The Behavior of Adoption
Rogers’ Diffusion of Innovation (DOI) theory [20], Davis’s Technology Acceptance Model (TAM) [21], and Ajzen’s Theory of Planned Behavior (TPB) [22] are key technology adoption and behavioral theories to employ. Combining more than one theoretical model usually leads to a better understanding of an adoption phenomenon [23]. Thus, our model for EMR innovation adoption integrates technical, social, human, and psychological factors from different theories and models, which is in line with what Jian et al. [24] did for uncovering the EMR adoption factors in Taiwan. Our research question focuses on identifying the drivers behind physicians’ adoption of an innovation. The DOI theory [20] describes the spread of technical innovations, such as EMR systems, through an innovation-decision process [20]. This process is a series of stages that a decision maker follows to reach a decision that can be favorable (to adopt) or not favorable (to reject).
The decision for EMR system adoption can be modeled as a rational behavior in the framework of the TPB [22]. The rational planned behavior is mainly a function of the individual’s intention to engage in the behavior, which is the indication of an individual's readiness to perform the behavior. The TPB variables can “capture unique variance in intention” (p. 178) [25]. Although the individual’s intention to act is not actual behavior, “there is considerable evidence that intention to perform a behavior predicts actual behavior” (p. 174) [25].
Intention Drivers
Per the TPB model [22], the intention to engage in a certain rational behavior is directly influenced by the attitude toward the behavior, the subjective norms, and perceived behavioral control. Attitude is the individual’s mental state, including feelings, values, and dispositions toward the behavior, and subjective norms are the collection of social pressures and beliefs that important others (e.g., peers or government) expect particular behavior [22]. Perceived behavioral control corresponds to the self-efficacy theory developed by Bandura [26], who defined it as the conviction by someone about his or her ability to successfully execute a behavior required to produce the expected result.
Ajzen [22] noted the TPB is intended to provide a general guideline of what determines or influences a behavior. The researcher is expected to creatively identify the main factors affecting a behavior that are relevant to a situation or setting. Hypotheses 1a and 1b are two of the hypotheses on predicting intention:
Hypothesis 1a: Attitude toward adoption has an effect on the intention to adopt.
Hypothesis 1b: Perceived behavioral control over adoption has an effect on the intention to adopt.
Subjective Norms Factors
Subjective norms’ social pressures can be coercive, mimetic, or normative [23]. Coercive pressure is practiced by a source of power to force conformity to demand or expectations; mimetic pressure is what makes an individual imitate others; and normative pressure is the tendency to behave in a manner that is deemed to be acceptable or approved by others [22, 23]. Social interferences are generally expected to play a positive role during and after the adoption of a new technology [27, 28]. Therefore, identifying the factors of the subjective norms helps to assess their impacts on the intention to adopt EMR.
The medical industry has tight networks that provide forums for peer knowledge and opinion sharing. Peer preference is the individual’s perception of what medical peers, colleagues and associates think about EMR systems. Having similar or congruent perspectives among physicians and associating with medical care professionals, is an important factor of behavior [11]. According to Bramble et al. [29], physicians who know other physicians supporting EMR systems enhance their desire to adopt these systems themselves. Peer preference exerts a mimetic pressure and is a main component of social norms. Hypothesis 1c is an additional hypothesis on intention:
Hypothesis 1c: Peer preference has an effect on the intention to adopt.
According to Watkins et al. [30], governments have been known to “play a central orchestrating role in the generation and diffusion of innovation in a national economy” (p. 1408). This orchestrating role is clearly visible in the U.S. government’s regulations, policies, and mandate requiring the adoption and use of EMR systems. This exerts coercive pressure on physicians who feel that their decisions should meet the expectations of the government and other influential organizations [23]. Hence, hypothesis 1d is proposed:
Hypothesis 1d: The government policy and mandate for adoption have an effect on the intention to adopt.
More recent literature shows that intermediary industry associations have an increasing involvement in cooperative relations between government and industry aimed at influencing an innovation’s diffusion, adoption, training, and standards [30]. These associations create and set industry protocols and common best practices to which physicians are driven to adhere. Lack of standardization is the biggest challenge according to Rathert et al. [2]. We expect physicians to take the normative pressure of meeting industry standards into consideration when adopting technologies, and this is reflected in hypothesis 1e:
Hypothesis 1e: Industry standards for adoption have an effect on the intention to adopt.
Attitude Drivers
Hypothesis 1a posits that attitude has an effect on intention. In this section, factors that influence attitude are considered. Complex innovation integration, like EMR systems, requires knowledge creation and diffusion [18, 31]. Knowledge is the extent to which the individual is aware of the innovation and its purpose, structure, components, requirements, benefits, and impacts. According to Rogers [20], knowledge is the first stage of the innovation-decision process by which a decision regarding the adoption of innovation matures prior to being made. Knowledge leads to physicians’ persuasion to adopt the system [20].
Having such knowledge helps promote persuasion and attitude formation. This definition supports hypothesis 2a:
Hypothesis 2a: Knowledge of innovation has an effect on the attitude toward adoption.
Ultimately, successful adoption of EMR systems in the United States and the nationwide diffusion of shareable electronic records should yield measurable benefits. Several studies associate the acquisition and adoption of technology innovation with enhanced performance, increased efficiencies, and improved quality [9, 32, 33]. The more the adopters believe they can achieve higher performance, efficacies, and quality, the more positive psychological feelings they will have toward it. The TAM [21] posits two determinants of user’s attitude toward acceptance of a technology system: perceived usefulness and perceived ease of use. Davis [21] defined perceived usefulness as the extent to which a person believes using a system will enhance the individual’s job performance. However, regarding benefits, an innovation can be differently useful to the user than it is to the overall industry. Thus, a distinction between perceived usefulness for the individual and perceived benefits for the industry is needed. While perceived usefulness is a behavioral belief, perceived industry benefits is an outcome evaluation [25]. Both are factors of attitude [25]. Hence, hypotheses 2b and 2c are as follows:
Hypothesis 2b: Perceived industry benefits have an effect on the attitude toward adoption.
Hypothesis 2c: Perceived usefulness has an effect on the attitude toward adoption.
Perceived ease of use, the second determinant of technology acceptance, is defined by Davis [21] as the degree to which a person believes using the system would be free from effort. Perceived ease of use can influence attitude and is stated in hypothesis 2d:
Hypothesis 2d: Perceived ease of use has an effect on the attitude toward adoption.
Perceived Behavioral Control Drivers
Researchers, such as Boonstra and Broekhuis [34], Bramble et al. [29], Felt-Lisk et al. [3], Häyrinen et al. [35], Hillestad et al. [4], and Mostashari et al. [36] discussed and identified existing and potential control barriers to EMR system adoption. The two main identified factors are first, not having the financial ability for adoption, and second, the negative impact EMR system adoption has on workflow and operations. Financial ability is having the necessary funds to support the initial setup and ongoing maintenance of the system. The impact on workflow is the extent to which the EMR system benefits or disrupts operations. These factors are what Ajzen [22] calls control beliefs. They affect behavior through their impact on the trust in self and the perception that one is feasibly able to adopt and use an innovation. Two of the hypotheses of factors impacting perceived behavioral control are as follows (hypotheses 3a and 3b):
Hypothesis 3a: Financial ability to adopt has an effect on the perceived behavioral control over adoption.
Hypothesis 3b: Workflow benefits from adoption have an effect on the perceived behavioral control over adoption.
Additionally, understanding how much exposure to EMR systems physicians have, may help to assess the role in their willingness to adopt and use the system, especially when this adoption comes with a financial or patient-care related advantage over other competing offices. Also, there are early adopters [20] who jump on opportunities to use an innovation before others in order to be differentiated. Relative advancement is the perception of how much more or less current the setup at the physician’s facility is compared with others. It is also related to the trialability and observability attributes of Rogers’ DOI theory [20], where people’s willingness to adopt an innovation is higher if they had the opportunity to experience it; thus, we propose hypothesis 3c:
Hypothesis 3c: Relative advancement of adoption has an effect on the perceived behavioral control over adoption.
Finally, Ajzen [22] showed that attitude and perceived behavioral control have respective effects on each other. So, we hypothesize (hypotheses 2e and 3d) that our results will show:
Hypothesis 2e: Perceived behavioral control over adoption has an effect on the attitude toward adoption.
Hypothesis 3d: Attitude toward adoption has an effect on perceived behavioral control over adoption.
Figure 1 illustrates the constructs, the hypotheses, and the overall model to be tested. The model combines the TAM and the TPB. Both are a specialization and derivation of the Theory of Reasoned Action (TRA) [37]. A unification of the TPB and the TAM combines compatible models and is therefore natural and possible. The predictive power of both the TAM and the TPB is empirically about the same [25], so that cannot guide the modeling. It is possible to use the TAM constructs perception of usefulness and ease of use as direct antecedents of intention to implement a higher level of EMR, or to use the TPB for that goal. Both have been done in the literature. The TPB is preferred here because industry-wide outcome evaluations, like benefits for the whole industry, are a part of attitude in the TPB. This and behavioral control, a natural and direct part of the TPB but not of the TAM, are (as established in the pilot phase below) necessary to understand the reason behind the intentions of the decision makers. Using the TPB as basis is therefore “the more suitable theoretical framework” (p. 961) [37] because it leads to a deeper understanding of the voluntariness of the decision maker. These considerations lead directly to the model of Figure 1.
Survey Creation and Distribution
A structured survey questionnaire targeting a large group of physicians was developed for the purpose of this study. The final survey questions were prepared to operationalize the variables
and test the hypotheses shown in Figure 1. There were 13 variables (see Table 1). Responses to the questions were collected using Likert scales. Invitations to complete the survey were sent via direct mail, email and social media advertising. A total of 2,012 mailings were sent to a network of Michigan physicians who were associated with groups such as Beaumont Hospital and Henry Ford Health Systems. In addition, 55,177 emails were sent to selected groups of physicians nationwide. Facebook and LinkedIn advertisements reached 5,978 physicians specifically by using the segmentation and audience targeting tools offered by both social platforms. Although three methods were used to disseminate the invitation, the survey itself was distributed digitally using Qualtrics software. An easy-to-access web address (www.adoptingEMR.com) was shared with invitees to link them to the survey. The distribution and collection of answers took approximately 10 weeks.
Table 1 Variables and their descriptive statistics
Variable
|
N
|
Mean
|
Std. Dev.
|
Std. Error Mean
|
Intention
|
208
|
5.05
|
1.830
|
0.127
|
Attitude
|
230
|
5.13
|
1.876
|
0.124
|
Perceived Behavioral Control
|
230
|
4.74
|
1.781
|
0.117
|
Peer Preference
|
220
|
4.40
|
1.678
|
0.113
|
Government policy and Mandate
|
343
|
4.99
|
1.855
|
0.100
|
Industry Standards
|
343
|
4.46
|
1.791
|
0.097
|
Knowledge
|
378
|
3.62
|
0.945
|
0.049
|
Perceived Industry Benefits
|
360
|
3.54
|
1.814
|
0.096
|
Perceived Usefulness
|
334
|
4.08
|
2.040
|
0.112
|
Perceived Ease of Use
|
332
|
3.60
|
1.710
|
0.094
|
Financial Ability
|
217
|
4.21
|
2.054
|
0.139
|
Workflow Benefits
|
331
|
3.25
|
1.737
|
0.095
|
Relative Advancement
|
342
|
5.25
|
1.329
|
0.072
|
The constructs of the conceptual model and their descriptive statistics of the mean, standard deviation and standard error mean. Responses were gathered on a Likert scale (1=negative, 7=positive), except for Knowledge (1=negative, 5=positive). N = Total received responses for that question.
|
The study was done in accordance to the Rules and Regulations of Grenoble Ecole de Management (GEM) Doctoral School (Grenoble, France) and was approved by GEM’s Ethics Review Board, which adopts the Academy of Management (AOM) Code of Ethics. The invitation had a clear description of the purpose of the study and all participants consented digitally to using the data and results for academic and scholarly purposes as a condition for entering the Qualtrics survey online.