Potential of Students to Become Entrepreneurs in the Chemical Industry

Abstract

Chempreneurs are revolutionary entrepreneurs who have been trained at a chemistry faculty. They are expected to find innovative solutions for current challenges to the chemical industry, such as sustainability. Innovative chemical entrepreneurship is the art of commercialising innovations from chemistry for a broad audience outside the classroom. Innovative ideas that are implemented with societal relevance offer the potential for individuals/graduates to generate income and promote the economy, thus offering students the opportunity to lay the foundation for a career with innovations developed in class. The probability of a German chemistry student founding a company is 54% lower than for the average German student. Previous studies show the potential effects of social capital, the subjective norm, motives and barriers for chemistry students in Germany. In this study, we tested these hypotheses by implementing entrepreneurship education. Two surveys, one before class and one with a one-week delay, were conducted with first- and third-semester chemistry bachelor students at the Fresenius University of Applied Sciences in Idstein, Germany. We conducted the surveys to understand how students view chemistry career opportunities through entrepreneurship and how to change this mindset. Finally, we propose a pedagogy to introduce/expand entrepreneurship in chemistry and discuss approaches for future research.

1. Introduction

“Chemical entrepreneurship” refers to the commercial application of innovations in chemistry to a market or buyer [1]. Innovations are defined as new processes, products or procedures that are characterised by a combination of ideas, inventions and diffusion [2]. Innovations can take the form of new processes, products or procedures, while technology transfer for innovations can take the form of patents, spin-offs from a university or the founding of a company [3]. Due to the products, applications and concepts of the chemical industry in everyday life, the application possibilities for innovations from chemistry are necessary and important in the context of global challenges such as human health, plant production, energy conversion and storage, safe and abundant water, climate change and others [4]. In Germany, the chemical industry accounts for 6.7% of gross domestic product, putting it in third place worldwide behind the USA and China [5,6,7]. Bridging the gap between science and entrepreneurship is therefore important for the expansion and continued existence of the chemical industry in Germany [8].

Academic research can be commercialised through patenting, licencing or the founding of companies and the marketing of products. To this end, these products from academic research require marketability [1,9]. For example, BioNTech SE, one of the companies that developed vaccines to combat the COVID pandemic, was founded in 2008 by German innovators [5]. Not many scientists and researchers with innovative ideas aim for commercialization or financial gain [10]. The identification of students with a potentially commercially successful idea is just as imperative as the attitude and behaviour of the individuals [11]. Classroom instruction is often limited for chemistry, neglecting its relevance and benefits to society, which causes students who recognise problems and develop innovative solutions to fail to implement them in the production of a marketable product [1,12].

Students make their career decisions based on the experiences they have during their training or studies. Therefore, a previous survey analysed the influencing factors on chemistry students in Germany and Poland [13], and based on these results a teaching model was prepared. This study focuses on the effects of entrepreneurship education based on the results of previous studies and the impact on chemistry students in Germany and serves to identify possible influencing factors on students with the aim of closing the gap between knowledge of the concept of chemical entrepreneurship and the realisation of innovations from chemistry with relevance to the real world. These research questions for this article deal with the question of whether and to what extent entrepreneurship education has an influence on the start-up attitude of chemistry students in Germany.

2. Literature Review

Entrepreneurship is considered an independent field of research. Due to the belief of many authors that entrepreneurship can be learned, many studies have been carried out on the entrepreneurial intentions of students in various disciplines with a majority in business administration at the beginning of their university careers [14,15,16]. Due to the specificities of STEM students and national or cultural differences, e.g., in terms of learning patterns, behaviour or self-actualisation, it is not advisable to generalise theories on entrepreneurship from other courses or countries [17]. Additionally, there may be differences in the explanatory power of the models and the applied elements depending on the country and their discipline [18]. The best-known models in the study of entrepreneurial readiness are the Entrepreneurial Events Model (EEM) [19] and the Theory of Planned Behaviour (TPB) [20]. The TPB model was cited most frequently, which is why it was favoured [21]. For this study, the applied model based on the TPB was expanded to include the factors of start-up knowledge, perceived educational support, perceived support from the university/research institution and perceived career opportunities. The fitted model can be seen in Figure 1.

The results for the start-up behaviour of German chemistry students show possible starting points via significant factors such as social capital, the subjective norm, motives and barriers. The significant variables of the factor Motives 1 are “my personal independence”, “the opportunity to be financially independent”, “improving the quality of life”, “earning more money than through wage labour”, “working in a varied profession”, “building personal wealth”, “having more free time” and “the difficulty of finding the right job”, and the factor Motives 4 is made up of the variables “the opportunity to realise my own ideas” and “creating something of my own”. The factor Barriers 3, which has a significant influence on the start-up behaviour of chemistry students, is made up of the variables “the tax burden (taxes, court fees, etc.)”, “paperwork and bureaucracy when starting a business”, “too much work when starting a business” and “lack of support from people around me” [22].

The links between entrepreneurship education and students’ entrepreneurial intentions are increasingly being investigated using the TPB [14,16,23,24]. In addition to the influence of entrepreneurship education on entrepreneurial intentions, the educational context (e.g., the university) is also important [25]. The results of previous studies and investigations are inconsistent and disagree on whether entrepreneurship can be promoted through education. While some studies report positive effects of entrepreneurship education on entrepreneurial intentions [26,27,28], others report evidence that the effect is statistically irrelevant or even negative [29,30,31].

In addition to the mode of action, the implementation of entrepreneurship education also varies. Entrepreneurship education is argued to be either a method or a process [24,32]. The path to student entrepreneurship is described as a journey and sequence of events or a series of opportunities or steps [33]. In the comparison of method and practise, entrepreneurship education is argued to be a method with a series of practises in which learning phases are juxtaposed with steps to be completed, as opposed to a process with known inputs and predetermined outputs, a focus on action rather than planning [32]. In addition, learning as an investment versus learning for an expected or predictable return and co-operative versus competitive learning were contrasted [32]. In the classroom, students can be introduced to a culture of innovation through projects, proposals, product development or research [34,35] for which they receive assessment or credit [14]. The growth and progress of innovation in the classroom is hindered by several factors; for example, the opportunities and innovations for chemistry education are still unexplored due to weaknesses in outcomes-based instruction [36]. Another issue may be the focus of academic institutions on publishing innovations that do not take into account the needs and wants of society [37].

The 21st century generational skills and attributes of critical thinking, creative thinking, collaboration and communication have a critical impact on the higher education period and in the training of chemists [38] and provide an opportunity to foster innovation in the classroom for the real world [35]. Limited knowledge of chemical entrepreneurship restricts students from exploring the business world and limits them to career paths in academia or industry, while entrepreneurship as part of the curriculum can create awareness of the business career option [39]. The German Fresenius University of Applied Sciences has developed a cooperative innovation strategy called PANDA (Powerful Actions for the Natural Development of microAccelerators) to inspire students to become entrepreneurs [40]. Since 2017, this approach has been implemented in various countries with companies in Germany and Poland. We argue that the mix of the PANDA approach to students’ experiences combined with entrepreneurship education for the theoretical facet can help students break down possible barriers, build start-up knowledge and prepare for their possible journey. This study aims to analyse the relationship between entrepreneurship education and entrepreneurial intentions. Using the known theoretical approaches, our hypothesis 1 is that entrepreneurship education has a direct positive effect on the entrepreneurial intentions of chemistry students.

3. Methods

This study is a comparative study of German chemistry students, before and after entrepreneurship education, which analyses innovation-oriented technology transfer from the students’ perspective. For reasons of time and cost, a longitudinal design was chosen for our study. The chosen method was a survey with two measurement points (November 2023 and December 2023) to collect prospective and current data using an online questionnaire. The students were asked to participate in the first survey without preparation or information. The target group of the survey were students in the departments of chemistry and their technician students (chemical technicians, analytical and digital forensics, applied chemistry for analytics, forensics and life science, industrial chemistry, business chemistry and industrial biology), who were contacted personally in lectures.

3.1. Entrepreneurship Education

After the survey, entrepreneurship education was provided by an experienced founder who also has a chemistry education at a low-threshold level, which is intended to establish the founder as a role model in order to increase the probability of starting a business [41]. The findings of the first survey (September 2022–January 2023) were used, such as low-threshold communication, the development of motives or the removal of barriers [42]. In the classroom, the focus was on reducing possible barriers such as the risk of a failed start-up and the capital required. At the same time, motives such as career prospects, the development of soft skills through management responsibility and the possibility of financial success were developed. The time gap between the surveys and the lectures was also intended to give the students the opportunity to exchange ideas with each other and possibly process them in their private environment, which was intended to increase the subjective norm [22]. The time gap was also intended to reduce the possibility of errors by deliberately influencing the participants. It was assumed that the participants forgot their answers to the questions after this period of time. The entrepreneurship lessons focused on idea generation, and the process of founding and closing a company. The lessons were conducted by a chemist in order to maintain proximity to the students. Possible obstacles such as a lack of business knowledge or start-up capital were addressed directly. The students had the opportunity to ask questions and clarify their own priorities at any time. Following the lecture, a start-up game was played in groups to find possible ideas for a start-up. The aim was to analyse an idea in depth and develop possible concepts. The second survey took place at the beginning of a new lecture and after a break of several days. The purpose of the surveys and the aim of the follow-up research were only explained after the second survey had been completed in order to rule out possible errors.

3.2. Questions

The questionnaire used in this article corresponds to the questionnaire from the first study on the start-up behaviour of chemistry students in Germany and Poland [41]. The questionnaire used in this study is made up of questions from various studies. The questions are divided into demographic factors, the probability of starting a business or career intentions, and the assumed influencing factors from the TBP with supplementary questions. The questions were asked in the specified order of demographic questions, external influencing factors, start-up probability and personal influencing factors. After the demographic questions on gender, migration, nationality and age, participants were excluded on the basis of their student status in order to obtain the desired data. Subsequently, study-related characteristics such as place of study, subject, intended degree, total duration of study, type of employment or the presence of founders in the environment were recorded and questions were asked to determine the latent constructs. For this purpose, questions from various studies were used, which were adapted to a 6-point Likert scale to force the selection: absolute ignorance (1) to comprehensive knowledge (5) for questions on basic knowledge, and I don’t know (0) and strongly disagree (1) to strongly agree (5) for the others. The likelihood of starting a business was surveyed using two different types of questions: firstly, the likelihood of starting a business after graduation, which ranged from very unlikely (1) to very likely (6), and secondly, career intentions with the options of public service, employment and starting a business. An overview of the question categories used, the number of questions, the presence of the “I don’t know” response option and the question source can be found in

Table 1. Overview of applied study questions with sources, numbers and additions.

Question Categories	Number of Questions	Answer Option “I Don’t Know”	Question Source	Addition of Own Questions
Foundation knowledge	3	no	[43]	no
Perceived educational support (PES) and perceived support of the university (PSU)	10	yes	[44]	yes
Subjective norm (SN)	2	no	[45]	no
Founding intention	2	no	[45]	no
Perceived career options (WCO)	6	yes	[43]	no
Perceived behavioural control (PBC)	9	yes	[46]	no
Self-assessment for the foundation (SF)	3	yes	[47]	no
Motives for Starting a Business	17	yes	[48,49]	yes
Barriers to Starting a Business	19	yes	[48,49]	yes

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3.3. Participants

The total group size for this course, consisting of students in their 1st and 3rd semesters, was 75 people. For the pre-class survey, we received completed questionnaires from 67 participants, a response rate of 89.3%. For the second survey, after class, we received 51 completed questionnaires from participants, a response rate of 68.0%. Before conducting our statistical analyses, we excluded 3 participants from the pre-lesson survey and 4 participants from the post-lesson survey due to incomplete data. The final sample thus consisted of 111 students: 64 before entrepreneurship education and 47 after entrepreneurship education. The average time spent on the questionnaire was 9.25 min. An overview of the distribution of the study participants can be found in

Table 2. Summary of the study participants.

Entrepreneurship Education	Before	After
Age [years]	23.6 ± 3.1	23.7 ± 3.2
Men	45.3%	46.8%
Women	53.1%	48.9%
Migration background	18.9%	14.8%
Foreign citizenship	15.6%	8.5%
Bachelor’s programme	93.3%	95.7%
No income	37.5%	34.0%
Full-time employment	6.3%	6.4
Social capital [yes]	48.4%	48.9%

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3.4. Statistical Analysis

Statistical analysis was carried using IBM SPSS Statistics version 28.0.1. The data were tested for normal distribution using Shapiro–Wilk tests. Not all variables in the data set had a normal distribution and some variables had an ordinal scaling. The (Fisher and Yates, 1938) logistic ordinal model was used to study the effect of continuous and categorical variables on a dependent variable [50].

Further processing is analogous to a study already conducted (Walther, Haubold, & Dobrucka, 2024). In the course of comparability, no new exploratory factor analysis (EFA) was carried out using the maximum likelihood extrusion method. In this way, 12 variable factors can be formed from 69 variables; see

Table 3. Questions in the variable factors.

Variable Factors	Questions from Survey
Motives 1	my personal independence
	the opportunity to be financially independent
	the improvement of quality of life
	earn more money than through wage labour
	to work in a varied profession
	the building of personal wealth
	to have more free time
	the difficulty of finding the right job
Motives 2	the creation of jobs
	to contribute to the economy
	to contribute to regional economic development
Motives 3	to be at the head of an organisation
	to lead people
	attainment of high social status
Motives 4	the opportunity to realise my own ideas
	to create something on my own
Barriers 1	a lack of entrepreneurial competence
	a of knowledge in the business world
	a lack of knowledge of the market
	a lack of experience of accounting
	a lack of guidance
	a lack of support in setting up a business
Barriers 2	excessive risk
	my own fear of failure
Barriers 3	the fiscal charges (taxes, court fees, etc.)
	paperwork and bureaucracy when starting up
	too much work when setting up a company
	a lack of support from people around me
Barriers 4	the search for a business idea for a company that has not yet been realised
	a lack of ideas about which company to found

.

In order to create ordinal logistic regression (OLR), the results were coded as Y = 1 or Y = 0, whereby the result either occurs (1) or does not occur (0). In addition, OLR was performed as an analysis of the factor variables. Thus, OLR was used to determine the effect size (Exp(B)) as a 95% Wald confidence interval, to predict effects, to detect trends and to predict the relationship between an endogenous variable at ordinal level and two or more categorical or continuous exogenous variables. The chi-square test and the goodness of fit according to Person and Deviance as well as the pseudo R-square according to Nagelkerke and Cox I Snell were specified as further parameters for the OLR. The second analysis method used was the Mann–Whitney-U Test for independent samples, which is a non-parametric alternative to ANOVA, as the data were not completely normally distributed. The Kruskal–Wallis test compared the ranks of the data. The result of an ANOVA analysis is the standardised z-value, which indicates by how many standard deviations the test statistic deviates from the expected value, and the significance level (p-value), which is determined by the distribution function of the test.

The significance level was set at 5%. The calculation of the probability of starting a business is based on the percentage of participants who chose starting a business as a career option. The results are presented in the form of a table containing the factor, the country, the number of “I don’t know” statements, the percentage of “I don’t know” statements, the sample size (n), the mean, the median, the Wald chi-square and the p-value (p).

4. Results

Figure 2 provides an overview of the respective mean values of the three surveys (the comparative survey, and those conducted before and after entrepreneurship education).

Figure 1 shows the mean values from the surveys; it is noticeable that the first survey and the survey prior to entrepreneurship education are almost identical. This is important because the first survey was not representative and the surveys for entrepreneurship education were only conducted at the Fresenius University of Applied Sciences in Idstein. As a result, the number of participants is even lower and could otherwise distort the results. The factor compositions are identical to the evaluations already carried out in Germany [22]. The subjective norm does not show any significant differences and is therefore not used below.

4.1. Entrepreneurial Intention

The ANOVA comparison revealed no significant differences in the probability of chemistry students starting a business before and after entrepreneurship education. At 1.97 and 2.15, respectively, the students’ assessments do not indicate significantly higher ratings. However, the limited number of participants after the entrepreneurship education and the fact that the lecture initially only took place once also had an impact.

4.2. Foundation Knowledge

The results of the ANOVA comparison of the variables for foundation knowledge can be found in

Table 4. ANOVA comparison of foundation knowledge.

Question Categories	Entrepreneurship Education	n	Mean	sd	Median	z	p
Do you know of any funding organisation that can help you with your start-up?	Before	64	1.89	1.31	1.0	2.389	0.017
	After	47	2.43	1.38	2.0

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The ANOVA comparison for the start-up knowledge variables revealed a significantly higher classification according to entrepreneurship education with the variable “Do you know of any funding organisation that can help you with your start-up?”.

4.3. Educational Support/Support by University

The results of the ANOVA comparison of the variables for educational support/support by university can be found in

Table 5. ANOVA comparison of educational support/support by university.

Question Categories	Entrepreneurship Education	“I Don’t Know” n	“I Don’t Know” %	n	Mean	sd	Median	z	p
My university has clear rules for the transfer of ideas from research to a start-up company.	Before	42	65.6	22	2.41	1.18	2.5	2.173	0.034
	After	29	61.7	18	3.39	1.46	4.0

As the ANOVA comparison shows, entrepreneurship education significantly increased the variable “My university has clear rules for the transfer of ideas from research to a start-up company.” While the ratings had a mean value of 2.41 at the beginning, they subsequently had a mean value of 3.39. This suggests that students had a better understanding of the university’s rules and programmes in the area of business foundations.

4.4. Perceived Career Options

The results of the ANOVA comparison of the variables for perceived career options can be found in

Table 6. ANOVA comparison of perceived career options.

Question Categories	Entrepreneurship Education	“I Don’t Know” n	“I Don’t Know” %	n	Mean	sd	Median	z	p
I give a lot of thought to choosing the right career.	Before	3	4.5	64	5.14	1.34	6.0	−2.594	0.009
	After	2	4.3	45	4.53	1.50	5.0

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A comparison of the responses from the chemistry students before and after entrepreneurship education revealed a significant difference in the variable “I give a lot of thought to choosing the right career.”. This question was previously answered significantly higher, which suggests greater determination in choosing a career and at the same time more self-confidence.

4.5. Perceived Behavioural Control

The results of the ANOVA comparison of the variables for perceived behavioural control can be found in

Table 7. ANOVA comparison of perceived behavioural control.

Question Categories	Entrepreneurship Education	“I Don’t Know” n	“I Don’t Know” %	n	Mean	sd	Median	z	p
I am not willing to take risks when choosing a job or a company.	Before	8	11.9	59	3.02	1.64	3.0	2.130	0.033
	After	1	2.1	46	3.65	1.42	4.0

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A direct comparison between the answers to the question “I am not willing to take risks when choosing a job or a company.” before and after entrepreneurship education shows a significantly higher rating. This suggests a higher deterrent to a possible start-up due to the associated risks and at the same time shows a higher level of determination.

4.6. Self-Assessment

The results of the ANOVA comparison of the variables for self-assessment can be found in

Table 8. ANOVA comparison of self-assessment.

Question Categories	Entrepreneurship Education	“I Don’t Know” n	“I Don’t Know” %	n	Mean	sd	Median	z	p
I know all about the practical details required to start a business.	Before	7	10.4	60	1.68	1.16	1.0	2.285	0.022
	After	2	4.3	45	2.11	1.34	2.0

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The assessment of the question “I know all about the practical details required to start a business.” shows a significant increase in a direct comparison before and after entrepreneurship education. This can be attributed to the learning successes from the lessons and speaks in favour of starting a business through the necessary knowledge.

4.7. Motives

The results of the ANOVA comparison of the variables for motives can be found in

Table 9. ANOVA comparison of motives.

Question Categories	Entrepreneurship Education	“I Don’t Know” n	“I Don’t Know” %	n	Mean	sd	Median	z	p
…to create something on my own.	Before	4	6.0	63	4.89	1.15	5.0	−2.024	0.043
	After	3	6.4	44	4.50	1.09	5.0

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The analysis of 17 questions with motives comparing the answers before and after entrepreneurship education revealed a significant difference for one question. When asked to what extent “…to create something on my own.” is a motive for starting a business, the students answered this question significantly lower than before entrepreneurship education.

4.8. Barriers

The results of the ANOVA comparison of the variables for barriers can be found in

Table 10. ANOVA comparison of barriers.

Question Categories	Entrepreneurship Education	“I Don’t Know” n	“I Don’t Know” %	n	Mean	sd	Median	z	p
…the current economic situation.	Before	18	28.9	49	4.53	1.28	4.0	−2.222	0.026
	After	10	21.3	37	3.84	1.30	4.0

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As the ANOVA comparison of the answers given before and after entrepreneurship education shows, the students rated the variable “…the current economic situation.” as a significantly lower barrier. This suggests the success of direct learning in the organisation of seed capital for business foundations, for example.

4.9. Regression

The results for the ordinal regression with the factors from the pre-entrepreneurship education survey can be found in

Table 11. The ordinal regression results—contrast ratios before entrepreneurship education.

Parameters	Exp(B)	95% Wald Confidence Interval Exp(B)		Hypothesis Test
		Lower	Upper	Wald Chi-Square	p
Foundation knowledge	1.369	0.944	1.983	2.748	0.097
Education support	1.002	0.930	1.081	0.004	0.949
Subjective norm	2.610	1.391	4.897	8.929	0.003
Perceived career options	0.864	0.645	1.158	0.953	0.329
Motives 1	1.096	0.970	1.238	2.157	0.142
Motives 2	0.948	0.730	1.232	0.158	0.691
Motives 3	1.024	0.758	1.383	0.025	0.876
Motives 4	1.319	0.884	1.969	1.836	0.0175
Barriers 1	1.020	0.869	1.197	0.057	0.811
Barriers 2	1.086	0.744	1.585	0.182	0.669
Barriers 3	0.869	0.712	1.060	1.912	0.167
Barriers 4	0.722	0.536	0.971	4.653	0.031

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Ordinal regression analysis revealed significant influences of the subjective norm and the fourth Barriers category. The subjective norm has an Exp(B) of 2.61, which means that this factor can have a very strong influence on the start-up behaviour of chemistry students in Germany. Here, the strength of the effect shows an increased value compared to the first survey with chemistry students throughout Germany, which showed an Exp(B) of 1.68 [22]. This can be explained by the low number of participants, as higher ratings from individual participants are weighted more heavily. Barriers 4, with the variables “the search for a business idea for a company that has not yet been realized” and “a lack of ideas about which company to found”, showed an effect of 0.772, which means that the lower these barriers are rated, the greater the willingness to found a company. This factor was not significant in the first survey of chemistry students in Germany [22]. The results for the statistical key figures can be found in

Table 12. Statistical key figures for the ordinal regression performed in the applied model before entrepreneurship education.

Test	Model	−2 Log Likelihood	Chi-Square	df	Sig	Pseudo R-Square
Test of Parallel Lines	Null Hypotheses	109.382
	General	95.736	13.646	48	1.000
Model fitting information	Intercept only	147.062
	Final	109.382	37.680	12	<0.001
Goodness of Fit	Pearson		238.268	258	0.806
	Deviance		109.382	258	1.000
Pseudo R-square	Cox/Snell					0.496
	Nagelkerke					0.533
	McFadden					0.256

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The statistical key figures show that the chi-square test of the parallel lines test does not reject the null hypothesis that the location parameters are consistent across the categories. The model fitting shows a significant chi-square test, indicating that the model is better than a constant term. The goodness of fit indicates the good calibration and fit of the model to the observed data [51].

The effect sizes for the factors after entrepreneurship education are shown in

Table 13. The ordinal regression results—contrast ratios after entrepreneurship education.

Parameters	Exp(B)	95% Wald Confidence Interval Exp(B)		Hypothesis Test
		Lower	Upper	Wald Chi-Square	p
Foundation knowledge	0.955	0.662	1.378	0.059	0.808
Education support	1.028	0.950	1.111	0.467	0.494
Subjective norm	1.373	0.773	2.439	1.170	0.279
Perceived career options	0.840	0.559	1.263	0.699	0.403
Motives 1	1.240	1.005	1.530	4.044	0.044
Motives 2	1.290	0.973	1.711	3.139	0.076
Motives 3	0.833	0.564	1.229	0.847	0.358
Motives 4	1.513	0.951	2.406	3.054	0.081
Barriers 1	0.861	0.712	1.041	2.379	0.123
Barriers 2	0.891	0.574	1.381	0.268	0.605
Barriers 3	1.124	0.798	1.582	0.446	0.504
Barriers 4	1.066	0.632	1.800	0.058	0.810

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The effect sizes after entrepreneurship education (Table 13) show clear differences to the previous results (Table 11). The factors of the subjective norm and Barriers 4 are no longer significant, but Motives 1 is. Motives 1, consisting of the variables “my personal independence”, “the opportunity to be financially independent”, “the improvement of quality of life”, “earn more money than through wage labour”, “to work in a varied profession”, “the building of personal wealth”, “to have more free time” and “the difficulty of finding the right job”, show higher Exp(B) values and at the same time a stronger effect after entrepreneurship education. The subjective norm and Barriers 4, as well as start-up knowledge, Motives 4 and Barriers 3 from the previous studies (the first survey of chemistry students in Germany and the survey before entrepreneurship education) are not significantly pronounced in this evaluation, which can be partly explained by the low number of participants in this survey. However, effects such as start-up knowledge, which can even be assessed negatively after entrepreneurship education, should be considered in the longer term. The same applies to Barriers 3 and 4, which even have a positive effect after entrepreneurship education. The results for the statistical key figures can be found in

Table 14. Statistical key figures for the ordinal regression performed in the applied model after entrepreneurship education.

Test	Model	−2 Log Likelihood	Chi-Square	df	Sig	Pseudo R-Square
Test of Parallel Lines	Null Hypotheses	99.375
	General	85.498	13.877	48	1.000
Model fitting information	Intercept only	116.815
	Final	99.375	17.441	12	0.134
Goodness of Fit	Pearson		208.114	193	0.217
	Deviance		99.375	193	1.000
Pseudo R-square	Cox/Snell					0.340
	Nagelkerke					0.362
	McFadden					0.149

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The results clearly show that the model used fits the data from the survey delivered after entrepreneurship education less well. The poorer results for the model fitting, which are no longer significant, are striking. One reason for this may be the change in the database due to the lessons or the use of the identical survey with a short time interval and the resulting dependencies. At the same time, the second survey has the lowest number of participants of the surveys with only 42 analysable participants.

With the present results, we reject hypothesis 1 that entrepreneurship education has a direct positive influence on the start-up behaviour of chemistry students in Germany.

5. Discussion

In our study, the willingness to start a business showed no significant improvement after attending entrepreneurship education compared to before. In contrast to the argument that attending entrepreneurship education does not increase the likelihood of an individual starting a business [52], we argue about the positive contribution of entrepreneurship education in terms of skills, know-how and better entrepreneurial attitude [53,54,55,56]. Entrepreneurship intention shows an improvement after just one lecture compared to the German or the second survey. The fact that this effect is not significant could be due to the small number of participants and the initial one-off nature of the lecture and short time to the second survey. Another possible reasoning, following Zhao, Seibert and Hills, 2005, is that entrepreneurship courses have often practised formal learning, which has the strongest positive relationship with intentions by teaching entrepreneurial self-efficacy [57]. Future research should compare different teaching methods and learning environments for their different effects on outcomes.

Although the ordinal regression after entrepreneurship education no longer shows a significant influence on the subjective norm as in the survey before the lessons, the effect is in the positive range. Due to the non-significant difference within the ANOVA comparison, it is assumed that the number of participants in the second survey was too low. Further investigations with continuous adjustments of the methodology and teaching to the students’ circumstances are recommended. We also recommend the introduction of the cooperative innovation strategy, PANDA, of the Fresenius University of Applied Sciences beyond the business chemistry programme, parallel to entrepreneurship education. In this way, students learn not only the theoretical basis but also the practical perspectives of a start-up, while possible barriers can be broken down and start-up knowledge can be built up. In addition, a combination with other subject areas would be conceivable in which students could benefit from their peers, such as marketing or advertising [58].

Limiting factors or possible sources of error in this study are the small number of participants. In addition, the survey after entrepreneurship education has an even smaller number of participants. The effects of this sample size could potentially distort the results. At the same time, the results in comparison with the cohort of German chemistry students from the previous study do not present any significant distortions, which means that reliable data can be assumed [22]. This means that the study results are sufficient as indices for answering the research question and as a starting point for further research. Thus, the results of this study do not represent a starting point for generalisation for chemistry students. A possible influencing factor on the survey results may also be the personal contact with the students during the first survey, which could raise expectations in the results for the second survey. Another potential error factor is the time between the surveys; although it was assumed that the students forgot their own answers within a week, there is also the possibility of bias. At the same time, the model fit after entrepreneurship education no longer shows a good fit compared to the survey before entrepreneurship education or to the data from the first survey [22]. Due to the desired comparability, no further adjustment of the model was made here, but in future studies and analyses, attention should be paid to smaller groups of participants when choosing a suitable analysis method. Due to the use of an anonymous survey, it was not possible to use a longitudinal survey with the additional variable of time. However, the use of the variable time is recommended for future analyses.

6. Summary

This study analyses the effects of entrepreneurial education on chemistry students in Germany. Insights into the changes in attitudes towards entrepreneurial activity before and after attending the relevant courses are explained. The results are based on survey data which were analysed using ANOVA comparisons and ordinal regression. The results show positive developments in the level of knowledge in relation to the understanding of funding organisations, which in turn indicates a direct influence of entrepreneurial education. Likewise, after the lessons, students show a higher understanding of the rules of the teaching institution in relation to the transfer of research ideas to start-ups, which indicates that the lessons promote the perception of institutional support. Teaching also strengthens the determination to choose a career among chemistry students in Germany. Both self-perception and entrepreneurial know-how are increased by entrepreneurial education. The motives show a lower rating for the motive “to create something of my own”, which indicates that other motives such as financial independence have moved into the focus of the students after the lessons. With regard to the barriers to starting a business, the economic framework conditions were rated lower after the course, which indicates that the course increased the ability of chemistry students in Germany to realistically assess potential obstacles and possibly overcome them. The ordinal regressions show the factors of motives, subjective norms and barriers as influencing factors on business start-ups among chemistry students in Germany. This study shows the complex role of entrepreneurial education in shaping the attitudes of chemistry students in Germany towards entrepreneurship. Overall, the results show the success of teaching in terms of increasing the willingness of German chemists to start a business in the future. Further research and interventions are needed to improve the effectiveness of teaching and to fully understand the willingness to start a business.