examination will not only help better understand the elderly population but will also help in designing effective strategies or interventions to promote the Internet as a health communication and education tool for seniors.

CONCEPTUAL FRAMEWORK The CMIS explains information-seeking behavior by looking at why and where individuals look for information. The model is composed of three parts: antecedents, information carrier factors, and information-seeking actions (e.g., Johnson et al., 1995; Johnson, Andrews, & Allard 2001). The antecedent factors include demographics (i.e., age, gender, education), experience, salience, and beliefs regarding the information being sought. Information carrier factors are composed of characteristics and utility, which both determine where the individual looks for information. Information carrier characteristics primarily relate

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to message-content attributes such as source credibility and trustworthiness as well as the style and format of the information presented. Utility refers to the relevancy, usefulness, and importance of the information to the individual. In essence, the CMIS presents a causal structure in which antecedent factors determine an individual’s perceptions of the characteristics and utility of an information carrier, which then determines use of the information carrier. The model has received empirical support from different research contexts such as in explaining employees’ use of informal and formal channels to communication information within an organizational setting (Johnson et al., 1995) and in explaining consumers’ health information-seeking actions (e.g., Johnson & Meischke, 1993; Johnson et al., 2001; Rains, 2008). In the case of health information seeking on the Internet, past research built upon the CMIS (e.g., Rains, 2008) has examined antecedent factors that include demographics, experience with health issues, salience or personal importance of the information, and a sense of efficacy in managing health. Age, education, and income correspond to demographic variables in the model and perceived health status has been used to indicate experience (or lack of experience) with certain health issues. Perceived importance of having access to health information on the Internet speaks to the salience and personal importance of the information while health locus of control conveys the sense of efficacy in managing one’s own health. Regarding information carrier characteristics, credibility and trustworthiness have been identified as important source characteristics in communication processes (c.f., Johnson et al., 1995). However, trust is becoming a critical issue of concern especially within the online context as consumers tend to have more doubts about the quality and authenticity of information obtained from the Internet (Kwon & Xie, 2003). Trust in health information websites is determined by perceptions of the trustworthiness of the Internet and websites as an information carrier and therefore is a variable of great importance in understanding consumers’ online health information-seeking behavior. Finally, perceived usefulness, another information carrier factor in the CMIS framework, relates to perceptions of the extent to which information obtained from the Internet is useful and relevant to the needs of an individual and therefore corresponds to the information carrier utility variable in the CMIS. The CMIS framework may be important in explaining health information seeking on the Internet. Age, education, income as demographic factors, health status as an experience factor, perceived importance of health information access on the Internet as a salience factor, and locus of control of as a belief factor are all potential antecedents that should each affect primary information carrier factors, specifically the utility factor of perceived usefulness of the Internet. Additionally, trust in health information websites, as an information carrier characteristic, should also impact perceived usefulness of the Internet.

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Utility and Information-Seeking Action The CMIS predicts that using a certain information carrier to search for information is determined by perceptions of the utility of the information carrier. In the context of health information seeking on the Internet, perceived usefulness of the Internet is indicated by the Internet’s perceived utility and is expected to determine the online health information seeking actions. This prediction is in line with the technology acceptance model (TAM; Davis, 1989; Davis, Bagozzi, & Warshaw, 1989, 1992) in which perceived usefulness of an information technology predicts intention to use the technology, which in turn, predicts the actual use of the technology. Applying this logic, we propose that: H1: Intention to use the Internet for health information will positively affect the behavior of using the Internet for health information. H2: Perceived usefulness of the Internet will positively affect the intention to use the Internet for health information. H3: Perceived usefulness of the Internet will positively affect the behavior of using the Internet for health information.

Information Carrier Characteristics and Utility According to the CMIS, perceptions of the information carrier’s characteristics affect both perceptions of the utility of the information carrier and informationseeking actions. If consumers attach great importance to the trustworthiness of websites that provide health-related information, then in the CMIS framework, trust in health information websites as an information carrier characteristic should influence perceived usefulness of the Internet, intentions to seek information on the Internet, and the actual behavior of using the Internet for health information. Research that integrates trust into the TAM supports this reasoning. For example, Pavlou (2003) demonstrated across two studies that trust towards a retailer’s website positively affects the perceived usefulness of the website and positively affects intention to purchase from the website. Similarly, W. Wang and Benbasat (2005) showed that trust has a significant positive effect on perceived usefulness of an online product recommendation agent and a significant positive effect on intention to adopt the agent’s recommendation. Given this background, we propose that: H4: Trust in health information websites will positively affect perceived usefulness of the Internet. H5: Trust in health information websites will positively affect intention to use the Internet for health information. H6: Trust in health information websites will positively affect the behavior of using the Internet for health information.

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Antecedent Factors and Utility The CMIS predicts that antecedent factors influence perceptions of the utility of the information carrier; a prediction supported by empirical findings. For example, online health information seekers tend to be younger, more educated, and have a higher income (e.g., Lorence & Park, 2006a, 2006b; Rains, 2008; Renahy et al., 2008; Rice, 2006; Weaver et al., 2009; Ybarra & Suman, 2006). Studies have also shown that health status affects use of the Internet for health information with healthier individuals less likely to seek information on the Internet because health is not a concern (e.g., Goldner, 2006; Rains, 2008; Rice, 2006). These research findings together with the prediction based on the CMIS suggest that age, education, income, and perceived health status will have direct effects on perceptions of the utility of the Internet and on the actual behavior of using the Internet for health information. We therefore propose that: H7a1: Age will negatively affect perceived usefulness of the Internet. H7a2: Age will negatively affect the behavior of using the Internet for health information. H7b1: Education will positively affect perceived usefulness of the Internet. H7b2: Education will positively affect the behavior of using the Internet for health information. H7c1: Income will positively affect perceived usefulness of the Internet. H7c2: Income will positively affect the behavior of using the Internet for health information. H7d1: Perceived health status will negatively affect perceived usefulness of the Internet. H7d2: Perceived health status will negatively affect the behavior of using the Internet for health information.

Perceived importance of access to health information on the Internet taps the concept of salience in the CMIS framework. Johnson et al. (1995) wrote that salience reflects the importance and relevance of the information to an individual and acts as a motivating force that drives information seeking actions. If a greater importance is attached to being able to access health information on the Internet, then it is more likely that the Internet will be viewed as a useful channel for health information and actually used for health information. Following this logic, we propose that: H7e1: Perceived importance of health information access on the Internet will positively affect perceived usefulness of the Internet. H7e2: Perceived importance of health information access on the Internet will positively affect the behavior of using the Internet for health information.

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Health locus of control refers to an individual’s enduring belief that health outcomes are controllable (Rotter, 1966). Whereas people with an internal locus of control believe that health outcomes are contingent upon their own behaviors or personal qualities, people with an external locus of control believe that health outcomes are controlled by others, luck, or fate (Wallston, Maides, & Wallston, 1976). Research provides support for the positive relationship between internal locus of control and intention to obtain health pamphlets (DeVito, Bogdanowicz, & Reznikoff, 1982) and preventive health behavior such as exercise, weight control, and rest=sleep and relaxation (Abella & Heslin, 1984). These findings suggest that higher levels of internal locus of control about health are associated with more proactive health care management. Applying this logic to using the Internet for health information, we propose that: H7f1: The level of internal health locus of control will positively affect perceived usefulness of the Internet. H7f2: The level of internal health locus of control will positively affect behavior of using the Internet for health information.

Hypotheses 1 through 7 are shown in Figure 1.

FIGURE 1 Health care information seeking and seniors: Determinants of Internet use. Note. Adapted from the comprehensive model of information seeking by Johnson, Donohue, Atkin, and Johnson (1995).

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METHOD Procedure Data for this study was collected from questionnaires inserted into a newspaper publication that targets seniors and retirees who visit far south Texas during winter months. The primary purpose of the questionnaire was to determine the economic impact of the wintering visitors on the regional economy but also had the purpose of examining perceptions of these seniors about obtaining health care information online. A total of 25,000 questionnaires were inserted in the newspaper and respondents were invited to return the completed questionnaire in their own stamped envelope or to answer the questions online.

Sample Characteristics A total of 1,138 participants responded, with 25.8% responding online. Of the 1,138 responses, 832 were usable and retained for further data analysis. Participants in the survey were primarily Caucasian (98%) and 58.1% were females. While a small percentage (6.5%) of the participants were 80 years of age or older, the rest were spread across age groups: 36.8% were between 70 and 79, 31.6% were between 65 and 69, and 23.3% were younger than 65. More than half of the respondents were either high school graduates (26.8%) or had some college but no degrees (29.6%). About 17.5% held graduate= professional degrees, 13.9% had a bachelor’s degree, and 9.8% had an associate’s degree. Finally, 53.5% of the respondents had an annual household income between $30,000 and $59,999, 25% between $60,000 and $99,999, 4.6% above $100,000, and 16.9% were lower than $30,000.

Measures Many of the measures used in the survey are single items developed for this study. The action variable, use of health information websites (WEBUSE), was assessed with the yes=no question, ‘‘Have you ever checked health information online?’’ The intention variable (INTENT) was measured by asking likelihood of using the Internet as a source of health care information. Respondents were asked to rate importance of being able to access health resources on the Internet as a measure of perceived importance of health information access on the Internet (IMPORT), the salience factor. The information carrier utility factor, perceived usefulness of the Internet (USEFUL), was also assessed using a single item that asked respondents to indicate, ‘‘How useful do you feel the Internet is in helping you make decisions about your health?’’ INTENT, IMPORT, and USEFUL were all measured on a 5-point scale. Five items from the health locus of control scale by Wallston et al. (1976), which assesses belief in internal locus of control (ILOCUS), were used

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to measure the beliefs antecedent in the model. Perceived health status (HEALTH) was assessed with four items. Three of the four items were from Lumpkin and Hunt (1989). The fourth item asked respondents to rate their health, in general on a 5-point scale (poor to excellent). The information carrier characteristic variable, trust (TRUST), was assessed using five items adapted from S. Wang, Beatty, and Foxx (2004). Five-point Likert scales were used for all multi-item measures except one item for HEALTH.

RESULTS Measure Validity Multi-item measures (i.e., HEALTH, TRUST, and ILOCUS) were subjected to exploratory factor analysis using principal components analysis and Varimax rotation. The results, as seen in Table 1, showed that the items for measuring HEALTH and TRUST loaded correctly. Four of the five items for internal locus of control loaded on the intended construct, but the fifth item cross-loaded on another variable and was therefore removed from further analysis. Correlations among the three constructs were 0.05 between HEALTH and TRUST, 0.21 between HEALTH and ILOCUS, and 0.03 between ILOCUS and TRUST. The Cronbach’s alpha values, shown in Table 1, were 0.84 for HEALTH, 0.96 for TRUST, and 0.64 for ILOCUS. Convergent validity of these multi-item measures was evidenced by the correct loading of the items on their intended constructs and by the substantial and significant factor loadings which were all well above the recommended threshold of 0.4 (Churchill, 1979). Low correlations between the constructs provided support for discriminant validity and the Cronbach’s values that were above 0.7 (c.f. Nunnally, 1978) demonstrated the reliability of the measures for HEALTHY and TRUST. While the Cronbach’s alpha for the ILOCUS variable was below the .70 level, it is within the range of .60 to .75 reported in hundreds of prior studies using the scale measure (Wallston, 2005) so it was retained in this study.

Hypotheses Testing Binary logistic regression was conducted to simultaneously test hypotheses H1, H3, H6, and H7a2–H7f2 because they share the same dependent variable and because the dependent variable was measured as a ‘‘yes’’ or ‘‘no’’ dichotomous variable. The results, in Table 2, show that of all the variables in the model, INTENT, USEFUL, TRUST, AGE, and EDUCATION were significant predictors of WEBUSE. The model had a Nagelkerke R2 value of 0.45. Beta coefficients were 0.47 (p ¼ .002) for INTENT, 0.37 for USEFUL (p ¼ .02), 0.52 for TRUST (p ¼ .009), 0.28 for AGE (p ¼ .02), and 0.30 for EDUCATION (p ¼ .003). The beta coefficients for these variables were significant and in the

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TABLE 1 Factor Analysis Results and Cronbach’s Alpha Values Construct

Items=Factor loadings Compared to others my age, I take less medicine. Compared to others my age, I think I’m in better health. I really don’t have any physical problems. In general, would you say your health is: Generally speaking, health websites appear to be very trustworthy. In general, health websites can be relied upon. I believe the information on health websites is correct. I am confident that health websites can be trusted. My overall faith in health websites is high. If I take care of myself, I can avoid illness. Whenever I get sick, it is because of something I’ve done or not done. When I feel ill, I know it is because I have not been getting the proper exercise or eating right. People’s ill health results from their own carelessness. I am directly responsible for my health.a Cronbach’s alpha

Perceived health status

Trust in health information websites

Internal locus of control

.838 .787

.015 .018

.061 .063

.858 .770 .017

.023 .048 .911

.098 .011 .022

.009 .005

.934 .942

.011 .007

.017 .023 .313 .078

.942 .922 .043 .024

.021 .025 .549 .759

.002

.033

.721

.012

.044

.707

.421 .84

.057 .96

.289 .64

Note. Factor loadings and Cronbach’s alphas are boldfaced. a Item dropped due to cross-loading.

right direction as predicted, providing support for H1, H3, H6, H7a2, and H7b2. Moreover, the beta coefficient for ILOCUS was 0.41(p ¼ .06). Contrary to H7f2, the results showed borderline support for the negative impact of ILOCUS on WEBUSE. Independent-samples t-tests were also run to determine whether the means for INTENT, USEFUL, TRUST, AGE, and EDUCATION were significantly different for the group that checked health information online and the group that did not. Results provide additional support for the hypotheses related to WEBUSE: H1, H3, H6, H7a2, and H7b2. The mean score for INTENT was higher for the Internet user group (M ¼ 3.35) than that for the nonuser group (M ¼ 1.91), t ¼ 13.87, p < .001. The mean score for USEFUL was higher for the Internet user group (M ¼ 3.49) than that for the nonuser group (M ¼ 1.90), t ¼ 16.74, p < .001. TRUST was higher for the Internet user group (M ¼ 3.12) than that for the nonuser group (M ¼ 2.39), t ¼ 12.02, p < .001. The Internet user group were younger (M ¼ 2.30) than the nonuser group (M ¼ 3.01), t ¼ 7.34, p < .001. And the Internet user group had a higher education level (M ¼ 3.70) than the nonuser group (M ¼ 3.18), t ¼ 4.38, p < .001. T-test results also indicated that Internet users had a slightly

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Health Care Information Seeking and Seniors TABLE 2 Summary of Hypotheses Testing Results Hypothesis H1: INTENT ! (þ) WEBUSE H2: USEFUL ! (þ) INTENT H3: USEFUL ! (þ) WEBUSE H4: TRUST ! (þ) USEFUL H5: TRUST ! (þ) INTENT H6: TRUST ! (þ) WEBUSE H7a1: AGE ! () USEFUL H7a2: AGE ! () WEBUSE H7b1: EDUCATION ! (þ) USEFUL H7b2: EDUCATION ! (þ) WEBUSE H7c1: INCOME ! (þ) USEFUL H7c2: INCOME ! (þ) WEBUSE H7d1: HEALTH ! () USEFUL H7d2: HEALTH ! () WEBUSE H7e1: IMPORT ! (þ) USEFUL H7e2: IMPORT ! (þ) WEBUSE H7f1: ILOCUS ! (þ) USEFUL H7f2: ILOCUS ! (þ) WEBUSE

Testing method Binary logistic regression Multiple regressions Binary logistic regression Multiple regressions Multiple regressions Binary logistic regression Multiple regressions Binary logistic regression Multiple regressions Binary logistic regression Multiple regressions Binary logistic regression Multiple regressions Binary logistic regression Multiple regressions Binary logistic regression Multiple regressions Binary logistic regression

Results

Supported or not supported

b ¼ 0.47, p ¼ .002

Supported

b ¼ 0.56, t ¼ 20.19, p < .001 b ¼ 0.37, p ¼ .02

Supported

b ¼ 0.22, t ¼ 4.94, p < .001 b ¼ 0.36, t ¼ 7.61, p < .001 b ¼ 0.52, p ¼ .009

Supported Supported Supported Supported

b ¼ 0.04, t ¼ 1.40, Not supported p ¼ .16 b ¼ 0.28, p ¼ .02 Supported b ¼ 0.03, t ¼ 1.42, p ¼ .16 b ¼ 0.30, p ¼ .003

Not supported Supported

b ¼ 0.008, t ¼ 0.52, Not supported p ¼ .60 b ¼ 0.03, p ¼ .64 Not supported b ¼ 0.05, t ¼ 1.18, Not supported p ¼ .24 b ¼ 0.09, p ¼ .60 Not supported b ¼ 0.81, t ¼ 30.28, p < .001 b ¼ 0.23, p ¼ .15

Supported

b ¼ 0.005, t ¼ 0.09, p ¼ .93 b ¼ 0.41, p ¼ .06

Not supported

Not supported

Not supported

lower mean ILOCUS score (M ¼ 2.92) than the nonuser group (M ¼ 3.02), t ¼ 1.80, p ¼ .07. Multiple regressions were used in testing H2 (USEFUL ! INTENT) and H5 (TRUST ! INTENT) together since both shared the same dependent variable. The overall model is significant (F ¼ 390.39, p < .001), with an R2 value of 0.53. USEFUL had a significant, positive effect on INTENT with a standardized b weight of 0.56 (t ¼ 20.19, p < .001) as did TRUST with a standardized b weight of 0.36 (t ¼ 7.61, p < .001). Therefore, both H2 and H5 were supported. USEFUL was hypothesized to be positively affected by trust in health information websites (H4), negatively affected by age and perceived health

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status (H7a1 and H7d1), and positively affected by education, income, perceived importance of health information access on the Internet, and internal locus of control (H7b1, H7c1, H7e1, and H7f1). Multiple regressions were run to test H4 and H7a1 through H7f1, simultaneously. The overall model was significant (F ¼ 204.65, p < .001), with an R2 value of 0.69. Of all the predictors in the model, TRUST and IMPORT were significant predictors and had positive impacts on USEFUL. The standardized b value was 0.22 (t ¼ 4.94, p < .001) for TRUST and 0.81 (t ¼ 30.28, p < .001) for IMPORT, providing support for H4 and H7e1. Table 2 summarizes these hypotheses testing results.

Post Hoc Analysis Although the CMIS did not make predictions regarding the relationship between antecedent variables and information carrier characteristics, perhaps seniors’ age, education, income, perceptions about their health and about the importance of health information access on the Internet, and beliefs in internal locus of control over health could impact their trust towards health information websites (i.e., the information carrier characteristic variable examined in this study). Regression analysis show that IMPORT had a positive effect on TRUST, b ¼ 0.44 (t ¼ 12.07, p < .001), and that AGE had a negative effect on TRUST, b ¼ 0.08 (t ¼ 2.07, p ¼ .04).

DISCUSSION Engaging seniors in using the Internet for health purposes is important because the Internet has become a primary communication channel for disseminating health information and promoting health (e.g., Cline & Haynes, 2001). This is especially important because about 70% of U.S. adults between the ages of 50 and 65 are diagnosed with a chronic disease (Centers for Disease Control and Prevention et al., 2009) and the percentages increase in even older groups. Having convenient, current, and accurate information about health and disease management can empower these seniors toward better health outcomes. Furthermore, using the Internet provides other potential benefits to the elderly such as helping them become more active about their health, keeping them socially connected, and assisting in a successful aging process (e.g., Adams et al., 2005; Mellor et al., 2008). This research examined a broad range of variables and their relationships by drawing upon the theoretical framework of the CMIS (Johnson, 1983; Johnson & Meischke, 1993; Johnson et al., 1995) to better understand the determinants of seniors’ Internet use for health information. The research found determinants of seniors’ use of the Internet for health information to be intention to use the Internet for health information, perceived usefulness

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of the Internet as an information source, trust in health information websites, age, and education level. This research also found that trust in health information websites and perceived usefulness of the Internet positively affected intention and that trust and perceived importance of health information access on the Internet positively affected perceived usefulness of the Internet.

Theoretical Contributions The current research makes several theoretical contributions. First, this research empirically tested the CMIS within the context of seniors’ health information seeking on the Internet, thus extending the CMIS to the online context and to the elderly population. Also, this research found general support for the CMIS chain of effects whereby information-seeking actions are determined by information carrier factors, which in turn, are determined by antecedent variables. This research makes another contribution by providing converging evidence of the model’s predictive validity and lends additional support to the causal structure of the interrelationships between antecedents and information carrier factors and between information carrier factors and information-seeking actions. The finding that both information carrier factors (trust in health information websites and perceived usefulness of the Internet) and antecedent variables (age and education) had direct impacts on seniors’ actual behavior of using the Internet for health information supports Johnson et al.’s (1995) contention that ‘‘the mediating role of Information Carrier Factors . . . may not be as universal as originally thought’’ (p. 298). This suggests that a direct link between antecedent variables and information seeking actions should be added to the model in future research. On the other hand, perceived importance of health information access on the Internet was the only antecedent variable found to positively affect perceived usefulness of the Internet, an information carrier factor. This finding is consistent with earlier work in which the paths between antecedents and information carrier factors were generally low and not significant (e.g., Johnson et al., 1995). However, these paths should not be trimmed from the model as suggested by Johnson et al. (1995), because the intervening effect of information carrier factors was shown to play a role in explaining the influence of antecedents on health information-seeking behavior. Post hoc analysis found that seniors’ trust in health information websites was negatively affected by age but positively affected by perceptions of the importance of health information access on the Internet. This finding offered empirical evidence of the effects of antecedent variables on information carrier characteristics variables, even though such effects are not included in the CMIS. Therefore, a link between antecedent variables and information carrier characteristics variables should be added to the model in future research.

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Our CMIS ‘‘belief’ factor, internal locus of control, was found to negatively impact seniors’ use of the Internet for health information, which runs counter to some previous findings that internal locus of control facilitated health behaviors (e.g., Abella & Heslin, 1984; DeVito et al., 1982). A possible explanation of this counterintuitive result is that potential moderating factors influenced the directionality of the effect of internal locus of control on Internet health information-seeking behavior. In fact, Moorman and Matulich (1993) found that health locus of control and media information acquisition were negatively related for motivated consumers but positively related for unmotivated consumers. Future research could study other potential moderators such as need for cognition and health information orientation to examine whether the effect of internal locus of control is moderated by these variables.

Managerial Implications Findings from this research should also provide guidance to health care officials concerned about improving the delivery of health care information to the elderly via the Internet. Trust in health information websites, intention to use the Internet for health information, perceived usefulness of the Internet, age, and education were found to be significant determinants of seniors’ health information seeking behavior on the Internet, with the trust variable having the largest effect compared to other determinants. These findings have several implications for strategies to improve seniors’ online health information-seeking behaviors. First, health care providers should work to build and improve seniors’ trust towards their health information websites because of the positive effects of trust on seniors’ health information seeking behavior on the Internet and on perceived usefulness of the Internet as a health information source. This is important for new websites that are unknown to consumers who might consider building trust by having the website sponsored or endorsed by a well-known entity such as the Mayo Clinic or the Centers for Disease Control and Prevention. Since age was shown to have a negative effect on trust, health care providers should prioritize their time and efforts in building trust among seniors. For example, educational programs that demonstrate the benefits of using the Internet to access health information by seniors might help enhance such perceptions. Second, this study’s findings suggest that improving perceived usefulness of the Internet as a health information source is another way of driving more seniors to use the Internet for health purposes. Promotions that emphasize the ease of using the Internet for finding health information and training programs that build seniors’ Internet and computer-using skills should be implemented to improve seniors’ perceptions of the Internet as a useful source for health information.

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Third, health care providers should use age and education to identify those seniors who are more likely to utilize the Internet for health information and segment them into ‘‘heavy users’’ and ‘‘light users’’ to better engage seniors of all ages and education levels in seeking health information on the Internet. Light Internet users will likely need much more attention by health care officials to encourage online health information seeking. That attention should focus on improving older, less educated seniors’ Internet searching abilities and their perceived usefulness of and trust in the online information.

LIMITATIONS AND FUTURE RESEARCH This study is limited by the sample, which may not be representative of seniors in general. These seniors tend to be more active, better educated, and have higher incomes than their similarly aged U.S. counterparts. To overcome this limitation, future research could employ a nation sample to verify results. Another study limitation is that seniors’ online health information seeking behavior was measured by one item. Checking health information using the Internet is no doubt a manifestation of seniors’ online information-seeking behavior in general, but future research should examine other information-seeking behaviors, such as searching and browsing patterns on the Internet and length of time spent on certain websites. Given that trust is found to positively impact seniors’ online health information-seeking behavior and their intentions to use the Internet for health information, future research should investigate factors other than age and perceived importance of health information that facilitate or inhibit trust building. Because disposition to trust, a personality variable, has been shown to affect trust in certain technology (e.g., McKnight, Choudhury, & Kacmar, 2002; W. Wang & Benbasat, 2008), future research could examine whether the disposition has an impact on seniors’ trust in health information websites. Perceived risk in using the Internet is another variable to study in future research as previous work supports the negative effect of perceived risk on trust building (e.g., Jarvenpaa, Tractinsky, & Vitale, 2000). Future research could also study factors that might moderate the main effects found in this research. For example, the positive effect of trust and that of perceived usefulness on intention and actual behavior of health information seeking on the Internet might be moderated by the level of experience that seniors have in using the Internet. As discussed previously, health information orientation and need for cognition might also strengthen or weaken these main effects because of the level of motivation that seniors have towards acquiring and understanding health information. Finally, future research could continue efforts to verify the CMIS model by using an experimental design to have a more stringent test of the model’s causality.

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