Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search

Computers in Human Behavior 70 (2017) 416e425

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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

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Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search Bobby Swar a, 1, Tahir Hameed b, *, Iris Reychav c a

Concordia University of Edmonton, Edmonton, Canada SolBridge International School of Business, Daejeon, South Korea c Industrial Engineering and Management Department, Ariel University, Ariel, Israel b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 September 2016 Received in revised form 17 December 2016 Accepted 28 December 2016 Available online 30 December 2016

Internet these days have been extensively used to access and search health information supplementing or substituting the traditional sources of online health information (OHI) like health professionals. With the increase in online health information search the production of health information on internet is also rapidly increasing. Due to the enormous volume of health information available on internet, it is hard to locate, process and manage the required valuable information effectively often overloading health information seekers. Information overload phenomenon occurs when more information is presented than the ability of information seekers to process and handle the information. Researchers argue that information overload phenomenon is significantly associated with health-related issues of information seekers. Therefore, the aim of this study is to empirically examine how OHI related information overload impacts the psychological state of information seekers and their behavioral intention to continue the use of OHI search. A research model based on Information Processing Theory and Theory of Planned Behavior is developed and tested using the data collected from 380 survey responses. The results show that perceived information overload has a positive impact on information seekers’ psychological ill-being influencing their behavioral intention to discontinue the use of OHI search. Theoretical and practical implications are discussed at the end of the paper. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Online health information Information search behaviour Information processing theory Information overload Psychological ill-being Behavioral intention

1. Introduction People generally seek online health information (OHI) to stay informed in preventing diseases, understanding more about diseases, general well-being and to find various treatment options for diseases. Traditionally, OHI was provided by health professionals to patients (McMullan, 2006). These days internet is being used as a substitute or supplement to traditional sources of health information (Kitchens, Harle, & Li, 2014). People are now going online to try self-diagnose their symptoms and find more information about their symptoms. “Most physicians are already experiencing the effects of patients showing up to their offices armed with printouts from the World Wide Web and requesting certain procedures, tests,

* Corresponding author. Management Science Department, SolBridge International School of Business, 128 Uam Ro, Dong Gu, Daejeon, 34613, South Korea. E-mail addresses: [email protected] (B. Swar), [email protected] (T. Hameed), [email protected] (I. Reychav). 1 Dr. Bobby Swar was at SolBridge International School of Business in South Korea prior to joining Concordia University of Edmonton. http://dx.doi.org/10.1016/j.chb.2016.12.068 0747-5632/© 2016 Elsevier Ltd. All rights reserved.

or medications” (Hesse et al., 2005). Internet may be used bypassing entirely some of the traditional sources for seeking health information (Case, Johnson, Andrews, Allard, & Kelly, 2004). Currently, health information topics are among the heavily searched topics on the Internet. According to Pew Internet Project’s research 72% of U.S. adult internet users sought OHI in 2012, up by 12% compared to the year 2009 (Fox & Duggan, 2013; Fox, 2011). To supply this demand for health information, thousands of healthcare-related pages are added daily on the Internet, to the extent that it now serves as a significant source of OHI. Health information on the internet is exaggeratedly increasing (Chan, 2012). The enormous volume of OHI has revolutionized patient education (Fahy, Hardikar, Fox, & Mackay, 2014). However, it also leads to a phenomenon called ‘information overload’, a common term for receiving too much of information that human brain cannot process and handle (Kim, Lustria, & Burke, 2007). Taking an example from social media exchanges on the internet, people find it difficult to process too many media messages they receive because it requires continuous cognitive effort the resources for which have limited capacity (Ji, Ha, & Sypher,

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2014). Similarly, due to the massive amount of OHI provided by the internet, internet users may find OHI functionally inaccessible, increasing their information overload. Due to the fact that many web search engines rank their search results according to link popularity, or other algorithms than relevance, internet users typically encounter a large amount of irrelevant information, which can result in difficulty in finding the most desirable results (Jiang & Beaudoin, 2016). The ease of online communication has led to humongous volumes of online postings and tweets on social networking sites (Soucek & Moser, 2010) including healthcare topics and experiences. Online searches conducted on some Facebook and Twitter groups related to chronic diseases revealed that search results might not be highly relevant on social media. Moreover, the search results for the health keywords, much like those of web search engines, are filtered by posting popularity, which may derive search results that are not highly relevant and can thus increase information overload of the information seeker (De la Torre Diez, Diaz Pernas, & Anton Rodriguez, 2012). Abundance of information actually threatens individual’s control of the situation more than improving it (Edmunds & Morris, 2000). Health related information overload has become a widespread problem in patients as well as physicians (Kim et al., 2007). Researchers argue that perceived information overload phenomenon is significantly associated with health-related issues of the information seekers (for example, Bawden & Robinson, 2008; Kim et al., 2007; Misra & Stokols, 2012; White & Dorman, 2000). Information overload influences how people determine whether to keep searching and processing OHI or stop it. When information supply exceeds one's information processing capacity, the individual confronts problems in identifying relevant information, becomes overtly selective and neglects a large amount of information, faces difficulties in understanding the association between details and the overall perspective (Eppler & Mengis, 2004). Similarly, when people are faced with overly complex health information, they may not perceive the information as being personally relevant or worthwhile and may not be able to surmise the necessary behavioural information (Davis, Williams, Branch, Green, & Whaley, 2000, pp. 123e146). Under these circumstances, people tend to develop negative attitudes toward OHI which lead to unwillingness to further seek or digest OHI and ultimately a reduction in healthcare knowledge and OHI search skills. Thus, information overload is an important barrier to health literacy in terms of reducing people's understanding of health information, including diagnoses and treatment, and limiting their ability to evaluate and act upon health information (Kim et al., 2007). Health information overload phenomenon can trigger psychological ill-being in information seekers. Psychological ill-being is often conceptualized as the experience of negative affect and explicit psychological malfunction, such as emotional and physical exhaustion (Stebbings, Taylor, Spray, & Ntoumanis, 2012). Despite the growing research in this area, there is scant research on how online health information overload influences individual psychological ill-being and individual’s behavioral intention to continue to use OHI search despite being subject to ill effects. According to Misra and Stokols (2012), the impact of perceived information overload on individual’s health and stress remain to be assessed. Hence, this paper empirically investigates how the availability of abundant OHI is correlated with the information seeker’s psychological ill-being and on their behavioral intention to continue the use of OHI sources. A research model based on theories of information processing and planned behaviour has been proposed and validated using PLS-SEM (Partial Least Square- Structured Equation Modelling). The result shows that OHI related information overload causes psychological ill-being that in turn impacts information

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seekers’ online search behaviour leading them to discontinue the OHI search. 2. Theoretical background 2.1. Information processing theory and information overload The information processing theory (IPT) is a cognitive approach in understanding information processing by humans (Atkinson & Shiffrin, 1968; Miller, 1956). This theory provides a three stage model of information processing, which is based on parallels drawn between digital computers and the human brain. According to this model, the human brain receives information from different sensory inputs and stimulus which are immediately moved to a cache like sensory memory (SM). Not all of the newly received information is valuable or relevant to the needs of the receiver, therefore only filtered information from SM based on the receiver’s attention is passed on to working or short-term memory (STM). There, it is subjected to additional processing, such as categorization, comparison or combining the pieces of received information for identification of situations, development of new responses, or recalling from learnt responses in the past for the situation similar to the one on hand. The process is generally concluded when a response is generated to the received information. Information in STM can be recalled within a few seconds up to a few months if a similar situation is encountered again. Contrarily, a lack of repetition of such situations leads to forgetting of the information unless rehearsals or conscientious efforts are made to transfer and retain it in long term-memory (LTM). The organized knowledge in LTM can be recalled even after years. Such abilities to process, store and retrieve information are associated with human cognition, learning and literacy (Atkinson & Shiffrin, 1968; Simon, 1978, pp. 271e295). However, in the area of information systems, the digital computer is viewed as a system with limited capacity to process information. Since the IPT model assumes that digital computers copy the human brain’s information processing abilities, they suffer from similar capacity limitations (Y. C. Chen, Shang, & Kao, 2009; Miller, 1956). In humans, STM can typically hold up to seven (7) pieces of information at one time (Miller, 1956). If the volume of incoming information is higher, the information processing capacity depletes quickly and the person can experience information overload. Selecting relevant information from large volume of received information and making sense of it, before it could be used for developing or choosing a response, requires several properly sequenced storage, retrieval and intermediate processing operations. It is difficult to perform all of them in a short period of time when new information arrives continuously and competes for the limited processing resources (Simon, 1978, pp. 271e295). Similarly, high variety of information received on the same subject necessitates filtering the most useful information separating it from noise (Broadbent, 1958; Sternberg & Sternberg, 2016). It is quite discernible that selective attention for filtering also strains information processing capacity. People also increase their efforts to process received information in case of information overload (Chen et al., 2009) but their information processing capability and response rate e ability to successfully making sense of the received information and use it to
& Gallupe, 1999). In such cases, their advantage e will drop (Grise they might choose to give up or withdraw from the task at hand (OHI search in this paper). Only if people have prior knowledge of the subject on which the information is being received, their information processing capacity is not that strained, therefore reducing the chances for information overload. It is a simple matter of extending these basic concepts to access and consumption of OHI search. Healthcare is a specialized field

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involving several deep bodies of knowledge in medicine, physiology, psychology, technology and management. Like all other fields information, knowledge and experiences are posted online by patients, caregivers, healthcare providers, governments, and professional and industry organizations for the benefits of those who might need such information. Typically, information seekers do not possess deep prior knowledge of the symptoms, diagnosis, treatment or administration of health and mental conditions about which they seek information online. In simpler words, they are not health literate or do not have enough absorptive capacity therefore not competent to interpret OHI. Every new OHI search on internet generates several thousand links or pages on the topic that are ranked according to algorithms used by the websites for example popularity. As a result, a first challenge for the information seekers is choosing the most valuable and relevant information from the returned results. Selective attention mechanism is a very costly (burdening) process in information processing contexts. Subsequently, the information seeker has to make sense of the most important parts related to symptoms, diagnosis, and treatment etc. at the least. Without prior knowledge of the field, both these steps require deep attention and reflection processes while the inflow of new OHI continues. Thirdly, new challenges arise when the received OHI has been understood and transferred to STM or LTM as newly acquired knowledge. For example, it is common for the information seekers not to trust information received from a single online source which leads them to search/access other websites, blogs, online reviews or testimonials for further verification. However, such additional searches just add to the variations (or noise) in their recently gained knowledge because several symptoms appear to be common for multiple illnesses. The task becomes even more complex while the information processing capacity is already under stress due to prolonged involvement in highly demanding tasks i.e. attention and reflection. After a certain time, the information seeker’s information processing capacity is fully constrained or information overloaded. 2.2. Theory of planned behavior Theory of Planned Behavior (TPB) (Ajzen, 1985), which evolved from the Theory of Reasoned Action (TRA) (Fishbein & Ajzen, 1975, p. 6), provides the basis for predicting behavioral intention. According to TPB, individual behavior is driven by individual’s attitude toward the behavior, subjective norms surrounding the performance of the behavior, and the individual’s perception of the ease (control) with which the behavior can be performed. Attitude toward the behavior refers to the individual’s positive or negative feelings about performing a behavior. It is determined through an assessment of one’s beliefs regarding the consequences arising from a behavior and an evaluation of the desirability of these consequences. Subjective norm reflects individual’s perception of whether people important to the individual think the behavior should be performed. Behavioral control refers to an individual’s perception of the difficulty of performing a behavior. As discussed in the previous section, information seekers might feel information overload when they confront large volumes or variety of OHI. An information seeker’s attitude and/or perceived control of the situation could be deeply affected by information overload in several scenarios (Stebbings et al., 2012). For example, a user’s inability to seek the pertinent or relevant information might make him/her anxious or angry. S/he may also experience difficulty in interpreting and making sense of the search results in situations where too much jargon and specialized concepts are involved in a piece of OHI which could also contribute to the feelings of anxiety and/or anger. Yet another scenario involves misinterpreting or misunderstanding the received OHI, for example when people

perceive additional illnesses based on symptoms common/similar to other than the actual illness. In such cases, it is common to feel depression and negative sentiments towards further search of OHI. In sum, it is not difficult to discern that information overload tends to affect information seekers’ attitude towards carrying out OHI search hence affecting their behavioural intention to continue searching OHI in the short run.2 Such attitude reflects the psychological state or mental well-being of a person which is referred as psychological ill-being and which remains a key focus of this paper. 2.3. Psychological ill-being Psychological well-being has been conceptualized as a range of positive emotional experiences or moods like happiness, pleasure, interest, enthusiasm and inspiration (Stebbings et al., 2012). On the other hand, psychological ill-being is the overt experience of negative affect, like distress, nervousness, anger, and aversion and are likely to occur if individual needs are explicitly thwarted (Stebbings et al., 2012). Researchers argue that psychological well-being and ill-being are not opposite of the same dimensions rather are distinct dimensions with different causes (Ryff et al., 2006; Stebbings et al., 2012). That simply means a person may not be considered to be experiencing anxiety, stress, anger or other negative feelings if a person is visibly not happy, interested or excited about something. Psychological ill-being is acknowledged as a separate, independent dimension of psychological functioning, which is measured in terms of several different emotions or feelings (Ryff et al., 2006). As noted in the past section, such feelings e leading to a state of psychological ill-being - could be experienced by an OHI seeker when information overload occurs during OHI search. Therefore it’s obvious that psychological ill-being negatively contributes to the behavioral intention to consume or continue OHI search. However, it has not been tested yet how psychological ill-being mediates between information load and OHI search behavior. Next section introduces a research model and testable hypotheses to fill this gap in online health information search literature. 3. Research model and hypotheses development A research model depicted in Fig. 1 has been developed to empirically examine the relationship and impact of OHI related information overload phenomenon on information seeker’s psychological ill-being and their behavioral intention to continue OHI search. The research model is classified into three parts namely perceived information overload, psychological ill-being, and behavioral intention. 3.1. Behavioral intention Behavioral intention is defined as an individual’s subjective probability that he/she will engage in a specified behavior. Behavioral intention is a well-established construct in information systems literature describing the acceptance of and continuance of information systems use. According to Huang (2013), there is a significant causal predictive relationship between behavioral

2 This paper focuses only on short run OHI search behavior because psychological conditions based on temporary information overloads and associated negative feelings/psychological state might not persist for long in changed contexts. However, this research continues to explore the behavioral intention to share OHI rather than self-consumption only. In that case, the focus would also be on information search behavior in the long run.

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Psychological Ill-Being

Negative Affect

Perceived Information Overload

H1-H4

Depressive Symptoms

H5-H8

Behavioral Intention

Trait Anxiety

Trait Anger

Fig. 1. Research model.

intention and the use of actual technology. In this study, behavioral intention refers to the possibility of continued use of online health information search. Research indicates that if individuals perceive the adoption of a system to be useful, then they exhibit behavioral intention to use the system (Igbaria, 1994). Igbaria (1994) further argues that a system perceived to be useful, important, and viewed as providing benefits to the user, will generate a strong task-technology fit. System deployment failures can be correlated with a mismatch between the user/system expertise levels (Arnold, Collier, Leech, & Sutton, 2004). Although a subset of the literature related to reuse intentions measure the user’s intention to adopt a system after an initial use (Wang & Benbasat, 2009), this study focuses on the users’ behavioral intent to use the system when faced with a similar situation in the future (AlNatour, Benbasat, & Cenfetelli, 2008). Prior research has documented a strong positive correlation between perceived usefulness and reuse intentions (AlNatour et al., 2008; F. D.; Davis, 1989; Wang & Benbasat, 2009). In this study, we use psychological ill-being as capturing the usefulness of the information related to OHI searches that involve information overload situations. 3.2. Perceived information overload There is no universally accepted definition of information overload. Some argue that it refers to the phenomenon where seekers have more relevant information than can be assimilated. Others use this phrase to indicate the possibility that an individual receives a large supply of unsolicited information, some of which may be relevant (as cited in Edmunds & Morris, 2000). In ordinary lingo, the term “information overload” refers to the situation where seekers receive excess supply of information, and which exceeds their capacity to process received information, resulting in dysfunctional consequences (such as stress or anxiety) and diminished decision quality (Eppler & Mengis, 2004). Information overload has been viewed as a mismatch between the neural capacity of the human being and the rate of expansion of human knowledge (Hanka & Fuka, 2000). The phenomenon of information overload is not particular to healthcare. In fact, literature related to information overload

appears in a variety of disciplines like business, computer science, information sciences, and the social sciences (Edmunds & Morris, 2000). However, the concept is treated similarly in the stated areas. For instance, a review of the relevant literature suggests that a variety of beliefs, causes, and theoretical background influence the information overload phenomenon (Hall & Walton, 2004). In management, information overload has mainly been examined in the areas of accounting, management information systems, marketing and consumer research, primarily to investigate how the performance (in terms of adequate decision making) of individuals vary with the quantity of information available to them (Eppler & Mengis, 2004). Further Eppler and Mengis (2004) also show that the performance of individuals decline rapidly if information beyond a certain threshold is provided. Prior research has examined information overload from the organizational and individual perspectives. This research has particularly examined it from individual perspective defining individual’s information overload as a perception that available OHI is greater than what the information seeker can manage effectively, leading to ineffective coping strategies and a perception of psychological ill-being (Hall & Walton, 2004). When the OHI related information load increases, information seekers tends to increase his or her efforts to process the information available. At some point, the processing effort of information seekers surpasses their processing capacity resulting in information overload with profound effects on individual’s physical, mental, emotional and social aspects (White & Dorman, 2000). Literature has identified the negative effect of information overload on information seeker’s health (Bawden, Holtham, & Courtney, 1999; Kim et al., 2007; Misra & Stokols, 2012; White & Dorman, 2000). It should be stated that OHI is unique when compared to other types of information because individuals tend to use the readily available OHI to self-diagnose by correlating it with their actual or imagined symptoms. Based on this self-diagnosis, information seekers may panic into a state of psychological ill-being thinking that they have serious health issues when in fact they may not. Indeed, research has shown that psychological ill-being is the higher-order construct consisting of negative affect, depressive symptoms, trait anxiety and trait anger (Ryff et al., 2006). If information seeker is overloaded with OHI, it is reasonable to assume

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that information overload condition leads to individual psychological ill-being. This context leads to the proposal of the following hypotheses. Hypothesis 1. Perceived information overload is positively associated with negative affect. Hypothesis 2. Perceived information overload is positively associated with depressive symptoms. Hypothesis 3. Perceived information overload is positively associated with trait anxiety. Hypothesis 4. Perceived information overload is positively associated with trait anger.

3.3. Psychological ill-being and intention to continue OHI search As stated earlier in section 2.3, researchers argue that psychological well-being and ill-being are not opposite of the same dimension rather are distinct dimensions with different causes. Unlike psychological well-being (psychological ill-being is acknowledged as a separate, independent dimension of psychological functioning (Ryff et al., 2006; Stebbings et al., 2012). Psychological ill-being in this research has been adapted from (Ryff et al., 2006), which is measured in terms of four different assessments: negative affect, depressive symptoms, trait anxiety, and trait anger. Negative affect is defined as the general experience of negative emotions such as guilt or shame regardless of the situation (Thatcher & Perrewe, 2002). Negative affect “is a general dimension of subjective distress and unpleasable engagement that subsumes a variety of aversive mood states, including anger, contempt, disgust, guilt, fear and nervousness, with low negative affect being a state of calmness and serenity” (Watson, Clark, & Tellegen, 1988). Watson et al. (1988) further argue that high negative affect is related to self-reported stress, poor coping, health complaints and frequency of unpleasant events. According to (Fiske & Taylor, 1991, pp. 16e15) individuals with high negative affect react more strongly to negative environmental stimuli (as cited in Thatcher & Perrewe, 2002). If OHI-related information overload leads to negative affect then it is more likely that information seeker tends to have a negative feeling about seeking OHI again. On this pretext, the following hypothesis is proposed. Hypothesis 5. Negative affect is negatively associated with behavioral intention to continue the use of online health information search. Depression is defined as feelings of intense sadness that include feelings of helplessness, hopelessness, and worthlessness. Moreover, depression has been linked with loss of interest in activities that were once enjoyed by the (WebMD, 2016). Depression is viewed as a common but serious illness that can interfere with daily activities (NIH., 2013). According to (Warmerdam, VanStraten, Twisk, Riper, & Cuijpers, 2008), depression is known to be one of the most prevalent mental disorders in the world. If individual’s online healthcare information search behavior leads to depression then it is more likely that the individual will tend to avoid online information searches behavior in future. This phenomenon leads to formulate the following hypothesis. Hypothesis 6. Depressive symptoms is negatively associated with behavioral intention to continue the use of online health information search. Trait anxiety is defined as feelings of stress, worry, discomfort, etc. that individuals can experiences on day to day basis (Spielberger & Sydeman, 1994). According to Thatcher and Perrewe

(2002), trait anxiety refers to the general feeling of anxiety individuals experience when confronted with problems or challenges. Trait anxiety describes a tendency to respond anxiously (Reiss, 1997) and is an important factor in determining person’s levels of state anxiety and have at least four facets: social evaluation, physical danger, ambiguous, and daily routines (Endler & Kocovski, 2001). People tends to avoid behaviors that invoke feelings of anxiety. Extant research indicates a negative relationship between anxiety and the use of computers and technology (Compeau & Higgins, 1995). Particularly for online information search behaviour, anxiety has been negatively related with the continuance intention due to the fact that online world involves social constructs like shyness and social phobias, hence social anxiety could be considered as an integral part of trait anxiety in addition to the general anxiety (Akehurst & Thatcher, 2010; I. Y. ; Chen, 2007; Hong, Hwang, Hsu, Tai, & Kuo, 2015). Based on the above observations, the following hypothesis about anxiety (including both general and social anxiety) and intention to continue OHI search is proposed especially for online contexts. Hypothesis 7. Trait anxiety is negatively associated with behavioral intention to continue the use of online health information search. Trait anger relates to individual differences in the frequency, intensity, and duration of state anger, where state anger is defined as “an emotional state marked by subjective feelings that vary in intensity from mild annoyance or irritation to intense fury and rage” . Many adverse consequences are associated with high levels of trait anger, for example, increased the likelihood of aggressive behavior, and also trait anger has a negative impact on physical, social, and psychological health variables of the individuals (Wilkowski & Robinson, 2008). In the current context, if online healthcare information search leads to individuals feeling displeasure, unpleasant experiences, and negative cognitions, then it is likely that individual may discontinue the healthcare related search activity, leading to the following hypothesis. Hypothesis 8. Trait anger is negatively associated with behavioral intention to continue the use of online health information search.

4. Research methods 4.1. Measures Field and online survey methods were used to collect data and examine the validity of the proposed hypotheses. The survey instruments are developed by identifying appropriate measurements from the literature. With only a few exceptions, survey instruments used in this study are adaptations of the well-established measures already used in the literature. All measures are rated on five-point Likert scales with larger values for a measure indicating a greater magnitude of the variable. First, perceived information overload was measured using an adapted version of the seven-item instruments developed by Chen et al. (2009). For instance, one of the items is, “…When I access online information on the internet related to a healthcare topic, I feel there was too much information about that health-related topic on the internet so I feel burdened in handling it”... The possible response options range from “strongly disagree” to “strongly agree” on the 5 point scale. Second, negative affect was measured using an adapted version of the ten-item instruments from Thatcher and Perrewe (2002). These items were originated from Positive/Negative Affect Scale (PANAS) (Watson et al., 1988) and later modified by Thatcher and

B. Swar et al. / Computers in Human Behavior 70 (2017) 416e425

Perrewe (2002) in the context of computer anxiety and computer self-efficacy. In this study, participants were asked as to the extent they felt anxiety (for e.g., distressed, upset, guilty etc.) after accessing health-related information on the internet. Possible responses ranged from “not at all” to “extremely” on the 5 point scale. Third, depressive symptoms was measured using the seven items in the “Hospital Anxiety and Depressive Scale” (HADS) depressive subscale (HADS-D) (Zigmond & Snaith, 1983). Participants were asked to indicate their feelings after accessing online health information (for e.g., I still enjoy the things I used to enjoy, I lost interest in my appearance etc.). The possible responses ranged from “not at all” to “extremely” on the 5 point scale. Fourth, trait anxiety was measured by adapting the six items specified in the short-form of the “Spielberger State-Trait Anxiety Inventory” (STAI) scale (Marteau & Bekker, 1992). Participants were asked to indicate their feelings after accessing OHI (for example, I was tense, I felt upset etc.). Possible responses ranged from “not at all” to “extremely” on the 5 point scale. Fifth, trait anger was measured by adapting the seven items from the “State-Trait Anger Expression Inventory” (STAEI) scale (Spielberger, 1988). Participants were asked to indicate their feelings upon accessing online health information (for example, was becoming angry quickly, felt like shouting out loud etc.). The possible responses range from “never” to “almost always” on the 5 point scale. Finally, the behavioral intention was measured by adapting the three items developed in the applications of the Technology Acceptance Model (TAM) (Agarwal & Prasad, 1999; Venkatesh & Davis, 2000). Participants were asked to indicate their feeling upon accessing OHI (for example, intend, predict and plan to use internet for health-related information within a short period). The possible responses range from “strongly disagree” to “strongly agree”, on the 5 point scale.

4.2. Sample and data collection The survey was conducted on university students and adults in South Korea and Israel. Total of 400 responses were collected through the survey. After excluding the responses who indicated that they do not access OHI and those surveys with missing values a sample of 380 valid responses were used for the analysis. The sample size of 380 is adequate to test the research model against the required 80 for the eight paths in the research model according to suggestions by (Hair, Ringle, & Sarstedt, 2011). Moreover, based on the power analyses for the eight independent variables in the measurement and structural model, 238 observations would be required to achieve a statistical power of 80% for detecting R2 values of at least 0.10 with a 1% probability of error (Cohen, 1992; Hair, Hult, Ringle, & Sarstedt, 2014). Therefore, the sample size in this case is more than adequate, though limitations in the demographics of the sample are acknowledged. The sample comprises relatively greater number of younger subjects therefore caution is warranted in broadly generalizing the results of the study. However, it would also be pertinent to note a strength of this sample. The inter-cultural diversity reflected by four large nationality groups (China, Israel, South Korea and Kazakhstan) with several other nationalities included in the “Others” group would render results applicable to the youth in general. Possible crosscultural biases between western and eastern cultures are reduced. Table 1 presents the demographics of the sample used in this study.

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Table 1 Sample demographics. Category Gender Age

Nationality

Education

Male Female <18 19e24 25e34 35e44 >44 China Israel Kazakhstan South Korea Others High School Undergraduate Masters PhD

Frequency

Percent (%)

194 186 34 193 106 21 26 69 206 20 49 36 168 178 32 2

51.06 48.94 8.95 50.79 27.90 5.53 6.84 18.16 54.21 5.26 12.90 9.47 44.21 46.84 8.42 0.50

5. Results 5.1. Assessment of the measurement model The proposed model and its hypotheses are tested using structural equation modeling (SEM) supported by partial least squares (PLS) method. PLS allows a simultaneous test of the psychometric properties of each measurement scale (measurement model) and the analysis of the strength and direction of relationships among constructs (structural model). PLS is suitable during the early stage of theory development and enables the modeling of latent variables, even with small-to-medium size samples (Chin, 1998). According to (Fornell & Bookstein, 1982), PLS is also better suited for explaining complex relationships because it avoids the problems of inadmissible solutions and factor indeterminacy. This study particularly uses SmartPLS software package for data analysis (Ringle, Wende, & Becker, 2015). Table 2 Shows the assessment of the measurement model. Internal consistency reliability is investigated by using composite reliability due to the limitation of Cronbach’s alpha (Hair et al., 2014). The constructs in the proposed model are above the 0.7 threshold indicating a high reliability of items used for each construct. Convergent validity is assessed by evaluating the average variance extracted (AVE) from the measures. The AVE is above the threshold value of 0.5, meeting the criteria of convergent validity. Discriminant validity is assessed by examining the square root of AVE as recommended by (Fornell & Bookstein, 1982). As shown in Table 3, the square root of AVE of each construct is greater than the correlations between itself and all other constructs. Moreover, all the constructs are found to have a stronger correlation with their own measures than to those of others. This shows the proper assessment of discriminant validity. 5.2. Testing the model While testing the research model this study examined the overall explanatory power of the structural model, the amount of variance explained, and the magnitude and strength of the paths. Fig. 2 presents the results obtained from the PLS analysis. The coefficient of determination, R2, is 0.055 for the Behavioral Intention. The structural model explained 5.5% of the variance in Behavioral Intentional. The results indicates that perceived information overload has a statistically significant positive relationship with all the constructs of psychological ill-being i.e., negative affect (b ¼ 0.365, p < 0.01),

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B. Swar et al. / Computers in Human Behavior 70 (2017) 416e425 Table 2 Assessment of the measurement model. Variables

Average variance extracted (AVE)

Composite reliability

Perceived information overload Negative affect Depressive symptoms Trait anxiety Trait anger Behavioral intention

0.640 0.639 0.774 0.780 0.749 0.800

0.840 0.841 0.872 0.913 0.954 0.923

Table 3 Fornell-Lacker test of discriminant validity.

(1) (2) (3) (4) (5) (6)

Perceived information overload Negative affect Depressive symptoms Trait anxiety Trait anger Behavioral intention

(1)

(2)

(3)

(4)

(5)

(6)

0.80 0.365 0.332 0.244 0.261 0.109

0.799 0.462 0.704 0.665 0.107

0.879 0.467 0.487 0.159

0.883 0.592 0.032

0.865 0.184

0.894

Note: The diagonal elements (in bold) represent the square root of AVE.

Negative Affect 0.365** (28.803) 0.332** (24.894) Perceived Informatio n Overload

Depressive Symptoms

0.224** (18.09) 0.26** (19.935)

-0.037 * (1.87) -0.124** (6.758)

0.170 (7.805) Trait Anxiety

Behavioral Intention

-0.2** (9.351)

Trait Anger

Fig. 2. Results of structural model with path coefficients (associated t-statistics are in parentheses). Note: *p < 0.1,

depressive symptoms (b ¼ 0.332, p < 0.01), trait anxiety (b ¼ 0.224, p < 0.01) and trait anger (b ¼ 0.26, p < 0.01), thereby supporting hypotheses 1 through 4. Among the four construct of psychological ill-being three shows statistically significant negative relationship with behavioral intention to reuse the online healthcare information i.e., negative affect (b ¼ 0.037, p < 0.1), depressive symptoms (b ¼ 0.124, p < 0.01), and trait anger (b ¼ 0.2, p < 0.01), supporting hypotheses 5, 6, and 8. Contrary to the expectations, trait anxiety is found to positively related to behavioral intention (b ¼ 0.17), rejecting the hypothesis 7. 6. Conclusions and discussion This research is motivated by a need to understand and examine the effects of healthcare related perceived information overload on

**

p < 0.01.

information seeker’s psychological ill-being and their behavioral intention to continue the use of online health information search. In this regard, this study empirically tested the proposed model and hypotheses by employing information processing theory and theory of planned behavior and survey data collected from adults in South Korea and Israel. The sample comprises large groups of Chinese, Israelis, South Korean, and Kazakh nationals. The summary of the results presented in Table 4 indicate strong support for all hypotheses with one exception only which are discussed below in detail. The result for seven of the eight supported hypotheses imply that perceived information overload have a significant positive relationship with psychological ill-being on the health information seekers. Three of the psychological ill-being constructs (negative affect, depressive symptoms, and trait anger) are found to be negatively

B. Swar et al. / Computers in Human Behavior 70 (2017) 416e425

423

Table 4 Summary of results. Paths

Hypothesis

Result

Perceived information overload e Negative affect Perceived information overload e Depressive symptoms Perceived information overload e Trait anxiety Perceived information overload e Trait anger Negative affect - Behavioral intention Depressive symptomse Behavioral intention Trait anxietye Behavioral intention Trait angere Behavioral intention

H1 H2 H3 H4 H5 H6 H7 H8

Supported Supported Supported Supported Supported Supported Not supported Supported

related with behavioral intention. That implies, due to psychological ill-being created by OHI related information overload, individuals tend to discontinue the OHI search. Earlier research on information overload also had identified negative affect of information overload on information seeker’s health (for example, Bawden et al., 1999; Kim et al., 2007; Misra & Stokols, 2012; White & Dorman, 2000) but it did not explore or relate it with future OHI search behavior. Negative affect and depressive symptoms considered as dis~o et al., 2009) and further described by trupting quality of life (Roma (Gaines & Burnett, 2014) due to their impact on individual self evaluation and also as reducing evaluation to the people around. Taking advantage of the Internet’s capacity to convey rich information on health to consumers easily and quickly is crucial. However, this study exposes that the ever-increasing amount of online health information could be challenging to consumers’ limited processing capacity leading to the negative traits related to psychological ill-being such as anxiety, anger, and depressive symptoms. Such a situation warrants consideration of further strategies on user’s end to decide which information is relevant (Klerings, Weinhandl, & Thaler, 2015). Therefore, from IS perspective more efforts are need to design rules that will act as information filters to help the information seekers (or patients) identify potentially relevant OHI according to these rules. Contrary to our assumption, rejection of one hypotheses indicates that trait anxiety is positively related with behavioral intention to continue the OHI search. This suggests that information seekers continue to seek OHI even when they largely feel anxiety from their earlier OHI search experience. One possible explanation could be that anxiety in online contexts is not created similarly as it would be experienced in offline contexts. In offline settings, high levels of anxiety could be experienced over shorter periods of time immediately after a physician discloses threatening symptoms/diagnosis to the health or life of the patient. In online contexts, anxiety builds up slowly while the OHI search progresses and the information-seeker collects more and more information about his or her symptoms. In fact, information-seeker is motivated to collect more information either to verify the recently gained pieces of knowledge or to reject them. Therefore, even when the information seekers get anxious due to the received negative information, they might continue to seek internet sources in a hope to ultimately receive some positive information. In such cases they also try to use alternate internet sources to either validate or rebut the OHI received earlier. This means the users who are just anxious but do not feel anger, negativity or depressive symptoms might still continue to use OHI search. Another explanation for the rejection of hypotheses 7 could be based on the fact that anxiety is a time-bound trait. In this study, the survey does not capture the data about the time the respondents experienced anxiety. The respondents who were involved in OHI search recently might still recall their anxiety strongly, but others who did OHI search a long time ago might not be able to recall their levels of anxiety clearly. Therefore, future

studies considering anxiety should also consider to note the time between data collection and when information seekers were actually engaged in the OHI search. A practical implication for most frequently used online health information websites managed by the most well-known healthcare organizations could be to develop a mechanism/platform for sharing OHI content about symptoms and possible treatments etc. This way specific, consistent and unambiguous OHI from various leading resources would help reduce the traits contributing to information overload and psychological ill-being in online health information seekers. Overall, the study suggests that abundance of OHI and increase in OHI search might actually be causing adverse impacts on keeping or making people healthy. Therefore special care should be taken to avoid information overload phenomena while accessing online healthcare information mainly on supply side. It particularly emphasizes on a heavier role of online healthcare providers and managers to adopt better strategies for OHI content and systems management which would be suitable for individual’s physical and mental well-being while involved in OHI search. In term of practical implications, our finding provides refined knowledge to online healthcare providers and the organizations managing health websites. Our study also highlights a need that healthcare policy makers and healthcare educators should not simply motivate people to use the internet for understanding about healthy behaviours and treatments but they should also incorporate strategies to raise awareness among the OHI information seekers about OHI related information overload, how to avoid it, and the importance of improving their computer and internet competencies. The contributions of this study and opportunities for future research should be considered in the light of some limitations. Firstly, larger part of the sample of this study is younger population which is usually healthier, therefore caution is needed in generalizing the findings. Relatively young persons may not strongly experience the traits composing psychological ill-being due to their physical strength or they may not last longer in them. Secondly, as a limitation of the research methodology this study assesses responses from participants taken as a snapshot in time which could have diluted the fact that people tend to forget about the state of psychological ill-being in the long run and might tend to use online health information again. This would particularly be relevant for measuring trait anxiety as noted above in this section. The state of psychological ill-being and corresponding traits should be stronger in people who had recently experienced the information overload from OHI search. Therefore, future research is also recommended to consider data collection from elder people and patients engaged in OHI search and a longitudinal type of study to overcome the above limitations. Lastly, although the study has a diverse multi-cultural sample, but collecting data from information-seekers from some other countries, especially from the west, would definitely improve the generalizability of the findings of this study.

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Appendix. Survey questions

Perceived information overload When I accessed online information on the internet related to a healthcare topic: (1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree) I could effectively handle all the health related information found on the internet. I was certain that the online information about that health related topic fit my need well to make better decisions. I had no idea where to find online information about that particular health related topic due to abundance of online information on internet. Negative affect After accessing online information about a health related topic on the internet, up to what extent did you feel: (1-Not at all, 2-Slightly, 3-Somewhat, Extremely) Upset (unhappy, disappointed, or worried) Guilty (conscious of or affected by feeling of guilt) Scared (fearful, frightened) Hostile (unfriendly, cold etc.) Irritable (having or showing a tendency to be easily annoyed or made angry) Ashamed (embarrassed or guilty) Nervous (feeling excited and worried) Jittery(unable to relax for long) Afraid (feeling fearful) Depressive symptoms After accessing online information about a health related topic on the internet, up to what extent did you feel: (1-Not at all, 2-Slightly, 3-Somewhat, Extremely) I still enjoyed the things I used to enjoy. I could laugh and see the funny side of things. I felt cheerful. Trait anxiety After accessing online information about a health related topic on the internet, up to what extent did you feel: (1-Not at all, 2-Slightly, 3-Somewhat, Extremely) I felt calm. (not at all affected by emotions such as excitement, anger, shock, or fear) I was relaxed. (calm and not worried) I felt content. (happy and satisfied) Trait anger After accessing online information about a health related topic on the internet, up to what extent did you feel: (1-Not at all, 2-Slightly, 3-Somewhat, Extremely) I was becoming angry quickly. (becoming angry in a short time) I found myself hard to control. (because of anger) I felt frustrated, like hitting someone. I felt like saying nasty things. I felt like breaking the computer. I felt like shouting out loud. Behavioral intention (1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Strongly Agree) I intend to use internet for information about health related topics again in a short period. I predict whether you would use internet for information about health related topics again in a short period. I plan to use internet for information about health related topics again in a short period.

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