Elsevier

Health Policy and Technology

Volume 5, Issue 3, September 2016, Pages 268-284
Health Policy and Technology

Predicting tablet computer use: An extended Technology Acceptance Model for physicians

https://doi.org/10.1016/j.hlpt.2016.03.010Get rights and content

Highlights

  • We provide an extensive review of the Technology Acceptance Model in healthcare.

  • Individual, organizational, and device characteristics influence tablet adoption.

  • Subjective norms, compatibility, and reliability explain variance in PU.

  • Compatibility and reliability explain variance in PEOU.

  • Organizations should design training programs prior to deploying an IT initiative.

  • Health policies need to consider factors that lead to the adoption of technologies.

Abstract

Objective

Information technology has rapidly changed work in the United States in the 21st century. Healthcare, however, is one industry that has lagged behind in IT investment for a variety of reasons. Recent federal initiatives to encourage IT adoption in the healthcare industry provide an ideal context to study factors that influence technology acceptance.

Method

Data from 261 practicing pediatricians were collected to evaluate an extended Technology Acceptance Model. We employed structural equation modeling to statistically test three theoretically plausible models.

Results

Results indicated that individual, organizational, and device characteristics collectively influence pediatricians’ intention to adopt tablet computers in their medical practice. Subjective norms, compatibility, and reliability explain 72% of the variance in perceived usefulness. Additionally, compatibility and reliability explain 38% of the variance in perceived ease of use.

Conclusions

These results extend the literature on technology adoption by modeling determinants of the two core attitudinal constructs in the Technology Acceptance Model. Understanding these constructs will facilitate the adoption of technology and the management of health policies.

Introduction

There has been a rapid increase in the prevalence of information technology (IT), defined as the use of hardware and software to store, analyze, access, and distribute information, in organizations [16]. This is underscored by the fact that investment in IT equipment and software by private U.S. firms increased by more than 300% from 1995 to 2010 [54]. Most IT spending has come from data intensive industries like financial services, manufacturing, and communications to effectively manage and utilize the massive amount of digital information available to organizations [23].

Discussions on the implications of new information technologies at work highlight productivity and efficiency gains from increased communication and collaboration among employees within (e.g., flattening hierarchical structure) and between organizations (e.g., outsourcing non-core competencies) [15], [16]. For example, a multinational corporation can assemble a virtual team of high performing individuals from the headquarters and regional offices and use a video conferencing system to hold meetings and share presentations (e.g., Cisco TelePresence) or collaborate on digital documents (e.g., GoToMeeting). These technologies reduce travel costs while speeding up the time it takes to schedule a meeting. Manning et al. [42] report that small, medium, and large firms are offshoring nearly any function that can be digitized, such as IT, product development (i.e., research & development, product design), and administrative functions (e.g., accounting, human resources), to reduce labor costs and gain access to qualified personnel. This collaboration between organizations located in different countries is possible because of the breakthroughs and adoption of various pieces of IT. While adopting new technologies is common for most industries, other sectors, such as healthcare, have lagged behind in IT adoption.

A Gartner [23] report found that the healthcare industry in the United States has spent approximately 50% less than other industries on IT investment, despite the fact that medical knowledge continues to grow at an exponential rate. This is unexpected considering the fact that national healthcare expenditures are 17.6% of gross domestic product (GDP) and are projected to increase to 19.8% by 2020 [9]. Given the escalating costs and the massive growth in clinical knowledge, observers note the potential of health information technology (HIT), defined as technologies which allow healthcare providers to “collect, store, retrieve, and transfer information electronically,” to help professionals operate more efficiently and make fewer errors ([44], p. 5).

Recently, the federal government has encouraged the adoption of HIT such as electronic medical records (EMR) and secure electronic health information exchanges [47]. Healthcare experts note the potential of EMR to control costs, reduce medical errors, and improve patient outcomes by providing complete patient history information (e.g., clinical history, medications, tests) to all medical facilities involved with the patient [44]. In addition, popular press articles are heralding tablet computers (e.g., Apple iPad, Samsung Galaxy Tab) as promising devices to be used in conjunction with new HIT software (e.g. [6]). Tablet computers combine the best features of earlier mobile technologies used by healthcare providers, with the computing power, high resolution screen, and ease of data entry of the computer on wheels (COW) and the portability, customizability, and wireless connectivity of the personal digital assistant (PDA) [21].

Despite the potential benefits of HIT, previous research has noted the high failure rate of widespread adoption initiatives in the healthcare industry [20]. Implementing HIT interventions present a number of challenges and barriers due to individual (e.g., individual acceptance, ease of use, and loss of control), organizational (e.g., cost, managerial support, and changes in workflow), and device (e.g., design, compatibility with tasks, and flexibility) characteristics. Given the promise of health information technology to improve quality of care, the purpose of the present study is to identify factors that predict IT adoption in the healthcare industry.

In sum, information technology has had a large impact on the way people work. However, research has found that healthcare has fallen behind other industries in terms of technological innovation. The lack of technological investment and the recent influx of new IT solutions in healthcare provide an excellent context to study factors related to IT adoption.

Section snippets

Theoretical background

The Technology Acceptance Model (TAM) is the most widely used IT adoption model [17]. The original TAM provides a parsimonious account of technology adoption based on the Theory of Reasoned Action [22]. The Technology Acceptance Model and its successor TAM-2 [56] posit individual (e.g., ease of use, usefulness) and organizational (e.g., social norms, facilitating conditions) antecedents to predict behavioral intention to use (i.e., acceptance) and/or actual use of a new technology in an

TAM and TAM-2 in healthcare

Relevant to the current research question, the healthcare industry has applied TAM and TAM-2 to predict medical professionals’ adoption of various health information technologies with generally consistent results. One question that remains unanswered is if TAM is equally appropriate for different industries (e.g., education, government, and healthcare) since it was developed primarily for private sector corporations. TAM meta-analyses (e.g., [32], [38], [51]) combine all professional samples

Extended Technology Acceptance Models

In addition to testing theoretically based extensions of TAM like TAM-2, researchers have proposed other contextually relevant constructs to improve the explanatory power of the Technology Acceptance Model. The extended TAM variables can be grouped into three broad categories: individual, device, and organizational characteristics. Individual characteristics include individual differences in affect, perceptions, and knowledge about the specific technology. For example, personal innovativeness,

Research models

Following advice from MacCallum and colleagues [39], [40], [41] we hypothesize and test multiple models, each of which is theoretically plausible. Based on the evidence supporting the Technology Acceptance Model and the success of including additional variables to better understand factors related to adoption, we test a theoretically-based research model (model 1) and two alternative plausible models with minor modifications (models 2 and 3) to determine which model has the best fit. See Figure

Sample and procedure

Current residents or physicians in pediatrics or medical-pediatrics in the United States were recruited to participate in this study via an email invitation. The population of interest was pediatricians because the project was a follow up to previous research that examined tablet computer use among this population [21]. Email addresses of pediatricians were obtained in two ways. First, a list of approximately 300 pediatricians’ email addresses was obtained from a fee-based service that

Descriptive statistics

Descriptive statistics (i.e., mean and standard deviation), scale reliabilities, and intercorrelations among study variables are presented in Table 3. Reported scale reliabilities are coefficient alpha. All values are above 0.74 and indicate acceptable internal consistency reliability for each subscale. Zero-order correlations provide preliminary support for many hypotheses.

Proposed model testing and evaluation

Covariance structure analyses with maximum likelihood estimation were conducted in LISREL 8.53. Prior to estimating the

Discussion

The present study examined an extended Technology Acceptance Model to understand factors that influence tablet computer adoption among pediatricians operating in a variety of settings including academic medicine, university hospitals, and private practice. After evaluating three equally plausible structural equation models with statistical, empirical, and conceptual evidence, results indicated that model 2 best captured the process of tablet computer acceptance among pediatricians.

Theoretical and practical contributions

This study contributes to our theoretical understanding of technology adoption in organizations in a variety of ways. First, the results indicate that the Technology Acceptance Model provides a parsimonious way to model tablet computer adoption among pediatricians. Prior research has not examined the viability of the TAM to predict tablet computer use. Therefore, this study contributes to the literature by suggesting that the TAM generally applies very well for this new piece of technology.

Limitations

It is possible that using a sample of pediatricians and only examining tablet computers limits the generalizability of the results and conclusions. The extensive research literature on the Technology Acceptance Model in healthcare suggests that this is only a minor concern because prior results have been remarkably consistent across different samples of healthcare professionals and/or technologies including physicians, public health nurses [13], medical staff [45], and physiotherapists [55],

Future research

It would be beneficial to the field for future research to clearly demonstrate a substantive link for physicians between intention to adopt technology and actual adoption behavior. Future research should also consider including other individual (e.g., image, self-efficacy, and IT knowledge), organizational (e.g., training, type of healthcare setting, and technical support), and device (e.g., operating system, size, and cost) characteristics as predictors of perceived usefulness and perceived

Conclusions

The present study examined variables that influence tablet computer adoption in a sample of pediatricians. Comparisons of three alternative and equally plausible structural equation models indicated that individual, organizational, and device characteristics collectively influenced physicians’ behavioral intention to adopt tablet computers. This research extends the Technology Acceptance Model by showing that subjective norms, compatibility, and reliability explain 72% of the variance in

Funding

None.

Competing interests

None declared.

Ethical approval

This research was approved by the Institutional Review Board at the University of South Florida.

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