Understanding continuance intentions of physicians with electronic medical records (EMR): An expectancy-confirmation perspective

Decision Support Systems 77 (2015) 112–122

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Decision Support Systems journal homepage: www.elsevier.com/locate/dss

Understanding continuance intentions of physicians with electronic medical records (EMR): An expectancy-confirmation perspective Anteneh Ayanso ⁎, Tejaswini C. Herath, Nicole O'Brien Department of Finance, Operations, and Information Systems, Goodman School of Business, Brock University, 500 Glenridge Avenue, St. Catharines, Ontario L2S 3A1, Canada

a r t i c l e

i n f o

Article history: Received 3 December 2014 Received in revised form 27 April 2015 Accepted 11 June 2015 Available online 18 June 2015 Keywords: Health care IT IS adoption IS continuance ECT EMR Perceived risk

a b s t r a c t This paper examines physicians' continuance intentions with electronic medical records (EMR) in the postadoption phase. Expectation-confirmation theory (ECT) is used with the incorporation of perceived risk as the theoretical foundation. Based on the extended ECT model, eight hypotheses were formulated to aid in the understanding of physicians' continuance intentions. A field survey of 135 Canadian physicians that utilize EMR systems was conducted to test these hypotheses. The study found that physicians are willing to continue using EMR systems. In addition, the empirical results suggest that perceived usefulness and perceived risk impact satisfaction, which in turn influences physicians' continuance intentions. In addition, perceived risk also has a direct influence on physicians' continuance intentions. These results contribute to the IT adoption and use literature, particularly in the post-adoption context. © 2015 Elsevier B.V. All rights reserved.

1. Introduction In the last few decades, implementing enterprise-wide systems has become a common practice in businesses and government institutions. During the same time period, health care systems worldwide have felt increasing pressure on both physical and financial resources due to increases in patient needs and operational costs. As a result, investing in information systems has been one of the top government priorities in many nations as a way of increasing efficiency and decreasing costs in their health care systems. The adoption of information systems in health care involves the compiling of patient records from different sources into a central digital repository. Several terms have been used in practice as well as in the literature to describe this software, including electronic health record (EHR), clinical information systems (CIS), clinical information technology (clinical IT), and EMR. In this paper, the term electronic medical records (EMR) is used, and its scope is defined to represent the software system and vendor services that are used to capture the medical records of patients from sources inside and outside a physician's practice. With the rise of big data and analytics in recent years, ERM systems play a significant role in the health care industry in facilitating access to timely information, sharing patient information among health care units, reducing medical errors, and improving health care operations and decision making. EMR systems provide benefits such as the ⁎ Corresponding author. E-mail addresses: [email protected] (A. Ayanso), [email protected] (T.C. Herath), [email protected] (N. O'Brien).

http://dx.doi.org/10.1016/j.dss.2015.06.003 0167-9236/© 2015 Elsevier B.V. All rights reserved.

improvement and support of patient care, as well as increased productivity while reducing costs [60]. Further benefits of EMR systems versus paper records include records being more legible, accurate, and easily retrieved for use and sharing [61,65]. In addition, EMR systems help ensure that the best medical practices are employed to provide the best patient outcome [37]. By reducing errors as well as operational time and costs, EMR systems also provide growth opportunities and scalability for health care practices. These strategic and operational benefits highlight that EMR systems serve as decision support tools, rather than just data repositories. EMR systems contain many different functions to aid in the delivery of health care services by health care providers and to enhance decision making. These functions are broken down into two separate categories: practice management and clinical support [41]. Most physicians use applications for practice management, which consists of scheduling appointments for patients and billing for services. However, the adoption of clinical support functions is becoming common among physicians [56,57,65]. Clinical support functions include the creation of patient notes in electronic form for all patient encounters; writing or renewing prescriptions; providing alerts for lab results; the ability to receive and import lab results electronically; the ability to compile patient information and documents from other sources within the health care community; and providing best practice alerts for the care and treatment of disease and illness. There are many different stakeholders within the medical community that utilize EMR systems. A few of the stakeholders include physicians, nurses, laboratory technicians, physician's office staff, hospital admitting, and researchers [45]. It was found that physicians carry the deciding vote

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on utilizing EMR systems, due to their dominance in health care and status within the medical community [34,49]. Therefore, this paper focuses on physicians as the key stakeholders due to their power to influence the utilization of EMR systems within the health care system. Research has shown that EMR systems are slowly being incorporated into medical practice, first with the adoption of basic functional components and more recently with the inclusion of extended components [26,56,57]. It is expected that physicians will adopt additional functional components of EMR systems over time. As observed in a 2007 study of physician practices that used an EMR system, only 60% utilized the function for incorporating lab results, and less than 20% had implemented the alerts, warnings, and reminder functions [56]. The Health Care Information and Management Systems Society (HiMSS) [26] has developed a six-step adoption model for EMR systems in physicians' practices, with each step incorporating additional functions into operation. Although the use of an EMR system is expected to create better outcomes for patients [37,44], while also decreasing costs and increasing efficiencies [44], the slow rate of adoption in comparison to other software applications has been a concern for many. The pre-adoption stage of the process has been studied in the form of resistance to new technology [6,42] and physicians' expectations in their adoption of an EMR system [9,20]. The level of EMR system adoption has been also analyzed through historical as well as cross-country comparisons in order to increase awareness and increase adoption [52,57]. For example, the number of physicians that use an EMR system is known from surveys done on a regular basis such as the National Physician Survey by the Canadian Medical Association (CMA). These types of surveys, however, lack a vital piece of information to accurately compare the adoption rates of physicians on a historical basis. More specifically, the number of physicians who have discontinued use of an EMR system or have stopped the adoption process at a specific stage has not been given significant attention. In one study done in 2006, roughly 50% of physicians abandon an EMR system after adoption [29]. This gives an indication that physicians will discontinue using an EMR if they find it too difficult to use or incorporate into their routines. While the pre-adoption opinions of physicians towards EMR systems have been examined in the literature [e.g., 12,18,63], their post-adoptive behaviors regarding their satisfaction with EMR systems and their intentions to continue use or adopt extended functions have received limited attention both in the information systems and health services fields. Quite simply, physicians' continuance intentions of EMR systems are not very well known at this time. In this paper, physicians' post-adoption intentions towards continued use of EMR systems and the extended functions during different stages of the adoption process are studied. In particular, this study investigates physicians' satisfaction with EMR systems in the postadoption phase and how their satisfaction impacts their intentions to continue the use of the systems and their extended functions within their practices. The expectation-confirmation theory (ECT) is used as the main theoretical foundation to aid in the understanding of physicians' continuance of EMR systems after adoption [4,39,40,43]. Furthermore, using ECT as the base model, perceived risk is incorporated [15,23] to examine its effects on physicians' continuance intentions of EMR systems. 2. Literature review A range of issues have been examined in current research about EMR systems, including the reasons for physicians' resistance to adoption, and the difference in demographics between those that use EMR systems to those that do not. However, most of the existing research focuses on the pre-adoption behavior of physicians and other stakeholders within health care [6,59]. These studies explain why physicians and the health care community have been slow to adopt EMR systems. The studies that examine the pre-adoption opinions of physicians have mainly used the technology acceptance model (TAM), in addition

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to a few other theories. TAM was used to understand physicians' intentions to adopt EMR systems or specific functions [3,6,55]. The studies that used TAM to understand physicians' resistance to EMR systems also incorporated additional constructs. For example, IT efficacy was examined in the study by Dixon and Dixon [19] to show that lack of IT knowledge increased the resistance to adoption of EMR systems. Dixon and Stewart [20] also analyzed physicians' resistance to EMR systems by dividing the physicians into demographic groups of high and low users of EMR systems. Bhattacherjee and Hikmet [7] found that the level of available organizational support of IT through technical support and infrastructure had a direct positive impact on physicians' willingness to adopt EMR systems. In another study, Boonstra et al. [9] used TAM to understand physicians' limited acceptance of the prescription function of EMR systems. They found that practice culture was a significant factor for adoption. Whereas, Sicotte et al. [55] used a modified TAM framework to understand physicians' acceptance of electronic prescribing that were early adopters. Other theoretical frameworks have been also used to understand the adoption of EMR systems. Angst et al. [2] analyzed the adoption of EMR systems from the perspective of a social contagion to study the diffusion of EMR systems within the health care system. Other researchers have tried to gain an understanding of the barriers to EMR systems adoption. Some of the barriers identified have been the perceived threats of EMR systems such as loss of control over work, loss of organizational status, loss of power, and loss of control over organizational resources [7]. Anderson [1] also found that physicians resist EMR systems for fear that the routines in their traditional practice would be altered. They fear that these routines would be altered by the imposition of restrictions on how individual records are recorded and organized, which may not be personalized to their requirements. To understand why some physicians have adopted EMR systems and others have not, some studies have analyzed the demographic characteristics of physicians [35,64]. It has been found that the categorical characteristics of physicians affect their willingness to adopt EMR systems. These categories include the number of years in practice, the practice setting, practice type, patient's characteristics, as well as the age and gender of the physician. Other studies have analyzed the differences between countries that have high adoption and utilization rates to those that have lower rates in order to understand the different catalysts and disincentives [19,27,48,51]. The major areas affecting adoption and utilization rates are the policies implemented by governments along with the different cultural perspectives within and outside of health care in the different countries. In contrast to the pre-adoption phase, the research in the postadoption context is very limited. The expectation-confirmation theory (ECT) was utilized in a study of users, patients, and other health care stakeholders who visited an e-health website [31]. In this study, the users' intentions to continue using the e-health website of the National Cancer Center (NCC) in Korea were examined. This study, however, focused mainly on the patient to evaluate if the website met the patient's information needs. In other related studies, physician satisfaction rates with EMR systems were examined [57,58]. Simon et al. [57] compared the satisfaction rates of non-adopters to adopters. They also examined three levels of intensity of EMR systems use with regards to the different dimensions of health care, which includes control of costs, quality of care, and interaction with team and patient. Although it was observed that physicians in larger practices were more likely to use most of the available functions of the EMR systems than those physicians in smaller practices, the study found that there was little difference in satisfaction between the types of users. Sittig et al. [58] also analyzed physicians' satisfaction about the user friendliness of the system and where system improvements are needed. The findings suggest that users need to be included in developing improvements of specific EMR systems functionalities. EMR systems adoption was also analyzed in comparison with the positive and negative experiences of using the systems [38]. It was found that

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positive experiences such as time savings and improved access to data increased physician adaptation of EMR systems. Negative impacts on physician adaptation of EMR systems were also uncovered, including the negative effects on productivity, as well as the mismatch between work practices and processes. King et al. [30] studied the impact on physician's perceptions on clinical benefits and patient care in the context of using EMR systems with meaningful use standards. They found that physicians that used EMR systems that met the meaningful use standards and had a few years experience with them had clinical benefits [30]. 3. Theoretical foundation and conceptual model The main objective of this paper is to examine the continuance intentions of physicians with EMR systems as well as physicians' intentions to adopt extended functions of EMR systems in their practice. To achieve this goal, the expectation-confirmation theory (ECT), with the incorporation of the perceived risk construct, is utilized as the theoretical foundation. The ECT originates from the consumer behavior research domain [39,40] and was later modified by Bhattacherjee [4,5] in understanding continuance intentions of information systems (IS) users. Using the extended expectation-confirmation framework [4,5] as the base model, this study incorporates perceived risk and provides an integrated model of physicians' continuance intentions with EMR systems. Perceived risk is the expected negative effects of using a product or service [23]. Fig. 1 shows the conceptual model. 4. Expectation-confirmation theory (ECT) The expectation-confirmation theory has been utilized in different contexts to understand the post-adoption purchase or usage behavior of individuals. The original model was proposed by Oliver [39] as a means to understand consumers' satisfaction with a product or service and how this satisfaction impacted their willingness to repurchase the product. Bhattacherjee [4,5] modified this model to understand a user's continuance intention with IS. In the original ECT model [39], both the post-adoption and pre-adoption opinions of users were analyzed to understand consumer repurchase intentions. Bhattacherjee [5] argued that pre-adoption opinions were not necessary to understand consumers' intentions to continue purchasing a product or using IS, thus suggesting that a longitudinal study was not required. In the modified ECT for IS continuance, users continually update their expectations as they gain

more experience with IT. This continual updating of user expectations can create very different expectations than the user's initial expectations. Consequently, “the effects of pre-acceptance variables are already captured within the confirmation and satisfaction constructs” [5, p. 355]. Expectations are seen as irrelevant, thus only the post-adoption expectation of IS are used in the model. EMR systems for our study have been defined as utilitarian as the intent of the system is to strengthen the physician's task performance and encourage efficiency [62]. Thus, the physician is motivated to use EMR systems primarily from a perspective of usefulness and not on how easy the system is to use. Bhattacherjee [5] also argued that while both perceived usefulness and ease of use may influence subsequent continuance decisions, empirical studies have assessed the relative effects of perceived usefulness on attitudes to be substantial and consistent during both stages of IS use. Prior studies [17,28] also found that ease of use has an inconsistent effect on attitudes in the initial stages and seems to become non-significant in later stages [5]. Therefore, following the arguments provided by Bhattacherjee [5] in the modified ECT framework, the perceived usefulness is considered to be the most salient ex post expectation influencing post-acceptance bahaviors. Subsequently, in this study, we use the constructs, satisfaction, confirmation, and perceived usefulness from the modified ECT [5] to aid in the understanding of physicians' post-adoption intentions with EMR systems. According to ECT [5], confirmation of previous expectations will affect both the user's satisfaction and the perceived usefulness of IS. The constructs of satisfaction and perceived usefulness affect a user's willingness to continue using IS. Perceived usefulness impacts the level of satisfaction of the user and satisfaction levels tend to change with the user's perception of the usefulness of IS. For example, if the IS is found to be useful, there will be a positive impact on the user's satisfaction. Similarly, physicians' satisfaction depends on the confirmation of their expectations with EMR systems. If their confirmation of expectations is positive, then this would reflect in the level of physicians' satisfaction. If physicians' expectations are negatively confirmed, then the impact on satisfaction would be diminished. Thus, we hypothesize that the confirmation of expectations by physicians with their initial use of EMR systems will positively impact satisfaction. Furthermore, satisfaction with EMR systems will have a positive effect on continuance intentions as well as the adoption of extended EMR system functions. Thus, the first hypothesis is as follows.

Perceived Usefulness

Confirmation

Satisfaction

Perceived Risk

Fig. 1. Conceptual model.

EMR systems Continuation Intention

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Hypothesis 1. Physicians' satisfaction with EMR systems is positively related to their continuance intentions with EMR systems. Furthermore, the initial confirmation of physicians' expectations with EMR systems leads to their satisfaction with the systems, whereas a disconfirmation of the physicians' expectations with EMR systems will have a negative impact on the level of satisfaction. Thus, following the expectation-confirmation model of IS continuance, the following hypothesis is made. Hypothesis 2. Physicians' confirmation of expectations is positively related to their satisfaction with EMR systems. During the adoption and utilization phase of EMR systems, physicians evaluate if their initial perceptions were correct. After physicians adopt and utilize EMR systems, their perception of usefulness may change. This change of perception regarding the usefulness of EMR systems impacts the level of satisfaction. As a result, the following hypothesis is made concerning the relationship between perceived usefulness and satisfaction with EMR systems. Hypothesis 3. Physicians' perceived usefulness is positively related to their satisfaction with EMR systems. As physicians utilize EMR systems, they assess its usefulness in their practice. If the system is deemed not useful in their practice, physicians may not continue using EMR systems or may not continue to adopt extended functions of EMR systems in order to expand the scope of the system. Therefore, following the ECT framework, the following hypothesis about the relationship between perceived usefulness and continuance intentions is formed. Hypothesis 4. Physicians' perceived usefulness is positively related to their continuance intentions with EMR systems. Physicians' expectations regarding the potential practicality of EMR systems are equivalent to perceived usefulness. The opinions about the usefulness of EMR systems by physicians may change when expectations are confirmed. Physicians' confirmation of expectations will have a positive effect on their perceptions of usefulness. Conversely, physicians' disconfirmation of expectations will negatively influence the perception of usefulness. Thus, following the ECT framework, the following hypothesis involving confirmation and perceived usefulness is formed. Hypothesis 5. Physicians' confirmation of expectations is positively related to their perceived usefulness of EMR systems.

5. Perceived risk (PR) Perceived risk has been used in conjunction with the technology acceptance model (TAM) to aid in the understanding of consumers' postadoption perceptions [23,32,33]. Perceived risk has been also used to aid in the understanding of consumers' adoption or resistance to IT. TAM and ECT both observe the consumer's expectations with regards to IT, but at different points in the adoption process. TAM is used to study the pre-adoption attitudes of consumers, whereas ECT analyzes the post-adoption attitudes of consumers. However, perceived risk has received limited attention in the IS continuance literature. Perceived risk is defined as “felt uncertainty regarding possible negative consequences of using a product or service” [23, p. 453]. When the level of uncertainty or the consequences are more substantial, the level of perceived risk increases [33]. Perceived risk is a combination of two different factors: the amount at stake and the level of uncertainty for the individual [15]. In the perspective of physicians, there are several different kinds of risk involved with the use of EMR systems. These different aspects of risk are not only relevant in the continued use of EMR systems, but also in the adoption of extended functions of the system.

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Because risk is an important factor that can potentially impact a physician's perception of EMR systems, this study integrates perceived risk into the ECT framework to gain a more comprehensive understanding of the continuance intentions of physicians with EMR systems. The perception of risk associated with EMR systems leads to physicians' resistance to adopt EMR systems [6]. In the pre-adoption literature, there are several critical areas of implementation for EMR systems. These critical areas include acceptance and change management, demonstration of benefits and funding, project management, health policy-related goals and implementation, basic legal and data protection, and technical difficulties [18]. There have been several dimensions of risk identified in the preadoption literature for physicians' resistance to adopt an EMR [9]. These include financial, performance (lack of computer skills), time, psychological, social, and privacy/ security. These perceived risks are part of the reason that physicians resist EMR systems. Perceived risk originated with Cox and Rich [15] and has been further expanded since then. The two major works include the studies by Featherman and Pavlou [23] and Lu et al. [33]. The dimensions of perceived risk defined by Featherman and Pavlou [23] include performance, financial, time, privacy¸ social, psychological, and overall risk. Lu et al. [33] also defined perceived risk to include the dimensions, physical, functional, social, time-loss, financial, opportunity risk, and information. Based on these studies, we identify seven dimensions of risk that broadly define a physician's perceptions of risk with EMR systems. These include performance, financial, time-loss, social, psychological, privacy, and overall risk. A brief description of each of these seven risk dimensions is given in Table 1. Performance risk is the risk associated with EMR systems not performing as expected or needed. The first issue of performance risk is related to the complexity of EMR systems in both the hardware and software requirements [42]. With more complex technology, there is a greater chance for glitches in the system that vendors may not be available or willing to address. The second issue with performance risk is the expectation of how the system should perform. Physicians expect to be able to access patient information in a timely manner with all relevant information about the patient available. If patients' records are unavailable to physicians or if the system cannot show all pertinent patient information, important information may be missed. Missing information can have serious consequences and a negative impact on patient care. Financial risks of EMR systems are the financial burden they place on the individual practices. Costs associated with EMR systems not only include the initial costs of implementation but also the continuing costs of maintaining the system and the periodic system and equipment upgrades. These additional costs due to EMR systems may not be covered by an increase in income from the physician's practice. Privacy risk for physicians is the lack of control over personal information [23]. This risk is significant to both the patient and the physician. Medical records in electronic format are easier to access than the paper Table 1 Perceived risk dimensions [23,33]. Dimensions

Definition

Performance risk

The risk that the EMR systems do not function as intended or as needed. The risk that the EMR systems are a financial burden on the physician's practice. The risk of losing control over confidential patient information. The risk that a physician will feel their self-image is harmed by using EMR systems. The risk for the physician that a loss of social status will occur within the physicians' community. The risk that using EMR systems will take excessive amounts of time to create and access patients' records; as a result, EMR systems waste time. The overall perceived risk of EMR systems from the physician's perspective.

Financial risk Privacy risk Psychological risk Social risk Time-loss risk

Overall risk

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versions. The ability to access medical records easily creates a higher chance that these records may be leaked or accessed by unauthorized individuals. The information in those records is considered confidential as they contain both patients' personal information and the personal notes of physicians. Unauthorized access is a concern not only for the physician but for patients as well. There are two key privacy issues for the physician. The notes of physicians are considered to be intellectual property, thus they are protected by copyright law. This legal protection of physicians' notes creates a need for physicians to protect their notes as well as the notes of other physicians when included in shared patients' records. The second issue is the security of the patients' personal information. The physician has a moral and legal obligation of maintaining patient confidentiality. Physicians are responsible to ensure that patients' records are kept private. There is an increased risk in security breaches with patient information when EMR systems are utilized. The increase is partly due to the sharing of patient files electronically with other physicians. In addition, privacy risks are increased when patients' records can be accessed remotely, as this creates a further opportunity for unauthorized persons to gain access. Psychological risk is the feelings of harm or loss of self-image [23]. Physicians may perceive that patients view them in a less professional manner when they adopt EMR systems, causing a loss in their professional self-image. A decrease in their professional image may also occur when a physician shares electronic copies of a patient's file. The electronic records are not typically self-edited, so other physicians may see notes or other material that may be damaging to the physician's professional image. Part of the physician's self-image is that of an employer. EMR systems may affect a physician's image as a competent boss and leader within their medical practice. The support staff will be using EMR systems in the medical office. If the system is difficult to manipulate or files from outside sources are problematic to input into the system, these problems will cause extra aggravation. If the physician's support staff find that the EMR systems are problematic to use, the physician might perceive his image as a capable leader diminished by the support staff. Social risk is the risk for the physician of losing social status within their peer group. The loss of social status within the health care system for the physician could result in a loss of power and income. For example, when EMR systems are not compatible with other physicians' EMR systems, physicians are unable to communicate electronically with others in their peer group. The time-loss risk of EMR systems for physicians is the risk associated with an increase in operational time needed to record patient information when caring for a patient as well as the time that may be lost due to fixing errors or other issues related to the use of the EMR system. The overall risk category is used to evaluate the physician's total risk. This gives an overview of the total risk physicians perceive with EMR systems and provides a structure to determine if all individual risk facets are incorporated in the model. The perceived risk construct is anticipated to have a relationship with the constructs, satisfaction, confirmation, and continuance intention. Thus, physicians' satisfaction with EMR systems depends on their perceptions of risk. Although physicians are able to evaluate their perceptions of risk after initial utilization of EMR systems, perceived risk is expected to affect their level of satisfaction with EMR systems. Accordingly, the following hypothesis is formed regarding the relationship between perceived risk and satisfaction with EMR systems. Hypothesis 6. Physicians' perceived risk is negatively related to their satisfaction with EMR systems. The expectations of the potential threats of EMR systems to the smooth and efficient running of the practice are perceived risks. Disconfirmation of these perceived threats may reduce physicians' perceptions of risks with EMR systems. As physicians use EMR systems, they will confirm if their initial perceptions of risk were accurate. Thus, the

positive confirmation of expectations of EMR systems by physicians will reduce their perceptions of risk. Accordingly, the following hypothesis is formed between confirmation and perceived risk. Hypothesis 7. Physicians' confirmation of expectations is negatively related to their perceived risk with EMR systems. During the adoption and utilization process of EMR systems, physicians verify their perceptions of risks. If EMR systems are perceived to have a large risk, the physicians may discontinue use. The perception of risk will also affect physicians' willingness to continue the process of adopting extended functions of EMR systems. The more risk perceived, the less willing physicians will be for further adoption. Therefore, the hypothesis regarding the relationship between perceived risk and IS continuance can be made as follows. Hypothesis 8. Physicians' perceived risk is negatively related to their continuance intentions with EMR systems.

6. Methodology To operationalize the conceptual model, a survey instrument was created that contained multiple measures for each construct. The survey instrument was adapted from current IS literature pertaining to expectation-confirmation theory and perceived risk. The constructs, perceived usefulness, confirmation, satisfaction, and continuance intention were adopted mainly from the ECT framework [4,5]. For the subconstructs of perceived risk, the measurements are defined based on the studies by Featherman and Pavlou [23] and Lu et al. [33] with some modifications to account for differences in the research contexts. All the constructs are measured using multiple items and a sevenpoint Likert scale ranging from strongly disagree (1) to strongly agree (7). The survey also includes a few new measurements to build upon the extended constructs of perceived risk and EMR systems continuance intention. The measurements were evaluated during a pre-testing phase and pilot of the survey, which consisted of 6 physicians [10]. This evaluation was done to determine if the measurements and survey questions could be easily understood by the respondents. The survey instrument includes 34 measurements to define the five constructs and a question to understand the demographic composition. A summary of the measurements for the constructs is presented in Appendix A (Measurement Constructs). 7. Data collection To test the model empirically, a cross-sectional field survey was conducted using a combination of an electronic and hard copy of the questionnaire. The survey was distributed during a 4-month period in 2011 to Canadian physicians that use EMR systems in their practices. Use of both electronic and paper forms of the survey allowed for the participation of physicians from different areas of Canada. The majority of physicians were from Southern Ontario. An honorarium of a $10 gift card was used as a token of appreciation for participation. For the electronic survey, an e-mail was sent to physicians inviting them to complete the survey online. The rate of response for the electronic request was low at 7%. In constrast, the response rate of physicians that were contacted in person was much higher at 48%. This much higher response rate was perhaps due in part to the fact that the survey was only left with physicians that used EMR systems. Compared to online surveys, paper-based surveys also tend to have a larger response rate [36]. In total, 135 filled surveys were received. Seven of the survey results were removed due to incomplete responses, providing a final data set with 128 responses for the analysis. To compare the demographics of the population of physicians to those in the survey sample, the survey

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included a general question related to physician demographics. In the survey, 37% of the physicians identified themselves as specialists, 28% were solo practitioners, and 34% practiced in a family health team. The remaining 1% of those surveyed practiced in a community health center. 8. Measurement model Prior to analyzing the model in SmartPLS, factor loadings were used to assess the measurements. Two items for the satisfaction were removed from the model, (ST6) with a value below the recommended threshold [14], and (ST1) which when removed improved the cross loadings of the measurements in the model [24]. The final model is composed of 32 measurements that create four reflective constructs—perceived usefulness, confirmation, satisfaction, and continuance intentions—and one multi-dimensional formative construct for perceived risk. The four ECT constructs were considered as reflective since each measurement represents certain elements of the constructs [47]. Perceived risk was considered to be a multi-dimensional formative construct created by six sub-constructs, captured with three items for each sub-construct. These sub-constructs are not expected to be interchangeable; in other words, if one is omitted, the other sub-constructs are not expected to define that portion of the construct [22,46]. The values for these sub-constructs were calculated by averaging the scores of 3 relevant items used to capture each of these sub-constructs. For testing the formative constructs, an examination of the weights was done utilizing principal component analysis (PCA), rather than the factor loadings [8]. The results (Fig. 2) show that five of the six sub-constructs for perceived risk are significant. The sub-construct for perceived privacy risk (PRPriv) was found to be non-significant. However, the perceived privacy risk was retained in the model to preserve the content validity [8]. Additionally, since multi-collinearity between the sub-constructs creates a problem in the model, it is examined using a variance inflation factor (VIF). All sub-constructs indicate a VIF below a stringent threshold of 3.3 [46]. Thus, multi-collinearity is not an issue for the multi-dimensional construct. The results of the VIF are shown in Fig. 2. Construct validity, convergent validity, and discriminant validity were used to assess the reflective constructs in the model. Composite reliability was used to assess the construct validity of the measurements. All the reflective constructs have composite reliability values above the 0.70 threshold as seen in Table 2 [24]. The reliability of the measurements is assessed using the Cronbach's alpha reliability coefficient [11,16]. In the model, all the reflective constructs have Cronbach's alpha values above 0.70 (see Table 2), thus indicating construct validity. Convergent validity is defined as the amount of variance the indicators have in common, or the amount that they converge [25]. The convergent validity was evaluated using factor loadings and average

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Table 2 Measures for constructs.

Confirmation Continuance intentions Perceived usefulness Perceived risk Satisfaction

AVE

Composite reliability

R2

Cronbach's alpha

0.7277 0.7097 0.7321 n/a 0.7251

0.8879 0.9067 0.9159 n/a 0.8874

0 0.6179 0.5756 0.3892 0.6157

0.8105 0.8654 0.8772 n/a 0.8083

variance extracted (AVE). The factor loadings were above the threshold of 0.7 (see Table 3) and were found to be significant with a tvalue N 1.95 [14]. As Table 2 also shows, the AVE for all of the constructs is above the 0.5 threshold [25]. Discriminant validity was assessed using the measurement cross loadings (see Table 4). The cross loadings for their assigned measurements were larger than their cross loadings on all other constructs [24]. The analysis of the cross loadings at this stage suggests that discriminant validity of the constructs cannot be rejected. The cross loadings are within the tolerance level of more than 0.1 difference between the measurements that load on the latent construct and the loadings of all other measurements [24]. An alternative method to evaluate discriminant validity was also employed by analyzing the square root of the AVE. The square root of AVE and the latent correlation of the constructs are given in Table 5. The square root of the AVE for each construct is greater than the cross correlations. Thus, discriminant validity is also shown to be evident from the constructs' cross correlations in comparison with the square root of AVE. Overall, the above tests suggest that the empirical model displays reliability, along with reasonable content, discriminant, and convergent validity. 9. Structural model The theoretical model was evaluated by examining the structural model. Bootstrapping of 500 samples was used to determine the significance of the path coefficients. The hypotheses were examined by analyzing the size and the significance of the structural paths and the percentage of variance explained (see Fig. 3). The model explains 62% of continuance intention. Satisfaction has a significant positive effect on continuance intention (β = 0.394, p b 0.01), thus supporting H1. Perceived risk has a significant negative effect on continuance intention (β = − 0.360, p b 0.01), supporting H8. On the other hand, the hypothesized relationship between perceived usefulness and continuance intention (H4) is not supported (β = −0.108 p b 0.3111). The model explains approximately 58% of the variance in perceived usefulness. Confirmation has a significant and positive relationship with

Fig. 2. Multi-collinearity test of perceived risk.

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10. Discussion

Table 3 Measurement loading and weights. Mean

St. dev.

Item loadings (t-value)

Item weight (t-value)

Continuance intention IN1. 0.3517 IN2. 0.3522 IN4. 0.2194 IN5. 0.2479

0.0269 0.0344 0.0305 0.0197

0.8809 (17.0544) 0.9151 (50.388) 0.7429 (8.8902) 0.8206 (14.0284)

0.3531 (12.5354) 0.3502 (9.6837) 0.2201 (6.8954) 0.2497 (12.4231)

Satisfaction ST2. 0.4144 ST4. 0.3465 ST5. 0.4041

0.029 0.0354 0.028

0.9088 (41.6083) 0.7758 (13.2293) 0.8647 (16.6714)

0.4175 (14.8556) 0.3526 (9.7091) 0.4013 (14.6272)

Perceived usefulness PU1. 0.3241 PU2. 0.2498 PU3. 0.2825 PU4. 0.3055

0.0226 0.0227 0.0226 0.0211

0.8821 (29.3923) 0.7699 (13.1956) 0.8779 (16.3957) 0.8871 (21.5167)

0.3253 (14.868) 0.2508 (10.1672) 0.281 (12.3609) 0.3081 (13.9397)

Confirmation CN1. 0.4287 CN2. 0.4381 CN4. 0.2876

0.0287 0.0223 0.0381

0.907 (49.1966) 0.9219 (62.6023) 0.7146 (10.9475)

0.4291 (15.0398) 0.441 (19.2908) 0.258 (7.7771)

perceived usefulness (β = 0.76, p b 0.0001), in support of H5 in the model. Confirmation had a significant negative influence on perceived risk (β = −0.624, p b 0.0001), supporting H7 in the model. Confirmation explains 38.9% of the variance in perceived risk. Furthermore, the model accounts for 63% of the variance in satisfaction explained by perceived usefulness, confirmation, and perceived risk. Perceived usefulness has a significant positive relationship with satisfaction (β = 0.491, p b 0.0001), supporting H3. Confirmation does not have a significant relationship with satisfaction (β = 0.361, p b 0.7187), therefore, H2 is not supported. Perceived risk is shown to have a significant negative impact on satisfaction (β = − 0.320, p b 0.01), thus supporting H6. In summary, the results support that both perceived usefulness and perceived risk affect physicians' satisfaction with EMR systems. However, perceived usefulness and perceived risk impact continuance intentions differently. The impact of physicians' perceived risk on their continuance intentions is observed directly, as well as indirectly through satisfaction. This differs from the impact of physicians' perceived usefulness, which is seen only indirectly through satisfaction. Finally, the results support H1 that physicians' satisfaction with EMR systems positively impacts their continuance intentions. Table 6 summarizes the outcomes of the hypotheses.

Table 4 Cross loadings for constructs.

CN1 CN2 CN4 IN1 IN2 IN4 IN5 PU1 PU2 PU3 PU4 ST2 ST4 ST5

Confirmation

Continuance intentions

Perceived usefulness

Satisfaction

0.907 0.922 0.715 0.534 0.490 0.301 0.341 0.653 0.595 0.623 0.722 0.567 0.549 0.441

0.477 0.470 0.348 0.881 0.915 0.743 0.821 0.676 0.434 0.539 0.613 0.657 0.507 0.688

0.720 0.718 0.465 0.646 0.655 0.415 0.483 0.882 0.770 0.878 0.887 0.713 0.602 0.604

0.540 0.609 0.375 0.693 0.730 0.444 0.523 0.747 0.561 0.625 0.627 0.909 0.776 0.865

Cross loadings for constructs are highlighted in bold.

The empirical results support most of the hypotheses in this study. In addition, the results provide important insights into users' postadoption behaviors with information systems. For example, Chen et al. [13], in their study of online shopping, found that a small portion of satisfaction was explained through the confirmation of user's beliefs, and 85% of satisfaction was explained through perceived usefulness. They found that confirmation had a significant positive impact on perceived usefulness. Their findings suggest that confirmation works through perceived usefulness to affect satisfaction to a greater extent. Our results in this study confirm their findings. More specifically, this study finds that the impact of physicians' confirmation on satisfaction is through perceived usefulness as well as perceived risk. On the other hand, the opposite effect was found by Bhattacherjee [5], where confirmation had a much larger impact on satisfaction than perceived usefulness does. This was found in his study of users of online banking who were examined using ECT to understand their continuance intentions. Similarly, Shiau et al. [54], in their study of continuance intention of blog users, found that confirmation has a larger impact on satisfaction than perceived usefulness does. In addition, they found that confirmation positively impacts usefulness. Our study shows that confirmation impacts physicians' view of both the usefulness and risk of EMR systems. This confirmation impacts satisfaction indirectly through physicians' perceived usefulness and perceived risk associated with EMR systems. From the results of this study, satisfaction seems to be a consequence of the physicians' confirmed beliefs that EMR systems are useful and the risk is manageable. Moreover, physicians' willingness to continue using and adopting extended functions of EMR systems is impacted by both satisfaction and the perceived risk. In other words, the impact of perceived risk on continuance intension is seen both directly as well as indirectly through satisfaction. However, satisfaction has a large positive impact on the continuance intentions of physicians with EMR systems. It appears from our results that the perceived usefulness of EMR systems and its impact on the continuance intentions of physicians are felt only indirectly through satisfaction. This outcome is similar to the findings by Bhattacherjee [5], in that perceived usefulness had a lesser impact on users' continuance intentions. He found that satisfaction with the IS product had a much greater effect on the users' continuance intentions. In his study, it was observed that satisfaction explained 32% of the variation in continuance intention for the user, while perceived usefulness only accounted for 10% of the variation in continuance intention [5]. In Bhattacherjee [5], it is also noted that the perception of usefulness and its influence on intentions changes over the various stages of IS use. In the pre-adoption stage, perceived usefulness has a larger impact on use of IS. The pre-adoption attitude is based on perceptions alone, while the post-adoption attitude is based on actual use of the IS. Satisfaction is supported by the user's experience, while the perceived usefulness can be based on uncertain beliefs. Shiau et al. [54] also found that satisfaction has a larger impact on continuance intention than perceived usefulness does, although both usefulness and satisfaction are positively related to continuance intention in their study of blog users. 11. Theoretical implications This paper contributes to the IT adoption and use literature, particularly in the post-adoption context. The ECT framework is used to ascertain the post-adoption attitude of physicians on EMR systems. The ECT framework was shown to enhance understanding of physicians' continuance intentions with EMR systems. This study also highlights the difference between the pre- and the post-adoption attitude of EMR systems users. The pre-adoption attitude of physicians was heavily weighted on perceived usefulness for the physician to be willing to adopt EMR systems [7]. In contrast, the post-adoption attitude of physicians is heavily weighted on their satisfaction, which then motivates them to

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Table 5 Latent correlations for construct with AVE.

Confirmation Continuance intentions Perceived usefulness Perceived risk Satisfaction

AVE

Confirmation

Continuance intentions

Perceived usefulness

Perceived risk

Satisfaction

0.7277 0.7097 0.7321 0 0.7251

0.8530⁎ 0.5115 0.7587 −0.6239 0.6075

– 0.8424 0.6691 −0.7165 0.7287

– – 0.8556 −0.7331 0.7525

– – – – −0.7021

– – – – 0.8515

⁎ The square root of AVE is on the diagonal in bold.

continue utilizing EMR systems. The difference between these findings highlights that perceived usefulness is a cognitive belief, whereas satisfaction is a reflection of the user experience. Therefore, in the postadoption context, users' opinions of satisfaction with the product should have a larger impact on continuance intentions [5]. The inclusion of perceived risk in the ECT framework has provided additional insights. The perceived risk construct is used in the preadoption context for understanding physicians' resistance to adopt EMR systems. The inclusion of the perceived risk construct in the theoretical framework has allowed for a broader picture of physicians' attitudes in the post-adoption phase and created a richer understanding of users' continuance intentions. The findings further suggest that the users' initial evaluation and confirmation are dependent on their perception of the impact that the IS has on either their work or lifestyle. Our findings suggest that the physicians' major focus is on the usefulness and risk of use of the IS product, and thus satisfaction with EMR systems is a result of these perceptions. In this study, satisfaction was found to mediate the impact of perceived usefulness on continuance intentions. To reiterate, Bhattacherjee [5] found that the perceived usefulness was found to have a small direct effect on continuance intentions. Thus, our findings confirm this and indicate that users' satisfaction has a large bearing on users' continuance intentions. Yet, the study by Chen et al. [13] shows a different result, with the perceived usefulness construct having a large effect on the continuance intention of online shoppers. This may imply that the characteristics or usage environment of the IS product/service examined may have a role in these relationships. 12. Practical implications For physicians, the perceived usefulness of EMR systems is important to their satisfaction with the product, which in turn impacts their

Perceived

H4 0.108 (1.017)

Usefulness

H5 0.759 (19.712)

Confirmation

H7 -0.624 (9.762)

Perceived Risk

intentions to continue using and adopting extended functions of EMR systems. Physicians' perceived usefulness of EMR systems also has a much larger bearing on satisfaction than does the perceived risk. The majority of physicians are satisfied with EMR systems and are willing to continue using and adopting additional functions. However, the perceived risks impact negatively on their decision concerning continuance intentions with EMR systems. For policy makers, this research brings to light several areas that should be considered when drafting policy around EMR systems. Policy makers need to be aware that the perceived risks cause dissatisfaction with EMR systems. Perceived risk also decreases the physicians' willingness to continue using and adopting extended functions of EMR systems. Therefore, the level of risk associated with EMR systems should be a consideration when making policy changes. Factors that may increase the risk associated with EMR systems should also be monitored in order to mitigate risks when changes occur within the medical system. The second area that policy makers need to be aware of is the issue of meaningful use of EMR systems for physicians. Physicians see EMR systems as similar to their paper records system, even though the two different mediums of recording information change the way in which physicians interact with patients [38,61]. This change in interaction can be seen as necessary and will be accepted by physicians if EMR systems are seen as more useful than a paper system. This suggests that physicians are willing to change and adapt to new ways of caring for patients as long as the changes create better outcomes for the patient. The more meaningful the use of EMR system is to the physician, the more willing they are to continue using and further adopting extended features of EMR systems. Existing and future changes in the medical system that could affect the meaningful use of EMR systems for physicians should be addressed. To increase the meaningful use of EMR systems, other stakeholders within the medical system also need to embrace the use of EMR systems.

H3 0.491 (4.050) H2 0.035 (0.361)

H6 -0.320 (2.835)

Satisfaction

H8 -0.360 (2.557)

Fig. 3. Model with path coefficients.

H1 0.394 (2.917)

EMR systems Continuation Intention

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Table 6 Hypotheses results. Hypothesis

Path

Coefficient

t-value

p-value

Outcome

H1 H2 H3 H4 H5 H6 H7 H8

Satisfaction → Continuance Intention Confirmation → Satisfaction Perceived Usefulness → Satisfaction Perceived Usefulness → Continuance Intention Confirmation → Perceived Usefulness Perceived Risk → Satisfaction Confirmation → Perceived Risk Perceived Risk → Continuance Intention

0.3942 0.0352 0.4912 0.1083 0.7587 −0.3200 −0.6239 −0.3604

2.917 0.361 4.050 1.017 19.712 2.835 9.762 2.557

b0.01 0.7187 b0.0001 0.3111 b0.0001 b0.01 b0.0001 b0.01

Supported Not supported Supported Not supported Supported Supported Supported Supported

Vendors can also use this research to improve marketing of EMR systems. From the vendors' perspective, there are several areas that need to be understood regarding EMR systems and physicians' satisfaction with the software and vendor services. After all, physicians are willing to continue using EMR systems and many are willing to continue incorporating further functions of EMR systems into their practice. This is good news for vendors as their customers are willing to continue using their services/products. Physicians are willing to continue using EMR systems as long as they are satisfied. Physicians' satisfaction comes from two different perceptions: how useful EMR systems are and how much risk it poses. Thus, both the issues of usability and risk need to be given attention when updating and marketing the software. In order for physicians to deem EMR systems as meaningful to use, the systems need to be relevant to patient care. Part of the issue with relevance is that patient information needs to be readily available and easily accessible by the physician. To make EMR systems more meaningful to use, the system needs to be able to easily incorporate information from outside the practice. Many of the physicians that were contacted stated that information from outside the practice was difficult and time consuming to incorporate into the patients' records. EMR systems need to incorporate ways for a physician to make the system more useable. The currently available versions of EMR systems do not create relevance for physicians as they are unable to modify it to their needs. Physicians practicing in the different fields of medicine have different requirements of their EMR systems. Thus, the ability to tailor EMR systems to the physicians' requirements would create a system that is easier and more relevant to the physician. 13. Limitations This study has limitations related to data collection and the conceptualization of the model. One issue is the small number of indicators used in the creation of the constructs for the ECT framework. Some of these constructs might have been better defined, thus allowing to measure more succinctly the behavior by using extra indicators. Unfortunately, the number of indicators needed to be limited in order for this study to be manageable and for physicians to complete the survey. The physicians that participated in the pilot study for the survey suggested that the length of the survey could be an issue in encouraging responses. In addition, as noted previously, the perceived privacy risk subconstruct was found not to be significant in building the perceived risk construct. This could be an indication that the perception of risk components from the pre-adoption phase change in the post-adoption phase. The perception of risk in the pre-adoption phase considered financial, performance, time, security/privacy, social, and psychological. In the post-adoption phase, this study considered the same risks. In other words, the perception of risk associated with EMR systems in the post-adoption phase may include other sources of risk that have not been examined in previous literature and in this study. Future research should address this issue to strengthen the argument on the impact of risk and highlight the differences in the pre-adoption versus postadoption stages. Furthermore, the sampling technique used in this study may have resulted in some biases. The majority of the sample is from physicians

located in Southern Ontario, although the survey was deployed throughout Canada. This large portion of the sample from one area could create a sample selection bias. However, the demographics of the population were compared to the demographics of the sample and the sample has demonstrated a reasonable degree of representation. Thus, we believe a sample bias is not a major issue. In general, there are differences in the regulatory environments as well as specific incentives provided by different governments for EMR adoption. Past surveys also found that there are differences in the levels of acceptance of electronic records and other elements of practice systems [51–53]. In addition, costs of providing health care per capita, access, and quality of care differ among countries. As a result, the results of this study may not be generalizable to other countries. Since the 1990s, the World Bank has been funding information technology infrastructure in health care facilities in several countries. The European Union has been advocating the use of technology to share medical records and expertise across the political borders of Europe [21]. In the U.S., the Senate has created a committee to explore an affordable health care system in which the use of technology is an overarching priority. In both the United Kingdom and Canada, an advisory board was created to facilitate the implementation of a nation-wide electronic medical system. Furthermore, physicians within Canada and other parts of the world such as the U.S. run their offices as independent businesses within the medical system. While most government organizations have not mandated that physicians use EMR systems in their practices, physicians are encouraged to use EMR systems. For example, American and Canadian government agencies are using incentives for physicians to adopt EMR systems in their practices. Both countries have a small penalty for not adopting EMR systems; namely, if a physician does not use electronic billing and bills a government organization for a patient's medical care, there will be a small claw back of fees. However, a 2006 survey of primary care physicians in seven countries (Australia, Canada, Germany, New Zealand, the Netherlands, the United Kingdom, and the United States) revealed striking differences in several elements of practice systems that support quality and efficiency [53]. The study identified wide gaps between leading and lagging countries in clinical information systems and payment incentives. A 2009 comparative study of 11 countries (Australia, Canada, France, Germany, Italy, the Netherlands, New Zealand, Norway, Sweden, the United Kingdom, and the United States) also found that 46% of physicians in the U.S. used EMR, while the Netherlands had the highest percentage of EMR systems use at 99% [51,52]. The study also found that even with the high rate of utilization in the Netherlands, just over 50% of the physicians actually utilized the system to its full capability. In Canada, the National Physician Survey [65], completed in 2011, had the proportion of doctors using EMR systems at 50.2%. Finally, about half of Canadian physicians currently use EMR systems. Diffusion theory suggests that the early adopters are more risk tolerant, while the late adopters are more risk adverse [50]. According to diffusion theory, the late adopter stage is over when 50% of the population of available users has adopted the product. The physician population has reached this point. Those physicians that adopt EMR systems later are considered late adopters and are more risk averse. Thus, the inclusion of the late adopters in the survey may change the

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perception of risk and its effect on satisfaction and continuance intentions. 14. Conclusions This study investigates physicians' continuance intentions of EMR systems as well as their adoption of extended EMR systems functions in their practice. In order to achieve this objective, the expectationconfirmation theory (ECT) and perceived risk are utilized. The ECT has its roots in consumer behavior [39,40] and looks at consumer satisfaction after the adoption of a product or service and how consumers' satisfaction with the product or service impacts their intentions for continued use. Perceived risk is the expected negative effects of using a product or service [23]. Using the extended expectation-confirmation framework [4,5] as the base model, this study incorporates perceived risk as a key construct and provides an integrated model of physicians' continuance intentions of EMR systems. A survey of 135 Canadian physicians that have used EMR systems was conducted to test the research model. The findings indicated that physicians' continuance intention with EMR systems is dependent on their satisfaction and their perceived risk. Satisfaction mediates the effect of perceived usefulness on physicians' continuance intentions with EMR systems. Perceived risk impacts continuance intention both directly, as well as indirectly through satisfaction. Physicians' confirmation of expectations impacts their perceived usefulness positively, while reducing perceived risks. Confirmation of expectations influences satisfaction through perceived usefulness and perceived risk. These findings contribute to the IS continuance literature and the ECT framework and help to explain the difference between the pre-adoption and the post-adoption attitudes of users towards EMR systems. References [1] J.G. Anderson, Clearing the way for physicians' use of clinical information systems, Communications of the ACM 40 (8) (1997) 83–90. [2] C.M. Angst, R. Agarwal, V. Sambamurthy, K. Kelley, Social contagion and informationtechnology diffusion: the adoption of electronic medical records in U.S. hospitals, Management Science 56 (8) (2010) 1219–1241. [3] N. Archer, M. Cocosila, A comparison of physician pre-adoption and adoption views on electronic health records in Canadian medical practices, Journal of Medical Internet Research 13 (3) (2011). [4] A. Bhattacherjee, An empirical analysis of the antecedents of electronic commerce service continuance, Decision Support Systems 32 (2) (2001) 201–214. [5] A. Bhattacherjee, Understanding informations systems continuance: an expectationconfirmation model, MIS Quarterly 25 (3) (2001) 351–370. [6] A. Bhattacherjee, N. Hikmet, Physicians' resistance toward healthcare information technology: a theoretical model and empirical test, 16 (2007) 725–737. [7] A. Bhattacherjee, N. Hikmet, Reconceptualizing organizational support and its effect on information technology usage: evidence from the health care sector, The Journal of Computer Information Systems 48 (4) (2008) 69–76. [8] K. Bollen, R. Lennox, Conventional wisdom on measurement: a structural equation perspective, Psychological Bulletin 110 (2) (1991) 305–314. [9] A. Boonstra, D. Boddy, M. Fischbacher, The limited acceptance of an electronic prescription system by general practitioners: reasons and practical implications, New Technology, Work and Employment 19 (2) (2004) 128–144. [10] A. Bowden, J.A. Fox-Rushby, L. Nyandieka, J. Wanjau, Methods for pre-testing and piloting survey questions: illustrations from the KENQOL survey of health-related quality of life, Health Policy and Planning 17 (3) (2002) 322–330. [11] S. Brahama, Assessment of construct validity in management research, Journal of Management Research (2) (2009) 59–71. [12] Brookstone, Barriers to the Adoption of EMRs in Medical Clinics, HCIM&C, 2010. 23–25 (accessedMay 24, 2014, available at http://www.healthcareimc.com/sites/ default/files/previous/Volume%2024/Volume%2024%20Number%203/Barriers% 20to%20the%20Adoption%20of%20EMRs.pdf). [13] Y.-Y. Chen, H.-L. Huang, Y.-C. Hsu, H.-C. Tseng, Y.-C. Lee, Confirmation of expectations and satisfaction with the internet shopping: the role of internet self-efficacy, Computer and Information Science 3 (3) (2010) 14–22. [14] W. Chin, How to Write Up and Report PLS Analyses, Handbook of Partial Least Squares, Handbooks of Computational StatisticsSpringer, Berlin 2010, pp. 665–690. [15] D.F. Cox, S.U. Rich, Perceived risk and consumer decision-making-the case of telephone shopping, Journal of Marketing Research 1 (4) (1964) 32–39. [16] L. Cronbach, Coefficeint alpha and the internal structure of tests, Psychometrika 16 (3) (1951) 297–334.

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[64] M. Wynia, G. Torres, J. Lemieux, Many physicians are willing to use patients` electronic health records, but doctors differ by location, gender and practice, Health Affairs 30 (2) (2011) 266–273. [65] National Physician Survey, The College of Family Physicians of Canada, Canadian Medical Association, The Royal College of Physicians and Surgeons of Canada, 2010. Anteneh Ayanso is an Associate Professor of Information Systems in the Goodman School of Business at Brock University, Canada. He received his Ph.D. in information systems from the University of Connecticut and a MBA from Syracuse University. He is also certified in Production and Inventory Management (CPIM) by APICS. His research interests are in data management, business analytics, electronic commerce, and electronic government. His current related studies include topics such as search engine advertising, the role of social media technologies and applications in the commercial as well as public sector, and methods for profiling and measuring ICT positions and e-government readiness of world nations. He has published many articles in leading journals such as European Journal of Operational Research, Decision Sciences, Decision Support Systems, Journal of Database Management, Communications of the AIS, International Journal of Electronic Commerce, Journal of Computer Information Systems, Government Information Quarterly, AIS Transactions on Human-Computer Interaction, Information Technology for Development, among others. His research has been funded by a Discovery Research Grant by the Natural Sciences and Engineering Research Council (NSERC) of Canada. Tejaswini Herath is an associate professor of information systems in the Goodman School of Business at Brock University, Canada. She received her Ph.D. in management science and systems from the State University of New York at Buffalo. She holds an MMIS and MSCE from Auburn University, and a B.Eng. from Pune University, India. She also holds advanced certification in information assurance from the University at Buffalo (USA) and is a certified general accountant (CGA-Canada). Her work has been published in the Journal of Management Information Systems, Decision Support Systems, European Journal of Information Systems, Information Systems Journal, Information Systems Management, among others. Her research interests are in information assurance and include topics such as information security and privacy, diffusion of information assurance practices, economics of information security and risk management. Her research has been funded by SSHRC Canada and other grants. Nicole O'Brien graduated from the MSc in management program with a major in management information systems from the Goodman School of Business (GSB), Brock University, Canada in 2013. Prior to that, Nicole completed her undergraduate degree in business administration from the GSB. She is currently pursuing her PhD in information systems in the DeGroote School of Business at McMaster University, Ontario, Canada. Her current research focuses on health information systems, particularly the adoption and postadoption behaviors of users.