Dealing with the inconsistency of studies in entrepreneurship education effectiveness: A systemic approach to drive future research

A RESEARCH AGENDA FOR ENTREPRENEURSHIP EDUCATION Alain Fayolle (ED) ISBN: 978 1 78643 290 2 Chapter 3 Dealing with the inconsistency of studies in entrepreneurship education effectiveness: A systemic approach to drive future research Michela Loi Department of Economic and Business Sciences University of Cagliari 1. Introduction This chapter aims to propose a systemic approach to the analysis of the effectiveness of entrepreneurship education. The theoretical root of this work is grounded in training effectiveness research and, especially, on a systems perspective that Baldwin and Ford (1988) have contributed to building in order to understand the impact of training and development activities (Bell, Tannenbaum, Ford, Noe, and Kraiger, 2017). The systems perspective has driven the last 30 years of research on training effectiveness, and empirical support has been produced regarding its basic assumptions (Baldwin, Ford, and Blume, 2009; 2017). Literature in entrepreneurship education has borrowed some of the basic concepts that sustain the Baldwin and Ford (1988) perspective (Fayolle and Gailly, 2015). Recently, Gielnik, Uy, Funken, and Bischoff (2017) have analysed the impact of entrepreneurship education by looking at long-term training effects. They have made reference to Baldwin and Ford’s model (1988) in supporting their hypothesis that the positive effects of training on motivational outcomes wear off over time. Specifically, they have analysed how the role of passion varies over 32 months after training, showing that passion mediates the relationship between training and business creation several months after training. Further, they have observed that entrepreneurship education has a 1 positive effect on entrepreneurial self-efficacy. According to their predictions, this finding is crucial for business creation because self-efficacy nurtures passion that, in the long term, fosters individuals’ efforts to create their own businesses. In the conclusion of their work, Gielnik et al. (2017) argued that the transfer literature in entrepreneurship education represents an important legacy for future studies in this field. This chapter is built upon this reflection. The literature on training effectiveness offers a theoretical basis to address the inconsistent results found in studying the impact of entrepreneurship education. In fact, the systems perspective of Baldwin and Ford (1988) was crucial in training effectiveness studies because it systematized the complex mechanisms that characterise a learning environment and that could either validate or nullify the effects of a programme. Understanding these mechanisms helps identify the factors that make a training programme effective or ineffective. Scholars have recognized that this perspective gives important support to address the question: “Why does a training programme work?”, and provides the research to dive deeper into the question: “What outcomes has the training programme achieved?” (Salas and Cannon-Bowers, 2001). In the same vein, we argue that future questions in entrepreneurship education should be related to better understanding why entrepreneurship education is working or not working. In this direction, the present chapter proposes a systemic approach of assessment. In so doing, it points out the theoretical assumptions supporting the systems perspective (Baldwin and Ford, 1988), by focusing specifically on the key points that have received empirical support over the last 30 years of research. These assumptions are used to create a future research agenda for entrepreneurship education that might sustain future assessment studies from a theoretical and empirical point of view. This work brings three main contributions to the current literature on the impact of entrepreneurship education. First, it draws a holistic perspective of entrepreneurship education assessment by relying on the main points that constitute the theoretical basis of the transfer of training literature. This allows us to design a research agenda that is built upon a comprehensive approach, in which concepts and assumptions are systematically borrowed from the training literature. In this way, future research on entrepreneurship education effectiveness might highlight new hypotheses and new lines of research by grounding their reflections on a theoretically-driven approach. Second, the chapter links the findings of entrepreneurship education studies to the key points that have emerged from the training literature. This clarifies what we 2 already know and the features that future studies should consider. Possible gaps and needs, in fact, might be easily recognized, helping figure out new possible lines of research to enhance the current knowledge. Third, the chapter offers a base for a reflection on variables and mechanisms that might be worth investigating in entrepreneurship education to better assess its impact. These suggestions, rather than being prescriptive, are intended to encourage a scientific debate regarding those issues that emerge as being crucial in strengthening the effectiveness of entrepreneurship education. The chapter has four sections: the first provides a synthesis of the studies focused on assessing the impact of entrepreneurship education. The second section briefly presents the systems perspective (Baldwin and Ford, 1988). The third section illustrates our systemic approach along with the research agenda for entrepreneurship education studies. The fourth section draws some conclusions of this work. 2. Studies on entrepreneurship education effectiveness Understanding the impact of entrepreneurship programmes is a highly debated issue in the field of entrepreneurship education. If we look at the most recent studies dealing with entrepreneurship education (published from 2015-2017), it is worth noticing that the scholars’ community is building one of the most impactful cluster of studies. Figure 1 shows a density map resulting from the application of a bibliographic coupling technique to recent studies on entrepreneurship education1. A density map in this field depends on both the number of neighbouring publications and the weights of each publication (Van Eck and Waltman, 2010) corresponding to the number of its occurrences. Red areas indicate that publications in these parts of the map have many references in common with their neighbouring publications—meaning that they are looking at similar 1. Bibliographic coupling is a bibliometric technique based on the number of references that two articles have in common. The greater the number of equal references between two articles (bibliographic couplings), the greater their similarities (Vogel and Guttel, 2013). As references are fixed attributes of a paper, bibliographic coupling is insensitive to the passing of time. This contributes to making this method the most suitable to detect research fronts. In this work, the bibliographic coupling technique covers papers working on entrepreneurship education that are indexed in the ISI-Web database, and published in the period 2015-2017. This time-frame is chosen for two reasons. The first one, by relying on a methodological base, is that the application of this bibliometric technique is more accurate for studying brief periods of time than other techniques, such as for instance co-citations (Boyack and Klavans, 2010; Castriotta and Loi, 2017). The second reason, most related to the internal dynamics of entrepreneurship studies, is that this time-frame allows this contribution to consider papers appearing after the publication of the latest literature reviews (Nabi, Liñán, Fayolle, Krueger, and Walmsley, 2017) or meta-analyses (e.g., Bae, Qian, Miao, and Fiet, 2014) working on entrepreneurship education or on its assessment. 3 arguments, and also that they are highly cited compared to other publications on the map. Conversely, green areas denote fewer publications with fewer references in common, and with low impact in terms of occurrences. Figure 1 Recent studies on Entrepreneurship Education (2015-2017) By looking at the content of the publications in the red area of Figure 1, on the left side of the map, we discover that they are mostly focused on understanding the impact of entrepreneurship education. Table 1 shows the list of the papers belonging to this most dense cluster of studies2. Table 1. Recent papers (2015-2017) on entrepreneurship education assessment Authors Fayolle, A., and Gailly, B. (2015) Title Journal Citations The impact of entrepreneurship Journal of Small education on entrepreneurial Business Management attitudes and intention: Hysteresis and persistence 45 Piperopoulos, P., and Burst Bubbles or Build Steam? Journal of Small Dimov, D. (2015) Entrepreneurship Education, Business Management, Entrepreneurial Self-Efficacy, and Entrepreneurial Intentions 23 2. A cluster analysis was performed in order to foster the interpretation of the internal structure of the map in Figure 1. Papers listed in Table 1 resulted to belong to the same cluster. 4 Rauch, A., and Hulsink, W. (2015) Putting entrepreneurship Academy of education where the intention to Management Learning act lies: An investigation into the and Education impact of entrepreneurship education on entrepreneurial behavior 21 Karimi, S., Biemans, H. J., Lans, T., Chizari, M., and Mulder, M. (2016) The impact of entrepreneurship education: A study of Iranian students' entrepreneurial intentions and opportunity identification Journal of Small Business Management 18 Lima, E., Lopes, R. M., Nassif, V., and Silva, D. (2015) Opportunities to improve entrepreneurship education: Contributions considering Brazilian challenges Journal of Small Business Management 13 Saeed, S., Yousafzai, S. Y., Yani‐De‐ Soriano, M., and Muffatto, M. (2015) The role of perceived university Journal of Small support in the formation of Business Management students' entrepreneurial intention 9 Do Paço, A., Ferreira, Entrepreneurial intentions: is J. M., Raposo, M., education enough? Rodrigues, R. G., and Dinis, A. (2015) International Entrepreneurship and Management Journal 7 Westhead, P., and Solesvik, M. Z. (2016) International Small Business Journal, 8 Kwong, C., and The when and why: student Thompson, P. (2016) entrepreneurial aspirations Journal of Small Business Management 6 Oehler, A., Höfer, A., Entrepreneurial education and and Schalkowski, H. knowledge: Empirical evidence (2015) on a sample of German undergraduate students. The Journal of Technology Transfer 5 Maresch, D., Harms, R., Kailer, N., and Wimmer-Wurm, B. (2016) Technological Forecasting and Social Change 5 Entrepreneurship education and entrepreneurial intention: Do female students benefit? The impact of entrepreneurship education on the entrepreneurial intention of students in science and engineering versus business studies university programs 5 Lanero, A., Vazquez, Social cognitive determinants of J.L., Aza, C.L. (2016) entrepreneurial career choice in university students International Small Business Journal 1 Entrialgo, M., and Iglesias, V. (2016) The moderating role of International entrepreneurship education on the Entrepreneurship and antecedents of entrepreneurial Management Journal intention 1 Giacomin, O., Janssen, F., Shinnar, R.S. (2016) Student entrepreneurial optimism International Small and overconfidence across Business Journal cultures 0 McNally, J.J., Martin, B., Honig, B., Bergmann, H., Piperopoulos, P. (2016) Toward rigor and parsimony: a Entrepreneurship and primary validation of Kolvereid's Regional Development (1996) entrepreneurial attitudes scales 0 Note. Citations are based on Isi-Web, and they are updated in September 2017. According to these studies, students’ preconditions have a relevant impact on the size of personal changes due to attending an entrepreneurship programme (Fayolle and Gailly, 2015). Compared to elective programmes, compulsory programmes are less powerful in sustaining students’ positive attitudes towards entrepreneurship (Karimi, Biemans, Lans, Chizari and Mulder, 2016). Similarly, compared to practically-oriented courses, theoretically-oriented courses are less effective in developing entrepreneurial intentions (Piperopoulos and Dimov, 2015). Complex dynamics help in part to address the contradictory results that often stem from studies that have built the theoretical foundations of entrepreneurship education assessment (Fayolle, Gailly, and Lassas-Clerc, 2006; Perterman and Kennedy, 2003; Souitaris, Zerbinati, and Al-Laham, 2007; Oosterbeek, Van Praag, and Ijsselstein, 2010; Von Graevenitz, Harhoff, and Weber, 2010). The scholarly importance of the assessment topic is validated by the high number of literature reviews and meta-analyses aimed at understanding and estimating the impact of entrepreneurship education programmes. Table 2 presents a list of reviews and meta-analyses that have appeared in highly regarded entrepreneurship journals, such as the Journal of Business Venturing, International Small Business Journal, or Academy of Management Learning and Education (Byrne, Fayolle, and Toutain, 2014; Fayolle, 2013; Teixeira, 2011). 6 Table 2. Literature reviews and meta-analyses on entrepreneurship education assessment Authors Title Journal Pittaway, L., and Cope, J. Entrepreneurship (2007). education: a systematic review of the evidence Citations International Small Business Journal 226 Martin, B. C., McNally, J. Examining the formation Journal of Business J., and Kay, M. J. (2013) of human capital in Venturing entrepreneurship: A metaanalysis of entrepreneurship education outcomes 109 Bae, T. J., Qian, S., Miao, The relationship between Entrepreneurship C., and Fiet, J. O. (2014) entrepreneurship education Theory and Practice and entrepreneurial intentions: A meta‐analytic review 78 Rideout, E. C., and Gray, Does entrepreneurship Journal of Small D. O. (2013). education really work? A Business Management review and methodological critique of the empirical literature on the effects of university‐based entrepreneurship education 38 Nabi, G., Liñán, F., Fayolle, A., Krueger, N., and Walmsley, A. (2017) 5 The impact of Academy of entrepreneurship education Management Learning in higher education: A and Education systematic review and research agenda Note. Citations are based on Isi-Web, and they are updated in September 2017. Apart from delineating important progress made by the research in this area —such as the consideration of moderators and mediators that might intervene in the relationship between entrepreneurship education and programmes’ outcomes (Bae, Qian, Miao, and Fiet, 2014)—these reviews and meta-analyses show there are still several drawbacks in the research. Below we recap some of these gaps that future research is strongly encouraged to address. These gaps were identified in systematic review of 7 studies published from 2004 to 2016 (Nabi, Liñán, Fayolle, Krueger, and Walmsley, 2017). The first drawback is the focus that studies put on short-term results, and subjective impact measures such as entrepreneurial attitudes and intentions, rather than long-term results like venture creation and business performance. Moreover, only a limited attention is paid to understanding the transition from intentions to behaviour. Second, studies tend to generate contradictory results regarding the impact of entrepreneurship education when short-term outcomes are considered, and a few tentative solutions are offered to explain this inconsistency. The question arises as to whether these contradictory results come from different research methodologies, or whether studies are observing different phenomena, or whether unstudied mechanisms nullify or magnify programme results. Lastly, very few details are given regarding the way these programmes are taught. This limits our ability to draw conclusions about whether the findings of these studies can possibly explain the extent to which is the impact of entrepreneurship education depending on the specific characteristics of the training context, and the extent to which findings are generalized to other settings. The systems perspective (Baldwin and Ford, 1988; Bell et al., 2017) is adopted as a theoretical lens to identify inputs to address each of these drawbacks. 3. The systems perspective in training effectiveness studies This chapter presents a brief synthesis of the systems perspective, drawing on recent literature reviews and meta-analysis on training effectiveness (for an extended discussion of these works see Tixier, Loi, LaPontois, Tavakoli, and Fayolle, 2017). More recently, Bell et al. (2017) have provided a complete overview of studies that have appeared in the Journal of Applied Psychology in the last 100 years, giving an in-depth and fresh perspective on this topic. This chapter takes their reflections into account. Further, Baldwin et al. (2017) have recently provided a synopsis of the training research that has been used, in this work, to better understand recent developments in the topic. Research on training effectiveness suggests that a systemic approach— looking at the person, the training and social environment, and their multiple interactions—is best suited to understanding the dynamics that surround the training and the post-training phases (Bell et al., 2017). Further, training outcomes should span a wide range of evaluation criteria that are both 8 multidimensional and multistage in nature. They are multidimensional as they might capture a change in affective, cognitive, and behavioural spheres (Ford, Kraiger, and Merritt, 2010; Kraiger, Salas, and Ford, 1993). They are multistage because such evaluations should take place multiple times across all phases of a training programme, from its beginning to well after it ends (Blume, Ford, Baldwin, and Huang, 2010). This allows us to understand the learning outcomes that have been achieved at the end of a programme, and the processes that individuals put in place once the training has ended (Baldwin and Ford, 1988). The systems perspective consists of three important nodes: inputs, outputs, and outcomes, also referred to as transfer conditions (Baldwin and Ford, 1988). The inputs include (1) the participants’ characteristics, (2) training characteristics, and (3) organizational context or environmental characteristics (Bell et al., 2017). The outputs refer to the learning and retention processes. The learning process is indicative of the personal changes that a learning context might enhance, including motivation, skills, ability, regulatory and metacognition processes, behaviour, and so on (Ford et al., 2010; Kraiger et al., 1993; Noe, Tews, and Dachner, 2010). Retention is more specifically related to a possession of knowledge or abilities. Outcomes refer to the maintenance of what people have learned over time (Barnett and Ceci, 2002; Huang, Ford, and Ryan, 2017; Ran and Huang, 2017). Transfer should be considered more than a mere applying what was learned in training. In fact, this process hinders trainees’ decisions that are worth considering (Baldwin et al., 2017; p. 24): “While traditional research has generally defined transfer as the use of the trained skills or the effectiveness in applying the training [...] more recent work has embraced the notion that the trainee is an active participant in learning and transfer [...]. Consistent with this perspective, we view transfer as a series of choices that trainees make to discard, maintain, apply, or modify trained knowledge and skills in their work context [...].” The systems perspective relies on a number of theoretical assumptions that have received empirical support over the last 30 years of research (Baldwin et al., 2017). These assumptions are built on a set of theories (Yamnill and McLean, 2001), including Bandura's social cognitive theory (Bandura, 2001), self-regulation theories (Sitzmann and Ely, 2011), Vroom’s expectancy theory (Vroom, 1964), equity theory (Adams, 1963), and goal setting theory (Locke, 1968). These theories emphasise the role of an individual as an active agent, whose behaviour is the result of continuous interaction among cognitive, affective, environmental, and behavioural aspects. Taken as a whole, these theories suggest that people have different levels of motivation and interest 9 when they attend training (Huang and Jao, 2016), often attend programmes with different goals in mind (Baldwin et al., 2010; Salas and Kozlowski, 2010), and react differently to training situations (Bandura, 2001; Frayne and Geringer, 2000; Gist and Mitchell, 1992; Meyer, Dalal, and Hermida, 2010; Ng and Lucianetti, 2016). The person-environment fit theory (Edwards, 1991) has also been used to better understand the relationship between the person and the generalization processes of what has been learned (Awoniyi, Griego and Morgan, 2001; Chiaburu and Marinova, 2005). These theories support the notion that each of the aforementioned inputs (personal, training, and environmental characteristics) are related to specific training outcomes, meaning that training itself can stimulate very different responses in each trainee, depending on their characteristics, training methods, and environmental context. Further, beyond the direct relationship between inputs and outcomes, the impact of a training programme depends on the coexistence of mechanisms that link a person to a training condition and a specific environment. The learning processes that occurs during training, and the adaptation processes that an individual faces after training, should be read in light of these multiple interactions (Bell et al., 2017). Lastly, the size of the effect of a training program can vary depending on the evaluation criteria. Variations might be observed depending on whether observed outcomes deal with motivational or behavioural aspects (Arthur, Bennett, Edens, and Bell, 2003; Ford et al., 2010), and whether they are observed during or following training. Among the personal characteristics, motivation to learn (Colquitt, LePine, and Noe, 2000), self-efficacy (Chen, Gully, Whiteman, and Kilcullen, 2000), goal orientation (Bell and Kozlowski, 2002; Heimbeck, Freise, Sonnentag, and Keith, 2003; Holladay e Quiñones, 2003; Sohn, Doane e Garrison, 2006; Towler e Dipboye, 2001), and personality (Herold, Davis, Fedor, and Parsons, 2002; Lievens, Harris, Van Keer and Bisqueret, 2003) have received a special attention by scholars. However, the research has demonstrated that the attributetreatment interaction perspective (Gully, Payne, Koles, and Whiteman, 2002; Gully and Chen, 2010; Bell et al., 2017)—the ways that individual differences can interact with training sessions to yield differential effects on training outcomes—is promising and should be considered as the future frontier of training research (Salas and Kozlowski, 2010). Training characteristics are among the most investigated issues in training research (Baldwin and Ford, 1988), especially after the shift from a passive to an active learning perspective that has characterised theories and practices since 10 the 1990s (Bell et al., 2017). In this respect, the idea is to understand which methods are more useful in supporting a learner-centred perspective, in which the responsibility of the learning process falls to trainees rather than to the instructors (Bell and Kozlowski, 2010). Some tentative models that integrate learners’ characteristics with an active learning approach have been provided by Bell and Kozlowski (2008), who found that exploratory learning (as opposed to proceduralized instruction) supports trainees in adapting what they have learned. Further, they have produced evidence that a strategy based on errorframing manipulation influenced trainees’ adaptability more than avoidanceerror training. One reason behind this finding is that people who learn from their errors are stimulated to reflect on their performance, alimenting their meta-cognitive processes that are mostly related to generalization processes (Keith and Frese, 2008). Keith and Frese (2008) showed in particular that error management training has a stronger impact on post-training transfer than on training performance. Their results suggest that trainings which focus on errors may be better suited, than error-avoidant training methods, for promotion of transfer to novel tasks. Regarding the environmental context, organizational studies have shown that “What happens prior to and after training can greatly influence training effectiveness, so it is critical to take a systems perspective that goes beyond instructional design and trainee characteristics and considers the context within which training occurs” (Bell et al., 2017; p: 315). In particular, the social context can be important as it can influence motivation to learn and to apply what was learned in training. 4. A systemic approach for entrepreneurship education effectiveness and inputs for a future agenda The translation of the systems perspective (Baldwin and Ford, 1988; Baldwin et al., 2017) into the studies of entrepreneurship education effectiveness can be summarized into five inputs.   The first input is the importance of looking at the ways that the effects of a programme vary depending on trainees’ personal characteristics. The second input is understanding the role played by the training strategies in favouring or hindering effective learning processes and results. 11    The third input is the importance of considering the environment and the social context as intervening variables that might foster training results. The fourth input is the need to consider the attribute-treatment interaction perspective. The fifth and final input is the need to look at the impact in terms of both learning results and generalization processes once a training programme is completed. For each of these inputs, we share results achieved thus far in entrepreneurship education studies, and put forward new suggestions that might be useful to advance future research on the effectiveness of entrepreneurship programmes. The overview on entrepreneurship education studies relies on recently published meta-analyses and literature reviews that embrace most of the works addressing the topic. These reviews are complemented with recent papers selected with the bibliographic coupling. Table 3 reports a synthesis of the inputs and suggestions for future research. Table 3. Inputs for Entrepreneurship Education Effectiveness Research Inputs Suggestions for future research Personal characteristics -Categories of individual differences Capabilities Demographics Personal traits Values and interests -A special focus on pre-training motivation, expectations and needs -Focus on the mechanisms linking demographic and background characteristics to training outcomes Training characteristics -Focus on variables and mechanisms fostering an active learning approach (e.g., games, simulation, technology base training) -Role of 12 different actors involved in an entrepreneurship learning environment teachers, mentors, coaches, peers) (e.g., -Role of collaborative and team learning Environment and Social context -Proximal social context (e.g., peer support) -Distal social context school/university context) Attribute-treatment interaction perspective (National, regional, -Focus on multiple interactions among personal, training and environmental characteristics -Focus on the fit between targets and training and environmental contexts Learning results and generalization process -Learning outcomes at the end of the programme -Maintenance of learning outcomes over time -Generalization process (with a focus on near and far transfer) -Focus on timing and variations over time in trainees’ behaviours in generalization process 4.1. The role of personal characteristics Entrepreneurship education studies have started paying attention to an ensemble of personal issues that might intervene in the relationship between entrepreneurship education and training outcomes, such as attitudes and intentions. In their meta-analysis, Bae et al. (2014) considered the role of gender and the status of belonging to a family business or having prior entrepreneurial goals as moderators in the relationship between education and intentions. Contrary to their expectations, their findings showed that neither gender nor belonging to a family business affected the role of entrepreneurship education in fostering or hindering training outcomes. Recently, Westead and Solesvik (2016) found that gender does play a role in the effect of entrepreneurship education on intentions; their study showed that women appear to take advantage of training less often than men. They found that women show a lower risk-taking approach with respect to their male counterparts; consequently, women exhibited fewer entrepreneurial intentions than men at the end of such courses. Bae et al. (2014) have also shown that pre13 training entrepreneurial intentions affect the impact of entrepreneurship education, in that entrepreneurial intentions increased more in people who showed lower intentions at the beginning of their training. More recently, in a study involving 275 French students enrolled in an entrepreneurship master class, Fayolle and Gailly (2015), have echoed the findings from Bae et al. (2014). Accordingly, they validate that previous entrepreneurial experiences and intentions can affect the impact of entrepreneurship education, showing that the entrepreneurship programme increased the level of intentions of students who initially did not envisage an entrepreneurial career, while decreasing that of students who initially had one in mind. We claim that future studies should maintain this line of research, by exploring further those personal characteristics that might enhance or diminish results. Taking together, these results suggest that trainees with different levels of experience and different intentions towards an entrepreneurial career likely have different motivations, expectations, and training needs that cause them to react differently to the training. These pre-training conditions might activate a combination of mechanisms that influence how people process information, focus their attention, direct their effort, and manage their affect during learning (Salas and Kozlowski, 2010). Gully and Chen (2010) have grouped individual differences into four general categories: (a) capabilities, (b) demographics, (c) personality traits, and (d) values and interests. Capabilities include general mental ability, specific talents, and skills. Demographics refer to physical and observable characteristics of the individual such as gender, ethnicity, and age. Personality traits include umbrella traits such as the Big Five -a five factor model to classify personality (Goldberg, 1990), as well as self-concept traits like goal orientation, general self-efficacy, self-esteem, and locus of control. Values and interests include career orientation, vocational interests, and education. Future studies on entrepreneurship education might look at these groups of variables to better understand the nexus between individual’s characteristics and the general impact of entrepreneurship education. However, we argue that it is also important to figure out the learning mechanisms that a programme fosters or hinders, which may depend on participants’ characteristics. For example, when a study aims to investigate the role of a background moderator, it should also try to explain the surrounding mechanisms that lead the moderator to intervene by modifying the training impact. In this respect, future research will benefit from considering more directly the role of trainees’ motivation, expectations, personal goals, and training needs with respect to the entrepreneurship programme as well as in the 14 pursuit of an entrepreneurial career. Carsrud and Brännback (2011) have highlighted the theoretical principles according to which motivation is a paramount variable in an entrepreneurial process. They propose that motivation is the theoretical link between intentions and behaviour. In the same vein, motivation is thought to play a key role in the entrepreneurship learning process. This issue is even more important in a learner-centred perspective, in which the trainee is an active agent of his or her own learning process (Noe et al., 2010). Motivation to learn, for example, has been shown to influence learning outcomes such as knowledge, post-training self-efficacy, and reaction to training (Aguinis and Kraiger, 2009; Colquitt et al., 2000; Salas and CannonBowers, 2001). People who are motivated to attend a training programme prepare themselves beforehand (Stanhope, Pond, and Surface, 2012), as a result, they are more engaged in learning activities (Noe et al., 2010). Further, people often have different expectations of training programmes, and have different levels of clarity about what they expect to learn. These expectations might have important implications with respect to the learning processes (Salas and Cannon-Bowers, 2001) that future studies should try to address. By connecting this to the recent study of Gielnik et al. (2017), potential intriguing questions come to light. For example, to what extent does entrepreneurship education foster individuals’ passion to solve real-life problems that might develop into a new business? 4.2 The role of training strategies An important focus of entrepreneurship education studies is on training pedagogy. The ways in which entrepreneurship should be taught has stirred up intense debate (Gibb, 2002; Fiet, 2001; Honig, 2004; Neck and Green, 2011). A focus on the “learning by doing approach” emerges as central to this research area (Fayolle, 2013), and results suggest that this approach correlates with positive effects of a wide range of training outcomes. Nabi et al. (2017) reviewed 159 papers published from 2004 to 2016, looking specifically at the pedagogies and their effects on different entrepreneurial outcomes. By relying on Béchard and Grégoire (2005), they have distinguished five pedagogical approaches, namely (1) the supply approach: described as the transmission and reproduction of knowledge and application of procedures; (2) demand approach: involves exploration, discussion, and experimentation activities; (3) competence approach: a problem-solving approach to real-life situations; and (4 and 5) two hybrid approaches that combine the supply and demand methods, and the demand and competence methods. Their findings suggest that all 15 pedagogical models positively impact attitudes and intentions. However, only the competency-based methods affect long-terms results (operationalised as start-ups activities and business creation). Along with this stream of research, a recent study involving 320 Iranian students from six different university contexts has shown elective courses are more effective in increasing students’ entrepreneurial intentions than compulsory courses (Karimi et al., 2016). According to Karimi et al. (2016) a possible explanation is that students’ motivation towards entrepreneurship might be higher in participants attending an elective course. Taken together, these studies suggest that an active learning approach is key for entrepreneurship. However, a fine-grained exploration of training strategies is useful in order to understand how entrepreneurship education might support participants’ engagement in such education. An engaged person expresses themselves physically, cognitively, and emotionally (Noe et al., 2010). This engagement is pivotal in training as it likely nurtures trainees’ motivation to learn or to accomplish tasks (Sitzmann and Ely, 2011). The question remains: which strategies can be employed to make entrepreneurship programmes more effective in engaging participants? Studies on training effectiveness have recently recognized that games, simulations, and technology-based training might be central in supporting an active learning approach. However, this is still an underdeveloped topic, and future research is needed to understand these interactive approaches play in a society whose citizens require a continuous learning approach (Bell et al., 2017). It’s also important to investigate these approaches in entrepreneurship education. In addition to these issues, however, we claim that it might be worth understanding the role that different actors have in an entrepreneurship learning environment that emphasises an active learning approach. In this respect, a set of questions might be answered, for example: what role should teachers, mentors, coaches, and teams play? To what extent are deliberate learning practices or informal learning crucial for entrepreneurship? Is collaborative learning—where trainees are trained in groups, but not necessarily to perform team tasks (Salas and Cannon-Bowers, 2001)—more effective in terms of training performance and generalization than a learning by teams approach (where tasks are required to be performed in teams)? 4.3 The role of the environment and social context Studies that assess entrepreneurship education have primarily concentrated on understanding the role of national contexts on programme effectiveness (e.g., 16 Lima, Lopes, Nassif, and da Silva, 2015). In their meta-analysis, Bae et al. (2014) have shown that, among the factors they investigated, national context was the only one that accounted for significant differences in the impact of entrepreneurship education on entrepreneurial intentions. They observed that nations high in collectivistic culture, low in gender egalitarianism, and low in uncertainty avoidance recorded higher levels of entrepreneurship education effects. More recently, Entrialgo and Iglesias (2016) have sought to understand the mechanisms linking entrepreneurship education, social norms, and entrepreneurial intentions. In a sample of 338 final-year undergraduate students of a Spanish university, they observed that students attending an entrepreneurship education programme achieved the same results in terms of perceived behavioural control compared to students without entrepreneurship education but high in social norms. Further, they showed that entrepreneurship education strengthens the role of social norms in increasing positive attitudes towards entrepreneurship. Entrepreneurship education seems then to equalise the role of social support, by reinforcing it with respect to attitudes and intentions. In a more extended study of 805 students in Pakistan, Saed, Yousafzai, Soriano, and Muffatto (2015) found that the social support coming from the university fosters students’ entrepreneurial self-efficacy, which in turn affects entrepreneurial intentions. It should be noted, however, that social contexts have been the focus of much research attention if we look at studies dealing with the formation of entrepreneurial intentions (Liñán and Chen, 2009). These works constitute an important legacy for studies on entrepreneurship education assessment in which entrepreneurial intentions are addressed as a training outcome. Taken together, these works suggest that studies of entrepreneurship education should dive deeper into the role of social environment and its implications on learning and generalization processes. In particular, studies that assess entrepreneurship education should control for the role that different social actors play in learning and generalization processes at the national, regional, organizational, and class level. The goal would be to understand the role that proximal social factors and distal social factors play in entrepreneurship education. Shifting attention to studies of training effectiveness, findings show that environment is pivotal in fostering the generalization process (Bell et al., 2017). Empirical evidence has demonstrated that social support, opportunities to apply new learnings, and lack of constraints in an organizational context are key variables in promoting generalization (Burke and Hutchins, 2007; Blume et al., 17 2010; Cromwell and Kolb, 2004). For instance, Martin (2010) examined the role played by peer support and by work climate—i.e. the general support coming from management. He found that support from peers played a stronger role than support from supervisors. It is worth noting that his study found that trainees in a negative climate with peer support achieved the same degree of transfer as trainees in a positive climate. These results suggest that more proximal factors, like peer support, can overcome the effect of more distal factors, like climate, in promoting the generalization process. Apart from considering proximal factors of social context, distal factors and their implications on the effectiveness of entrepreneurship education should be investigated. Entrepreneurship education at a regional level might play an important role in assessing entrepreneurship programmes outcomes, yielding to different scenarios in terms of participants’ decision to generalize what they have learned. In this regard, Lindh and Thorgren (2016) have shown ways in which territories shape entrepreneurship education programmes. According to their study, policymakers might see entrepreneurship education as a job creation activity, or as a way to train people to be more autonomous and capable of fully managing their careers and life aspirations. In the first case, entrepreneurship is secondary to the need to create employment opportunities. In the second case, territories cultivate people’ talents, encouraging people to come back with new ideas that will enrich their social and economic context. This occurs in territories in which entrepreneurship is part of people’s daily lives and where entrepreneurial motivation is already high. In territories where manufacturing and mining are the most important industries, entrepreneurship education is seen as a tool for municipal development to strengthen existing businesses. These findings suggest that the assessment of entrepreneurship programmes deserves a systemic approach of analysis, in which the social and environmental contexts are investigated to better understand the transition from training to transfer. 4.4 The role of an attribute-treatment interaction perspective Research on training effectiveness has shown that a promising direction for future studies is the attribute-treatment interaction perspective (Salas and Kozlowski, 2010) —the ways that individual differences interact with training interventions or social context to yield differential effects on training outcomes (Gully et al., 2002; Gully and Chen, 2010; Bell et al., 2017). The assumption behind this perspective is that training characteristics, social context, and personal characteristics might interact to affect learning outcomes (Gully and 18 Chen, 2010). This suggest that better training results might come from the fit between an individual and a context. For example, scholars have demonstrated that low-ability learners seem to benefit from learning situations that are more structured and less discovery-based, while high-ability learners benefit from less structured and more self-guided exploration. A similar mechanism has also been observed for level of experience and cognitive ability (Day, Arthur and Gettman, 2001). The same logic also applies to personal traits (Herold et al., 2002), motivations, and aptitudes (Bell and Kozlowski, 2002; Chiaburu and Tekleab, 2005; Towler and Dipboye, 2001). These findings suggest that studies of entrepreneurship education should carefully explore learning strategies to better identify which strategies are suited to different levels of pre-training entrepreneurial experience, different magnitudes of pre-training entrepreneurial intentions, attitudes, interests, and motivation towards entrepreneurship. The goal would be to understand how to create pedagogical environments that foster successful learning mechanisms for different groups of trainees, rather than simply selecting the best trainees for entrepreneurship courses. 4.5 Looking at the impact in terms of learning results and generalization process Nabi et al. (2017) claim that much research is needed to investigate the transition from training to a work context, and recommend looking specifically at the development of business behaviour once entrepreneurial intentions have been declared. By looking at research into training effectiveness, it is clear that this topic is one of the most important research fields that training scholars have ever addressed (Baldwin et al., 2017). Recent research has tried to better identify the different shapes that this process might take in real contexts, yielding a better definition and operationalization of the generalization phenomenon (Bennett and Ceci, 2002; Blume et al., 2010), and how this process might evolve over time (Huang et al., 2017). An important premise of this work is that all the training outcomes and transfer conditions are worth being investigated. Although research has emphasised intentions, we argue that it is useful to continue to consider them as a training outcome. Future questions should better identify why entrepreneurship education can or cannot change people’s attitudes, interests, and intentions regarding entrepreneurship, knowing that different results should 19 be expected depending on the three inputs and their mutual interactions mentioned in the previous sections of the chapter. In accordance with previous studies (e.g., Nabi et al., 2017), future works should try to better understand generalization outcomes and observe the posttraining phase more systematically. In line with the current research on training transfer (Bell et al., 2017), the relationships among the different stages of evaluation should especially be considered. We argue that studies that assess entrepreneurship education might benefit from distinguishing maintenance from generalization results (Blume et al., 2010). Maintenance is the extent to which changes that result from a learning experience persist over time. Regarding generalization, we rely on a taxonomy devised by Barnett and Ceci (2002) to introduce the concepts of near transfer and the far transfer. The basic distinction between the two is the level of similarities of the content in the training and the post-training phase. Near transfer concerns outcomes that are observed during training: for example, if a trainee is engaged in developing the same business idea after the training. Far transfer looks at new forms of behaviour, such as participating in different entrepreneurial activities, identifying new opportunities, and pursuing a portfolio experience after training. In this respect, future research needs to address the role of post-training timing to better assess the impact of entrepreneurship education and, at the same time, the role that the environment might have in enhancing or hindering entrepreneurial behaviour, by modifying the effect of timing. This reflection comes in light of the fact that entrepreneurship can include, but does not necessarily require, the creation of new organizations (Shane and Venkataraman, 2000), and that the relationship between intentions and behaviour should be conceived not simply as a linear or a unidirectional process (Carsrud and Brännback, 2011). Related to this issue, interesting findings are emerging in entrepreneurship education. In a study involving students in business and enterprise courses at a U.K. higher education institution, Kwong and Thompson (2016) have classified students depending on their intention to start a business immediately or in the future following completion of their studies. They have defined three typologies of students: rapid, waiting, and doubtful potential entrepreneurs. Both rapid and waiting entrepreneur students displayed strong desires for entrepreneurial careers, but when the issue of timing was considered, most students were not looking for rapid involvement. In fact, the most favoured path to entrepreneurship was to work for others first, and then become an entrepreneur at a later time. 20 If we look at the research into training effectiveness, timing is an important variable that has recently received heightened focus. Scholars have shown that following training, trainees can vary in putting to use what they have learned, and that these variations have been observed both at the between-person, and at the within-person level. Regarding the between-person level, Huang et al. (2017) showed that people with high levels of self-efficacy (measured after training) try to use what they have learned following training more than people with lower levels of post-training self-efficacy. At the within-person level, the same study shows that only highly motivated people increase their effort in generalizing what they have learned over time, demonstrating that transfer is primarily a motivational process. The ways these motivational processes can be fostered represent an important issue to be addressed in future research. In this respect, Bell et al. (2017) have shown that post-training interventions designed to sustain and support people to generalize might enhance the motivation to transfer after training. In the same vein, entrepreneurship education programmes should consider whether and how to support participants in transforming and pursuing what they have learned in other contexts. A question to be answered is whether entrepreneurship education might be more effective if programmes provide specific interventions that help to identify, for example, contextual constraints along with their possible solutions. Further, by echoing recent entrepreneurship studies that call for broadening the range of entrepreneurship education outcomes (Gielnik et al., 2017; Nabi et al., 2017), we claim that assessment studies should also consider organizational and societal outcomes. In their literature review of training research, Aguinis and Kraiger (2009) pointed out several reasons that training is important for societies, even more so when individuals are confronted with important technological and societal changes (Bell et al., 2017), such as when artificial intelligence is introduced into the work place. In this respect, entrepreneurship education assessment should also consider its role in fostering entrepreneurial employees’ behaviours within organizations. Courpasson, Dany, and Martì (2016) have shown that organizational entrepreneurship might be an “[…] expression of transformative and creative efforts confronting the established structures, practices, and strategies as opposed to a realization of possibilities articulated in management strategy” (p. 132). A question for future entrepreneurship education research is whether it contributes to the development of a more proactive society of future workers that might challenge organizational dynamics. 21 5. Conclusion This chapter presents a research agenda for future studies on the effectiveness of entrepreneurship education. It relies on the systems perspective developed in training effectiveness literature (Baldwin and Ford, 1988; Baldwin et al., 2017) to build a systemic approach of assessment that might drive future research questions regarding the impact of entrepreneurship education. Our research agenda integrates current entrepreneurship education results with systems perspective (Baldwin and Ford, 1988) assumptions. Our analysis of entrepreneurship education literature shows interesting connections and overlaps with the aforementioned perspective, and suggests fruitful insights that need to be addressed in the future. In particular, the chapter systematizes the role of personal, training, and environmental characteristics to better explain the effects of entrepreneurship education. It proposes to look at mutual interactions across these variables in connection with the attitude-treatment interaction perspective (Gully and Chen, 2010). Further, in the transition from training to the post-training phase, it emphasises the need for more research on timing, and specifically on the relationship between time passed since training and entrepreneurial behaviours. In the post-training phase, it suggests investigating the mechanisms connecting learning results and the maintenance and generalization processes, knowing that variations might occur over time in the ways in which these mechanisms work. We claim that the systemic approach this chapter offers is useful to handle the main drawbacks that have been highlighted in the literature (Nabi et al., 2017). Regarding the pitfalls of focusing on short term results, we provide a framework that emphasises both learning results and the processes of maintenance and generalization. In this respect, we believe that additional research is needed to understand proximal and distal outcomes of training. We have presented the difference between maintenance and generalization. Regarding generalization, we have added a further distinction between near and far transfer. Moreover, we have shown that future research should seek to understand the mechanisms linking these outcomes together over time. Regarding the inconsistency of results, our systemic approach provides a base to better identify the intervening variables that have the potential to foster or hinder the effects of entrepreneurship education. Specifically, it encourages researchers to focus on the mechanisms that surround these effects. Literature on training effectiveness gives theoretical and empirical suggestions in this direction, some of which are synthesised in this chapter. 22 Regarding the pedagogy of entrepreneurship education programmes, we suggest that training characteristics play a paramount role in influencing training effectiveness. Much research is needed to better understand which strategies are crucial for supporting an active learning approach that previous works have shown as crucial for increasing transfer process. References Adams, J. S. (1963). Toward an understanding of inequity. Journal of Abnormal and Social Psychology, 67, 422-463. Aguinis, H., and Kraiger, K. (2009). Benefits of training and development for individuals and teams, organizations, and society. Annual Review of Psychology, 60, 451-474. Arthur, W. Jr., Bennett, W., Edens, P. S., and Bell, S. T. (2003). Effectiveness of training in organizations: A meta-analysis of design and evaluation features. Journal of Applied Psychology, 83, 234-245. Awoniyi, E. A., Griego, O. V., and Morgan, G., A., (2002). Person Environment fit and transfer of training. International Journal of Training and Development, 6, 25-35. Bae, T. J., Qian, S., Miao, C., and Fiet, J. O. (2014). The relationship between entrepreneurship education and entrepreneurial intentions: A meta‐ analytic review. Entrepreneurship theory and practice, 38(2), 217-254. Baldwin, T. T., and Ford, J. K. (1988). Transfer of training: A review and directions for future research. Personnel Psychology, 41(1), 63-105. Baldwin, T. T., Ford, J. K., and Blume, B. D. (2009). Transfer of training 1988– 2008: an updated review and agenda for future research. International Review of Industrial and Organizational Psychology, 24(1), 41-70. Baldwin, T. T., Kevin Ford, J., and Blume, B. D. (2017). The State of Transfer of Training Research: Moving Toward More Consumer‐Centric Inquiry. Human Resource Development Quarterly, 28(1), 17-28. Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1-26. Barnett, S. M., and Ceci, S. J. (2002). When and where we apply what we learn? A taxonomy of far transfer. Psychological Bulletin, 128, 612-637. 23 Béchard, J. P., and Grégoire, D. (2005). Entrepreneurship education research revisited: The case of higher education. Academy of Management Learning and Education, 4(1), 22-43. Bell, B. S., and Kozlowski, S. W. J. (2002). Goal Orientation and ability: Interactive effects on self-efficacy, performance, and knowledge. Journal of Applied Psychology, 87, 497-505. Bell, B. S., and Kozlowski, S. W. (2008). Active learning: effects of core training design elements on self-regulatory processes, learning, and adaptability. Journal of Applied psychology, 93(2), 296. Bell, B. S., and Kozlowski, S. W. (2010). Toward a theory of learner-centered training design: An integrative framework of active learning. Learning, Training, and Development in Organizations, 263-300. Bell, B. S., Tannenbaum, S. I., Ford, J. K., Noe, R. A., and Kraiger, K. (2017). 100 years of training and development research: What we know and where we should go. Journal of Applied Psychology, 102(3), 305–323. Blume, B. D., Ford, J. K., Baldwin, T. T., and Huang, J. L. (2010). Transfer of training: A meta-analytic review. Journal of Management, 36(4), 10651105. Boyack, K. W., and Klavans, R. (2010). Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the Association for Information Science and Technology, 61(12), 2389-2404. Burke, L. A., and Hutchins, H. M. (2007). Training transfer: An integrative literature review. Human Resource Development Review, 6(3), 263-296. Byrne, J., Fayolle, A., and Toutain, O. (2014). 15. Entrepreneurship education: what we know and what we need to know. Handbook of research on small business and entrepreneurship, 261-288. Carsrud, A., and Brännback, M. (2011). Entrepreneurial motivations: what do we still need to know?. Journal of Small Business Management, 49(1), 926. Castriotta, M. and Loi, M. (2017). Entrepreneurship education and the rise of new organizations. Franco Angeli: Milano. Chen, G. Gully, S. M., Whiteman, J. A., and Kilcullen, R. N. (2000). Examination of the relationships among trait-lik individual differences, 24 state-like individual differences and learning performance. Journal of Applied Psychology, 85, 835-847. Chiaburu, D. S., e Marinova, S. V. (2005). What predict skill transfer? An exploratory study of goal orientation, training self-effcacy, and organizational supports. International Journal of Training and Development, 9, 110-123. Chiaburu, D. S., and Tekleab, A. G. (2005). Individual and contextual influences on multiple dimensions of training effectiveness. Journal of European Industrial Training, 29(8), 604-626. Colquitt, J. A., LePine, J. A., and Noe, R. A. (2000). Toward an integrative theory of training motivation: A meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85, 678-707. Courpasson, D., Dany, F., and Martí, I. (2016). Organizational entrepreneurship as active resistance: A struggle against outsourcing. Entrepreneurship Theory and Practice, 40(1), 131-160. Cromwell, S. E., and Kolb, J. A. (2004). An examination of work‐environment support factors affecting transfer of supervisory skills training to the workplace. Human Resource Development Quarterly, 15(4), 449-471. Day, E. A., Arthur Jr, W., and Gettman, D. (2001). Knowledge structures and the acquisition of a complex skill. Journal of Applied Psychology, 86(5), 1022. Do Paço, A., Ferreira, J. M., Raposo, M., Rodrigues, R. G., and Dinis, A. (2015). Entrepreneurial intentions: is education enough? International Entrepreneurship and Management Journal, 11(1), 57-75. Edwards, J. R. (1991). Person-job fit: A conceptual integration, literature review, and methodological critique. International review of industrial and organizational psychology, 1991, 6, 283-357. Entrialgo, M., and Iglesias, V. (2016). The moderating role of entrepreneurship education on the antecedents of entrepreneurial intention. International Entrepreneurship and Management Journal, 12(4), 1209-1232. Fayolle, A., and Gailly, B. (2015). The impact of entrepreneurship education on entrepreneurial attitudes and intention: Hysteresis and persistence. Journal of Small Business Management, 53(1), 75-93. 25 Fayolle, A. (2013). Personal views on the future of entrepreneurship education. Entrepreneurship and Regional Development, 25(7-8), 692-701. Fayolle, A., Gailly, B., and Lassas-Clerc, N. (2006). Assessing the impact of entrepreneurship education programmes: a new methodology. Journal of European industrial training, 30(9), 701-720. Fiet, J. O. (2001). The pedagogical side of entrepreneurship theory. Journal Of Business Venturing, 16(2), 101-117. Ford, J. K., Kraiger, K., and Merritt, S. M. (2010). An updated review of the multidimensionality of training outcomes: New directions for training evaluation research. Learning, Training, and Development in Organizations, 135-165. Kraiger, K., Ford, J. K., and Salas, E. (1993). Application of cognitive, skillbased, and affective theories of learning outcomes to new methods of training evaluation. Journal Of Applied Psychology, 78(2), 311. Frayne, C. A., e Geringer, M. (2000). Self-Management Training for Improving Job Performance: A Field Experiment Involving Salespeople. Journal of Applied Psychology, 85, 361-372. Giacomin, O., Janssen, F., and Shinnar, R. S. (2016). Student entrepreneurial optimism and overconfidence across cultures. International Small Business Journal, 34(7), 925-947. Gibb, A. (2002). In pursuit of a new ‘enterprise’ and ‘entrepreneurship’ paradigm for learning: creative destruction, new values, new ways of doing things and new combinations of knowledge. International Journal of Management Reviews, 4(3), 233-269. Gielnik, M. M., Uy, M. A., Funken, R., and Bischoff, K. M. (2017). Boosting and sustaining passion: A long-term perspective on the effects of entrepreneurship training. Journal of Business Venturing, 32(3), 334-353. Gist, M. E., and Mitchell, T. R. (1992). Self-efficacy: A theoretical analysis of its determinants and malleability. Academy of Management Review, 17, 183-211. Goldberg, L. R. (1990). An alternative" description of personality": the big-five factor structure. Journal of Personality and Social Psychology, 59(6), 1216-1229. 26 Gully, S., and Chen, G. (2010). Individual differences, attribute-treatment interactions, and training outcomes. Learning, training, and development in organizations, 3-64. Gully, S. M., Payne, S. C., Koles, K. L. K., and Whiteman, J. K. (2002). The impact of error training and individual differences on training outcomes: An attribute-treatment interaction perspective. Journal of Applied Psychology, 87, 143-155. Heimbeck, D., Freise, M., Sonnentag, S., and Keith, N. (2003). Integrating errors into the training process: The function of error management instructions and the role of goal orientation. Personnel Psychology, 56, 333-361. Herold, D. M., Davis, W., Fedor, D. B., and Parsons, C. K. (2002). Dispositional influences on transfer of learning in multistage training programs. Personnel Psychology, 55, 851-869. Holladay, C. L., and Quinones, M. A. (2003). Practice variability and transfer of training: the role of self-efficacy generality. Journal of applied psychology, 88(6), 1094. Honig, M. I., and Hatch, T. C. (2004). Crafting coherence: How schools strategically manage multiple, external demands. Educational Researcher, 33(8), 16-30. Huang, W. R., and Jao, Y. J. (2016). Comparison of the influences of structured on-the-job training and classroom training approaches on trainees’ motivation to learn. Human Resource Development International, 19(2), 116-134. Huang, J. L., Ford, J. K., and Ryan, A. M. (2017). Ignored no more: Within‐ Person variability enables better understanding of training transfer. Personnel Psychology, 70(3), 557-596. Karimi, S., Biemans, H. J., Lans, T., Chizari, M., and Mulder, M. (2016). The impact of entrepreneurship education: A study of Iranian students' entrepreneurial intentions and opportunity identification. Journal of Small Business Management, 54(1), 187-209. Keith, N., and Frese, M. (2008). Effectiveness of error management training: a meta-analysis. Journal of Applied Psychology, 93, 59-69. 27 Kraiger, K., Ford, J. K., e Salas, E. (1993). Application of cognitive, skill-based and affective theories of learning outcomes to new methods of training evaluation. Journal of Applied Psychology, 78 , 311-328. Kwong, C., and Thompson, P. (2016). The when and why: student entrepreneurial aspirations. Journal of Small Business Management, 54(1), 299-318. Lanero, A., Vázquez, J. L., and Aza, C. L. (2016). Social cognitive determinants of entrepreneurial career choice in university students. International Small Business Journal, 34(8), 1053-1075. Lievens, F., Harris, M. M., Van Keer, E., and Bisqueret, C. (2003). Predicting cross-cultural training performance: The validity of personality, cognitive ability, and dimensions measured by an assessment center and a behavior description interview. Journal of Applied Psychology, 88, 476-489. Lima, E., Lopes, R. M., Nassif, V., and Silva, D. (2015). Opportunities to improve entrepreneurship education: Contributions considering brazilian challenges. Journal of Small Business Management, 53(4), 1033-1051. Liñán, F., and Chen, Y. W. (2009). Development and Cross‐Cultural application of a specific instrument to measure entrepreneurial intentions. Entrepreneurship Theory and Practice, 33(3), 593-617. Lindh, I., and Thorgren, S. (2016). Entrepreneurship education: the role of local business. Entrepreneurship and Regional Development, 28(5-6), 313-336. Locke, E. A. (1968). Toward a theory of task motivation and incentives. Organizational Behavior and Human Performance, 3 , 157-189. Maresch, D., Harms, R., Kailer, N., and Wimmer-Wurm, B. (2016). The impact of entrepreneurship education on the entrepreneurial intention of students in science and engineering versus business studies university programs. Technological Forecasting and Social Change, 104, 172-179. Martin, H. J. (2010). Workplace climate and peer support as determinants of training transfer. Human Resource Development Quarterly, 21(1), 87-104. McNally, J. J., Martin, B. C., Honig, B., Bergmann, H., and Piperopoulos, P. (2016). Toward rigor and parsimony: a primary validation of Kolvereid’s (1996) entrepreneurial attitudes scales. Entrepreneurship and Regional Development, 28(5-6), 358-379. 28 Meyer, R. D., Dalal, R. S., and Hermida, R. (2010). A review and synthesis of situational strength in the organizational sciences. Journal of Management, 36(1), 121-140. Nabi, G., Liñán, F., Fayolle, A., Krueger, N., and Walmsley, A. (2017). The impact of entrepreneurship education in higher education: A systematic review and research agenda. Academy of Management Learning and Education, 16(2), 277-299. Neck H.M., and Green, P.G. (2011). Entrepreneurship education: Known world and new frontiers. Journal of Small Business Management, 49, 55-70. Ng, T. W., and Lucianetti, L. (2016). Within-individual increases in innovative behavior and creative, persuasion, and change self-efficacy over time: A social–cognitive theory perspective. Journal of Applied Psychology, 101(1), 14-34. Noe, R. A., Tews, M. J., and Dachner, A. M. (2010). Learner engagement: A new perspective for enhancing our understanding of learner motivation and workplace learning. Academy of Management Annals, 4(1), 279-315. Oehler, A., Höfer, A., and Schalkowski, H. (2015). Entrepreneurial education and knowledge: Empirical evidence on a sample of German undergraduate students. The Journal of Technology Transfer, 40(3), 536-557. Oosterbeek, H., Van Praag, M., and Ijsselstein, A. (2010). The impact of entrepreneurship education on entrepreneurship skills and motivation. European Economic Review, 54(3), 442-454. Peterman, N. E., and Kennedy, J. (2003). Enterprise education: Influencing students’ perceptions of entrepreneurship. Entrepreneurship Theory and Practice, 28(2), 129-144. Piperopoulos, P., and Dimov, D. (2015). Burst bubbles or build steam? Entrepreneurship education, entrepreneurial self‐efficacy, and entrepreneurial intentions. Journal of Small Business Management, 53(4), 970-985. Ran, S., and Huang, J. L. (2017). Enhancing adaptive transfer of cross-cultural training: Lessons learned from the broader training literature. Human Resource Management Review. Doi.org/10.1016/j.hrmr.2017.08.004 Rauch, A., and Hulsink, W. (2015). Putting entrepreneurship education where the intention to act lies: An investigation into the impact of 29 entrepreneurship education on entrepreneurial behavior. Academy of Management Learning and Education, 14(2), 187-204. Saeed, S., Yousafzai, S. Y., Yani‐De‐Soriano, M., and Muffatto, M. (2015). The role of perceived university support in the formation of students' entrepreneurial intention. Journal of Small Business Management, 53(4), 1127-1145. Salas, E., and Cannon-Bowers, J. A. (2001). The science of training: A decade of progress. Annual Review of Psychology, 52(1), 471-499. Salas, E., and Kozlowski, S. W. (2010). Learning, training, and development in organizations: Much progress and a peek over the horizon. Learning, Training, and Development in Organizations, 461-476. Shane, S., and Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25(1), 217-226. Sitzmann, T., and Ely, K. (2011). A meta-analysis of self-regulated learning in work-related training and educational attainment: what we know and where we need to go. Psychological Bulletin, 137(3), 421-442. Sohn, Y. S., Doane, S. M., and Garrison, T. (2006). The impact of individual differences an learning context on strategic skill acquisition and transfer. Learning and Individual Differences, 16, 13-30. Souitaris, V., Zerbinati, S., and Al-Laham, A. (2007). Do entrepreneurship programmes raise entrepreneurial intention of science and engineering students? The effect of learning, inspiration and resources. Journal of Business Venturing, 22(4), 566-591. Stanhope, D. S., Pond III, S. B., and Surface, E. A. (2013). Core selfevaluations and training effectiveness: Prediction through motivational intervening mechanisms. Journal of Applied Psychology, 98(5), 820-831. Teixeira, A. A. (2011). Mapping the (in) visible college (s) in the field of entrepreneurship. Scientometrics, 89(1), 1-36. Tixier J., Loi, M., Le Pontois, S., Tavakoli, M. and Fayolle, A. (forthcoming). Entrepreneurship Education Effectiveness: What we can learn from education and organizational studies. In Enterprising Education: International Perspectives, Challenges, and Practice. Routledge. 30 Towler, A. J., and Dipboye, R. L. (2001). Effects of trainer expressiveness, organization, and trainee goal orientation on training outcomes. Journal of Applied Psychology, 86, 664-673. Van Eck, N. J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523538. Vogel, R., and Güttel, W. H. (2013). The dynamic capability view in strategic management: A bibliometric review. International Journal of Management Reviews, 15(4), 426-446. Von Graevenitz, G., Harhoff, D., and Weber, R. (2010). The effects of entrepreneurship education. Journal of Economic Behavior and Organization, 76(1), 90-112. Vroom, V. H. (1964). Work and motivation. New York: Wiley. Westhead, P., and Solesvik, M. Z. (2016). Entrepreneurship education and entrepreneurial intention: Do female students benefit? International Small Business Journal, 34(8), 979-1003. Yamnill, S., and McLean, G. N. (2001). Theories supporting transfer of training. Human Resource Development Quarterly, 12, 195-208. 31