Antecedents for use of Information Systems Development Methodologies in Health Information Systems

Transcription

1 December, Antecedents for use of Information Systems Development Methodologies Health Information Systems Conradie, Pieter Department of Information and Communication Technology Vaal University of Technology, South Africa Huisman, Magda Department of Computer Science and Information Systems NorthWest University, South Africa Abstract The use of Systems Development Methodologies (SDMs) is widely considered beneficial to the development of formation systems. To confirm this premise, this study explored the antecedents of acceptance and contued used of SDMs health formation systems. Identifiable as a behavioural study, the ma theoretical artefact was a conceptual research model, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Expectancy Disconfirmation Theory (EDT). The UTAUT was applied to evaluated factors of SDMs acceptance, while the EDT was utilized to evaluated factors regardg the contued used of SDMs. Statistical analysis to evaluate the conceptual research model was performed by means of structural equation modellg. The study employed a crosssectional survey method with 1000 webbased questionnaires send to dividuals engaged health formation systems development. A total of 224 usable questionnaires were returned. A high percentage of SDM usage was found, with only 15.5% of respondents dicatg they were not usg a SDM. Furthermore, agile development methodologies were found to be the major SDM used, followed by house developed and structured SDMs. Keywords Expectancy Disconfirmation Theory, Health Information Systems, Systems Development Methodologies, Unified Theory of Acceptance and Use of Technology. I. INTRODUCTION NUMEROUS methods and techniques have been developed for Information Systems (IS) development over the last four decades [21]. As these methods and techniques have matured, those that were found not to be effective have been replaced by more advanced solutions. This process of adaptation and replacement of Systems Development Methodologies (SDMs) IS development is ever present. Avison and Fitzgerald [3] list some of the reasons for the use of SDMs 1, cludg their ability to improve the efficiency and effectiveness of the IS development process. Barriers identified by the authors to 1 An SDM constitute the combation of four elements, namely system development approach(es) (e.g., structured, objectoriented, agile), system development process model(s) (e.g., cremental, evolutionary, spiral), system development method(s) (e.g., XP, Scrum) and system development technique(s) (e.g., entityrelationship diagrams, data flow diagrams) [20]. the acceptance of SDM clude the variety of SDMs, the ability to match the project type to SDM, suppression of developer creativity, failure of many SDMs to meet the need for commercial packages and the complexity of SDMs. These impediments are applicable to all IS development, cludg Health Information Systems 2 (HIS). In response to these barriers, new progressive SDMs have emerged over the last decade, cludg objectoriented SDMs and agile SDMs. By troducg these new progressive SDMs, it is postulated that SDMs will contue to be an tegral part of IS development [21]. This study will explore whether this premise is valid, or whether SDMs have become irrelevant, focusg only on SDMs use HIS. For HIS, the proposed benefits of creased efficiency and effectiveness are noteworthy [29]. An SDM is not efficient if it cannot enhance system development productivity, whereas it is not effective if it cannot improve the quality and reusability of IS. McChesney and Glass [29] further assert that the contued use (i.e., contuance) of SDMs, is related to whether SDMs are efficient and effective for IS development. Whereas efficiency and effectiveness is important for all IS, it is especially critical for HIS, where HIS is used for diagnostics and medical treatments of patients. To explore antecedents of the acceptance and contuance of SDMs HIS, technology acceptance studies, also classified as behavioural studies, is best suited ([6], [8]). Technology acceptance research is considered to be mature, and it is encourage literature that researchers move beyond its constrats. Venkatesh et al [49] propose that researchers need to focus on important and terestg questions, focusg on new research areas, related theories and constructs. One of these terestg questions is contued use after itial acceptance. The concepts of acceptance and contued use are based on the novation s depth of usage implementation model, first troduced by Kwon and Zmud [27]. Consistg of six 2 Health formation systems (i.e., health formatics) are the application of formation technology and formation science to the theoretical and practical problems of medical education, clical practice and biomedice [41]

2 December, phases rangg from itiation, adoption, adaptation, acceptance, routization and fusion, it is the acceptance phase, related to the itial use of an novation 3, on which most technology acceptance research has been directed [30]. regardg the fusion phase, associated with the contued use of the novation, is limited [39]. Karahanna [23] postulate that antecedents that drive itial use differ from those that fluence the postacceptance or contued use. By vestigatg contuance, buildg on extant acceptance and use theories, it is envisaged that their explanatory power will be improved. This strategy is evident is the further work of Venkatesh et al [50], which extended the Unified Theory of Acceptance and Use of Technology (UTAUT) by addg consumption theory (i.e., the constructs hedonic motivation, price value, and habit), thereby creatg the UTAUT2. This study associates with this approach technology acceptance research, by attemptg to extend the UTAUT with the constructs of the Expectancy Disconfirmation Theory (EDT). Freudenberg and Sharp [15] identified the top ten burng questions of practitioners. Among the questions identified, was the use of SDMs across organizations and functional areas, specifically related to agile SDMs. The research objectives of this study, based on abovementioned requirements identified literature, is first to establish whether SDMs are accepted and used HIS development across health organizations, second, whether the UTAUT is a valid theory explag the acceptance of SDMs HIS development and third, whether the EDT is a valid theory explag the contued use of SDMs HIS development. The remader of this paper is structured as follows. Section two presents the a priori research model by tegratg sights from apposite theoretical literature. Section three describes the research method implemented, followed by section four, the research fdgs. In the fal section, the theoretical and practical contribution of study and future research opportunities are discussed. II. BACKGROUND Although SDMs are believed to help improve the IS development process, their deployment frequently faces resistance from IS developers [20]. This is not only true for traditional SDMs, but is also experience with agile SDMs ([2], [13], [28]). Existg research has attempted to exame developers' acceptance and use of SDMs from the technology adoption perspective, by categorizg SDMs as an Information Technology (IT) artefact or novation. Technology acceptance models are then utilized to exame SDMs acceptance and use. Although these models have been found to be suitable for examg the acceptance and use of traditional SDMs, they focus maly on the technology characteristics, such as perceived usefulness and perceived ease of use ([16], [36], [46]). Nontechnology antecedents have received less attention. However, it is reported that dividual and 3 Innovation is any new development, implementation, and matenance of IS an organizational context [43]. organizational characteristics have a significant impact on the use of SDMs ([20], [26], [42]). This study addressed this requirement by considerg technical, dividual and organizational characteristics, explorg the acceptance and contued use of SDMs. It is important to distguish between antecedents (i.e., factors) that fluence the dividual acceptance and use of an SDM, compared to antecedents that fluence an organization. It is postulated that there would be some relation or mutual fluence between the dividual and the organizational decision to use SDMs [17]. If a large number of dividuals with an organization are usg an SDM, it could encourage the organization to standardize the use of SDMs. Conversely, if an organization mandates the use of an SDM, it supports the use of SDMs by dividuals. In this study, similar to the study of Templeton and Byrd [46], the dividual is considered as the actual user of SDMs, and the one that evaluates its efficiency and effectiveness. In addition, this study is based on the confirmation of a conceptual research model, of which the foundation is human cognitive behavioural theories. This study s unit of analysis is accordgly the dividual engaged with HIS development. Recent studies on SDMs, specifically related to agile methodologies, frequently focus on the use of only one or two methodologies. For example, Pikkaraen et al [34] focus on the acceptance of XP and Scrum, whereas Mangalaraj et al [30] only focus on XP. Although these studies have added to the extant literature of SDMs, they are limited terms of only focusg on one or two types of agile SDMs. In practice, most organizations do not strictly adhere to any one particular SDM, but use a tailored approach by blendg a number of SDMs [2]. In this study, all SDMs used HIS development was vestigated, not only agile SDMs. It is, however, anticipated that agile SDMs will be a major group of methodology, used by IS developers health organizations [21]. Reflectg on prior research on traditional SDMs acceptance and use, a number of studies can be highlighted. Riemenschneider et al [36] tested the applicability of five behavioural theories an dividual s acceptance and use of structured SDMs. Of the 11 antecedents identified, only four were significant, namely perceived usefulness, voluntaress, perceived compatibility and subjective norm. In a related study, Hardgrave et al [16] used two behavioural theories to vestigate an dividual s acceptance of structured SDMs. It was reported that perceived usefulness, social pressure, perceived compatibility and organizational mandate was significant. Templeton and Byrd [46] narrated similar fdgs, with perceived ease of use and perceived compatibility beg significant, when vestigatg formal SDMs. Kaemar et al [24], vestigatg the acceptance of formal SDMs utilizg social exchange, tasktechnology fit and technology acceptance theories, found that outputs positively fluence perceived usefulness while negatively fluencg ease of use. In all above mentioned studies, the ma focuses were technical characteristics

3 December, When considerg dividual characteristics (e.g., experience) and organizational characteristics (e.g., culture), the followg studies are relevant. Sultan and Chan [42] vestigatg objectoriented development, reported that characteristics of the technology cludg perceived compatibility and complexity were not significant, contradictg the fdgs of Riemenschneider et al [36], Hardgrave et al [16] and Templeton and Byrd [46]. This contradiction was explaed by the fact that developers were experienced, knowg the benefits of SDM use. However, antecedents cludg management support, organizational culture and technology policy, were found to be significant. Co and Kim [9], also explorg objectoriented development, found that maturity of technology and trag are significant. Khalifa and Verner [26], focusg on the waterfall model and prototypg, found team size to be relevant whereas Ilvari and Huisman [20], considerg formal SDMs, established the importance of organizational culture, mandatoress and social norms. Studies on agile methodology acceptance are evident, mostly utilizg case studies. Cockburn and Highsmith [11] vestigated the use of agile methodologies and identified the antecedents compatibility, team size, dividual competence and management support relevant. Nurer et al [32] found antecedents organizational culture, customer relationship, trag and competence as important, when considerg agile methodology acceptance. Talfo and Wazlawick [47], considerg extreme Programmg (XP), found that organization culture was significant. Drobka et al [12] reported significant results regardg organizational culture, trag and coachg, aga with reference to XP. A clear distction between studies on traditional SDMs and agile methodologies is the added factor of customer or user relationship, therefore, the fluence of the customer on the acceptance and use of agile SDMs. This is essential, sce customers or users play a much more active role IS development when agile SDMs are used [32]. As such, this study will corporate customer fluence as a determant, specifically to address this requirement. A. Model IS researchers generally strive towards makg contributions to the IS disciple through the refutg and refg of theories that expla specific phenomena. Naturally, these contributions become more relevant when the disciple creates fluential and authoritative theories about its core concepts [5]. In the context of identifyg antecedents impactg on the acceptance and use of SDMs HIS, considerg both the rigor required for valid research and the relevance of the research topic to the real world [35], the UTAUT, developed by Venkatesh et al [48], was utilized (depicted Fig. 1). Figure. 1 Unified theory of acceptance and use of technology (derived Venkatesh et al [48]:47). The study of acceptance and use of formation technology has resulted a number of different theoretical models, not only the disciple of IS, but also the disciples of psychology and sociology. Venkatesh et al [48] subsequently proposed the need for a review and synthesis of these major theoretical models, thereby progressg toward a unified view of user acceptance and use formation technology. As a result, the authors combed eight behavioural theories to construct the UTAUT. Noteworthy, is that the UTAUT not only considers technology antecedents (i.e., performance expectancy, effort expectancy), but also nontechnology antecedents (i.e., social fluence, facilitatg conditions, experience, voluntaress), highlighted previously as important antecedents acceptance and use studies. Although the amount of IS research focusg on the acceptance and use of IT artefacts has been numerous, research on the contuance of IT artefacts are limited. This is maly based on the premise that if an IT artefact is accepted, it will automatically lead to contued use. While itial acceptance and use of IT artefacts are important, longterm viability and eventual success depend on contued use, rather than itial acceptance. Contuance is not entirely an alien concept formation systems research. The Innovation Diffusion Theory (IDT), its fivestage decision process, postulates that adopters reexame their earlier acceptance decision durg a fal confirmation stage, only then decidg to contue or discontue the use of a particular novation [37]. However, most acceptance and use studies view contuance as an extension of acceptance and use behaviour, and subsequently employ similar acceptance and use antecedents to expla both acceptance and contuance decisions. It is assumed that contuance covaries with acceptance, a premise that is improbable. This congruity has been explored by Bhattacherjee [4], defed as the acceptancediscontuance anomaly, the discontued use by dividuals after acceptance. Discontuance, specifically related to SDMs, is lked to its effectiveness [29]. McChesney and Glass [29] defed core criteria for assessg SDMs effectiveness, namely improved productivity of development process, improved quality of the development process and customer satisfaction. These

4 December, criteria are postulated to affect the dividual s satisfaction of usg SDMs [31], a construct found a number of consumer behavioural theories. To explore the contued use of SDMs, this study utilizes a contuance theory. The EDT, developed by Oliver [33], is an example of a contuance theory, used consumer behaviour literature, illustrated Fig. 2. Important to note is that each relation depicted between the constructs represent a hypothesis, evaluated durg the statistical analysis of the research model. Based on the literary review and applicability check performed durg conceptual model creation, relevant constructs were defed the combed UTAUT and EDT research model, operationalized a webbased questionnaire. All constructs of the conceptual research model are described Table I, with reference to literary source(s). Figure. 2. Expectancy disconfirmation theory (derived Bhattacherjee [4]:353). The EDT has transpired to become a notable theory for explag antecedents that motivates dividuals to contue usg formation technology. This has resulted it beg used a number of studies to explore contuance ([4], [7], [25]). This study hence combes UTAUT and EDT to vestigate the antecedents of acceptance and use of SDMs, as well as the contuance behaviour of dividual users the development of HIS. The conceptual research model, portrayed Fig. 3, served two purposes. Figure. 3 Conceptual research model. Firstly, it provided a rationale for the clusion of identified constructs, and secondly, it provided context for the terpretation of empirical results by allowg the generation of testable hypotheses. In addition, to ensure a more parsimonious model, it was decided to group the constructs of the conceptual research model based on the generic framework for technology acceptance ([1], [19]). This framework, used widely health formatics research, allows for the groupg of constructs pertag to the dividual context, technological context and implementation context. As such, the dividual context denotes the fundamental characteristics of the dividual (e.g., experience, selfefficacy), the technological context focuses on the attributes of the technology (e.g., performance expectancy), and the implementation context refers to the relevant elements the environment or organization (e.g., social fluence, organizational culture). TABLE I. CONCEPTUAL RESEARCH MODEL CONSTRUCTS (MEASUREMENT INSTRUMENT OPERATIONAL DEFINITIONS NOT DEPICTED DUE TO SPACE CONSTRAINTS) Construct Description Operational defition for followg constructs obtaed from Venkatesh et al [48]. Performance Expectancy (PE) Expectation of dividual improved job performance by usg SDMs. Effort Expectancy (EE) Experience (EX) Customer Influence (CI) Voluntaress (VO) Social Influence (SI) Behaviour(al) Intent (BI) Facilitatg Conditions (FC) Degree of ease related to the use of SDMs. Prior use or knowledge of SDMs. Degree of customer fluence on the use of SDMs. Level at which an dividual perceives there is a choice SDM use. Degree to which an dividual deems important the opion of others regardg SDM Acceptance use. or tention to use an SDM. Level of organizational and technical frastructure to support SDM use. Operational defition for followg construct obtaed from Venkatesh et al [48] and Bhattacherjee [4]. Use Behaviour (UB) Actual utilization (i.e., contued use, contuance) of SDMs. Operational defition for followg constructs obtaed from Bhattacherjee and Premkumar [7]. Confirmation (CF) Satisfaction (SF) Individual s perception between performance expectancy vs. actual performance. Individual s belief/judgement regardg SDM effectiveness and efficiency. Operational defition for followg construct obtaed from Ilvari and Huisman [20]. Organizational Culture (OC) Basic assumptions, beliefs, values, rituals and practices a group of dividuals. Operational defition for followg construct obtaed from Riemenschneider et al [36] and Rogers [37]. Compatibility (CO) Extent of SDM consistency with dividual preexistg IS development style. Operational defition for followg construct obtaed from Nerur et al [32]. Selfefficacy (SE) Individuals beliefs or judgements on capabilities to use SDMs. Operational defition for followg constructs obtaed from Williams et al [47]

5 December, Personal Innovativeness (PI) Inclation of an dividual to try/attempt/sample new SDMs. User Attitude toward Use (UA) Individual s sense of favourableness towards SDMs use. Operational defition for followg construct obtaed from Sultan and Chan [42]. Policies (PL) Level to which external regulations (e.g., canon, laws) regulate SDM use. III. RESEARCH METHOD This research is based on the pragmatic paradigm, sce it focuses on the practical elements of the research. B oth quantitative (i.e., survey) and qualitative (i.e., applicability checks 4 implemented as structured case studies) methods of data collection and analysis can be recognized the study. The study thus prescribe to the compatibility thesis, proposg that combg quantitative and qualitative methods is advantages and disagreeg that they are herently epistemologically coherent [45]. A. Survey Webbased surveys have become popular IS studies, sce they are relatively uncomplicated to admister and are effective collectg large quantities of data at low cost. Furthermore, location of participants is not a concern, allowg researchers worldwide access to participants. Notable is the concern of relative low response rates with survey based research, especially if a specific number of responses are required for statistical analysis. Initially, a sample frame was selected, based on dividuals volved IS development, which were members of medical formatics associations South Africa, Australia and North America. A total of 1000 participants were selected from the sample frame by usg simple random samplg per country. The questionnaire (i.e., measurement strument) was operationalized based on literature ([4], [7], [20], [32], [36], [37], [42], [47], [48]). To ensure the theoretical research model was relevant, an application check was performed, whereby four case studies were executed by means of structured terviews, usg the developed questionnaire as guidele [38]. After confirmation of all identified constructs, the webbased survey was pretested and a codebook created. Data collection took place utilizg the tailored design method, implyg a respondentfriendly webbased questionnaire, a fourcontact strategy and personalized correspondence. Survey data was automatically transferred to the Statistical Package for Social Sciences (SPSS, ver.17), based on the codebook. Structural Equation Modellg (SEM) analysis was subsequently performed Analysis of Moment Structures (AMOS, ver.17). 4 Applicability check is an additional step the traditional research life cycle used to evaluate, establish and improve research relevance, focusg on theories, models and frameworks, utilizg case studies or focus groups [38]. B. Data Analysis and Results It is essential to state the reason why this study utilized SEM for statistical analysis. In practice, obtag meangful results with SEM can be problematic, sce it is a complex multivariate statistical technique, requirg careful planng by the researcher [43]. As such, SEM is used to model multifaceted hypothetical relations among constructs. This is accomplished by modellg covariances, which consist of representg the hypothetical relations (i.e., the estimated population variancecovariance matrix) and evaluatg its consistency with the relations manifested the observed sample variancecovariance matrix [40]. Furthermore, the structural equation model consists of two complementary models. Firstly, the measurement model, representg the loadgs of observed measurement items on their expected latent constructs. Secondly, the structural model, which depicts the relations among dependent and dependent variables (i.e., constructs). Based on the relative complex conceptual model under vestigation, and crosssectional survey method employed, SEM was the ideal solution. C. Data Analysis and Results The measurement model, also referred to as the outer model, shows the lk of each construct to a set of dicators (i.e., measurement variables) measurg the construct [44]. To validate the measurement model, it is general practice to employ confirmatory factor analysis (CFA), especially SEM. With CFA, the pattern of loadgs of the measurement items on the latent constructs is specified explicitly the measurement model. The fit dices of the prespecified measurement model are examed, providg a measure of the extent to which the measurement model accounts for the covariance the data. It is further recommended by Schumacker and Lomax [40] that five distct phases be followed when measurement model analysis is performed. These phases clude model specification (i.e., development of measurement model), model identification (i.e., establish model constrats), model estimation (i.e., executg statistical analysis of model), model testg (i.e., considerg the model fit dices) and model modification (i.e., modify model for better fit). Model estimation comprises the use of a specific fittg function to estimate parameters. The most common fit functions or estimation procedures clude Maximum Likelihood Estimation (MLE), used this study, as well as ordary least square and generalized least squares [44]. To confirm the stability of the estimates, it was decided to employ a resamplg method (i.e., bootstrappg), set at 200. Durg model testg, it is determed how well the measurement model fits the data. This was accomplished by the use of fit dices (e.g., Chisquare (CMIN/DF), Comparative Fit Index (CFI), Tucker Lewis Index (TLI), Root Mean Square Error Approximation (RMSEA)), depicted Table II. TABLE II. FIT INDICES OF MEASUREMENT MODEL

6 December, Fit dices CMIN/DF CFI TLI RMSEA Recommended <3 >0.9 >0.9 <0.05 value Actual value All fit dices were recommended value ranges, thereby validatg the measurement model. In addition, reliability of the measurement strument was ensured by the use of Cronbach s alpha (>0.6), whereas convergent validity of measurement strument was confirmed by the use of composite reliability (>0.7) and average variance extracted (>0.5) ([14], [44]), for reflective constructs, depicted Table III. Furthermore, terconstruct correlations were greater than the square root of average variance extracted, accepted as adequate proof of measurement strument discrimant validity. TABLE III. CRONBACH S ALPHA (CA), COMPOSITE RELIABILITY (CR), AND AVERAGE VARIANCE EXTRACTED (AVE) OF REFLECTIVE CONSTRUCTS Reflective Constructs Performance Expectancy Acronym CA CR AVE (>0.6) (>0.7) (>0.5) PE Effort Expectancy EE Experience EX Selfefficacy SE Personal Innovation PI Compatibility CM Social Influence SI Policies PL User Attitude UA Behavioural Intent BI Customer Influence CI Voluntaress VO Use Behaviour UB (Contuance) Satisfaction SF Confirmation CF Organisational Culture Facilitatg Conditions OC FC * formative construct Important to note is that formative constructs are validated differently to reflective constructs, as defed by Jarvis et al [22]. First, nomological validation must be considered by evaluatg the correlations among the measurement items of the formative construct, followed by calculatg the VIF (=2.932) value. Both measurements were the prescribed ranges. D. Structural Model After measurement model validation, structural model validation was performed. The structural model, also referred to as the ner model, depicts the relationships, effect the hypotheses, between constructs. Similar to measurement model validation, five distct phases were followed (i.e., model specification, model identification, model estimation, model testg and model modification). Fit dices obtaed are depicted Table IV. TABLE IV. FIT INDICES OF STRUCTURAL MODEL Fit dices CMIN/DF CFI TLI RMSEA Recommended value <3 >0.9 >0.9 <0.05 Actual value All fit dices were prescribed range, validatg the structural model. The path coefficients of the structural model are presented Table V, as obtaed from AMOS. TABLE V. PATH COEFFICIENTS FROM STRUCTURAL MODEL (** P<0.001, * P< 0.05) Hypothesis Antecedents β H1 PE UA 0.498** H2 EE UA H3 PE BI 0.270** H4 EE BI H5 PI UA H6 CO UA 0.396** H7 SE UA 0.375** H8 EX SE 0.282** H9 EX UA H10 UA BI 0.432** H11 SI BI H12 VO BI 0.336** H13 CI BI 0.194* H14 PL BI 0.193** H15 OC BI H16 OC UB 0.184** H17 FC UB 0.163** H18 UA BI 0.432** H19 PE CF 0.591* H20 PE SF 0.106* H21 CF UB H22 SF UB 0.426** H23 CF SF 0.208** The fal endogenous variable (i.e., use behaviour or contuance) coefficient of determation (R 2 ) value was 0.681, dicatg that the explanation ability of the conceptual research model to be 68.1%. IV. RESULTS The descriptive statistical results revealed that the study was representative of the population, with 152 male and 72 female participants. In total, 97 (42.9%) participants were from South Africa, 84 (37.2%) from the United States and 45 (19.9%) from Australia. The most represented group of 101 participants were associated with hospital formation systems, followed by pharmacy and laboratory formation systems. With regard to the major SDM types, 16.9% of respondents dicated the use of structured SDMs, 18.6% the use of custom house SDMs, 19.8% the use of agile SDMs, 11% the use of process maturity

7 December, frameworks, 4.8% the use of objectoriented SDMs and 4.1% the use of software engeerg. In total, 15.5% dicated that they are not usg SDMs. Interestg was the high level use of the structured SDMs (16.9%) and custom house developed SDM (18.6%) the development of HIS. Furthermore, the 19.8% use of agile SDMs is also noteworthy, dicatg a significant use percentage. In total, 47.3% of respondents utilize commercial SDMs. No significant difference was found between the percentage use of SDMs South Africa, Australia, and the United States. [18]. Based on the globalization of IT services and the outsourcg from developed countries to developg countries, this result is noteworthy [18]. No correlation between the use of SDMs and organizational size (rho=0.035, p>0.05) and the use of SDMs and department size (rho=0.024, p>0.05) could be established. The ferential statistical results, depicted Table V, are reviewed based on the generic framework of technology acceptance, specifyg the determant factor groupgs of technology context, dividual context and implementation context. Refer Fig. 3. Technology Context: Results dicated that performance expectancy had a direct fluence on behavioural tent to use an SDM (H3:β=0.270, p<0.001), whereas effort expectancy s direct effect on behavioural tent was unsupported (H4:β=0.208, p>0.05). Effort expectancy s direct effect on users attitude towards use was also unsupported (H2:β=0.086, p>0.05), clearly dicatg that the determant effort expectancy was nonsignificant the research model. This may dicate that developers do not consider SDMs as difficult, beg a mature artefact which most IS graduates have been traed, similarly reported by Sultan and Chan [42]. A significant direct fluence of performance expectancy on satisfaction was found (H20:β=0.106, p<0.001), but not on confirmation (H19:β=0.591, p>0.05). This was surprisg, but aga this can be due to the fact that SDMs are relatively mature, allowg developers to measure the value of SDMs on actual effectiveness and not expected effectiveness. In addition, a significant direct fluence of performance expectancy on the user s attitude towards SDM use was found (H1:β=0.496, p<0.001). Performance expectancy can thus be identified as the major determant factor the technological context regardg the behavioural tent of the dividual to use SDMs. Individual Context: Results dicated that user attitude towards SDM use had a direct fluence (H10:β=0,432, p<0.001) on the behavioural tent to use an SDM. Antecedents havg a direct fluence on attitude towards use clude selfefficacy (H7:β=0.375, p<0.001) and compatibility (H6:β=0.396, p<0.001). Personal novativeness did not have a significant effect on attitude towards SDM use (H5:β=0.002, p>0.05). The novativeness of a person is thus not significant with regard to the dividual s attitude towards SDMs use. An direct effect of experience through the mediator selfefficacy (H8:β=0.105, p<0.001) on user attitude towards use was found, but no direct effect (H9:β=0.321, p>0.05). Experience with SDMs direct effect on attitude towards SDMs use is noteworthy, sce it can be postulated that the more experienced a developer is, the more positive his or her attitude towards SDM use will be. With reference to the dividual context, attitude towards use is a major determant towards behaviour tent, with compatibility and selfefficacy as the ma antecedents fluencg attitude towards SDM use. Implementation Context: Results dicate that customer fluence (H13:β=0.194, p<0.001), voluntaress (H12:β=0.336, p<0.001) and policies (H14:β=0.193, p<0.001) had a direct effect on behaviour tent to use an SDM. Thus, the more SDMs are prescribed by customers, required by policies and considered less voluntary, the higher the tended use of SDMs. Social fluence (H11:β= 0.064, p>0.05) and organizational culture (H15:β=0.052, p>0.052) were found to be nonsignificant. Important, however, is that a significant direct effect was found between organizational culture and use behaviour (H16:β=0.184, p<0.001), with the hierarchical culture (f 2 =0.28) the most significant, followed by developmental (f 2 =0.16), group (f 2 =0.11) and rational (f 2 =0.06) (organizational culture framework based on the competg values model ([20], [21])). This dicates that, after itial acceptance of SDMs, the contued use of SDMs is reliant on the herent culture of the organization. Facilitatg conditions, which clude constructs like trag, also had a significant direct effect on contued use of SDMs (H17:β=0.163, p<0.001), dicatg that aspects of ternal resources, external resources and time fluences the contued use of SDM. With regards to the implementation context, organizational culture, policies, voluntaress, customer fluence and facilitatg conditions seem to be the major antecedents. With reference to antecedents that fluence the use behaviour (i.e., contued use) of SDMs, it was found that satisfaction (f 2 =0.24) with SDMs had the most significant effect, followed by behavioural tent (f 2 =0.19), facilitatg conditions (f 2 =0.09) and organizational culture (f 2 =0.06). Thus, contued use depends on the level of satisfaction of the dividual with SDMs. If the paths coefficients are examed, a significant direct positive effect between satisfaction and use behaviour (H22:β=0.426, p<0.05) is evident. In addition, there is also a significant direct effect between confirmation and satisfaction (H23:β=0.208, p<0.001) and behavioural tent and use behaviour (H18:β=0.453, p<0.001). No significant direct effect was found between confirmation and use behaviour (H21:β=0.006, p>0.05), although an direct effect was noted. Based on this analysis, satisfaction constitutes the major antecedents affectg the contued use of SDMs. If the dividual is not satisfied with the use of an SDM, even if the ideal organizational culture or facilitatg conditions are conducive, it will negatively affect the contued use of an SDM

8 December, V. CONCLUSIONS It is necessary to note that this study extends the work of previous researchers, most noteworthy that of Hu et al [19], Riemenschneider et al [36], Venkatesh et al [48], and Bhattacherjee and Premkumar [7]. Several theoretical contributions can, however, be highlighted. From the viewpot of acceptance and use theory, this research adds to the body of knowledge relatg to the applicability of UTAUT and EDT HIS. Although UTAUT was largely supported, the direct association between effort expectancy and behavioural tent and social fluence and behavioural tent, as postulated, could not be validated. With regard to EDT, this study also could not validate the direct association between performance expectancy and confirmation. All other antecedents the UTAUT and EDT were found to be significant, confirmg that the combed UTAUT and EDT conceptual research model are valid and theoretically justifiable. Determates compatibility (from Hardgrave et al [16], trag and customer fluence (from Nurer et al [32]), organizational culture (from Iivari and Huisman [20]), voluntaress (from Riemenschneider et al [36]), selfefficacy (from Cockburn and Highsmith [11]), and policies (from Sultan and Chan [42]), were all found to be significant. However, organizational size and department size was found not significant, contradictg the fdgs of Khalifa and Verner [26] with regard to SDM use HIS. This could relate to the antecedents policies and customer fluence, mandatg the use of SDMs, irrespective of organization size. Future research to verify this premise is proposed. Results of this study contributed to practice a number of ways. Firstly, the study made a contribution to the disciple of formation systems and health formatics by providg sights with regard to antecedents affectg the use of SDMs HIS, identified as a need by [15]. Among all constructs, user attitude towards the use of SDMs, performance expectancy, voluntaress, policies and customer fluence were found to directly fluence the acceptance of SDMs. Antecedents fluencg users' attitude towards SDM use clude compatibility and selfefficacy. Individuals considerg SDMs more compatible with the work methods they employ, have a more positive attitude towards SDM use. Managers therefore need to confirm that the SDM that is considered for implementation is compatible wi t h the developer's IS development style. Selfefficacy, which is fluenced by the dividual's experience, was also found to positively affect attitude towards SDM use. Selfassured and confident dividuals who know how to use an SDM are therefore beneficial to health organizations. Noteworthy, is that the antecedents customer fluence and policies were found to be statistically significant. These constructs, with reference to healthcare, must be highlighted. In general, healthcare is regulated by government. Considered an essential service entrenched the majority of constitutions, healthcare service providers are not free to act dependently. Therefore, if a customer is required to comply with a canon, makg the use of SDMs mandatory, it is theoretically reasonable to expect that it will impact on the acceptance of SDMs. Satisfaction, facilitatg conditions, organizational culture and behavioural tent were found to directly fluence the contued use of SDMs. A direct fluence of confirmation on satisfaction was found. This dicates that user satisfaction depends on positive confirmation that SDMs perform as expected, that they are efficient and effective. If not, this will result negative confirmation, leadg to dissatisfaction and ultimately discontuation of SDMs use. The UTAUT is a relatively new theory of use and acceptance, whereas EDT has not been extensively used the IS field. Future research for corroboratg UTAUT and EDT as pertent theories conceptual model creation is recommended. Conductg the same study other countries and cultural environments could be formative, further addg to the body of knowledge. Given the dynamic nature of systems development and healthcare, longitudal studies are recommended, usg latent growth modellg statistical analysis. It must be taken to account that causal ferences made from crosssectional data are never more than ferences. As such, longitudal studies, unlike the crosssectional study employed this research, are better suited for testg causality. Fally, as the number of constructs theoretical models creases, the likelihood of confusion escalates. To mimize this aspect, it is suggested that researchers consider the use of a framework to group related constructs specific contexts or focus areas. This effect dimishes the theoretical model s tricacy to a more coherent structure, allowg for a more parsimonious model. In conclusion, regardg research objectives, it was found that the use of SDMs is relevant HIS development, specifically agile SDMs, with only 15.5% of respondents dicatg that they are not usg SDMs, that the UTAUT is relevant elucidatg antecedents of acceptance of SDMs HIS development, and that the EDT is relevant revealg antecedents of contued use of SDMs HIS development. REFERENCES [1] V.P. Aggelidis and P.D. Chatzoglou, Usg a modified technology acceptance model hospitals, International Journal of Medical Informatics, vol. 78, pp , [2] T. Dgsøyr, S. Nerur, V.G. Balijepally, and N.B. Moe, A decade of agile methodologies: Towards explag agile software development, The Journal of Systems and Software, vol. 85, pp ,. [3] D. Avison and G. Fitzgerald, Information systems development: methodologies, techniques and tools, 4 th ed., Maidenhead: McGraw Hill, [4] A. Bhattacherjee, Understandg formation systems contuance: an expectation confirmation model, MIS Quarterly, vol. 25, pp , [5] I. Benbasat and R. Zmud, The identity crisis with the disciple: defg and communicatg the disciple's core properties, MIS Quarterly, vol. 27, pp , [6] I. Benbasat and H. Barki, Quo Vadis, TAM, Journal of AIS, vol. 8, pp , [7] A. Bhattacherjee and G. Premkumar, Understandg changes belief and attitude towards IT usage: a theoretical model and longitudal test, MIS Quarterly, vol. 28, pp ,

9 December, [8] F.Y.K. Chan and J.Y.L. Thong, Acceptance of agile methodologies: A critical review and conceptual framework, Decision Support Systems, vol. 46, pp , [9] I. Cho and Y.G. Kim, Critical factors for assimilation of objectoriented programmg languages, Journal of Management Information Systems, vol. 18, pp , [10] C.P. Chou and P.M. Bentler, Model modification structural equation modelg by imposg constrats, Computational Statistics and Data Analysis, vol. 41, pp , [11] A. Cockburn and J. Highsmith, Agile software development: the people factor, IEEE Computer, vol. 34, pp , [12] J. Drobka, D. Noftz, and R. Raghu, Pilotg XP on four missioncritical projects, IEEE Software, vol. 21, pp. 7075, [13] D. Falessi, G. Cantone, S.A. Sarcia, G. Calavaro, P. Subiaco, and C. D Amore, Peaceful coexistence: agile developer perspectives on software architecture, IEEE Software, vol. 27, pp. 2325, [14] C. Fornell and D.F. Larcker, Evaluatg structural equation models with unobservable variables and measurement error, Journal of Marketg, vol. 18, pp. 3950, [15] S. Freudenberg and H. Sharp, The top 10 burng research questions from practitioners, IEEE Software, vol. 27, pp. 89, [16] B. Hardgrave, F.D. Davis, and C. Riemenschneider, Investigatg determants of software developers' tentions to follow methodologies, Journal of Management Information Systems, vol. 20, pp , [17] B.C. Hardgrave and R.A Johnson, Toward an formation systems development acceptance model: the case of objectoriented systems development, IEEE Transactions on Engeerg Management, vol. 50, pp , [18] R. Heeks, Health formation systems: Failure, success and improvisation, International Journal of Medical Informatics, vol. 75, pp , [19] P.J. Hu, O.R. Chau, L. Sheng, and K.Y. Tam, Examg the technology acceptance model usg physician acceptance of telemedice technology, Journal of Management Information Systems, vol. 16, pp , [20] J. Ilvari and H.M. Huisman, The relationship between organizational culture and the deployment of systems development methodologies, MIS Quarterly, vol. 31, pp. 3558, [21] J. Iivari and N. Iivari, The relationship between organizational culture and the deployment of agile methods, Information and Software Technology, vol. 53, p , [22] C.B. Jarvis, S.B. Mackenzie, and P.M. Podsakoff, A critical review of construct dicators and measurement model misspecification marketg and consumer research, Journal of Consumer, vol. 30, pp , [23] E. Karahanna, Symbolic adoption of formation technology, Proc. of Decision Sciences International Conference, Athens, Greece, [24] C.J. Kaemar, D.J. McManus, E.W. Duggan, J.E. Hale, and D.P. Hale, Software development methodologies organizations: field vestigation of use, acceptance, and application, Information Resources Management Journal, vol. 22, pp. 1639, [25] M. Khalifa and V. Liu, Satisfaction with ternetbased services, Proc. of the 35 th Hawaii International Conference on System Sciences, Hawaii, 2002, pp [26] M. Khalifa and J.M. Verner, Drivers of software development method usage, IEEE Transactions on Engeerg Management, vol. 47, pp , [27] T.H. Kwon and R.W. Zmud, Unifyg the fragmented models of formation systems implementation, Critical Issues Information Systems, John Wiley & Sons, pp , [28] G. Lee and W. Xia, Toward agile: an tegrated analysis of quantitative and qualitative field data on software development agility, MIS Quarterly, vol. 34, pp , [29] I.R. McChesney and D. Glass, Postimplementation management of CASE methodology, European Journal of Information Systems, vol. 2, pp. 09, [30] G. Mangalaraj, R. Mahapatra, and S. Nerur, Acceptance of software process novations the case of extreme programmg, Empirical Software Engeerg, vol. 18, pp , [31] S.C. Misra, V. Kumar, and U. Kumar, Identifyg some important success factors adoptg agile software development practices, The Journal of Systems and Software, vol. 82, pp , [32] S. Nerur, R. Mahapatra, and G. Mangalaraj, Challenges of migratg to agile methodologies, Communications of the ACM, vol. 48, pp. 7378, [33] R.L. Oliver, A cognitive model for the antecedents and consequences of satisfaction, Journal of Marketg, vol. 17, pp , [34] M. Pikkaraen, X. Wang, and K. Conboy, Agile practices use from an novation assimilation perspective: a multiple case study, Proc. of the 28 th International Conference on Information Systems (ICIS 2007), Montreal, Dec [35] R. Rademacher, The changg profile of formation systems research: , Journal of Computer Information Systems, vol. 42, pp. 1316, [36] C.K. Riemenschneider, B.C. Hardgrave, and F.D. Davis, Explag software developer acceptance of methodologies: a comparison of five theoretical models, IEEE Transactions on Software Engeerg, vol. 28, pp , [37] E.M. Rogers, The diffusion of novation, 5 th ed., New York: Free Press, [38] M. Rosemann and I. Vessey, Towards improvg the relevance of formation systems research to practice: The Role of Applicability Checks, MIS Quarterly, vol. 32, pp. 122, [39] M. Senapathi and A. Srivasan. Understandg postadoptive agile usage: An exploratory crosscase analysis, Journal of Systems and Software, vol. 85, pp ,. [40] R.E. Schumacker and R.G. Lomax, A begner's guide to structural equation modelg, 3 rd ed., New York: Routledge, [41] E.H. Shortliffe and M.S. Blois, The computer meets medice and biology: emergence of a disciple, Shortliffe, E.H., Perreault, L.E., Wiederhold, G. and Fagan, L.M., eds. Medical Informatics: Computer Applications Healthcare and Biomedice, New York: Sprger, [42] F. Sultan and L. Chan, The adoption of new technology: the case of objectoriented computg software companies, IEEE Transactions on Engeerg Management, vol. 47, pp , [43] E.B. Swanson, Information systems novation among organizations, Management Science, vol. 40, pp , [44] B.G. Tabachnick and L.S. Fidell, Usg multivariate statistics, 5 th ed, New York: Pearson, [45] C. Teddie and A. Tashakkori, Foundations of Mixed Methods, California: Sage, [46] G.F. Templeton and T.A. Byrd, Determants of the relative advantage of a structured SDM durg the adoption stage of implementation, Information Technology and Management, vol. 4, pp , [47] C. Tolfo and R.S. Wazlawick, The fluence of organizational culture on the adoption of extreme programmg, The Journal of Systems and Software, vol. 81, pp , [48] V. Venkatesh, M.G. Morris, G.B. Davis, and F.D. Davis, User acceptance of formation technology: toward a unified view, MIS Quarterly, vol. 27, pp , [49] V. Venkatesh, F.D. Davis, and M.G. Morris, Dead or alive? The development, trajectory and future of technology adoption research, Journal of the AIS, vol. 8, pp , [50] V. Venkatesh, T.A. Sykes, and X. Zang, Just what the Doctor Ordered: A revised UTAUT for EMR system adoption and use by doctors, Proc. of the 44 th Hawaii International Conference on System Science, p. 1530,