The first two decades of 21st century mark the start of an age characterised by globalisation, as well as transition from an industrial society to a knowledge and information technology society, coupled with rapid changes that they bring in the economic, technological, political, social and cultural levels of each country. These evolutions result in an urgent demand for more efficient operation, reform and modernisation of public administration organisations. According to Sannia et al. (2009), one essential ingredient in achieving efficiency and in improving the quality of public administration organisations is to educate and continuously train the staff of such agencies based on the recent developments in science and technology, so as to ensure that their services operate to their maximum efficiency. The European Commission, in a series of recommendations published in 2017, invited Member States’ administrations to stimulate high-quality, innovative ways of learning and teaching using new technologies and digital content. This is clearly related to the use of open and distance learning, an education and training method facilitated by information and communication technologies which lessens physical and psychological distance, increasing interactivity and communication among learners, learning sources and facilitators. Distance learning can therefore be regarded as a tool that enables administrations to devise a strategy focusing on skill needs as well as on job training, retraining and upskilling of their employees.
In this context, for the Independent Authority for Public Revenue (IAPR) to safeguard Greece’s public revenues and to rise to the major challenge of implementing the targets set by the country’s creditors following the period of economic recession, IAPR has set up its own training body and has incorporated flexible methods into its training practices. It should be noted that, apart from the need for efficient tax collection and the respective training needs of employers, there is an ongoing upgrade of e-government in Greece following the period of economic recession. This requires increased and specific training, especially for the employees of IAPR. It is only in the past 3 years that distance learning has been included in the comprehensive training methods provided by the IAPR, with the aim of creating a culture of operating through common ways and principles, avoiding handling cases subjectively and promoting the staff’s ability to detect money laundering and to combat tax evasion. The overriding objective of distance learning courses is to adapt employee knowledge and skills to ever-increasing requirements and to increase productivity and efficiency. It will help employees to be in a position to engage in continuous learning and development and to respond to the challenges and the constantly developing conditions of the modern working environment.
The inclusion of distance education and the use of digital technologies in education and training programmes worldwide brought to the foreground a range of challenges. It highlighted inefficiencies and difficulties in relating knowledge and skills to workplace practices, and revealed issues related to disappointment and high dropout rates for online programmes (Carr, 2000; Doe et al., 2017; Moody, 2004; Schreurs et al., 2008; Willging & Johnson, 2009). Therefore, it is necessary to identify specific factors that could contribute to creating a high level of e-training readiness in order to ensure the successful planning and execution of effective distance learning courses, meeting the needs of employees while serving the public interest and taking into account the financial constraints.
The term ‘e-training readiness’ refers to a set of skills that allow an employee to actively participate in the learning process using distance learning to enrich, upgrade and further develop academic/theoretical or practical, professional and personal interests, skills, knowledge and capabilities. To date, no major efforts have been made to develop one single, standard diagnostic tool to measure the various aspects of e-training readiness. On the contrary, each researcher uses a combination of theories to develop their own tool depending on the purpose and objectives of research.
Initially, learner e-training readiness was considered to be associated with the ability of learners to use technology for academic purposes. Warner et al. (1998) identified e-training readiness in the context of vocational education and training (VET) as a combination of learners’ preferences for participating in e-learning courses, their ability and self-confidence in e-communication and their capacity for self-directed learning. Lynch (2001) and Smith et al. (2003) equated readiness for online education to ‘feeling comfortable about engaging in e-learning’ and ‘self-management of learning’. Smith (2005) accepted the application of these technological aspects of readiness in research and practice in the areas of learner inclination/disposition to e-learning.
In the years that followed, the concept of readiness was primarily associated with the ability of learners to use computer technology, their satisfaction with the training course, factors associated with successful distance learning such as persistence, internal sense of control and self-efficacy and innate features such as self-directed learning, self-motivation, emotional self-regulation and persistence. Hung et al. (2010) presented a redesigned view of readiness to learn online which argued that learners’ e-training readiness is reflected in their ability to learn through self-directed learning, the degree of control learners have and the learners’ motivation for learning. This relationship between self-directed learning, motivation, control and self-esteem, with e-training readiness as an emerging concept, transformed this issue into a psychological one, thus opening up the possibility of psychometric research in this field.
Over recent years, the presence of emotions has become a fundamental aspect of e-learning. Consequently, Doe et al. (2017) included an emotional involvement question in a multidimensional tool and pointed out the need to explore the psychometric skills of learners to better understand their online learning readiness. Emotions are associated with the very nature of learning and cannot be viewed separately from it. Dirkx (2008) argued that understanding the nature of emotions and their impact on learning can help the learning process itself. Flood (2003) argued that learning is more dependent on an individual’s emotional response to a learning environment than the educational methods or structures used in it. Consequently, emotions play a decisive role in shaping learning experiences, whether those learning experiences are face-to-face or remote. Despite the fact that the dynamic of emotions is less evident in distance learning, there are no clear indications that the existence of learner emotions differs depending on whether the educational process is face-to-face or online (Derks et al., 2008).
The role and importance of emotions in the distance learning process is confirmed by numerous studies. Increasing emphasis is placed on understanding the emotions of the individuals involved as a factor in the success of distance learning programmes. These studies explore emotions that either impede the learning process or contribute to its successful completion (Angelaki & Mavroidis, 2013; Berenson et al., 2008; Chaffar & Frasson, 2006; Derks, 2007; Dirkx, 2008; Juutinen & Saariluoma, 2010; O’Regan, 2003; Zembylas et al., 2009). A large number of researchers attribute the failure of distance learning courses to the lack of emotional intelligence skills and overlooking the dynamic of emotions (Chaffar & Frasson, 2006; Martinez, 2001), while the emotional response of a learner has also been linked to the achievement of distance learning outcomes.
Emotional intelligence, which is related to the set of skills and abilities that allow the persons who hold them to successfully manage various emotional situations in their day-to-day life, is a complex concept. Many theories have been formulated to capture its content, and various measurement methods and tools have been developed. However, there is no absolute agreement among the researchers in the scientific community on these matters. Over recent years, there has been an increase in the number of studies into the level of emotional intelligence among employees (Extremera et al., 2018; Miao et al., 2017; Toyama & Mauno, 2017). The common denominator in such studies was the high level of emotional intelligence among the executives who took part in them. The correlation between emotional intelligence and specific work-related behaviours highlighted its exceptional role in the more effective operation of private sector businesses as well as public administration organisations.
One common finding is that the emotional intelligence of learners is a major factor affecting their online learning readiness and, consequently, their academic success in distance learning environments (Ahmad & Salim, 2021; Alenezi, 2020; Behnke & Greenan, 2011; Berenson et al., 2008; Brackett & Salovey, 2006; Brackett et al., 2004; Buzdar et al., 2016; Engin, 2017; Goodwin, 2016; Mayer et al., 2004; Williford, 2000; Zahed-Babelan & Moenikia, 2010). Researchers examined emotional intelligence in relation to the degree of online learning readiness, the attitudes of learners towards teaching methods and the academic performance of learners in distance learning courses. The role of emotional intelligence was highlighted as important in explaining deviations in online learning readiness (Alenezi, 2020; Buzdar et al., 2016). The combination of the level of emotional intelligence among learners and their personality traits (such as sociability, nervousness, irritability, persuasiveness, self-control and focus on achieving targets) proved to be a powerful diagnostic factor for successful online learning processes (Berenson et al., 2008). Recently, Priya (2021) considered emotional connectivity with e-learning as a key aspect of effective learning and development during Coronavirus disease 2019 (COVID-19). Moreover, it was found that students with a high level of emotional intelligence sought asynchronous and self-directed learning methods and more easily responded to the requirements of e-learning (Behnke & Greenan, 2011). Finally, a high level of correlation between emotional intelligence and academic performance was confirmed (Goodwin, 2016; Zahed-Babelan & Moenikia, 2010).
The majority of studies highlighted the great importance of two specific attributes for designing online training courses that lead to a successful learning: understanding how distance learners think and learn and how their emotional intelligence and personality impact the success of distance learning courses. In many cases, there was a need to adopt pedagogical strategies and methods, which would bolster emotional intelligence and other psychometric skills of learners to improve their e-training readiness (Berenson et al., 2008; Buzdar et al., 2016).
Against this background, considering the current understanding that e-learning readiness is not related only to technical and computer/internet skills of learners, the present study examines the role of psychometric abilities and the emotional involvement of learners in relation to their readiness to participate in online learning courses. This is done through a survey research on the employees of the IAPR, an authority with increased mandate and demands for delivery due to the financial crisis recently faced in Greece, considering also the ongoing upgrade of e-government in the country.
Employees of the IAPR can participate in training courses run by the National Centre for Public Administration and Local Government and the newly established Tax and Customs Academy. Therefore, they can enjoy a comprehensive set of training and personal development services which integrates distance online learning methods into educational practice. Public administration employees frequently face difficulties in accepting and utilising innovative training methods. To this end, the main objective of this study was to explore the degree of e-training readiness among IAPR employees in active service and its relationship with their level of emotional intelligence.
Following the above and based on the literature review, the following research questions were set:
What is the degree of e-training readiness of IAPR employees regarding the following factors: computer-internet self-efficacy, self-directed learning, learner control online, motivation for learning online and online communication self-efficacy?
What is the level of emotional intelligence among IAPR employees?
How is the level of emotional intelligence among IAPR employees related to their degree of e-training readiness?
In terms of educational practice, this study seeks to bolster understanding of the factors that positively affect the overall e-readiness of employees in active employment, with an emphasis to their emotional intelligence, so as to enable high-quality distance learning courses. Such courses should reflect the needs of the learners, the mission of the organisation and the requirements for serving the public interest.
The methodological approach used is the quantitative method, more specifically a survey research using random research design. The chosen target population consisted of 11,893 active employees of the IAPR allocated across the entire territory of Greece. Convenience sampling was chosen as sampling method, since it allows data to be collected from a subset of a geographically dispersed target population, such as IAPR employees, to provide useful information about the entire target population. It should be noted that all employees were deliberately not chosen to participate via a mass mailing to their email addresses in order to avoid the risk of limiting responses to a subgroup of the target population which is not so representative (i.e. a subgroup that is more familiar with computers and the internet).
The tool used was a questionnaire to be filled out by the respondents. It was accompanied by a short informational letter containing all required elements related to the consent of participants. These elements included the identity and the status of the researcher, the aims and the design of the research, the voluntary nature of participation as well as guarantees about the anonymity of the participating employees and the confidentiality of their personal data. This information was also presented orally during the face-to-face distribution of the questionnaires.
The questionnaire included 6 questions related to the personal characteristics of participants, a set of 18 closed questions measured on a 5-point Likert scale related to the employees’ e-training readiness and a set of 30 closed questions measured on a 7-point Likert scale related to their level of emotional intelligence. The second set of questions was divided into five factors that affect distance learning: computer/internet self-efficacy, self-directed learning skills, learner control online, motivation for learning online and online communication self-efficacy. The questions in the third set could be allocated to the factors of well-being, self-control, emotionality and sociability, leaving four questions unclassified. To prepare the questionnaire on e-training readiness, the Online Learning Readiness Scale (OLRS) (Hung et al., 2010) was adopted. Regarding the emotional intelligence scale, the Short Form of the Trait Emotional Intelligence Questionnaire – TEIQue by Petrides and Furnham (2001) was used in the present study. This questionnaire has already been translated into Greek and weighted by Stamatopoulou (2014), with written permission from the authors.
The research tools ensured satisfactory internal consistency of reliability since the value of the Cronbach’s alpha index was calculated as 0.90 for all questions. The values for questions in the e-training readiness measurement scale and those in the emotional intelligence measurement scale were 0.89 and 0.88, respectively. The internal consistency was then calculated separately for each of the five factors of the e-training readiness measurement scale and for each of the factors of the emotional intelligence measurement scale. All questions in each subsection of the measurement scale for e-training readiness were used to create complex variables since their internal consistency values were considered reliable. On the contrary, the measurement scale for emotional intelligence was evaluated as a single variable, since the Cronbach’s alpha values were low; therefore, it would be exceptionally risky to use subsections without satisfactory consistency when creating complex variables.
The content validity of the questionnaire was also ensured: before the data collection process was initiated, concepts were defined, and an attempt was made to identify all dimensions comprising the variables to be measured. Suitable tools were chosen to measure e-training readiness and emotional intelligence and were included as subscales in the measurement tool. Conceptual construct validity was ensured given that the adopted scales had already been used by various researchers focusing on different types of learners and various countries and were tested for their reliability and validity.
Convenience sampling was used for the survey. A total of 302 hard copy questionnaires were distributed in person to all the employees on duty in the four largest Tax Offices in the city of Thessaloniki, during the period 19–23 April 2019. Notably, 233 completed questionnaires were obtained in return in the period up to 25 April 2019, which constituted the sample of the survey. The selection of the subjects between the employees on duty in the four Tax Offices of Thessaloniki was based on their availability and willingness to participate in the survey. The size of the sample, although limited, was considered adequate for good assessment of the characteristics of the population.
To statistically analyse the data, a descriptive statistical analysis of the variables was initially carried out by calculating the average, variance and standard deviation of their values. Then, an inductive analysis was carried out by checking assumptions to correlate the e-training readiness and the factors comprising the emotional intelligence level of respondents. The statistical analysis was carried out with the assistance of the IBM SPSS Statistics 24.0 software software.
The socio-demographic characteristics of the IAPR employees participating in the survey are presented in Table 1. The greater number of women in relation to men who participated in the survey is in line with the official human resource allocation rates per gender presented by the organisation itself regarding all IAPR employees. As far as the age distribution of participants in the study is concerned, there is a relatively small percentage of employees aged under 40 years. In addition, the vast majority of participants declared that they were married with children. Furthermore, they were higher education graduates, with 36.48% of them holding a postgraduate or doctoral degree. As far as work experience in the tax sector is concerned, the majority of employees have 11–20 years of past service. Employees with up to 10 years and with more than 20 years of past service accounted less. Finally, 19.31% of all participants stated that they already had previous experience in distance learning.
Socio-demographic characteristics of the IAPR employees participating in the survey
| Variable | Number | Percentage (%) | |
|---|---|---|---|
| Gender | Men | 71 | 30.47 |
| Women | 162 | 69.53 | |
| Age (years) | ≤30 | 2 | 0.86 |
| 31–40 | 48 | 20.60 | |
| 41–50 | 107 | 45.92 | |
| >50 | 76 | 32.62 | |
| Marital status | Single/divorced without children | 42 | 18.03 |
| Single/divorced with children | 13 | 5.58 | |
| Married without children | 18 | 7.72 | |
| Married with children | 160 | 68.67 | |
| Educational level | High school | 13 | 5.58 |
| Post-secondary certificate or diploma | 5 | 2.14 | |
| Bachelor | 130 | 55.80 | |
| Master | 82 | 35.19 | |
| PhD | 3 | 1.29 | |
| Work experience in the sector (years) | ≤10 | 51 | 21.89 |
| 11–20 | 101 | 43.35 | |
| 21–30 | 60 | 25.75 | |
| >30 | 21 | 9.01 | |
| Previous experience in distance learning | Yes | 45 | 19.31 |
| No | 188 | 80.69 | |
IAPR, Independent Authority for Public Revenue.
The average and standard deviation values for the e-training readiness scale and for each of its subscales are presented in Table 2 (the higher the average, the higher the level of readiness). As it can be seen from the relevant table, the highest average assessment of readiness was observed for motivation for learning online with an average value of [4.33] on the 5-point evaluation scale. A high average value [4.10] was also observed for self-directed learning. Slightly lower values were observed for the other readiness factors. Computer/internet self-efficacy, learner control online and online communication self-efficacy received average values of [3.99], [3.93] and [3.73], respectively. Consequently, a high degree of e-training readiness is demonstrated by the responses to almost all questions on the OLRS and therefore for all the subscales measured.
Means and standard deviations of the responses given by the IAPR employees participating in the survey for each of the five e-training readiness factors
| Scale | Subscale | Mean | Standard deviation |
|---|---|---|---|
| E-training readiness | Computer/internet self-efficacy | 3.99 | 0.77 |
| Self-directed learning | 4.10 | 0.64 | |
| Learner control online | 3.93 | 0.67 | |
| Motivation for learning online | 4.33 | 0.57 | |
| Online communication self-efficacy | 3.73 | 0.76 | |
| OLRS | 4.04 | 0.51 | |
IAPR, Independent Authority for Public Revenue; OLRS, Online Learning Readiness Scale.
The average value and standard deviation for the scale on emotional intelligence level are presented in Table 3 (the higher the average, the higher the emotional intelligence). The overall value showed that the IAPR employees participating in the survey were emotionally intelligent to a satisfactory degree since they had an average emotional intelligence level of 4.98 on the 7-point evaluation scale.
Mean and standard deviation of the responses given by the IAPR employees participating in the survey related to their emotional intelligence
| Scale | Mean | Standard deviation |
|---|---|---|
| TEIQue-SF | 4.98 | 0.68 |
IAPR, Independent Authority for Public Revenue; TEIQue-SF: Trait Emotional Intelligence Questionnaire—Short Form.
To examine the relationship between respondents’ e-readiness and their level of emotional intelligence, a normality check was conducted using the Kolmogorov–Smirnov test. Monte Carlo simulations were used to determine both the degree of e-training readiness and the complex variables which arose from the five readiness factors. The analysis generated values below the statistical level of significance of 5%, and therefore the alternative hypothesis that there is no normality in data distribution was accepted. The normality test conducted on the degree of e-training readiness using the Kolmogorov–Smirnov test generated a value of p = 0.041, a value lower than the statistical level of significance of 5%. The normality test on the emotional intelligence level using the Kolmogorov–Smirnov test generated a value of p = 0.200, a value higher than the statistical level of significance of 5%. Therefore, the zero hypothesis about data distribution was confirmed, which does not differ from the normal distribution. To evaluate whether there is relevance between the two continuous variables, the Spearman’s rho, also known as the nonparametric rank correlation coefficient, was used.
The results of the correlation analysis carried out to determine the relationship between the emotional intelligence level of IAPR employees and their degree of e-training readiness are presented in Table 4. The correlation analysis followed the process of checking the working hypotheses (setting the nonexistence of a relationship between the employee’s emotional intelligence level and their e-training readiness as the zero hypothesis).
Spearman’s nonparametric correlation analysis between the emotional intelligence level of IAPR employees and their degree of e-training readiness
| Spearman’s rho | TEIQue-SF | |
|---|---|---|
| OLRS | 0.422* | |
| Subscales of OLRS | Computer/internet self-efficacy | 0.292* |
| Self-directed learning | 0.301* | |
| Learner control online | 0.321* | |
| Motivation for learning online | 0.374* | |
| Online communication self-efficacy | 0.345* | |
Correlation is significant at the 0.01 level (two-tailed).
IAPR, Independent Authority for Public Revenue; OLRS, Online Learning Readiness Scale; TEIQue-SF: Trait Emotional Intelligence Questionnaire—Short Form.
The observed value (rs = 0.422), which resulted from the correlation analysis, indicates the existence of a positive correlation trend between the degree of e-training readiness and the level of emotional intelligence and is statistically significant at a level of 0.01 (p = 0.000 <0.01). The results from the correlation test also show a low positive correlation between all individual factors comprising the degree of e-training readiness and the level of emotional intelligence (rs = 0.292, rs = 0.301, rs = 0.321, rs = 0.374 and rs = 0.345). It is worth noting that the greatest correlation of emotional intelligence can be seen with overall e-readiness and then with motivation for learning online. It appears that emotionally intelligent IAPR employees have a higher degree of readiness for successful distance learning.
This study aimed to assess the e-training readiness of IAPR employees, their level of emotional intelligence and the degree of correlation between these two parameters. This would contribute in assessing ways to increase employee e-training readiness, improving the design and effectiveness of e-training programmes, increasing satisfaction, avoiding disappointment of participants and limiting dropout from distance learning courses. The research hypotheses have been partially verified by the results of the research in a sample of 233 active employees.
The responses indicate the readiness of the majority of employees participating in the survey to use online learning environments for their training. The high degree of readiness is highlighted by the responses of employees to almost all questions of the scale and is evident in all the individual factors measured. These findings are similar to the results of relevant studies conducted earlier on employees in other sectors of the Greek public administration (Hasiotis, 2017; Tsiaousi, 2014) as well as on employees in other countries (Kaur & Abas, 2004; Moolman & Blignaut, 2008; Ouma et al., 2013). However, they do not seem to be in line with the findings of Warner et al. (1998) about the high percentage of Australian VET learners who did not have technological skills and readiness for flexible educational methods. This difference between the two studies can be attributed to the different tool used and mainly to the 20-year period which have elapsed between the surveys: during these years, there has been a substantial evolution with the rapid development of digital technologies and their use for educational and training purposes.
As far as the high levels of computer/internet self-efficacy of IAPR employees is concerned, the result agrees with the results of previous studies conducted on the context of VET (Hasiotis, 2017; Lai & Wang, 2012; Lai, 2011; Ouma et al., 2013; Tsiaousi, 2014). One could argue that it is a natural consequence of employees’ increasing day-to-day involvement with computers and the internet and their high level of technical skills. This is in line with the results of Moolman and Blignaut (2008) in their study on online learning readiness for warehouse workers in South Africa. Moreover, the interaction between employees’ self-efficacy and their technical knowledge and skills in using computers and the internet is clear. Of course, an evaluation of the level of technical skills of employees alone, although considered critical in the past by a large number of studies, is becoming less important. This is due to the fact that the majority of employees belong to the ‘digital natives’ generation, i.e. those who were born in an age of rapid technological progress and are fully familiar with technologies such as computers, internet, smartphones, digital tablets and video games (Prensky 2001).
The findings related to the high level of self-directed learning among IAPR employees when using online applications are equally important in determining the overall e-training readiness of employees. The successful inclusion of learners in a self-defined reference framework is a necessary condition for effective distance learning courses. The positive correlation between readiness for self-directed learning and e-learning efficacy is also observed in a survey conducted among civil servants in Taiwan (Lai, 2011; Lai & Wang, 2012).
To successfully implement online training courses, and in direct conjunction with the self-directed ability to learn online, it is necessary to have learner control of the online process. The results of the present study show that employees of the IAPR have the flexibility and ability to take overall control over their learning development, to have self-discipline, to be focused on their learning activities and to understand all the teaching materials used in the context of an online training process. These findings are in agreement with those of a similar study carried out on secondary education teachers (Hasiotis, 2017).
The vast majority of IAPR employees appear to have strong motivation for learning online, are open to new ideas, learn from their mistakes and share their ideas with others. The existence of needs and desires, which could motivate employees to participate in online distance learning courses, has a positive impact on how they approach and perceive their professional career and development. These findings are of exceptional importance considering that the contribution of both internal and external motives to the success of e-learning courses is of key importance (Borotis & Poulymenakou, 2009; Moolman & Blignaut, 2008; Roca & Gagne, 2008).
The improved online communication self-efficacy of IAPR’s employees is a consequence of the extensive use of electronic text and web tools when performing their duties to communicate with citizens and services. Moreover, the electronic dispatch of incoming and outgoing documents by email for many years has become well-established as a legitimate mean of communication for the Greek public administration. In the context of distance learning, online communication replaces face-to-face communication and interaction between learners and their instructors and is used to assist with feedback and encouragement. Therefore, online communication self-efficacy has become important for employees who have opted to engage in distance vocational training.
The satisfactory level of emotional intelligence among IAPR employees maybe a subjective assessment of emotional intelligence, as perceived by the employees themselves, and not an objective measurement of their skills. However, it indicates that self-assessment of emotional aspects of their personality constitutes a satisfactorily high emotional intelligence quotient. It allows them to be flexible and to constantly adapt to the new circumstances arising in the modern competitive and constantly changing environment. However, it is important not only to have emotional intelligence stationary at some satisfactory level, but also to enable it to develop and grow as much as possible. This is especially since emotional intelligence is an important personal attribute that significantly contributes to the effective and efficient operation of businesses and organisations as well as to their qualitative transformation.
Finally, the results from the analysis of the correlation between the level of emotional intelligence among IAPR employees and their readiness to engage in online vocational training revealed that the level of emotional intelligence among employees accounts for 42.2% of the overall variance in their e-training readiness. The existence of a positive correlation (even if moderate) between these two variables is in agreement with the results of Alenezi (2020), Engin (2017) and Buzdar et al. (2016), and demonstrates the usefulness of the developed psychometric skills of learners on the way they perceive their learning trajectory. The role of emotions and emotional intelligence on the effectiveness of adult education and training has been highlighted in a previous study by Imel (2003). Goodwin (2016) as well as Zahed-Babelan and Moenikia (2010) highlighted the significant correlation between emotional intelligence and the academic performance of students. Berenson et al. (2008) showed that a powerful diagnostic factor in a successful e-learning process was the combination of the emotional intelligence level of learners and aspects of their personalities such as sociability, nervousness, irritability, persuasiveness and self-control as well as their focus on achieving targets. The inextricable connection between emotions and the learning process in online environments is bolstered by the findings of Behnke and Greenan (2011) in a survey conducted on students of a nursing school in the USA, which found that learners with a high level of emotional and social intelligence more easily respond to the requirements of e-learning. It appears that emotional intelligence, and emotions in general, exert a major influence on the performance of learners in distance learning courses. Consequently, one can assume that the low levels of e-training readiness, the ineffective transfer of knowledge to workplace practices and the unwillingness, dissatisfaction and frustration observed among some of the learners are—at least partly—due to their non-emotional involvement and low psychometric skills they may have developed.
The positive statistical correlation observed between emotional intelligence and all measured e-training readiness factors/subscales shows the ability of the former to be possibly used as a predictor of the e-training readiness of IAPR employees regarding the implementation of the necessary technical knowledge and skills for computer and online communication, the existence of internal motivations for online distance learning and their ability to self-determine and direct their learning path and to have overall control over the learning process. These findings are in agreement with those of studies conducted on university students in Saudi Arabia, Pakistan and Turkey using emotional intelligence measurement and OLRSs (Alenezi, 2020; Buzdar et al., 2016; Engin, 2017). It is noteworthy that the emotional intelligence of IAPR employees appears to predict 37.4% of the existence of their online motivation for learning. Since the emotional involvement of learners in the learning process entails the creation of motivation as a necessary condition for them being mobilised and actively involved in their learning trajectory and personal development, the correlation between the two variables would be expected to be even stronger. However, one should not overlook the fact that the responses from participants in this study reflect their beliefs and their own personal perception about their degree of e-training readiness and their level of emotional intelligence.
The study showed that emotional intelligence is a critical predictive factor in employee readiness to effectively respond to alternative, more flexible forms of training such as e-learning. Furthermore, it showed that the respondents, IAPR employees, have an adequate level of emotional intelligence and are ready to use online learning environments for training. The evaluation and prediction of the emotional intelligence of employees, which lead to effective training in online learning environments, are very important issues that need to be addressed for improving their performance and work culture. In this specific case, it will help safeguard Greece’s public revenues and provide top-quality services to citizens and businesses, also facilitating transition to an e-government era. Consequently, interventions and programmes which bolster and develop emotional intelligence, as well as pedagogical strategies and methods which incorporate elements of emotional education, should be adopted during the staff’s continuous professional development process that takes place online in an increasing rate. The assistance of psychology and sociology would be also very useful in this respect.
One of the limitations of this study is the relatively small sample size. Therefore, further research, with a larger sample size, including employees from tax offices from other cities and towns around Greece with possibly different characteristics, would be useful to enable the generalisation of the findings of this study to a larger employee base. Furthermore, the participation of civil servants in a process of continuous learning and development is now an unavoidable path for the transformation of the public administration into a more effective, flexible and modern one. Therefore, further research could look into the development of a predictive model which would include a greater variety of variables in order to assess the readiness and to increase engagement in distance vocational training of both the organisation and its employees. Finally, from a more theoretical perspective, it would be interesting to study and assess the possible relation of emotional intelligence and e-training readiness with the transactional distance theory of Michael G. Moore.