ScienceDirect. A Literature Review on Attitudes of Health Professionals towards Health Information Systems: From e-health to m-health

Transcription

1 Available online at ScienceDirect Procedia Technology 16 (2014 ) CENTERIS Conference on ENTERprise Information Systems / ProjMAN International Conference on Project MANagement / HCIST International Conference on Health and Social Care Information Systems and Technologies A Literature Review on Attitudes of Health Professionals towards Health Information Systems: From e-health to m-health Emre Sezgin a, *, Sevgi Özkan Yıldırım a a Middle East Technical University, Informatics Institute, Department of Information Systems, Ankara 06800, Turkey Abstract This paper presented a literature review research about health professionals acceptance of HIS and m-health based on a systematic review procedure. The results were derived from 31 scholar studies of which consisted of 27 HIS studies and 4 m-health studies. It was aimed to provide insight about acceptance theories and constructs being employed to assess current health information systems and their implementation on mobile platform. Results presented the relevance and contradictions in theories in comparison to HIS and m-health domains. It is believed that this study can bring new measures in determining influencing factors of m-health technology users, which can lead the researchers to develop effective models, and so, to assist the developers to improve applications of HIS in the mobile platform The Authors. Published by by Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the Organizing Committees of CENTERIS/ProjMAN/HCIST 2014 Peer-review under responsibility of the Organizing Committee of CENTERIS Keywords: e-health; Health Information Systems; Literature Review; Mobile Health; Technology Acceptance 1. Introduction The use of information systems in health services emerged by the extensive use of information technologies and computers in different branches of medical industries [1]. However, the term of Health Information System (HIS) has * Corresponding author. Tel.: ; fax: address: esezgin@metu.edu.tr The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the Organizing Committee of CENTERIS doi: /j.protcy

2 1318 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) emerged in the early of 1990, by the employment of variety of information and communication technology developments to be used in health services. HIS defined as health service applications and technologies which have electronic background to provide basis for communication and processes of health affairs [2]. By the increasing use of HIS in different fields of healthcare services, development of health systems and applications gained importance. In parallel to these developments, studies about assessing acceptance of HIS also increased [3,4]. Today, providing healthcare services on the mobile platform is considered as a promising technological development. It has increased reachability, accessibility and ability for effectively performing tasks [5,6]. Even though risk issues about individuals health and use of mobile devices were emphasized in the reports [7,8], yet, it has not shadowed the increasing use of mobile devices. Gartner s report presented that worldwide smart phone use have been rapidly increasing since 2007 [9] as well as the use of mobile applications. In addition to that, use of mobile platform in health service technologies, within the context of Health Information Systems (HIS), gained importance [10]. The impact of mobile developments was allegedly affected use of technology in in health services. There were number of studies in the literature about the use of mobile devices and applications in health services [11,12]. However, increasing use of mobile health services may reduce the quality of service delivered [13]. In other words, use of mobile technology has increased in health service and the service quality has been depending on the degree of relation between the user and the system [14]. Thus, in parallel to developments in health information systems, there have been questions raised regarding the success of the mobile health services: What does the success of the health service depends on? The studies demonstrated that success level of health services highly depends on the level of user acceptance and adoption [4,15]. Acceptance studies has proved their influence in terms of assessing user intentions towards technologies, and assisting quality improvement of technology and its use [16]. In this case, it is a need to figure the influencing factors for mobile health services in order to assess the effective use of the services. Literature provided that recent studies aimed to investigate the acceptance of mobile health services including management systems for patients and professionals [17 24]. The assessment of human behavior towards particular subjects has been studied in psychological studies for a long time [25,26], however, its employment for assessing technology acceptance by the users emerged in the beginning of 1990s and the studies in acceptance field increased dramatically [16,27 30]. One of the pioneering studies in technology acceptance was technology acceptance model (TAM) which was developed by Davis [27]. TAM is used for determining behaviors of users towards particular technologies, as the successor of Theory of Planned Behavior (TPB) [29]. The model argues that actual system use is affected by two main elements, perceived ease of use (PEOU) and perceived usefulness (PU). Thus, these constructs are fragments of reflection of an individual s behavioral intention (BI) towards using a system. Addition to that, the model had been expanded with additional variables in order to measure effects of different behavioral factors on particular technologies [31]. It was observed that TAM theory has been successfully applied in variety of HIS studies [4]. The model was modified and expanded by the changes of technologies and user needs [16,28]. In this context, Roger s Diffusion of Innovations theory (IDT) used to expand the scope of acceptance [17]. However, TAM was criticized due to its shortcomings such as difficulties in generalization, explanatory power and inconsistent relationships between constructs [28,32,33]. As a further step, Unified Theory of Acceptance and Use of Technology (UTAUT) was developed by Venkatesh et. al. [28] in the field of information systems, and it has been validated by many studies [14,34 38]. The UTAUT proposed an acceptance model which helps to understand likelihood of success of new technologies and determine drivers of acceptance. Here, the aim of this research is to present studies in the literature about acceptance of m-health technologies by health professionals. The study consisted of three fundamental parts. They were developing and implementing a review method and reporting of the review results. For the study, the research question can be stated as what are the influencing factors for user attitudes towards m-health? This question was the primary issue to be addressed in order to reveal trajectories in users attitudes in the domain of m-health. It is believed that this study can bring new measures in determining influencing factors of m-health technology users, which can lead the researchers to develop effective models, and so, to assist the developers to improve applications of HIS in the mobile platform. In the following sections the method of the literature research method and results were presented.

3 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) Literature Research 2.1. Method In this literature research, a literature review procedure was developed and employed in order to conduct the review systematically. Three systematic review guidelines were held as the resources to build the procedure [39 41]. The steps of literature review were given in Fig. 1 and defined as the followings: i. Identification of keywords and database search Within the context of the study, keywords for database research were selected in order to reach studies in relevant fields of health information systems. The keyword search was elaborated by the first results for increasing accuracy in the search. In total, combinations of following keywords were used: health information system, e-health, m- health, mobile health, health informatics, acceptance, information system acceptance, adoption, technology acceptance, technology adoption. The search was conducted on academic databases which have large repository of academic studies and high popularity in web based academic researches. In this context, scopus.com, sciencedirect.com and webofknowledge.com were used as the academic databases. The initial search resulted with over a thousand articles. ii. Refining results: Phase 1 In this phase, the keywords of the articles as well as the titles were reviewed. The relevance to the context was investigated. In addition to that, a set of exclusion criteria was applied in order to receive articles from reliable sources, and to ensure to receive articles suits to the context of the research. Exclusion criteria were specified as followings considering review guidelines: a. Papers should be published in English language b. Papers should be published within 10 years ( ) c. Papers should be published in peer reviewed journals which also had impact factor over 1.0 d. Objective of the papers should be about acceptance or adoption of HIS as well as mobile HIS e. The target sample of the researches should be health professionals including physicians, doctors, nurses, and other healthcare providers. f. Papers should present information about quantitative results At the end of phase 1, according to exclusion criteria a, b and c, the studies were refined to 360 articles. iii. Refining results: Phase 2 At the phase 2, abstracts of 360 articles were reviewed. They were read by the relevance of the study and the exclusion criteria d and e. 309 papers were eliminated due to context mismatch defined in these criteria. iv. Refining results: Phase 3 The remaining 51 articles were reviewed. Twenty articles were excluded as the result of exclusion criteria d, e and f, method of the use of acceptance theories and irrelevant to context results. At the end, 31 papers were included to the literature of the study. Four of the studies grouped as m-health studies. The remaining 27 studies were investigating acceptance of health professionals in e-health domain.

4 1320 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) Results Fig. 1. Flow diagram of selection process of articles The literature review was conducted accordingly the procedure of research and m-health studies were listed by Appendix A presented constructs being tested in the studies, the theories employed and the definitions. The list of the literature research in m-health acceptance by health professionals was given in Appendix B (Unfortunately resulting chart of ehealth studies could not be shared due to page limitations). It included the study titles, significant variables in each study, the theory being employed by the study, target sample, sample size, variance explained for BI or actual use and references. The Table 1, 2 and 3 demonstrated overall results of the review. Even though m-health results are very limited, they slightly enlightens the trend of researches in m-health domain. Table 1 presented the types of samples and the number of studies in HIS and m-health which these sample types. The types were grouped in 4: physicians, nurses, others and mixed. Their definitions were given in the table. According to Table 1, physicians were the popular samples in the acceptance studies in general. Table 1. List of samples participated to HIS and m-health studies Samples # of studies in HIS (Out of 31) Physicians* 15 1 Nurses 5 - Others** 6 1 Mixed*** 5 2 # of studies in m- Health (Out of 4) * Physicians include physicians, clinicians and paediatricians; ** Others involve physiotherapists, caregivers, healthcare workers, technicians, ER teams and therapists; *** Mixed samples include a mixed set of participants consist of physicians and nurses. Table 2 demonstrated the theories employed by the studies. Here, integrated models were the trending approaches which were developed by integrating TAM and another behavioural theories (such as UTAUT, IDT, IS success model and TPB) to build a new model.

5 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) Table 2.. List of theories employed in HIS and m-health studies Theory employed # of studies in HIS (Out of 31) TAM* 12 - UTAUT 5 - Integrated models** 14 4 # of studies in m- Health (Out of 4) * TAM includes TAM and TAM2; ** Integrated models include theories of UTAUT, IDT, IS success model and TPB integrated with TAM. Table 3 presented the fundamental constructs of TAM and their relations with behavioural intention in order to explain user behaviours. The main reason to use constructs of TAM was its popularity and involvement in each study. Thus, constructs of TAM outlines main significant relations in the studies. Table 3 included statistically significant relations, number of these significant relations observed in HIS studies and m-health studies. Table 3. List of significant relations in HIS and m-health studies Significant relations* # of relations in HIS studies (31) # of relations in m- Health studies (4) PU- BI 23 4 PEOU- BI 14 3 PEOU- PU 17 2 Attitude BI 8 2 PU- Attitude 8 2 PEOU- Attitude 5 - Others BI ** 17 4 * The main constructs of TAM and their statistically significant relations with each other were presented; ** Others involve constructs of perceived behavioural control, social norms, self-efficacy, hospital type, self-identity, normative factors, perceived readiness, computer level, logical access, image, compatibility, trust, perceived financial cost and perceived system performance. Literature research of HIS studies, which employed TAM, TPB, UTAUT and extended models, demonstrated that they have succeeded to explain intention to health professionals use of health technologies with high rates of variance explained (Appendix B). It was observed that trend in behavioural theories in assessing user acceptance of HIS shows varieties with respect to users environment. In a study, as a new variable, habit was identified as effective factor in telemedicine adoption [42]. However, in spoken dialog system acceptance study, perceived system response was argued as influential factor for PEOU [43]. In most of the studies, the relations of PEOU-PU, PE-BI, EE-BI, Perceived Innovativeness and its relations with BI, SN and PEOU found significant [3,14,34,36,38,44 47]. Exclusively, the impact of image [44], facilitating conditions, computer anxiety and self-efficacy [34,35], compatibility [48], training [35], service quality [3], trust, perceived risk and information integrity [49], knowledge and ICT feature demands [45] and perceived threat to professional autonomy [15] were found significantly related to influencing factors like PEOU and PU, which enabled them indirectly to be effective on intention to use health information systems. The literature research of m-health studies showed that, the factors influencing mobile system use in health services demonstrated similar factors as in the HIS studies. With this respect, PU, PEOU, compatibility, self-efficacy, training and support, personal innovativeness, SN were identified as prominent factors in adoption of mobile health services by health professionals [19,50,51]. In addition to that, hospital type [51] and age [20] were found as influencing mediating factors. From another point of view, in a study of mobile healthcare service for patients, it was found that perceived usefulness, perceived ease-of-use, external cues to actions, and innovativeness influencing factors of BI [23]. The study of Hung and Jen [17] supported the major relations in acceptance studies about mobile

6 1322 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) health services. In the bottom line, the studies presented similarities in HIS and m-health domains and suggested further research about investigating influencing factors and identifying key variables in different branches of health services [10,20,23,45]. In this context, non-communicable diseases were presented as one of the intact field of study in terms of acceptance [21] Discussion and Implications At the final point of the study, it was found that extended TAM model and UTAUT was a powerful approach to explain intention to use in health technologies [4]. It also refers to that current acceptance models are better off with an integrated approach which merges more than one model in one, in order to understand more about user needs [19,38,46,50]. When the researchers consider the major theories (such as TAM, TPB, IDT and UTAUT), it was observed that there have been important improvements on the theories over the time (expanding with additional variables). However, in each study, almost similar technologies, which were HIS and mobile systems to use for medical communication and health services, were tested [4,19,20,50,51]. Furthermore, variables in HIS and mobile system acceptance studies were also observed to be similar as well as the explained variances of relations. It can be concluded that HIS and mobile systems did not demonstrate highly differences in terms of explaining user intention towards a health service technology. It was also noted that in most of the studies perceived usefulness and ease of use found effective on behavioural intention. On the other hand, new variables were emerging with regards to the changes in technologies in HIS acceptance studies [1,8,21]. But they were mostly not so useful to explain intention to use. Even though new variables may fail to explain intentions, it is required to extend to scope, to consider latent facts and to generate new variables with respect to the needs of technology users [28]. It was also observed that there was a particular increase in degree of variance to explain intention in recent studies. It may be the result of mediating effects of new variables, or increasing power of explanation by existing constructs. From broader point of view, it can be interpreted as there are positive changes in people s lifestyles (involving technology in their lives more than before), mind-set, and so, attitudes towards new technologies. In the bottom line, acceptance models are prone to be developed with new approaches or variables. In the domain of health service, it was demonstrated that branches of health services remained intact in terms of acceptance studies, and it was required to conduct further researches in those fields in order to explain human-technology relation. With this study, one of the health service branches, cardiovascular services, will be investigated for acceptance of mobile systems by cardiologists. From the point of research design, the results showed that emerging studies should include more qualitative approaches and longitudinal studies in order to understand user needs effectively [52]. In addition to that, mediating factors should be considered as important inputs to seize variety of relations within the model. Most of the studies, in future research sections, emphasized on including mediating factors in proposed model, to extend current study with longitudinal studies and involving qualitative methods. But in action, they remained so few. Furthermore, some studies also underlined the impact of social conditions and developments in health services. Eysenbach and Diepgen [53] argued that low health literacy leads poor health and underlined the contradiction in technology access and wealth (or level of literacy) in preventable health problems. Thus, even though new technologies have emerged, if accessibility by public remained low, the systems will not be serving to their purposes. In addition to that, new technologies have always been questioned for effects on human health. Visvanathan et al [13] argued that increasing use of mobile communication technologies may contain risks and cause pitfalls such as electromagnetic risks, patient security, confidentiality and data security and distraction. Thus, when the system view considered as a part of the big equation, other impacts of technologies and their indirect effects on human behaviour should be investigated as future studies. 3. Conclusion This paper presented a literature review research about health professionals acceptance of HIS and m-health based on a systematic review procedure. The results were derived from 31 scholar studies of which consisted of 27 HIS studies and 4 m-health studies. It was aimed to provide insight about acceptance theories and constructs being

7 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) employed to assess current health information systems and their implementation on mobile platform. Results presented the relevance and contradictions in theories in comparison to HIS and m-health domains. However, m-health studies were in premature level and it was hard to deduct comprehensive and explanatory results about new trends. In any case, the current practices underlined that theories in m-health studies are successors of HIS studies and as in other studies in HIS, qualitative approaches as well as longitudinal data collection methods were needed in the literature. Further studies were planned to extend the scope and coverage (including recent years studies) of this study to provide a comprehensive review in acceptance of health information technologies. References [1] Reichertz PL. Hospital information systems--past, present, future. Int J Med Inform 2006;75: [2] Haux R. Health information systems - past, present, future. Int J Med Inform 2006;75: [3] Pai F-Y, Huang K-I. Applying the Technology Acceptance Model to the introduction of healthcare information systems. Technol Forecast Soc Change 2011;78: [4] Holden RJ, Karsh B-T. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010;43: [5] Sarker S. Understanding Mobile Handheld Device Use And Adoption. Commun ACM 2003;46: [6] Nah F, Siau K. The value of mobile applications: a utility company study. Commun ACM 2005;48: [7] Repacholi MH. Health risks from the use of mobile phones. Toxicol Lett 2001;120: [8] IEGMP. Mobile Phones and Health. Report of an Independent Expert Group on Mobile Phones [9] Gartner Inc. Gartner: World-Wide Smartphone Sales [10] Tachakra S, Wang XH, Istepanian RSH, Song YH. Mobile e-health: the unwired evolution of telemedicine. Telemed J E Health 2003;9: [11] Istepanian RSH, Laxminarayan S, Pattichis CS. M-Health: Emerging Mobile Health Systems. Springer-Verlag, London; [12] Hampton T. Recent Advances in Mobile TechnologyBenefit Global Health, Research, and Care. Am Med Assoc 2012;307: [13] Visvanathan A, Gibb AP, Brady RRW. Increasing clinical presence of mobile communication technology: avoiding the pitfalls. Telemed J E Health 2011;17: [14] Dünnebeil S, Sunyaev A, Blohm I, Leimeister JM, Krcmar H. Determinants of physicians technology acceptance for e-health in ambulatory care. Int J Med Inform 2012:1 15. [15] Walter Z, Lopez MS. Physician acceptance of information technologies: Role of perceived threat to professional autonomy. Decis Support Syst 2008;46: [16] Venkatesh V, Davis FD. A Theoretical Acceptance Extension Model : Four Longitudinal Field Studies. Manage Sci 2000;46: [17] Hung M-C, Jen W-Y. The Adoption of Mobile Health Management Services: An Empirical Study. J Med Syst 2010;36: [18] Wu I, Li J, Fu C, Wu S. The Acceptance Of Wireless Healthcare For Individuals : An Integrative View. Proc 12th Int Conf Enterp Inf Syst 2010;5:1 6. [19] Chen J, Park Y, Putzer GJ. An Examination of the Components that Increase Acceptance of Smartphones among Healthcare Professionals. Electron J Heal InformaticsInformatics 2010;5:1 12. [20] Shengnan Han, Pekka Mustonen, Matti Seppanen MK. Physicians acceptance of mobile communication technology an exploratory study 2006: [21] Piette JD, Blaya JA, Sanchis JBB, Box PO, Arbor A. Experiences in mhealth for Chronic Disease Management in 4 Countries. Proc 4th Int Symp Appl Sci Biomed Commun Technol 2011:0 4. [22] Lin S-P, Yang H-Y. Exploring key factors in the choice of e-health using an asthma care mobile service model. Telemed J E Health 2009;15: [23] Lin SP. Determinants of adoption of Mobile Healthcare Service. Int J Mob Commun 2011;9:298. [24] Iredale R, Hilgart J, Hayward J. Patient perceptions of a mobile cancer support unit in South Wales. Eur J Cancer Care (Engl) 2011;20: [25] Fishbein M, Ajzen I. Belief, attitude, intention and behaviour: An introduction to theory and research. Addison-Wesley; [26] Bandura A. Self-efficacy toward a unifying theory. Psychol Rev 1977;84: [27] Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Manag Inf Syst 1989;13: [28] Venkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. Manag Inf Syst 2003;27: [29] Ajzen I. The Theory of Planned Behavior. Organ Behav Hum Decis Process 1991;50: [30] Wood R, Bandura A. Social Cognitive Theory of Organizational Management. Acad Manag Rev 1989;14: [31] Bagozzi RP, Warshaw PR. Development and Test of a Theory of Technological Learning and Usage. Hum Relations 1992;45: [32] Legris P, Ingham J, Collerette P. Why do people use information technology? A critical review of the technology acceptance model. Inf Manag 2003;40: [33] Sun H, Zhang P. The role of moderating factors in user technology acceptance. Int J Hum Comput Stud 2006;64: [34] Schaper LK, Pervan GP. ICT and OTs: a model of information and communication technology acceptance and utilisation by occupational therapists. Int J Med Inform 2007;76 Suppl 1:S

8 1324 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) [35] Aggelidis VP., Chatzoglou PD. Using a modified technology acceptance model in hospitals. Int J Med Inform 2009;78: [36] Chang I-C, Hwang H-G, Hung W-F, Li Y-C. Physicians acceptance of pharmacokinetics-based clinical decision support systems. Expert Syst Appl 2007;33: [37] Kijsanayotin B, Pannarunothai S, Speedie SM. Factors influencing health information technology adoption in Thailand s community health centers: Applying the UTAUT model. Int J Med Inform 2009;78: [38] Pynoo B, Devolder P, Duyck W, van Braak J, Sijnave B, Duyck P. Do hospital physicians attitudes change during PACS implementation? A cross-sectional acceptance study. Int J Med Inform 2012;81: [39] Kitchenham B. Procedures for Performing Systematic Reviews [40] Brereton P, Kitchenham B, Budgen D. Lessons from applying the systematic literature review process within the software engineering domain. J Syst Softw 2007;80: [41] Creswell JW. Research design: qualitative, quantitative, and mixed method approaches. vol. 2nd. Sage Publications; [42] Gagnon M-P, Godin G, Gagné C, Fortin J-P, Lamothe L, Reinharz D, et al. An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians. Int J Med Inform 2003;71: [43] Barker DJ, Schaik P Van, Simpson DS, Corbett WA. Evaluating a Spoken Dialogue System for recording clinical observations during an endoscopic examination. Informatics Heal Soc Care 2003;28: [44] Yi MY, Jackson JD, Park JS, Probst JC. Understanding information technology acceptance by individual professionals: Toward an integrative view. Inf Manag 2006;43: [45] Melas CD, Zampetakis L a, Dimopoulou A, Moustakis V. Modeling the acceptance of clinical information systems among hospital medical staff: an extended TAM model. J Biomed Inform 2011;44: [46] Holden RJ, Brown RL, Scanlon MC, Karsh B-T. Pharmacy workers perceptions and acceptance of bar-coded medication technology in a pediatric hospital. Res Social Adm Pharm 2012;8: [47] Yu P, Li H, Gagnon M-P. Health IT acceptance factors in long-term care facilities: a cross-sectional survey. Int J Med Inform 2009;78: [48] Tung F-C, Chang S-C, Chou C-M. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. Int J Med Inform 2008;77: [49] Ortega Egea JM, Román González MV. Explaining physicians acceptance of EHCR systems: An extension of TAM with trust and risk factors. Comput Human Behav 2011;27: [50] Wu J-H, Wang S-C, Lin L-M. Mobile computing acceptance factors in the healthcare industry: a structural equation model. Int J Med Inform 2007;76: [51] Wu I-L, Li J-Y, Fu C-Y. The adoption of mobile healthcare by hospital s professionals: An integrative perspective. Decis Support Syst 2011;51: [52] Kaplan B. Qualitative research methods for evaluating computer information systems. Impact Healthc Inf Syst 2005: [53] Eysenbach G, Diepgen TL. The role of e-health and consumer health informatics for evidence-based patient choice in the 21st century. Clin Dermatol 2001;19:11 7.

9 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) Appendix A. Constructs and Definitions Constructs Definitions References Perceived Usefulness (PU) Behavioral Intention (BI) Perceived ease of use (PEOU) Compatibility Self-Efficacy Technical support and training The degree to which a person believes that using a particular system would enhance his or her job performance An individual s performing a conscious act, such as deciding to accept (or use) a technology The degree to which a person believes that using a particular system would be free of effort The degree to which the use of the system is perceived to be consistent with health- care professionals existing values, prior experiences and needs The healthcare professional s perceptions of his or her ability to use the system in the accomplishment of healthcare task The technical support and the amount of training provided by individuals or groups with the system knowledge Attitude Individual's positive or negative feeling about performing the target behavior [27] Task Task includes structure of the task, jurisdiction, and uncertainty [19] Perceived Service Availability Personal Innovativeness in IT Social Norms (SN) Perceived Behavioral Control (PBC) Hospital type Perceived service availability refers to the degree to which an innovation is perceived as being able to support pervasive and timely usage Personal innovativeness represents the degree to which an individual is willing to take a risk by trying out an innovation The degree to which the social environment perceives particular technology as desirable Reflects perceptions of internal and external constraints on behavior and encompasses self-efficacy, resource facilitating conditions, and technology facilitating condition Hospital type was defined to include three major types, medical center, regional hospital, and local hospital, based on a combination of some hospital attributes, such as quality of medical service, annual revenue, and number of employees [27] [27] [27] [51] [51] [51] [47] [47] [29] [28] [47]

10 1326 Emre Sezgin and Sevgi Özkan Yıldırım / Procedia Technology 16 ( 2014 ) Appendix B. Literature chart Title of the study Significantly related variables Model /Theory Physicians' PU-BI TAM and acceptance of (Age on) ease of use-bi UTAUT mobile communication (Age on) compatibility-bi technology an Personal Innovativeness- BI exploratory study (Moderating) Mobile computing acceptance factors in the healthcare industry: a structural equation model An Examination of the Components that Increase Acceptance of Smartphones among Healthcare Professionals The adoption of mobile healthcare by hospital's professionals: An integrative perspective Target sample Professional physicians Compatibility Self Efficacy Compatibility- BI Compatibility- PU Compatibility- PEOU Self-Efficacy- PU TAM and IDT Physicians, nurses, and medical technicians in Taiwan Self-Efficacy- PEOU Technical support and training- Self efficacy PEOU-PU PEOU-BI PU-BI Attitude BI TAM- IDT Physician and PU- BI nurses PU- Attitude Self-efficacy- PEOU Self-efficacy - BI Compatibility - PU Task- Attitude Compatibility- PEOU Perceived Service TAM and Hospital Availability- PU TPB Professionals Attitude- BI PEOU-PU PU- BI PU- Attitude Personal Innovativeness in IT- PEOU Personal Innovativeness in IT -PBC SN- BI PBC- BI Hospital type- BI Sample size Variance Explained References [20] [51] [19] [47]