Management and Professional Development of Employees in the Era of Digitalization (Part 1)

Introduction and Theoretical Background

Digitalization and technological development bring new opportunities for employee development and modern instruments of further education, which in the last several years have been influenced by social and economic changes – particularly the covid-19 pandemic. Further education and development of employees in this period naturally enabled the transition to an online environment and are currently a key technology for employee education and development. In educational practice, this brought, among other things, a change in didactic principles and the search for suitable evaluation tools, including feedback in the processes of assessment, remuneration, and career development of employees. Among other personnel activities influenced by these factors, the selection and adaptation of new employees should also be mentioned.

According to a number of studies, companies currently use mainly hybrid tools for employee education and development, which combine in-person and online elements of education and workplace learning. The aim is to motivate individuals toward original thinking, adaptability, creativity, and also to foster the development of employees' digital and specific professional competencies – those competencies that are significant for current developments and ongoing changes in society.

The manufacturing industry in particular fundamentally influences economic and social progress, as well as scientific knowledge and innovation. The Industry 4.0 initiative has gained significant attention from the business, professional, and scientific communities. Although this idea is not entirely new and has been the subject of academic research for many years, its perception has evolved with respect to a number of variables. "This is mainly the result of economic development and related social needs, which create pressure for the implementation of lifelong learning/education, retraining or qualification changes, etc." (Průcha, 2014, p. 77).

While academic research currently focuses mainly on "understanding and defining this concept and attempts to develop related systems, business models and relevant methodologies, industry focuses its attention on the adaptation of industrial machines, intelligent products and on potential customer progress" (Ordieres-Meré et al., 2023, pp. 1-2). Experts estimate that Industry 4.0 and related progress in this area will have a huge impact on social life. This will naturally encourage manufacturing companies to improve their production processes to meet customer demands and maintain a competitive advantage (Oztemel and Gursev, 2020, p. 128).

According to Ordieres-Meré et al. (2023), the birth of the Industry 4.0 (I4.0) concept also brought the need for new approaches to effective data management. In the following years, a significant increase in the volume of processed data is expected across all manufacturing sectors. The adoption of I4.0 technologies in industries is closely linked to so-called data-driven manufacturing. At the same time, a new paradigm has recently begun to be discussed – namely, another industrial revolution – the concept of Industry 5.0, which will place new demands on the very essence and philosophy of lifelong learning, particularly those areas related to data integration, transfer, and analysis (see Adel, 2024). In the future, it will therefore be desirable for employers to provide education on this topic so that employees are prepared for new technologies and, consequently, for working with them (cf. Ordieres-Meré et al., 2023). Shrouf et al. (2014) prepared a literature review on "smart" factories and examined, among other things, energy and knowledge management systems. The main idea of this study is the continuous experimentation of production and knowledge performance prediction throughout the entire production lifecycle (Oztemel and Gursev, 2018).

Therefore, companies that want to succeed in the market and be competitive must develop creative and strategic management models, formerly called human resource management or more recently human capital management, with new approaches reflecting new knowledge (Matouš et al., 2020). As Armstrong and Taylor (2015) state, human resource management has evolved into a sophisticated method, an effective approach for engaging and developing employees within companies. This discipline, also referred to as the philosophy of workforce (employee) management/development, is based on a broad spectrum of theoretical areas that explain the dynamics of individual behaviour and structures in organisations. It draws primarily from management psychology, human motivation, and sociology of management.

In the context of any company where people are key to achieving the (strategic) goals of the organisation, human resource management appears to be an essential foundation – something without which a company cannot be managed, administered, and developed. Defence of this practice is unnecessary, as the existence and expansion of every organisation naturally requires effective management of its human resources, professional development of employees at all levels, adaptability, and loyalty (Armstrong and Taylor, 2015, p. 55).

Therefore, corporate practice clearly shows that systematic further professional education is of fundamental importance and enables both the company and individual employees to cope with difficult situations and the current challenges of a social, technological, and economic nature. Non-formal education in this context is characterised by a structured process of acquiring knowledge, skills, and professional competencies (Veteška, 2016). This educational activity is characterised by a higher degree of flexibility in content and process, and typically lasts a shorter time, making it an effective tool for responding to current needs in personal and professional life (Neformální vzdělávání, 2018). For example, adult participation in non-formal education in the Czech Republic in 2016 reached 40% for persons aged 18–69 and 45% for the productive age group 25–64, representing an increase compared to previous years. The rate of adult participation in this form of education reflects involvement in various educational activities regardless of their duration or current status (Neformální vzdělávání, 2018). Non-formal education and informal learning (see Kříž, 2024a) are understood as educational tools for the management, development, and further education of employees, in all areas and preferred individual contexts.

As mentioned above, the world industry is currently in the midst of the so-called "Fourth Industrial Revolution." According to Quoex (2018), this era brings radical changes in many sectors, requiring more from workers than just technical skills and social competencies. Soft skills, also known as behavioural skills, are becoming increasingly important and are intensely sought after, but above all demanded. Human capital, encompassing these soft skills together with technical knowledge, plays a key role in the prosperity, competitiveness, and development of companies. Quoex (2018) points to the importance of cross-functional skills, which enable employees to collaborate effectively across different disciplines and departments, which is essential in today's rapidly changing work environment.

Within further professional education, "one type of education is also implemented, called organisational learning (workplace learning). This type of learning is considered significant for the successful functioning of enterprises" (Průcha, 2014, p. 81).

Because it remains true that every organisation "needs enough capable and motivated people ... (because) the abilities and motivation of people (competence and willingness to perform agreed work) determine the performance of people (the result of work and behaviour), which determines the performance of the organisation (the result of business and management)" (Šikýř, 2014, pp. 20-21). The extension of the model to the sphere of human capital therefore seems logical and justified at the turn of the 21st century (Dostálek, 2021, p. 5).

As Dostálek (2021) states, the search for new strategies and methods in the field of human resource management and development is often driven by a certain effort to eliminate the shortcomings of the traditional directive approach. Motivation, loyalty, and engagement are topics that determine the work climate and its positive development within the organisation. As is the emphasis on work performance, although in recent years we encounter new constructs in the form of well-being, understood as the overall or personal well-being of an individual. And further, work-life balance, which contributes to reducing stress and greater satisfaction of people not only at work but also in personal life (Dostálek, 2014).

Veteška and Tureckiová (2020, p. 145) state that employee development strategies are based on the needs of the educational organisation and the decisions of its management; however, they should also reflect the individual development needs and goals of specific employees. We proceed here from the fact that every work organisation, and thus also educational organisation, fulfils the premise of a plurality of interests, needs, and goals of individuals and work teams. According to various authors, functional development strategies may combine, for example: a) a strategy of creating a culture of learning, organisational learning, and individual employee learning (Reynolds 2004, cited in Armstrong, 2014, pp. 286-287, detailed further); b) strategy axes in three dimensions with boundary points: the axis of organisational development → development of individual employees, the axis of differentiation → integration, and the axis of big leap → continuous improvement (Hroník, 2007, pp. 18-25 In Veteška and Tureckiová, 2020, p. 145).

Strategic development and employee learning are key to achieving organisational goals, with the main objective being to keep the company equipped with qualified workers for all important roles, as Veteška and Tureckiová (2020) state. In current conditions, this is a complex challenge, especially in the context of constant pressure for development and very difficult to predict changes in technological, economic, and social areas. According to these authors, what is essential is not only "the development of individuals, but also the sharing of experiences, the development of professional and occupational competencies, and support for individual and team counselling, the use of coaching and mentoring, as well as other methods suitable for the creative development of the company" (Veteška and Tureckiová, 2020, pp. 143–144).

For example, the effectiveness of e-learning in the education of employees of small and medium-sized enterprises (SMEs) depends on a whole range of factors that influence the success of the implementation and subsequent application of these educational tools (Veteška, 2024). Key factors include technological availability and infrastructure, which must be sufficiently robust to support online learning platforms and other technological tools. Furthermore, employee motivation is important, as it influences the level of involvement and engagement in e-learning, and generally in educational activities.

Another factor is organisational culture and support in the implementation of development and further professional education. SMEs often face challenges related to limited resources and time constraints, which can affect employees' willingness to participate in e-learning. The success of e-learning in companies also depends on adapting the educational content to the specific needs of employees and their professional development. Personalisation of learning and the ability to access various forms of educational materials – videos, quizzes, interactive modules – can improve the learning experience and contribute to its higher effectiveness. These are important factors that influence the process and effectiveness of learning, or learning outcomes.

Analysis of Selected ICT and Digital Tools for Further Education

In this context, Veteška (2016) states that the term e-learning began to be widely accepted in the Czech Republic at the end of the 20th century, which correlates with the expansion of computer technologies and their integration into the educational sector, particularly in the context of adult education. This term, originating from English, became a commonly used concept in the field of education and was adopted into Czech without any modifications, a trend that can also be observed in other languages. Electronic education (e-education), often referred to by the abbreviation "e", has already become established in practice. The abbreviation "e" symbolises the English word "electronic", which in the broadest sense reflects the use of information and communication technologies in the educational process, as defined by Mužík (2011).

The spectrum of e-learning includes diverse applications and processes that are mediated by electronic media, primarily the internet. Due to its dynamic nature and adaptability, the definition of e-learning in professional literature is varied, reflecting its complex and constantly evolving character. Vaněček (2011) points to the unsettled nature of the term e-learning and the existence of a number of related terms that are used in the English language space to describe various modalities of electronic education. These include, for example:

• WBL (web-based learning): Education via the web or internet, i.e., online.
• CBT (computer based training): Training or education of employees through a computer, which can be both synchronous (in real time) and asynchronous (not in real time).
• CAL (computer assisted learning): Learning supported by a computer.
• Blended learning: Mixed education, a combination of in-person (face-to-face) and distance (online) forms of education.
• TBT (technology-based training): Education supported by technologies, a term used particularly in the USA.
• CAI (Computer Assisted Instruction): Computer-assisted learning.
• LMS (Learning Management System): A system for managing instruction, a platform for the administration, distribution, and organisation of educational courses and content. It facilitates communication between learning subjects and a tutor (lecturer).
• M-learning: Education through mobile devices, such as smartphones and tablets (Vaněček, 2011, adapted and supplemented In Dostálek, 2024). Each of these terms emphasises different aspects and possibilities that technology offers in the field of education. E-learning can be a very effective way to provide access to education to a wider range of people, enable flexible study schedules, and adapt educational content to individual needs and learning pace (Dostálek, 2024).

For example, Veteška and Kursch (2019) present nine key trends that will significantly influence education within a few years. These are:

1. Temporal and spatial freedom in education; education will take place anytime and anywhere with the help of new technologies (online, digitalisation, hybrid distance education, micro-learning). Virtual tools will significantly dominate in the coming years. Content and evaluation will move to the online environment.
2. Personalised instruction, i.e., the adaptation of the pace of instruction to the learning individual and their abilities, needs, and requirements.
3. Free choice of means and forms of education, individuals will be able to choose their own "educational path" and select between different ways of learning.
4. Project-based education will be based on projects, with emphasis on organisational management, mutual cooperation, and effective time management.
5. Emphasis on gathering experience, i.e., more fieldwork, work in a real environment, and sharing of best practice.
6. Processing and use of data, emphasis on logical connection of contexts, orientation in large amounts of information (knowledge), their interpretation and analysis.
7. Change in the area of certification – new methods and forms of examination and testing, de-emphasis of various tests and metrics, focus on evaluating the use of knowledge in the subsequent work process, orientation towards the educational process.
8. Greater participation of education participants in shaping the curriculum, dynamic and progressive adjustments to the curriculum based on feedback from education participants (identification of educational needs).
9. Use of mentoring as one of the main forms of support for education participants (Fisk, 2017 In Veteška and Kursch, 2019, p. 20, adapted) (Dostálek, 2024).

Based on these identified methods, we present a more detailed elaboration of digitalisation methods and the use of information and communication technologies (ICT) in corporate education, which companies can use for the further development of their employees' skills and knowledge:

• Blended Learning – Combines traditional (in-person) approaches to face-to-face teaching with online methods (distance), providing flexible and effective education.
• Videoconferencing – Enables the delivery of training and meetings through video calls, saving time and travel costs.
• Digital libraries and databases – Provide easy access to professional articles, studies, and books, supporting employee self-education.
• Cloud-based learning platforms – Offer centralised access to learning materials and resources stored in the cloud, enabling easy accessibility and updates.
• Online simulations – Digital simulations of real work situations that help develop critical thinking and decision-making skills.
• Interactive e-books – Electronic books with interactive elements such as quizzes, videos, and simulations that increase engagement and understanding.
• Peer-to-peer learning – A method where employees teach their colleagues, strengthening team spirit and knowledge sharing within the organisation.
• Big Data and learning analytics – The use of big data, databases, and analytical tools leads to easier personalisation of educational content and optimisation of further education and employee development.
• Virtual Learning Environment (VLE) – An integrated learning environment that combines various digital tools and resources to support learning. It represents a virtual learning environment in educational technologies, such as a web platform for the digital aspects of study courses, typically in educational institutions. It provides resources, activities, and interactions within the course structure and offers various assessment stages.
• AI-driven tutoring – The use of artificial intelligence or machine learning tools to create personalised tutorial programmes that adapt to the needs and progress of each individual employee. AI-driven tutoring uses data collected from student interactions with educational content (study material) to identify their strengths, weaknesses, and learning patterns. Based on this analysis, the AI system adjusts the content, style, and pace of the educational material to best suit the individual needs of learners.

Key benefits of AI-driven tutoring include:

• Personalisation: The ability to adapt learning plans and materials according to students' individual abilities and preferences.
• Immediate feedback: Providing students with instant feedback on their progress and areas needing improvement.
• Flexibility: Allowing students to learn at their own pace, anywhere and anytime, increasing the accessibility and convenience of education.
• Efficiency: Increasing learning efficiency by focusing on students' individual needs, which can lead to faster and deeper understanding of the material.

AI-driven tutoring thus represents a revolutionary approach in education that has the potential to transform traditional teaching and learning methods through the use of advanced technologies and artificial intelligence (Conati, 2020). The follow-up research study by Conati, Baaral et al. (2021) represents a significant step towards identifying the need for personalisation in the field of explainable artificial intelligence (XAI), focusing on evaluating the value of AI-driven hints and feedback in so-called intelligent tutoring systems (ITS). As part of the research, the authors integrated an explanation function into the interactive simulation Adaptive CSP (ACSP), which helps students learn algorithms and simulations through AI-driven hints tailored to their knowledge level. Within this, an explanation function and the results of a controlled study evaluating its impact on students' learning process and their perception of hints provided by the ACSP application were designed (Conati, Baaral, et al., 2021).

Another review study focuses on the application of artificial intelligence in collaborative learning of teachers and students, specifically within intelligent tutoring systems, automated assessment, and personalised learning – details can be found in the work of Kamalov, Calonge, and Gurrib (2023). The high performance of the ChatGPT model on several standardised academic tests has recently attracted attention to the topic of artificial intelligence (AI), bringing this topic to the centre of the discussion on the future of education. Since this type of learning has the potential to fundamentally change the teaching paradigm, emphasis is placed on the necessity of understanding its impacts on the current educational system and the development of educational policy. This is essential for ensuring sustainable development and effective deployment of AI-driven technologies in schools and universities (Kamalov, Calonge, and Gurrib, 2023).

In light of digitalisation and Industry 4.0, as Kursch (2022) notes, the connection of things to the Internet of Things (IoT), mass automation, analysis of huge volumes of data (big data), and the use of artificial intelligence or advanced communication technologies are key. Stankovski et al. (2019 In Kursch, 2022) points out that development in industry and education is not proceeding at the same pace, which may lead to unfulfilled expectations regarding the qualification of future workers. For study (educational/development) programmes to be able to prepare students for current and future challenges, they must be able to respond to the rapid changes that characterise the current Industry 4.0. Adaptability and the ability to innovate are key to keeping pace with these developmental trends in all areas of human activity.

Research findings are presented in the second part of the study.

Authors: prof. PhDr. Jaroslav Veteška, Ph.D., MBA Mgr. et Mgr. Radovan Dostálek, MBA Department of Andragogy and Educational Management Faculty of Education, Charles University Czech Republic This text was created within the Cooperatio programme (Charles University, Faculty of Education, 2022-2026), research area Education and Pedagogy (GEED).

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1So-called big data are more diverse data that come in greater volume and with greater speed. These aspects are also known as the three Vs. Simply put, big data are larger and more complex datasets primarily from new sources. 2AI-driven tutoring refers to the use of algorithms and systems based on artificial intelligence to personalise and optimise the educational process. This approach enables the creation of individually tailored educational experiences that respond to the specific needs, preferences, and learning pace of each student.