Understanding and Analyzing Critical Success Factors for Physician’s Continuance Usage Intention towards Evidence-Based Medicine: Extended Expectation Confirmation Model

Fida Hussain Chandio (Corresponding author) Kuliyyah of Information & Communication technology Internation Islmaic Uniververisty Malaysia Email: [email protected] Fozia Anwar COMSATS Institute of Information Technology Islamabad Campus, Pakistan Email: [email protected] Abdul Waheed Mahesar Kulliyyah of Information & Communication Technology International Islamic University Malaysia Email: [email protected] Akram M Z Khedher Kulliyyah of Information & Communication Technology International Islamic University Malaysia Email: [email protected]

Abstract This research proposes an extended expectation confirmation model (EECM) to investigate the continued IT usage behavior among health care professionals. The proposed model integrates key constructs from the health inofmatics and technology acceptance research streams into the theoretical frame of the expectation confirmation model. The model was tested on a sample of 352 physicians. Data analysis was performed using structural equation modeling tools and techniques. The results of this study showed considerable support for EECM. Altough the impact of both perceived usefulness and perceived ease of use was found significant in influencing user satisfaction and continued usage intention, perceived ease of use, however, had shown stronger impact than perceived usefulness. In addition, results suggested strong influence of perceived usefulness on continuous usage intention, this implies that the nature of the target technology can be an important boundary condition in understanding the continued IT usage behavior in evidence-based medicine (EBM) context. By underatanding continued EBM usage behavior from physicians point of view, this study will provide a deeper insight into how to increase acceptance and usage of healhe care information technologies by intended health care professionals. Keywords: Continued IT usage; Evidence based medicine; Extended Expectation-confirmation model; Health technolgies acceptance; Structural equation modelling. 1. Introduction

With the rapid growth of information systems and web-based health care technologies, physicians are required to operate health care information systems and relavent applications for providing better health care facilities to the paptients. Due to the dramatic growth in new types of diseases, it is believed that patients visit clinic with prefrerences, unique concerns and expectations about the services offered in the clinic (Greenhalgh, Howick, & Maskrey, 2014). Evidence-based medicine (EBM) is the integration of clinical expertise, patient values, and the best research evidence into the decision making process for patient care. Where clinical expertise refers to the cumulated experience of medical and clinical education, and skills of the medical practitioners (Haynes, Wilczynski, McKibbon, Walker, & Sinclair, 1994). EBM is a type of new information systems that uses the innovative resources of the internet and health care technologies to enable physicians to provide better health care facilties to their patients . Therefore, it is important for physicians to apply current health care information systems into their clinical settings to effectively improve the quality of patients’ healthcare services. There is consensus among the researchers around the globe (BHASKAR, 2011; Organization, 2009) that health care information systems are, and, will continue to have a considerable effect on the health care industry. Novel trends of medical practice (scuh as EBM) requires fairly complex understanding and mental capability to fully employ IS functionalities. Utilization of these new type of information systems may be hard to achieve due to potential users’ (such as physicians) limited exposure and access to it (Majid et al., 2011). Therefore, in order to better understand and exaplain the maximum usage of these systems, it merits a systematic mechanism to understand the factors that can facilitate physcisians acceptance and continued usage of health infromation systems. This issue is of particular importance for health care industry policy makers because by understanding crucial factors, they will be able to understand user perceptions and continued usage intentions towards evidence based medicine. As a result, increase acceptance and continued usage of such systems. In literature many technology acceptance theories such as technology acceptance model (Davis, Bagozzi, & Warshaw, 1989), innovation diffusion theory (Rogers, 2003), and the theory of planned behavior (Ajzen, 1991), studied initial technology adoption and explored the variables that motivate individuals to accept new technology and information system (IS). Although initial acceptance is first step toward realizing IS success yet long-term successful sustainability of an IS depend on its continued use rather than initial use (Bhattacherjee & Lin, 2014). Continuous usage intention is very critical in IS implementation. Consequences of inappropriate and ineffective long-term use of IS results in corporate failures (Bhattacherjee & Lin, 2014; Cecez- Kecmanovic, Kautz, & Abrahall, 2014; Lyytinen & Hirschheim, 1988). Understanding physicians’ continuance usage intention towards EBM by using e-information resources is the goal of this study. The concept of continuance is not an entirely alien concept in IS research. As "implementation" (Zmud, 1982), "incorporation" (Cooper, 1990), and "routinization" (Cooper, 1990) are the various ways available in the IS implementation literature, where post acceptance stage of IS usage excels conscious behavior and becomes part of normal routine activity. Many studies in literature, view continuance intention as an acceptance behaviors extension as they are employing the same set of pre-acceptance variables to explain both acceptance and continuance decisions, thus, indirectly it is assumed in these studies that continuance covaries with acceptance (Cecez-Kecmanovic et al., 2014; Davis et al., 1989; Karahanna, Straub, & Chervany, 1999). Therefore, this literature is unable to explain acceptance- discontinuance anomaly. This paper is based on the proposed model of EECM-IT (Hong, Thong, & Tam, 2005). EECM- IT is based on expectation-confirmation theory (Oliver, 1980). This study empirically validates the EECM-IT using a field survey data of 352 physicians. We believe the lack of a theoretical foundation for this stream of research has limited the contributions of previous research and prevented health care organizations from understanding what makes a practical use of technology to practice evidence based medicineThe rest of the paper is organized as follows. The next section describes ECT and integrates it with prior IS usage research to postulate the hypothesis in EECM-IT. The third section describes the research methodology adopted to test the research model. The fourth section presents the results of data analysis and discusses the implications of the study. The final section summarizes the study's core findings and its contributions.

2. Theory and Hypothesis 2.1 Expectation Confirmation Model -IS The Expectation Confirmation Model (ECM)-IS model give provision to evaluate the requirements of individual users in their own environment (may be at home or at their workplace) and the continuance intention of use of specific system in the absence of organizational supporting factors. However, the modifications on the original ECM-IS are necessary in the case of integrated and complex systems where a lot of strongly associated users are involved who depends on each other to have full utilization of the IS (Sharma & Yetton, 2007). In 2001, Bhattacherjee developed ECM rooted in expectancy-confirmation paradigm for continued information technology (IT) usage. This modified model is based on three antecedents constructs, which are user satisfaction, user confirmation and post-adoption expectations, which is represented by perceived usefulness (PU) in this modified model. Key determinants of satisfaction are perceived usefulness and user’s levels of confirmation (Bhattacherjee, 2001). As it is proved in the expectancy-confirmation paradigm that satisfaction is positively influenced by the perceived usefulness providing a baseline for reference against confirmation judgments. Helson’s adaptation level theory also provides a theoretical support to this relationship (Helson, 1964). Adaption level theory postulates that one perceives stimuli only in relation to an adapted level (Yi, 1990). A directly proportional relationship between user’s expectation and subsequent satisfaction achieved, is also found in prior user’s behavior research (Hussein, Moriarty, Stevens, Sharpe, & Manthorpe, 2014; Oliver & DeSarbo, 1988). Additionally, studies on IT adoption have reliably found that perceived usefulness is the most imperative factor in defining and explaining users’ adoption intentions (Davis et al., 1989; Venkatesh & Davis, 2000).

2.2 Extended Expectation Confirmation Model

Extended Expectation Confirmation Model (EECM-IT) added user’s support and maintenance of the IS as a salient feature in shaping the IS user behavior, which can further influence the user’s decision to either continue or discontinue with IS use (Bhattacherjee, 2001). In EECM-IT the post-adoption expectation is characterized by perceived ease of use and perceived usefulness. Furthermore, as hypothesized in technology acceptance model (TAM) that perceived ease of use may have both direct influence and indirect influence via perceived usefulness on sustained IT usage intention. With the same logic of reasoning applied to the relationship between confirmation and perceived usefulness in the ECM-IT, the level of confirmation is also hypothesized to positively affect perceived ease of use. As a user gains confirmation experience, the user’s perceived ease of use will become more concrete and updated (Hong et al., 2005). In TAM, perceived usefulness has an immediate effect upon behavioral intention for IS, which is helpful towards the actual behavior (Bhattacherjee, 2001; Davis et al., 1989; Karahanna & Straub, 1999). Perceived Usefulness (PU) is proved as a significant influencing factor in determining the user acceptance, user intention, satisfaction and usage behavior. Literature review provided the evidence of the significant effect of PU on IS acceptance and usage (Davis et al., 1989; Sun, Wang, Guo, & Peng, 2013). Bhattacherjee verified that perceived usefulness is a significant determinant of satisfaction (Bhattacherjee, 2001). A positive correlation between PU and perceived ease of use (PEOU) is also established in prior literature (Hayashi, Chen, Ryan, & Wu, 2004; Rai, Lang, & Welker, 2002; Seddon, 1997). Devaraj et al. found that perceived ease of use and perceived usefulness both are antecedent of satisfaction (Devaraj, Fan, & Kohli, 2002). Correspondingly, in the usage of online information resources with the context of EBM, if physicians have an opinion that system is useful then they are more likely to accept it. Therefore, it is hypothesized that PU will have a significant positive effect on physician’s satisfaction and continued usage behavior towards online information resources to practice EBM. Accordingly, the hypotheses are as follows:

H1a. Perceived usefulness will have a significant positive effect on satisfaction H1b. Perceived usefulness will have a significant positive effect on continued usage intention

Literature review proved that PEOU is also among the major factors of user acceptance, satisfaction and usageintention, which eventually have a significant positive effect on actual system usage behavior (Davis et al., 1989; Gefen & Straub, 2000; Igbaria, Zinatelli, Cragg, & Cavaye, 1997; Sun et al., 2013; Venkatesh & Davis, 2000). As physicians’ experience with online information resources (to get authenticated and valid evidences during their clinical practice) is different from a common person. Because of the technical nature of medical databases and their interactive characteristics, physicians require more cognitive effort as compared to other IT users. Specifically, physicians first need to get access to find the relavant resources and then make a quick evaluation of whether it is worthy to rely on the obtained information for clinical decision making process. Therefore, the easier they perceive using available online information resources, the more likely they are to engage them in their decision making process (Davis et al., 1989; Venkatesh & Davis, 2000). Other researchers established the significant relation between PEOU and PU (Adams, Nelson, & Todd, 1992; Davis et al., 1989; Gefen & Straub, 2000; Igbaria et al., 1997). The literature suggested that ease of use may be an antecedent of usefulness, rather than a parallel, direct determinant of usage” (p. 334) (Davis et al., 1989). Therefore, the construct of satisfaction is introduced between ease of use and perceived usefulness, suggesting that perceived usefulness and perceived ease of use can be adjusted by confirmation experience. Similarly, if physicians find online medical information resources easy to use then they are more likely to accept and subsequently continue using the system.r. Therefore, it is hypothesized that PU and PEOU has an influence on user satisfaction and usage intention. The hypotheses related to PEOU are summarized as follow:

H2a. Perceived ease of use and perceived usefulness will have a significant positive effect on satisfaction H2b. Perceived ease of use and perceived usefulness will have a significant positive effect on continued usage Intention H2c. Perceived ease of use will have a significant positive effect on perceived usefulness.

Perceived usefulness is influenced by users ‘confirmation level in online banking services, business-to-consumer and e-commerce services (Bhattacherjee, 2001). This causal relationship have been confirmed in virtual learning environment as well and in the use of a web portal (Hayashi et al., 2004; Lin, Wu, & Tsai, 2005). According to ECT both disconfirmation and expectations is going to affect satisfaction where gap between expectations and perceived performance is indicated by disconfirmation. Swan et al. studied the concepts of disconfirmed expectations and satisfaction in retail businesses and results showed that a higher level of positive disconfirmation indicates higher satisfaction (Swan & Trawick, 1981). Spreng et al. proposed an updated model complementing the previous ECT model. This updated model indicated that disconfirmation has a significant influence upon satisfaction of product attributes and information, thereby influencing overall satisfaction (Spreng, MacKenzie, & Olshavsky, 1996). This suggests that users positive confirmation expections about the system will significantly increase the level of satisifaction with system. Furthermore, Bhattacherjee (2001) in their study concluded that confirmation had a positive influence on satisfaction and perceived usefulness. Thus, the hypotheses for confirmation are presented below: H3a. Confirmation will have a positive significant relation with PU. H3b. Confirmation will have a positive significant relation with PEOU. H3c. Confirmation will have a positive significant relation with the satisfaction.

In the context of IS acceptance, satisfaction is a decisive factor affecting continuous usage intentions of the customers towards that IS; thus, there is a significant correlation between satisfaction and intention (Hussein et al., 2014; Swan & Trawick, 1981). According to Bhattacherjee, satisfaction of previous experience is the main factor, which affects the continuance usage intention . In an empirical study of 1000 customers of banking IS network, satisfaction was found to be a determinant of IS continuance usage intention (Bhattacherjee, 2001). Satisfaction has a positive effect upon repurchase intentions of trading partners in e- commerece domain (Devaraj et al., 2002). Behavioral intention is primarily predicted by satisfaction in the studies where satisfaction is viewed as an attitude. Several studies show that satisfaction is a strong indicator of continuance intention. Researchers found that there is a strong link between satisfaction and continued use (Liao, Chen, & Yen, 2007; Roca, Chiu, & Martínez, 2006). Therefore, the following hypothesis is proposed:

H4. Satisfaction will have a significant positive effect on continued usage intention.

Figure1: Proposed research model

3. Methodology This is a cross sectional quantitative survey study where data was collected from a sample size of 352 physicians by using random sampling technique. A self-administered survey procedure was adopted and questionnaire was designed by taking items from previous studies and then reworded these questions to match the scenario of the current study. PU and PEOU construct were measured by using Davis pre-defined tool (Davis et al., 1989) while continued usage intention (CUI) was measured by original scale of measurement items proposed by Bagozzi (Davis et al., 1989), for the rest of the constructs the items were taken from Bhattacherjee (Bhattacherjee, 2001). Six items for PU, five for CUI, six for the PEOU 2 for confirmation and 2 items for satisfaction construct were included in the questionnaire. All items were measured by using a 7 point Likert scale that ranges from strongly disagree (1) to strongly agree (7). Questions to know the demographic details of the participants were added in the beginning of the questionnaire. The questionnaire was pilot tested on 50 responses to improve validity and reliability of the used measurement scale. Responded were asked to point out the confusing items in the questionnaire while filling out the questionnaire. The identified items by the respondents of the pilot test were reexamined and modified to improve their readability and understandability. Pilot test data were analyzed using exploratory factor analysis (EFA) with Varimax method of rotation. Poorly loaded items (on their hypothesized scales) of the questionnaire were reworded or dropped. Pilot test responses were excluded from our larger hypotheses testing survey. The final survey data(consisting of 352 responses) were analyzed via a two-step structural equation modeling (SEM) approach suggested by Anderson & Gerbing (Anderson & Gerbing, 1988). In the first step confirmatory factor analysis (CFA) wasemployed to assess reliability and validity of the scale, while in the second step the structural model was examined to test our proposed hypotheses empirically .

4. Results Data was screened for missing values. Expectation maximization technique was applied to identify the extent and pattern of the missing data by using SPSS version 20. The results of Little’s MCAR test was insignificant at each construct level (i.e. Chi-Square = 3301.523, DF = 2178, Sig. = .230) indicating that pattern of missing values was completely at random (Tabachnick & Fidell, 2001). Because of the low percentage of the missing data in current study, researchers applied mean substitution imputation method recommended in literature as it is widely accepted method and calculated means by this method can be best single replacement for any missing value (Cordeiro, Machás, & Neves, 2010; Joseph F. Hair, 2006; Tabachnick & Fidell, 2001). 41.41% response rate was noted for current study. More than half (57.7%) of the respondents were male and 42.3% were female. 29.3% of the participants of the survey belongs to the age group between 25-30 years and majority of the participants were having a clinical experience of 1-5 years. Demographic details are shown in Table 1. Data were screened for outlier detection and to check the normality of the data. This screening process was compulsory before performing structure equation modelling (SEM) on analysis of movement structure (AMOS) software, as the SEM technique is very sensitive to such issues. Mahalanobis distance (D2) was applied to identify the presence of outliers. Few outliers were present in the data, however, it was decided that these outliers will be retained in the further analysis as insufficient evidences are available in literature suggesting that these outliers were not part of the entire population (Hair, Anderson, Tatham, & Black, 2006). Data normality was checked by using skewness and kurtosis, which indicates the normal distribution of data. Exploratory factor analysis was performed by using principle component analysis with the varimax rotation method. Value of 0.938 was noted for Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlett’s Test for spherecity, which was significant at .000 level. All of the above mentioned tests were performed by using SPSS (Statistical Package for the Social Sciences) version 20. Table 1: Demographic Details Variable Category Frequency % Gender Male 203 57.7 Female 149 42.3 Age 25-30 years 103 29.3 31-35 years 92 26.1 36-40 years 55 15.6 Above 40 years 102 29.0 Organization Private 246 69.9 Government 106 30.1 Clinical Experience 1-5 years 125 35.5 6-10 years 91 25.9 11-15 years 50 14.2 More than 15 years 86 24.4 Highest degree Bachelor 159 45.2 Entry level master 90 25.6 Advance Master 57 16.2 Entry Doc 26 7.4 Advance Doc 20 5.7 Facility Location Rural 12 3.40 Urban 340 96.5

4.1 Structure Equation Modelling Analysis Two step procedure was performed for structural equation modeling (SEM) analysis. In the first step, relationship among latent and observed factors are specified by using the inner model or measurement model. Confirmatory factor analysis (CFA) is performed to check the reliability, validity and uni-dimensionality of the measures to validate the relationship between construct and structure model. The second step was testing the hypothesis by specifying structural model . SPSS and AMOS v.20 were used for the data analysis. 4.1.1 First step: examination of reliability and validity by Measurement Model

Measurement model (MM) was specified using five factors, which are PU, PEOU,SAT,CON and CUI. Standard regression weights for all the items were above the threshold value of 0.7, as recommended by (Joseph F. Hair, 2006). When standardized residual were analyzed and it was found that PEOU4 is not within the acceptable range (above 2.58 or below –2.58) defined in (Joseph F. Hair Jr, 2009). As this item was sharing a high degree of residual value, thus , it was dropped at this stage. Results for the three main fit indices obtained by running MM are shown in Table 2.

Table 2: Goodness of fit index for measurement model Absolute Fit Measures Parsimony Incremental Fit Measure Fit Measures CMIN Df CMIN/df GFI RMSEA AGFI CFI NFI Obtained fit 151.530 142 1.067 .941 .016 .941 .989 .956 CMIN=minimal value for discrepancy, df=degree of freedom, GFI=goodness of fit, RAMSEA=Root mean square error of approximation, AGFI=adjusted goodness of fit NFI=Normed fit index, CFI=comparative fit index

The results of these goodness of fit indices super passed the minimum thresh hold values or recommended criteria (Joseph F. Hair Jr, 2009), which indicates the confirmation that the model adequately fitted the data. Results for calculated average variance extracted (AVE) and construct reliabilities value along with the factor loading are given in Table 3.

Table 3: Items with factor loading, construct reliability abd average variance extracted Construct Item Standardized factor Construct AVE loading reliability Perceived PU1 .857 0.95 0.76 usefulness PU2 .864 PU3 .882 PU4 .873 PU5 .883 PU6 .902 Perceived ease of PEOU1 .879 0.94 0.64 use PEOU2 .886 PEOU3 .837 PEOU5 .875 PEOU6 .895 Usage Intention UI1 .892 UI2 .865 0.95 0.78 UI3 .890 UI4 .883 UI5 .888 Satisfaction SAT1 .919 0.892 0.81 SAT2 .876 Confirmation CON1 .947 0.871 0.78 CON2 .816

The model was checked for convergent validity by calculating AVE (average variance extracted) of each construct. Rule of thumb is that AVE should be equal or above 0.5. Therefore, results confirm the presence of adequate convergent validity. Then, these AVE were compared with the corresponding squared inter-construct correlation ( principal diagonal elements in Table 4) with inter-factor correlations to verify the presence of discriminant validity. Results (Table 4) confirmed that the values for corresponding squared inter-construct correlation are lower than the AVE extracted of each, which assure the presence of discriminant validity of our observed data at construct level (Hair et al., 2006).

Table 4: Discriminant Validity

PU PEOU UI CON SAT PU 0.76 PEOU 0.13 0.64 UI 0.41 0.24 0.78 CON 0.09 0.24 0.09 0.78 SAT 0.10 0.14 0.17 0.01 0.81 Note: Bold values are Average Variance Extracted and off diagonal are inter-construct squared correlations.

4.1.2 Second step: Structural Model Assessment

Paths for a causal relationship were evaluated by using structural model (SM). SM specifies the influence of one factor on the other. The standardized regression coefficients for hypothetical validity of four hypotheses with their sub hypotheses are shown in Table 5. The rule of thumb is that a relationship can only be significant if the C.R value is above 1.96 (Hair et al., 2006). All the relation are significant on the basis of two tailed tests except the relationship of confirmation and satisfaction. All the loadings were positively significant (p<0.05) except confirmation and satisfaction (i.e. CON → SAT), where p value was found 0.226 and C.R value was -0.067. Table 5: Hypothesis validation

Positive Relationship Estimate β Value C.R. Supported (p) PEOU→PU .315 .058 6.669 Supported *** PU→UI .494 .049 9.792 Supported *** PEOU→UI .252 .049 5.163 Supported *** CON → PU .258 .041 4.574 Supported *** CON → PEOU .265 .054 4.898 Supported *** CON → SAT -.067 .055 -1.212 Not Supported (.226) SAT→ UI .147 .044 3.336 Supported ***

Table 6: Goodness of fit for structure model Absolute Fit Measures Parsimony Incremental Fit Measure Fit Measures

CMIN Df CMIN/df GFI RMSEA AGFI CFI NFI Obtained fit 177.364 161 1.102 .943 .017 .936 .981 .961 CMIN=minimal value for discrepancy, df=degree of freedom, GFI=goodness of fit, RAMSEA=Root mean square error of approximation, AGFI=adjusted goodness of fit NFI=Normed fit index, CFI=comparative fit index

5. Discussion Results of this research are significant with the EECM stating that perceived usefulness have a significant effect on satisfaction of users towards continuance usage of the technology. Several researcheers supported this finding (Davis et al., 1989; Kim & Chang, 2007; Pai & Huang, 2011; Venkatesh, Morris, & Ackerman, 2000; Yeo, Parkin, Aurum, & Handzic, 2002) as perceived usefulness is a cognitive belief salient to IS use (Davis et al., 1989). Perceived usefulness is also an adequate expectation in the IS continuance context as it is the only belief confirming the reliable influence of user intention transverse with the temporal stages of information system use (Davis et al., 1989; Karahanna et al., 1999). PU is found to have a positive significant relationship with the continuance usage intention towards online information systems to practice EBM in this research. Therefore it is suggested that usefulness of available online information systems is an important criteria in acceptance of these systems. Thus, it can reasonably be suggested that PU is one of the driving forces behind users continuous usage of the EBM. . It was hypothesized that perceived usefulness and perceived ease of use can be adjusted by user’s confirmation and this relationship found significant in current study. These findings are in accordance with the previous research findings (Holden & Karsh, 2010; Hsu, Wu, Chen, & Chang, 2012; Wixom & Todd, 2005), which suggested a significant relationship between perceived usefulness, perceived ease of use and confirmation in technology acceptance studies. Satisfaction construct was having varying and conflicting conceptualizations in the prior studies (Yi, 1990). Hence, one may have a pleasant experience and confirmation with a service, but still feel dissatisfied if it is below expectation as initial studies of ECT present varying and conflicting conceptualizations of the satisfaction construct similar to that in other attitude theories (Ajzen & Fishbein, 1980). Although the confirmation-satisfaction association is yet to be examined empirically in IS, industry studies provide idiosyncratic support for the confirmation and satisfaction association. The result of confirmation and satisfaction relationship can be justified by Tse and Wilton study demonstrating that satisfaction and confirmation may differ in their predictive abilities (Tse & Wilton, 1988), while Oliver observes that confirmation temporally and causally precedes towards satisfaction in a path-analytic model (Oliver, 1980). Limitation to linear models in expectation disconformation theory (EDT) research in IS is another reason for this effect. As linear models fail to reveal complexities that are anticipated in theories of congruence (EDT is one of the examples of such theories where attitudes and behaviour’s result from the comparison between experiences and expectations) (Edwards, 1994, 2002). Another reason of this insignificant relation is that IS research has used direct measurement of confirmation (or disconfirmation) rather than separately measuring the components (expectation and experience with technology use). The joint effects of individual component measures on various outcomes distorts by the direct measurement (Irving & Meyer, 1995, 1999). The most likely reason and explanation for this inconsistent result between satisfaction and confirmation by the physicians may lie within the time constrained, inaccurate results, and non-availability of the items for which they were using the information system and the fact that the type of confirmation is objective in the proposed research model. However confirmation is found to have indirect influence on the satisfaction through perceived usefulness and perceived ease of use. It was hypothesised that perceived ease of use (PEOU) will have a positive significant effect on the satisfaction. Results revealed statistical significance of PEOU on satisfaction. This finding is in confirmation with prior research studies (Kim & Chang, 2007; Yeo et al., 2002). In addition, this study also revelaed a positive correlation between perceived ease of use and continued usage intention; this finding is consistent with previous findings (Davis et al., 1989; Igbaria et al., 1997; Mathieson, Peacock, & Chin, 2001). In addition, it was hypothesized that PEOU will have a significant effect on perceived usefulness (PU). This relationship was derived from the prior work in techolgy acceptance and IS domain. The results of this study revealed that PEOU had a signifacnt influence on PU. This finding is in confirmation to the prior work (Davis et al., 1989; Igbaria et al., 1997; Lee, 2010). However some researchers found this relation insignificant in their findings (Abbasi, Chandio, Soomro, & Shah, 2011; Chandio, Abbasi, Nizamani, & Nizamani, 2013; Hussain Chandio, Irani, Abbasi, & Nizamani, 2013). For example, (Hu, Chau, Sheng, & Tam, 1999)found no relationship between PU and PEOUin their study of technology acceptance among physicians. Finally, the relationship between satisfaction and continuance usage intention was also found significant in the current study.

6. Conclusion This research did not take in account all the contextual consideration in acceptance and intention to practice EBM by using online information resources. Especially, it cannot incorporate all the factors determining the technology acceptance for EBM by the physicians. Also, the possibility of mediating and moderating variables which may reinforce the hypothesis of the research model is not considered in this research. As this research focuses on the technology acceptance model and tries to link the causal relationship of the original model with this contextual setting, as result; it will encourage the use of online medical databases for gathering information and finding the evidences for EBM. It also raises the thought for the educational makers’ awareness of how Pakistani physicians are going to utilize the online medical databases so that they can design the future strategy according to the physician’s needs and preferences. Reference

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