Adoption of electronic patient records by Iranian hospitals´ staff

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2008:043

M A S T E R ' S T H E S I S

Adoption of Electronic Patient Records by Iranian Hospitals´ Staff

Mahbod Hamidfar

Luleå University of Technology Master Thesis, Continuation Courses

Marketing and e-commerce

Department of Business Administration and Social Sciences Division of Industrial marketing and e-commerce

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MASTER’S THESIS

Adoption of Electronic Patient Records by Iranian Hospitals’ Staff

Supervisors:

Dr. Moez Limayem

Dr. Seyed Hessameddin Zegordi Referees:

Dr. Amir Albadvi Dr. Esmail Salehi-Sangari

Prepared by:

Mahbod Hamidfar 846831009

Tarbiat Modares University Faculty of Engineering Department of Industrial Engineering

Lulea University of Technology

Division of Industrial Marketing and E–Commerce

Joint MSc PROGRAM IN MARKETING AND ELECTRONIC COMMERCE

2008

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Abstract

There has been an increasing interest in the area of Electronic Patient Records (EPR) and more and more hospitals all over the world try to keep their patients’ records electronically. The adoption of EPR has become a major concern in the healthcare industry, as it is a key factor to the healthcare quality improvement. Today, despite the immense investment in EPR systems in hospitals, these systems are not used by the clinical staff in most Iranian hospitals. The usage of these systems would be the key to the return on investments in these systems.

The purpose of this study is to gain a better understanding of the factors affecting Iranian hospitals’ staff intention to use EPR. To do so, the literature on EPR and the use and importance of Information Technology (IT) in healthcare industry is reviewed.

Different technology adoption theories are introduced and compared. Consequently, an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) is proposed to perform the study. Finally, the proposed research model is statistically tested using the data from the conducted survey in 6 Iranian hospitals.

The findings provide strong empirical support for most of the main constructs mentioned in the research model, which posits five direct determinants of intention to use EPR as follow: performance expectancy, effort expectancy, social influence, facilitating conditions and personal innovativeness in IT. In addition the results show that the effect of social influence on behavioral intention is even stronger for women.

Considering the fact that the achieved conceptual framework considers the particular characteristics of the medical profession, contributions and implications of this study are noteworthy at the theoretical level as well as the practical level.

Keywords: Electronic Patient Record (EPR), Healthcare Industry, Information Technology (IT), Technology Adoption, Unified Theory of Acceptance and Use of Technology (UTAUT)

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Acknowledgement

This dissertation concludes my Master of Science degree in Marketing and e–

Commerce at Tarbiat Modares University joint with Lulea University of Technology.

Completion of this work has been both interesting and challenging to me. I would like to extend my gratitude to all the people who helped and supported me during this process.

I wish to express my deepest appreciation to members of Industrial Engineering Department of Tarbiat Modares University and Industrial Marketing and e–Commerce division of Lulea University of Technology, especially my supervisors, Prof. M.

Limayem and Dr. S.H. Zegordi for their guidance and encouragement that gave me an opportunity to progress and broaden my knowledge.

I would like to especially thank managers of Day, Kasra, Laleh, Shahid Rajaee, Dr. Shariati and Toos hospitals and all the doctors and nurses who took the time to answer my questions, for their cooperation and patience.

Last but not least, I wish to express my sincere gratitude to my family and friends for their love and support. I hereby dedicate this piece ofwork to my beloved parents to whom I owe all the joy and success in my life.

March 2008 Mahbod Hamidfar

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List of Tables

Table 1-1 IT Adoption Related Studies in Healthcare Industry ... 14

Table 1-2 Healthcare Professionals Technology Acceptance Related Studies... 17

Table 2-1 EPR Adoption Related Studies... 30

Table 2-2 IDT ... 33

Table 2-3 Refined IDT. Source: Venkatesh et al., 2003... 34

Table 2-4 TRA. Source: Venkatesh et al., 2003 ... 35

Table 2-5 TPB and DTPB. Source: Venkatesh et al., 2003... 36

Table 2-6 TAM and TAM2. Source: Venkatesh et al., 2003... 38

Table 2-7 C–TAM–TPB. Source: Venkatesh et al., 2003 ... 39

Table 2-8 UTAUT... 41

Table 2-9 MPCU. Source: Venkatesh et al., 2003... 43

Table 2-10 MM. Source: Venkatesh et al., 2003 ... 44

Table 2-11 SCT. Source: Venkatesh et al., 2003... 44

Table 2-12 Performance Expectancy Root Constructs. Source: Venkatesh et al., 2003... 46

Table 2-13 Effort Expectancy Root Constructs. Source: Venkatesh et al., 2003 . 46 Table 2-14 Social Influence Root Constructs. Source: Venkatesh et al., 2003 .... 46

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Table 2-15 Facilitating Conditions Root Constructs. Source: Venkatesh et al.,

2003... 47

Table 3-1 Relevant Situation for Different Research Strategies. Source: Yin, 1994 ... 62

Table 3-2 Definition of Constructs ... 66

Table 4-1 Cronbach Alphas ... 76

Table 4-2 Factor Loadings ... 77

Table 4-3 Factor Structure Matrix of Loadings and Cross–Loadings ... 79

Table 4-4 AVE and Square Root of AVE... 80

Table 4-5 Correlation of Latent Variables ... 81

Table 4-6 Demographic Characteristics of the Respondents... 81

Table 4-7 Results of Hypotheses Tests... 83

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List of Figures

Figure 1-1 A Model of Data–Driven Healthcare Quality. Source: Lorence and

Jameson, 2002... 16

Figure 1-2 Structure of the Study... 23

Figure 2-1 Innovation Decision Process. Source: Rogers, 1995 ... 30

Figure 2-2 IDT. Source: Rogers, 1995... 33

Figure 2-3 Refined IDT. Source: Moore and Benbasat, 1991 ... 35

Figure 2-4 TRA. Source: Fishbein and Ajzen, 1975 ... 36

Figure 2-5 TPB. Source: Ajzen, 1991... 37

Figure 2-6 DTPB. Source: Taylor and Todd, 1995a... 37

Figure 2-7 TAM. Source: Davis, 1989 ... 39

Figure 2-8 TAM2. Source: Venkatesh and Davis, 2000... 39

Figure 2-9 C–TAM–TPB. Source: Chau and Hu, 2002... 40

Figure 2-10 UTAUT. Source: Venkatesh et al., 2003 ... 42

Figure 2-11 Proposed Research Model... 52

Figure 5-1 Results ... 92

Figure 5-2 Final Theoretical Model... 93

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Chapter 1 Introduction

1. Introduction

The first chapter presents the research overview and background, the motivation of the study and a review on the current state of Iran and, then introduces the reader to the problem statement, research objective and question which leads to the importance of the research. Subsequently it reports the structure of the thesis.

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1.1 Overview

The rapid growth of investment in Information Technology (IT) by organizations worldwide has made user acceptance an increasingly critical technology implementation and management issue. While such acceptance has received fairly extensive attention from previous researches, additional efforts are needed to examine or validate existing research results, particularly those involving different technologies, user populations, and/or organizational contexts (Hu et al., 1999b).

The importance of technological change in the health sector is a widely discussed topic in the economic literature (Selder, 2005). Among these changes is the introduction of Electronic Patient Records (EPR) which can promote higher quality, lower costs, and increased patient and clinician satisfaction. Yet one important player in the healthcare market has so far been neglected in the discussion: the provider of healthcare services (ibid). After all, it is the physicians and nurses who decide whether to use a technical innovation such as EPR or not.

Regarding the literature review and the current state of Iranian hospitals, among the research opportunities in healthcare industry and technology adoption context, investigating the factors influencing Iranian hospitals’ staff intention to adopt EPR systems, is chosen to be studied in this research.

1.2 Background

The shift from industrial to information society has also its phenomena in medicine (Maass and Eriksson, 2006). Traditionally, medicine is an information–

intensive branch where patient treatment is triggered by the availability of diagnostic knowledge (ibid). Today, healthcare computing or medical informatics is one of the fastest growing areas of Information and Communication Technology (ICT) applications (Rogerson, 2000). It is a multifaceted application concerned with EPR, performance indicators, paramedical support, emergency service, computer aided diagnosis, clinical governance, research support, and hospital management (ibid).

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In the past decade, EPR has become an important tool for the healthcare providers (McDonald et al., 1999; Sim et al., 2001). Use of an EPR is shown to produce more complete clinical documentation than the paper record, leading to more appropriate clinical decisions (Barahona et al., 2001; Daugaard, 2002; Sim et al., 2001). In addition, EPR is recognized for its potential to implement guideline–based healthcare and to identify and limit medical errors (Mikulich et al., 2001; Morgan et al., 1998; Shiffman et al., 1999).

Years ago, entry of a clinical encounter summary into an EPR meant transcription of voice files or data entry by clerks (Whiting–O’Keefe et al., 1988). But now, the intermediary between the clinician and EPR may no longer be needed, and a great opportunity exists for streamlining clinical record keeping and increasing clinical access to medical records (Johnson et al., 2004). Still, despite the increasing availability of EPR systems, anecdotal evidence suggests that its use has not been well accepted by physicians and nurses (Anderson, 2000; McDonald, 1997). The investments in this new technology are immense and seen from a cost–benefit perspective most implementations of EPR are more or less "trial–and–error" projects (Nikula, 2005). Therefore, there is a need for academically investigating the factors influencing the adoption of EPR by the hospitals’ staff.

1.3 Motivation of the Study

Public healthcare services have been under scrutiny in terms of productivity and efficiency for several years now and IT applications are sought to assist the re–

engineering of these services (Maass and Eriksson, 2006). Among these IT applications, EPR is recognized as one of the most important strategic IT tools to improve a hospital’s productivity and competitiveness. In addition, employing EPR systems help to improve the healthcare quality and patient care in hospitals. On the other hand, it is the physicians and nurses who decide whether to use EPR systems or not and unfortunately anecdotal evidence suggests that they have not well accepted the use of these systems (Anderson, 2000; McDonald, 1997). Consequently the researcher is motivated to conduct the current study in order to identify the factors influencing Iranian hospitals’ staff intention to adopt

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EPR systems. The above mentioned motivating areas are more discussed in the following sections.

1.3.1 IT in Healthcare Industry

To cope with the dramatic changes and fierce competition, healthcare industry is experiencing major transformation in its IT base (Wilson and Lankton, 2004). By nature, hospitals are in an information–intensive industry and hence they would gain great benefit by adopting IT applications, ranging from medical to administration systems (Chang et al., 2005). IT has had its applications within healthcare for decades now (Maass and Eriksson, 2006). Primarily, until ten years ago, IT was used for administrative tasks, such as statistics and personnel data (ibid). Some newcomers are the Hospital, Laboratory, Pathology and Radiology Information Systems (ibid). Nowadays Healthcare Information Technology (HIT) is broadly defined as including in patient and out patient care settings clinical information management systems used by clinicians and ancillary staff for the purpose of clinical information management, order entry, documentation of care services, and decision support (Middleton et al., 2005).

IT solutions are sought to assist the re–engineering of public healthcare services (Maass and Eriksson, 2006). Therefore, the importance of technological change in the health sector is a widely discussed topic in the economic literature (Selder, 2005). Table 1-1 lists several studies which have been conducted in the IT adoption context in healthcare industry, examining the factors affecting IT acceptance by either healthcare organizations or physicians and other healthcare professionals.

Table 1-1 IT Adoption Related Studies in Healthcare Industry

Authors Year Title

Al–Qirim, N. 2007 Championing telemedicine adoption and utilization in healthcare organizations in New Zealand

Maass, M.

Eriksson, O.

2006 Challenges in the adoption of Medical Information Systems

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Table 1-1 IT Adoption Related Studies in Healthcare Industry (Continued)

Zheng, J.

Bakker, E.

Knight, L.

Gilhespy, H.

Harland, C.

Walker, H.

2006 A strategic case for e–adoption in healthcare supply chains

Lubrin, E.

Lawrence, E.

Zmijewska, A.

Navarro, K.F.

Culjak, G.

2006 Exploring the Benefits of Using Motes to Monitor Health:

An Acceptance Survey

Chang, I. C.

Hwang, H.

Yen, D. C.

Lian, J. W.

2005 Critical factors for adopting PACS in Taiwan: Views of radiology department directors

Lorence, D. P.

Jameson, R.

2002 Adoption of information quality management practices in US healthcare organizations: A national assessment

Anderson, J. G. 2002 Evaluation in health informatics: Social network analysis Hu, P. J.

Chau, P. Y.

Sheng, O. L.

2000 Investigation of factors affecting healthcare organization’s adoption of telemedicine technology

Nabali, H. M. 1991 Hospital Information Systems in Arab Gulf countries:

Characteristics of adopters 1.3.2 EPR and Healthcare Quality

Proper and correct adoption of IT can significantly affect the quality and performance of medical services provided by a hospital (Chang et al., 2005). A comprehensive EPR system is a viable solution for optimizing patient management and

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providing high quality care while maintaining costs under economic restrictions (Ratib et al., 2003). As a result, to increase the hospitals’ competitiveness, hospital managers try to employ Hospital Information Systems (HIS) supporting EPR to improve the quality of patient care and productivity of their staff (Chang et al., 2005).

Figure 1-1 also shows that evidence–based medicine, which is reached as a consequence of EPR usage, is one of the main data–driven factors to improve healthcare quality.

Figure 1-1 A Model of Data–Driven Healthcare Quality. Source: Lorence and Jameson, 2002

1.3.3 Role of Hospital staff

One of the most challenging areas of EPR development is integrating it into the workflow of the physicians and nurses (Johnson et al., 2004). In a typical day, as physicians and nurses visit patients, they document the patients’ symptoms and any physical findings discovered during the encounter in the form of encounter summaries (ibid). These summaries may be initial history and physical reports, follow–up visit notes, progress notes, surgical procedure notes, or consult summaries, depending on the reason for the patient to be seeing the clinician (ibid). The problem is that, not all hospitals’ staff adopt IT applications such as EPR without any hesitation (Chang et al., 2005). In fact, the greatest barrier to EPR adoption is the resistance by physicians and nurses (Brailer and

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Terasawa, 2003). There have been several researches conducted to study factors affecting healthcare professionals and hospitals’ staff technology adoption which are listed in Table 1-2.

Table 1-2 Healthcare Professionals Technology Acceptance Related Studies

Chang, I. C.

Hwang, H. –G.

Hung, W. –F.

Li, Y. –C.

2007 Physicians' acceptance of pharmacokinetics–based clinical decision support systems

Schaper, L.

Pervan, G.

2007 ICT and OTs: A model of information and communication technology acceptance and utilization by occupational therapists

Wu, J. –H.

Wang, S. –C.

Lin, L. –M.

2007 Mobile computing acceptance factors in the healthcare industry: A structural equation model

Litwin, A. S. 2006 Information technology and the employment relationship:

Examining physicians’ adoption of health information technology

Ford, E. W.

Menachemi, N.

Phillips, M. T.

2006 Predicting the adoption of Electronic Health Records by physicians: When will healthcare be paperless?

Pare, G.

Sicotte, C.

Jacques, H.

2006 The effects of creating psychological ownership on physicians' acceptance of clinical information systems

Yi, M. Y.

Jackson, J. D.

Park, J. S.

Probst, J. C.

2006 Understanding information technology acceptance by individual professionals: Toward an integrative view

Liu, L.

Ma, Q.

2005 The impact of service level on the acceptance of application service oriented medical records

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Table 1-2 Healthcare Professionals Technology Acceptance Related Studies (Continued)

Lu, Y. –C.

Xiao, Y.

Sears, A.

Jacko, J. A.

2005 A review and a framework of handheld computer adoption in healthcare

Schectman, J. M.

Schorling, J. B.

Nadkarni, M. M.

Voss, J. D.

2005 Determinants of physician use of an ambulatory prescription expert system

Selder, A. 2005 Physician reimbursement and technology adoption Zheng, K.

Padman, R.

Johnson, M. P.

Diamond, H. S.

2005 Understanding technology adoption in clinical care:

Clinician adoption behavior of a point–of–care reminder system

Lee, T. –T. 2004 Nurses' adoption of technology: Application of Rogers' innovation–diffusion model

Gagnon, M. –P.

Godin, G.

Gagne, C.

Fortin, J. –P.

Lamothe, L.

Reinharz, D.

Cloutier, A.

2003 An adaptation of the theory of interpersonal behavior to the study of telemedicine adoption by physicians

Chismar, W. G.

Wiley–Patton, S.

2002 Does the Extended Technology Acceptance Model apply to physicians

Chau, P. Y. K.

Hu, P. J. –H.

2002 Investigating healthcare professionals' decisions to accept telemedicine technology: An empirical test of competing theories

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Table 1-2 Healthcare Professionals Technology Acceptance Related Studies (Continued)

Johnston, J. M.

Leung, G. M.

Wong, J. F. K.

Ho, L. M.

Fielding, R.

2002 Physicians' attitudes towards the computerization of clinical practice in Hong Kong: A population study

Croteau, A. –M.

Vieru, D.

2002 Telemedicine adoption by different groups of physicians

Hu, P. J. –H.

Chau, P. Y. K.

Sheng, O. L.

Tam, K. Y.

1999 Examining the technology acceptance model using physician acceptance of telemedicine technology

Hu, P. J. –H.

Sheng, O. R. L.

Chau, P. Y. K.

Tam, K. –Y.

Fung, H.

1999 Investigating physician acceptance of telemedicine technology: A survey study in Hong Kong

Hu, P. J. –H.

Chau, P. Y. K.

1999 Physician acceptance of telemedicine technology: An empirical investigation

Succi, M. J.

Walter, Z. D.

1999 Theory of user acceptance of information technologies:

An examination of healthcare professionals

Jayasuriya, R. 1998 Determinants of microcomputer technology use:

Implications for education and training of health staff

1.4 Review of the Current State of Iran

According to the interviews conducted with different hospitals’ staff and EPR software developer companies, at the time being there are different levels of using computers in Iranian hospitals. In some hospitals patients’ files are still kept paper–based

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and the systems which are computerized are only the administration of the patient and financial affairs, of course their laboratories, radiology and other parts taking examination from patients might have computerized systems of their own but it does not join a main system recording everything for a single patient. Other hospitals have implemented HIS supporting EPR, but for each patient it only includes the items that are needed later for calculating the patients’ paycheck. For example, there is an order for different blood tests for the patient in his/her file but there is no result entered. In this case, it is nearly only a system for their financial records not an EPR, and physicians and nurses are not forced to use the clinical part of the system.

Software developer companies claim that their software supports clinical issues but hospitals’ staff (doctors and nurses) do not use those items and therefore managers are not interested in those parts, either.

Even in hospitals most using HIS, details of daily observations and notes of physicians and nurses are not entered to the computerized system, and as a result they do not have a complete EPR. According to Johnson et al. (2004), there are two key aspects of the observation notes that make them attractive and important for inclusion in the EPR.

First, they represent a rich source of data about the patient. These data may be used to generate reports about the quality of care being delivered, and they may be useful for research or for billing. Second, the act of completing this documentation is typically associated with decision making.

1.5 Problem Statement

Over the past years, the adoption of information and communication technologies in the healthcare sector has been the focus of many studies (Gagnon et al., 2003).

Physicians and nurses represent one of the principal groups of EPR users and their acceptance of this technology constitutes one of the prerequisites to the usage and sustainability of EPR systems (Hu et al., 2000).

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The problem stated in this research is that Iranian hospitals spend a great deal of money on buying EPR software and implementing EPR systems but the hospitals’ staff (physicians, nurses) do not use it.

1.6 Research Objective

EPR adoption refers to physician or nurse’s psychological state with regard to his/her intention to use EPR in his/her practice (Croteau and Vieru, 2002). EPR acceptance can be defined in different manners and adoption (or utilization) represents a common indicator of the degree of EPR acceptance (Gagnon et al., 2003). An individual’s intention to use EPR is considered as an appropriate measure of his/her actual use of the technology (Hu et al., 1999a). Moreover, meta–analysis on the use of psychosocial models in the study of healthcare professionals’ behaviors has found high correlation between the intention to perform a given behavior and the actual behavior (Godin and Kok, 1996). Thus, the dependant variable measured in this study is the intention to use EPR.

Regarding the problem statement, this study is an attempt to understand how the hospitals’ staff, those directly involved in the care and treatment of patients, accept and utilize the new technology in this case, the EPR. Therefore, the research objective is to investigate the factors affecting physicians and nurses’ intention to use EPR. This objective is achieved, using an extension of an already existing adoption theory which has not been applied to the hospitals’ staff EPR acceptance context before, and conducting a survey as the research strategy through a quantitative approach.

1.7 Research Question

Regarding the literature review and current state of Iranian hospitals, among the research opportunities in HIT and technology adoption contexts, investigating the factors influencing Iranian hospitals’ staff intention to adopt EPR systems, is chosen to be studied in this research. Thus, the research question is: “What are the factors influencing the Iranian hospitals’ staff intention to use EPR?”

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It should be noted that this study does not focus on the applications or how daily routines linked to patient treatment or care is affected but how the technology is viewed.

1.8 Importance of the Study

The development, implementation and adoption of IT is a high–risk undertaking (Maass and Eriksson, 2006). Regarding the increasing usage of IT, the determinants of IT usage have been widely studied in different researches as a key dependent variable (Davis, 1989, 1993; Davis et al., 1989; Hartwick and Barki, 1994; Mathieson, 1991;

Thompson et al., 1991). The usage of IT is a necessary condition to ensure the productivity payoffs from IT investments (Davis, 1989; Mathieson, 1991). It is common knowledge that a number of projects fail (Lyytinen and Robey, 1999). The main reason to this failure is that when a new technology is implemented, it changes the work practices of the organization members and consequently they may not accept its usage (Ågerfalk and Eriksson, 2004). Therefore, understanding why people use a technology and investigating the factors influencing the new technology adoption, helps to ensure effective deployment of IT resources in organizations and ensure a successful implementation (Taylor and Todd, 1995a).

EPR software which is used to record histories, physical exams, and progress or procedure notes, is touted as an important addition to the HIS (Johnson et al., 2004).

Although EPR has great influence on improving the healthcare quality, its functionality has remained static over the past 30 years, which may be because of the limited adoption of this tool (ibid). Despite the increasing availability of EPR, anecdotal evidence suggests that its use has not been well accepted by physicians and nurses (Anderson, 2000;

McDonald et al., 1999). The vast investments and great expectations from the system, present a challenge (Nikula, 2005), and yet few researches have been conducted to investigate the factors influencing the hospitals’ staff acceptance of EPR. Much more needs to be known about the adoption of EPR and in particular how the new technology is taken into use.

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To conclude, the vast investment in IT in organizations, the need to understand the factors influencing IT adoption in healthcare industry, the numerous effects of using EPR on healthcare quality, and the role that hospitals’ staff play in this context, brings great importance to this study.

1.9 Terminology

Electronic Patient Record: EPR embraces all departmental sources of patient information (Maass and Eriksson, 2006). EPR system is a similar system to the analogue patient record (ibid), supporting text documents and clinical data in electronic format (Chang et al., 2007).

1.10 Structure of the Study

This dissertation is organized into 5 chapters as shown in Figure 1-2.

Figure 1-2 Structure of the Study

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1.11 Summary

Regarding the increasing usage of IT in different industries and organizations, understanding why people use a technology has become an important factor to researchers. Healthcare industry is also an information–intensive industry and many researches have been conducted so far to gain a better understanding of technology adoption in this industry.

Due to the fact that, it is the healthcare professionals who decide whether or not to use a new technology, finding out about the determinants of their decision to use a specific technology is of great importance. Among these technologies is EPR which is one the key factors to quality improvement in healthcare industry. Unfortunately, although many Iranian hospitals have implemented HIS supporting EPRs; these systems are not well accepted by the hospitals’ staff. Consequently, the vast investment in IT in organizations, the need to understand the factors influencing IT adoption in healthcare industry, the numerous effects of using EPR on healthcare quality, and the role that hospitals’ staff play in this context, brings great importance to this study. Therefore the research objective is to find out what the factors influencing the Iranian hospitals’ staff (physicians, nurses) intention to adopt EPR, are.

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Chapter 2 Literature Review

2. Literature Review

Chapter two is structured along several themes. First of all, this chapter explains the basic terminology of Electronic Patient Record (EPR) and its benefits. Second, it outlines the definition of adoption and different intention based adoption theories.

Finally, the research model and hypotheses are introduced.

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2.1 Definition of EPR

Computing technologies were introduced to the clinical setting as early as the mid–1960s when a number of hospitals rapidly began clinical information system projects for storage and retrieval of medical documents (Saba and McCormick, 1996).

Progress slowed as the complexity of such projects became apparent with limited success in implementation but with the abandonment of mainframe computers and the advent of the smaller microcomputers and minicomputers, interest in computerized clinical information systems resumed in the 1970s and 1980s (Chamorro, 2001). Today, computer has become integral to healthcare delivery, driven in part by accelerated development of digital applications and communication technologies over the last two decades and consequently diagnostics ranging from laboratory tests to more complex imaging studies are healthcare functions that may be totally computer–driven (ibid). EPR is a key infrastructure requirement in information management which is essential to maintaining a scientific basis for healthcare (ibid).

Functional EPRs embrace all departmental sources of patient information (Maass and Eriksson, 2006). In this scenario, the EPR is a similar system to the analogue patient record (ibid), supporting text documents and clinical data in electronic format (Chang et al., 2007).

According to Chamorro (2001), EPR is achieved through the integration of the following items:

• Admission, discharge, and transfer systems

• Scheduling systems

• Order entry and results reporting systems

• Point of care clinical data entry systems, including o Physician documentation

o Nursing information systems

o Ancillary healthcare provider documentation

• Laboratory information systems

• Pharmacy information systems

• Radiology information systems

• Medical logic modules aiding decision support

• Research databases

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• Charge capture and patient billing

Throughout this research the term EPR will be used but since healthcare industry lacks a commonly accepted set of definitions and terminologies for clinical information tools, many different terms are used throughout the literature to describe EPR (Brailer and Terasawa, 2003). These terms are as follow (ibid):

• Automated Medical Record (AMR)

• Clinical Data Repository (CDR)

• Computer–based Patient Record (CPR)

• Computer–based Patient Record System (CPRS)

• Computerized Medical Record (CMR)

• Computerized Patient Record (CPR)

• Electronic Health Record (EHR)

• Electronic Medical Record (EMR)

• Electronic Patient Record (EPR)

• Lifetime Data Repository (LDR)

• Virtual Health Record (VHR)

• Virtual Patient Record (VPR)

2.2 Benefits of EPR

EPR is indicative of the advances in medical informatics and allows providers, patients and payers to interact more efficiently and in life–enhancing ways (Rogerson, 2000). It offers new methods of storing, manipulating and communicating medical information of all kinds, including text, images, sound, video and tactile senses, which are more powerful and flexible than paper based systems (ibid).

Not only EPR assists the handling of patient information but also facilitates organizational and structural changes within healthcare delivery due to the enhanced accessibility to patient information in time and space that it provides, and therefore the main benefit of EPR is seamless care i.e. coordination between institutions involved in the care and treatment of the individual patient (Nikula, 2005).

It is obvious that implementing EPR makes it possible to simplify the routines concerning the patient record; no more looking around for the record, the physician can

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countersign his notes almost anywhere (ibid). In addition, research is greatly facilitated, investigations are completed on a timelier basis through the acquisition of aggregate data directly from EPR databases for analysis and medical management of the patient is accelerated (Chamorro, 2001).

By using EPR, aside from reducing paper cost and human energy, medical expertise becomes available regardless of the location of the patient, which increases patient democracy and quality of care (Maass and Eriksson, 2006).

Chamorro (2001) summarizes the advantages of EPR systems as follow:

• Accessible simultaneously to multiple users and multiple settings

• Integrates variable types of data media

• Data are legible

• Reduces medical errors

• Prompts user for completeness and quality data

• Supports structured data entry

• Processes are accurately calculated

• Can provide tools for decision support

• Allows analysis if backed by database

• Vehicle for health outcomes research that will drive practice changes

2.3 Technology Adoption

Rogers (1995) describes that adopters of any new innovation or idea could be categorized as innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%), and each adopter's willingness and ability to adopt an innovation would depend on their awareness, interest, evaluation, trial, and adoption.

Some of the characteristics of each category of adopters are as follow (ibid):

• Innovators are venturesome and educated, with multiple information sources and greater propensity to take risk.

• Early adopters are social leaders, popular, and educated.

• Early majority are deliberate, with many informal social contacts.

• Late majority are skeptical and traditional, with lower socio–economic status.

• Laggards’ main information sources are neighbors and friends and have fear of debt.

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A potential adopter passes through certain stages before decision is made on whether to adopt or reject an innovation (ibid). Rogers (1995) defines the adoption process as “the process through which an individual or other decision–maker unit passes from first knowledge of an innovation, to forming an attitude toward the innovation to a decision or rejection to implementation of the new idea, and to confirmation of this decision”. Regarding Rogers and Shoemaker (1971), consumers go through “a process of knowledge, persuasion, decision and confirmation'” before they are ready to adopt a product or service. The stages of innovation decision process are graphically presented in Figure 2-1 and described as follow (Rogers, 1995):

1. Awareness: Socio–economic characteristics, personality variables and communication behavior all relate to innovativeness. Innovativeness is the degree to which an individual or other adoption unit is relatively early in adopting new ideas compared to other members of a system.

2. Persuasion: The potential adopter’s attitude towards the innovation is formed in this stage. By anticipating and predicting future use satisfaction and risk of adoption, the potential adopter develop positive or negative attitudes toward the innovation, which plays an important role in modifying the final decision.

Perceived attitudes of an innovation as its relative advantage, compatibility and complexity are especially important here.

3. Decision: The decision stage occurs when an individual engages in activities that lead to adoption or rejection of the innovation. In this stage the adopter starts to actively seek out information about the innovation that assists the decision making.

4. Implementation stage: In this stage, mental information processing and decision making come to an end, but the behavioral change begins.

5. Confirmation stage: After the adoption of innovations, the adopter keeps evaluating the results of his/her decision. If the level of satisfaction is significant enough, the use if innovation will continue; however, it is also possible that the rejection occurs after adoption. In the latter case, the reverse of previous decision is called “discontinuance”.

Finally adoption is defined as “the acceptance and continued use of a product, service or idea” (Rogers and Shoemaker, 1971).

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Figure 2-1 Innovation Decision Process. Source: Rogers, 1995

Table 2-1 lists the researches conducted studying the adoption of EPR but none has quantitatively investigated the EPR adoption by the hospitals’ staff.

Table 2-1 EPR Adoption Related Studies

Simon, S. R.

Kaushal, R.

Cleary, P. D.

Jenter, C. A.

Volk, L. A.

Poon, E. G.

Orav, E. J.

Lo, H. G.

Williams, D. H.

Bates, D. W.

2007 Correlates of Electronic Health Record adoption in office practices: A statewide survey

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Table 2-1 EPR Adoption Related Studies (Continued)

Anderson, J. G. 2007 Social, ethical and legal barriers to e–health Tang, P. C.

Ash, J. S.

Bates, D. W.

Overhage, J. M.

Sands, D. Z.

2006 Personal Health Records: Definitions, benefits, and strategies for overcoming barriers to adoption

Ford, E. W.

Menachemi, N.

Phillips, M. T.

2006 Predicting the adoption of Electronic Health Records by physicians: When will healthcare be paperless?

Nikula, R. E. 2005 A study of the adoption and definition of the electronic patient record by clinicians

Middleton, B.

Hammond, W. Ed.

Brennan, P. F.

Cooper, G. F.

2005 Accelerating U.S. EHR adoption: How to get there from here. Recommendations based on the 2004 ACMI retreat

Berner, E. S.

Detmer, D. E.

Simborg, D.

2005 Will the wave finally break? A brief view of the adoption of electronic medical records in the United States

Rose, A. F.

Schnipper, J. L.

Park, E. R.

Poon, E. G.

Li, Q.

Middleton, B.

2005 Using qualitative studies to improve the usability of an EMR

Ash, J. S.

Bates, D. W.

2005 Factors and forces affecting EHR system adoption:

Report of a 2004 ACMI discussion

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Table 2-1 EPR Adoption Related Studies (Continued)

Johnson, K. B.

Ravich, W. J.

Cowan, Jr J. A.

2004 Brainstorming about next–generation computer–

based documentation: An AMIA clinical working group survey

Walsh, S. H. 2004 The clinician's perspective on electronic health records and how they can affect patient care

Brailer, D. J.

Terasawa, E. L.

2003 Use and adoption of computer–based patient records

Lorence, D. P.

Spink, A.

Richards, M. C.

2002 EPR adoption and dual record maintenance in the U.S.: Assessing variation in medical systems infrastructure

Van Ginneken, A. M. 2002 The computerized patient record: Balancing effort and benefit

2.4 Adoption Theories

Several researches have focused on identifying the determinants of intention to use a technology and therefore employed intention–based theories, using behavioral intention to predict usage (Davis et al., 1989; Hartwick and Barki, 1994; Mathieson, 1991). The following sections briefly introduce each of the theories most employed in the studies of technology adoption by healthcare professionals.

2.4.1 Innovation Diffusion Theory (IDT)

Innovation Diffusion Theory (IDT) is a model that explains the process by which innovations in technology are adopted by users (Rogers, 1995). The definition and core constructs of IDT are explained in Table 2-2.

The validity of IDT has been demonstrated in a study of technology adoption by nurses by Lee (2004). Also, Wu et al. (2007a) successfully tested a combination of IDT and the Technology Acceptance Model (TAM) to investigate the mobile healthcare

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systems acceptance factors in the healthcare industry. The graphical model of IDT is presented in Figure 2-2.

Table 2-2 IDT

Innovation Diffusion Theory (IDT)

Grounded in sociology, IDT (Rogers, 1995) has been used since the 1960s to study a variety of innovations, ranging from agricultural tools to organizational innovation (Tornatzky and Klein, 1982).

Core Constructs

Definitions

Relative Advantage

“The degree to which an innovation is perceived to be better than the idea it supersedes” (Rogers, 1995).

Compatibility “The degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters”

(Rogers, 1995).

Complexity “The degree to which an innovation is perceived as relatively difficult to understand and use” (Rogers, 1995).

Trialability “The degree to which an innovation may be experimented with on a limited basis” (Rogers, 1995).

Observability “The degree to which the results of an innovation are visible to others” (Rogers, 1995).

Figure 2-2 IDT. Source: Rogers, 1995

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Moore and Benbasat (1991) developed and refined IDT by adding few constructs.

Croteau and Vieru (2002) have studied telemedicine adoption by different groups of physicians and validated their research model combining the refined IDT and TAM. Yi et al. (2006) also performed a research on technology acceptance by individual professionals and successfully tested a combined model of refined IDT, TAM and Theory of Planned Behavior (TPB). The definition and core constructs of the refined IDT are explained in Table 2-3. Figure 2-3 shows the graphical model of this theory.

Table 2-3 Refined IDT. Source: Venkatesh et al., 2003

Refined Innovation Diffusion Theory

Within Information System (IS) domain, Moore and Benbasat (1991) adapted the characteristics of innovations presented in Rogers and refined a set of constructs that could be used to study individual technology acceptance. Moore and Benbasat (1996) found support for the predictive validity of these innovation characteristics (see also Agarwal and Prasad, 1997, 1998; Karahanna et al., 1999; Plouffe et al. 2001).

Definitions

Relative

Advantage “The degree to which an innovation is perceived as being better than its precursor” (Moore and Benbasat, 1991: 195).

Ease of Use “The degree to which an innovation is perceived as being difficult to use” (Moore and Benbasat, 1991: 195).

Image “The degree to which use of an innovation is perceived to enhance one’s image or status in one’s social system” (Moore and Benbasat, 1991: 195).

Visibility The degree to which one can see others using the system in the organization (adapted from Moore and Benbasat, 1991).

Compatibility “The degree to which an innovation is perceived as being consistent with the existing values, and past experiences of potential adopters”

(Moore and Benbasat, 1991: 195).

Results

Demonstrability “The tangibility of the results of using the innovation, including their observability and communicability” (Moore and Benbasat, 1991: 203).

Voluntariness

of Use “The degree to which use of the innovation is perceived as being voluntary or of free will” (Moore and Benbasat, 1991: 195).

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Figure 2-3 Refined IDT. Source: Moore and Benbasat, 1991

2.4.2 Theory of Reasoned Action (TRA)

The Theory of Reasoned Action (TRA) is a widely studied model from social psychology, which is concerned with the determinants of consciously, intended behaviors. The definition and core constructs of TRA are explained in Table 2-4.

Table 2-4 TRA. Source: Venkatesh et al., 2003

Theory of Reasoned Action (TRA)

Drawn from social psychology, TRA is one of the most fundamental and influential theories of human behavior. It has been used to predict a wide range of behaviors (see Sheppard et al. (1988)). Davis et al. (1989) applied TRA to individual acceptance of technology and found that the variance explained was largely consistent with studies that had employed TRA in the context of other behaviors.

Definitions

Attitude Toward Behavior

“An individual’s positive or negative feelings (evaluative affect) about performing the target behavior” (Fishbein and Ajzen, 1975: 216).

Subjective

Norm “The person’s perception that most people who are important to him think he should or should not perform the behavior in question”

(Fishbein and Ajzen, 1975: 302).

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Figure 2-4 shows the graphical model of TRA.

Figure 2-4 TRA. Source: Fishbein and Ajzen, 1975

2.4.3 Theory of Planned Behavior (TPB) and Decomposed TPB

The definition and core constructs of TPB and Decomposed TPB (DTPB) are explained in Table 2-5. The validity of TPB was demonstrated in a study conducted by Hu et al. (1999b) investigating physician acceptance of telemedicine technology.

Table 2-5 TPB and DTPB. Source: Venkatesh et al., 2003

Theory of Planned Behavior (TPB) and Decomposed TPB (DTPB)

TPB extended TRA by adding the construct of perceived behavioral control. In TPB, perceived behavioral control is theorized to be an additional determinant of intention and behavior. Ajzen (1991) presented a review of several studies that successfully used TPB to predict intention and behavior in a wide variety of settings. TPB has been successfully applied to the understanding of individual acceptance and usage of many different technologies (Harrison et al. 1997; Mathieson, 1991; Taylor and Todd, 1995b). A related model is the Decomposed Theory of Planned Behavior (DTPB). In terms of predicting intention, DTPB is identical to TPB. In contrast to TPB but similar to TAM, DTPB “decomposes” attitude, subjective norm and perceived behavioral control into the underlying belief structure within technology adoption contexts.

Core Constructs Definitions

Attitude Toward Behavior Adapted from TRA.

Subjective Norm Adapted from TRA.

Perceived Behavioral Control

“The perceived ease or difficulty of performing the behavior” (Ajzen, 1991: 188). In the context of IS research, “perceptions of internal and external constraints on behavior” (Taylor and Todd, 1995b: 149).

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Figures 2-5 and 2-6 show the graphical models of TPB and DTPB respectively.

Figure 2-5 TPB. Source: Ajzen, 1991

Figure 2-6 DTPB. Source: Taylor and Todd, 1995a

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2.4.4 Technology Acceptance Model (TAM) and Extended TAM (TAM2)

The definition and core constructs of TAM and Extended TAM (TAM2) are explained in Table 2-6. Hu et al. (1999a) have empirically tested TAM for examining the physicians’ acceptance of telemedicine technology. In the same year Succi and Walter used an adaptation of TAM to explore the factors affecting acceptance of information technologies by healthcare professionals. Years later, an investigation of the effects of creating psychological ownership on physicians' acceptance of clinical information systems by Pare et al. (2006) also found reasonable support for TAM. As for TAM2, Chismar and Wiley–Patton (2002) have studied whether it applies to physicians or not, and demonstrated its validity. Figures 2-7 and 2-8 show the graphical model of TAM and TAM2 respectively.

Table 2-6 TAM and TAM2. Source: Venkatesh et al., 2003

Technology Acceptance Model (TAM) and Extended TAM (TAM2)

TAM is tailored to IS contexts, and was design to predict information technology acceptance and usage on the job. Unlike TRA the final conceptualization of TAM excludes the attitude construct in order to better explain intention parsimoniously.

TAM has been widely applied to a diverse set of technologies and users. TAM2 extended TAM by including subjective norm as an additional predictor of intention in the ease of mandatory settings (Venkatesh and Davis, 2000).

Definitions

Perceived Usefulness

“The degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989:

320).

Perceived Ease of Use

“The degree to which a person believes that using particular system would be free of effort” (Davis, 1989: 320).

Subjective Norm

Adapted from TRA/TPB. Included in TAM2 only.

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Figure 2-7 TAM. Source: Davis, 1989

Figure 2-8 TAM2. Source: Venkatesh and Davis, 2000

2.4.5 Combined TAM and TPB (C–TAM–TPB)

The definition and core constructs of the Combined TAM and TPB (C–TAM–

TPB) are explained in Table 2-7. Chau and Hu (2002) used C–TAM–TPB and successfully tested it to investigate healthcare professionals’ decisions to accept telemedicine technology.

Table 2-7 C–TAM–TPB. Source: Venkatesh et al., 2003

Combined TAM and TPB (C–TAM–TPB)

This model combines the predictors of TPB with perceived usefulness from TAM to provide a hybrid model (Taylor and Todd, 1995a).

Attitude Toward Behavior Adapted from TRA/TPB.

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Table 2-7 C–TAM–TPB. Source: Venkatesh et al., 2003 (Continued)

Subjective Norm Adapted from TRA/TPB.

Perceived Behavioral Control Adapted from TRA/TPB.

Perceived Usefulness Adapted from TAM.

Figure 2-9 shows the graphical model of C–TAM–TPB.

Figure 2-9 C–TAM–TPB. Source: Chau and Hu, 2002

2.4.6 Unified Theory of Acceptance and Use of Technology (UTAUT)

The purpose of formulating the Unified Theory of Acceptance and Use of Technology (UTAUT) was to integrate the fragmented theory and research on individual acceptance of information technology into a unified theoretical model that captures the essential elements of eight previously established models (Venkatesh et al., 2003). To do so the eight specific models of the determinants of intention and usage of information technology were compared and conceptual and empirical similarities across these models were used to formulate UTAUT (Venkatesh et al., 2003). The eight discussed models were: IDT, TRA, TAM, TPB, C–TAM–TPB, Model of PC Utilization (MPCU), Motivational Model (MM), and the Social Cognitive Theory (SCT).

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According to Venkatesh et al. (2003), UTAUT is a definitive model that synthesizes what is known. By encompassing the combined explanatory power of the individual models and considering key moderating influences, UTAUT advances cumulative theory while retaining a parsimonious structure (ibid). The definition and core constructs of UTAUT are explained in Table 2-8.

Schaper and Pervan (2007) have empirically tested UTAUT for examining the technology acceptance and utilization by occupational therapists. Chang et al. (2007) also used UTAUT to explore the factors affecting Physicians’ acceptance of pharmacokinetics–based clinical decision support systems. Figure 2-10 shows the graphical model of UTAUT.

Table 2-8 UTAUT

Unified Theory of acceptance and Use of Technology (UTAUT)

Venkatesh et al. (2003) combined the views of user acceptance from eight previously established theoretical models to formulate four core determinants of key relationship and proposed a unified model called Unified Theory of Acceptance and Use of Technology (UTAUT) to predict user intentions to use IT. This model has been successfully employed in many technology adoption studies and has provided a useful tool for managers to assess the success of new IT introductions (Chang et al., 2007).

Definitions

Performance

Expectancy “The degree to which an individual believes that using the system will help him or her to attain gains in job performance” (Venkatesh et al., 2003: 447).

Effort Expectancy

“The degree of ease associated with the use of the system”

(Venkatesh et al., 2003: 450).

Social Influence

“The degree to which an individual perceives that important others believe he or she should use the new system” (Venkatesh et al., 2003:

451).

Facilitating Conditions

“The degree to which an Individual believes that an organizational and technical infrastructure exists to support use of the system”

(Venkatesh et al., 2003: 453).

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Figure 2-10 UTAUT. Source: Venkatesh et al., 2003

Among the eight theoretical models that Venkatesh et al. (2003) reviewed, IDT, TRA, TAM, TPB, and C–TAM–TPB have so far been introduced. In the following sections, the definition and core constructs of MPCU, MM, and SCT are presented to help the reader gain a better understanding of UTAUT.

2.4.6.1 Model of PC Utilization (MPCU)

To ensure a fair comparison of the different models in their research, Venkatesh et al. (2003) examined the mentioned determinants of intention in MPCU. The definition and core constructs of MPCU are explained in Table 2-9.

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Table 2-9 MPCU. Source: Venkatesh et al., 2003

Model of PC Utilization (MPCU)

Derived largely from Triandis’ theory of human behavior, this model presents a competing perspective to that proposed by TRA and TPB. Thompson et al. (1991) adapted and refined Triandis’ model for IS contexts and used the model to predict PC utilization. However, the nature of the model makes it particularly suitable to predict individual acceptance and use of a range of information technologies.

Definitions

Job–fit “The extent to which an individual believes that using [a technology]

can enhance the performance of his or her job” (Thompson et al., 1991: 129).

Complexity Based on Rogers and Shoemaker (1971), “the degree to which an innovation is perceived as relatively difficult to understand and use”

(Thompson et al., 1991: 128).

Long–term Consequences

“Outcomes that have a pay–off in the future” (Thompson et al., 1991:

129).

Affect Toward Use

Based on Triandis (1977), affect toward use is “feelings of joy, elation, or pleasure, or depression, disgust, displeasure, or hate associated by an individual with a particular act” (Thompson et al., 1991: 127).

Social Factors Derived from Triandis (1977), social factors are “the individual’s internationalization of the reference group’s subjective culture, and specific interpersonal agreements that the individual has made with others, in specific social situations” (Thompson et al., 1991: 126).

Facilitating Conditions

Objective factors in the environment that observers agree make an act easy to accomplish. In an IS context, “provision of support for users of PCs may be one type of facilitating condition that can influence system utilization” (Thompson et al., 1991: 129).

2.4.6.2 Motivational Model (MM)

Venkatesh et al. (2003) examined the constructs of MM and their effects on intention in order to enrich their research model justification. The definition and core constructs of MM, are explained in Table 2-10.

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Table 2-10 MM. Source: Venkatesh et al., 2003

Motivational Model (MM)

A significant body of research in psychology has supported general motivation theory as an explanation for behavior. Several studies have examined motivational theory and adapted it for specific contexts. Vallerand (1997) presents an excellent review of the fundamental tenets of this theoretical base. Within the IS domain, Davis et al. (1992) applied motivational theory to understand new technology adoption and use (see also Venkatesh and Speier, 1999).

Definitions

Extrinsic Motivation

The perception that users will want to perform an activity “because it is perceived to be instrumental in achieving valued outcomes that are distinct from the activity itself, Such as improved job performance, pay, or promotions” (Davis et al., 1992: 1112).

Intrinsic

Motivation The perception that users will want to perform an activity “for no apparent reinforcement other than process of performing the activity per se” (Davis et al., 1992: 1112).

2.4.6.3 Social Cognitive Theory (SCT)

Venkatesh et al. (2003) examined the predictive validity of SCT in the context of intention and usage to allow a fair comparison of the models. The definition and core constructs of SCT, are explained in Table 2-11.

Table 2-11 SCT. Source: Venkatesh et al., 2003

Social Cognitive Theory (SCT)

One of the most powerful theories of human behavior is social cognitive theory (see Bandura, 1986). Compeau and Higgins (1995) applied and extended SCT to the context of computer utilization (see also Compeau et al., 1999).

Definitions

Outcome Expectations–

Performance

The performance–related consequence of the behavior. Specifically, performance expectations deal with job–related outcomes (Compeau and Higgins, 1995).

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Table 2-11 SCT. Source: Venkatesh et al., 2003 (Continued)

Definitions

Outcome Expectations–

Personal

The personal consequence of the behavior. Specifically personal expectations deal with the individual esteem and sense of accomplishment (Compeau and Higgins, 1995).

Self–efficacy Judgment of one’s ability to use a technology (e.g., computer) to accomplish a particular job or task (Compeau and Higgins, 1995).

Affect An individual’s liking for a particular behavior (Compeau and Higgins, 1995).

Anxiety Evoking anxious or emotional reactions when it comes to performing a behavior (Compeau and Higgins, 1995).

2.5 Comparison of Theories

Among the adoption theories presented in this chapter, UTAUT was found to be the most complete model to investigate technology adoption determinants. According to Venkatesh et al. (2003), UTAUT was tested using data from four organizations over a six–month period and was found to outperform the eight individual models used to formulate it (IDT, TRA, TAM, TPB, C–TAM–TPB, MPCU, MM, and SCT).

Explanatory power of UTAUT was then confirmed with data from two new organizations with similar results (ibid).

Each of the constructs mentioned in IDT, TRA, TAM, TPB, C–TAM–TPB, MPCU, MM, and SCT, pertained to one of the main constructs of UTAUT regarding the substantial similarities that existed among their definitions and measurement items (ibid).

Tables 2-12 to 2-15 present the root constructs of performance expectancy, effort expectancy, social influence and facilitating conditions, respectively.

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Table 2-12 Performance Expectancy Root Constructs. Source: Venkatesh et al., 2003

Construct Theory

Perceived Usefulness (Davis 1989; Davis et al., 1989) TAM/TAM2 and C–TAM–

TPB Extrinsic Motivation (Davis et al., 1992) MM

Job–fit (Thompson et al., 1991) MPCU

Relative Advantage (Moore and Benbasat, 1991) IDT Outcome Expectations (Compeau and Higgins, 1995;

Compeau et al., 1999) SCT

Table 2-13 Effort Expectancy Root Constructs. Source: Venkatesh et al., 2003

Perceived Ease of Use (Davis, 1989; Davis et al., 1989)

TAM/TAM2

Complexity (Thompson et al., 1991) MPCU Ease of Use (Moore and Benbasat, 1991) IDT

Table 2-14 Social Influence Root Constructs. Source: Venkatesh et al., 2003

Subjective Norm (Ajzen, 1991; Davis et al., 1989;

Fishbein and Azjen, 1975; Mathieson, 1991; Taylor and Todd, 1995a, 1995b)

TRA, TAM2, TPB/DTPB and C–TAM–TPB

Social Factors (Thompson et al., 1991) MPCU Image (Moore and Benbasat, 1991) IDT

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Table 2-15 Facilitating Conditions Root Constructs. Source: Venkatesh et al., 2003

Perceived Behavioral Control (Ajzen, 1991; Taylor and Todd, 1995a, 1995b)

TPB/DTPB and C–TAM–

TPB Facilitating Conditions (Thompson et al., 1991) MPCU Compatibility (Moore and Benbasat, 1991) IDT

To conclude, UTAUT advanced individual acceptance research by unifying the theoretical perspectives common in the literature and incorporating four moderators to account for dynamic influences including organizational context, user experience, and demographic characteristics (Venkatesh et al., 2003).

2.6 Other Important Factors Influencing the Intention to Adopt

Other than the factors mentioned in previously discussed adoption models, there were three other factors influencing the intention to adopt new technologies, which were recognized based on the results of conducted interviews, previous empirical studies and literature review (Boyle and Ruppel, 2004; Featherman and Pavlou, 2003; Gagnon et al., 2003; Limayem et al., 2000; Lu et al., 2005; Rosen, 2004; Wu et al., 2007b). These factors (Facilitating Conditions, Perceived Time Risk and Personal Innovativeness in IT) are explained in the following sections.

2.6.1 Facilitating Conditions

According to Triandis (1979), behavior is determined by three dimensions:

intention, facilitating conditions, and habit. Facilitating conditions represent objective factors that can make the realization of a given behavior easy to do (Gagnon et al., 2003) and is defined as “the degree to which an Individual believes that an organizational and technical infrastructure exists to support use of the system” (Venkatesh et al., 2003).

Facilitating conditions was hypothesized to be linked directly to behavioral intention in this study. This was done because firstly, the definition of facilitating

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conditions in UTAUT captured the concept of perceived behavioral control, which was directly linked to behavioral intention in TPB, DTPB and C–TAM–TPB (Venkatesh et al., 2003) and secondly, previous studies that employed Triandis’ theory had found that facilitating conditions was an important predictor of behavioral intention (Boots and Treloar, 2000). Wu et al. (2007b) have also empirically tested the direct effect of facilitating conditions on behavioral intention in a study of 3G mobile communication acceptance and found reasonable support for it.

Additionally, in order to confirm the effect of facilitating conditions on behavioral intention 25 healthcare providers were interviewed as experts and among them 21 interviewees agreed to the findings of literature and confirmed that facilitating conditions had an important influence on their intention to use EPR, because they believed that having sufficient organizational and technical infrastructure to support the new technology in hospitals, was a very important factor to them, when deciding to use EPR.

2.6.2 Perceived Time Risk

Perceived risk (PR) is commonly thought of as felt uncertainty regarding possible negative consequences of using a product or service (Featherman and Pavlou, 2003). It has formally been defined as ‘‘a combination of uncertainty plus seriousness of outcome involved’’ (Bauer, 1967), and ‘‘the expectation of losses associated with purchase and acts’’ (Peter and Ryan, 1976). Featherman and Pavlou (2003) defined perceived risk as

‘‘the potential for loss in the pursuit of a desired outcome of using an e–service’’.

Perceived risk enters the IS adoption decision when circumstances of the decision create (a) feelings of uncertainty, (b) discomfort and/or anxiety (Dowling and Staelin, 1994), (c) conflict aroused in the consumer (Bettman, 1973), (d) concern, (e) psychological discomfort (Zaltman and Wallendorf, 1983), (f) making the consumer feel uncertain (Engel et al., 1986), (g) pain due to anxiety (Taylor, 1974), and (h) cognitive dissonance (Festinger, 1957; Germunden, 1985).

Cunningham (1967) identified two major categories of perceived risk (a) performance and (b) psychosocial. He broke performance into three types (i) economic,

Adoption of electronic patient records by Iranian hospitals´ staff

M A S T E R ' S T H E S I S

Adoption of Electronic Patient Records by Iranian Hospitals´ Staff

Mahbod Hamidfar

MASTER’S THESIS

Adoption of Electronic Patient Records by Iranian Hospitals’ Staff

Supervisors:

Dr. Moez Limayem

Dr. Seyed Hessameddin Zegordi Referees:

Dr. Amir Albadvi Dr. Esmail Salehi-Sangari

Prepared by:

Mahbod Hamidfar 846831009

Abstract

Acknowledgement

Table of Contents

List of Tables

List of Figures

Chapter 1 Introduction

Chapter 2

Literature Review