Introduction

The new digital area is being part of the lives of the Society. People are using mobile devices and new technologies for making their lives easier. Step by step, new technologies have been introduced in our lives, first of all as only a telephone with the possibility to call or send a message. After that, it was possible to access Internet but Society was demanding more and more products to be used with mobile phones. Then, the smartphones emerged with the possibility to do sports, control your diet, or even learn cooking using mobile applications (“apps”), which play an important role in the use of mobile devices.

In fact, according to the last report of International Telecommunications Unit [1], there will be around 7.000 million users in the world with a mobile line by end 2014, which represents a penetration rate of 96 %. The number of Internet users globally will be almost 3 billion (40 % of world’s population). In Europe, ITU estimates the mobile users penetration will be around 125 % and the Internet penetration rate will reach 75 % by end 2014.

As for the number of apps, there are more than one million apps in each main marketplace [2]: Google Play market (for Android System Operating) and App Store (for iOS) . Not only that, the Global Research Study [3] reports that the mobile technology offers real opportunities for mhealth. Besides, some figures also support this new emerging market. The physicians have a preference for using smartphones and tablets [4]. In Spain, this trend is also very similar as 90 % of the physicians access Internet at least with two different devices [5]. Some Medical Schools have adopted this new trend [6] making mobile devices and apps become a new tool for learning.

However, there have been few researches about the inquiries and the acceptance of mobile technology in medical education. In order to enhance the understanding of that issue, a TAM model was selected and implemented in the University of Salamanca. The purpose of this paper is to assess and verify that this TAM model can be employed to measure and explain the acceptance of introducing mobile devices and apps within Medical Education. The methodology was based on a survey distributed to students and medical professionals from University of Salamanca. This model will help us to justify and understand the current situation of the mobile technologies within Medical Schools.

The paper is structured in four parts. The first part consists on a brief introduction; the second part describes the methodology and the items of the TAM model used in this research. The third part presents the results of the study divided in two types: descriptive and inferential statistics. Finally, the last part draws the discussion and the conclusion.

Methodology

Method

The study carried out within this research, performed a survey in University of Salamanca among students and professionals of medical sector that investigated to which extent they accept the mobile devices and the apps in their medical curriculum.

The survey consisted of 29 questions grouped into two sections and the number of participants was 124. The first section included 19 questions related with demographic information and the second section included ten measurement items. The first TAM designed by Davis [7] omitted the external influences of others on behavior intention, but these external variables are added in the model as it is explained in [8]. In this research, the first eight items considered for the survey were based on the constructs reported by both articles [7, 8]. In addition, this study added two constructs more (Reliability and Recommendation) [9].

Table 1 shows the constructs and the assigned variables for this research framework. In order to quantify the different dimensions or constructs, the survey used a 5-point Likert scale. The participants were asked to respond to each statement in terms of their own degree of agreement of disagreement [10]. Likert scale is based on five possible answers ranging from strongly disagree (mapped to number 1) and strongly agree (mapped to number 5). The ANX construct has been reversed-scored.

Table 1 Description of the constructs of the survey
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Results

Descriptive statistics

The total number of participants that answered the survey was 124. There was a major proportion of female participants than male participants (64 % female, 36 % male). The reason could be that there is a growing tendency of women signed up in Medical Schools. Approximately, since 1998, the proportion of new female students in Medical Schools in Spain is roughly 65–71 % [11]. The dominant age presented in the sample was ranging from 18 to 35 years with a 71 % of the participants [12]. Most part of the participants (93,6 %) owned only a Smartphone or a Smartphone and a tablet. Then, we analyze the main descriptive statistics of the selected constructs.

The Table 2 describes the different values for media, mean, standard deviation, standard error, and variance. All means are above the midpoint 3000 and the standard deviations are within the range from 0,99 to 1,34 indicating a narrow spread around the mean.

Table 2 Descriptive statistics
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Besides, the study calculates the skewness and kurtosis tests normality. Kline [13] suggests cutoff of absolute values of 3.0 and 8.0 for skewness and kurtosis respectively to ensure univariate normality. The results show that skewness values (≤|1,88|) and kurtosis values (≤|3,447|) are within the range of recommended values, so it is possible to assume that the responses were relatively normally distributed.

Inferential statistics

When using Likert-type scales it is imperative to calculate and report Cronbach’s alpha coefficient for internal consistency reliability [14]. The Cronbach’s alpha is an index of inter-item homogeneity [15], that is, how related a set of items is as a group [16].

The value of Cronbach’s alpha is 0,787, a value considered by experts as acceptable [17]. However, it is needed to run the factorial analysis to confirm if the proposed TAM model is adequate and the set of items is homogenous.

The objective of Factorial Analysis is “to represent a set of variables in terms of a smaller number of hypothetical variables” [18]. Structural Equation modeling was used to test all the hypothesized relationship.

A measure of sampling adequacy is a useful method for determining the appropriateness of running a factor analysis. These measures are also known as Keiser-Meyer Olkin (KMO statistics). In this case, the value obtained with SPSS program v21 is 0,748, what means that the correlation is good [19] and the factorial analysis is possible.

The CFA method is widely used for examining hypothesized relations among Likert-type items [20] and it provides a general analytic approach to assess construct validity. In this case, it is necessary to use AMOS program v16 to run the structured model and to test the relationship between the constructs of the research.

The significance tests of the hypothesis are presented in Fig. 1. This figure shows the path coefficients and R2 represents the proportion of the variance of the variable that could be explained. The path coefficients are marked with * if t > 1,96 (the level of ρ < 0,05). They are marked with ** if t-value > 2,58 (the level of ρ < 0,01) and it is marked with *** if t-value > 3,3 (the level of ρ < 0,001). In this research, the proposed model can explain 47 % of variance. It is difficult to find a standard criterion for how much a variance must be explained to be considered adequate. Some authors consider the adequate range from 40to 70 % [21]. It should be noted that all direct hypothesis were supported.

Fig. 1
figure 1

Path Diagram obtained with AMOS Program v16

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This research based the examination of the goodness of fit (GOF) indices on the assumption that the observed variables were normally distributed. The Table 3 reveals the output data of the proposed hypothesized model.

Table 3 Hypothesized model (Goodness-of-Fit Indices)
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In this study, the final GOF indices considered for the analysis are the following: relative X 2, TLI, CFI and RMSEA parameters [13]. The relative X 2 is calculated as X 2/df as an alternative to X 2 goodness of fit which has been falling out of favor because it is influenced by sample size [25]. RMSEA and CFI seem to be less sensitive to sample size [26]. According to Mars & Balla [27] TLI (NNFI) is also no dependent of sample size.

The acceptable values of the parameters are obtained from different sources. RMSEA tells how well the model would fit the populations covariance matrix [28] and the accepted values were within the range 0,05–0,08 [22–24]. The CFI cutoff must be equal or higher than 0,95 [22]. On the other hand, NNFI parameter should have values equal or higher than 0,95 [22].

The TLI, CFI and RMSEA values obtained in the hypothesized model are within the recommended cutoff values. Therefore, these fit indices indicate that the proposed measurement model exhibits a good fit with the observed data.

Discussion and conclusion

This study attempts to provide some insights about factors that may affect the acceptance of mobile devices and apps by undergraduate students and medical professionals in their curriculum.

The use of mobile devices for learning has been analyzed by several articles (among others) [29–31] and several studies have been conducted to measure the acceptance of different technologies [32–35]. However, the results therefore need to be interpreted with caution to extend the results to different sectors as it is as well reported by Huang et al. [35]. Most part of the researches does not describe the type of the participants involved in the survey in order to determine the homogeneity of the respondents.

There are few studies that investigate specifically the medical education area [36, 37]. This paper is conducted within this area and the participants of the survey were undergraduate students and medical professionals. As a result, there are many factors influencing the mobile technology use that have yet to be fully explored [6]. This investigation can be considered a first research framework to demonstrate that individual characteristics and external variables may have a significant influence on individuals to predict BI.

Hence, this study found that overall two constructs (REC and SE) are key determinants for the BI of using apps for learning (ρ < 0,0001). The ATU is also important to predict the BI and this research also suggests that SI may affect the ATU in the same proportion than PU does. This is very important, as external factors must be considered to predict the attitude for using apps. The FC and ANX variables have been considered as external variables to affect indirectly the ATU and the BI. Consistent with prior research [6, 7], PEOU has a significant influence on PU, with ρ < 0,001.

However, while PU has a significant effect on ATU, PEOU does not. Other important finding of the research is that ATU significantly increases the degree of recommendation (REC) or the necessity of an app certification (REL).

Future research can be conducted to analyze the score results obtained and the benefits and drawbacks to encourage the use of mobile devices and apps for learning. In addition, it should be recommended to compare the collected data by using different subgroups such as profile, age or gender to gain deeper insights into the impact of these external variables on this TAM model.