Keywords

1 Introduction

We are facing a technological revolution that is changing the world. Our young people will be the ones who live in today’s and tomorrow’s world and will have to make use of these technologies or even contribute to their development. But what is the attitude of our young people towards technology?

Many of them face the challenge as part of the time they have to live. They get involved with technologies, are willing to adopt them as soon as they are available and make use of them and investigate how to improve them. Many others, however, remain indifferent to this technological revolution, thinking that the old is better known than the new to know. Some others are even afraid of change. Their primary worry is fear of the unknown, of not being able to understand and manage these advances and of being relegated to a marginal place in society.

How young people, especially those in training, face the challenge of technologies can shape their future and that of society as a whole. Therefore, teachers should try to take advantage of technological tools to improve the learning process. We tend to think that by the mere fact of having been born in the digital age, our young people are digital natives [10] and therefore they know and handle the technologies perfectly. The reality, however, is very different. Just like any child needs to be literate to deal with written information, students also need digital literacy, since digital technologies are nowadays an essential life skill. Digital literacy is the usage and understanding of information in the digital age [8]. But it is not a question of developing in any way, true digital literacy means learning to use technology effectively and efficiently, so that you learn to live with it and use it to create. One of the ultimate aims of this literacy is to prepare students to confront technology with a positive attitude.

Let us first define what we mean by attitude. An attitude can be defined as an evaluative judgement, either favourable or unfavourable, that an individual possesses and directs towards some attitude object [4]. In the context of technology, the attitude towards technology is one’s positive or negative evaluation towards the introduction of new kinds of technology in any environment.

To try to measure this attitude in an objective way, different models have been defined. For example, the Technology Acceptance Model (TAM) [3] is an information systems theory that models how users come to accept and use a technology. This model aims to provide insight into the factors that determine the acceptance of technology, treats attitude towards technology as a key determinant of one’s behavioural intention to use such technology.

Another interesting model is the Gartner’s technological Hype Cycle [5,6,7]. It is a graphical representation of the maturity, adoption and commercial application of emerging technologies. This report is published annually and provides an overview of trends in the technology industries.

The Unified Theory of Acceptance and Use of Technology (UTAUT) is a model of acceptance of technology defined by Venkatesh et al. [15]. This model attempts to explain how individuals begin to use a technology and the subsequent behaviour with it.

Rogers’ Theory of the Diffusion of Innovations [13] is a sociological theory that seeks to explain how, why and at what speed new technological ideas move in society or in a group within the society.

All of these models and a few others have tried to explain how technology develops and how people are confronted with it. The objective of this research is to know, in a specific environment such as the university, what is the attitude of its members towards technology, using some of the main models found in literature. In order to achieve this project, a survey has been designed and validated by experts. Its results will allow us to draw conclusions on the degree of knowledge of some of the main emerging technologies and adaptability shown by students at a Spanish university. This paper presents the distribution of university students in comparison to the Rogers diffusion model considering several variables, but the survey is prepared obtain data that allow, in the future, the study of students’ attitudes to technology based on the key determinants of the UTAUT model and the comparison of their knowledge of specific technologies to the Gartner’s Hype Cycle.

Section 2 of the paper presents the background of the research, reviewing some previous papers that have dealt with similar topics and presenting the main theories with which we will compare our data. Section 3 explains the research methodology and design of the experiment. Section 4 presents the experiments. Comparison with the Rogers’ Theory of the Diffusion of Innovations and discussion of results form Sect. 5. Finally, Sect. 6 provides the main conclusions and findings of the study.

2 Background

This study is based on several theories related to technological aptitude and its acceptance in the population. Therefore, this section will explain the most relevant theories used during the development of this project. First, some general studies about the attitude of several collectives (professionals, students and general population) are presented. The three studies that have served as the basis for comparison with our results are presented in more detail next.

In the professional field, Elias et al. [4] state that understanding the employees’ attitudes towards technology is essential given the prevalence of technology in the workplace. They consider that this attitude is quite often linked to issues such as the successful implementation of new technologies in the workplace, employee intent to use technology, and the actual usage of technology by employees. They also note that age can be an important factor in the attitude towards technology.

In order to measure this attitude objectively, different models have been defined. For instance, the Technology Acceptance Model (TAM) [3] is an information systems theory that models how users come to accept and use a technology. This model aims to provide insight into the factors that determine the acceptance of technology, treats attitude towards technology as a key determinant of one’s behavioural intention to use such technology. The model suggests that when users are presented with a new technology, a number of factors influence their decision about how and when they will use it, notably:

  • Perceived usefulness: this was defined by the author as “the degree to which a person believes that using a particular system would enhance his or her job performance”.

  • Perceived ease-of-use: the author defined this as “the degree to which a person believes that using a particular system would be free from effort”.

In the field of education, Raat and de Vries [11] conducted the international research project Pupils’ Attitudes Toward Technology (PATT). They verified that learners’ attitudes toward technology attract serious attention among scholars of technology education. Another interesting study about the attitude of pupils towards technology is that of Klerk Wolters [9]. It is a deep study that reviews the literature about research into the attitude towards science and technology and proposes a measurement instrument and a technology attitude scale.

The work of Yu et al. [17] establishes, tests, and verifies a model of junior high school students’ attitudes toward technology by using the Attitudes Toward Technology Scale for Junior High School Students developed by themselves, to help researchers and teachers in the domain of technology education understand learners’ attitudes and enhance the benefits of learning. More specifically, the purposes of this study were to establish a model of junior high school students’ attitudes toward technology, determine the appropriateness of the model, and examine correlations among factors in the model. They distributed questionnaires to research participants with the following results: the made up a complete model of student attitudes toward technology and identified the main factors influencing the pursuit of careers in technology, identification with technology, and experiences with technology-related curricula.

2.1 Gartner Hype Cycle for Emerging Technologies

Gartner’s Hype Cycle [5,6,7] is a graphical representation of the maturity, adoption and commercial application of emerging technologies. This report is published annually and provides an overview of trends in the technology industries, so that you can find out whether a technology has potential or whether there is an over-expectation that could lead to failure.

Each hype cycle is composed of five phases that represent the life cycle of a technology. These phases are:

  • Innovation trigger. In this first phase a new potential technology and the first news about it appears. The first advertisements are beginning to appear, but it is not common for them to be applied to products.

  • Pick of Inflated Expectations. Advertising begins to grow and a series of expectations for success are created due to the success stories of the technology.

  • Through of Disillusionment. At this stage the interest decreases if no technology implementations have been created. Investments in technology continue to be made if suppliers succeed in satisfying the early adopters of their products.

  • Slope of Enlightenment. At this stage, due to the successes in the first adopter companies, the other companies begin to invest in technology, although the more traditional ones are still waiting.

  • Plateau of Productivity. Widespread adoption of technology begins and good results can be seen.

Figure 1 shows the curve with the five phases described above. Each year, each technology is placed in a position in the curve, depending on its maturity and adoption. The position indicates how the technology is potentially relevant to solving real problems and exploiting new opportunities.

Fig. 1.
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Gartner Hype Cycle for emerging technologies

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2.2 Unified Theory of Acceptance and Use of Technology

Unified Theory of Acceptance and Use of Technology (UTAUT) is a model of technology acceptance defined by Venkatesh et al. [15]. This model attempts to explain how individuals begin to use a technology and the subsequent behaviour with it.

The model considers four factors and four moderators that influence an individual when using a given technology. The factors are:

  • Performance expectancy: It is the degree to which individuals perceive that the use of a technology will help them in their work.

  • Effort expectancy: It is the degree of ease associated with the use of a technology.

  • Social influence: It is the degree to which an individual perceives that those around him or her want the technology to be used.

  • Facilitating conditions: It is the degree to which individuals consider that they have the technical or organizational infrastructure to support them in the use of a technology.

The influence of the expectation of effort and performance in terms of users adopting the technology has already been proven in different research studies. For example, Schultz and Slevin [14] and Robey [12] have already analysed the impact of expected performance on technology utilization. The last author focuses his study on the performance that a technology will have on the work of the individual and notes that a system that does not help people to develop their work is not favourably received despite implementation efforts.

Bandura [1] analyses the importance of ease of use perceived by the user. This author considers this key variable and associates it with performance expectation, so that in a given situation, behaviour is easier to predict considering both performance expectation and effort expectation. In addition, Bandura determines that the effort expectation itself cannot be considered an adequate general measure, since it depends on the analysed scenario.

2.3 Rogers’ Theory of the Diffusion of Innovations

The Rogers’ Theory of the Diffusion of Innovations [13] is a sociological theory that tries to explain how, why and at what speed new technological ideas move in society or in a group of society. A new idea, or innovation, is one that is perceived by individuals as novelty.

Within this model, five categories of adopters of new ideas are defined, ranging from the fastest adopters to the later adopters. These five categories are:

  • Innovators: they are venturesome and their new ideas leads them out of a local circle of peer networks and into more cosmopolite social relationships. They are able to understand and apply complex technological knowledge and cope with a high degree of uncertainty. They play an important role in the diffusion of innovative technologies.

  • Early adopters: they are more integrated in the local social system. They usually have the highest degree of opinion leadership in most systems and they are the users to whom others ask for advice. In a sense, they put their stamp of approval on a new idea by adopting it.

  • Early majority: they adopt the ideas just before the average member of a system. They are one of the most numerous adopter categories and follow with deliberate willingness in adopting innovations but seldom lead.

  • Late majority: they adopt new ideas just after the average member of a system. For them, adoption is a economic necessity or the result of increasing peer pressures. They are, in general, sceptical and cautious.

  • Laggards: their decisions are usually made in terms of what has been done previously. They tend to be suspicious of innovations and of change.

Rogers proposes a non-symmetrical distribution of technology adopters based on a normal distribution. Figure 2 shows the distribution of adopters and their adoption curve. Additionally, it is possible to establish a relationship between Gartner Hype Cycle and the Rogers’ theory. Specifically, during the trigger phase of Gartner’s curve, it is innovators who access the technology. The peak of inflated expectations occurs when early adopters access this technology. The early adopters join the technology during the slope of enlightenment, and finally it is adopted by the late majority when the plateau is reached.

Fig. 2.
figure 2

Categorization of adopters based on their level of innovation

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3 Methodology

The data collection is based on a closed-ended opinion survey validated by experts. The study can be divided into four main phases, which are:

  1. 1.

    Tool design: In this first phase, the necessary information is collected to determine what aspects of the study are to be analysed. Once completed, a first survey is designed and validated by experts. Based on the analysis carried out by the experts, a final survey will be created and answered by the sample.

  2. 2.

    Conducting the survey: Once the final survey is held, it will be published so that it can be answered by the sample. The survey needs to reach as many people as possible, so it has been decided that it will be published in different channels, such as social networks or messages on the virtual campus.

  3. 3.

    Analysis of results: At the end of the response period, the survey will be closed and responses downloaded. These results will be cleaned if necessary. Once the results have been refined, various graphs will be obtained that will allow the conclusions of the study to be drawn. Among other aspects, we are especially interested in comparing the results with the Rogers’ innovation diffusion model.

  4. 4.

    Conclusions: Once the graphs have been obtained, they will be analysed and the conclusions of the study will be obtained to obtain the necessary knowledge to determine the population’s technological aptitude.

4 Experiment

The following is a detailed description of the aspects to be analysed with the study and how the questionnaire has been designed to enable this analysis to be carried out. In addition, the ways in which the questionnaire was applied and the characteristics of the sample obtained are explained below.

4.1 Tool Design

The aim of the experiment is to compare the attitude towards technology of students at the University of Alicante (UA) using the three models we have discussed, although this paper is focused on the Rogers’ model. For this reason, it has been determined that there will be three blocks of questions, one for each model, in addition to a block of personal information. In short, the four blocks of questions are:

  1. 1.

    Block of personal information. In this block, the necessary personal questions will be asked to determine the relationship between the individuals surveyed and the University of Alicante, their sex, age, the size of their town, their field of study and the disabilities they may suffer. This information will be used to analyse the influence of these variables on the other aspects analysed in the study.

  2. 2.

    Technology adoption block. In this block, questions will be asked to individuals to determine when they adopt a new technology and how they learn of its existence. This block is related to Rogers’ innovation diffusion model and the aim is to determine the distribution of adopters for the analysed sample.

  3. 3.

    UTAUT variable block. In this block, questions will be asked corresponding to the variables of the UTAUT approach in order to analyse their influence on the other aspects.

  4. 4.

    Block of specific technologies. In this block, individuals will be asked the degree of knowledge they possess about specific technologies. Responses will be used to determine the hype distribution of the sample with respect to Gartner’s Hype Cycle. Based on these four blocks, a preliminary questionnaire was designed and validated by experts.

In particular, six experts participated in the validation. To this end, these experts were provided with a second questionnaire asking for the clarity of each question and making it possible to offer suggestions for modifications to the preliminary questionnaire. The changes proposed by the experts were, in summary, aimed at reformulating some questions to simplify them or eliminate ambiguities, eliminating some redundancies and adding examples in the questions on specific technologies to help respondents identify them. The final questionnaire, once the corrections incorporated, is available in the Appendix.

4.2 Conducting the Survey

The questionnaire was published through the Google Forms tool and advertised to a large number of students. For this dissemination, social networks (official Twitter of the UA) and messages on the institutional virtual campus of the UA have been used, the latter especially aimed at certain engineering and education degree courses.

5 Results and Discussion

The survey resulted in a total of 194 responses. Of these responses, 187 (96.4%) were answered by students from the University of Alicante and the rest by teaching or administrative staff from the UA or people with no relation to the UA. Given the low representativeness of these collectives in the final sample, it has been decided to eliminate these data and focus the study only on the group of students.

5.1 Demographic Data

In this section, the main demographic data are presented. Table 1 summarizes the distributions of participants in the survey, indicating the number of participants, their gender distribution and their average age.

Table 1. Distribution of participants, gender and average age
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It is also interesting to know the knowledge area to which the participants’ studies belong, as well as the size of the town they live in. All this information is presented in Fig. 3.

Fig. 3.
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Knowledge area and town size

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5.2 Comparing Rogers’ Model and UA Results

Before continuing with the analysis of the data, it is important to check whether the distribution of the results is normal. A Shapiro-Wilk normality test has been carried out for this purpose. The results \((p<0.05)\) indicate that we can reject a normal distribution. However, F-test has been shown to be robust to moderate departures from normality when sample sizes are reasonably large and are equal [2, 16]. A sufficient sample size (n = 187) allows us to justify the use of these tests with sufficient guarantees, despite not fulfilling the criterion of normality.

An interesting first comparison can be made between the data provided by the Rogers’ model and the results obtained for question 8 of the survey (see Appendix) among UA students. To check if there is any difference between the two groups, a t-test was carried out on the mean differences. The results of this test evidence that there is a significant difference between the two groups, since p is practically 0 \((p<2.2 \times 10^{-16})\). The box plot of the two distributions (Fig. 4) helps us to verify the differences between them. In this figure, the Y-axis indicates the timing of technology adoption, where 1 indicates immediately (group of innovators) and 5 at the last moment (group of laggards).

Fig. 4.
figure 4

Box plot for the Rogers’ model and the UA results

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The graph in Fig. 5 is also interesting. In the graph, the bars indicate the percentage of individuals classified in each category, in red the data extracted from the Rogers’ model, and in blue the results of the UA survey. In addition, curves have been added in order to make the graph easier to understand (actually, the corners of the curves represent the same values as the corresponding bars).

Fig. 5.
figure 5

Comparison of the Rogers’ model and the UA results

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This graph shows a clear shift of the categories in which UA students are classified to the left, i.e. this group tends to adopt technological innovations earlier than in the rest of the population. This can be seen very clearly at the extremes of the curves: the number of UA users who consider themselves innovative is 16% compared to the 2.5% indicated in the Rogers study. Just the opposite occurs in the case of laggards that represent 16% for Rogers, and among the UA population are almost non-existent (a single person of 187 is considered a laggard, a 0.5% of the sample).

5.3 Technology Adoption and Gender

One of the main issues of concern to us is whether there are gender differences in the adoption of technology. Firstly, in Table 2, we present the main descriptive statistics corresponding to the results of the survey. In Table 2, each participant has self-classified into one of the five categories: Innovators (1), Early adopters (2), Early majority (3), Late majority (4) and Laggards (5). It can be observed that the numerical value also corresponds to the estimated time for the adoption of a new technology, with 1 being a very short time and 5 being a very long time.

Table 2. Descriptive statistics
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Although there are differences in the means of the two groups (on average men adopt technologies earlier than women), it is necessary to know whether this difference is statistically significant. For this we have made an ANOVA, which allows us to analyze the differences among group means. In this case, the independent variable is the gender, and the dependent variable the timing of technology adoption (again, 1 indicates immediately—innovators—and 5 the last moment—laggards—). Table 3 presents the results of ANOVA.

Table 3. ANOVA technology adoption vs. gender
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Since the significance level value is 0.06204 and this value is greater than 0.05, we accept the null hypothesis that there are no differential effects between the two genders. This means that there are no statistically significant differences in the adoption of technology by women and men, even though the means of both groups are slightly different.

5.4 Technology Adoption and Knowledge Area

Another aspect that we are interested in studying is the possible relationship between the participants’ knowledge area and their adoption of technology. In this case we have decided to analyze just the data from the students of Engineering and Architecture and Social Sciences, since the studies of Science, Health Sciences and Arts and Humanities have a too low representativeness in the sample. The main descriptive statistics are shown in Table 4. In the table, some differences in the means are remarkable depending on the studies the participants are taking.

Table 4. Descriptive statistics
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Again, we question whether these differences in mean values are statistically significant. The corresponding ANOVA is presented in Table 5. The independent variable is the knowledge area, and the dependent variable the timing of technology adoption.

Table 5. ANOVA technology adoption vs. knowledge area
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The significance level value is now 0.00649, which is lower than 0.05. Therefore, the null hypothesis is rejected, and we can conclude that the group means are not equal. In other words, there are statistically significant differences in the adoption of technology by students taking Engineering and Architecture studies and those taking Social Sciences studies. We can clearly see these differences if we graphically represent the results for these two groups. Figure 6 shows the shape of the curve of technology adoption according to the students’ knowledge area. The shift of the curve to the left corresponding to Engineering and Architecture studies with respect to those of Social Sciences indicates that the former tend to adopt technologies before the latter. The difference is especially significant among innovators, who are 23.96% in the case of Engineering and Architecture students compared to only 6.78% in the case of Social Science students.

5.5 Interactions Between Gender and Knowledge Area

In the sample, an irregular distribution between men and women can be detected depending on the area of knowledge. We have therefore questioned whether there are interactions between these two factors. Table 6 presents the main descriptive statistics for the four groups resulting from crossing these two factors (we have eliminated participants who did not specify their gender). It can be observed that, among Engineering and Architecture students, the male gender is much more abundant than the female gender, while among Social Science students, the precisely the opposite is true.

Table 6. Descriptive statistics
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Fig. 6.
figure 6

Technology adoption depending on knowledge area

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To test whether the differences in mean are statistically significant we have conducted a Two-way ANOVA, whose results are presented in Table 7. There are now two factors: the knowledge area and the gender, and the dependent variable is the timing of technology adoption.

Table 7. ANOVA technology adoption vs. knowledge area and gender
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The only significance level value which is lower than 0.05 is that of the Knowledge area factor, as it was detected in Sect. 5.5. However, the level of significance for the interaction of the two factors considered is higher than 0.05. In short, we can affirm that there are no statistically significant interactions between the two factors.

In addition to these analyses, significant differences have been sought in the adoption of technology according to the other demographic variables (age and size of the locality in which the participants live) and they have not been found in any case.

6 Conclusions and Further Work

Our university students, in a short time, will face a future that will be conditioned by technology. The attitude with which they are confronted can make the difference between being able to cope safely in this society or being relegated to a marginal position. In this work we wanted to know which is that attitude using as a guide prestigious models on behavior before technology. To this end, we have proposed a survey among our students in which we asked about different aspects in order to compare the results in our university with the contributions of three well-known theories. In particular, in this paper, we study how students adopt technology by classifying them into the five categories Rogers raises in his Theory of the Diffusion of Innovations. We must also say that the questionnaire has been reviewed by a group of six experts, who have suggested some changes and finally validated the questions contained in the survey.

Once the survey has been conducted, we have proceeded to analyze the data. To this end, a t-test has been carried out to assess whether the mean of the results of the UA survey are statistically different from the results obtained by Rogers in his study. It has been found that both samples behave differently and that, in general, UA students present a higher number of innovators than those observed by Rogers, and a lower number of laggards than in the baseline study.

Furthermore, we wanted to investigate whether there are gender differences in adopting technology. The one-way ANOVA carried out to assess if there are significant differences indicates that they do not exist from the statistical point of view, although we can observe a certain tendency for male students to be more innovative than female students, from the point of view of adopting new technologies.

One aspect in which there is a statistically significant difference is the time of adoption of a new technology depending on the area of knowledge of the students’ courses. In general, engineering and architecture students adopt technologies earlier than social science students.

In addition, there are no interactions between the area of knowledge and gender. For this purpose, a two-ways ANOVA has been carried out.

As future work, we plan to expand the study to compare the results of the UA students with the Gartner’s Hype Cycle and Unified Theory of Acceptance and Use of Technology. Data collected in the survey included in the Appendix are already available for this purpose.