دانلود مقاله ISI انگلیسی شماره 5834
عنوان فارسی مقاله

عوامل موثر بر پذیرش سیستم پشتیبانی تصمیم گیری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
5834 2013 9 صفحه PDF سفارش دهید 8760 کلمه
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عنوان انگلیسی
Factors influencing decision support system acceptance
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Decision Support Systems, Volume 54, Issue 2, January 2013, Pages 953–961

کلمات کلیدی
- سیستم های پشتیبانی تصمیم گیری - نظریه یکپارچه از پذیرش و استفاده از فناوری - پزشکان عمومی - پذیرش فناوری
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پیش نمایش مقاله عوامل موثر بر پذیرش سیستم پشتیبانی تصمیم گیری

چکیده انگلیسی

While clinical DSS have many proven benefits, their uptake by GPs (general practitioners) is limited. The purpose of this research was to develop and explore a UTAUT (Unified Theory of Acceptance and Use of Technology) based model of how and why GPs accept DSS. Insight into the reasons why GPs do not use clinical DSS combined with knowledge of why GPs use DSS will allow the development of strategies to facilitate more widespread adoption with consequent improvements across many areas. Depth interviews were conducted with 37 GPs comprising a mix of education backgrounds, experience and gender. The developed model indicated that four main factors influence DSS acceptance and use including usefulness (incorporating consultation issue, professional development and patient presence), facilitating conditions (incorporating workflow, training and integration), ease of use and trust in the knowledge base.

مقدمه انگلیسی

Decision support systems (DSS) research has been undertaken for over 35 years with such systems proving useful in supporting semi-structured and unstructured problems [2]. The main aim of DSS is to provide the user with tools that enhance their decision making process, resulting in more informed decisions [2]. However, despite increasing developments in DSS, the usage breadth increase has been modest [4]. DSS are most widely used in corporate functional management fields, such as marketing and logistics, with limited use within non-corporate areas such as medicine [21]. While there has been some research into DSS acceptance in fields such as agriculture [37] and marketing [30], there is little research on the acceptance of DSS within the medical field, despite the potential of clinical DSS to provide improvements in areas such as: the quality of medical care [13]; disease prevention [55]; disease management and drug dosing [59]; management of chronic physical illness [7]; decision variations between practices [51]; and compliance with guidelines [66]. Clinical DSS are knowledge bases which contain the ability to perform inferences on known information based on prior experience or knowledge [16]. Clinical DSS have been developed since the 1990s [41]. However, most of these DSS do not go beyond the trial stage, and are often only adopted by those who created them. Although there are numerous studies that show the benefits of using DSS by general practitioners (GPs) [19] and [50], their uptake is very low [20]. It is therefore important to identify the factors that influence GPs' acceptance of these systems to facilitate their usage and improve decision making. A starting point in exploring the reasons for low usage of clinical DSS lies in the area of user acceptance of information technology. An inventory of DSS in health was created in 2002 as a part of the National Electronic Decision Support Taskforce (NEDST) report [41] and [53]. This inventory identified 35 DSS that were either in use or in progress at the time. However, this report is now out-dated. Since the NEDST report [41] there has not been an updated report on the status of these DSS or on the possible existence of new DSS. The NEDST report [41] categorised DSS into four types. Type 1 DSS provide information that then requires further analysis before the user can make a decision. Type 2 DSS provide trend analyses of patients' clinical status and/or clinical alerts. Type 3 DSS use knowledge bases and inference engines to generate recommendations. Finally, type 4 DSS are closely related to type 3, but are equipped with autonomous learning capabilities such as case-based reasoning, neural networks, and discrimination analysis for more advanced decision making support. Applying the categories of DSS and the definition of clinical DSS, NEDST's [41] types 1 and 2 categories do not classify as DSS, with only types 3 and 4 DSS considered to be actual DSS. It was identified in the inventory that only five of the 35 systems were either type 3 or type 4. Type 1 and 2 systems are more like MIS systems that can help in decision making, but are not actually typical DSS. Therefore, for this research, only type 3 or type 4 clinical DSS will be considered DSS. The area of user acceptance of information technology (IT), not just in the areas of DSS or health, has spawned considerable research. A number of models aim to explain the acceptance and intention to use IT [58], [61] and [65]. For example, Roger's Innovation Diffusion Theory examines the relationship of the characteristics of an innovation (not specifically IT) with the rate of its adoption at an organisational rather than an individual level; as a result, this is found to be somewhat limited with regard to individual adoption [11]. The focus of this study is individual adoption, and the Unified Theory of Acceptance and Use of Technology (UTAUT) [65], which supports this perspective, is therefore the model upon which this research will be based. The UTAUT is based on eight IT acceptance models, including the widely researched technology acceptance model (TAM). The UTAUT synthesises these eight previous models based on their unique and significant elements [65]. The UTAUT comprises four main determinants of intention and use: Performance Expectancy, Effort Expectancy, Social Influences, and Facilitating Conditions, as well as four moderating variables: gender, age, experience and voluntariness of use. The UTAUT has explained up to 70% of the variance in behavioural intention, compared to 30–40% for competing models [61] and [65], and represents a major step in acceptance research [35]. Due to its infancy, the UTAUT has only been incorporated in a few studies to date [8], [22] and [34], which found support for most of the constructs as well as the overall model. Although technology acceptance research has been conducted for many different types of systems [60] and [65], its application to DSS is limited. Existing research often uses the TAM [17]. Other studies do not make any reference to a particular acceptance model, but rather examine specific issues [24] and [43]. DSS differ from other technologies in their ability to provide advice to the user making the decisions, and therefore the factors influencing the use of DSS need to be established. It has been argued that the current technology acceptance models are not suited for more complex, advanced technologies, but are more appropriate for simpler technologies such as email and word processing [5]. Many studies on the adoption and acceptance of technologies have focused on the use of these simpler technologies, and have used university students as subjects. It is therefore important to look at these models using a more complex technology applied within a new context to subjects other than students. This research will hence examine the use of DSS within a health context using general practitioners (GPs) as subjects. The purpose of this research is theory building in order to develop and explore a theoretical model that will, in future, provide a basis to examine the acceptance of DSS, and perform some preliminary testing of this model. By using the UTAUT as a starting point, this research will add to the area of technology acceptance by further investigating the UTAUT and adapting it to DSS acceptance. Moreover, this research will examine technology acceptance in the context of GPs, who are independent workers who make individual decisions. Thus the research question is: How and why do general practitioners use decision support systems? The next section will present the initial model developed for this study, followed by a description of the case methodology used to gather data to further develop and test the model. Results are then discussed and the final model developed through this research is presented. The paper concludes with implications for research and practice.

نتیجه گیری انگلیسی

In summary, this research asked “How and why do general practitioners use decision support systems?” To answer this research issue, all three groups of GPs were able to provide insight. Firstly, Group 3, those who have not heard of DSS, identified factors that would enable them to use DSS. Secondly, Group 2, those who had heard of but do not use DSS, identified factors for not using DSS. These factors would need to be addressed for them to use DSS. Finally, since the GPs in Group 1 were currently using DSS, they identified factors that would enable them to use more DSS. Analysis of these issues led to the development of the model presented previously (Fig. 3). This research has addressed three gaps in the literature: a lack of research testing the UTAUT model, DSS acceptance, and context specific technology acceptance research. This research adds to the area of technology acceptance by providing support for the UTAUT and refining it to DSS acceptance, specifically in the context of GPs. The original UTAUT model was modified by removing the social influence factor and including one additional factor, trust in the knowledge base. Although this research was targeted at GPs, the findings have a wider relevance to general users of DSS. Technology acceptance research is often conducted using similar environments and subjects, namely university students, whereas this research examined technology acceptance in the context of GPs, independent professionals who make individual decisions. This research has practical implications for those who are involved in the use and dissemination of DSS. First are the DSS developers, who will create the systems for the GPs to use with greater knowledge of user requirements. The second group are the GPs, who will be making the decisions to use DSS in practice. Finally, the last group are the people within the health sector who are responsible for increasing the use of computerisation in GPs' practices. The health sector comprises a range of people, from practice managers to government officials. This research enables these people to understand why GPs do not readily use DSS, and use the findings to remedy this situation. For example, practice managers may arrange more DSS training rather than promoting DSS for reasons that are not important to the GPs, such as cost savings. In addition, government officials, often the sponsors of DSS development, can target clinical areas where DSS would be seen as useful by the GPs, such as dermatology. Finally, the main practical implication of this research for GPs is that they will be more aware of the reasons why DSS are used and thereby able to evaluate their own justifications for using or not using DSS with greater clarity. An awareness of such factors can help the GPs re-examine why they actually do or do not use DSS and in turn lead to higher uptake of DSS. Limitations include the larger number of overseas trained GPs compared to the actual population of practising GPs. In order to gain access to GPs and be able to interview them, referrals were obtained from other GPs. Therefore, only the GPs who were referred were interviewed and as a result their educational backgrounds were slightly skewed towards overseas trained GPs. However, by having this slightly higher proportion it made the comparisons more valid and reliable as it reduced the difference in size between the Australian and overseas GPs. In terms of other characteristics, such as gender and age, the sample appeared representative of the actual GP population. Finally, the research was conducted using a qualitative approach. However, the aim of this research was to build theory rather than test theory. Therefore, the qualitative approach taken in this research was appropriate. Areas for future research include exploring additional factors that may influence the use or non-use of DSS. More specifically it was noted that, while Group 1 was predominantly overseas trained, Group 2 was mainly Australian trained, and Group 3 was also mainly overseas trained. This pattern of overseas training suggests that an additional factor may play a role in further influencing the use or non-use of DSS, perhaps related to how GPs are trained to make decisions. Furthermore, although the influence of patients and the development of DSS were examined through the inclusion of the patient presence and involvement construct, further examination of both the influence of patients and DSS developers could be undertaken. The degree of depth required to examine these areas was outside the scope of this research and would require comprehensive examination from the perspective of patients and DSS developers.

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