Influence of digitalization on the requirements and competencies in the area of sales management

Table of Contents

Executive Summary

Table of Contents

List of Abbreviations

List of Figures

List of Tables

1 Introduction
1.1 Relevance of the topic
1.2 Problem and objective target
1.3 Course of action & Methodology

2 A theoretical approach
2.1 Digitalization as a mega trend
2.1.1 Industry 4.0
2.1.2 Digital transformation
2.1.2.1 Artificial intelligence
2.1.2.2 Cyber-physical systems
2.2 Leadership
2.2.1 Definition
2.2.2 Leadership 4.0 – Expected changes due to digitalization
2.2.3 Competences
2.3 Displacement of competences from human to intelligent system
2.4 Intermediate conclusion

3 Empirical study
3.1 Research questions & topics
3.2 Questionnaire
3.2.1 Target group
3.2.2 Operationalization
3.2.3 Development
3.2.4 Pretest
3.2.5 Implementation
3.3 Type of evaluation

4 Result evaluation
4.1 Summary of collected data
4.2 Evaluation and testing of research topic
4.2.1 Results from literature review
4.2.2 Data analysis combined as overview
4.2.3 Data analysis of the companies
4.2.4 Data analysis of the educational institutes
4.2.5 Data analysis of the most important competences

5 Discussion

6 Conclusion & Outlook

Bibliography

Appendix

Index of appendices

Appendices

Executive Summary

Can humans be replaced by robots or intelligent systems such as artificial intelligence? Are they becoming less important because of the digital revolution? Are humans changing in terms of the orientation of their competences, also due to the effects of the digital revolution? These questions are often asked in the context of the work between man and machine.

In order to find about more about a possible change in competences, caused by the effects of the digital revolution, this Master thesis deals with the “Influence of digitalization on the requirements and competencies in the area of sales management An empirical study of DAX companies and educational institutions”

First of all, a basic theoretical knowledge will be provided in order to work on and answer this topic. On the one hand, the digital transformation, the area of leadership and on the other hand the area of competence takeover by intelligent systems will be presented. To collect and evaluate data, an empirical study was conducted at DAX companies and Germany's leading universities and colleges in order to be able to identify possible shifts in competence on the basis of valid data.

The result of the empirical analysis is a competence structure, which results in an interesting form. The study has shown that, contrary to expectations, the technical and IT-heavy competences as well as the competences related to the digital revolution are not as important for leaders as other competences, such as personal, ethical and social-communicative competences, which are important for leaders acting in the digital age.

This and other results, such as the distribution within the competence classes and whether a competence shift from man to machine and other intelligent systems is possible, are presented in the final part of this Master thesis.

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

List of Figures

Figure 1: Development path industry 3.0 to industry 4.0

Figure 2: Level of digitalization in the EU in 2019

Figure 3: Workplace of the future

Figure 4: Usage of digital technologies in Germany in 2018

Figure 5: CPS combined by autonomous people and autonomous technical systems

Figure 6: Combination of leadership sub-areas

Figure 7: Classic leadership model extended by the digital transformation

Figure 8: Competence atlas by Heyse and Erpenbeck

Figure 9: Target group I: Identified professional categories and its distribution

Figure 10: Operationalization of questions

Figure 11: Number of competence citation in analyzed sources

Figure 12: Color code competence class

Figure 13: Bar chart analysis personal and activity and action competences

Figure 14: Bar chart analysis social- and comm. and prof. and method. c.

Figure 15: Competence ranking

Figure 16: Result of most important competences

Figure 17: Leader in the digital age with results of the empirical analysis

List of Tables

Table 1: Detailed view on different areas of competences

Table 2: Professional and personal competences for the future

Table 3: Overview of research topics

Table 4: Result of analysis of second target group: Education institute

Table 5: Applied statistical methods

Table 6: Evaluation schemes for empirical analysis

Table 7: Evaluation example

Table 8: Summary of the questionnaire’s response

Table 9: Investigated research literature

Table 10: Result of competence shift of personal competences of DAX

Table 11: Result of competence shift of activity and action competences of DAX

Table 12: Result of competence shift of social- and comm. competences of DAX

Table 13: Result of competence shift of prof. and method. competences of DAX

Table 14: Result of competence shift of personal competences of EI

Table 15: Result of competence shift of activity and action competences of EI

Table 16: Result of competence shift of social- and comm. competences of EI

Table 17: Result of competence shift of prof. and method. competences of EI

Table 18: Ranking DAX vs. EI divided in their competence classes

Table 19: Top 10 competences for leaders based on DAX results

Table 20: Top 10 useless competences for leaders based on DAX results

Table 21: Top 10 competences for leaders based on EI results

Table 22: Top 10 useless competences for leaders based on EI results

Table 23: Final result of empirical analysis

Table 24: Final result combined with most important competences

1 Introduction

This scientific paper was written in order to obtain the academic degree "Master of Business Administration" at the FOM University of Applied Sciences for Economics and Management. The research subject of this Master's thesis is an empirical study on the change of key competences in the context of the digital revolution. This empirical study was conducted at DAX companies and Germany's leading educational institutions.

1.1 Relevance of the topic

Digital topics such as Blockchain and Augmented Reality as well as Internet of Things (IoT) are still at the very beginning of their technological development. Nevertheless, there have already been some drastic changes in our environment in terms of business ideas:¹ AirBnB, one of the big names in the hotel industry, has no hotels of its own, Google and Facebook control the internet all over the world and that as software companies. Tesla, Elon Musk's e-car group, which in 2017 had stronger shares than the traditional carmaker BMW despite lower sales volumes,² and Amazon, the global player in the online department store sector, which does not own a single sales shop and sells the goods exclusively on the internet.³ It is clear that this change has a lot to do with changes in the market or the global environment, which are centrally summarized in the term VUCA. VUCA means Volatility, Uncertainty, Complexity, and Ambiguity and has its origin in the military and summarizes all the challenges that a company has to face in the digital world: They are confronted with volatile (V) markets, struggle with uncertain (U) and complex (C) situations and have to deal with misinterpretations (A = ambiguity).⁴ Since this can often be observed in disruptive changes such as digitization, it can be said that VUCA and digital revolution belong together indispensably.⁵ Fact is that companies have to rethink in order not to stay behind in times of the digital revolution.⁶

A study by the Berlin University of Applied Sciences shows that 40% of DAX companies have already hired and are employing a Chief Digital Officer (CDO), which is a key instrument for the digital revolution. Among the M-DAX companies there are hardly any occupied positions, altogether only 4% of the M-DAX companies rely on the expertise of a CDO. This shows that DAX companies attach great importance to a digitization strategy. However, it is essential that a CDO can only be a company's first step towards a digitization strategy - in the long term, all managers must think digitally.⁷

1.2 Problem and objective target

Good leadership is essential for the success of a company. Companies that attach great importance to good leadership are particularly successful because they have satisfied, efficient and motivated employees. In order for employees to be led correctly, individually and above all goal-oriented, leaders must be aware of their own competences, strengths and weaknesses as well as their abilities.⁸ Especially due to the increasing digitalization and the global and dynamic changes described by VUCA, the field of leadership is undergoing an immense change of values, which is called: paradigm shift in leadership.⁹ Robots and intelligent systems can already take over some tasks. Since 1980, robots have been performing a number of manufacturing tasks that could be harmful to human health in the long term.¹⁰ Meanwhile, intelligent systems are so far advanced that they can also do mental work. Artificial intelligences should be able to better analyze work processes, use resources more efficiently or function as assistance systems for human workers. However, it is unclear what this means in general. Is there already a shift of competence from humans to machines, in this case to artificial intelligence?¹¹ Studies by the "Offensive Mittelstand - Gut für Deutschland - Stiftung "Mittelstand - Gesellschaft - Verantwortung" show that a shift in competence from people to machines is possible, but not in detail to what extent this is possible.¹²

It is obvious that to cope with complex leadership tasks not only knowledge and skills are necessary, but in particular whether the actual basis of a leader, namely his or her own (key) competences, fit into the digital revolution or have to be partly adapted.¹³

The aim of the Master's thesis is therefore to identify a change in competence / shift in competence due to digital revolution. Although some current studies already prove that competence changes will take place, they do not deal with an actual evaluation of which competences will change to what extent.¹⁴ Based on this, the focus of this Master's thesis is on the two current research gaps "Competence changes / shifts in leadership and displacement of competences from human to intelligent systems".

1.3 Course of action & Methodology

As previously mentioned, the aim of this Master's thesis is to answer the question of whether a shift / change in competence due to the effects of the digital revolution (or I4.0) can be identified. This global question has been divided into two research topics for easier answering, which are introduced in chapter 3.1. However, before the research topics and therefore also the research question can be answered, theoretical background knowledge is necessary. For this reason, a short introduction to the topic was already described in chapter 1. For further description and especially for theoretical knowledge enhancement, chapter 2 was divided into three main sub-chapters and a summary at the end. The first two chapters digitalization as a megatrend and leadership form the two main theoretical chapters of this thesis. In the section digitalization (2.1) a definition and the effects of the digital revolution, especially the topics artificial intelligence and cyber-physical systems are presented. The area of leadership (2.2) also begins with a definition, followed by an explanation of what is meant by leadership 4.0 and ends with the area of competences, which is very detailed since this area forms the main part of the empirical work. In the third subchapter (2.3) a shift in competences from humans to machines is examined. The result of this will also be included in the overall evaluation of the research question. Subsequently, in chapter 2.4, an intermediate conclusion is mentioned which defines the end of the theoretical background.

The theoretical background is followed by the actual core of this Master thesis, the empirical study. For this purpose, the competencis already explained in chapter 2.2.3 were operationalized. They form the basis of the questionnaire, together with other questions which can be taken in detail from appendix 1-40. In addition to the presentation of the questionnaire, chapter 3, the empirical study mentions which target group is involved, how the questionnaire was designed and implemented and how it was finally evaluated. Chapter 4 contains the evaluation of the results and is divided into a summary of all collected data (4.1) as the start chapter. This is followed by detailed answers of the different research topics, which are presented in 4.2. Finally, in chapter 5 the discussion of the results follows. The Master thesis ends with chapter 6 in the form of a conclusion.

Since, on the one hand, the competence atlas (which will be explained in more detail in the following chapter) invented by two German scientists was used as the basis for the empirical investigation and, on the other hand, since the empirical investigation was conducted exclusively at companies and educational institutions in Germany, many of the sources used are German-language. Thus, cultural influences of other countries, which might have been included in their studies and journals, can be eliminated.

2 A theoretical approach

In this chapter, a theoretical background is provided which is required to understand the procedure of the further explanations. The theoretical background is divided into four subchapters: In the first subchapter (2.1), general information on digitization is mentioned and presented. The second subchapter (2.2) deals with the explanation of leadership in general. Afterwards, in the third subchapter (2.3) a possible displacement of competences from human to intelligent systems is presented. These three subchapters must be considered in combination, which is done in the last section, called intermediate conclusion (2.4). Combined, they form the basis of this work, called "Digital Leadership".

2.1 Digitalization as a mega trend

This chapter is divided into two subchapters. In the first subchapter the term industry 4.0 is explained in more detail (2.1.1). The second subchapter, which describes the digital transformation, is divided into two subchapters, namely artificial intelligence (2.1.2.1) and cyber-physical systems (2.1.2.2). These two sub-areas are of central importance in the further course of this work, therefore they will be explained in more detail in the following chapters.

2.1.1 Industry 4.0

"Smart Robots, Smart Workplace, Augmented Reality (AR), and Virtual Reality (VR), Machine Learning, Virtual Personal Assistants or Internet of Things (IoT) are [...] only excerpts from a long list of technological innovations that are already in use [...] or will be developed in the next five to ten years [...]".¹⁵

Industry 4.0 describes the networking of individual industrial infrastructures such as people, plants and products to digitally controlled systems that work completely autonomously, without human intervention, only on the basis of information and communication technologies (e.g. internet or data).¹⁶ Industry 4.0 focuses on the development of intelligent products and processes and, in some areas, on intelligent services such as predictive maintenance.¹⁷ According to Jäger et al., industry 4.0 is divided into three sub-areas:¹⁸

- Cloud computing,
- Cyber-physical systems and
- Smart Factory.

Cloud computing, the first large sub-area, is the supply of data stored on central servers, regardless of location and time, and the processing of this data including analyses. The combination of this data, which comes from people, machines, equipment and material resources and is stored on cloud-based servers (cloud computing), forms the basis for the combination of the real and digital world in cyber-physical systems.¹⁹ By further networking the individual CPSs, an intelligently controlled factory is created, called Smart Factory, the third sub-area of Industry 4.0.²⁰ In the Smart Factory, the aim is to connect cyber-physical systems that allow people, machines, material resources and processes to interact with one another, with the aim of increasing production efficiency and eliminating inefficiencies such as malfunctions before they become serious problems.²¹

Figure 1 shows the development path from industry 3.0 to industry 4.0 and in particular what contributed to the development of this new technological revolution called digitization. The figure supports the statements of Jäger et. al. that cloud computing and cyber-physical systems can be regarded as main prerequisites for a smart factory.²²

Figure 1: Development path industry 3.0 to industry 4.0

Abbildung in dieser Leseprobe nicht enthalten

Source: Own figure in accordance with Kagermann, H., Wahlster, W., Helbig, J., Implementation of industry 4.0, 2013, p. 7; Roy, D. T., Cyber-physical systems, 2017, p. 47

Changes can be expected in the shortest possible time, whereby the degree of impact does not matter for the time being. It is currently assumed that the volume of data and information will double every 18 months.²³ Furthermore, not only positive effects are mentioned. Especially with drastic technical changes, which end in a technical revolution, people who are generally negative about this revolution show up. The fourth industrial revolution, in general, focuses on the fear of taking over humanity from robots and artificial intelligence and on the reduction of human labour. That this is not the case is shown by studies of labour market developments in recent years. The perspectives for low-skilled occupations are not good, as some will disappear in the course of digitization. On the other hand, jobs with higher skill levels will increase, especially in those professions that are driving the digitization.²⁴ In addition to the jobs that require only a low level of qualification as a prerequisite for exercise, the professions in particular are at risk of being taken over by intelligent systems that mainly perform routine tasks. The scenario of (partial) takeover of (intelligent) machines is already known. Since 1980, robots used in production have taken over some tasks from human employees. They take over activities that are harmful to human health in the long term (heavy weight) and enable humans to perform other activities, such as flexible reaction to changing tasks. The extent to which social skills are required to perform this task will determine whether or not an activity can be performed by intelligent systems in the future. These cannot yet be imitated by intelligent systems or robots.²⁵ Ittermann et. al. further explains about I4.0 that it (I4.0) can only be successfully implemented if, in addition to technical interactions, interactions between new (intelligent technical) technologies and social as well as operational aspects are guaranteed.²⁶ Hirsch-Kreinsen extends the statement of Ittermann et. al. for the area of social aspects to the extent that at the employee level not only jobs, tasks and qualifications are affected, but the entire organizational-social structure of the company must think digitally in order to become digital. This means that companies that want to implement the advantages of I4.0 must develop / change their entire structure accordingly.²⁷ A study by the management consultancy Staufen AG predicts that the topic of leadership will become increasingly important in times of digital change.²⁸ Schwarzmüller et al. further elaborate on the area of leadership in the context of I4.0²⁹, which is described in more detail in chapter 2.2. The fact that Germany is slower in the area of the use of I4.0 is shown by a study by the management consultancy Staufen AG, which found out that in 2018 [only³⁰ ] 52% of all German companies implemented parts of I4.0 operationally.³¹ Within one year, Germany's digitization rate increased by approximately 2.5% to 54.4%, which shows a Europe-wide comparison of the digitization rate for 2019. As figure 2 shows, Germany occupies the middle position behind the digitization giants Finland, Sweden, the Netherlands and Denmark with almost 70% digitization rate.³²

Figure 2: Level of digitalization in the EU in 2019

Abbildung in dieser Leseprobe nicht enthalten

Source: Own figure in accordance with European Commission, Degree of digitalization, 2019, n. p.

Until 2025, however, clear growth processes can be seen among companies in Germany with regard to the use of I4.0.³³

Whether it is actually a fourth industrial revolution, or a classical further development of the third industrial revolution, is not considered further in the present work and, above all, is not evaluated. To define the terms and to understand them, however, the terms "I4.0", "fourth industrial revolution" or "digital revolution" are used in this paper as the same meaning for the current developments in this segment.

2.1.2 Digital transformation

Identifying a standardized definition is difficult, as there are some different definitions of digital transformation in the literature, according to the motto: "Ask hundred people what digital transformation means, and there will be hundred answers". And if there is a definition it focusses most often on what digital transformation can do, but not explicitly what it meant by it.³⁴ Bracken describes the digital transformation as follows: Digital transformation is "applying the culture, practices, processes and technologies of the internet era to respond to people’s raised expectations.“³⁵ In fact, digital transformation is more than disruptive technical changes - it is at least as much about strategies and processes as about people's behavior.³⁶ Altimeter, a leading digitalization consulting firm, describes digital transformation as follows: „[...] realignment of, or new investments in, technology and business models to more effectively engage digital customers at every touchpoint in the customer experience lifecycle.“ ³⁷ The author Rogers, who specifically wants to help companies to implement the digital transformation, names five areas in which the digital transformation will bring remarkable changes: Customers, Competition, Data, Innovation and Value. So, Rogers says that only one in five areas is technology.³⁸ In summary, the term digital transformation can be understood as networking between the economy and society. It is mainly about the creation of new business ideas and opportunities, the digitalization of processes and the expansion of relationships across several stages of the value chain.³⁹ For a better understanding, however, the terms digitization and digital transformation must be separated: Digitization describes the process of digitizing analog data and information. Digital information technologies serve as the basis for this process.⁴⁰ The focus of the digitization is on converting data, for example scanning paper-based registration forms.⁴¹ The digital transformation deals with the networking of these digitized data within value chains and business models, with the goal of a more efficient production of services and satisfaction of the requirements of the management and the customers.⁴² The focus of digital transformation lies in knowledge levering, with the aim of changing the corporate culture, i.e. the way processes are carried out.⁴³

In the development of the digital transformation, the major focus in the 20th century in particular is on the economic redesign and reorientation of workplaces, mainly through the use of computers and the internet, whereby in the 21st century the focus is on the revision and redevelopment of innovative business processes, as well as on the integration of intelligent systems to improve production processes and a merging of the boundaries between man and machine.⁴⁴ Digital transformation attacks the relationship between social and economic structures. The interaction of man and machine is changed by the increasing use of technological solutions, which can do more and more, whereby it can be assumed that a variety of tasks, from all stages of the value chain,⁴⁵ can be taken over by intelligent systems, both by robots and by software. The digital transformation also changes the operational use of human resources and the question arises to what extent the possibilities and limits of human resources change.⁴⁶

The combination of a digital workplace and the networking / merging of the boundaries between people and intelligent systems is shown in figure 3. The necessary details of networking within the workplace of the future are described here: space, person and devices. In detail, the human being acts via a human machine interaction with the device and with a cyber-physical system with different spaces. Devices and spaces can communicate with each other through IoT. The central tool within each connection is AI, as can be seen in the figure.⁴⁷

Figure 3: Workplace of the future

Abbildung in dieser Leseprobe nicht enthalten

Source: Own figure in accordance with Fisher, M., Future workspace, 2019, n. p.

By illustrating the digital workplace, dependencies between people, artificial intelligence and the cyber-physical system can be clearly identified.⁴⁸

For this reason, these two areas are explained in more detail with regard to a possible human competence change and competence shift from humans to intelligent systems.

2.1.2.1 Artificial intelligence

The term artificial intelligence is not consistently defined, as the meaning of the term has always adapted to the respective technical possibilities since the end of the 1950s.⁴⁹ Nevertheless, artificial intelligences are said to have a lot of potential and possibilities of application.⁵⁰ Gartner says that artificial intelligence will be one of the most disruptive technologies ever within the next few years.⁵¹ The description of the effects of the scenario in which an intelligent system replaces an employee is usually the main focus. Especially in the area of standardized tasks and processes, artificial intelligence shows its strength because it can implement and work through these very effectively and efficiently.⁵² Artificial intelligences are able to arrange unimaginably large, complex and ambiguous amounts of data into real structures and take over tasks that are based on these amounts of data much more efficiently.⁵³

The aim of AI research is to copy the intelligence observed and recognized in humans in the most similar way possible. Several multidisciplinary areas and scientific disciplines can be assigned to the AI area: Computer science, psychology, neurology, philosophy, sociology, linguistics, art, law, etc.⁵⁴

Experts such as Leonhard and Kospoth believe that artificial intelligence will be as intelligent as the human brain, which is called singularity, by 2025.⁵⁵ This form of artificial intelligence is called strong artificial intelligence (Artificial general intelligence). However, this is not the primary goal of AI research. The primary goal, which is called weak artificial intelligence⁵⁶, is to make it easier for humans to work through intelligent human-machine connections, such as cyber-physical systems⁵⁷, and to make the fulfillment of tasks more efficient.⁵⁸ The intelligent system, AI, can, for example, recognize user behavior and make suggestions based on it. This process can be applied, for example, to machines that are exposed to abrasion. By combining man and machine (with the help of artificial intelligence), processes can be made more efficient, operational safety can be maintained and costs can be reduced.⁵⁹

The long-term goal of artificial intelligence is to relieve people of some of their competences. Based on this, new competences will emerge and develop in humans, such as enhanced IT expertise to program and maintain intelligent systems.⁶⁰ In view of the task to be solved within the effects of digital transformation, the competences between man and machine must therefore be rearranged and redistributed.⁶¹

2.1.2.2 Cyber-physical systems

Cyber-physical systems have the ability to take some competences from humans⁶², which is explained in detail in chapter 2.3. For this reason, they are mentioned in more detail in this paper, as well as artificial intelligence, in order to identify opportunities for this competence shift.

Chapter 2.1.1 mentioned that the focus of digitization is on the development of smart factories. As can be seen in figure 1, CPS are one of the prerequisites for the development of smart factories.⁶³ The question therefore arises as to what cyber-physical systems actually represent and what they are mainly used for? According to the definition of the Fraunhofer IWU, CPS are characterized by the possibility of data acquisition via sensors, the storage and evaluation and communication of this data both digitally with other intelligent systems and with humans. Based on the previously generated data, human-machine interfaces (HMI) are used to perform actions through actuators.⁶⁴ As already mentioned, as the digital revolution continues to evolve, some changes can be expected within companies, especially within the enterprise architecture. The most significant changes are to be expected within production and its processes.⁶⁵ This is also shown in figure 4, which illustrates the results of a study conducted in December 2017, which states that 80% of the surveyed German companies that actually implemented digitization used it mainly to improve automation processes in production. Furthermore, 60% of the surveyed companies would like to convert their production from an inflexible into a flexible one.⁶⁶

Figure 4: Usage of digital technologies in Germany in 2018

Abbildung in dieser Leseprobe nicht enthalten

Source: Own figure in accordance with Breitkopf, A., Usage of digital technologies, 2018, n. p.

This is exactly what CPS are made for, they form the area of the most elementary changes, especially for the production area. CPS are digital components that communicate via the internet. By using CPS in production systems and concepts as well as in materials or people, these systems can communicate with each other via the internet. RFID chips are used for people and materials to be able to request status messages in order to integrate them into a central data tool.⁶⁷

Cyber-physical systems are currently able to independently recognize, analyze and subsequently evaluate certain situations up to a certain limit. Based on this knowledge, CPS can make decisions completely autonomously and therefore act autonomously based on these decisions. The result of this action serves, among other things, to feed their own system further with data in order to always make accurate decisions, whereby they themselves become more and more efficient machines (this is called machine learning). CPS do not work on the basis of fixed program sequences, but on the basis of programmed technical models. This means that CPS are enabled to act independently and self-learning. Based on the possibility that CPS can learn independently, it can also solve problems if the objective was formulated differently or if the initial situation changes. This is made possible by simple adjustment of algorithms or partly by the use of artificial intelligence.⁶⁸ These features make it possible to transform monolithic production systems (large systems that are difficult to change) into agile, modular and, above all, networked production systems that can be expanded by external and mobile terminals. These end devices enable employees to access evaluations and other control functions via the use of apps.⁶⁹ Application examples for CPS are intelligent electricity meters or logistics systems that control themselves.⁷⁰ Further application examples can be found not only in industrial companies that want to optimize their production, but also in everyday situations, such as in the fire brigade. Here, protective suits can be equipped with CPS, which monitor and analyze the body functions of the suit wearer. If some human functions are permanently overloaded, the system sounds the alarm, makes recommendations based on experience and informs the respective person. Another example is monitoring the wearing of personal protective equipment on construction sites. Here, a person will only be allowed to enter a construction site if the CPS recognizes that the employee is wearing all necessary parts of the personal protective equipment.⁷¹ The visionary Elon Musk and his company Neurolink are working on the idea that microchips with the structure of a CPS should be inserted in the brain of test persons so that people with disabilities, such as paraplegics, can control machines that can offer help with their thoughts.⁷² As it can be seen, there are many possible applications for CPS. It can be assumed that these will be expanded many times over in the future.

The following figure 5 shows how the CPS system works together with people and the social system to initiate a decision-making process:⁷³

Figure 5: CPS combined by autonomous people and autonomous technical systems

Abbildung in dieser Leseprobe nicht enthalten

Source: Own figure in accordance with „Offensive Mittelstand – Gut für Deutschland“ Stiftung „Mittelstand – Gesellschaft – Verantwortung“, Implementation aids, 2019, p. 27.

Human individuals make decisions mainly on the basis of individual characteristics, such as their own awareness, expectations and other attributes, such as competences, moral bases and other characteristics shown in figure 5, as well as on the basis of the design of the social system, which includes norms, laws, ethical attitudes or social identity. Fact is, that the same person can react differently due to different social system. An example of this is that a person who is at work often decides differently than in private life, for example with friends. Autonomous technical systems are based on technical models of interpretation implied by people. The basis for this are technical models and programming languages. Autonomous technical systems use sensors and actuators, algorithms, ontologies as well as data analytics and other properties to have an effect on the person and the social system. In short, it can be said that people act in socio-cultural patterns, with autonomous technical systems acting according to technical models. The goal is to combine these different characteristics and fundamentals in order to imply autonomous action successfully and efficiently. An example of this combination is the programming of artificial intelligence. A human being has programmed this system and set a thought model for it. Based on this technical model, the system ultimately decides for itself, but always has a connection to the values and interests of the human programmer.⁷⁴

2.2 Leadership

This chapter is divided into a total of three subchapters, in which a detailed definition of the area of leadership is given first (2.2.1). In chapter 2.2.2 the expected effects of digitization on leadership are then presented and explained. In the third subchapter (2.2.3) the area of competences is presented.

2.2.1 Definition

Leadership in general is the creation of room for action in which employees can move in order to be able to fulfill their tasks in a success-oriented and goal-oriented manner.⁷⁵ Leaders must be able to recognize the behavior of the individual and influence it in the sense that the desired goal is achieved.⁷⁶ In this context, leadership is to be understood as a goal-oriented influence carried out by communication processes, which is based on social aspects and follows previously defined corporate goals.⁷⁷ Leadership always describes a relationship in the form of a vertical hierarchical system, which means that employee leadership from the same hierarchical level (horizontal) is unusual.⁷⁸ Despite this still applied hierarchical structure of leadership, the focus is on participation, co-determination, flexibility and individualization, especially with regard to the led employees.⁷⁹ Leadership is carried out both with individuals or groups of individuals, such as in teams, or also with social entities like companies or organizations.⁸⁰ Accordingly, leadership is the special characteristic of a person who, through his or her actions and powers of persuasion, is able to communicate visions, values, corporate goals and necessary actions to employees. Leaders should demonstrate charisma and determination through their leadership style and must set an example.⁸¹ Generally speaking, leadership can be divided into three sub-areas: Corporate leadership, which describes the leadership of the organization, employee leadership, which deals with the leadership of employees, and self-guidance, whose goal describes the leadership of one's own person. These three sub-areas together form the basis of an ideal leader, which is illustrated in figure 6.⁸²

Figure 6: Combination of leadership sub-areas

Abbildung in dieser Leseprobe nicht enthalten

Source: Own figure in accordance with Bruch, H., Vogel, B., Krummaker, S., Trends in leadership, 2006, p. 5; Grolman, F., AI, n. d., n. p.

Within the company, there are one or more specialists for each area. The managing director, for example, will focus on the area of corporate leadership, which means that he will focus on the areas of planning, leadership and control of the company as a whole, whereby departmental or team leaders will focus on the area of employee leadership, which is described by a target-oriented influence on the employees. This can be accomplished by structural guidance, thus the guidance of the employees over enterprise principles or by personal guidance, which intervenes directly into the motivation behavior of the employee and in the selection, the evaluation and the development of the employee results. Nevertheless, the managing director also leads employees, namely the department heads. The department heads, in turn, pass on to their employees the company goals as well as the missions and visions that are to contribute to the company's success, which ultimately supports the company's success.⁸³ By the above-mentioned execution and by looking at figure 7, it can be clearly identified that synergies can be found between the individual areas for which similar or partly different competences are required.

Figure 7: Classic leadership model extended by the digital transformation

Abbildung in dieser Leseprobe nicht enthalten

Source: Own figure in accordance with Gasche, R., Leadership, 2016, p. 30; Stöwe, C., Keromosemito, L., Lateral leadership, 2013, p. 5.

Leadership is carried out by a leader who belongs to a certain hierarchy level. This includes the lower hierarchy level, which is held by team leaders, the middle hierarchy, which is usually held by department heads, and the upper management level, to which the managing directors belong to. The higher the hierarchical level, the higher the degree of responsibility. Controversially, the proportion of specialist tasks decreases the higher the hierarchical level of the manager is, as can be seen in figure 7.⁸⁴ Due to the increasing digitalization, the internal networking of various structures as well as the networking with external service providers and the resulting increase in leadership that has to get by without this classic hierarchy system, other people are included in the circle of leaders. This includes highly qualified people who do not have any personnel and disciplinary responsibility, but who nevertheless lead professionally. These include, for example, project managers or other staff positions.⁸⁵ This group of people is included in the group of managers, which is also illustrated in figure 7.

As a result of the increasing digitalization, the demand for digital leaders is increasing. The aim of this digital leader is to implement and maintain the digital transformation of the company in terms of leadership of employees, corporate leadership and self-guidance.⁸⁶ The special changes that the digital revolution has brought to leadership are explained in the following chapter 2.2.2.

2.2.2 Leadership 4.0 – Expected changes due to digitalization

As already mentioned in chapter 2.1.1, Schwarzmüller et. al. see some changes that also affect the area of leadership due to I4.0, which is now described as Leadership 4.0. Schwarzmüller et al. divides the area of changes into three core areas:⁸⁷

- Changed opportunities to influence employees by delegating authority to intelligent, digital systems,
- Increased need for relationship-building behavior towards employees (based on the first core area), and
- Differentiated demands on leaders’ competences through changed leadership forms (leadership at a distance, necessity of quick decisions due to volatile market conditions, use of highly complex IT applications (both hardware and software).

According to a study from the Initiative Neue Qualität der Arbeit (new quality of work initiative), in which 400 leaders were interviewed by telephone, it was found that the future model of leadership 4.0 is based on networks within the organization. Leaders can provide targeted creative impulses, generate a high degree of innovative power, and accelerate processes if the strongest advantage of self-organizing networks, the collective intelligence of the network, is used.⁸⁸ A new task for leaders working in the context of I4.0 will be to be able to identify digital trends based like Big Data, analyze them in an interdisciplinary manner and implement digital business models based on them.⁸⁹ Especially when leading virtual teams, leaders face the challenge of leading them correctly and adequately. As a result, they have to identify and level suitable digital communication channels.⁹⁰ In addition, it will be the leader's task to implement a digital feedback culture, as the possibility of personal feedback (praise, criticism) is made more difficult by virtual teams working at different locations and times. It is easy to see that communication competence is probably the most important competence of a leader in times of digitalization.⁹¹ Leaders have an active chance to prevent the negative effects of I4.0, like job losses, by identifying the strengths and weaknesses of employees through a targeted network organization together with a practiced digital feedback culture to give them perspectives for working in the digital world.⁹²

2.2.3 Competences

The term "competence" has its origin in the Latin word "competere" and means to be capable of something. However, the term "competence" can be understood in very general terms, since it is used in a wide variety of areas. These include linguistics, sociology, ergonomics, psychology, occupational pedagogy and strategic management.⁹³

In this Master's thesis, however, the concept of competence is used purely in relation to strategic management, as this most closely matches the topic of the thesis.

According to Heyse and Erpenbeck, "Competences [...] are the complex, partly hidden potentials [...]. They encompass the complex experiences, the knowledge, the values and ideals of a person or of groups".⁹⁴ Nevertheless, the term competence is to be considered in more detail and is therefore described with the following two meanings. On the one hand, the term competence describes a responsibility or a professional but factual connection to a situation, a problem or a task.⁹⁵ On the other hand, the term competence can be almost equated with the term ability⁹⁶ and, according to Echterhoff, means a cognitive, emotional and physical ability that leads to success-oriented and goal-oriented behavior.⁹⁷ This is also demonstrated by Wunderer and Bruch, who understand competence as a kind of mix of the behavioral and psychological characteristics of an individual, especially basic orientation, values, motives, knowledge, skills and abilities.⁹⁸ With this mix of behavioral and psychological characteristics, operational tasks in the company can be performed "according to the job, in a goal-oriented, situation-related and responsible manner, and problems can be solved, either alone or in cooperation with others, depending on the organizational circumstances".⁹⁹ When developing physical and cognitive abilities and competences, one thing must be considered, namely the personal development time required or the age of the person who has to deal with the new competence. Physical abilities, such as muscle strength as an example, decrease with age. If leaders are to learn new physical skills, this becomes more difficult or takes longer with increasing age. The social competence, i.e. the correct contact with people, increases due to the varied experiences the person experiences in his life.¹⁰⁰ The following paragraph can be used to briefly summarize the term "competence": A competent person is described as a person who is able to identify the necessary solution steps during a problematic situation, to bundle them into a solution package and finally to apply this solution package. For that, the motivation¹⁰¹ is essential. Finally, the effectiveness and efficiency of the implemented solution is evaluated and the path to finding a solution is reflected. This reflection can be used as an empirical value for future problematic situations. Accordingly, competence is the result of a dynamic learning process.¹⁰²

According to Hülshoff, competences are divided into four individual competence areas, namely:¹⁰³

- Professional competence,
- Methodological competence,
- Social competence and
- Personality or self-competence.

That is why a competence can only be exercised as matter of course if all four areas of activity are dealt with in the area of cooperation.¹⁰⁴ In the following table 1, these four areas of competences are described in more detail and reference is made to a leader.

Table 1: Detailed view on different areas of competences

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¹ Cp. Seitz, J., Seitz, J., Digital competences, 2018, p. 358.

² Cp. Manager Magazin, Tesla, 2017, n. p.

³ Cp. Melchior, L., Amazon, 2016, n. p.

⁴ Cp. Codreanu, A., VUCA framework, 2016, p. 31; Kail, E. G., VUCA, 2011, n. p.

⁵ Cp. Wagner, G., Changes through CPS, 2017, p. 167.

⁶ Cp. Rogers, D. L., Playbook, 2016, p. 11 ff.

⁷ Cp. Businesscloud.de, Willingness to digitalize, 2018, n. p.

⁸ Cp. von Rosenstiel, L., Gaining competences, 2013, p. V-VI.

⁹ Cp. von Au, C., Paradigm shift, 2016, p. 13.

¹⁰ Cp. Gruhn, V., Team, 2018, p. 18-19.

¹¹ Cp. Didacta DIGITAL, Artificial intelligence, 2019, n. p.

¹² Cp. „Offensive Mittelstand – Gut für Deutschland“ Stiftung „Mittelstand – Gesellschaft – Verantwortung“, Implementation aids, 2019, p. 111 ff.

¹³ Cp. Bröckermann, R., Leadership competence, 2011, p. V.

¹⁴ Cp. „Offensive Mittelstand – Gut für Deutschland“ Stiftung „Mittelstand – Gesellschaft – Verantwortung“, Implementation aids, 2019, p. 117 ff.

¹⁵ Franken, S., Prädikow, L., Vandieken, M., Industry 4.0, 2018, p. 1.

¹⁶ Cp. Lang, F. P., Digital revolution, 2019, p. 4; Franken, S., Prädikow, L., Vandieken, M., Industry 4.0, 2018, p. 5.

¹⁷ Cp. Kagermann, H., Wahlster, W., Helbig, J., Implementation of industry 4.0, 2013, p. 23.

¹⁸ Cp. Jäger, J., et. al., Opportunities due to industry 4.0, 2015, p. 6.

¹⁹ Cyber-physical systems will be introduced in detail in chapter 2.1.2.2.

²⁰ Cp. Jäger, J., et. al., Opportunities due to industry 4.0, 2015, p. 6-10.

²¹ Cp. Kagermann, H., Wahlster, W., Helbig, J., Implementation of industry 4.0, 2013, p. 23.

²² Cp. Kagermann, H., Wahlster, W., Helbig, J., Implementation of industry 4.0, 2013, p. 23; Roy, D. T., Cyber-physical systems, 2017, p. 47; Jäger, J., et. al., Opportunities due to industry 4.0, 2015, p. 6-10.

²³ Cp. Treue, D., Intelligent assistants, 2019, p. 28.

²⁴ Cp. Specht, F., Job killer, 2019, n. p.

²⁵ Cp. Gruhn, V., Team, 2018, p. 18-19.

²⁶ Cp. Ittermann, P., et. al., Job design, 2016, p. 9.

²⁷ Cp. Hirsch-Kreinsen, H., Changes within industry 4.0, 2014, p. 12.

²⁸ Cp. Staufen AG, German industry index, 2014, p. 8.

²⁹ Cp. Schwarzmüller, T., Brosi, P., Welpe, I. M., Leadership 4.0, 2017, p. 620.

³⁰ Personal comment of the author.

³¹ Cp. Staufen AG, Digital tools, 2018, p. 10.

³² Cp. European Commission, Degree of digitalization, 2019, n. p.

³³ Cp. BITKOM – Bundesverband Informationswirtschaft, Telekommunikation und neue Medien e. V., IAO - Fraunhofer-Institut für Arbeitswirtschaft und Organisation, Potential of industry 4.0 for Germany, 2014, p. 35.

³⁴ Cp. Perkin, N., Abraham, P., Agile business, 2017, p. 31.

³⁵ Bracken, M., Blog, 2016, n. p.

³⁶ Cp. Perkin, N., Abraham, P., Agile business, 2017, p. 50.

³⁷ Altimeter, Solis, B., Transformation, 2014, n. p.

³⁸ Cp. Rogers, D. L., Playbook, 2016, p. 1.

³⁹ Cp. Schallmo, D. R. A., Digital transformation, 2016, p. 3ff.

⁴⁰ Cp. Gläß, R., Leukert, B., Digitization of the trade, 2017, p. 85.

⁴¹ Cp. Savic, D., Stages of development, 2019, p. 37.

⁴² Cp. Schallmo, D. R. A., Herbort, V., Doleski, O. D., Procedure, 2017, p. 2ff.

⁴³ Cp. Savic, D., Stages of development, 2019, p. 37.

⁴⁴ Cp. Brucker-Kley, E., Keller, T., Kykalová, D., Process management, 2016, p. 5-7.

⁴⁵ Cp. Dietz, U., Information technologies, 2016, p. 19.

⁴⁶ Cp. Gratton, L., Futuristic work, 2010, p. 16-21.

⁴⁷ Cp. Fisher, M., Future workspace, 2019, n. p.

⁴⁸ Cp. Fisher, M., Future workspace, 2019, n. p.

⁴⁹ Cp. BITKOM, DFKI, Decision by artificial intelligence, 2017, p. 61.

⁵⁰ Cp. Treue, D., Intelligent assistants, 2019, p. 28.

⁵¹ Cp. Gartner, Trends, 2017, n. p.

⁵² Cp. Treue, D., Intelligent assistants, 2019, p. 28.

⁵³ Cp. UNGC, Intelligent future, 2017, p. 1.

⁵⁴ Cp. BITKOM, DFKI, Decision by artificial intelligence, 2017, p. 28.

⁵⁵ Cp. Leonhard, G., G. v. Kospoth, C.-A., Sustainability, 2017, p. 78.

⁵⁶ Cp. Hecker, D., et al., Potential of artificial intelligence, 2017, p. 5.

⁵⁷ Author's note: The CPS is not an artificial intelligence in itself, it serves as a link between man and machine and can be completed by artificial intelligence.

⁵⁸ Cp. BITKOM, DFKI, Decision by artificial intelligence, 2017, p. 29.

⁵⁹ Cp. BITKOM, DFKI, Decision by artificial intelligence, 2017, p. 19.

⁶⁰ Cp. Hartmann, V., Tschiedel, R., Artificial competence, 2016, p. 10.

⁶¹ Cp. PROKOM 4.0, Management of competences, 2017, p. 86.

⁶² Cp. „Offensive Mittelstand – Gut für Deutschland“ Stiftung „Mittelstand – Gesellschaft – Verantwortung“, Implementation aids, 2019, p. 111 ff.

⁶³ Cp. Kagermann, H., Wahlster, W., Helbig, J., Implementation of industry 4.0, 2013, p. 17; Roy, D. T., Cyber-physical systems, 2017, p. 47.

⁶⁴ Cp. Drossel, W.-G., et. al., CPS, 2018, p. 198.

⁶⁵ Cp. Burger, A., Lang, A., Müller, Y., System architectures, 2017, p. 60-61.

⁶⁶ Cp. Breitkopf, A., Usage of digital technologies, 2018, n. p.

⁶⁷ Cp. Burger, A., Lang, A., Müller, Y., System architectures, 2017, p. 60-61.

⁶⁸ Cp. „Offensive Mittelstand – Gut für Deutschland“ Stiftung „Mittelstand – Gesellschaft – Verantwortung“, Implementation aids, 2019, p. 26-27.

⁶⁹ Cp. Burger, A., Lang, A., Müller, Y., System architectures, 2017, p. 60-61; Bischoff, J., et. al., Potentials of industry 4.0, 2015, p. 267-268; Sauer, O., Manufacturing execution system, 2014, p. 10-12.

⁷⁰ Cp. Drossel, W.-G., et. al., CPS, 2018, p. 198.

⁷¹ Cp. Zeidler, M., Leader, 2019, p. 16.

⁷² Cp. Hegmann, G., Potentials of cyber-physical-systems, 2019, n. p.

⁷³ Cp. „Offensive Mittelstand – Gut für Deutschland“ Stiftung „Mittelstand – Gesellschaft – Verantwortung“, Implementation aids, 2019, p. 27.

⁷⁴ Cp. „Offensive Mittelstand – Gut für Deutschland“ Stiftung „Mittelstand – Gesellschaft – Verantwortung“, Implementation aids, 2019, p. 26-27.

⁷⁵ Cp. von Rosenstiel, L., Basics, 2014, p. 3.

⁷⁶ Cp. Becker, L., Ehrhardt, J., Gora, W., Leadership concept, 2006, p. 28.

⁷⁷ Cp. von Rosenstiel, L., Kaschube, J., Staff psychology, 2014, p. 681.

⁷⁸ Cp. Eck, C. D., Personality, 2014, p. 1.

⁷⁹ Cp. von Au, C., Paradigm shift, 2016, p. 2 ff.

⁸⁰ Cp. Becker, L., Ehrhardt, J., Gora, W., Leadership concept, 2006, p. 92-93; Bea, F. X., Introduction into leadership, 2011, p. 23.

⁸¹ Cp. Peters, T., Concepts of leadership, 2015, p. 2.

⁸² Cp. Bruch, H., Vogel, B., Krummaker, S., Trends in leadership, 2006, p. 5; Grolman, F., Good leadership, n. d., n. p.

⁸³ Cp. Bruch, H., Vogel, B., Krummaker, S., Trends in leadership, 2006, p. 5; Grolman, F., Good leadership, n. d., n. p., Becker, M., Human capital development, 2009, p. 305.

⁸⁴ Cp. Gasche, R., Leadership, 2016, p. 30.

⁸⁵ Cp. Stöwe, C., Keromosemito, L., Lateral leadership, 2013, p. 5.

⁸⁶ Cp. Velten, C., et al., Digital era, 2015, p. 9.

⁸⁷ Cp. Schwarzmüller, T., et al., Leadership 4.0, 2017, p. 620.

⁸⁸ Cp. Initiative neue Qualität der Arbeit, Leadership culture, 2016, p. 7.

⁸⁹ Cp. Franken, S., Futuristic leadership, 2016, p. 58.

⁹⁰ Cp. Crummenerl, C., Kemmer, K., Development of leaders, 2015, p. 4.

⁹¹ Cp. Institut für Führungskultur im digitalen Zeitalter, Competences in digital era, 2016, p. 7.

⁹² Cp. Franken, S., Futuristic leadership, 2016, p. 60.

⁹³ Cp. Becker, M., Human capital, 2008, p. 38-39.

⁹⁴ Heyse, V., Erpenbeck, J., Competence management, 2007, p. 19.

⁹⁵ Cp. Wenninger, G., Psychology, 2001, p. 366.

⁹⁶ In this Master's thesis the terms "competences" and "abilities" are considered equally. They both fall under one term, namely "competence".

⁹⁷ Cp. Echterhoff, W., Competence, 2009, p. 527.

⁹⁸ Cp. Wunderer, R., Bruch, H., Implementation competence, 2000, p. 71; Krumm, S., Mertin, I., Dries, C., Competence models, 2012, p. 4.

⁹⁹ Münch, J., Development, 1995, p. 11.

¹⁰⁰ Cp. Jaeger, C., Performance of employees, 2015, p. 41 ff.

¹⁰¹ Employee motivation, its effects (both positive and negative) and the possibility to increase them are not mentioned further in this master thesis.

¹⁰² Cp. North, K., Reinhardt, K., Sieber-Suter, B., Identification of competences, 2013, p. 44-46.

¹⁰³ Cp. Dewe, B., Further education, 2000, p. 365 ff.; Mertens, D., Requirements in modern world, 1974, p. 36 ff.

¹⁰⁴ Cp . Fleps, J. G., Büser, T., Leadership role, 2007, p. 26.

¹⁰⁵ Cp. Brommer, U., Key qualifications, 1993, p. 80.

¹⁰⁶ The individual leadership styles that can be applied will not be explained further in this Master's thesis.

¹⁰⁷ Cp. Brake, J., Requirements profile, 1997, p. 157.

¹⁰⁸ Cp. Brommer, U., Key qualifications, 1993, p. 81.

¹⁰⁹ Cp. Brake, J., Requirements profile, 1997, p. 158.

¹¹⁰ Cp. Erpenbeck, J., Sauter, W., Competence development, 2013, p. 35.

¹¹¹ Cp. Brommer, U., Key qualifications, 1993, p. 81.

¹¹² Cp. North, K., Reinhardt, K., Sieber-Suter, B., Identification of competences, 2013, p. 59.

¹¹³ Cp. Brake, J., Requirements profile, 1997, p. 159.

¹¹⁴ Cp. Fleps, J. G., Büser, T., Leadership role, 2007, p. 32.