Next Generation Entrepreneurs. How Do Digital Skills Affect the Intention to Start Up a New Business?

Master's Thesis, 2019

93 Pages, Grade: 1,3


Table of Content


List of Figures

List of Tables

List of Abbreviations

Index of Appendices

1. Entrepreneurial Intentions and the Potential of Digital Skills

2. Literature Overview

3. Theoretical Background and Hypothesis Development
3.1 Theory of Planned Behavior
3.2 Entrepreneurial Intentions and Self-Efficacy
3.3 The Moderating Role of Perceived Digital Skills
3.4 DigComp Framework

4. Development of Questionnaire and Methodology
4.1 Sample and Data Collection
4.2 Designing the Digital Skill Instrument
4.3 Study Measures
4.4 Control Variables

5. Data Analysis and Findings

6. Opportunities and Challenges for Developing Entrepreneurial Intentions in the Digital Era
6.1 Research Implications
6.2 Practical Implications
6.3 Limitations and Outlook on Future Research
6.4 Conclusion




Facing the progressive digitalization process as well as the increasing resources allocated from the European Union to promote digital basic competences as important 21st century skills on the one hand and support the growth opportunities of the European Digital Economy on the other hand, the purpose of the present study is to explore the interplay between perceived digital skills, entrepreneurial self-efficacy beliefs, and the intention to start-up a business venture on the individual-level. Based on the theory of planned behavior extended by the social cognitive career theory, self-efficacy beliefs play a crucial role in the development of entrepreneurial intentions and, ultimately, entrepreneurial behavior. Perceived digital skills may shape this relationship through an interaction mechanism as a source of self-efficacy expectations. In order to make perceived digital skills measurable, a multidimensional scale was developed on basis of the DigComp Framework published by the European Commission. An explorative and confirmative factor analysis was performed to ensure a consistent and reliable scale. Based on a sample with 181 German Master’s students, collected and analyzed data (OLS Regression) reveal that entrepreneurial self-efficacy beliefs and perceived digital skills have a direct and significant effect on entrepreneurial intentions. A moderation effect of perceived digital skills could not be verified. After testing a mediation model, however, the relationship between perceived digital skills and entrepreneurial intentions is partially mediated by entrepreneurial self-efficacy beliefs. The results highlight the importance of the acquisition and perception of a broad set of digital skills as a relevant driver for entrepreneurial career intentions and an increasing confidence level in one's entrepreneurial skills. The findings are discussed against the background of economic opportunities as well as challenges in the digital era and open up a new research perspective on the understanding of the interdependencies between the main constructs.

Keywords: Entrepreneurial Intention, Entrepreneurial Self-Efficacy, Perceived Digital Skills, Entrepreneurship, DigComp Framework, Business Start-up

List of Figures

Figure 1: Development of Entrepreneurial Intentions within the Theory of Planned Behavior

Figure 2: A Simplified View on Social Cognitive Career Theory and the Crucial Role of Self-Efficacy

Figure 3: Conceptual Framework of the Theoretical Constructs and their Relationships

Figure 4: Confirmatory Factor Analysis Model of the Perceived Digital Skill Factors

Figure 5: Mediation Model Comprising the Main Constructs and their Relationships

List of Tables

Table 1: Literature Overview on Related Studies in Entrepreneurial Research

Table 2: Overview of Basic Skills Based on the DigComp Framework

Table 3: Demographic Data Summary of the Sample

Table 4: Selected Scale Items Including Factor Loadings, Eigenvalues, Scale Means and CR Alpha

Table 5: Overview of Instruments and Control Variables for Measurement

Table 6: Descriptive Statistics and Correlations

Table 7: OLS Regression Estimation of Entrepreneurial Intention

List of Abbreviations

CFA Confirmatory Factor Analysis

DV Depend Variable

EEM Entrepreneurial Event Model

EFA Explorative Factor Analysis

EI Entrepreneurial Intentions

EIQ Entrepreneurial Intention Questionnaire

EPM Entrepreneurial Potential Model

ESE Entrepreneurial Self-Efficacy

ICT Information and Communication Technology

IV Independent Variable

OLS Ordinary Least Squares

PDS Perceived Digital Skills

SCT Social Cognitive Theory

SCCT Social Cognitive Career Theory

SEM Structural Equation Modeling

TPB Theory of Planned Behavior

Index of Appendices

Annex 1: Questionnaire Including all Items of the Main Constructs and Control Variables

Annex 2: Communalities after Entering all Perceived Digital Skills-Items

Annex 3: Explanation of the Total Variance, Factor Loadings and Eigenvalues of the Final Scale

Annex 4: Factor Loadings of the Final Scale in a Rotated Component Matrix

Annex 5: Regression Weights of the Final Scale Based on Maximum Likelihood Method

Annex 6: Standardized Residual Covariances between the Final Scale Items

Annex 7: Modification Indices between all Covariances in the Structural Equation Model

Annex 8: Model Fit Summary Including CMIN, GFI, NFI, CFI and RMSEA

Annex 9: Model Summary of OLS Regression Estimation of Entrepreneurial Intention

Annex 10: Analysis of Variance of the OLS Regression Estimation of Entrepreneurial Intention

Annex 11: OLS Regression Estimation of Entrepreneurial Intention in a Three-Step Model Including all Regression Coefficients, Confidence Intervals and Collinearity Statistics

Annex 12: Total, Direct and Indirect Effects of Perceived Digital Skills (X) on Entrepreneurial Intentions (Y) in a Mediation Model

Annex 13: Mediation Model Summary with Entrepreneurial Self-Efficacy as Outcome Variable

Annex 14: Mediation Model Summary with Entrepreneurial Intention as Outcome Variable

Annex 15: Total Effect Mediation Model with Entrepreneurial Intention as Outcome Variable

Annex 16: Entrepreneurial Intentions between Different Study Courses

Annex 17: Perceived Digital Skills between Different Study Courses

Annex 18: Social Influence and its Shaping Effect on the Relationship between Entrepreneurial Intention and Entrepreneurial Self-Efficacy

1. Entrepreneurial Intentions and the Potential of Digital Skills

Digital technologies will fundamentally change business models, institutions and society as a whole, as new ecosystems emerge.” (European Commission, 2019b)

In light of the progressive digitalization process in economic and social spheres, in 2006 the European Union declared digital competences as an important key resource "essential to citizens for personal fulfilment, a healthy and sustainable lifestyle, employability, active citizenship and social inclusion." (European Commission, 2019a, p. 4). In order to accommodate this conclusion, the European Structural and Investment Funds (ESI[*] ) were set up recently aiming, inter alia, the promotion of digital competences on the one hand, and the support of business start-ups to increase the growth potential of the European Digital Economy on the other hand. However, a closer look at the business demographic statistics within the euro area reveals that the overall birth rate of enterprises decreased slightly over the recent years (Eurostat, 2019). This observation raises the question of whether the promotion of digital basic competencies can cause economic growth by increasing entrepreneurial activities. From this point of view, entrepreneurs occupy a relevant key function by creating more jobs, developing innovations and, ultimately, supporting economic growth (Baptista, Escária, & Madruga, 2007; Galindo & Méndez, 2014). Considering that entrepreneurial activities in a certain country are also affected by macroeconomic, social and national-political conditions, the present study is aimed at investigating the interplay of perceived digital skills, entrepreneurial self-efficacy beliefs and entrepreneurial intentions on the individual level.

In the entrepreneurial research, intention-based models are applied to discover the influence of personality traits, environmental factors or behavioral characteristics on the intention to become an entrepreneur (Krueger & Brazeal, 1994; Shapero & Sokol, 1982). Entrepreneurial self­efficacy (abbreviated below as ESE) plays a crucial role in the development of entrepreneurial intentions (abbreviated below as EI) and refers the confidence in one's abilities to master different challenges during the start-up process successfully (Cardon & Kirk, 2015). A wide range of studies acknowledge that ESE as motivational driver of EI constitutes a relevant link between external contributing factors and emerging start-up intentions (Atitsogbe, Mama, Sovet, Pari, & Rossier, 2019; Murugesan & Jayavelu, 2017; Rosique-Blasco, Madrid-Guijarro, & García-Pérez-de-Lema, 2018; Wilson, Kickul, & Marlino, 2007). Based on this, the perception of one's digital skills can fill an important role in the interplay between these variables. Modern basic skills comprise the capabilities to use digital technologies in a creative, responsive and critical way for communication, content creation, as well as problem-solving and, therefore, may enable supportive effects during the start-up process (Hatlevik, Guómundsdóttir, & Loi, 2015b). Entrepreneurial research provides several indications that individual skills are positively associated with the development of intentions to start-up a business venture (Farooq, 2018; Liñán, 2008; Mamabolo, Kerrin, & Kele, 2017). However, there exists only a small body of literature focusing on the impacts of IT-related skills on EI, consequently, a lack of research can be identified.

To close this gap in the present research context, this study examines the direct effects of perceived digital skills (abbreviated below as PDS) and ESE on the intention to create a new business. Based on the theory of planned behavior (Ajzen, 1991), ESE is considered as a main antecedent of EI encouraging people to develop long-term efforts, efficient strategies and persistence in setbacks with regard to the business start-up process (Drnovsek, Wincent, & Cardon, 2010; Shane, Locke, & Collins, 2003). In Addition, the social cognitive career theory by Lent et al. (1994) is included to examine the influence of PDS on the process to start a career as a self-employee. Considering that the individual conviction to possess a certain level of digital skills can shape ESE beliefs and their influence on EI, PDS are also assumed as a moderation variable. In this manner, this quantitative empirical study aims to provide an answer to the main research question of how PDS shape ESE as a driver of entrepreneurial intentions. As one of the first studies addressing this research focus, findings can provide an important contribution for a better understanding of the role of digital skills and start-up intentions as a crucial predictor of entrepreneurial behavior (Krueger, Reilly, & Carsrud, 2000).

For this purpose, a theoretical foundation is developed to clarify the association between the main constructs and theoretical models in a first step. Subsequently, the theoretical constructs and their relationships with each other are derived in a conceptual framework to formulate the hypothetical assumptions. In a second step, a scale is developed based on the DigComp Framework published by the European Commission for measuring PDS. After conducting a survey on German Master's students, the collected dataset is analyzed in a multiple regression estimation model of EI to identify relevant influencing factors and interaction effects during a third step. In the fourth and final step, findings are discussed against the background of opportunities and challenges regarding the development of entrepreneurial intentions in the digital era. It includes the derivation of implications for practice and research as well as limitations of this work and an outlook on future research. A conclusion completes the present study.

2. Literature Overview

In the following chapter, the focus is on identifying and summarizing relevant studies in the area of the current research context. It comprises a literature overview of studies related to an investigation of the interplay between personal skills, self-efficacy beliefs and the intention to start-up a new venture. In addition, intention-based theories relevant for the entrepreneurial research are identified and discussed.

Early research mainly focused on the influence of personal and psychological factors (cognitive approach) as well as environmental factors (contextual approach) on the process of entrepreneurial intention building (Bird, 1988). Evidence suggests that both contribution factors have an significant influence on EI (Karimi et al., 2017). According to a systematic review by Liñán and Fayolle (2015), most studies in entrepreneurial research examine personal-level variables such as background factors, personality traits and gender issues.

More specifically it includes such variables as individual skills and prior knowledge, demographical factors, psychological characteristics, and social aspects (Phuong & Hieu, 2015). Following the cognitive approach, the present study is centered on the interplay between PDS, ESE and the EI, based on a sample of German Master’s students. In recent years, a wide range of studies examine the influence of ESE on intentionality toward venture founding. Most researchers have found a significant positive link between EI and ESE as relevant predictor (Austin & Nauta, 2016; Fitzsimmons & Douglas, 2011; Geenen, Urbig, Muehlfeld, van Witteloostuijn, & Gargalianou, 2016; Hockerts, 2017; Horvath, 2016; Hou, Su, Lu, & Qi, 2019; Kassean, Vanevenhoven, Liguori, & Winkel, 2015; Krueger et al., 2000; Lanero, Vázquez, & Aza, 2016; Pfeifer, Sarlija, & Zekic Susac, 2016; Piperopoulos & Dimov, 2015; Sequeira, Mueller, & McGee, 2007; Wilson et al., 2007; Zellweger, Sieger, & Halter, 2011; Zhang & Cain, 2017). Based on Ajzen’ s (1991) theory of planned behavior (abbreviated below as TBP), scholars argue that strong beliefs in own capabilities (e.g. subject-specific abilities, related knowledge and individual skills) to perform entrepreneurial tasks successfully, increases the persons intention to create a business. Thus, a high level of ESE may support the mobilization and allocation of one's internal and external resources to pursue entrepreneurial start-up goals.

According to the literature of entrepreneurial research, relevant skills and knowledge of how to be a successful entrepreneur are highly diversified. A broad set of skills in financial management, human resource management, leadership, start-up, social- and interpersonal, personality, marketing, business management, and technical issues are identified to run a business successfully (Mamabolo et al., 2017). Recent research examines also the impact of individual (technological) skills and personal abilities on EI (see Table 1). For example, a study by Rosique-Blasco et al. (2018), conducted on a sample of 1126 university students, indicates that strong personal abilities such as creativity, proactivity, risk aversion, and locus of control have a positive direct and indirect impact on EI, partially mediated by ESE. Other studies confirm these results in regard of the determinants creativity (Zampetakis, Gotsi, Andriopoulos, & Moustakis, 2011), proactivity (Crant, 1996; Hu, Wang, Zhang, & Bin, 2018) and risk aversion (Costa & Mainardes, 2016; Zhang & Cain, 2017), however, there is no clear consensus about the significance of locus of control (Gurel, Altinay, & Daniele, 2010; Zellweger et al., 2011). Further research examined the influence of entrepreneurial skills and knowledge on EI. A study by Liñán et al. (2013) tested the entrepreneurial skill-level and its impact on the intention to start-up a new venture across Great Britain and Spain, based on a sample of 1005 business students. Results suggest that an enhancing grade of entrepreneurial skills and knowledge increase a person's level of self-efficacy beliefs, whereby positively mediates the relationship to EI. Findings were replicated by Yaghoubi Farani et al. (2017) in a similar study design by focusing on the intention to become a digital entrepreneur.

TABLE 1: Literature Overview on Related Studies in Entrepreneurial Research

Abbildung in dieser Leseprobe nicht enthalten

IEM = International Entrepreneurship and Management Journal; REM = Revista de economía mundial; JCIS = Journal of Information Systems Education; IJE = International Journal of Entrepreneurship; EPM = Entrepreneurial Potential Model; IV = Independent variable; DV = Depend variable; TPB = Theory of Planned Behavior; SCCT = Social Cognitive Career Theory; EPM = Entrepreneurial Potential Model; Source: Author’s own work

In this research area, only a few studies examine individual's IT-related competences and knowledge referring to intentionality towards venture creation. Chen (2014) found that a high level of confidence in a person’s applied computer skills increase entrepreneurial self-efficacy beliefs as a main driver of EI and its effect on start-up an IT business. A recent study by Sitaridis and Kitsios (2019) confirms these results and reveals that a high level of computer and software literacy increases people's learning motivation, risk behavior and openness to new experiences and, thereby, relevant entrepreneurial qualities required for mastering entrepreneurial challenges during the start-up process. Another study by Dutta et al. (2015), based on a survey of 164 U.S. students, focuses on the interplay between related knowledge, experience and technical knowhow in emerging digital business areas, personal innovativeness in technology and its effect on EI through perceived feasibility and desirability. Firstly, the findings demonstrate that a high level of technical knowhow, related knowledge and experience in venture founding increase a person’ s confidence in their own capabilities to start-up a digital business successfully and positively affect their attitude toward entrepreneurship. Secondly, a strong interest and innovativeness on new information technologies positively influence perceived feasibility and desirability directly. Similar results are provided by Zarefard and Cho (2018) in their comprehensive study by examining a sample of 418 Iranian students. Managerial competences in the area of “leadership, creativity and innovation as well as knowledge and technology are the most effective factors influencing innovative start-up intentions through self-efficacy as a mediator. However, creativity and innovation, knowledge and technology, and administrative competency have the strongest effects through mediating the role of attitude” (Zarefard & Cho, 2018, p. 15). Thus, existing literature suggests that practical and theoretical knowledge as well as a wide range of skills support individual self-confidence referring to entrepreneurial task fulfilment and thereby the development of EI. The Table 1 above provides an overview of the outlined literature in the present research context.

In the area of entrepreneurial research, intention-based models were increasingly used by different business researcher and psychologist in the 1990's to form the theoretical foundation of their investigations (Ajzen, 1991; Autio, H. Keeley, Klofsten, G. C. Parker, & Hay, 2001; Bird, 1988; Bird, 1992; Davidsson, 1995; Kolvereid & Moen, 1997; Krueger, 1993; Shapero & Sokol, 1982). These cognitive models focus on internal personal traits and attitudes as well as external environmental factors and their antecedents to explain the intention building process related to starting a new business and, consequently, entrepreneurial behavior as a descendant of EI. Therefore, intentions and behavioral actions are closely related (Krueger, 1993). Evidence suggests that EI are a strong predictor of entrepreneurial behavior in a consistent setting (Adekiya & Ibrahim, 2016; Armitage & Conner, 2001). A meta-study by Schlaegel and Koenig (2014) shows that a large majority of studies developed a theoretical framework based on the TPB (Ajzen, 1991) or the entrepreneurial event model (Krueger, 1993; Shapero, 1975; Shapero & Sokol, 1982) also known as Krueger-Shapero model (abbreviated below as EEM).

According to Shapero and Sokol’s (1982) EEM, EI depends on an individual's (a) perceived attractiveness toward entrepreneurship, (b) willingness to act and (c) confidence in personal abilities to start-up a new business successfully. In early 1990’s, a similar conceptualization for more variable contexts was provided by the social psychologist Icek Ajzen in the TPB as a consequence of refining his first Theory of Reasoned Action (Ajzen & Fishbein, 1980). A few years later (Krueger & Brazeal, 1994) created a further development of the EEM to an entrepreneurial potential model, for a clearer demarcation. The differences between the TPB and EEM are based on varying relationships between the main predictors and their representing information value (Ajzen, 2002; Krueger et al., 2000). Nevertheless, a wide range of empirical evidence supports the reliability and validity of Ajzen’ s (1991) TPB and Shapero and Sokol’s (1982)EEM(Conner & Armitage, 1998; Fayolle, Gailly, & Lassas-Clerc, 2006; Godin & Kok, 1996; Kautonen, van Gelderen, & Tornikoski, 2013; Krueger, 1993; Schlaegel & Koenig, 2014; Sutton, 1998). With regard to the TPB, recent research in the area of entrepreneurship shows that the three predictors (a) perceived attitude towards a behavior, (b) perceived behavioral control and (c) subjective norms explain 30-45% of the variance in EI (Kautonen, van Gelderen, & Fink, 2015; Kolvereid & Isaksen, 2006; Liñán & Chen, 2009; van Gelderen et al., 2008). A further study by Schlaegel and Koenig (2014) also indicates a larger amount of variance in EI compared to the EEM. The high robustness of the TPB, broad contextual application possibilities, more detailed specifications of theoretical constructs and a prevalent integration, testing and evaluation in different research areas are several reasons for justifying the inclusion of TPB in the present study. The TPB is being described more precisely in the following subsection.

3. Theoretical Background and Hypothesis Development

In the following chapter, the theoretical foundation of this work will be explained by including Ajzen’ s (1991) TPB in a first step. This theoretical model will be extended by the social cognitive career theory by Lent et al. (1994) in a second step. Moreover, a theoretical conceptualization of the main constructs PDS, ESE, and EI will be conducted, grounded on existing literature in the entrepreneurial and psychological research. In this respect, the interrelationships between the main constructs are to be clarified and, therefore, form the starting point for developing the hypothesis. Finally, the theoretical background of the DigComp Framework published by the European Commission will be highlighted as a prerequisite for developing a digital skill-scale in the methodical part of this study.

3.1 Theory of Planned Behavior

The current study include the TPB, according to Ajzen (1991), as a concept of cognition psychology to clarify the interplay between ESE, PDS and EI. TPB based on the main assumption, that intentions influence behavioral actions driven by three motivational factors as planned and target-oriented behavior (Ajzen, 1991). Ajzen (1991, p. 181) describes intentions as “indications of how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the behavior”. In other words, a planned and conscious decision is the result ofan intentionality affected by the conceptual determinants (a) personal attitude, (b) perceived behavioral control and (c) subjective norms. For a better understanding of how entrepreneurial career intentions are developed, these motivational predictors are described in more detail in the following (see Figure 1).

The antecedent (a) personal attitude is defined as attitude toward a specific behavior and refers to a person’s subjective evaluation of positive and negative factors that influence the behavior to be performed (Ajzen, 1991). Regarding to entrepreneurship, it describes the individual assessment of advantages and disadvantages about founding a new venture (Liñán & Chen, 2009). For Example, if a person forms an attitude toward being an entrepreneur, it depends on individual perceptions and beliefs such as striving for autonomy, earning opportunities, professional self-fulfillment, or other barriers and motivational factors (Bhaskar & Garimella, 2017). For explaining the process of intention forming, several scholars have proven personal attitude as a reliable predictor (Kautonen et al., 2015; Kolvereid, 1996; Krueger & Carsrud, 1993).

In the present study (b) perceived behavioral control plays a central role in theoretical conceptualization. Perceived behavioral control refers to the perceived simplicity and difficulty of performing a specific behavior (Ajzen, 1991). Based on individual beliefs about an access to resources and opportunities, people assess the success potential and feasibility of a planned behavior (Krueger, 1993). This means, on the one hand, forming entrepreneurial intentions is affected by the perception of individual confidence in own capabilities, skills and potentials to perform different tasks during the venture creation process successfully. On the other hand, entrepreneurial behavior is restricted by a certain level of controllability of the start-up process (Ajzen, 2002). For Example, a high level of self-efficacy beliefs can encourage people’s willingness to become an entrepreneur, but a lack of financial resources or loss of control simultaneously restrict entrepreneurial actions and opportunities. The concept of perceived behavioral control is closely related to Bandura’s (1977; 1982) concept of self-efficacy. Perceived self-efficacy refers to “people’s beliefs about their capabilities to exercise control over their own activities and over events that affect their lives” (Bandura, 1991, p. 257). In essence, both concepts focus on factors of the individual perception referring to target behavior. In Fayolle et al.’s (2006, p. 708) point of view, the main distinction is "that perceived behavioral control is rather focused on the ability to perform a particular behavior." Accordingly in a later study, Ajzen (2002) specified the concept of perceived behavioral control by a more precise differentiation between the determinants of self-efficacy and locus of control as a part of perceived behavioral control. Therefore, Bandura's concept of self-efficacy can be considered as a comprehensive and essential element of perceived behavioral control, which substantially overlaps in terms of persons individual beliefs in their own ability related to behavioral goals. Evidence suggests that extended concepts of generalized self-efficacy can also include elements of locus of control, whereas other studies identify self-efficacy as key contributor ofEI through perceived feasibility (Judge, Erez, Bono, & Thoresen, 2002; Krueger, 1993; Krueger et al., 2000). Consistent with several other studies, the current study uses the concept of perceived behavioral control and self-efficacy in a similar way (Kolvereid & Isaksen, 2006; Rosique- Blasco et al., 2018; Roy, Akhtar, & Das, 2017; van Gelderen et al., 2008). Based on this view, ESE can be considered as a main motivational driver of EI.

Finally, (c) subjective norms refer the individual perception of social pressure to perform (or not perform) a specific behavior (Ajzen, 1991). Based on the subjective perception, people assess different opinions in their own social environment about being an entrepreneur and get influenced by their positive or negative attitudes toward venture creation. For Example, if self­employment plays a role in the family background and thereby creates a positive image of entrepreneurship, it enhances the efficacy of entrepreneurial intention building through the social influence (Zellweger et al., 2011). Research literature suggests that the reliability of the predictor subjective norms is not unequivocally clear. Most researchers have found a high level of significance (Armitage & Conner, 2001; Gird & Bagraim, 2008; Kolvereid, 1996; Kolvereid & Isaksen, 2006; Liñán & Chen, 2009), whereas some studies indicate no support for this result (Autio et al., 2001; Fini, Grimaldi, Marzocchi, & Sobrero, 2012; Krueger et al., 2000). As a possible reason was considered that empirical findings in various cultures suggest a different influence on an individual's subjective norms (Schlaegel & Koenig, 2014).

FIGURE 1: Development of Entrepreneurial Intentions within the Theory of Planned Behavior

Abbildung in dieser Leseprobe nicht enthalten

Source: Adapted from Ajzen (1991, p. 182)

Figure 1 shown above, illustrates the relationships between the different determinants of the TPB. Summing up, it can be noted that EI is mainly determined through three motivational factors. These determinants are affected simultaneously by a person's human capital and demographic variables such as age, skills, education, and family background. According to Ajzen’ s (1991) TPB, the intention to start-up a new business increases if (a) a person has a positive attitude towards entrepreneurship, (b) a person has a strong confidence in their own abilities to challenge entrepreneurial start-up goals successfully and (c) a person has a positive perception about their social environment and prevailing opinion’ s towards entrepreneurship. The present study centers on the relationship between ESE, as a similar construct of perceived behavioral control, and EI. The stronger the entrepreneurial intentions, the more likely it is that entrepreneurial behavior will be performed. In the next subchapter, the relationship of ESE and EI is more closely analyzed by applying the TPB.

3.2 Entrepreneurial Intentions and Self-Efficacy

“It’s not about ideas. It’s about making ideas happen.” (Belsky, 2014, p. 1)

In most cases, process of venture creation begins with an approximate idea for a future organization and is based on an emerging intention to achieve these defined goals (Bhave, 1994). According to the organizing model of venture creation by Shook et al. (2003), this process will be initiated through an developed intention to start a new business, followed by the search for new opportunities and the decision to exploit through new venture creation and activities of opportunity exploitation. Thus, an individual's EI is highly relevant as a main starting point to build up new organizations (van Gelderen et al., 2008). Further, EI could be considered as crucial antecedent for identifying as well as developing entrepreneurial opportunities and, therefore, play an important role for understanding the venture-creation process. The present study follows the recommendation to choose EI as depend variable instead of entrepreneurial behavior. This is reasoned by the fact that it is difficult to predict exact future behavior on an individual level. However, EI seems to be a suitable predictor to estimate a strong tendency of future behavior (Krueger, 1993).

In prior literature, scholars in the research field of entrepreneurship have interpreted EI in different ways. Bird (1988, p. 442) defined intentionality in general as “a state of mind directing a person's attention (and therefore experience and action) toward a specific object (goal) or a path in order to achieve something (means).” With regard to entrepreneurship, the decision to become an entrepreneur is a target-oriented and planned cognition process influenced by EI, after considering all available information and consequences of start-up relevant issues to achieve business-related goals (Katz & Gartner, 1988; Shapero & Sokol, 1982). Phuong and Hieu (2015) describe EI as willingness to pursue entrepreneurial goals and desire a career as self-employee. Many scholars agree that EI evince the first step of a long-term venture creation process and refer to people's commitment to start a new business in future (Adekiya & Ibrahim, 2016; Krueger et al., 2000; Lee & Wong, 2004; Murugesan & Jayavelu, 2017; Popescu, Bostan, Robu, Maxim, & Diaconu, 2016). Following this explanation, EI is defined as cognitive conviction and willingness to mobilize internal as well as external resources with the aim to start-up a new business venture and challenge entrepreneurial goals over the long term. In case of the current study, EI state the aspiration of Master’ s students to pursue entrepreneurial purposes and, consequently, a career as self-employee after their university graduation. To choose this professional path, student's self-evaluation of own capabilities plays an important role in their decision-making process.

Albert Bandura, a Canadian psychologist, conceptualized these considerations in his construct of self-efficacy by developing his social cognitive theory (abbreviated below as SCT) (Bandura, 1986). According to the SCT, Bandura (1986) assumes a reciprocal interaction between an individual's behavior, cognitive determinants and environmental factors, which mutually influence each other. For Example, a certain behavior to achieve a goal can change the environmental conditions, and vice versa, different environmental factors and individual beliefs also cause on behavioral consequences. To carry out a specific conduct, Bandura emphasizes the importance of cognitive processes such as capabilities of reflection, comparison and self­assessment. Considering that individuals possess a set of essential knowledge and skills, additional capabilities can be learned through the observation and reproduction of behavioral patterns conducted by others. Furthermore, the cognitive evaluation-process serves as a helpful mechanism to decide which behavior should be imitated and which is less useful. In addition, individual's outcome expectations and self-reinforcement can restrict and enable a specific behavior or action. Outcome expectations refer to anticipated behavioral consequences, based on past experiences and their probability of success. Reinforcement is defined as a person’ s stimuli for continuation or discontinuation a certain behavior depending on internal and external environmental factors. Therefore, the decision to engage in a goal-directed behavior is made in a complex interplay between previous experiences, individual expectations and beliefs, environmental influences and gained abilities. Moreover, Bandura added self-efficacy as a key construct in the SCT (for refining his previous Social Learning Theory) to develop a further motivational factor in humans cognitive decision-making system.

Bandura (1997a, p. 3) defines the general construct of self-efficacy as "beliefs in one’ s capabilities to organize and execute the course of action required to produce given attainment". Thus, motivational, cognitive and action processes are controlled by an individual’s self­judgement of his or her own abilities (Wood & Bandura, 1989). Different studies indicate a motivational function of self-efficacy and its significant influence on people's goal choices, persistence, performance and level of effort (Chen, Gully, & Eden, 2004; McGee, Peterson, Mueller, & Sequeira, 2009; Wood & Bandura, 1989; Zhao, Seibert, & Hills, 2005). In other words, comparing two individuals with an opposed level of self-efficacy and a similar set of skills, the person with a higher degree of confidence in his or her own capabilities can perform a specific task more persistently, efficiently and ambitiously. Therefore, most individuals prefer decision paths accompanying with a higher level of personal controllability (in a certain sense perceived feasibility) as well as perceived self-efficacy, so it become more probable to finish certain activities successfully (Bandura, 1977; Bandura, 1982). However, contextual factors of a specific task, for example perceived difficultly or particular task requirements, have an relevant impact on self-evaluation of efficacy beliefs (Ineson, Jung, Hains, & Kim, 2013). Thus, an individual's level of self-efficacy vary in different activity fields. Bandura (1997b, p. 43) states that personal "assessments linked to activity domains and situational contexts reveal the patterning and degree of generality of people’s beliefs in their efficacy. " Consistent with Bandura’s (1997b) conceptualization of self-efficacy, the theoretical implementation of the construct in the current study must be viewed from the perspective of the entrepreneurial domain and their related tasks. Based on this, a wide range of scholars in the entrepreneurial research area used a domain-specific adjustment of the self-efficacy construct - Entrepreneurial self-efficacy.

Referring to appropriate specialist literature, various definitions of ESE are existing. For instance, Drnovsek et al. (2010, pp. 329-330) define ESE as "individuals’ beliefs regarding their capabilities for attaining success and controlling cognitions for successfully tackling challenging goals during the business start-up process." They suggest that ESE is a multidimensional construct including task or outcome-related goal beliefs as well as positive or negative control beliefs referred to the entrepreneurial start-up process (Drnovsek et al., 2010, p. 335). Besides, Fini et al. (2012) focuse on a more behavioral-oriented definition and state ESE as belief in a person's ability to mobilize resources, skills and knowledge for challenging the start-up process through a goal-oriented entrepreneurial behavior and risk-taking. In general, ESE refers to an individual's cognitive assessment of his or her abilities to pursue start­up-related tasks and achieve the aspired level of entrepreneurial outcomes (Chen, Greene, & Crick, 1998). Therefore, ESE can be considered as a key for self-evaluation by assessing the own individual resources regarding entrepreneurial challenges and a consequential buildup of efficacy beliefs. In the present study, ESE is defined as belief in one's capabilities to perform entrepreneurial goals during the start-up process successfully. In line with the theoretical approach by McGee et al. (2009), ESE is considered as a multidimensional construct comprising five entrepreneurial subdomains. In view of this, it describes people’ s confidence in their abilities, related to the searching and developing of business opportunities, controlling and planning of enterprise processes, achieving of persuasive efforts toward employees, stakeholder and other relevant business partner, the executing of leadership tasks, and, finally, the mastering of financial issues. Regarding to the sample of students, a high level of ESE is related to a high degree of confidence in their skills, competences and know-how to perform entrepreneurial tasks in different domains (opportunity recognition, business planning, persuasion, leadership and financial literacy) successfully. For example, a higher level of ESE is accompanied with a higher level of persistence, aspiring ambitious goals and a better performance, effected by an individual's self-confidence toward entrepreneurial task­fulfillment. From a student's perspective this gives rise to the belief: "When I do my best, I can solve difficult challenges related to the start-up process” or "Even in difficult times, I am sure to achieve my entrepreneurial goals”.

Meanwhile, the concept of ESE was applied in a wide range of different activity fields to predict entrepreneurial context factors such as performance, career path, preferences, and intentionality (Hmieleski & Baron, 2008; Lent & Brown, 2006; Piperopoulos & Dimov, 2015). Especially in case of venture creation, ESE plays a crucial role for explaining the formation ofEI and, finally, entrepreneurial behavior and action. Several studies suggest that ESE is an important antecedent of EI, however, there are divided considerations about the influence of ESE as mediator (Austin & Nauta, 2016; Mortan, Ripoll, Carvalho, & Bernal, 2014), moderator (Lee, Wong, Foo, & Leung, 2011) or IV (Murugesan & Jayavelu, 2017). According to Bandura (1982; 1986; 1991), self-efficacy is a significant driver of student's aspiration level, performance, persistence, effort and the selection behavioral patterns. Goal-oriented behavior and thereby intentionality is related to these parameters. In view of this, it is to be assumed that individuals with a high level of ESE are more likely to start-up a new business. It is based on the following 3 considerations: Firstly, individuals with a high level of ESE possess a high degree of confidence in their personal capabilities to perform different challenges during the venture founding process. Hence, they feel more confident that upcoming entrepreneurial tasks will be performed successfully. Secondly, motivational effects for entrepreneurial planning, a greater persistence against setbacks and efficient ways to perform pending tasks are several effects resulting from positive efficacy beliefs (Bandura, 1986; Shane et al., 2003). Thus, mentioned effects reduce perceived requirements and barriers related to entrepreneurial challenges and encourage considerations about start-up a new business. Finally, the stronger the individual beliefs in one’s entrepreneurial abilities, the more likely it is that business opportunities will be discovered and pursued (Krueger & Dickson, 1994; Tumasjan & Braun, 2012). According to Ajzen (1991, p. 184), "the theory of planned behavior places the construct of self-efficacy belief or perceived behavioral control within a more general framework of the relations among beliefs, attitudes, intentions, and behavior. " Including the TPB, it can be assumed that a high level of ESE, as a main part of PCB, set a wide range of positive effects on person's confidence to challenge entrepreneurial goals successfully and, consequently, encourage the development of EI. For instance, if students feel sufficiently capable to lead employees, manage financial resources, develop creative ideas or recognize business opportunities, they tend to reinforce their efforts toward entrepreneurial goals and, accordingly, enhance the intentionality to start-up a new venture in future. The following hypothesis can, therefore, be derived:

Hypothesis 1: Entrepreneurial self-efficacy is positively related to entrepreneurial intentions.

After the explanation of the link between ESE and EI, the focus is on the important role of PDS and its moderation effect on this relationship. In a first step, a theoretical classification is carried out for a better understanding of the term digital skills. In a second step, an attempt is made to develop the construct PDS and integrate it in the social cognitive career model. Based on it, the moderation effect can be derived for the theoretical foundation. In the third and final step, the variable is integrated in a conceptual framework to explain the interrelationships between the main constructs and, as a result, develop the final hypotheses.

3.3 The Moderating Role of Perceived Digital Skills

Underpinned by digitisation, interconnection and the growing ecosystem of digital technologies, digitalisation is transforming our economies and societies by changing the ways people interact, businesses function and innovate, and governments design and implement policies.” (OECD, 2017, p. 25)

It can be assumed that the global dissemination of information and communication technology (abbreviated below as ICT) will restructure economies as well as related business processes in a comprehensive extent and, therefore, change prospective requirements of digital skills in working environments (Calvani, Fini, Ranieri, & Picci, 2012; Hatlevik, Ottestad, & Throndsen, 2015a). In this transformation process digital skills become an important resource at the individual level to participating in social and economic spheres. In view of this, the European Commission (2019a, p. 4) added digital competences in their framework of key competences for lifelong learning "essential to citizens for personal fulfilment, a healthy and sustainable lifestyle, employability, active citizenship and social inclusion." Accordingly, digital competences refer to people’ s confident, critical and responsible use of ICTs in a variety of contexts (European Commission, 2019a, p. 10). Several (inter-)governmental organizations and scholars of different research areas are engaged to identify, measure and evaluate relevant fields of competences related to the use of ICTs in a knowledge society. Hence, digital skills will be explored in a dynamic process as political construct (identify future needs) as well as normative research construct (make skills measurable), led by the implementation of new ICTs through economic competition (Punie, 2007). This is how new technologies change digital skill requirements in a continuous way. It should be noted that various future expectations from cultures, economies and governments could lead different strategies of digital transformation and, for that reason, the emergence of digital competence levels.

Regarding to the theoretical conceptualization of digital skills in current research, diverse approaches and a wide variety of definitions making it more difficult to clarify a generally admitted construct (Stopar & Bartol, 2019). For instance, digital skills (Matzat & Sadowski, 2012; Zhong, 2011), media literacy (Erstad, 2015; Gutiérrez-Martín & Tyner, 2012; Livingstone, 2004), digital competences (Calvani et al., 2012; Ferrari, 2013; Hatlevik & Christophersen, 2013; Vito, 2017), internet skills (Litt, 2013; van Deursen, van Dijk, & Peters, 2012), digital literacy (Eshet-Alkalai, 2004; Gui & Argentin, 2011), computer skills (Goldhammer, Naumann, & Keßel, 2013) and ICT proficiency (Handley, 2018) are developed concepts which focused a domain-specific view (digital, media, internet, computer, ICT) on the one hand and a knowledge perspective (skills, literacy, competences, proficiency) on the other hand (Hatlevik et al., 2015a, p. 221). This diversity of theoretical concepts reflects the broad variety of different domains and phenomena of digital technologies. In view of this, the development of a theoretical construct should be considering the various contexts. Further, van Deursen and van Dijk (2009) note a lack of knowledge regarding the measurement of digital skills grounded on empirical studies with a too small sample size, limited survey designs and ambiguous definitions. In order to simplify, the present study focuses on the conceptualization of digital skills in a broader definition, including elements within the same domain area - Digital competences as well as digital literacy.

Furthermore, the term digital technologies is used to describe a wide range of smart devices, applications, software and information and communication systems in a broader sense. According to Nambisan (2017), digital technologies comprise digital artifacts, infrastructure and platforms. In a study by Nambisan (2017, p. 1031) the author defined digital artifacts as "digital component, application, or media content that is part of a new product (or service) and offers a specific functionality or value to the end-user (Ekbia, 2009; Kallinikos, Aaltonen, & Marton, 2013)". These include, for example, smartphones, apps, gaming consoles, smart home devices and computers. Furthermore, digital infrastructure refers to systems for enabling information exchange between users and digital artifacts (e.g. social media, cloud computing, market platforms, etc.). Finally, Nambisan (2017, p. 1032) describes a digital platform as "a shared, common set of services and architecture that serves to host complementary offerings, including digital artifacts (Parker, van Alstyne, & Choudary, 2016; Tiwana, Konsynski, & Bush, 2010)". This refers to operational systems such as Windows, Android or IOS as a multilevel interface for digital devices. This definition of digital technologies forms the theoretical foundation of the term in the further course of this work.

The concept of skills is generally defined as "ability to apply knowledge and use know-how to complete tasks and solve problems." (Ala-Mutka, 2011, p. 17). In a narrow sense, digital skill referring an individual's cognitive and practical capability to perform tasks in digital environments successfully. Depending on various circumstances and contexts, previous research literature used the term digital skills in an extended definition which partly overlaps with the concept of competence. (Ilomäki, Kantosalo, & Lakkala, 2011). A previous study by Le Deist and Winterton (2005, p. 38) provides an overview of country-related competence taxonomies and states a domain-specific competence as "willingness and ability, on the basis of subject-specific knowledge and skills, to carry out tasks and solve problems and to judge the results in a way that is goal-oriented, appropriate, methodological and independent." In order to apply this definition in the digital domain, Hatlevik et al. (2015b, p. 346) derived the term competences as "skills, knowledge and attitudes that make learners able to use digital media for participation, work and problem solving, independently and in collaboration with others in a critical, responsible and creative manner." These definitions demonstrate that in several studies the concept of digital competence is interpreted more widely compared to digital skills (Ala- Mutka, 2011; Hatlevik et al., 2015a). For instance, it involves people’s social integration through digital technologies and the capability to solve complex problems by the application of a creative, but also critical thinking in the use of ICTs. A further concept closely related to digital competences is digital literacy. The term coined in the 1990's to define people's ability to understand and deal with digital information (Bawden, 2001). In a broader definition, Erstad (2015) provides essential aspects of digital literacy, comprising basic skills and the ability to download, search, navigate, classify, integrate, evaluate, communicate, cooperate, and create digital information with any media. Thereby the focus lies on operational interactions between data, information and humans through a wide range of digital technologies. Including these considerations, the present study understands digital skills as a wide set of skills and specific knowledge in operating ICT handling (skill approach), including abilities and attitudes to use digital technologies responsibly in social as well as professional contexts (competence approach), and varied practices in the use of information to communicate, solve problems and complete tasks (literacy approach). Therefore, the understanding of digital skills is not restricted on student's ability to apply their resources on a specific task in digital spheres, it also includes a creative expression and awareness of handling new technologies in a critically manner (Janssen et al., 2013). This definition is in line with the conceptualization of the DigComp Framework by the European Commission (Ferrari, 2013).

After consideration of the theoretical background of digital skills, the present study focuses the subjective perception of an individual's skill level in handling with digital technologies. Therefore, PDS are defined as conviction to possess a certain level of digital competences in five different subdomains: Information and data literacy, communication and collaboration, digital content creation, safety issues and problem-solving approaches (Vuorikari, Punie, Carretero, & van den Brande, 2016). Consistent with the DigComp Framework, these competence areas cover a wide range of digital basic skills (Ferrari, 2013). These individual basic skills, based on the DigComp Framework, are explained more detailed in the next chapter. It must be emphasized that the construct PDS follows a similar cognitive mechanism such as self-efficacy. Both constructs based on a cognitive self-evaluation process of one's abilities (PDS) or the confidence in these abilities (ESE). Thus, PDS are not suitable for comparing the digital skill level between different individuals, since no objective valuation standard was provided. In view of the subjective character and the effect of a possible under-/overconfidence bias, the determined level of PDS can differ from the actual skill level.

One of the objects in entrepreneurial research is to analyze the relationship between personal factors (e.g. abilities, skills, knowledge, etc.) and EI. For instance, a study by De Noble et al. (1999) suggests that risk management and opportunity recognition skills have an direct and significant effect on students intentionality to build up a new company. They argue that a broad set of skills enhance people's general feeling to launch a business successfully. Another study by Liñán (2008) indicates that entrepreneurial capabilities (e.g. leadership, management and innovation skills, etc.) have an positive influence on the motivational factors of the TPB and, ultimately, increase peoples willingness to found a venture. In line with Krueger and Carsrud (1993), actual skills can be considered as a relevant driver of people's attitude toward venture creation. Thus, intentionality affected by a certain skill level can be also explained through different mediator variables of the TPB. However, the present study assumes a shaped effect of PDS on the relationship between ESE and EI on the one hand, and a direct effect of PDS on EI on the other hand by including the SCCT. According to the SCCT, Lent et al. (1994) follow the basic assumption of Bandura (1986) and state that future intentions and behavior emerging in a complex process influenced by a set of personal capabilities, self-efficacy beliefs, cognitive traits and the social environment. It can be noted that PDS are changing the conditions how people interact with their environment and processing information (Sousa & Rocha, 2019).

Consistent with the SCCT, PDS can be considered as a source of self-efficacy and outcome expectations on the one hand, and a direct driver of future career choice intentions on the other hand. Therefore, the conviction to possess a certain level of digital skills may affect the cognitive decision-making process toward founding a venture in the way that the pursuit of entrepreneurial goals seems to be more attractive. Conceivably the use of digital technologies lowers the general barriers to peruse an own business idea, and simultaneously serve as a source for new ideas or inspirations. Further, a problem-solving approach can be grounded on a creative use of digital technologies and enable new impulses for business opportunities. For example, people with high programming skills can identify a lack of potential in digital devices, apps or tools and can more likely generate improvement ideas and transform them into a business idea. Thus, the following hypothesis can be derived:

Hypothesis 2: Perceived digital skills are positively related to entrepreneurial intentions.

To clarify the moderation role of PDS and its effect on the relationship between ESE and EI, the present study includes a deduced and more domain-specific approach of Albert Bandura’s SCT - The Social Cognitive Career Theory (abbreviated below as SCCT) by Lent et al. (1994). One Reason for the integration of the SCCT into TPB is a greater focus on the interdependence between self-efficacy beliefs and their drivers (e.g. personal abilities, skills, experience, etc.). According to Brown and Lent (2013, p. 117) the “SCCT acknowledges the important roles that interests, abilities, and values can play within the career development process.” As already stated, SCCT based on the theoretical assumption that individual’s decision-making process is influenced by environmental factors, cognitive traits and an internal control mechanism of self­evaluation (Lent & Brown, 2006; Lent & Brown, 2008). However, SCCT focuses on the three intricately linked determinants a) self-efficacy beliefs, b) outcome expectations and c) personal goals or career choice intentions to describe the process of career development as shown in Figure 2 (Lent et al., 1994). In line with Bandura (1977), the SCCT is founded on the assumption that self-efficacy beliefs are sourced on four antecedents: Personal performance accomplishments (e.g. (un-)successful execution of tasks), vicarious experience (e.g. observing others in their task-strategies), social persuasion (e.g. encouragements, doubts or (dis- )motivation by others) and physiological and emotional states (e.g. anticipation of an project or a high stress level) (Wood & Bandura, 1989). Moreover, Bandura emphasizes the strong effect of successful experience related to a certain task on a person’s efficacy beliefs (Bandura, 1977). According to the SCCT, individuals need a set of necessary skills, knowledge and abilities to perform specific tasks successfully and gather positive experience in this way. A positive feedback, goal achievements and other sources of inspiration enhance the level of self-efficacy (path 1) as well as outcome expectations and enable the interest toward cognitive forming of career intentions (see Figure 2) (Liguori, Bendickson, & McDowell, 2018). In contrast, a low skill-level is more likely to lead to further negative experience by an insufficient task fulfillment and reinforce doubts, recurrent failures and, consequently, negative efficacy beliefs. Thus, “success or failure in reaching personal goals, in turn, becomes important information that helps to alter or confirm self-efficacy beliefs and outcome expectations.” (Hackett, 2008, p. 751). It is likewise conceivable that perceived abilities and experience affect the formation of intentionality directly (path 2).

FIGURE 2: A Simplified View on Social Cognitive Career Theory and the Crucial Role of Self-Efficacy

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Source: Adapted from (Lent et al., 1994, p. 88)

The contribution of different skills, knowledge and abilities to self-efficacy is a central research issue of several studies. For example, findings from Ineson et al. (2013) suggest that student's prior knowledge and abilities in relation to management tasks have a significant positive effect on their self-efficacy beliefs. A further study by Hutchison et al. (2006) indicates that a high level of student's perceived abilities with computing strengthened the conviction to master specific goals successfully. The study also supports the important role of prior experience as most influential predictor of self-efficacy beliefs. The influence of computer experience on self­efficacy was explored in a study by Doyle et al. (2006). Findings also reveal that student's positive experience in the use of computers support their confidence in one’ s abilities to challenge computer-related tasks. With reference to the DigComp Framework published by the European Commission, digital skills include a wide range of abilities, knowledge and attitudes in different competence areas (information management, communication, content creation, safety issues and problem-solving strategies) when using ICTs (Ferrari, 2013). It can be assumed that a high level of perceived digital skills is an important prerequisite for the development of self-efficacy beliefs and has a supportive effect on mastering challenges resulting from the entrepreneurial start-up process and its relationship to the development of EI. For example, an individual's high skilled research strategy in digital spheres can make an important contribution to find relevant information needed for the development of a business idea. Advanced digital communication skills can support the process of networking with important stakeholder or help to spread the business vision for interested people. Moreover, the effective use of digital tools can provide solutions in a different manner. For instance, nascent entrepreneurs with digital skills are using apps for financial planning, business modelling, competition analysis, design thinking and much more to implement their own business project. In line with the SCCT the following considerations are assumed: Based on positive experience within the application of digital skills, a high level of perceived digital skills enables the development of beliefs that entrepreneurial-related tasks can be performed successfully when using ICTs and ultimately increases the effect to encourage EI. In this way, PDS shape the relationship between ESE and EI. However, a low level of digital skills can lead to a perceived disproportion between necessary skills and an individual’s perceived resources to solve problems related to the entrepreneurial start-up process and, therefore, are more likely to result in negative experience. Thus, defective performance grounded on a low level of digital skills restricts the own ESE beliefs and intensify doubts about the intention to start-up a business in future. Accordingly, the effect of ESE on EI decrease. For instance, if a student has strong / low abilities in the use of ICTs, it generates positive / negative experience by applying these skills and ultimately increases / decreases his or her confidence in own capabilities to master challenges in the founding phase with digital technologies and know-how. Thus, the following proposition is suggested:

Hypothesis 3: Perceived digital skills positively moderate the relationship between entrepreneurial self-efficacy and entrepreneurial intention.

A summary of the conceptual framework is illustrated in Figure 3. As this figure implies, ESE has a direct and positive effect on EI (Hypothesis 1). Based on the TPB, the present study uses the constructs perceived behavioral control and ESE as overlapping terms. Therefore, ESE can be considered as a main predictor of EI. PDS have also a direct and positive effect on EI (Hypothesis 2). In line with the SCCT, intentions and behavior emerge through a complex interaction between individual expectations and beliefs, previous experiences and environmental influences. Hence, the individual conviction to possess a certain level of digital skills simultaneous has a direct effect on the development of future intentions and, ultimately, behavior. Finally, PDS shape the relationship between ESE and EI (Hypothesis 3). According to the SCCT, PDS can be assumed as important source (mastery experience) of self-efficacy expectations and therefore as enabler for a high level of ESE and its positive effect on EI.

FIGURE 3: Conceptual Framework of the Theoretical Constructs and their Relationships

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Source: Author’s own work

For the measurement of the construct PDS, the present study develops a scale on basis of the DigComp Framework by Vuorikari et al. (2016). Therefore, the next chapter outlines the theoretical background of the DigComp Framework for a better understanding. Accordingly, it comprises the definition, methodical approach and illustration of all digital basic competences. In addition, the theoretical implementation of the Framework in a research design is discussed.

3.4 DigComp Framework

Out of political considerations several countries and organizations are concerned with the definition and classification of digital skills as a central resource for participating in social and economic environments. For example, Norway developed a framework for digital skills in a national curriculum for comparing abilities (e.g. searching, produce, communicate, and judge of digital media) of students in different schools (Norwegian Directorate for Education and Training, 2012). A further popular concept is the 21st century framework by Binkley et al. (2012), which differentiates between people’s ways of thinking (e.g. creative, critical, etc.), ways of working (communication, collaboration), tools for working (information literacy) and contextual situations (social, economic) to classify relevant capabilities of an knowledge society. In accordance with above mentioned Frameworks, the present study includes a more recent and evidence-based conceptualization published by the European Commission for measuring people’s PDS - The DigComp Framework (Carretero, Vuorikari, & Punie, 2017; Ferrari, 2013; Vuorikari et al., 2016). This conceptual framework serves as basis for the development of the scale in the next chapter.

In 2011, the Joint Research Centre of the European Commission started a two-year project launched by the Information Society Unit at the Institute for Prospective Technological Studies (IPTS). The first version was publicized by Ferrari (2013), followed by an updated DigComp 2.0 Framework (Vuorikari et al., 2016) and the latest version, the DigComp 2.1 (Carretero et al., 2017). The DigComp 2.0 is an improved version considering linguistic peculiarities by adopting a new terminology driven by the progressive development of digital technologies. Changes in the DigComp 2.1 Framework are not relevant for the later scale development. For this reason, the present study uses the comprehensive upgraded version 2.0 for the derivation of digital competences. Main aim of the DigComp Framework project is "the better understanding and development of Digital Competence in Europe", including the identification of key components regarding to digital competences, the development of a evaluable framework as guideline for the competence level of European citizens and the proposition for a future roadmap to adapt people’ s digital competences (Ferrari, 2013, p. 2). In particular, this approach is appropriate for individual's self-evaluation of one's digital competence level to identify strengths and weakness in different digital application areas. Therefore, it can be used for the improvement of digital skills, helping policy makers to adapt legal conditions and giving new approaches as integrated model in education and research areas. The starting point for the project was grounded on a systematic review of 15 analyzed frameworks (Ferrari, 2012) and a conceptual mapping of digital competences (Ala-Mutka, 2011). To collect valuable opinions about digital basic competences, an online consultation with 95 stakeholders and experts from various workspaces was completed (Janssen et al., 2013). Finally, the preliminary data was refined and structured in an expert workshop for a draft proposal. The final proposal was approved, after a multi-stakeholder consultation has discussed and validated the results within further workshops and interviews. The resulting framework comprises five competence areas with 21 competences (see Table 2) which are described below.

TABLE 2: Overview of Basic Skills Based on the DigComp Framework

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The DigComp Framework was divided into five competence areas: Information and Data Literacy, Communication and Collaboration, Digital Content Creation, Safety and Problem Solving (Vuorikari et al., 2016). According to Vuorikari et al. (2016, pp. 14-16), Information and Data Literacy is defined as the ability to search for necessary information, to analyze, to compare as well as to evaluate them and to organize digital content in a structured manner. Communication and collaboration describe people’ s interaction through a variety of digital technologies by sharing content, working together on projects or using social platforms in digital environments. The creation, modification, refinement, improvement and understanding of digital content and coherent technologies was summarized in the term Digital Content Creation. Furthermore, Safety is an aspect that includes the protection of devices, personal data, health issues as well as an individual risk-assessment in digital spheres. Finally, Problem Solving strategies comprise the identification and solving of technical problems, creative methods of resolution and existing competence gaps. For the purposes of illustration, the competence areas (Dimension 1) and sub competences (Dimension 2) are shown in Table 2 above.

According to Ferrari (2013, p. 10), “the framework could be used by curricula and initiative developers who want to develop the digital competence of a specific target group, and could be inspired by or gain ideas from this model.” Thus, the framework also addresses applied research areas. Recently, several publications in different research fields have integrated the DigComp Framework in their study design (Evangelinos & Holley, 2014; Gewerc, 2015; Napal Fraile, Peñalva-Vélez, & Mendióroz Lacambra, 2018). For example, Siiman et al. (2016) developed a self-report questionnaire for school-age children in alignment with the DigComp Framework to understand children’s usage behavior of digital devices. A study by Kuzminska et al. (2019) derives the DigComp Framework to measure the digital skill levels of Ukrainian teachers and students by prescribing five competence areas. They conclude a high reliability of the developed questionnaire which is grounded on DigComp Framework. Moreover, Evangelinos and Holley (2015) investigate a target-group-specific derivation of digital competences in the health-care profession. On basis of the DigComp Framework, they defined and refined a more job-specific questionnaire about digital competences for people in healthcare provision. In accordance with the publications mentioned above, the current study includes the five-dimensional DigComp Framework as a theoretical guide for the development of a questionnaire to measure Master student’s PDS.

4. Development of Questionnaire and Methodology

In the following chapter, initial conditions of the quantitative analysis will be explained and substantiated in detail. This comprises the selection and specification of the sample, a scale development of the construct PDS, the integration of existing measurement methods for measuring EI as well as ESE and finally an explanation of selected control variables to exclude potential confounding factors. The primary focus is set on the development of a questionnaire based on the DigComp Framework and a subsequent explorative factor analysis to examine a set of meaningful items as well as develop a valid instrument for measuring PDS. In conclusion, it provides the preconditions to analyze the collected data in the next step through a descriptive procedure.

4.1 Sample and Data Collection

Data were collected using an online survey (with LimeSurvey 2.73) to reach as many students as possible in an interactive and modern manner. The survey incorporates 68 questions in five thematic blocks (EI, ESE, Perceived Digital Skills, Financial Literacy and Demographic Variables) and addresses Master’s students of all study courses on German universities. A sample of Master’ s students was selected by following a main argument from Krueger (1993, p. 6): Regarding to entrepreneurial career intentions as research subject, it seems to be reasonable to select "samples of subjects currently facing actual major career decision". In order to follow the advice by Mortan et al. (2014) and Piperopoulos and Dimov (2015), a sample of students with a high variety of interest fields was selected to make a useful contribution for the generalization of the results and counteract against a possible self-selection bias. It can therefore be assumed that entrepreneurs also emerge strongly from different disciplines, although they have a non-business background (Austin & Nauta, 2016; Tessema Gerba, 2012). Before beginning, participants were informed about the ethical guidelines of research practices which include the principles of voluntariness, anonymity and transparency, based on the recommendation of the Deutsche Gesellschaft für Psychologie (2018), regarding ethical action in practical research areas. Participants were also informed that the questionnaire required around 10 minutes. To prevent a lack of data, almost all items were designed as mandatory questions, excluding sensitive data such as year of birth, family status, number of siblings, and monthly net income. On the one hand, the embedded link was shared on the central e-mail distribution list provided by Justus-Liebig-University for seeking volunteers in all university departments. On the other hand, fellow students, friends and acquaintances were personally asked if they are willing to participate on the online survey and share the link among other Master’ s students. In order to avoid the participation of non-master’s students, there is firstly a reference on the start page and secondly a control variable asks for the current intended degree. Finally, the online survey was open for responses for one month. After carrying out the online survey by the respondents, the collected data were converted to SPSS (Version 26), checked for inconsistencies, corrected of outliers, edited and prepared for statistical analysis.

In total, a sample of 181 Master’s students was surveyed. A large majority of responses were completely filled out, excluding four respondents (2.2%) who did not mention their family status, monthly net income or year of birth. Only six participants revealed themselves as bachelor students and responses were discarded afterwards. Data reveals a well-balanced gender distribution (coded as 1=”f”, 0=”m”), among them 55.2% females and 44.8% males aged between 21 and 38 years. The average age is 25 years (SD = 2.24) , consistent with the age span of 25 to 30 years in which the most Master’s students graduate on German universities (Federal Ministry of Education and Research, 2018, p. 277). As might be expected, almost none of the students are divorced or widowed. Only 12 respondents (6.6%) reported that they are married, might be reasoned by a little delay in family planning with regard to the comparatively lengthened education. In view of this, family status was coded as 1=”single” and 0=”married”. Moreover, it can be stated that every third participant has an economic background (29.3%), whereas 70.7% of students are from other disciplines. The strongest represented study courses encompass the disciplines law and economics, social science and psychology (16.0%) as well as medicine and health care (8.8%). This is followed by the study programs art, design and music (7.7%), natural science (7.7%), linguistic and cultural studies (7.2%), education and teaching (7.2%), computer science and IT (6.1%), engineering (6.1%) and agriculture and forestry (3.9%). In total, student’s economic status is mainly determined through a lower income level. 74.0% of the respondents indicated that they have a net income less than 1000€ per month. It is conspicuous that 59 respondents (32.6%) have self-employed parents, while 46.4% are connected with entrepreneurs through their circle of friends and 74.0% through their acquaintances. The average amount of working years the students have spent in a marginal part­time job, internship or vocational training is 3 years. Moreover, the median level of the number of siblings came to 1. Table 3 provides an overview of the main demographic data of all participants.

TABLE 3: Demographic Data Summary of the Sample (N=181)

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4.2 Designing the Digital Skill Instrument

A secondary purpose of the current study is to develop a questionnaire for measuring an individual’s PDS on basis of the DigComp Framework by Vuorikari et al. (2016). In consideration of the integration in different research studies and a broad coverage of digital competences in a modern knowledge society, the DigComp Framework is particularly suitable for a derivation of measurable competence factors in using digital technologies. In order to cover a wide range of competence areas and to describe the construct PDS in its entire spectrum, all 21 competences (see Table 2) have been transformed to contentual equivalent items. In other words, each of the total 21 core competences was compiled in a statement similar in content for measuring students self-report in faced activities within digital spheres (see Annex 1). In context of scale development, this formulation of statements is in line with the common practice in applied psychology research (Cardon, Gregoire, Stevens, & Patel, 2013, p. 378). Therefore, the scale for representing perceived digital skills is derived from the competence areas information and data literacy (3 items), communication and collaboration (6 items), digital content creation (4 items), safety (4 items) and problem solving (4 items). During the transformation of these 21 broadly-based competences in tangible and concrete activities, a minimum of information loss is to be expected. In line with the theoretical conceptualization of perceived digital skills in the previous chapter, the designed items comprise statements related to individual's skills ("I am able to..."), knowledge ("I know...") and attitude ("I am aware..."). Using a seven-point Likert scale (1 = I totally disagree, 7 = I totally agree), respondents were confronted with statements such as "I know many ways to search for information on the Internet" derived from the framework competence “Browsing, searching and filtering data, information and digital content“, or "I use a variety of applications (e.g. mails, chats, blogs, etc.) for online communication" which based on the digital competence "Interacting through digital technologies". It must be emphasized that students were asked about a self-evaluation of their skills, knowledge and attitudes in the digital context. Therefore, PDS have a subjective character and were not determined through a standardized testing procedure with true or false questions and an objectified scoring model.

The second step is mainly devoted to analyzing the correlative structure of the 21 items and ultimately select those with a high explanatory power for complexity and redundancy reduction. By using SPSS (Version 26), an exploratory factor analysis (abbreviated below as EFA) has been performed with a dataset of 181 Master’s students to examine the factor structure of the construct PDS in more detail. To measure whether the present dataset seems to be suitable for EFA, a Kaiser-Meyer-Olkin measure of Sampling Adequacy was run and revealed a good value of .93. A value more than .5 is recommended and indicates that variables have sufficient commonalities to cover a broad variance (Kaiser & Rice, 1974). The Bartlett’s Test of Sphericity (Chi-Square (210) = 2888.08) supports this result, by confirming the null hypothesis (p < .05) with a statistical significance of .000. Overall, the dataset in its entirety seems to be qualified for generating useful results through an EFA. Another important prerequisite is an appropriate number of respondents and the selection of reliable items with a high explanatory value. According to MacCallum et al. (1999) and Mundfrom et al. (2005), a sample size of n=60 is applicable, if the item communalities exceed a value of .60. The commonality indicates how much of the variance is explained through an item in all factors. Therefore, all items with an extraction value lower than .60 were discarded to select only variables with a high proportion on the common variance, explained through all factors. Consequently, the diagonal line of the Anti-Image Correlation Matrix was examined on values below .60 to identify further items for elimination and verify the prior results, however, all alpha values achieve a level above .70. In total, 3 items were eliminated with a communality value of .55 (PDS25), .55 (PDS41) and .58 (PDS54) (see Annex 2). The remaining 18 items seemed to be quite appropriate for further examination.

After checking the above-mentioned quality criteria, EFA was run again with the adjusted data. On basis of the refined dataset, the determination of numbers of factors was conducted. Taking into consideration the Kaiser-Guttman-criterion with an Eigenvalue of 1 as cut-off point, the EFA revealed a four-factor structures, which explained a total variance of 75.0% in the data (Guttman, 1954; Kaiser, 1960). This four-factor solution will be maintained in the following analysis. Based on the principal components analysis with varimax rotation, all factor loadings above .70% were retained in the rotated factor matrix. Following common practice, further 6 items (PDS11, PDS12, PDS13, PDS26, PDS33, PDS42) with a low rated factor assignment (< .70) were excluded to ensure a high meaningfulness of the measurement model. Considering that an identified single item (PDS34) is fully loaded on one factor, this item will be examined in more detail. An analysis of the item difficulty indicates that the vast majority of respondents negated the question "I am able to develop my own program (e.g. software or app)". That allows one to conclude that programming skills are not a part of digital basic competences, but rather an advanced knowledge in the use of digital technologies. For this purpose, the above­mentioned item was discarded afterwards. After running EFA again, an 11-item scale with a four-factor structure was provided. Hence, remaining solutions indicated that the selected factors poll an overproportion predictive power (explains 83.3% of the total variance) and have an adequate reliability for the invention of a four-dimensional multi-item construct (see Annex 3). The selected 11 items for the PDS factors along with the appropriate loadings, eigenvalues, scale means, and Cronbach’ s alpha are presented in Table 4 (see Annex 4). The following table illustrates that remaining factor loadings are all above .60 and therefore suggest a high level of reliability for the multidimensional construct.

TABLE 4: Selected Scale Items Including Factor Loadings, Eigenvalues, Scale Means and CR Alpha

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Source: Author’s own work

Identified side loadings indicate that two items (PDS23, PDS24) of factor one cannot clearly separate from factor two. Therefore, it suggests that some overlaps exist in the construct communication and collaboration and creative problem solving. Based on prior knowledge of the theoretical construct PDS, this solution can be accepted. Further, it can be shown that factor one summarizes items related to the communication and collaboration through digital technologies and factor two describes skills such as creative problem-solving approaches. The creation and modification of digital content are pooled through variables in factor three and self-protection measures characterize abilities are linked to factor four. Therefore, four dimensions are identified: (1) Communication and Collaboration, (2) Creative Problem Solving, (3) Digital Content Creation, (4) Health and Safety Prevention.

In the third and final step, a confirmatory factor analysis (abbreviated below as CFA) was carried out to assess and review the factor structure of the PDS construct. This also refers to the intercorrelations structure among the four factors of the CFA model (see Annex 5). Using AMOS (Version 26), a covariance-based structural equation model was developed to calculate relevant model-fit indices. However, the selection of indices is also depending from the sample size, estimation method, misspecifications and breach of conditions during Structural Equation Modelling (abbreviated below as SEM) (Byrne, 2001, pp. 79-88). Following those considerations, an estimation of indices in different measuring ranges could be useful, if they are adoptable for the principal components analysis. Before running the CFA, all covariances in the respective sub-dimensions were examined by checking the modification index (M.I) for outliers (see Annex 7). The modification index for the covariance of PDS52 and PDS53 come to 6.83% and therefore indicates that both indicators probably have a similar significance. By interconnecting these indicators, an improved model fit can be reached. The standardized residual covariances show no sign for further outliers, since all values fall below 2.58 (Byrne, 2001, p. 89) (see Annex 6). In line with Kline (1998), the model was evaluated by means of absolute fit indices (CMIN, RMSEA), incremental fit indices (CFI, NFI) and parsimony measurements (CMIN/df). CMIN is equivalent to Chi2 and refers to the minimum of the discrepancy function. Discrepancy value (CMIN = 73.732). The Root Mean Square Error of Approximation (RMSEA = .07) indicates an adequate model fit (Hair, 1998). The Normed Fix Index (NFI = .95) as well as Comparative Fit Index (CFI = .97) were included to compare the proposed model with a basis model. Both indices suggest a high model accuracy by reaching values above .95. According to Hair (1998), a CMIN/df value below 2.0 suggests an appropriate fit, which is also achieved in the present model (CMIN/df = 1.99). Following theory-based considerations, the indicators PDS52 and PDS53 are maintained and reasoned by their contentual discrepancy. Overall, model-fit indices reveal that a reliable model has been identified. The structural equation model, including all correlations is presented in Figure 4. It can be illustrated that all covariances are statistically significant (p > .05). Therefore, the remaining item-sets represent each construct in a comprehensive manner by sharing a high degree of variance. Ultimately, it can be stated that the CFA confirms a convergent and discriminant validity of the model structure considering the significant factor loadings, corresponding t-values (>2.0) and model-fit indices (see Annex 8). A reliability analysis was performed to examine the inter-item correlation and determine Cronbach’s Alpha. Findings are presented in Table 4 and give evidence for the internal consistency of each construct.

FIGURE 4: Confirmatory Factor Analysis Model of the Perceived Digital Skill Factors

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4.3 Study Measures

Entrepreneurial Intention. The dependent variable EI was measured using 6 pure-intention items that were adopted from Liñán and Chen (2009). Based on the TPB, they developed an Entrepreneurial Intention Questionnaire (abbreviated below as EIQ) for a cross-cultural application and validated items for the constructs personal attitude (5 items), subjective norms (3 items), perceived behavioral control (6 items) and EI (6 items). The EI-scale focuses people’ s intentionality to become an entrepreneur and launching an own business in future. In previous research several approaches to measure EI are existing, which is due to the fact that the complexity of this construct is not finally clarified yet (Rosique-Blasco et al., 2018). According to Armitage and Conner (2001), the measurement of EI overlaps with similar measurement concepts of self-prediction, desire and interest. For complexity reduction and avoidance of measuring errors, the Liñán and Chen (2009) scale includes only pure-intention items which can be rated on an seven-point Likert scale (1= total disagreement, 7 = total agreement). To collect information about the intentionality of participants the EIQ comprises items such as "I am ready to do anything to be an entrepreneur" or "I have very seriously thought of starting a firm". In recent years, the EIQ by Liñán and Chen (2009) became one of the most widely used instruments for measuring EI in different research areas (Liñán & Fayolle, 2015). Reliability of the EI-scale was tested with a high Cronbach’s alpha value of .943 and also revalidated in current studies (Adekiya & Ibrahim, 2016; Atitsogbe et al., 2019; Nowinski, Haddoud, Lancaric, Egerová, & Czeglédi, 2019; Rosique-Blasco et al., 2018). Therefore, the robustness of measurement is particularly suitable for heterogenous samples of students in different study courses and thus a broader field of interests.

Entrepreneurial Self-Efficacy. ESE was measured by using a task-specific scale from McGee et al. (2009) for different phases in the venture creation process. Based on the widely used Chen et al. (1998) measure, they proposed a refined measurement and developed a 19-item questionnaire about self-evaluated abilities to challenging entrepreneurial tasks in five entrepreneurial sub-dimensions: Searching for opportunities, planning business-related processes, marshalling resources and implementing people as well as financial resources. A CFA also suggest that the ESE-construct is a five-dimensional instrument (all Cronbach alphas exceeding .8) and therefore differs from previous measures in identified phases of the start-up process. For the rating of individual's confidence-level in their capabilities to perform entrepreneurial tasks successfully, a seven-point Likert scale (1 = very little confidence, 7 = very much confidence) was used instead of the five-point Likert scale used by McGee et al. (2009). After implementing a seven-point Likert scale, measurements have shown that the prior scale mean (AVG = 0.85) based on Cronbach's Alpha is even marginally below the current average value of .89 and reveals therefore nevertheless a statistically significance without constraints in measurement. A further reason for this is a more differentiated rating of evaluated abilities and providing a clear visualization through a uniform scale formation. Participants are asked how much confidence they have in their ability to “identify the need for a new product” (Searching), “design an effective marketing campaign” (Planning), “network” with others (Marshalling), “supervise employees” (Implementing People) or “manage financial assets” (Implementing Financial). Therefore, it covers the essential task domains related to the business launching process. The multi-dimensional construct proposed by McGee et al. (2009) is one of the most widely tested in recent years and has proven to be a rugged and reliable measurement (Karlsson & Moberg, 2013; Newman, Obschonka, Schwarz, Cohen, & Nielsen, 2019; Nowinski et al., 2019; Schlaegel & Koenig, 2014; Sondari, 2014). Especially, in use of an sample including students from various disciplines a robust instrument is important for an accurate measurement (Karlsson & Moberg, 2013). Moreover, a systematic review by Newman et al. (2019, p. 415) advocate the integration of the McGee et al.’s (2009) measure as an improved version of the Chen et al.’s (1998) scale and recommend it "for researchers who wish to examine ESE in different stages of the venture creation and development process." Considering that the current study focuses students self-evaluation process of their abilities related to entrepreneurial activities in a potential stage of entrepreneurial intention building, the McGee et al. (2009) scale seems to be an appropriate measurement in the present context.

Perceived Digital Skills. Summarized, PDS was measured by a self-developed scale, based on the DigComp Framework of the European Commission. The results provide a 11-item scale for measuring digital basic skills in the four sub-dimensions "Communication and Collaboration", "Digital Content Creation", "Health and Safety Prevention" and "Creative Problem Solving". The questions are rated on a seven-point Likert scale. After performing an EFA and subsequent an CFA, results give a strong support for a reliable and valid model fit.

4.4 Control Variables

To take the influence of demographic variables and situational factors on EI into account, the present study examined several control variables. Previous literature suggests that demographic factors on the individual level such as gender, age or family status are associated with the decision to become an entrepreneur (Hatak, Harms, & Fink, 2015; Hsu, Wiklund, Anderson, & Coffey, 2016; Shinnar, Giacomin, & Janssen, 2012). For instance, increasing age is related to positive effects on human capital factors such as experience or education and ultimately enhances the probability to intend a business launch (Lee et al., 2011). However, the positive impacts decrease at a certain age as consequence of increasing opportunity costs and a compensative effect through a higher income level (Alba-Ramirez, 1994; Bates, 1995). To examine this, monthly net income was also asked as further control variable. Moreover, in recent years many studies have been conducted to derive the influence of gender-specific characteristics on entrepreneurial career intentions (BarNir, Watson, & Hutchins, 2011; Díaz- García & Jiménez-Moreno, 2010; Shinnar et al., 2012). Considering context-specific factors (culture, age, family background, etc.), several scholars argued that comparatively more young woman limited their career aspirations as a result of underrating their own abilities in a perceived male field (Wilson et al., 2007). Findings of other studies suggest no significant difference between the gender-based confidence-levels related to abilities needed for the venture founding process (Murugesan & Jayavelu, 2017; Zhao et al., 2005). Considering the dissent in previous research, gender was asked in the survey to be on the safe side. Furthermore, the family status of individual’ s may be an issue in their intend to become an entrepreneur and was also integrated as a control variable (Davis & Shaver, 2012).

Another important part of the survey captures questions about participant's family background as well as educational and experiential issues. The background factors comprise the questions "How many siblings do you have?" and "Are your parents’ self-employees?" (coded as 1 = “yes” and 0 = “no”). In additional, it was separately asked about existing relations to entrepreneurs in the circle of friends or the circle ofacquaintances. Finally, the social interaction with entrepreneurs in the circle of friends, acquaintances and family is aggregated to a new variable - Social influence. Consequently, the more social environments are shaped by entrepreneurs, the stronger is the social influence on an individual, driven by the confrontation with entrepreneurial issues or attitudes. There is a wide range of evidence that the number of siblings and a perceived positive image of entrepreneurship in social contexts drive entrepreneurial career intentions (Altinay, Madanoglu, Daniele, & Lashley, 2012; Athayde, 2009; Farrukh, Khan, Shahid Khan, Ravan Ramzani, & Soladoye, 2017; Tognazzo, Gubitta, & Gianecchini, 2016; Zellweger et al., 2011). Since the survey addressed Master’s students of all disciplines, the study course was captured as educational control variable. This is grounded on the consideration that student's in economic-related study programs may have potentially more points of contact with entrepreneurial issues and therefore are more willing to start a career as entrepreneur in future. In view of this, students were also asked three questions (three response options with only one right answer) about economic and financial interrelations to determine their financial literacy. These items (The Big Three) were adopt from Lusardi and Mitchell (2014) and represent people's knowledge in interest compounding, inflation and risk diversification related to economic questions. For a more detailed ascertainment, it was additionally asked if an entrepreneurial course was attended before. In the entrepreneurial research a broad consensus that prior entrepreneurial education has a significant influence on people's intention to start-up a new business is available (Bae, Qian, Miao, & Fiet, 2014; Kassean et al., 2015; Nowinski et al., 2019; Souitaris, Zerbinati, & Al-Laham, 2007; Wilson et al., 2007). Especially, prior experience and gathered knowledge related to entrepreneurship are strongly associated with EI (Farooq, 2018; Watchravesringkan et al., 2013). Therefore, years of prior work experience and self-employment experience that already gained were also asked for.

Finally, the last part of surveyed control variables comprises questions about individual characteristics and attitudes. For example, participants were asked to rate the question "For me Entrepreneurship means a considerable risk" on a seven-point Likert scale to include a variable for representing student's entrepreneurial risk-attitude. This item is based on a measurement of entrepreneurial risk perception developed by Fedakova et al. (2018). A stronger willingness to take risks in business decisions has some positive effects on the intentionality to launch a business, because start-up-related uncertainties will be more accepted (Yurtkoru, Acar, & Teraman, 2014). Another cognitive factor affecting EI is the way in which people are searching, identifying and perusing business opportunities systematically. Kuckertz et al. (2017) developed and validated an instrument that incorporates four items for measuring individual’ s opportunity recognition on a seven-point Likert scale. In the survey, two scale-items with the highest loadings resulting by a confirmatory factor analysis (Item 1 = .79, Item 2 = .84) were adopted: "I search systematically for business opportunities" and "I regularly scan the environment for business opportunities". Moreover, based on a study by Gielnik et al. (2014) two slightly modified questions from their interview with business owners were adopted to measure business opportunity identification and exploitation. The participants must specify if they have identified or pursued a business opportunity in the last 5 years. Lastly, students must rate the question "To start up a digital business is interesting for me" on a seven-point Likert scale. The providing answers are useful to ex- or include the conclusion that people with a high level of digital skills tending to launch a business in the technological and digital sector.

TABLE 5: Overview of Instruments and Control Variables for Measurement

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5. Data Analysis and Findings

To summarize, a scale for measuring PDS was developed by (a) deriving 21 items on basis of the DigComp Framework, (b) conducting an online survey among 181 Master’ s students for testing the items, (c) performing a EFA to examine the scale structure as well as select reliable items for each construct and (d) carrying out a CFA to evaluate the model for confirming validity and reliability. Results indicate a consistent 11-item scale comprising a 4-dimensional factor model. Moreover, tested and valid scales developed by Liñán and Chen (2009) and McGee et al. (2009) were included for measuring students EI and ESE. The data analyses of the constructs include descriptive statistics such as means (M), standard derivation (SD), Pearson correlations and a regression estimation based on the method of ordinary least squares. The main findings are presented in more detail in the following.

Before starting the main data analysis, some control variables were examined more closely. Reasoned by the content-related proximity between the systematic searching of opportunities and the identification of business opportunities, the determinants opportunity recognition and business identification were reviewed in more detail. Both variables are highly correlated (r = .55, p < .001) and seem to have a similar meaningfulness related to their content. Therefore, business identification is discarded as control variable in the further analysis. In Addition, no stronger intentionality toward entrepreneurial aspirations among business-related students could be determined (see Annex 16). Hence, in the collected sample the academic background may play a tangential role in the decision to become an entrepreneur. Study course was therefore discarded as possible control variable.

The descriptive statistics and inter-correlations for the sample are given in Table 5. It can be stated that all bivariate relationships between the main constructs are highly significant and achieve absolute correlation coefficients greater than .44 based on a p < .05 significance level. An unambiguous relationship between ESE and EI may be evidenced by a strong correlation of .68 (p < .001). The examination of the results of the relationship between EI and the remaining variables (Column 1) shows that almost all correlations are highly significant. However, there are three exceptions: Age, family status and the number of siblings show no statistical significance, whereas the monthly net income demonstrate an adequate significance. Moreover, it can be noted that ESE, entrepreneurial risk perception, opportunity recognition and the interest on a digital business launch are highly correlated (r > .50) to EI (Cohen, 1988). An adequate correlation of .30 < r < .50 can be found between the variables PDS, social influence, entrepreneurial experience, entrepreneurial course attendance, perceived financial literacy and EI (Cohen, 1988). Furthermore, findings provide some indications of a significant and adequate correlation between the perception regarding a high level of digital skills and the interest to launch a business in the digital- and technology sector (r= .34, p < .001). In other words, people are more interested to start-up a business in the technology sector, if they feel well equipped in their digital skills and know-how. Moreover, a negatively weak but significant correlation can be stated between the gender and EI: Women demonstrate a comparatively lower inclination to be intentional founders than men. It is also striking that student's perceived financial literacy correlated much higher with EI than measured financial literacy according to Lusardi and Mitchell (2014). There are indications of an existing overconfidence bias, hence this phenomenon will be examined in the regression model in more detail.

With the purpose of investigating the relationship between ESE (as the IV) on EI (as the DV) which is moderated by PDS, several control variables were entered to form the baseline in Model 1 by using a hierarchical multiple regression analysis (see Table 7). In Model 2 the main effects by ESE and PDS were entered to evaluate the incremental validity in the prediction of EI. Finally, the interaction effects of these variables were provided in Model 3. A detailed model summary is listed in Annex 9. In order to examine whether the regression model is overall significant, a F-Test was performed. Results indicate that the base model (F(13, 167) = 35.02; p = .000) contributes significantly towards an explanation of entrepreneurial career choice intentions. The overall goodness-of-fit of chosen regression model is verified by a high and significant adjusted R2 of .71 (p = .000). In view of Model 1, some interesting observations about several control variables and their effect on EI can be made. For instance, age has no significant impact on the likelihood of choosing a career as self-employee (ß = 0.04; t(167) = 1.06; p > .10). Given the fact that 95.0% of the respondents are aged between 20 and 29, any conclusive statements about a u-shaped correlation between student’ s age and EI can thereby not be made. Contrary to expectations implied by the correlations, findings indicate that the influence of gender (ß = 0.09; t(167) = 0.49; p > .10) or the interest on a digital business launch (ß = -0.01; t(167) = -0.17; p > .10) does not seem to play an important role in the decision to become an entrepreneur. However, considering the social influence, the embossment of self­employed parents, friends and acquaintances have a significant effect on the formation of students' EI (ß = 0.17; t(167) = 1.99; p < .05). Therefore, the interaction with self-employed people across different social environments can strengthen the willingness to pursue own entrepreneurial goals. Interestingly, the attendance on an entrepreneurship course shows a positive and significant effect in model 1, whereas personal experience in entrepreneurial activities is significantly related to EI in model 2. In addition, the number of siblings is a significant factor in the prediction of EI in all models. Findings show that EI is positive affected by an increasing number of siblings. Finally, personality traits and attitudes also play an important role in supporting student's intentionality to pursue entrepreneurial activities. Especially, three variables stand out by a very high significance in their prediction of EI. Results suggest that a risk-averse attitude toward entrepreneurship has a negative influence on EI (ß = -0.22; t(167) = -3.59; p < .001), whereas a systematically looking for business opportunities strongly increases the likelihood to become an entrepreneur (ß = 0.66; t(167) = 9.76; p < .001). In addition, EI are negatively affected by the monthly net income (ß = -0.21; t(167) = -2.90; p < .01) in all models and reveal that a higher income lowers the willingness to start-up a business. One further interesting finding is that the perceived financial literacy is significant and positively associated with EI in the base model, whereas the measured financial literacy construct by Lusardi and Mitchell (2014) negatively influence the entrepreneurial career choice in model 2 and 3. This provides indications for a gap between peoples perceived financial literacy and their actual knowledge in financial issues. In other words, the individual perception of a strong financial expertise increases the aspiration to pursue entrepreneurial goals on the one hand. On the other hand, existing knowledge and basic competences about financial issues decreased the intention to become an entrepreneur. Theoretical backgrounds will be discussed in the following chapter.

The main hypotheses were tested in model 2, which reveals a significant increase in the explanation of variance compared with the base model (AR2 = 0.03). Considering the coefficient of determination (adjusted R2) in this model, 74.61% of the total variance in EI can be explained through the entered variables. The F-Test (F(15, 165) = 36.27; p = .000) confirms the explanatory power of the regression model (see Annex 10). As expected, and in line with previous literature there is a highly significant and positive relationship between ESE and EI (ß = 0.39; t(165) = 3.59; p < .001), confirming hypothesis 1. Therefore, a high level of confidence in one’ s own abilities to perform entrepreneurial tasks successfully strongly predicts the intentionality to start-up a business in future and makes it more likely. Furthermore, PDS are significantly related to EI, however, the effect is much weaker (ß = 0.18; t(l65) = 2.00; p < .05). Thus, PDS have a low, but significant influence on the intention to become an entrepreneur, suggesting that student's perception of high leveled digital skills increase the probability to pursue career as self-employee. Consequently, hypothesis 2 finds full support.

TABLE 6: Descriptive Statistics and Correlations (N=181)

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Source: Author’s own work

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TABLE 7: OLS Regression Estimation of Entrepreneurial Intention (N=181)

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In Model 3, the addition of the interaction term of ESE and PDS does not lead to an improvement in the model fit (AR2 = 0.0; p = .736). Findings reveal that there is no significant interaction effect of PDS on the relationship between ESE and EI in this regression model (ß=0.198; p < .05). A view on the F-value (F(16, 164) = 33.83; p = .000) demonstrates a decrease of the total explanatory power by entering the interaction effect. Moreover, the inclusion of the variable ESE x PDS induced strong indications for a multicollinearity, suggested by a high variance inflation factor (VIF = 65.14). The removal of the interaction variable has resolved the multicollinearity problem and recovered the independent variable effects. Results suggest that a strong confidence in one's business-related skills and their positive influence on the willingness toward a business creation is not significantly shaped by an individual’s perception of his or her own digital skill level. Therefore, hypothesis 3, that PDS moderates the relationship between ESE and EI, cannot be statistically confirmed. A detailed illustration of all models of OLS regression estimation including regression coefficients, confidence intervals and collinearity statistics are listed in Annex 11.

In a next step, an attempt shall be made to conceptualize and to test an alternative model within a mediation analysis. It is conceivable that PDS does not moderate the relationship between ESE and EI significantly, but rather the effect mechanism is to be understood as a causal association in which ESE mediates the relationship between PDS and EI. In line with the TPB, human capital and other demographic variables are predictors of perceived behavioral control and, grounded on previous theoretical considerations, self-efficacy beliefs. According to Krueger (1993), human capital comprises, inter alia, acquired skills and knowledge which are important antecedents of self-efficacy. Thus, PDS could be considered as a predictor of ESE, which in turn predicted support for EI through a mediation effect. After entering all control variables and the main constructs, a simple mediation was performed using the PROCESS macro (v3.4) by Hayes (2012) to analyze whether PDS predicts support for EI and whether the direct path would be mediated by ESE. Based on an ordinary least squares regression and a bootstrapping process with 5000 samples for percentile bootstrap confidence intervals, PROCESS generates total, direct and indirect effects with the variables X (PDS), M (ESE), and Y (EI) (see Annex 12). A 95.00% confidence interval as well as a heteroscedasticity consistent standard error (HS3) and covariance matrix estimator was used for significance test and inferential statistics (Davidson & MacKinnon, 1993). Effects were approved as significant when the confidence interval did not include zero. On the basis of a theoretical examination procedure by Peterman and Kennedy (2003), four conditions for proving a mediation structure are defeated to a verification. The main results of the moderation analyses are displayed in Figure 4. In a first step, a significant total effect between PDS and EI can be confirmed (c = 0.29; t(166) = 3.38; p < .001) (see Annex 15). In a second step, a highly significant relationship between PDS and ESE can also be verified (ß = 0.29; t(166) = 4.24; p < .001) (see Annex 13). Moreover, the influence of ESE on EI is also proved as positive and significant in a third step (ß = 0.39; t(165) = 3.67; p < .001) (see Annex 14). Finally, a reduced effect size can be confirmed between PDS and EI compared with the total effect in step one, however, the significance has been retained (c’ = 0.18; t(166) = 2.06; p < .05). Therefore, results indicate that the relationship between PDS and EI is partially mediated by ESE through an indirect effect XY = 0.11; 95%-CI [0.04; 0.19]. In other words, self-confidence in one's capabilities to master different challenges during the start-up process can be increased by the perception of a high level of digital skills and thereby influence the intention to pursue entrepreneurial career activities. In addition, entrepreneurial career intentions are directly affected by an individual’ s PDS.

FIGURE 5: Mediation Model Comprising the Main Constructs and their Relationships

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6. Opportunities and Challenges for Developing Entrepreneurial Intentions in the Digital Era

In this chapter, the main research results will be described and discussed. Moreover, research as well as practical implications will be derived and addressed to scholars, nascent entrepreneurs, educational institutions and policy makers. Finally, the limitations of this work are examined and further research needs are identified. A summarizing conclusion completes the current study.

The present study takes a closer look on the influence of ESE as an important driver ofEI and the question of how PDS shape this relationship on the individual level. The main aim is to achieve a better understanding of people’ s perception towards their own digital skills and its effect on their aspiration to start a career as self-employee. As one of the first studies to investigate the interplay of these constructs, this work makes an important contribution to a better comprehension of the development of entrepreneurial career choice intentions in the digital era. Based on the TPB, ESE is considered as a main motivational factor of EI on account of the similarity with perceived behavioral control. The moderation effect of PDS is undergirded by the SCCT and elucidates the interaction between PDS, self-efficacy beliefs and career choice intentions. In order to make PDS measurable, a scale was developed in a multi­step process, grounded on the DigComp Framework published by the European Commission. After collecting and analyzing 181 surveys by German Master’ s students, the results of the study provide a strong evidence that ESE and PDS have a positive and direct effect on EI, however, no significant moderation effect could be found. After testing a mediation model, it was discovered that ESE partially mediates the relationship between PDS and EI. Possible reasons for this will be discussed in the following.

In fact, in line with the previous literature, ESE has a positive and significant impact on students’ EI (Hypothesis 1) (Hockerts, 2017; Mortan et al., 2014; Murugesan & Jayavelu, 2017; Piperopoulos & Dimov, 2015; Rosique-Blasco et al., 2018; Wilson et al., 2007). It can therefore be concluded that a high level of confidence in one's abilities to successfully perform different tasks in the start-up process, enhances the willingness to pursue a career as entrepreneur. Conversely, the individual belief of people perceiving themselves as ill-equipped for entrepreneurial challenges reduces the intention to start-up a new business in future. This may be due to the assumption that peoples self-efficacy beliefs are an essential factor associated with cognitive processes of thinking, emotion regulation, motivation and action selection (Bandura, 1997a). The various dimensions of self-efficacy indicate that an individual’s self-assessment of internal resources plays a crucial role in developing intentions and executing behavior. Therefore, a high level of self-efficacy in the specific area of entrepreneurship is suspected to support people’ s motivation in the start-up process, increasing personal persistence, recognizing challenges as an opportunity and dealing positive with setbacks (Chen et al., 2004; McGee et al., 2009; Wood & Bandura, 1989; Zhao et al., 2005). Consistent with the TPB, ESE seems to be a strong motivational factor of EI, similar with perceived behavioral control. Previous literature also indicates that the construct ESE is a key determinant in the prediction of EI (McGee et al., 2009) and the career development (Lent et al., 1994; Lent & Brown, 2008). Therefore, the current study confirms the predictive power of people’ s self-efficacy beliefs in entrepreneurial-related tasks. According to Ajzen (1991), the effect size of PDS on EI is depending upon the social influence and the resulting norms and values of an individual. The present findings suggest that the stronger the social environment is marked by self-employees through the family, friends and acquaintances, the greater is the influence of ESE on EI (see Annex 18). Hence, frequented social contacts with self-employees can positively influence the view on entrepreneurship, incite the personal interest, and encourage people to mobilize their resources to achieve their entrepreneurial goals. These results are supported by a wide range of studies which suggest that the family background and social imprints play an important role in the development of EI (Farooq, 2018; Sequeira et al., 2007; Zellweger et al., 2011).

In entrepreneurial literature, considerable studies have demonstrated that various skills, useful for business-related challenges, are important determinants in the prediction of entrepreneurial career intentions (Dutta et al., 2015; Liñán et al., 2013; Rosique-Blasco et al., 2018; Zarefard & Cho, 2018). Following this approach, the present study considers the individual perception of one's digital skills as relevant key resource in light of the progressive digitalization in economic and social spheres. Considering that no previous study investigated on the relationship between PDS and EI before, this study significantly supported that PDS positively impact EI (Hypothesis 2). As expected, PDS seems to be a perceived resource that is helpful for mastering different tasks during the start-up process, lowering the barriers to enable a career as self-employee and supporting aspirations to pursue an own business creation. Following a similar cognitive mechanism like self-efficacy, individual beliefs toward possessing a high level of digital competences may strengthen motivational effects to pursue own entrepreneurial goals. Moreover, there seems to be an obvious link between a high level of PDS and the interest to launch a venture in the digital and technology sector. Possible reasons for this correlation may be, for example, that individuals with high level of PDS are convinced to solve practical problems with ICT and identify possible business ideas and opportunities in this way. Furthermore, strong perceived skills and knowledge in a certain field of application may results in an increased interest and passion to take a closer look at the subject. Similar considerations are provided by Chen (2014) focusing on a link between self-efficacy beliefs in handling with computers and intentions to found a venture in the IT sector.

It was not possible to confirm the moderation effect of PDS on the relationship between ESE and EI, therefore hypothesis 3 must be rejected. This finding suggests that an individual's self­efficacy belief in the entrepreneurial context and its effect on EI is not significantly shaped by a high or low level of PDS. For instance, if people have a lacking confidence in their ability to perform entrepreneurial tasks successfully and therefore do not develop the intention to start­up a venture in future, this causal link cannot be significantly improved by a high level of PDS. In other words, people’s conviction to possess a certain level of digital skills and knowledge cannot be considered as prerequisite to develop entrepreneurial career intentions driven by self­efficacy beliefs to master different challenges during the start-up process. For an explanation of this non-significant and small moderation effect two considerations can be proposed. Firstly, both constructs, ESE and PDS, are based on a common cognitive mechanism within their theoretical conceptualization. The construct PDS refers to an individual self-evaluation of existing abilities when dealing with digital technologies, whereas ESE describes the self­assessment of confidence in one's capabilities in regard to successfully performing a certain task. Therefore, both constructs are based on a cognitive self-evaluation process referring to a subjective reflection of own skills or the individual confidence in these skills. In other words, the theoretical mechanism behind the constructs self-efficacy and PDS cannot be clearly differentiated. In view of this theoretical overlapping, it may be assumed that the perception of a broad set of digital skills which are useful in social and economic spheres may simultaneously lead to a perception of stronger confidence in one’ s resources to perform entrepreneurial tasks successfully. In this case, irrespective of the direction of the causality, a causal association between PDS and ESE is more likely than an interaction effect on the relationship between ESE and EI. Secondly, the present study used a sample with high educated Master’ s students who mainly grew up as digital native and therefore possess comparatively high basic skills in the use of digital technologies (see Annex 17). This in turn leads to a lowering of the range of variation in the measurement, since students’ answers are pooled in a certain scale range. This restriction of range can be limiting the effect size of moderation and makes it more difficult to identify a significant interaction between the variables. In Addition, a too small sample size exacerbates this problem by narrowing the statistical variance. For example, Judd et al. (2014) recommend a sample size of 500 participants to ensure a broad range of variation in collected data and enable to find significant moderation effects in a study based on a questionnaire. To summarize, an identified range restriction by a similar digital skill level of the participants of this study and a too small sample size can cause an underestimation of the actual interaction effect. For these reasons, the interrelationship between the main constructs needs to be reconsidered by opening a new perspective on the question of how PDS develop its effect on people’s intention to launch an own business and which role ESE might be play.

After rejecting hypothesis 3, a mediation model was developed to examine the interplay of PDS (X), ESE (M) and EI (Y) based on prior insights. On the one hand, findings have shown that the relationship between PDS and EI is partially mediated by ESE, on the other hand, PDS have also a low but significant effect on EI in a direct association. Hence, these discoveries ultimately provide stronger arguments for an existing (partial) mediation effect than for a moderation effect. From this, it can be concluded that people who feel well-equipped in term of a broad range of digital skills have a stronger confidence in their personal resources to master different challenges in the entrepreneurial process and thereby strengthen the willingness to start a career as self-employee. In line with several studies, acquired skills and its associated perception of a similar skill level are able to predict individual self-efficacy beliefs in business-related tasks and thereby EI (Bandura, 1997a; Hatlevik et al., 2015a; Lent & Brown, 2008). According to Wood and Bandura (1989), the first and foremost source of self-efficacy is mastery experience: Perceived high skills in a certain domain increase the probability to perform tasks successfully and, in turn, a successful accomplishment of tasks reinforce the individual confidence in one's internal resources. From this perspective, PDS is a relevant predictor of ESE via an assumed causal relation, whereas ESE effects EI as a main driver. Accordingly, this work endorses with the findings of prior studies identifying the role of skills as driver of ESE (Liñán, 2008; Liñán et al., 2013; Zarefard & Cho, 2018).

In addition, several control variables were included in this quantitative study. In the final regression estimation model (Model 2), age, gender, family status, social influence, entrepreneurial course attendance, perceived financial literacy and the interest on a digital business launch show no significant effect on EI. It can therefore be assumed that the main control variables were considered for the present research field. However, the number of siblings, entrepreneurial experience, financial literacy and, eminently, entrepreneurial risk perception, business opportunity recognition and the monthly net income are identified as significant influence factors during the intention forming process related to entrepreneurial ventures. The current research findings are therefore based on the main variables and the complementation of further determining factors in the regression estimation of EI to increase its predictive significance. Based on these discoveries, the present study agrees with the findings of prior studies, which emphasized the predictive power of personal traits such as a penchant for systematically searching of business opportunities or the risk attitude toward entrepreneurial venturing on the one hand, and demographic backgrounds such as the available income level on the other hand (Alba-Ramirez, 1994; Bates, 1995; Kuckertz et al., 2017;

Yurtkoru et al., 2014). Accordingly, the development of EI should be considered as process influenced by educational, demographic, cognitive and characteristic factors. Theoretical and practical implication will be discussed in the next subchapter.

6.1 Research Implications

This quantitative-empirical work provides various implications for research, which will be discussed in the following chapter. An objective in the entrepreneurial research is to explore the development of entrepreneurial career intentions by identifying important determinants for explaining the process of intention forming. The present study takes a closer look on peoples' PDS as an essential resource for participating in economic and social spheres within a modern knowledge society. As one of the first studies conducting these constructs, the main findings provide a strong support for the influence of PDS on the development of EI. Hence, PDS can be identified as a factor playing a crucial role in the prediction of EI. In view of an individual’s personal abilities, the readiness to pursue entrepreneurial activities in future is therefore not only driven by internal resources such as management skills, creative capabilities and financial literacy, but rather is additionally affected by digital skills and the self-perception of these set of competences. For a better understanding of the development of entrepreneurial career intentions, digital skills should be more emphasized as an important factor in theoretical research. However, reasoned by a small but significant effect size of people’ s perception of their own digital skill level on start-up intentions, PDS should be considered as one of many relevant (perceived) competences necessary for the venture founding process.

As mentioned before, this work reveals that PDS also play an important role in the decision to become an entrepreneur during the digital era. In the current study, one of the pivotal questions is how PDS are associated with ESE and EI and which interaction mechanisms can be proved between these variables. After testing a moderation effect of PDS on the relationship between ESE and EI, results suggest that the influence of ESE on EI is not increasing significantly when people feel themselves well-equipped in their digital skills. Thus, the theoretical conceptualization in this study needs to be rethought by developing new approaches for proving possible interdependencies of the main constructs. For research, this discovery opens up an entirely new perspective on the role of PDS and the interplay with ESE and EI, reasoned by emerging indications for a possible mediation effect.

The examination of the mediation link between the PDS and EI provides new insights into ESE as a main driver for the development of EI. This was confirmed by the positive significant relationship between ESE and EI. ESE can also be promoted by the significant positive effect of PDS. Therefore, people’ s conviction to possess a broad range of digital basic skills increases the confidence in one's entrepreneurial skills for different tasks during the start-up process and thereby strengthen the intentionality to start-up a new business in future. This model is based on the assumption that PDS and ESE, PDS and EI as well as ESE and EI are interacting in a causal relationship. Consistent with the TPB as well as SCCT, PDS can also be considered in a role as predictor of ESE. As a part of human capital, the perception of one's digital skills can be treated as antecedent of perceived behavioral control which is closely related to ESE. Moreover, a main source of self-efficacy expectations is experience of mastery. In line with the SCCT, a high level of PDS can contribute to master different challenges during the start-up process and, therefore, reinforce the self-efficacy beliefs to perform these tasks successfully. For research, PDS should be considered as independent variable with a) a significant and direct effect on entrepreneurial career intentions and b) an indirect effect on EI mediated by self­efficacy beliefs regarding entrepreneurial tasks.

Finally, the present study provides a developed scale for measuring perceived digital basic skill on the individual level. Items were derived by transforming the 21 basic competences of the DigComp Framework into equivalent questions (see Annex 1). After performing an explorative and confirmatory factor analysis, the scale was tested as valid and reliable instrument for theoretical and practical application. Hence, the questionnaire can be implemented in further studies with view on the respective research context. In addition, the DigComp Framework seems to be a suitable guideline for the derivation of digital basic skills necessary in a modern knowledge society by reflecting essential competence areas in handling with digital technologies. In consideration of the specific research context, the present study gives a recommendation for the theoretical integration and adaption of the Digcomp Framework. Beside the mentioned discoveries for research, this work addresses also several implications on practical decision-makers.

6.2 Practical Implications

The findings of the present study provide direct practical implications for entrepreneurship educators, nascent entrepreneurs and public policy makers responsible for developing and supporting entrepreneurial framework conditions in the economic system. Central to this work is the concept of EI as a strong predictor of entrepreneurial behavior (Shirokova, Osiyevskyy, & Bogatyreva, 2016). Thus, EI should be considered as a starting point of the entrepreneurial process. For aspiring entrepreneurs, a profile of individual factors can be derived that supports efforts to develop and pursue an own business idea. Besides individual attributes which cannot be directly influenced (e.g. Age, Gender, Social influence), findings provide a set of personality traits with a strong effect on EI. For instance, the readiness to take some risks, regularly searching for business ideas and a high level of confidence in business-related skills are essential cognitive factors associated with a strong support of the willingness to become an entrepreneur. Therefore, people with a general interest in self-employed activities should deepen an entrepreneurial mindset and business-related skills to reinforce their intentions to launch a business and ultimately pursue their entrepreneurial goals.

Moreover, findings have direct implications for teaching in entrepreneurship courses. The present study includes a five-dimensional instrument by McGee et al. (2009) for measuring ESE in the domains searching, planning, marshalling, implementing people and implementing financial. The quantitative evaluation of the relationship between the ESE-construct and EI revealed different effect sizes among the different dimensions. Especially, the confidence in searching and identifying business opportunities and handling the financial issues during the foundation phase are comparatively strong drivers of EI on the sub-dimensional level. In entrepreneurship courses, a particular attention should be therefore given to strengthening the capabilities of business recognition, financial controlling and asset management to enhance the confidence to challenge tasks successfully in these competence areas. This also agrees with the findings on the main control variables. However, this does not mean that the other two dimensions are not relevant, but rather the more influential dimensions should have a higher weight in entrepreneurial teaching.

Another main finding is that PDS directly support the formation of EI. Thus, Digital skills may be considered as valuable competences necessary for the process of conceiving, planning and realizing a modern business idea. In addition to the promotion of entrepreneurial skills, management competences and personal abilities (e.g. creativity, proactivity, innovativeness), the development of digital skills should be given an increased focus in entrepreneurial courses. A more detailed analysis of the sub-dimension of the PDS-construct shows that particularly the competence areas communication and collboration and creative problem solving have a stronger effect on EI. Therefore, digital communication strategies (e.g. for networking, bureaucratic procedures, collaborative networking, data sharing) and the functionalization of digital technologies for own goals (e.g. for idea creation, innovativeness, solution approaches) should be specifically emphasized. For example, a practical teaching method could be focusing the knowledge transfer of a broad set of digital skills to forming fundamental basic competences (e.g. in line with the DigComp Framework) and finally build up entrepreneurial digital skills which are relevant for the business start-up process. To clarify which business-related digital competences are relevant for the entrepreneurial process, more research is needed. Furthermore, collected data suggest that students in scientific and technical studies (Computer Science and IT, Engineering, Natural Science) show stronger aspirations to become self-employed than students of other study courses (see Annex 17). Educational institutions should therefore reflect these interests and ensure that especially students from these faculties have also the possibility to attend on entrepreneurial courses. With a view on the findings, universities should be aware that entrepreneurship courses can be an important instrument for sensitize and motivate students for a career as self­employee.

The results allow further implications for policy makers on the systemic and meta level. In this respect, a difference should be made between the national level and the European level. Moreover, it should be noted that a implementation of individual measures must be considered in its overall context of structural, macroeconomic and social particularities in an country (Liñán & Fayolle, 2015). For instance, an appropriate measure for promoting digital skills in a certain country cannot develop its full effect if the economic and business conditions are suboptimal for entrepreneurs (e.g. in an economic crisis, difficulties in raising capital, etc.). The political agenda of the European Union is, inter alia, aimed at developing an EU's single market which match the constantly growing demands of the digital era. It includes providing and maintaining the ICT infrastructure, reducing barriers for internet companies and start-ups, increasing the growth potential of the European Digital Economy and supporting the development of digital skills for European citizens (European Commission, 2015). With a view to achieving these objectives, the European Union has set up a European Structural and Investment Fonds (ESI) to invest 2.2 billion Euro from the European Social Fund (ESF) in human capital development. According to the European Commission (2015, p. 4), "investments will focus on ICT skills, support for business creation, e-justice, as well as ensuring cross-country and cross-entity inter-operability of systems." Findings of the present study provide a strong support for these measures in two aspects. Firstly, through targeted encouragement of people’ s digital skills on the European level, the increasing competence level leads stronger intentions to launching businesses. This development can simultaneously affect the European Digital Economy in a positive manner. It is crucial, however, that the expansion of the digital infrastructure goes hand in hand with the development of promoting people’s digital skills. These considerations are based on the assumption that the results of the present study are transferable from an individual level, between a person and his or her EI, to the collective level regarding a general population and their career intentions. In addition, macroeconomic, social and national-political conditions are hidden in this consideration. The next chapter will give a deeper insight into this issue. Secondly, the DigComp Framework seems to be a suitable guideline to reflect necessary digital basic skills in a modern knowledge society. It is apparent from the scale development that the DigComp Framework covers a wide range of relevant digital skills and knowledge in different competence areas. Therefore, the framework is particularly well adapted to use it as common guideline for orientation and comparability of digital skills in the European Union, especially for education institutions, research organizations and the private sector. The limitations resulting from this will be discussed in the next section.

6.3 Limitations and Outlook on Future Research

Of course, this study is subject to some limitations related to the derivation of results, which can enable a new perspective and impulses for future research. Firstly, the using sample for data collecting focuses Master’s students of all study courses on German universities. Therefore, the sample comprises only digital natives with a similar education level and a low age span between 21 and 38 years in a modern industrial society. In view of this, the findings of the present study are not generalizable for the entire population in Germany and other countries. Cultural distinctions and the technological development can also play an important role for derivation these findings in other countries (Adekiya & Ibrahim, 2016). In order to make the results comparable, future research should set a heterogenous sample that provide a better reflection of the working-age population. More specifically, a sample can be obtained that include people with different educational attainments, social backgrounds, ethical affinities and career paths.

Secondly, it is necessary to emphasize that the measurement of PDS is based on a self-report of the respondents. Hence, these individuals evaluate their own digital skills in a subjective point of view. This has two major drawbacks: firstly, the digital skills among the respondents are not comparable by objective standards and, secondly, the actual capabilities and PDS may differ due to cognitive biases. Especially, an overconfidence bias is associated with scales aiming at questions related to a self-evaluation of individual perceptions, attitudes or skills. This may have lowly distorted the results by including scales with the respective valuation system. For future research it could be interesting to develop a scale for measuring digital skills by defining an objective assessment standard. For example, items can be transposed to a single­choice questionnaire with right or wrong answers to assess and compare digital skills in a standardized scoring system. Furthermore, the PDS-scale was developed to measure a wide range of digital basic skills to handle with digital technologies in various contexts. By transforming the DigComp Framework competences in 21 equivalent items, a slight loss of information cannot be excluded. In order to gain a deeper insight into the influence of digital skills on ESE and EI, a more contextual and task-specific entrepreneurial digital skills-scale development is recommended. Accordingly, it could be encompassed more specified digital skills which are necessary during the process of business launching. On the one hand, it can contribute to identifying important digital skills which can help to master different challenges in the entrepreneurial process. On the other hand, it allows a more detailed conclusions on the relationship between digital skills and peoples willingness to start-up a new business. Additionally, previous research mainly focuses the positive effects of ESE and other individual­based variables on EI. Therefore, more consideration should be given to negative effects such as an overconfidence bias or over-optimistic and their impact on ESE, PDS and EI.

Thirdly, the present results are based on an empirical data collection conducted at a certain point in time. As a cross-sectional study, this research is limited in their interpretation of the direction of causal links. This means that a high level of PDS can positively influence people’ s intent to launch a venture in future. From another point of view, entrepreneurship in a digitalized and mobile society often requires a basic know-how in the handling of digital technologies to secure competitive advantages. Accordingly, a wide set of digital skills can also be considered as a result of a strong interest in entrepreneurship and therefore as a part of required entrepreneurial skills. In addition, there are some indications that self-efficacy can also predict the development of digital skills (Hatlevik et al., 2015a). In order to draw conclusions about this causal connection, further research should perform long-term studies in the present context. Additionally, these studies provide the possibility to investigate the relationship between EI and entrepreneurial behavior. For instance, it should be further assessed whether people with very pronounced EI have actually start-up a business at a later stage. Hence, the practical realization of entrepreneurial activities is a research object of great interest to the policy makers and entrepreneurial research area.

Fourthly, the present study include the DigComp Framework published by the European Commission to derive digital competences that are determined as relevant 21st Century skills.

According to Ferrari (2013), a main purpose of the DigComp Framework is to identify digital key competences that are important to use current digital technologies for economic and social participating at the present time. It should be taking into account that digital technologies are undergoing a constant change through the processes of innovation. Hence, the practical requirements related to future technologies are also subject of changes in during the following decades. In other words, innovative technologies generate new requirements and, ultimately, new digital basic skills in future which may differ from the current key competences. Against this background, the findings of the current study should be interpreted with a focus on the temporal context and the societal change regarding the process of digitalization. Future research should regularly review the conformity between current digital basic skills and requirements of well-established technologies in economic and social contexts. Modified digital skills should be considered in future study designs to adapt the measurement of digital constructs.

Fifthly, the relationship between PDS and EI may be subject to certain restrictions. The theoretical conceptualization of the present study is grounded on the consideration that a high level of PDS facilitate certain tasks related to the business start-up process and reducing barriers on the way to becoming an entrepreneur. However, it should be noted that there are still several traditional sectors which are not or only partially affected by the process of digitalization. For instance, ina small boutique or traditional farming business the inclusion of digital technologies in the start-up and value-added process plays a minor role. It is therefore possible that founders in these sections have a strong intention to launch their venture and simultaneous have very limited competences in handling with digital technologies. This conversely means: a perception of strong digital skills does not necessarily need to drive the willingness to become an entrepreneur. To verify these considerations, future research should integrate branch-specific influences as moderator variable and examine this effect on the relationship between PDS and EI.

And last but not least, it should be noted that the TPB was adopted in a slightly modified version. During theoretical conceptualization the focus was on one of the three motivational predictors of EI, namely perceived behavioral control. Given the similarity between perceived behavioral control and Badura’s concept of self-efficacy, the present study used ESE instead of perceived behavioral control to explain the influence of people’ s self-efficacy beliefs on their intend to start-up a new business. These considerations are not uncommon and were also implemented in several other studies (Rosique-Blasco et al., 2018; Roy et al., 2017; Shirokova et al., 2016; van Gelderen et al., 2008). However, "studies that used perceived behavioral control to predict EI showed higher effect sizes than studies that employed ESE." (Schlaegel & Koenig, 2014, p. 305). Therefore, it exists nevertheless deviations in the explanatory power between PCB and ESE. Future research should pay more attention to the identification of differences as well as similarities of both constructs and try to make these particularities measurable. The EEM by Shapero and Sokol (1982) seems to be a suitable alternative for theoretical foundation and should be taken into considerations with view on the contextual research question. Furthermore, the present study examines the interplay between PDS, ESE and EI on basis of the TPB as well as SCCT on an individual level. The constructs of the TPB are associated with an individual’s subjective perception and therefore describe the intention forming process of one person. Considering the fact that the business creation process will often be carried out in teams, future research should investigate the process of intention forming on the team-level.

6.4 Conclusion

This research work is a first step to open up a new research perspective with the aim of building a bridge between modern basic skills in the handling with digital technologies and entrepreneurial start-up intentions. Results indicate a strong evidence for the predictive power of an individual's perceived digital skills and entrepreneurial self-efficacy beliefs on the willingness to pursue entrepreneurial goals and start-up a business in future. The conviction to possess a broad set of digital skills does not shape the relationship between entrepreneurial self­efficacy beliefs and entrepreneurial career intentions significantly, however, individual self­efficacy beliefs partially mediates the relationship between perceived digital skills and entrepreneurial intentions. Moreover, monthly net income, the individual risk attitude toward entrepreneurial activities and the characteristic to search systematically for business opportunities are also identified as important drivers associated with the decision to become an entrepreneur. Thereby, wide variations in the strength of entrepreneurial intentions among different study courses cannot be confirmed in the conducted sample. In this way, important conclusions can be derived for scholars, nascent entrepreneurs, educators and policy makers.

The present study gives evidence for the relevance of digital skills as a key resource for developing entrepreneurial intentions and ultimately a participation in economic spheres during the process of digitalization. Therefore, findings support the promotion of digital basic skills in the course of a political agenda within the European Union, however, in consideration of macroeconomic and social aspects. In view of transferring digital knowledge and skills in educational institutions, creative problem-solving approaches and collaborative skills in the use of digital technologies are closely related to the development of entrepreneurial intentions. Furthermore, an entrepreneurial profile of personal traits, attitudes and abilities for nascent founders can be derived on basis of the quantitative outcomes. As mentioned above, the discovered mediation effect opens up a new research perspective on the interplay between perceived digital skills, entrepreneurial self-efficacy beliefs and entrepreneurial intentions.

Considering, as one of the first studies which examined the interplay of these main constructs and developed a scale for measuring a broad set of perceived digital basic skills, more future research is needed. For example, the developed scale can be refined by testing the items in other research contexts. Moreover, new technologies require new knowledge and competences over time, therefore, the measurement of digital skills has to be adapted in future. In order to improve the evidence level and data availability, more research studies should focus modern phenomena in the digital area in relation to entrepreneurial intentions.


ANNEX 1: Questionnaire Including all Items of the Main Constructs and Control Variables

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ANNEX 2: Communalities after Entering all Perceived Digital Skills-Items

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ANNEX 3: Explanation of the Total Variance, Factor Loadings and Eigenvalues of the Final Scale

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ANNEX 4: Factor Loadings of the Final Scale in a Rotated Component Matrix

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ANNEX 5: Regression Weights of the Final Scale Based on Maximum Likelihood Method

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ANNEX 6: Standardized Residual Covariances between the Final Scale Items

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ANNEX 7: Modification Indices between all Covariances in the Structural Equation Model

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ANNEX 8: Model Fit Summary Including CMIN, GFI, NFI, CFI and RMSEA

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ANNEX 9: Model Summary of OLS Regression Estimation of Entrepreneurial Intention

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ANNEX 10: Analysis of Variance of the OLS Regression Estimation of Entrepreneurial

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ANNEX 11: OLS Regression Estimation of Entrepreneurial Intention in a Three-Step Model Including all Regression Coefficients, Confidence Intervals and Collinearity Statistics

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ANNEX 12: Total, Direct and Indirect Effects of Perceived Digital Skills (X) on Entrepreneurial Intentions (Y) in a Mediation Model

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ANNEX 13: Mediation Model Summary with Entrepreneurial Self-Efficacy as Outcome Variable

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ANNEX 14: Mediation Model Summary with Entrepreneurial Intention as Outcome Variable

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ANNEX 15: Total Effect Mediation Model with Entrepreneurial Intention as Outcome Variable

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ANNEX 16: Entrepreneurial Intentions between Different Study Courses

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ANNEX 17: Perceived Digital Skills between Different Study Courses

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ANNEX 18: Social Influence and its Shaping Effect on the Relationship between Entrepreneurial Intention and Entrepreneurial Self-Efficacy

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1 The ESI Funds are a labor market policy tool by the European Union to ensure opportunities for citizen’s social and economic participation. A funding priority includes investments amounting to EUR 23.6 billion in the digital economy and infrastructure as well as the promotion of ICT skills and business creation for the funding period 2014 to 2020 (European Commission, 2015).

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Next Generation Entrepreneurs. How Do Digital Skills Affect the Intention to Start Up a New Business?
Justus-Liebig-University Giessen  (Department for Business Administration with the focus on Technology, Innovation and Startup Management)
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Entrepreneurial Intention, Entrepreneurial Self-Efficacy, Perceived Digital Skills, Entrepreneurship, DigComp Framework, Business Start-up
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Jan-Nicolai Pitz (Author), 2019, Next Generation Entrepreneurs. How Do Digital Skills Affect the Intention to Start Up a New Business?, Munich, GRIN Verlag,


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