JDIS

Journal of Data and Information Science >

2017 , Vol. 2 >Issue 2: 76 - 95

DOI: https://doi.org/10.1515/jdis-2017-0009

Research Paper

Seeking Health Information Online: The Moderating Effects of Problematic Situations on User Intention

  • Lidan Xia ,
  • Shengli Deng ,
  • Yirong Liu
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  • School of Information Management, Wuhan University, Wuhan 430072, China
† Corresponding author: Shengli Deng (E-mail: ).

Online published: 2017-02-25

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Abstract

Purpose: This study investigates how online user intention in searching health information is affected by problematic situations.

Design/methodology/approach: Based on the Theory of Reasoned Action, the Technology Acceptance Model, and Sense-making theory, we propose two dimensions of problematic situations: urgency and severity of health issues being searched online. Data were collected through a questionnaire survey among 214 Wuhan University students and analyzed using hierarchical regression analysis.

Findings: Perceived usefulness, perceived ease of use, and subjective norm can influence user intention to seek health information online. The urgency of problematic situations has a negative moderating effect on the relationship between perceived ease of use and user intention and the relationship between subjective norm and user intention. The severity of problematic situations has a negative moderating effect on the relationship between subjective norm and user intention.

Research limitations: The respondents of the survey are limited to students in one Chinese university, so whether this study’s results can be applied to another population or not remains to be verified. In addition, only two dimensions of problematic situations are considered in this study.

Practical implications: The paper puts forward the moderating effect of problematic situations and verifies it, which is the compensation for online health information-seeking behavior research. Besides, our analyses have implications for professional design of health care systems and related consumer information searches, and improve their performance.

Originality/value: Previous work has reported the effects of problematic situation on user intention to seek health information online, ignoring its influence on other factors. This empirical study extends that work to identify the influence of problematic situation when seeking intention-behavior data in two dimensions, urgency and severity.

Key words： Health information seeking; Situation; Moderating effect; Urgency; Severity

Cite this article

Lidan Xia , Shengli Deng , Yirong Liu . Seeking Health Information Online: The Moderating Effects of Problematic Situations on User Intention[J]. Journal of Data and Information Science, 2017 , 2(2) : 76 -95 . DOI: 10.1515/jdis-2017-0009

1 Introduction

The Internet has now become one of the most important information sources for people seeking health information. Recent developments of online health knowledge bases and social media have also opened up a wide range of channels for health information seeking and sharing. According to China’s Online Medical Industry Research Report (iResearch, 2015), there are about 140 million active users of online medical searches between January and June 2015. As the Internet and social media have become increasingly popular among health information seekers, a number of studies have been conducted to examine people’s online health information seeking behaviors.
At present, user health information-seeking behavior research focuses on population characteristics, individual health differences, and other user perspectives, but generally ignores the influence of problematic situations on information seeker behaviors. This paper therefore examines how situational factors affect people’s intention to search health information online. Specifically, we attempt to answer the following research questions:
1) What factors could affect online health information-seeking intention?
2) Can the problematic situation with online information seekers affect their health information-seeking intention, and if so, how?
Based on the Theory of Reasoned Action, the Technology Acceptance Model, and Sense-making theory, this article introduces the factor problematic situations as a moderating variable and proposes two dimensions of problematic situations: urgency and severity of health issues being searched. This approach is designed to build a model for identifying influencing factors of online health information-seeking intention and testing the hypotheses. Studying these factors can be beneficial not only to health information-seeking behavior research, but also to improving the efficiency and accessibility of health information systems.

2 Literature Review

2.1 Effects of Situation on Information-seeking Behavior

As a purposeful activity of attempting to meet information needs, information-seeking behavior has become a hot issue in the field of library and information science (LIS). Belkin (1980) proposed that information needs arise because the user does not know something, where there is an anomaly in the user’s state of knowledge in relation to the problem faced. A summary of the literature (Khazer & Ganaie, 2014) in recent years indicates that research into information-seeking behavior mainly concentrates on two aspects of online searches: job-related information and daily information (Figure 1). And health information-seeking behavior has become an increasingly important issue in daily information-seeking behavior studies (Ajuwon & Popoola, 2015; Kim, 2015). When it comes to the process of information search, the related studies tend to focus on user information needs, the selection of information channels, and certain influencing factors of information-seeking behaviors (Zhang, Liu & Yang, 2008).
Figure 1. The research focus of information-seeking behavior in recent years.
In the early 1980s, research into information search behavior shifted its attention from an information system perspective to a user perspective, in which individual user differences, including cognitive ability, have become the focus of information behavior studies. Due to the complexity of information search behavior, there are many factors that can influence people’s information search behavior. Along with the study of people’s cognitive abilities and individual differences in search behavior, the effects of user situation or context should not be ignored (Li & Hu, 2012). At present, the studies on the effects of situational factors on information-seeking behavior can be mainly classified into two categories: (1) considering the particular situation as the research background, research is conducted to find how users search for information in that situation; (2) considering the situation as the research variable, studies are carried out to find the effects of situational factors on user information behavior (Tang, 2007).
In the field of health information seeking, scholars have investigated the effects of situational factors on information-seeking behavior. For instance, Wallengren, Segesten, and Friberg (2010) investigated the information needs of the relatives of stroke patients and studied the characteristics of their information-seeking processes shortly after the stroke event and six months later from the angle of personal involvement, situational circumstances, and sources of information. They found that the information needs of the relatives change with developments in their own health condition as well as that of the patients: the more stable their health condition is, the less their information need is. Avery (2010) studied the choice of information channels in regular or emergency situation via a public telephone survey. The results show that compared with less urgent situations, television news, radio news, doctors, and health institutions are more important sources of information in a critical situation than the Internet. These studies considered situation as an independent variable and evaluated its direct impact on the dependent variable, but did not consider situation as a precondition of information-seeking behavior, which has an interaction effect on other dependent variables.

2.2 Online Health Information-seeking Behavior Research

Much research has been done on health information-seeking behavior. As the literature reports, from 1993 to 2013 there were 1,157 academic papers published about health information seeking, and 60% of the research was done in the UK
and US (Shi & Xu, 2013). As more and more people prefer the Internet for health information, researchers are paying more attention to people’s online health information-seeking behavior. At present, online health information-seeking research mainly concentrates on issues such as impact factors, search content, search platform construction, and application of evaluations. The research on impact factors of online health information seeking in the last decade (Table 1) shows that the type of tasks, subjective factors, and information resources have been extensively studied, whereas situational factors are rarely mentioned.
Table 1 The impact factors investigated in online health information-seeking research in the last decade.
Type Overseas researchers Chinese researchers
Influencing factor Resource Influencing factor Resource
Demographic characteristics Gender Cotten & Gupta (2004);
Yun & Park (2010)		 Personal psychological factor and implementation cost Zhu & Deng (2015)
Race Yi, Stvilia, & Mon ;
Ramanadhan & Viswanath (2006);
Rooks et al. (2012)		 Cognitive ability, knowledge, and experience Sun, Wang, & Cao (2015)
Education Ramanadhan & Viswanath (2006);
Lemire et al. (2008)		 Gender
Age
Education
Income		 Wang (2013)
Wang (2013)
Wang (2013)
Wang (2013)
Income Ramanadhan & Viswanath (2006);
Zhao (2009)		 Health condition Zhou & Cai (2014)
Internet use level Neumark et al. (2013)
Health condition Shaw et al. (2008)
Disability level Liang, Xue, & Chase (2011)
Information resources Perceived usefulness
Perceived ease of use		 Kim (2015)
Ajuwon & Popoola (2015);
Zhang (2011)		 Information demand satisfaction
Reality satisfaction
Internet interaction and
risk perception		 Mo & Deng (2014)
Mo & Deng (2014)
Mo & Deng (2014)
Trust level of information Yun & Park (2010) Ease of use of search tools and
health website construction		 Zhou & Cai (2014)
Credibility of website Lemire et al. (2008) Information factor Zhu & Deng (2015)
Information quality awareness Xiao et al. (2014)
Social factors Subjective norm Chau & Hu (2002);
Zhang (2011)		 Social factor
Social support		 Zhu & Deng (2015)
Mo & Deng (2014)
Other factors Self-efficacy and information anxiety Lim et al. (2011) Task type Zhou & Cai (2014)
Users’ interaction and emotional support Hether, Murphy, & Valente (2014) Health self-efficacy and
health consciousness		 Mo & Deng (2014)
There are a few studies that consider the impact of situational factors, but they either take situational factors as the research background or just investigated the direct impact of situational factors on search intention. For instance, Wu and Li (2015) analyzed the elderly’s diversity of emotion and cognition, and their different ways of dealing with information in online information retrieval processes in different health situations via user experiment, questionnaire survey, and interview methods. But this study only considered different situations as the research background, and did not investigate the situations’ impact on health information retrieval. Zhang (2014) put forward a more complete health information source selection model via the interview method. Her study proposed that five factors including problematic situation can influence the choice of information sources, but did not specify how the problematic situation will affect source selection. Xiao et al. (2014) found that perceived health status could affect both frequency and diversity of online search of health information, but there was no evidence that perceived health status could lead to using the Internet as a preferred source for health information. So when considering information-seeking behavior, along with having a direct impact on search intention, situation is found to have an interactional effect on other factors. Few scholars are addressing this issue, however.

3 Research Model and Hypotheses

3.1 Research Model

In this paper, the core issue we study is user intention to access the Internet to seek health information, which presents a problem in relation to behavior prediction. We conduct a conceptual model based on the Theory of Reasoned Action (TRA) developed by Ajzen and Fishbein (1980), and considered one of three classic models of persuasion. TRA is used to predict how people will behave based on their attitudes and behavioral intentions, and adds the process of persuasion to behavioral intention. The Theory introduces two factors that determine intention: attitudes (about a person’s opinion, e.g. positive or negative) and subjective norms (the expectations of others or perceived social pressure to comply with others’ opinions) (Figure 2).
Figure 2. Theory of Reasoned Action.
Based on the TRA, Davis (1989) put forward the Technology Acceptance Model (TAM), which is used for forecasting the possibility of a person accepting and using an information system. TAM is remarkable tool for making such predictions, and more and more scholars apply its theoretical framework to different fields, such as health information seeking (Lemire et al., 2008; Liang, Xue, & Chase, 2011) and social networking (Al-Ghaith, 2015). The Model (Figure 3) proposes two determinants: perceived usefulness (e.g. a particular system would enhance one’s job performance) and perceived ease of use (a particular system would not require extensive effort) and their impact on attitude, intention to use, and actual use. We combine the TRA and TAM as the foundation for our research model.
Figure 3. Technology Acceptance Model.
Sense-making is the process people use to give meaning to their experiences. Dervin’s (1983) introduction of Sense-making theory to the field of information science has had a profound influence on information behavior research. This approach developed theories on the “cognitive gap” individuals can experience when trying to understand observed data, with the goal to provide measurements for concepts and performance and ways to make theories testable. Applied in many other disciplines, notably human-computer interaction and organizational studies, Devin (1992) developed the theory further into a formal methodology that considers the factors of situation, gap, and outcome, where situation is the precondition of information-seeking behavior. As this methodology emphasizes situation’s impact on information channels and content selection, we choose this theory as the theoretical basis of this paper. As online information query and use behavior are applied here in specific situations, such as when facing the same health information system, users in different situations will make different choices. Sense-making and situational awareness are thus viewed as a concept that enables us to investigate and improve our understanding of the interactions between people and information technology. For instance, people’s intention of using the Internet for health information will decrease when they face an urgent health problem, where other sources (e.g. doctors and health institutions, television, and radio) are available. In other words, the change of situation will affect other factors’ impact on intention to use the Internet for seeking health information. So we believe it is reasonable to introduce the factor of situation as a moderator to conduct our research model.
As the TRA plays an important role in cognitive behavioral research, and the TAM has also been confirmed to be effective in explaining users’ information-seeking behaviors, we use the subjective norm of the TRA and perceived usefulness and perceived ease of use of the TAM for our three variables, and use the factor of situation put forth in Devin (1992) Sense-making methodology to build the online health information-seeking intention model (Figure 4). Considering the various characteristics of health problems, we choose urgency and severity (of health issues) as the two dimensions of problematic situation.
Figure 4. Online health information-seeking intention model.

3.2 Research Hypothesis

Based on the TAM model, this paper puts forward two variables: the perceived usefulness and perceived ease of use. Zhang (2011) found that social networking sites’ (SNS) perceived usefulness and perceived ease of use have an impact on college students’ user intention of SNS to access health information. Kim (2015) and Ajuwon and Popoola (2015) came to the same conclusion in research on other health information systems. And many researchers have applied the TAM to the field of health information seeking. According to the TAM model and the existing research results, we hypothesize:
H1: Perceived usefulness (PU) has a positive correlation with online health information-seeking intention (UI).
H2: Perceived ease of use (PEU) has a positive correlation with online health information- seeking intention (UI).
There is no doubt that subjective norm has an influence on user intention based on the TRA. For example, research has shown that users who are new to an information system are significantly affected by others’ opinions (Luo & Zhu, 2015). Traditional health information channels always refer users to medical institutions for professional support, so users may be confused as to whether they can gain accurate health information on the Internet. Subjective norm is therefore likely to have a significant impact on user intention. So we propose the following hypothesis:
H3: Subjective norm (SN) has a positive correlation with online health information-seeking intention (UI).
Based on Devin (1992) Sense-making methodology, our model puts forward “problematic situation” as a key variable to better understand the factors of cognitive gap and problematic situations. As users seek information in order to reduce their uncertainty about a topic (Belkin, Oddy & Brooks, 1982), the problematic situation influences users’ information needs and motivates their processes. When users face different health problems, however, they will have different reactions to the same information channel. Using interviews, Zhang’s (2014) found that participants experiencing chronic diseases are more inclined to use the Internet as a source of information. As the Internet cannot directly solve the health problem, when users are in urgent problematic situation, they are inclined to choose more direct and accurate information channels, such as consulting doctors or health institutions. At this time of health urgency or severity, the intention to seek health information on the Internet is generally weak. The urgency of problematic situations may moderate the relationship between other factors and user intention, in that the more urgent the problematic situation is, the weaker other factors’ influence on user intention to search information online.
Liang, Xue, and Chase (2011) found via a questionnaire survey that the disability level of users has a positive moderating effect on the relationship between perceived ease of use and online health information-seeking intention, and a negative moderating effect on the relationship between perceived usefulness and online health information-seeking intention. By investigating 114 breast cancer patients, Shaw et al. (2008) found that patients who feel worse tend to seek information on the Internet. In addition, users in severe problematic situations will have stronger information demand, in that they are more anxious to obtain additional health information. These studies therefore confirm that the severity of problematic situations may influence the relationship between other factors and user intention. Based on the above research results and analysis, this paper divides problematic situation into two dimensions: urgency and severity of health issues being searched, and makes the following hypotheses:
H4a: The urgency of problematic situation (UPS) moderates the relationship between the perceived usefulness and online health information-seeking intention (UI).
H4b: The severity of problematic situation (SPS) moderates the relationship between the perceived usefulness and online health information-seeking intention (UI).
H5a: The urgency of problematic situation (UPS) moderates the relationship between the perceived ease of use and online health information-seeking intention (UI).
H5b: The severity of problematic situation (SPS) moderates the relationship between the perceived ease of use and online health information-seeking intention (UI).
H6a: The urgency of problematic situation (UPS) moderates the relationship between the subjective norm and online health information-seeking intention (UI).
H6b: The severity of problematic situation (SPS) moderates the relationship between the subjective norm and online health information-seeking intention (UI).

4 Research Methods

To validate the research model, a questionnaire survey was conducted. The questionnaire is divided into two parts. The first part is the demographic characteristics, Internet usage, and physical health, and the second part is the measurement items of variables in the model, graded by the 5-point Likert scale (1 = definitely does not agree with this proposal, 5 = definitely agrees). In order to guarantee the validity of measurement items, we use the scale found in the existing literature. Combined with the characteristics of the health information-seeking behavior, the scale items were designed. At the beginning, each latent variable was designed to represent more than three observed variables. We then did a pretest of 46 samples to test the item validity. Deleting four items of low validity, we obtained the final questionnaire. The measuring scale items and literature sources are shown in Appendix Table A1.
Appendix Table A1 Variable, measurement item, and source.
Variable Measurement item Source
UPS Most health problems that I am seeking on the Internet need to be solved immediately. Zhang’s (2014)
Most health problems that I am seeking on the Internet are urgent.
SPS Most health problems that I am seeking on the Internet are serious. Zhang’s (2014)
When I meet with serious health problems, I tend to seek health information online.
PU I think using the Internet to seek health information can ease my anxiety over personal heath. Davis (1989);
Liang, Xue, & Chase (2011)
I think using the Internet to seek health information can increase my knowledge of health.
I think the health information online can solve my problems.
I think the health information online is useful.
PEU I have no difficulties with the operation of health information websites. Davis (1989);
Liang, Xue, & Chase (2011)
I do not need to spend a lot of energy in using the Internet to seek health information.
Learning to use health information websites is easy for me.
Being familiar with the operation of websites makes it very easy for me to find health information.
SN Quite a few people around me will use the Internet to seek health information. Ajzen (1985);
Chau & Hu (2001);
Chau & Hu (2002)
People who have influence on me think I should use the Internet to seek health information.
People who are important to me think I should use the Internet to seek health information.
UI I would like to use the Internet to seek health information. Taylor & Todd (1995a);
Taylor & Todd (1995b);
Bhattacherjee (2001)
I often seek health information on the Internet.
I would recommend people around me to use the Internet to seek health information.
Due to the rise of health apps and the spread of health information on social media, more and more college students are beginning to search information related to their own health status. The Internet has become an indispensable part of college students’ life, where they tend to learn emerging technologies quickly. As their use of the Internet is frequent and longer online health information-seeking behavior is common, we choose college students as our survey respondents. This survey uses an offline investigation via questionnaires at Wuhan University Library.

5 Results

The data analysis has three parts. The first part is demographic description. The second part is evaluation of measures that explains the reliability and validity of measurement items. The third part is the hypotheses testing and model analysis.

5.1 Sample Demographics

The survey collected a total of 230 questionnaires. Eliminating 16 questionnaires with obvious problematic or default values, we performed descriptive statistics analysis of the remaining 214 questionnaires (Table 2).
Table 2 Demographic description.
Descriptive variable Item Frequency Percentage
Sex Male 79 36.9%
Female 135 63.1%
Age Under age 18 9 4.2%
18-22 years old 123 57.5%
23-28 years old 78 36.4%
More than 28 years old 4 1.9%
Education Bachelor degree 147 68.7%
Master degree 58 27.1%
Ph.D. 9 4.2%
Internet time per day Within 1 hour 14 6.5%
1-4 hours 124 57.9%
5-8 hours 57 26.6%
Above 8 hours 19 8.9%
Physical condition Good health 66 30.8%
Occasionally uncomfortable 141 65.9%
Often feeling unwell 6 2.8%
Suffering from a chronic disease 1 0.5%

5.2 Evaluation of Measures

First of all we need to evaluate the reliability and validity of measurement items. Researchers usually use Cronbach’s alpha to measure internal consistency and reliability, so the paper uses SPSS19.0 for data analysis. The whole measurement item alpha coefficient is 0.858, which indicates the questionnaire has good reliability as a whole. The alpha coefficient of each variable and the average variance extracted (AVE) values are shown in Table 3. As all alpha coefficients were greater than 0.7, this means the measurement of each item has good reliability. AVE reflects the average variance shared between a construct and its measures. All variables’ AVE values are greater than 0.50, which means the items have good convergent validity.
Besides convergent validity, the item validity analysis includes discriminant validity. Convergent validity and discriminant validity can be expressed with factor loadings and cross loadings. Table 4 shows that the factor loading of each item of each variable is greater than 0.5, and all factors’ own loadings are greater than the cross loadings (bold numbers), which indicates that the questionnaire has good convergent validity and discriminant validity. As the AVE values in Table 3 were greater than the correlation coefficient of this variable with other variables, the item measurement is shown to have good discriminant validity.
Table 3 Reliability and correlation coefficient.
Variable Mean SD alpha AVE PU PEU SN UPS SPS UI
PU 3.3002 0.68183 0.789 0.610 1
PEU 3.3879 0.71350 0.796 0.513 0.235** 1
SN 2.8255 0.76891 0.805 0.636 0.376** 0.287** 1
UPS 3.2056 0.92425 0.851 0.536 0.231** - 0.030 0.099 1
SPS 2.6402 0.88491 0.717 0.830 0.371** 0.080 0.326** 0.338** 1
UI 3.1511 0.78624 0.814 0.691 0.608** 0.217** 0.472** 0.248** 0.438** 1

Note. SD: standard deviation; alpha: Cronbach’s alpha; PU: perceived usefulness; ** p < 0.01 testing the null hypothesis of zero correlation.

Table 4 Factor loadings and cross loadings.
PEU PU SN UI UPS SPS
PEU3 0.864 0.099 0.021 0.114 - 0.009 0.015
PEU4 0.828 0.132 0.187 - 0.023 0.018 - 0.015
PEU1 0.741 - 0.161 0.124 0.233 - 0.052 0.014
PEU2 0.677 0.206 0.039 - 0.033 0.007 0.049
PU4 0.147 0.757 0.313 0.100 - 0.024 0.023
PU3 0.021 0.743 0.193 0.141 0.132 0.132
PU2 0.066 0.726 - 0.046 0.350 0.180 0.090
PU1 0.139 0.631 0.037 0.318 0.016 0.174
SN3 0.067 0.132 0.860 0.232 0.051 0.098
SN2 0.048 0.083 0.855 0.247 0.056 0.096
SN1 0.312 0.195 0.662 - 0.009 - 0.042 0.104
UI2 0.031 0.308 0.198 0.760 0.126 0.164
UI3 0.046 0.198 0.339 0.732 0.050 0.143
UI1 0.213 0.366 0.075 0.704 0.082 0.116
UPS2 - 0.032 0.096 - 0.001 0.041 0.915 0.143
UPS1 - 0.002 0.101 0.057 0.135 0.907 0.127
SPS1 0.025 0.146 0.055 0.115 0.258 0.837
SPS2 0.029 0.156 0.218 0.211 0.055 0.825
Note. Principle component analysis (PCA) is used in data analysis.

5.3 Structural Model Analysis

5.3.1 Moderating Effect of Urgency
There are moderating variables in the model, and the independent variables and moderating variables are continuous variables. To verify the moderating effect, we need to use the hierarchical regression analysis. We first focus on the regression analysis of how the independent variables and moderating variables affect the dependent variable, and we determine the coefficient R12. We then calculate the product of the independent variable and the moderating variable and perform the regression analysis of how it affects the dependent variable and determine the coefficient R22. If R22 was significantly higher than R12, it thus shows a strong moderating effect. Using the SPSS to perform hierarchical regression analysis to validate the urgency of problematic situation, we first input independent variables to the linear regression model, and verify their main effects. Then we input interactive items into the second linear regression model to study the interaction relationship between the variables. The results show that Sig. F in the second regression changes to 0.014, less than 0.05. This means the amount of change in R2 is significant, confirming that the moderating effect exists.
Hierarchical regression results are shown in Table 5. The regression coefficient of the urgency of problematic situation, perceived usefulness, perceived ease of use, and subjective norm is respectively significant (b = 0.141, p = 0.004; b = 0.486, p < 0.001; b = 0.134, p = 0.042; b = 0.264, p < 0.001, respectively). When adding the interactive items, the regression coefficient of the urgency of problematic situation, perceived ease of use, and subjective norm is respectively significant, but the regression coefficient of perceived usefulness is not significant. The coefficient of the urgency of problematic situation (×) perceived ease of use, and the coefficient of the urgency of problematic situation (×) subjective norm is significant, respectively, indicating that the urgency of problematic situation can moderate the relationship between perceived ease of use and user intention, and the relationship between subjective norm and user intention. From the interaction coefficient (b = - 0.134 and b = - 0.158, respectively), we find that the urgency of problematic situation has a negative moderating effect on the relationship between perceived ease of use and user intention, and the relationship between subjective norm and user intention. Hypotheses H1, H2, H3, H5a and H6a are therefore verified, but H4a is not verified.
Table 5 Hierarchical regression results for user intention.
Variable Step 1 Step 2
Step 1: Main effects
UPS 0.141** 0.615*
PU 0.486*** 0.065
PEU 0.134* 0.610**
SN 0.264*** 0.785*
Step 2: Moderations
UPS × PU 0.130
UPS × PEU - 0.134*
UPS × SN - 0.158*
R2 0.462 0.491
Adjusted R2 0.451 0.472
ΔR2 0.462 0.029
ΔF 40.974*** 3.618*

Note. * p < 0.05; ** p < 0.01; *** p < 0.001.

5.3.2 Moderating Effect of Severity
Due to the fact that severity and urgency are the two different dimensions of problematic situation, we need to validate the model separately. Putting in the independent variables, including severity of problematic situation, perceived usefulness, perceived ease of use, and subjective norm into the first linear regression model, and inputting interactive items, the severity of problematic situation (×) perceived usefulness, the severity of problematic situation (×) perceived ease of use, and the severity of problematic situation (×) subjective norm to the second linear regression model, Sig. F in the second regression changes to 0.007, less than 0.05. This means that the amount of change in R2 is significant, so the moderating effect exists.
The results of hierarchical regression are shown in Table 6, where the regression coefficient of the severity of problematic situation, perceived usefulness, perceived ease of use, and subjective norm is respectively significant (b = 0.217, p < 0.001; b = 0.432, p < 0.001; b = 0.128; p = 0.047; b = 0.216, p < 0.001, respectively). And after bringing in the interactive items, the regression coefficient of the severity of problematic situation, perceived ease of use, and subjective norm is respectively significant, but the regression coefficient of perceived usefulness is not significant. The coefficient of the severity of problematic situation (×) subjective norm is significant, indicating that the severity of problematic situation moderates the relationship between subjective norm and user intention. And the interactive items’ coefficient (b = - 0.179) shows that the severity of problematic situation has a negative moderating effect on the relationship between subjective norm and user intention. Hypotheses H1, H2, H3, and H6b are therefore supported, but H4b and H5b are not supported.
Table 6 Hierarchical regression results for user intention.
Variable Step 1 Step 2
Step 1: Main effects
SPS 0.217*** 0.934***
PU 0.432*** 0.388
PEU 0.128* 0.355*
SN 0.216*** 0.696***
Step 2: Moderations
SPS × PU 0.024
SPS × PEU - 0.083
SPS × SN - 0.179*
R2 0.481 0.514
Adjusted R2 0.471 0.496
ΔR2 0.481 0.033
ΔF 44.323*** 4.195**

Note. * p < 0.05; ** p < 0.01; *** p < 0.001.

6 Discussions and Conclusions

This paper studies the factors that influence health information-seeking intention, using the results of 214 college student questionnaires to verify the model by means of regression analysis. We summarize our findings in the following sections.

6.1 The Effects of Independent Variables on User Intention

We verify the direct influence of the independent variables on user intention, and in line with previous studies, we find that perceived usefulness, perceived ease of use, and subjective norm have a positive influence on user intention, respectively, where the stronger the factors of perceived usefulness, perceived ease of use, and subjective norm, the stronger the user intention of online health information seeking.
Seen from the coefficient of three independent variables, perceived usefulness’ influence on user intention is the strongest, significantly greater than the influence of perceived ease of use. The results are consistent with Lim et al. (2011), Liang, Xue, & Chase (2011), and others. For health information-seeking behavior, the influence of perceived usefulness on user intention is stronger than that of perceived ease of use on user intention. This shows that the influence of perceived usefulness is more intense for related health information system searches. This paper also finds that the urgency and severity of problematic situation can affect user intention, in that the more urgent and serious the health issues being searched are, the more likely users will use any source including the Internet to find health information. This outcome seems to contradict Zhang’s (2014) conclusion that people tend to choose the information channel except the Internet to ensure immediate access to treatment in the urgent problematic situation. But we study the intention to use the Internet for seeking health information, which is different from the user choice of information channels. When health problems occur, depending on specific kinds of information sources, we find that users hope to get as much information as quickly as possible. So with the increased severity or urgency of health issues, users are more likely to search health information online.

6.2 The Moderating Effect of Problematic Situation

From the perspective of the moderating effect of problematic situation, the urgency of problematic situation has a negative effect on the relationship between perceived ease of use and user intention and the relationship between subjective norm and user intention. This suggests that the more urgent the health issues being searched online, the weaker the influence of perceived ease of use and subjective norm on user intention. This result means that when the users are faced with a health problem, the more urgent the problem is, the more they are eager to get health information from any sources to better understand their health problems. They are therefore not concerned with whether the information system is easy to use or how others will evaluate it.
The severity of the problematic situation has a negative moderating effect on the relationship between subjective norm and user intention, in that for the user with a serious health issue, others’ opinions (subjective norm) become a less important influence on user intention. But the severity of the problematic situation does not moderate the relationship between perceived ease of use and user intention. Liang, Xue, & Chase (2011) found that the severity of disabled persons’ conditions can moderate the relationship between perceived ease of use and user intention, mainly because the disability level will affect the use of an information system. Our survey respondents are college students who are generally in good health, however. Their health problems seldom affect their use of information systems, so the two conclusions are not in conflict. The urgency and severity of health issues being searched does not generally moderate the relationship between perceived usefulness and user intention, which suggests that the influence of perceived usefulness on user intention is significant, and is thus not overly affected by the problematic situation. The perceived usefulness of an information system is the key factor that determines whether the user intends to use the system or not.

6.3 Implications and Further Study

From the above analysis, we put forward the following suggestions about how to improve health care information systems.
1) For system design, an emphasis should be placed on the construction of information content and the quality ensurance of information, and should include encouragement to use medical institutions and experts in participating in the site’s content construction. It is necessary to strengthen the audit of information content to ensure the usefulness and accuracy of the information system. With a better understanding of the user’s health information-seeking behavior, we could improve the system functions, as the system needs to meet users’ seeking habits to improve system usability;
2) It is important to enhance the publicity for information websites and build up the professional image and branding for websites in the health industry. Online marketers should make full use of opinion leaders’ knowledge and influence, and encourage information seekers to share the content of the system to increase its possible use; and
3) In the process of system design, apart from considering user characteristics, the factors of problematic situation should be taken into account. In the meanwhile, it is important to learn users’ search contexts, and consider different system processes in different situations of health information seeking.
This study has some limitations. First of all, the investigation respondents of our research are college students who are generally in good health and have high tech skills and acceptance of Internet use for information seeking. So whether this model can be applied to other populations remains to be verified. Second, while the two dimensions of problematic situation we address in this study contribute to the literature, urgency and severity are not necessarily applied in other contexts. Third, the theoretical limitation of this paper is that our research model is not a complete framework. We focus on the two dimensions of problematic situation, and may ignore other important factors. In a follow-up study, we may consider the investigation of other groups of people (such as patients with certain diseases) or specific health information needs (such as physical or mental health), and extend dimensions that measure problematic situations.

Acknowledgements

This research is supported in part by the Key Projects of Philosophy and Social Sciences Research supported by the Ministry of Education of the People’s Republic of China (Grant No.: 15JJD870001) and Luo Jia Youth Scholar of Wuhan University.

Author Contributions

L.D. Xia (kexialidan@163.com) put foward the research problems, designed the research framework and the methods, searched literature, analyzed data, and wrote the manuscript. S.L. Deng (victorydc@sina.com, corresponding author) instructed the research team how to carry out research work and revised the manuscript. Y.R. Liu (787955423@qq.com) searched the literature, designed the questionnaire, and collected data.

The authors have declared that no competing interests exist.

References

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[18]
Kim , Y.M. (2015). Is seeking health information online different from seeking general information online? Journal of Information Science, 41(2), 228-241.Increasing use of the Internet for health information delivery has created considerable discussion among digital divide scholars (i.e. how online information delivery benefits those individuals in higher socioeconomic brackets more than their counterparts). Because it is health information, we need to integrate how patients seek out online information. This study included patients’ information-seeking behaviour along with digital divide scholars’ constructs (i.e. literacy and computer skills). Using 1617 observations from the 2010 Pew Internet and American Life Project, this study found that individuals with a significant number of health problems, who are likely to be in a lower income bracket, are proactive online health information seekers; however, they are less likely to search general information. This finding adds value to existing research revealing that usefulness, which has been overlooked in online health information seeking, is important and should be a part of the research model.

DOI

[19]
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(in Chinese). Information Science, 1, 110-114.

[21]
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DOI PMID

[22]
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[25]
Neumark Y., Lopez-Quintero C., Feldman B.S., Hirsch Allen A.J., & Shtarkshall R. (2013). Online health information seeking among Jewish and Arab adolescents in Israel: Results from a national school survey. Journal of Health Communication, 18(9), 1097-1115.This study examined patterns and determinants of seeking online health information among a nationally representative sample of 7,028 Jewish and Arab 7th- through 12th-grade students in 158 schools in Israel. Nearly all respondents (98.7%) reported Internet access, and 52.1% reported having sought online health information in the past year. Arab students (63%) were more likely than Jewish students (48%) to seek online health information. Population-group and sex differences in health topics sought online were identified, although fitness/exercise was most common across groups. Multivariate regression models revealed that having sought health information from other sources was the strongest independent correlate of online health information-seeking among Jews (adjusted odds ratio = 8.93, 95% CI [7.70, 10.36]) and (adjusted odds ratio = 9.77, 95% CI [7.27, 13.13]). Other factors associated with seeking online health information common to both groups were level of trust in online health information, Internet skill level, having discussed health/medical issues with a health care provider in the past year, and school performance. The most common reasons for not seeking online health information were a preference to receive information from a health professional and in health/medical issues. The closing of the digital divide between Jews and represents a move toward equality. Identifying and addressing factors underpinning online health information-seeking is essential to improve the health status of Israeli youth and reduce health disparities.

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[26]
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[27]
Rooks R.N., Wiltshire J.C., Elder K., BeLue R., & Gary L.C. (2012). Health information seeking and use outside of the medical encounter: Is it associated with race and ethnicity? Social Science & Medicine, 74(2), 176-184.Increasing numbers of adults in the United States of America (USA) are seeking and using health information within their medical encounters. The theory of uncertainty management suggests that patients reduce health care uncertainty by increasing their understanding of disease etiology or treatment options, improving patient-doctor communication, and enhancing knowledge of disease self-management through health information seeking. However, research shows racial and ethnic minorities are less likely than Whites to seek health information and use it in their physician visits. How racial and ethnic minorities use health information outside of their medical encounters is unknown. In this study we used data from the 2007 USA Health Tracking Household survey, a nationally-representative survey of civilian, non-institutionalized Americans (n=12,549). Using logistic regression we found African Americans were no different from Whites in seeking health information and using it when they talked with their doctors. Latinos were significantly less likely than Whites to seek health information and less likely to use it when they talked with their doctors. But, among those who sought health information, African Americans and Latinos were significantly more likely than Whites to use health information to change their approach to maintaining their health and better understand how to treat illnesses. Also, education significantly moderated the relationship between race/ethnicity and health information seeking. However, results were mixed for education as a moderator in the relationship between race/ethnicity and health information use. Future research should focus on interventions to improve how African Americans and Latinos interface with providers and ensure that health information sought and used outside of their medical encounters augments treatment protocols.

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[28]
Shaw B.R., DuBenske L.L., Han J.Y., Cofta-Woerpel L., Bush N., Gustafson D.H., & McTavish F. (2008). Antecedent characteristics of online cancer information seeking among rural breast cancer patients: An application of the cognitive-social health information processing (C-SHIP) model. Journal of Health Communication, 13(4), 389-408.Little research has examined the antecedent characteristics of patients most likely to seek online cancer information. This study employs the Cognitive-Social Health Information Processing (C-SHIP) model as a framework to understand what psychosocial characteristics precede online cancer-related information seeking among rural breast cancer patients who often have fewer health care providers and limited local support services. Examining 144 patients who were provided free computer hardware, Internet access, and training for how to use an interactive cancer communication system, pretest survey scores indicating patients' psychosocial status were correlated with specific online cancer information seeking behaviors. Each of the factors specified by the C-SHIP model had significant relationships with online cancer information seeking behaviors, with the strongest findings emerging for cancer-relevant encodings and self-construals, cancer-relevant beliefs and expectancies, and cancer-relevant self-regulatory competencies and skills. Specifically, patients with more negative appraisals in these domains were more likely to seek out online cancer information. Additionally, antecedent variables associated with the C-SHIP model had more frequent relationships with experiential information as compared with to didactic information. This study supports the applicability of the model to discern why people afflicted with cancer may seek online information to cope with their disease.

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[29]
Shi , Y.L. , & Xu, X. (2013). Advances in seeking health information on the Internet and its inspiration

(in Chinese). Library and Information Service, 57(24), 123-131.

[30]
Sun L., Wang Y.T., & Cao J.D. (2015). Effect of task type on user health information seeking behavior on the web(in Chinese). Information Science, 9, 131-135.

[31]
Tang , M.C. (2007). Browsing and searching in a faceted information space: A naturalistic study of PubMed users’ interaction with a display tool. Journal of the American Society for Information Science & Technology, 58(13), 1998-2006.The study adopts a naturalistic approach to investigate users' interaction with a browsable MeSH (medical subject headings) display designed to facilitate query construction for the PubMed bibliographic database. The purpose of the study is twofold: first, to test the usefulness of a browsable interface utilizing the principle of faceted classification; and second, to investigate users' preferred query submission methods in different problematic situations. An interface that incorporated multiple query submission methods he conventional single-line query box as well as methods associated the faceted classification display was constructed. Participants' interactions with the interface were monitored remotely over a period of 10 weeks; information about their problematic situations and information retrieval behaviors were also collected during this time. The traditional controlled experiment was not adequate in answering the author's research questions; hence, the author provides his rationale for a naturalistic approach. The study's findings show that there is indeed a selective compatibility between query submission methods provided by the MeSH display and users' problematic situations. The query submission methods associated with the display were found to be the preferred search tools when users' information needs were vague and the search topics unfamiliar. The findings support the theoretical proposition that users engaging in an information retrieval process with a variety of problematic situations need different approaches. The author argues that rather than treat the information retrieval system as a general purpose tool, more attention should be given to the interaction between the functionality of the tool and the characteristics of users' problematic situations.

DOI

[32]
Taylor , S. , & Todd, P. (1995a). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570.A variety of models that incorporate attitudinal, social, and control factor have been advanced to explain IT usage, of which the Technology Acceptance Model (TAM) is the most well know. One goal of such models is to develop diagnostic tools to predict information systems acceptance and facilitate design changes before users have experience with a system. However, empirical tests of these models have generally focused on either systems that were already in use by the study participants, or systems that the participants were familiar with, such as word processing packages and spreadsheets. Given this, it is unclear (1) whether models such as TAM are predictive of behavior for inexperienced users and, more importantly, (2) whether the determinants of IT usage are the same for experienced and inexperiened users of a system.

DOI

[33]
Taylor , S. , & Todd, P.A. (1995b). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176.The Technology Acceptance Model and two variations of the Theory of Planned Behavior were compared to assess which model best helps to understand usage of information technology. The models were compared using student data collected from 786 potential users of a computer resource center. Behavior data was based on monitoring 3,780 visits to the resource center over a 12-week period. Weighted least squares estimation revealed that all three models performed well in terms of fit and were roughly equivalent in terms of their ability to explain behavior. Decomposing the belief structures in the Theory of Planned Behavior provided a moderate increase in the explanation of behavioral intention. Overall, the results indicate that the decomposed Theory of Planned Behavior provides a fuller understanding of behavioral intention by focusing on the factors that are likely to influence systems use through the application of both design and implementation strategies.

DOI

[34]
Wallengren C., Segesten K., & Friberg F. (2010). Relatives’ information needs and the characteristics of their search for information: In the words of relatives of stroke survivors. Journal of Clinical Nursing, 19(19-20), 2888-2896.To explore relatives' information needs and the characteristics of their information-seeking process shortly after the stroke event and six months later. Providing relatives of stroke survivors with information is important, as lack of information increases their uncertainty and risk becoming the 'second patient in the family' and early death. Therefore, it is essential to be aware of relatives' information needs and information-seeking process the first six months after stroke. This qualitative study has a descriptive design. Open-ended interviews were conducted with sixteen relatives after stroke survivor's admission to stroke unit and six months later with nine of these relatives. Data were analysed by means of content analysis. The identified information needs covered the spectrum from stroke survivor's medical condition because nurses' actions to relatives' changed health and life situation. Furthermore, relatives' information-seeking process was found to be related to their level of personal involvement, situational circumstances, different forms of knowledge and sources of information. Relatives' search for information emerges when health and lifestyle changes occur in survivors or themselves. It is important that this information affect them personally. Also, they need to develop different forms of knowledge when they cannot trust their own competences. As a result, instead of following established curricula based on their beliefs of relatives' information needs, nurses need to practice on identifying relatives' information needs. Different information needs and characteristics described in the study can serve as guidance in the development and implementation of pedagogical interventions to support relatives of stroke survivors. One pedagogical implication is to explore what a specific relative wants to know by how he/she talks or thinks about it. Thus, it must be taken into consideration that level of personal involvement, situational circumstances, sources of information and factual knowledge, understanding and skills are intertwined.

DOI PMID

[35]
Wang , M. (2013). Research on critical factors of behavior of obtaining online heath information in America(in Chinese). Journal of Information Resources Management, 3, 47-58.

[36]
Wu , D. , & Li, Y. (2015). Older adults’ online health information searching behavior and cognition under different contexts(in Chinese). Library Tribune, 2, 38-43.

[37]
Xiao N., Sharman R., Rao H.R., & Upadhyaya S. (2014). Factors influencing online health information search: An empirical analysis of a national cancer-related survey. Decision Support Systems, 57, 417-427.People are increasingly using the Internet to access health information and the information obtained has an impact on their healthcare outcomes. This paper examines the impacts of IT enablers and health motivators on peoples' online health information search behavior. We characterize users' online health information search behavior along three dimensions: the frequency of online health information search, the diversity of online health information usage, and the preference of the Internet for initial search. Using the 2003 Health Information National Trends Survey (HINTS) data on cancer, we find that ease of access to Internet services and trust in online health information could affect the three dimensional search behavior listed above. While perceived quality of communication with doctors has an impact on diversity of use and preference of use, we surprisingly do not find an impact on the frequency of search for online health information. In addition, our results find that perceived health status could affect both frequency and diversity of search for online health information. But we do not find evidence that perceived health status could lead to a preference for using the Internet as a source for health information.

DOI

[38]
Yi Y.J., Stvilia B., & Mon L. (2012). Cultural influences on seeking quality health information: An exploratory study of the Korean community. Library & Information Science Research, 34(1), 45-51.The study explored consumer health information seeking behavior of members of a local Korean community in Tallahassee, Florida; how they assessed the quality of health information; and what cultural values influenced their understanding of its quality. The study employed semi- structured interviews. The study identified accuracy and reliability as the two most valuable quality characteristics. Commercial type of a webpage was considered as a negative indicator for reliability, while the same information found on more than one website as an indicator of the information's reliability. In addition to functional quality criteria, the study found non-functional characteristics such as empathy. Most of all findings highlighted that cultural differences limited the use of health information. Quality based selection of information is an important part of a health information seeking process. This study can help the designers of consumer health information systems on the Web with important insights into how to support the evaluation of quality of health information by consumers, and to reduce barriers to information-seeking and use caused by cultural differences

DOI

[39]
Yun , E.K. , & Park, H.A. (2010). Consumers’ disease information-seeking behaviour on the Internet in Korea. Journal of Clinical Nursing, 19(19-20), 2860-2868.

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Zhang J., Liu Y., & Yang S. (2008). A research framework for information seeking on the Web(in Chinese). Library and Information Service, 52(6), 87-89.

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Zhang , Y. (2011). College students’ uses and perceptions of social networking sites for health and wellness information. Information Research, 17(3), Paper 523.

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Zhang , Y. (2014). Beyond quality and accessibility: Source selection in consumer health information searching. Journal of the Association for Information Science and Technology, 65(5), 911-927.

[43]
Zhao S. (2009). Parental education and children’s online health information seeking: Beyond the digital divide debate. Social Science & Medicine, 69(10), 1501-1505.

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Zhou , X. , & Cai, W. (2014). University students’ online health information seeking behavior patterns and influencing factors(in Chinese). Information and Documentation Services,4, 50-55.

[45]
Zhu , S. , & Deng, X. (2015). Study on influencing factors of older adults’ online health information seeking (in Chinese). Library and Information Service, 59(5), 60-67.

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