Exploring the inhibitors of online health service use intention: A status quo bias perspective

Accepted Manuscript Title: Exploring the Inhibitors of Online Health Service Use Intention: A Status Quo Bias Perspective Authors: Xiaofei Zhang, Xitong Guo, Yi Wu, Kee-hung Lai, Doug Vogel PII: DOI: Reference:

S0378-7206(17)30088-5 http://dx.doi.org/doi:10.1016/j.im.2017.02.001 INFMAN 2976

To appear in:

INFMAN

Received date: Revised date: Accepted date:

23-12-2014 18-10-2016 2-2-2017

Please cite this article as: Xiaofei Zhang, Xitong Guo, Yi Wu, Keehung Lai, Doug Vogel, Exploring the Inhibitors of Online Health Service Use Intention: A Status Quo Bias Perspective, Information and Management http://dx.doi.org/10.1016/j.im.2017.02.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Exploring the Inhibitors of Online Health Service Use Intention: A Status Quo Bias Perspective

Xiaofei ZHANGa,c, Xitong GUOa*, Yi WUb, Kee-hung LAIc, Doug Vogela

a

School of Management, Harbin Institute of Technology, Harbin, China

b

c

Department of Information Management and Management Science, Tianjin University, Tianjin, China

Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong

Kong

*Corresponding to: Xitong GUO Address: School of Management, Harbin Institute of Technology, 92 West Dazhi Street, Nan Gang District, Harbin, 15001, China E-mail: [email protected] Tel: 86-18646223358 Fax: 86-0451-86414024

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Highlights 

1 Factors in traditional healthcare hinder individuals’ OHS use intention.

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2 Perceived benefits mediate the effects of sunk costs and health habits on use intention.

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3 Perceived costs mediate the effects of transition costs and privacy beliefs on use intention.

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4 The status quo bias can influence new behavior decision through rational choice.

Abstract Why do online health services (OHSs) have a relatively low visit-use rate? Drawing on the status quo bias (SQB) theory and the rational choice theory, we developed an integrated research model to explore this phenomenon. To test the model, an online-survey was conducted in China with 339 valid responses for analysis. There are three insightful theoretical of the results: (1) Inhibitors in traditional healthcare hinder OHS use intention; (2) The rational choice theory can be used to measure the effects of the SQB on behavioral intentions, indicating that the SQB is capable of influencing behavioral intention from a rational decision process; and (3) The rational choice theory can be used in less rational situations.

Keywords: Online health service, inhibitors, status quo bias, rational choice theory, perceived benefits, perceived costs

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1. Introduction

The acquisition of necessary medical support is inconvenient and expensive for most patients, and remains a worldwide problem, especially for patients living in undeveloped areas (Qu et al. 2012). To manage medical needs cost-effectively, many online platforms (e.g., Hao Daifu Zaixian, Best doctors, Zocdoc and WellDoc) are providing medical health services. These online health services (OHSs) can be defined as a form of Internet-based health service delivery in terms of professional health services and medical consultations (Castrén et al. 2008). Ecommerce is defined as enabling the purchasing and selling of services and goods on the Internet (Benjamin et al. 1995). Accordingly, an OHS is the application of e-commerce in the healthcare context by providing an online platform for doctors to deliver health services requested by patients. According to a report by Fox et al. (2010), an increasing number of people are searching online for health information, and accordingly, an increasing number of OHS platforms have been built. For instance, the most popular Chinese OHS platform, namely, Hao Daifu Zaixian1, has attracted more than 10,000,000 patient visits since its launch in 2006. The platform provides background information (affiliations, titles, expertise, among others) and online information (patients’ reviews, interactions with patients, medical advice, among others) of physicians. Based on this information, patients can choose their physicians through OHS platforms. Patients are able to access health information, consultations, and recommendations by request at the price of 150200 Yuan (about 25-30 USD) for every 15-minute phone call or at the price of 20 Yuan (about 3 USD) for every question posed online.

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http://www.haodf.com

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Indeed, OHS is considered as beneficial for patients; however, the usage rate of such a service is relatively low in China. We conducted an in-depth statistical analysis of “Hao Daifu Zaixian” involving all its doctors’ websites in 20142. The results verified that the average usage rate of its service was relatively low (i.e., less than 0.2% of doctors’ website visitors used the paid conversations). In other words, the resistance rate of the OHS was much higher. This is not just applicable of the healthcare sector, in fact, the unwilling use of e-services (e.g., e-banking (Baraghani 2008), and e-government websites (Heeks et al. 2007)) is much higher. Although considerable attention has been devoted to the triggers of using OHSs (Fisher et al. 2008), limited research has been devoted to the inhibiting factors. Resistance is often manifested as a failure of a user’s transition from the incumbent business model to a new one (Polites et al. 2012; Ye et al. 2006). An OHS does provide a new form of healthcare i.e., one that is available seven days weekly without any spatial limitations; and moreover, it does substitute the traditional health services (THSs) to some extent. As THSs have been the primary healthcare resource for quite an extensive period in China, the antecedents of users’ switching behavior to an OHS can directly be related to their THS experiences (Ye et al. 2006). It is possible that the potential resistance to OHSs is related to the THSs. However, most previous literature has focused on explaining the enablers (mainly positive perceptions), with limited attention devoted to the impacts of the THS on online health behavior, especially regarding the inhibitors. Seeking insights into these factors will not only enable OHS providers to develop effective promotional strategies, but also enhance our understanding of the enablers from a new perspective. Based on the preceding context, our first research question is: Which inhibitors from the THS affect OHS use intention?

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We analyzed the websites of about 8,000 doctors. Their average website visitors are 498,365 and average patients are 577.

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To narrow this research gap, we draw on the rational choice theory to investigate individual users’ decision-making processes with regard to the inhibitors. The rational choice theory argues that individuals are rational, and would make an evaluation of the benefits and costs before making any decisions (Becker 1974). Accordingly, this theory is applicable for manifesting the decision processes of how inhibitors from THS affect OHS use intention. To examine the impacts of THSs, we deem the status quo bias (SQB) theory as a useful theoretical lens for our research. This theory provides a context dependent lens to explain why people prefer to maintain their current status rather than to change it even though the new status is a better choice (Samuelson et al. 1988). It argues that an individual’s decision-making between the status quo and a new status may be biased by internal factors such as switching costs, cognitive misperceptions, and psychological commitment. This theory is appropriate for explaining the phenomenon that, in the transition from a THS to an OHS, individuals tend to choose their current status, i.e., to continue using the THS, rather than switching to an OHS. Although the SQB is a useful theoretical lens for identifying the inhibiting factors from THSs, a knowledge gap between these factors and decision-making still exists. There is limited research on examining whether and how the inhibitors from SQB influence the behavior through benefits and costs perceptions. Thus our second research question is: Can status quo bias factors influence behavior through a rational process? To address the preceding questions, an integrative model based on a modified SQB theory and the rational choice theory was developed. Thereafter, the model was empirically tested using survey data. Our research provides both theoretical and practical contributions. Through developing an integrative model based on the SQB theory and the rational choice theory, this research extends findings on the SQB theory by investigating the mechanisms by which this

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theory influences behavioral intention through a rational process. It also extends the rational choice theory by acknowledging that the theory can be used even when biases exist. Finally, this research assists in explaining the influence of offline inhibitors on individuals’ OHS behavior. Moreover, this research also provides suggestions for health service providers on how to design health websites and what needs to be emphasized on these websites. 2. Theoretical Foundations

2.1 The Online Health Service

The online health service (OHS) can be defined as the delivery of health services through the Internet (Castrén et al. 2008). Due to the limitations of the Internet-based technologies, only a limited range of health services (e.g., provision of professional healthcare information and medical consultations, and conducting of patient education) can be provided by the OHS providers (Hadwich et al. 2010). Patients with different diseases can use the OHS to learn more about diseases, to obtain a second opinion on their treatments, to become more involved in health decision-making, to seek emotional support after treatment, and to access healthy lifestyle choices (Fisher et al. 2008; Yan et al. 2010). Through these services, patients can obtain informational and emotional support from physicians (Yan et al. 2010), which is invaluable in the making of health decisions before and during treatment as well as in reducing concerns and anxiety during the recovery period (Nettleton et al. 2005). Although OHSs are becoming increasingly popular, empirical studies have not adequately explored this phenomenon. Regarding the interactions with the OHSs, previous literature has studied the formation of trust (Mou et al. 2014) and user satisfaction (Koo et al. 2010). Regarding the outcomes of using the OHS, Chen et al. (2014) found that an online health community can enhance patients’ health conditions by providing them with social support; and Yan et al. (2010) showed that online health interaction does benefit patients by improving their 6

health management. However, there is limited research that explores users’ decision-making regarding the OHS, including the inhibitors of its use and the impacts from the THS. Given that the OHS is an extension of the THS and can substitute the THS to a certain extent, this research will explore the inhibitors from THS that influence OHS use intention. 2.2. Rational Choice Theory

The rational choice theory offers an economic approach that explains how individuals make decisions when faced with many different choices. The theory works under the basic assumptions that: (1) human beings are rational and self-interested; (2) when making decisions, they are rational; and (3) they try to gain more positive outcomes through analyses of costs and benefits (Paternoster et al. 2009). The rational choice theory supposes that before a decision is made, individuals will evaluate the potential benefits and costs of all alternatives, and then choose the one with the best outcome (Becker 1974). Perceived benefits and perceived costs are used to measure the benefits and costs. This theory was first developed to study criminal behavior regarding the benefits and costs of committing a crime (Becker 1974), after which it was widely used to explain human choices between conforming to or deviating from the current status at the individual level, such as in economics, finance, political science, marketing, information systems and other fields (Li et al. 2010). The reasons for our choice of the rational choice theory as the theoretical lens to study the acceptance of the OHS are threefold. First, most previous models and theories on technology acceptance, such as the Technology Acceptance Model, the Theory of Reasoned Action and the Theory of Planned Behavior, were mainly developed in workplaces, in which the technologies or systems are often characterized as productive tools (Venkatesh et al. 2001). While the OHS is a personal choice in competitive environments, the decision requires individuals to evaluate different alternatives and make personal choices. Second, the rational choice theory is based on 7

costs and benefits evaluations, which enables measuring the process of how the SQB factors influence the costs and benefits perceptions on using the OHS (Becker 1974). Third, we propose that SQB factors can influence human decisions through rational decision-making. Thus the rational choice theory is more effective in measuring the rational process (Becker 1974). While other adoption models can measure the decision process, they are invalid for reflecting the rational mechanisms in decision-making as does the rational choice theory. The rational choice theory, which has been shown to effectively explain human decisions in many contexts, has also received some criticisms. One criticism is that human behavior is sometimes deemed irrational due to the impacts of biases, emotions, and errors (Liang et al. 2013; Mellers et al. 1998). According to Mellers et al. (1998), there are two academic streams on the theory that support its applicability. Apart from psychologists, social scientists are focused on modifying or restricting the behavioral assumptions of the whole process to make a decision rational (Mellers et al. 1998). In the psychology discipline, most efforts are aimed at defining biases and errors in rationality assumptions (assumptions that suggest that the decision-making is rational) and then labeling behavior as irrational. Most current research on this theory posits rationality assumptions or merely ignore the irrational factors (Liang et al. 2013; Mellers et al. 1998). However, few empirical studies have been conducted to examine whether the theory can be used in less rational situations, and to which extent can the theory manifest the rational process of less rational decision-making. Engaging in an OHS will result in benefits such as saving of time and costs, health resource accessibility, and others; but it also means incurring costs, such as costs from incorrect diagnoses, offering of misleading advice, leaking of private information, biases, and so on (Eysenbach et al. 2002). Based on the rational choice theory, we apply perceived benefits and perceived costs as

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two main beliefs regarding usage of the OHS to measure individuals’ benefits and costs perceptions. We define the perceived benefits of the OHS as the total expected favorable consequences of its use, and the perceived costs as the total expected unfavorable consequences of using an OHS. 2.3. The Status Quo Bias (SQB) Theory

While an individual’s decision making of OHS use is the tradeoff between perceived benefits and perceived costs, these perceptions can be determined by factors of the THS from the SQB theory. This theory upholds the phenomenon that one would be “doing nothing or maintaining one’s current or previous decision” rather than changing it (Samuelson et al. 1988). It provides a brand new approach for studying IS innovation acceptance from the perspective of inhibitors (Polites et al. 2012), while other theories and models, such as the Technology Acceptance Model, the Theory of Reasoned Action and the Theory of Planned Behavior, are focused on the enablers. Additionally, this theory provides a useful theoretical lens to explain the impacts of the previous status, when an individual is making a decision regarding a new behavior (Kim et al. 2009). Samuelson et al. (1988) postulated that the SQB is caused by three factors: cognitive misperceptions, switching costs, and psychological commitment. First, cognitive misperceptions, the first aspect of the SQB theory, is related to loss aversion (Samuelson et al. 1988). Individuals who are risk-avoiders tend to weigh losses more than benefits in deciding whether to switch to a new situation (Samuelson et al. 1988). The purpose of our study is to explore the effects of the SQB from the THS on costs and benefits perceptions of OHS use, and hence, cognitive misperceptions, i.e., an individual specificity, is not within the scope of our research. Second, switching costs have an impact when individuals evaluate the costs and benefits of a change before making a decision. When measured costs are greater than benefits, the SQB is applied. As the theory focuses on biases, costs are the main emphasis in this aspect (Samuelson 9

et al. 1988). Two types of costs are identified: transition costs and uncertainty costs. In this context, costs incurred by changing to an OHS are transition costs. When considering the use of an OHS, potential users will experience a sense of uncertainty about using it, which will result in uncertainty costs. Most previous research on the SQB theory does not use concrete constructs to manifest uncertainty costs (see a review by Kim et al. (2009)). Some researchers have considered only uncertainty (Jiang et al. 2000) and fears (Joshi 2005) as uncertainty costs, and others have failed to measure such costs (Kim et al. 2009; Polites et al. 2012). In this research, we use the privacy factor as one main uncertainty cost in health behavior transition. Privacy refers to the rights of individuals to decide on the extent to which their information is disclosed to others (Westin 1968). The illegal use of such information by service providers or others may result in many privacy issues, a concern which is attracting increasing attention (Angst et al. 2009; Hong et al. 2013), especially in the area of healthcare (Angst et al. 2009). In the THS, information transmission mainly occurs in the form of the oral tradition, paper documents, or internal networks; hence, patients may perceive more privacy protection compared to an OHS. As privacy concerns generate uncertainty about the new situation, according to the social contract theory (Donaldson et al. 1994), the privacy protection beliefs on the THS could lead users to perceive uncertainty about using an OHS, thereby leading to SQB. Finally, the SQB may also be the result of psychological commitment, the third factor of the SQB theory. The greater the number of sources that individuals have invested in incumbent situations, also known as sunk costs, the greater the probability that they will continue with their current commitments, and therefore their situation would be more likely to remain unchanged (Samuelson et al. 1988), even though considering previous investments in subsequent decisions

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is not rational. Similarly, the sunk costs in the THS tend to encourage individuals to continue their use. Therefore, sunk costs also result in the SQB. Moreover, Polites et al. (2012) have proposed that habits can be a subconscious part of the SQB when system switching behavior is investigated. Habits refer to behaviors that are automatically repeated due to specific cues (Verplanken et al. 1999). As habits involve unconsciously repeating or continuing former behavior, they will hinder one from making changes. Therefore, in technology-based behavioral transition, the incumbent health service habits may also assist in rendering the incumbent services unchanged, thus leading to the SQB. In line with previous literature, our research argues that the SQB factors influence behavioral intentions through a decision-making process, by weighing individuals’ perceptions (Polites et al. 2012). While some previous studies have explored the effects of SQB factors on costs and benefits perceptions (Li et al. 2014), the SQB theory has not been empirically investigated with respect to how its factors are precisely allocated to costs and benefits perceptions on new behavior. On the one hand, sunk costs and health service habits are factors prompting the tendency of individuals to prefer the THS directly, which may adversely affect their initial perceptions of OHS benefits. In particular, more sunk costs amount to larger previous investments on the incumbent status, resulting accordingly in a greater inclination (for more benefits) towards the incumbent status (Samuelson et al. 1988). Previous behavioral habits will provide guidance in attitude forming, and create a more positive attitude (for more benefits) towards the incumbent status (Kim et al. 2005; Polites et al. 2012). On the other hand, transition costs and privacy protection beliefs are the results of a transition, indicating that using a new service requires more effort and time, and provokes more uncertain negative consequences, which will contribute to

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the costs perceptions of using an OHS directly. In summary, to explore the effects of SQB factors in the decision making process of OHS use, this research proposes that sunk costs and health service habits are determinants of perceived benefits, whereas transition costs and privacy protection beliefs determine perceived costs. 3. Research Model and Hypotheses Development

To study the inhibitors of OHS use intention, we proposed an integrated model (see Figure 1) based on the theories of SQB and rational choice. The SQB theory focuses on the factors leading to individuals’ biases towards THSs, such as sunk costs, health service habits, transition costs, and privacy protection beliefs. These factors are the inhibitors of the transition to the OHS. As these inhibitors are caused by the THS, they influence OHS use intention indirectly through individuals’ benefits and costs perceptions. Effects of Perceived Benefits and Perceived Costs on Use Intention According to the rational choice theory, when faced with the question of whether to use the OHS, individuals will perform a benefits-costs analysis. The OHS presents various benefits: it delivers services more conveniently at low costs; it forms multiple-channel health information sources; it provides informational support for medical decision making before/during treatments (Yan et al. 2010); and it also offers emotional support for minimizing concerns after treatment (Nettleton et al. 2005). All these factors comprise the benefits related to individuals’ benefits perceptions when they contemplate using the service. The rational choice theory proposes that, before a decision is made on whether to use an OHS, benefits perceptions of the behavior will motivate individuals to acquire the benefits by using the service (Liang et al. 2013; Mellers et al. 1998). This leads to our first hypothesis: H1: Perceived benefits are positively associated with OHS use intention.

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Simultaneously, OHS may also result in the exerting of more time and effort on learning its use, as well as other problems such as financial harm, incorrect diagnoses, misleading advice, privacy leakage, and so on (Liang et al. 2013). All these factors contribute to individuals’ costs perceptions when they are considering using the service. The rational choice theory supposes that, before a decision is made on whether to use an OHS, the costs perceptions will demotivate individuals into refusing its use, to avoid the costs (Liang et al. 2013; Mellers et al. 1998). This leads to our second hypothesis: H2: Perceived costs are negatively associated with OHS use intention. Effects of Sunk Costs and Health Service Habits on Perceived Benefits Sunk costs refer to the resources invested in previous decisions (Samuelson et al. 1988). Rationally, these investments may have no effect on subsequent decisions. As people do not always exhibit perfect rationality, they will justify these investments. The greater the investments in previous decisions, the greater the likelihood to replicate their earlier decisions (Samuelson et al. 1988). In our research context, sunk costs are the time and effort individuals have invested in learning how to use the THS, even regarding the unspoken rules in hospitals. If individuals expend more time and effort on the THS when considering a transition to OHS, they would find it less attractive to switch to the OHS (Brockner et al. 1985). Additionally, investing more resources on the THS will enable better familiarization with traditional healthcare, and thereby they may believe that they can obtain more benefits from traditional healthcare than from the OHS. Relatively, they will perceive that fewer benefits will be gained from using an OHS. Thus, we posit that: H3: Sunk costs in the THS are negatively associated with the perceived benefits of the OHS.

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Habits lead to automatic responses to cues and repeated behaviors (Verplanken et al. 1999). There are two different perceptions regarding this phenomenon. The first is based on automatic processing. Habitual behavior is automatic to individuals, and they do not perceive the need to evaluate the benefits and costs of their ongoing health behavior unless triggered by some circumstances (e.g., the OHS) according to Polites et al. (2012). The second perception is based on the self-perception theory (Bem 1973). Habitual behavior causes individuals’ lack of motivation to change their ongoing behavior, as is evident in attitudes such as: “I am always practicing Behavior X, therefore I must like it” (Kim et al. 2005; Polites et al. 2012). Health service habits may relate to an automatic process that causes individuals to make no evaluations of the benefits of the THS, let alone the benefits of the OHS. On the other hand, when individuals are satisfied with the THS and have developed offline habits, they will tend to retain the THS as guidelines for health decisions, which can lead to their poor perceptions of the benefits of the OHS. Thus, we posit that: H4: THS habits are negatively associated with the perceived benefits of the OHS. Effects of Transition Costs and Privacy Protection Beliefs on Perceived Costs Transition costs refer to the costs incurred in changing a situation. There are two kinds of transition costs: temporary costs incurred during a transition, and permanent costs incurred after the transition (Samuelson et al. 1988). The temporary costs for health behavior transition are the time and effort spent in learning how to use the OHS. The permanent costs are related to the fact that using the OHS could influence individuals’ attitudes towards traditional healthcare that has long been the basic health business model, and consequently, individuals distrust the OHS (Goidel et al. 2013). Specifically, temporary costs can make up a proportion of the overall costs

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for using an OHS, and the permanent costs may lead individuals into believing that they have made sacrifices by using the new health service. These costs positively intensify their costs perceptions on using the new service. As the temporary and permanent costs increase during the transition from the THS to the OHS, people will perceive more costs regarding the use of the OHS. Thus, transition costs can positively affect individuals’ costs perceptions on using the OHS. Therefore, we posit that: H5: Transition costs are positively associated with the perceived costs of the OHS. The privacy protection beliefs refer to the subjective probability of individuals’ believing that service providers will protect their privacy as promised (Li et al. 2011). The privacy protection beliefs can reduce individuals’ privacy concerns (Pavlou et al. 2001). The online environment increases privacy fears (Cranor et al. 2000). If they have more privacy protection beliefs on the THS, individuals are likely to be more concerned about privacy when using the OHS. The perception of greater concerns on privacy will lead the individuals to perceive more risks and threats on their personal health information in the online context. Accordingly, they will have to expend greater effort in using the OHS, such as making efficient use of the service with less privacy information, having greater control over information disclosure, and increasing their ability to protect sensitive information (Raschke et al. 2014). These costs can be treated as the potential costs of using OHSs, and therefore will affect costs perceptions. Thus, we posit that: H6: Privacy protection beliefs on the THS are positively associated with the perceived costs of the OHS.

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Control variables

We also included several respondents’ demographic characteristics in the model as control variables to alleviate the influence due to covariance issues. Previous literature suggests that individuals’ age, gender, levels of education, and computer usage experiences can influence their purchase behavior on the Internet (Pavlou et al. 2006). We thus controlled the effects of these factors on the endogenous constructs, i.e., perceived benefits, perceived costs, and use intention. 4. Method and Analysis

4.1. Data Collection

An online survey was conducted to test our hypotheses. We surveyed online for two reasons. The first relates to our research topic, the OHS, which is delivered online, and that its consumers are mainly Internet users. Second, a report from the China Internet Network Information Center (CNNIC) indicated that in 2013, 45.8% of China’s population had become Internet users (CNNIC 2014). Hence, an online survey can be representative to a certain extent. The questionnaire was arranged in two parts. In the first part, respondents answered questions about their offline health experiences, which was followed by a brief introduction of the OHS. In the second part, they were asked about their perceptions of the OHS. The measures of all constructs, developed from previous literature (see Appendix A), were evaluated by a sevenpoint Likert scale. Demographic characteristics, such as age, gender, and education, were included as control variables. Some reliability-test questions were also included to remove unreliable samples. The questionnaire was first created in English, and then translated into Chinese. Next, the content validity was reviewed by a group of IS academicians. The data was collected in May 2014. The link (URL) of our questionnaire was delivered via email and social networks. A total of 374 respondents completed the questionnaire, and 338 valid questionnaires were collected after removing the invalid responses and those who had used the 16

service. Those who had used it were deemed unsuitable for testing our model as our focus is on the individual’s OHS use intention. The respondents were from 27 provinces in China. Of these, 55.03% were male. The young (i.e., younger than age 35) made up 55.0%, while the middle-aged (36-55 years) made up 40.9%. Most of them (86.7%) had previously attended college, and 84.9% had more than three years’ experience in computer usage. 4.2. Measurement Model

Smart PLS was used to test our measurement and structural models. The measurement model was first tested to ensure measurement quality. As habit was measured as a second-order construct, we first assessed the measurement model with all first-order reflective constructs, and then used these first-order constructs as formative measures of habit (Polites et al. 2012). Following this, we first tested the reliability and validity of the constructs. The results are shown in Table 1 and Table 2.

Composite reliabilities exceeded 0.840, significantly above 0.707, thus indicating composite reliability. Most of the loadings of items were above 0.7, except for PC4 (0.669), which were close to the threshold, thus indicating convergent validity (Chin 1998). Moreover, the factor loadings of each construct was much greater than the cross-loadings on other constructs, and correlations of the constructs were much smaller than the square root of the AVE (average variance explained) of each construct, thus indicating discriminant validity (Chin 1998). As some of the cross-loadings were higher than 0.450, there could be potential issues of multicollinearity. We further tested the variance inflation factors (VIFs) of PB and UI. Results show that all VIFs were less than 5, which were far lower that the recommended cutoff of 10 (Kutner et al. 2004). This indicates that multicollinearity was not an issue in this study.

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4.3. Common Method Bias Testing

As survey data was collected from single respondents using a single method, common method bias might threaten the validity of the results (Podsakoff et al. 2003). In our research, we checked the common method bias using Harman's (1967) single factor approach. Following this approach, we found the first unrotated factor explaining only 27.5% of the covariance of the main constructs in our model. Some scholars have argued that Harmon’s test is insufficient in testing common method bias (Podsakoff et al. 2003). Hence, we used a modified marker variable analysis to further test the common method bias (Rönkkö et al. 2011). Following Rönkkö et al. (2011), we chose three items of Internet Technicality in our data set that had low correlations with items in our research model as marker variable items. The marker variable was then included in the model and its impact was tested on three endogenous latent variables. The results showed that the marker variable had no significant impacts on user intention, perceived benefits, and perceived costs, and that the hypothesized relationships were qualitatively equal to the results before the marker variable was incorporated. These results indicate that common method bias had little influence on the results of our research. 4.4. Structural Model

To measure the second-order construct, i.e., health service habits, we ran the full model with its three dimensions (i.e., Awareness, Controllability and Mental Efficiency). The resulting construct scores were used as formative measures for habit (Polites et al. 2012). To address the collinearity concerns, we calculated the VIF values for the three formative measures. The values ranged from 1.030 to 1.292, which were much smaller than 3.30 (Diamantopoulos et al. 2006). This indicates that there was no serious collinearity threat. The results of the entire structural model are presented in Figure 2. 18

The results indicate that all direct effects are significant. Both perceived benefits and perceived costs have significantly different effects on use intention, and 35.4% of the variance of use intention is explained. Sunk costs and health service habits have negative effects on perceived benefits, and account for 17.6% of the variance. Both transition costs and privacy protection beliefs have positive effects on perceived costs, and explain 25.3% of the variance. After running the entire model, we tested the mediating role of perceived benefits and perceived costs based on the criterion identified by Baron et al. (1986). The results indicate that perceived benefits mediated both the effects of sunk costs (z=-2.57, p<.01) and habits (z=-2.43, p<.01), and that perceived costs mediated both the effects of transition costs (z=-1.77, p<.05) and privacy protection beliefs (z=-1.68, p<.05). These preceding results indicate that there is a mediating role of perceived benefits and perceived costs on the relationships between SQB factors and OHS use intention. 4.5. Post-hoc Analysis

This study aims at exploring how the effects of the SQB resulting from the THS are allocated as the costs and benefits perceptions of OHS usage. To further verify the proposed paths showing how the SQB factors influence OHS use intention, we conducted a post-hoc analysis. Two sets of mediation tests were conducted. The first comprised the mediation effects of perceived benefits between transition costs and perceived costs; and between privacy protection beliefs and perceived costs. The second comprised the mediation effects of perceived costs between sunk costs and perceived benefits; and between habits and perceived benefits. We adopted the criteria identified by Baron et al. (1986) to test the mediation effects. We tested: (1) the effects of DV on IV; (2) the effect of DV on mediator (path a); (3) the effects of the mediator on IV (path b); and; (4) we determined the mediation effect by a formula z-value=ab/√(b2sa2+a2sb2), in which sa is the

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standard error of a and sb is the standard error of b. Table 3 shows the results of the post-hoc analysis.

The results indicate that perceived benefits mediated most of the effects of transition costs and privacy protection beliefs on perceived costs, while perceived costs mediated most of the effects of sunk costs and health service habits on perceived benefits. Thus, the proposed paths were supported: transition costs and privacy protection beliefs influenced use intention through perceived costs, while sunk costs and health service habits influenced use intention through perceived benefits. 5. Discussion

Our study aimed at investigating how the SQB theory based inhibitors from traditional healthcare influence OHS use intention through a rational process. Furthermore, the roles of four antecedents (sunk costs, health service habits, transition costs, and privacy protection beliefs) and how they influence behavioral intention through benefits and costs perceptions were tested. Perceived benefits positively influence use intention, and perceived costs positively influence use intention. Sunk costs and health service habits negatively influence perceived benefits. Transition costs and privacy protection beliefs positively influence perceived costs. Moreover, the post-hoc analysis verifies that perceived benefits and perceived costs had mediation effects on the relationship between SQB factors and behavior intention. In addition, the effects of control variables, gender, age and education were positively seen to influence perceived benefits, while age negatively influenced perceived costs. The other relationships were not significant. From these results, five aspects of key findings can be concluded.

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5.1. Key Findings

First, factors in traditional healthcare hinder individuals’ OHS use intention. Sunk costs and health service habits negatively affect perceived benefits, and thus weaken use intention. Transition costs and privacy protection beliefs positively affect perceived costs and, in turn, weaken use intention. All these factors suggest that the SQB factors from a THS can hinder individuals’ intention to use the OHS, further contributing to the poor usage of the OHS. These results consequently extend the previous findings in the IS context that the status quo bias (SQB) exists not only in IS implementation (Kim et al. 2009; Polites et al. 2012), but also in offlineonline service transition. This finding also indicates that, in the diffusion of an innovation, the previous status will hinder individual’s perceptions on the innovation, which in turn induces low use intention or high resistance intention. Second, this research incorporates privacy protection beliefs into the SQB theory. We found that privacy protection beliefs positively influence individuals’ costs perception on using OHSs. As strong PPBs (Privacy Protection Beliefs) on THSs lead to uncertainty on the part of individuals about online privacy protection, PPBs can be seen as part of uncertainty costs leading to the SQB. This finding indicates that in an offline-online service transition, once the individuals believe the offline service can protect their privacy better than in the online situation, there is a probability for them to stay offline. Third, Hypotheses H3 to H6 are supported, indicating that SQB factors can influence behavioral intention through a rational choice perspective. Moreover, our analyses also show perceived benefits and perceived costs do mediate most of the effects of SQB factors on use intention. Thus, in decision-making, although individuals may factor biases into consideration, their decisions can also be partially rational through a rational benefits and costs evaluation process. Thus, even when biases exist in decision-making, the decision-makers can also make rational evaluations on 21

the alternatives. This finding indicates that decision-making may not be purely rational or irrational, and can be the results of interplay between the irrational factors and a rational process. Fourth, this study explored how the SQB factors, namely transition costs, privacy protection beliefs, sunk costs and health service habits, are allocated on the costs and benefits perceptions in a rational decision process. The results indicate that transition costs and privacy protection beliefs contribute to cost perceptions directly, while sunk costs and health service habits contribute to benefits perceptions directly. These results indicate that the SQB factors will not only influence human decisions, but also are able to exert their effects through a rational decision process. Hence, this finding supports the link between the SQB theory and human behavior, and indicates a rationale path showing how SQB factors influence future behavioral intentions. Finally, our research also indicates the effects of the control variables on the OHS decision process. Gender, age and education have positive effects on perceived benefits, indicating that males, the elderly and highly educated participants perceive the OHS more positively. Age has negative effects on perceived costs. This result reflects that young Internet users may consider more potential risks than the elderly as the former are more familiar with the Internet. Computer experience has no effect on the perceptions and intentions regarding the OHS. 5.2. Theoretical Contributions

This research offers several theoretical contributions. By combining the SQB theory and rational choice theory, this research furthermore augments both theories, as it divides SQB factors into two parts (e.g., benefits related and costs related), and creates links between the two theories. As a result, this research augments both the SQB theory and the rational choice theory. First, regarding the SQB theory, this research extends the previous understanding of this theory that considers only the SQB as the reason for resistance to new changes and has failed to discover how the SQB factors affect the benefits and costs perceptions of new changes (Kim et al. 22

2009; Polites et al. 2012; Samuelson et al. 1988). Our model shows a path from SQB related factors to perceived benefits and perceived costs in using the OHS. Moreover, this research theoretically proposed and empirically verified that the SQB theory evaluates less rational factors in decision-making and results in “less optimal choices”. But once the factors are decided, the individual’s decision process can also be rational. Moreover, another contribution of our research is the dissecting and testing of how the SQB influences human decisions in two stages, initially less rational, but developing into rational decisions in subsequent processes. This is a research area that has received little empirical attention in the SQB literature. Second, our study is, to the best of our knowledge, with regard to the rational choice theory, the first to propose and empirically verify that the rational choice theory can be used despite the existence of biases. The rational choice theory has been criticized, as an individual’s behavior is not always rational because of pressures, biases, and so on. (Mellers et al. 1998). Social scientists and psychologists disagree over strengthening assumptions to enhance the theory (Mellers et al. 1998). In this research, we used the theory as a theoretical perspective to measure the rational decision processes occurring when biases exist. The results indicate that although biases and errors that are taken into account can cause less rational choices, the decision process can be rational. Therefore, when future research on the rational choice theory involves rationality issues, researchers can simply locate and overcome the less rational factors to make the theory adaptable. Third, this research provides a new insight into user acceptance behavior in a new context. The use of the SQB theory to study the inhibitors of online service use intention has received limited attention in the IS literature (Kim et al. 2009). Based on the SQB theory and the rational choice theory, our research examines the inhibitors of the OHS from a traditional healthcare perspective. Through theorizing and testing the inhibitors from THSs, this study provides an initial

23

understanding of how THSs affect individuals’ online health behavior, and also offers new insights into behavioral research on new e-services that the impacts from the offline context cannot be neglected.

Finally, this research also contributes to extending the SQB theory in the context of health behavior by incorporating privacy factors. Although the SQB theory has been introduced and empirically studied in the health context over an extensive period (Boonen et al. 2011; Schut et al. 2002), limited research has ensued on explaining the transition from offline to online healthcare. Furthermore, this study explores one of the primary concerns in health services, i.e., privacy, as a measure of uncertainty costs. Therefore, our study extends the SQB theory and makes it more applicable in the health service context. 5.3. Practical Contributions

This research also provides certain practical contributions. Studying the SQB based inhibitors of OHS can provide practitioners with guidelines on accelerating its diffusion. From our results, we offer service providers the following suggestions, i.e., to: 1) promote the benefits of the OHS in order to break the effects of sunk costs and habits; 2) reform individuals’ offline health service habits; 3) reduce the costs perceptions of using the OHS; and 4) develop privacy protection mechanisms and make them known to potential users. For instance, the combined efforts of service providers, policy makers and health professionals can prove useful in breaking the inhibiting impacts of traditional healthcare on OHS behavior. Second, this research also provides insights for technology designers in designing online health websites. Owing to the negative effects of transition costs, habits and privacy protection beliefs, designers need to make the platforms more accessible and to emphasize the benefits and privacy protection mechanisms on the websites. To mitigate the inhibiting effects of traditional healthcare habits, designers could conduct offline scene simulations in the online service to reduce the influence of previous habits. 24

Third, for potential users of OHS, our findings advise against the irrationality of their decisionmaking processes in their use of the OHS. The psychological commitment and incumbent service habits can lead to their continuing use of offline health services, which can be irrational, and are aspects that should be addressed. Finally, regarding the effects of control variables, our research found that males, the elderly, and highly educated individuals evaluate the OHS more positively. Thus, the service providers can target these groups as their priority, and subsequently encourage them to influence other groups. 5.4. Limitations and Directions for Future Research

This research is not without its limitations. First, the SQB can be the result of three factors: switching costs, cognitive misperceptions, and psychological commitment (Samuelson et al. 1988). Our model does not examine the effects of cognitive misperceptions. Second, this study used the OHS as a research context to explore a human decision process, but was conducted solely in the Chinese context. Thus, there may be cultural differences and healthcare regulation issues that threaten external validity in studies of other contexts. Third, for the inhibitors against using the OHS, there can be other inhibitors not belonging to the THS. This research mainly explored the inhibitors from the THS, and we shall consider other inhibitors for future research. In the light of our limitations, we propose that future research can focus on enriching the SQB theory and examining the contingent factors in different research contexts. In the future, researchers can also examine the inhibitors from both the OHS and THS to provide an overall explanation of the low usage rate of the OHS. 6. Conclusion

The ongoing growth of OHS usage indicates that currently many individuals are gradually accepting it. However, as the study on “Hao Daifu Zaixian” suggests, most visitors only visit the website and do not engage in transactions, resulting in a low usage rate of the OHS. Hence, 25

understanding individuals’ decision-making processes on their OHS use intention, especially the inhibitors in the processes, serves not only to meet the primary goals of OHS practitioners, but also to explain the low e-service usage rate. We aimed to account for the low visit/use conversion rate of the OHS from the theoretical and empirical perspectives. Two research questions were proposed to explore which inhibitors from the THS can influence OHS diffusion and how they influence the use intention. This study then developed an integrated model by combining the inhibitors from the SQB theory and rational choice theory. It verified that the inhibitors from the THS (i.e., sunk costs, health service habits, transition costs, and privacy protection beliefs) hinder OHS use intention, and that these SQB factors can influence human behavior through a subsequent rational process. Moreover, this study discovered how SQB factors influence human decision-making through costs and benefits perceptions. Our research contributes to the SQB theory by examining whether its subsequent decision processes can be rational; and to the rational choice theory by examining whether it can be used when biases exist. It also contributes to e-service acceptance research by exploring its low usage rate. This study offers practitioners insights into increasing the diffusion of the OHS, such as how to break the influence of biases in consumers’ decision-making. As the cultural differences and healthcare regulations among others limit this study, future research should focus on enriching the SQB theory and examining the contingent factors in different research contexts. 7. Acknowledges

This study is a learning outcome of the Guided Study subject of the first author at The Hong Kong Polytechnic University. This study was also partially funded by the National Natural Science Foundation of China (71531007, 71471048, 71471049, and 71490724).

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Appendix A

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Xiaofei ZHANG is a joint PhD candidate of Harbin Institute of Technology and The Hong Kong Polytechnic University. His research interest is in the area of IT-related healthcare. His research has appeared in Electronic Commerce with Research and Applications, Telemedicine and eHealth, Technology and Health care, and others.

Xitong GUO is a Professor of Information Systems at the Harbin Institute of Technology. He received his PhD in Information Systems at the City University of Hong Kong and PhD in Management Science and Engineering at the University of Science and Technology of China. His current research focuses on e-Health, social computing, and IT-enabled innovation. His work has been published in peer-reviewed journals, including Journal of Management Information Systems, ACM Transactions on Management Information Systems, Decisions Support Systems, Electronic Commerce with Research and Applications, Electronic Markets, and others.

Yi WU is an Assistant Professor at the Department of Information Management and Management Science, Tianjin University. He received his PhD in Information Systems at the National University of Singapore. His research interests include e-healthcare, social media, social network theory and analysis, and human-computer interaction. Currently, his work has appeared in the information systems conferences such as International Conference on Information Systems (ICIS), Americas Conference on Information Systems (AMCIS), IFIP Working Group 8.2 Conference, and others.

Kee-hung Lai is an Associate Professor in the Department of Logistics and Maritime Studies at the Hong Kong Polytechnic University. He obtained his PhD in Business from the same

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university. He has co-authored seven books and published over 100 papers in journals such as Production and Operations Management, Information & Management, Journal of Management Information Systems, International Journal of Production Economics, California Management Review, Communications of the ACM, Journal of Business Logistics, and others.

Douglas R. VOGEL is a Professor of Information Systems and is an Association for Information Systems (AIS) Fellow as well as previous AIS President. He received his PhD in Information Systems from the University of Minnesota in 1986. He has published widely and directed extensive research on eHealth, group support systems, knowledge management, and technology support for education. He has recently been recognized as the most cited IS author in AsiaPacific. He is currently engaged in introducing mobile devices and virtual world support for collaborative applications in educational and health systems.

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Sunk Costs (SCST) Health Service Habits (HSH)

-

H3

-

H4

Perceived Benefits (PB)

H1+

-

Use Intention (UI)

H2

Transition Costs (TCST)

+

H5

Perceived Costs (PC) Rational Choice Theory

Privacy Protection Beliefs (PPB)

H6+

Beliefs

Intention Control Variables

Status Quo Bias

Antecedents

Figure 1. Research Model

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Sunk Costs H3 -.152**

R2=.176

Health Service Habits

Transition Costs

H4 -.178*

Perceived Benefits

H5 .174**

Perceived Cost

H6 .102*

Perceived Benefits

H1 .547**

H2 -.267**

Use Intention R2=.354

R2=.253

Privacy Protection Beliefs (PPB)

Note 1: dashed lines represent nonsignificant effects; solid lines represent significant effects. Note 2: ns :Not Significant; * :p<.05; **:p<.01

Gender

Perceived Costs

Age

Education

Figure 2. PLS Results of the Structural Model

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Use Intention

Computer Experience

Hypotheses Results Hypothesis Supported H1 Yes H2 Yes H3 Yes H4 Yes H5 Yes H6 Yes

Table 1. Loadings and Cross Loadings UI SCST TCST HBTA HBTC HBTM PB PC PPB UI1 -0.217 -0.247 -0.226 -0.238 -0.061 0.563 -0.265 -0.246 0.916 UI2 -0.176 -0.171 -0.188 -0.175 -0.029 0.496 -0.223 -0.207 0.910 UI3 -0.225 -0.283 -0.251 -0.237 -0.016 0.516 -0.252 -0.309 0.908 SCST1 -0.204 0.349 0.250 0.228 0.128 -0.274 0.172 0.298 0.952 SCST2 -0.225 0.259 0.266 0.219 0.098 -0.224 0.134 0.277 0.927 TCST1 -0.187 0.379 0.198 0.165 -0.026 -0.225 0.211 0.262 0.881 TCST2 -0.242 0.176 0.198 0.056 -0.056 -0.229 0.187 0.165 0.845 HBTA1 -0.180 0.295 0.109 0.369 0.065 -0.142 0.049 0.323 0.737 HBTA2 -0.233 0.213 0.222 0.390 -0.090 -0.232 0.095 0.356 0.939 HBTA3 -0.218 0.233 0.244 0.429 -0.117 -0.199 0.125 0.395 0.913 HBTC1 -0.246 0.206 0.121 0.383 0.068 -0.165 0.068 0.249 0.903 HBTC2 -0.196 0.241 0.145 0.444 0.104 -0.211 0.052 0.305 0.965 HBTC3 -0.234 0.216 0.101 0.435 0.105 -0.194 0.022 0.311 0.931 HBTM1 -0.024 0.066 -0.061 -0.076 0.154 -0.054 -0.143 0.016 0.870 HBTM2 -0.057 0.134 -0.037 -0.064 0.077 -0.099 -0.110 0.025 0.940 HBTM3 -0.028 0.118 -0.040 -0.060 0.081 -0.122 -0.084 0.000 0.957 PB1 0.528 -0.257 -0.255 -0.183 -0.181 -0.096 -0.197 -0.196 0.920 PB2 0.544 -0.234 -0.189 -0.193 -0.163 -0.121 -0.191 -0.196 0.908 PB3 0.486 -0.230 -0.269 -0.236 -0.214 -0.071 -0.245 -0.209 0.871 PC1 -0.198 0.153 0.236 0.079 0.049 -0.071 -0.221 0.188 0.890 PC2 -0.342 0.166 0.195 0.118 0.092 -0.087 -0.296 0.115 0.892 PC3 -0.043 0.094 0.117 0.074 -0.069 -0.145 0.060 0.091 0.753 PC4 -0.002 -0.047 0.095 -0.012 -0.108 -0.170 0.097 0.013 0.669 PPB1 -0.260 0.264 0.221 0.367 0.273 0.038 -0.212 0.139 0.904 PPB 2 -0.239 0.290 0.239 0.353 0.261 0.032 -0.186 0.103 0.924 PPB 3 -0.264 0.289 0.251 0.399 0.309 -0.021 -0.208 0.175 0.922 Note: HBTA= Habit (Awareness); HBTC= Habit (Controllability); HBTM= Habit (Mental Efficiency).

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Table 2. Correlation Matrix AVE C.R. UI SCST TCST HBTA HBTC UI 0.830 0.936 0.911 SCST 0.883 0.938 -0.227 0.940 TCST 0.745 0.854 -0.258 0.328 0.863 HBTA 0.753 0.900 -0.244 0.273 0.229 0.867 HBTC 0.871 0.953 -0.239 0.238 0.131 0.453 0.933 HBTM 0.852 0.945 -0.040 0.122 -0.046 -0.069 0.100 PB 0.810 0.927 0.578 -0.267 -0.263 -0.226 -0.205 PC 0.578 0.841 -0.272 0.165 0.231 0.107 0.050 PPB 0.841 0.941 -0.280 0.306 0.250 0.411 0.311 Note: AVE= average variance explained; C.R.= composite presented data refer to the square roots of AVEs

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HBTM PB

PC

PPB

0.923 -0.107 0.900 -0.113 -0.233 0.760 0.013 -0.222 0.158 0.917 reliability; the bold diagonally

Table 3. Results of the post-hoc analysis IV

Mediator

DV

Transition costs Privacy protection beliefs Sunk costs Health service habits

Perceived costs Perceived costs Perceived benefits Perceived benefits

Perceived benefits Perceived benefits Perceived costs Perceived costs

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Mediation Effect (Z-value) 1.684 2.303 -1.647 1.980

p-Value .0461 .0106 .0498 .0239

Table A. Measurements Constructs

Habit–Awareness (Polites 2009)

Habit – Controllability (Polites 2009)

Habit –Mental Efficiency (Polites 2009)

Privacy Protection Beliefs (Pavlou et al. 2001)

Sunk Costs (Polites et al. 2012) Transition Costs (Polites et al. 2012) Perceived Benefits (Liang et al. 2013)

Perceived Costs (Bulgurcu et al. 2010)

Use Intention (Johnston et al. 2010) Internet Technicality (Kim et al. 2007)

Measurements Whenever I need to see a doctor/have health consultations, I choose to use [offline health services] without even being aware of (making) the choice. Whenever I need to see a doctor/have health consultations, I unconsciously start using [offline health services]. Choosing [offline health services] when I need to see a doctor/having health consultations is something I do unconsciously. I (would) find it difficult to overrule my impulse to use [offline health services] when I need to see a doctor/have health consultations. I (would) find it difficult to overcome my tendency to use [offline health services] when I need to see a doctor/have health consultations It would be difficult to control my tendency to use [offline health services] when I need to see a doctor/have health consultations. I do not need to devote much mental effort to decide that I will use [offline health services] when I need to see a doctor/have health consultations. Selecting [offline health services] when I need to see a doctor/have health consultations does not involve much thinking. Choosing [offline health services] when I need to see a doctor/have health consultations requires little mental energy. Using [offline health services] would help me control the privacy of my information. Using [offline health services] would help me protect my privacy because my personal information could not be used without my knowledge. There would be little possibility for others to control my information if I used [offline health services]. I have already invested a lot of time and effort in getting used to seeing a doctor/ having health consultations in the hospital. I have already invested a lot of time and effort in perfecting my skills at seeing a doctor /having health consultations in the hospital. Learning how to use [online health services] would take too much time. Becoming skillful at using [online health services] would be easy for me. [reverse coded item] [Online health services] improve my capability in managing my health conditions. [Online health services] increase my knowledge of my personal health conditions. [Online health services] help to relieve stresses about my new symptoms or my worries about new symptoms. [Online health services] could be harmful. [Online health services] could impact my health negatively. I could make wrong decisions regarding my health based on the poor quality of [online health services]. Using [online health services] would generate losses for me. I intend to transit to [online health services] in the future when needed. I predict I will use [online health services] in the future when needed. I plan to use [online health services] in the future when needed. It is easy to use the Internet. The Internet can be connected instantly. It is easy to get the Internet to do what I what it to do.

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