Catalyzing green entrepreneurial behavior: the role of intentions and selective factors

Abstract

This study aims to validate a model on the green entrepreneurial intention (GEI), attitude, subjective norms (SN), perceived behavioral control (PBC) and green entrepreneurial behavior (GEB) among students pursuing higher education in Delhi &NCR. This study also captures the mediating role of GEI between attitude, SN, PBC and GEB. About 327 samples were used for data analysis purposes using Smart-PLS. The result of the study suggests that attitude, SN and PBC have a significant positive relationship with GEB. Whereas, GEI as a mediator has no effect between attitude and GEB, while GEI as a mediator has a significant effect between SN, PBC and GEB. GEB among young professionals has been found a changing paradigm in the area of entrepreneurial practices in India. It has a significant impact on the sustainability aspects of society as well as the organization. Millennials get ignited toward such orientation which improved global environmental dynamics. The result of the study provides a significant source to the policymakers to promote GEBs among professionals.

IMPACT statement

The article on GEB sheds light on the critical nexus between business and environmental sustainability. It explores how entrepreneurs, by integrating green practices into their ventures, not only drive economic growth but also mitigate environmental impact. This research is paramount in today’s landscape, where global issues like climate change necessitate innovative, eco-conscious solutions. By delving into the motivations, challenges and outcomes of green entrepreneurship, the article offers valuable insights for policymakers, business leaders and society at large. Understanding and promoting GEB is crucial for fostering a more sustainable and responsible business environment. It’s an imperative step toward a future where economic prosperity aligns harmoniously with environmental conservation, benefitting both present and future generations.

Keywords:

• Green entrepreneurial intention (GEI)
• attitude
• subjective norms (SN)
• perceived behavioral control (PBC)
• green entrepreneurial behavior (GEB)
• higher education
• India

Reviewing Editor:

• Ala Dandis, Applied Science University, Jordan

Subjects:

• Environmental Economics
• Economics
• Business, Management and Accounting

1. Introduction

The environmental impact of natural disasters has increased interest in the concept of ‘going green’ in contemporary civilizations (Allen & Malin, 2008). Schumpeter’s concept of creative destruction, also known as the ‘green wave’ of gale, is gaining momentum and has impacted entrepreneurship and higher education institutions. Observers are recognizing a rise in green entrepreneurial activities among policymakers, practitioners and scholars, who emphasize the necessity of entrepreneurial endeavors for economic growth in environmental sustainability (Abdur Rouf, 2012; Ahmad et al., 2013; Grinevich et al., 2017). Academics worldwide have focused on environmental protection in business and non-business activities, with green entrepreneurship being recognized as a solution to social and environmental challenges (Demirel et al., 2019; Jamison, 2001; Purvis et al., 2019).

Green entrepreneurship is an effective option for addressing company sustainability and environmental protection through entrepreneurial activity. Companies cannot single-handedly implement such changes just to promote the green economy (Isaak, 2016). Higher education institutions should engage in research and managerial activities, with teachers and students contributing theoretical support through major research, as Farinelli et al. (2011) and Bovill (2020) suggested. Significant research has been conducted on entrepreneurial intention (E.I.) among higher technical education students and associated fields such as sustainable entrepreneurship, social entrepreneurship and eco entrepreneurship (Huq et al., 2017; Katz, 2003; Kuratko, 2005). Furthermore, the goal of higher education students to engage in green entrepreneurship is still developing. Past research has explored emotional intelligence (e.i.) and supported factors and theories such as the theory of planned behavior (TPB) and Entrepreneurial Intention (E.I.) (Ozaralli & Rivenburgh, 2016; Saleh & Athari, 2023; Tong et al., 2011).

Encouraging entrepreneurship is widely agreed upon as essential for stimulating economic growth and creating jobs, as emphasized in recent research by Entrialgo and Iglesias (2016), McClelland (2019) and Trapp and Kanbach (2021). Entrepreneurship education is crucial for enhancing individuals’ entrepreneurial mindsets (Liñán et al., 2011; Rauch & Hulsink, 2015). Education is a powerful method for introducing ideas into society (Collins & Halverson, 2018; Dewey, 2013). Educational initiatives should be implemented to develop the green entrepreneurial attitude of emerging entrepreneurs (Cui et al., 2021; Jabeen et al., 2017). For the sustainable economic development of any country, it is always highly promising to increase the bunch of young minds with entrepreneurial potential. The sustainable development of any green economy will depend on the supply of potential entrepreneurs. However, there is no consensus on the factors influencing an individual’s decision to establish a business (Al-Azab Zaki, 2023; Goleman, 2017). As a result, it is necessary to determine which factors influence a person’s decision to establish a business. This would make it possible to create more effective educational initiatives. The study aims to examine how attitude, subjective norms (SN) and perceived behavioral control (PBC) influence the intention of students to engage in green entrepreneurship, hence shaping their behavior in this field. The following objectives give rise to these research inquiries.

RQ1: How do Attitude, SNs and PBC impact green entrepreneurial intention (GEI)?

RQ2: What is the role of GEI in adopting green entrepreneurial behavior (GEB) among higher education students?

2. Theoretical underpinning and hypothesis construction

This study utilizes the TPB to assess how contextual circumstances and GEI influence green entrepreneurship behavior. Fishbein and Ajzen (1977) suggested that the TPB elucidates the elements influencing individuals’ choices. The theory of planned conduct was utilized to explain the connection in the present investigation. This concept expands the theory of reasoned action (TRA), which has been beneficial in comprehending individuals’ inclination toward engaging in green entrepreneurial activities. The hypothesis posits that individuals’ attitudes influence their actions. Universities contribute significantly to society through research and education, cultivating highly skilled individuals and driving innovative progress in diverse fields of study (Carayannis et al., 2022; Rahman & Khan, 2023). Business businesses are crucial in introducing new concepts and turning them into successful and long-lasting products. These three industries collaborate well, creating a strong driving force for ecologically sustainable innovation.

Similarly, intentions are a powerful indicator of conduct. A nation’s legislation and policies concerning green entrepreneurship should promote the self-efficacy required to initiate entrepreneurial endeavors. It is crucial to determine the authenticity of this link pertaining to green entrepreneurial conduct. India, a growing nation, has significant potential to influence individuals toward green purchasing habits, which significantly contributes to environmental sustainability. Research green behavior in the Indian context and analyze it from the younger generation’s perspective. This study provides valuable information on the topic. We analyzed green purchasing behavior through the notion of planned behavior. India is the world’s second most populous country and must instill these ideals in the younger generation. This study aimed to comprehend individuals’ intentions and attitudes, which are essential for green purchase behavior. This study utilized the idea of planned behavior to examine the connection between the studied variable and green entrepreneurship.

2.1. Attitude, subjective norms and green entrepreneurial intention

Entrepreneurship is a business unit that has started new and has the potential to provide social and economic value to society. Lumpkin and Dess (1996) define ‘entrepreneurship as an act of pursuing new ways of doing things in a real context, or more concretely, the essential act of entrepreneurship is new entry’. ‘Starting up a new venture has often been correlated with having a positive attitude, intention, and disposition towards it, particularly in the case of the opportunity entrepreneurs’ (Ismail, 2015, p.53).

Even an abstract idea becomes reality and capitalizes on the available resources to meet our business objectives. In the study by Spears and Singh (2004) it is evident that attitude is related to consumer purchase intention. The purchase intention of an individual is significantly associated with their attitudes. In a study, it was highlighted that the intentions of sportspeople are mediated by their attitude. The study highlighted a positive link with intention (Cho et al., 2019). Various other studies highlight that people’s behavior is affected by their attitudes (ElHaffar et al., 2020; Jena, 2020; Mahfud et al., 2020). It is essential to understand that respondents’ attitudes are significantly associated with GEIs. SNs are an essential construct that influences the individual’s intentions. It is the opinion of people in your family and friend circle and what you perceive. The study defines SNs as their perception, which is influenced by close groups. It influences people’s perception concerning any products and services at all stages. Previous studies have also suggested that people’s intention is determined by subjective influence (Grimes & Marquardson, 2019; Ruslim et al., 2022; Santos & Liguori, 2019; Winter et al., 2021; Zhuang et al., 2021). Therefore, it can be said that attitude and SNs are associated with intention. The alternative hypotheses are:

H1:

Attitude has a positive and significant effect on GEI.

H2:

SN has a positive and significant effect on GEI.

2.2. Perceived behavioral control, green entrepreneurial intention and green entrepreneurial behavior

PBC is defined as ‘the perception of the difficulty of enacting a behavior’. Here the TPB can account for it more accurately than TRA. Ajzen (2002) argued that ‘perceived behavioral control is comprised of two highly related (and correlated) variables: perceived self-efficacy (one’s belief about their ability) and perceived controllability (the belief that one’s behavior is volitional; Ajzen, 2002)’. They considered these two variables as one can directly predict the behavior and indirectly predict behavior through intention. Previous studies have found that PBC is significantly associated with intention and behavior (Barba-Sánchez et al., 2022; Pradeep et al., 2021; Sembada & Koay, 2021; Sultan et al., 2020). Therefore, it can be concluded that PBC significantly affects GEI and GEB. The alternate hypothesis of the same are:

H3:

PBC has a positive and significant effect on GEI.

H4:

PBC has a positive and significant effect on GEB.

H5:

GEI has a positive and significant effect on behavior.

2.3. Green entrepreneurial intention as a mediator

GEI can be defined as ‘a circumstance or state of mind in which a person becomes interested in, pays attention to, and decides to take a specific activity’ (Meoli et al., 2020, p. 32; Santos & Liguori, 2019, p. 26). According to the TPB theory, the intention to act involves a variety of driving variables that directly influence behavior (Fishbein & Ajzen, 1975). The step of intention precedes behavior. Many previous studies show that intention toward entrepreneurial activities positively influences the materialization of entrepreneurship (Alvarez-Risco, Mlodzianowska, García-Ibarra, et al., 2021; Alvarez-Risco, Mlodzianowska, Zamora-Ramos, et al., 2021; Meoli et al., 2020; Neneh, 2019; Rauch & Hulsink, 2015; Yasir et al., 2023). This study considers the GEI as a mediator between the theory of planned behavior (attitude, SN and PBC) and GEB in the Undergraduate and post-graduate level students. So the alternative hypothesis is

H6:

GEI mediates the relationship between PBC and behavior.

H7:

GEI mediates the relationship between attitudes and behavior.

H8:

GEI mediates the relationship between SN and behavior.

3. Research method

It is evident from Figure 1 that there is an association between various variables selected for this study and the direction of their relationship among the mentioned variables. This study used five variables: attitude, SNs, PBC, GEI and GEB. The research was conducted with various objectives to determine the relationship between attitude and GEI, SNs and GEI, PBC and GEIs and PBC and GEB. A relationship between GEI and GEB is found.

Figure 1. Hypothesized model (Source: based on LR).

The final mediating role of GEI is found between PBC, SNs and GEB. A structured questionnaire was adapted for this study. The questionnaire comprises 27 items. There are four statements in attitude; SNs constitute five statements. PBC includes seven statements. GEIs comprise five statements, and lastly, GEB shall consist of six statements. The reliability of variables was tested using these items and found that variables fulfill the reliability criteria (i.e. alpha = 0.811). Confirmatory factor analysis was done using the SMART PLS software version and found that variables satisfy the criteria of path analysis.

The hypothesized model in the study was also tested using SMART PLS software. The study sample was collected from pass-out students from MBA programs from various higher educational institutes affiliated with GGSIP University, Delhi. Pass-out students were sent the questionnaire online as a Google form. They were targeted, keeping in mind that their approach toward green entrepreneurship can be assessed appropriately. Students who completed MBA programs have been selected for this study only. The sample for this study includes both male and female students of different age groups. Data was collected from higher educational institutions through Google form (online). Questionnaires were sent to more than 2000 students separately. A total of 722 responses were received, of which only 327 responses were found suitable for this study. The response rate was only 36.1%, but finally, it was 16% only. The researcher has calculated the mediating effect of GEI using SMART PLS. A Likert five-point scale was used for data collection: ‘1 = strongly disagree, and 5= strongly agree’.

3.1. Questionnaire design and pre-testing

The question begins with asking general questions pertaining to the respondents’ demographic details, as suggested by Dillman et al. (2009). Further, the questionnaire includes four statements related to the Attitude of the Students adapted from Chen (2009), five statements based on SNs of the consumer adapted from Shin et al. (2018), Seven statements pertaining to PBC adapted from Kang et al. (2012), five statements of GEI (adapted from Zeithaml et al. (1996)) and six statements associated with GEB of the students (adapted from Giampietri et al. (2018)). Once the scale was developed, its face validity was confirmed and verified by a panel of four experts from the entrepreneurial domain of research.

Pilot testing of a GEI Questionnaire on higher education students is a crucial step in the research process to validate the questionnaire’s effectiveness and appropriateness for the target population. Pilot testing helps ensure that the GEI Questionnaire is a robust instrument, enhancing the quality and validity of the data collected from higher education students in your research. The authors did the pilot testing using 100 samples to test the reliability and validity of the data.

3.2. Response rate

Previous studies have suggested that a sample of two hundred is enough for applying the Structural Equation model approach (Hoelter, 1983). However, Whittaker et al. (2007) have justified the use of a sample size of a hundred respondents for running Structural equation modeling (SEM). Further, it is recommended that running SEM can be applied when the sample size should be at least reach the number of samples we get from multiplying five by the total statements present in the scale (Bentler & Chou, 1987). However, it is desirable to have a big data set to avoid any possibility of sampling error (Randall & Gibson, 1990). Therefore, this study was done using a 36.1% response rate, and the sample size was used for applying running SEM analysis, which is sufficient to avoid biases and sampling error.

4. Data analysis

This study has used PLS-SEM to confirm the validity and reliability of the scale for each construct and verify the relationships between variables proposed in the model. PLS-SEM is considered appropriate and provides robust results for analyzing and confirming the causal relationship between the variables proposed based on a review of previous research (Ma et al., 2018). Moreover, this PLS-SEM approach can be applied to small to medium size sample size and does not consider normality of data as one of its basic assumptions, which was an issue under the CB-SEM approach. Another reason supporting PLS-SEM use is that this method can work on both reflective and formative scales (Rodríguez-Entrena & Salazar-Ordóñez, 2013). All the factors mentioned above justify PLS-SEM use in this study.

4.1. Results

Table 1 comprises the data of the demographic characteristics of the respondents. It is evident from the above table that the gender of the respondents has been categorized into male and female, which comprises of 60% and 40%, respectively. The age of the respondents has been characterized into three categories. The categories are; 17–22, 23–28 and more than 28 years of age. These age categories are 4%, 88% and 8%, respectively. Respondents are categorized into three categories of job experience: less than 5 years, 5–10 years, more than 10 years. The percentages of these categories are 75, 20 and 5, respectively. The Educational details of the respondents are categorized into three categories Post graduate (P.G.), P.G. + Certification and pursuing higher education. The percentages of these categories are 70%, 22% and 8%, respectively.

Table 1. Demographic profile of respondents (sample size 327).

Category	Group	Frequency	Percentage
Gender	Male	195	60
	Female	132	40
Age of Respondents	17–22	16	4
	23–28	287	88
	>28	24	8
Year of Experience	Less than 5 years	244	75
	5–10 years	65	20
	More than 10 years	18	5
Education	PG	229	70
	PG + Certification	70	22
	Pursuing Higher Education	28	8

Source: Research Output.

4.2. Assessment of the measurement model

The robustness of the measurement model can be verified from the values of the factor loading, Cronbach’s alpha (C.A.) and rho-A. Further, the validity and reliability of the measurement model can be confirmed through composite reliability (C.R.) and convergent and Discriminant validity of the constructs. Table 1 ensures the adequacy of the factor loading, which is above 0.7 for each statement (Fornell & Larcker, 1981; Hair et al., 2014). The details of all the measurement scale is given in Annexsure-1.

4.3. Factor loading

Table 2. Factor loading.

Statements	Attitude	Green entrepreneurial behavior	Green entrepreneurial intention	Perceived behavioral control	Subjective norms
AT1	0.868	–	–	–	–
AT2	0.701	–	–	–	–
AT3	0.855	–	–	–	–
AT4	0.84	–	–	–	–
GEB1	–	0.793	–	–	–
GEB2	–	0.76	–	–	–
GEB3	–	0.742	–	–	–
GEB4	–	0.713	–	–	–
GEB5	–	0.764	–	–	–
GEB6	–	0.71	–	–	–
GEI1	–	–	0.771	–	–
GEI2	–	–	0.777	–	–
GEI3	–	–	0.762	–	–
GEI4	–	–	0.736	–	–
GEI5	–	–	0.786	–	–
PBC1	–	–	–	0.726	–
PBC2	–	–	–	0.773	–
PBC3	–	–	–	0.857	–
PBC4	–	–	–	0.826	–
PBC5	–	–	–	0.83	–
PBC6	–	–	–	0.763	–
PBC7	–	–	–	0.725	–
SN1	–	–	–	–	0.748
SN2	–	–	–	–	0.813
SN3	–	–	–	–	0.806
SN4	–	–	–	–	0.857
SN5	–	–	–	–	0.883

Source: Research output.

4.4. Internal consistency reliability

Table 3 confirms the presence of adequate internal consistency for each construct through the values of C.A. and rho-A. Here, all the values are above 0.70, which is sufficient to establish internal consistency. Further, the construct’s reliability is found to be satisfactory as all the values of C.R. for each construct are above 0.60 (Hair et al., 2014). In addition to this, Fornell and Larcker (1981) suggested the use of rho-A as additional criteria to confirm the reliability. The results of the rho-A test are satisfactory as all the values are above 0.6. All the values mentioned above lead to the confirmation of the internal consistency and reliability of the constructs.

Table 3. Results of convergent and Discriminant validity tests.

	Cronbach’s Alpha	rho_A	Composite Reliability	Average Variance Extracted (AVE)	Attitude	GEB	GEI	PBC	SN
Attitude	0.89	0.895	0.89	0.67	0.819		–	–	–
GEB	0.884	0.885	0.884	0.559	0.379	0.748	–	–	–
GEI	0.877	0.877	0.877	0.588	0.374	0.682	0.767	–	–
PBC	0.919	0.921	0.919	0.62	0.431	0.625	0.504	0.787	–
SN	0.913	0.915	0.913	0.677	0.415	0.271	0.303	0.269	0.823

^aSelf-reported.

CA, Cronbach’s alpha; CR, composite reliability; AVE, average variance extracted

(Source: Research Output).

4.5. Convergent and discriminant validity

This can be found in Table 3 that all the AVE values are more than 0.5, which is a necessary condition to establish convergent validity. Moreover, the value of AVE for each construct is more than the corresponding values of MSV (Gaskin & Lim, 2016). In addition to this, it can be observed that the square root of the AVE is higher than the corresponding correlation value for other relationships. Therefore, discriminant validity is also established.

In addition to the above verification of the discriminant validity, another test named ‘Heterotrait Monotrait Ratio (HTMT)’ was conducted to verify discriminant validity. Table 4 confirms that all the values of HTMT are below 0.85, thus, reassuring the Discriminant (Henseler et al., 2015).

Table 4. Heterotrait monotrait ratio (HTMT).

HTMT	Attitude	GEB	GEI	PBC	SN
Attitude	–	–	–	–	–
GEB	0.378	–	–	–	–
GEI	0.373	0.681	–	–	–
PBC	0.431	0.623	0.503	–	–
SN	0.415	0.27	0.302	0.27	–

Source: Research output.

4.6. Structural model fitness

In PLS-SEM, the fitness of the structural model was through standardized root mean square residual (SRMR) as a goodness of fit measure and the normed fit index (NFI) incremental fit measure. Table 5 confirms the fitness of the model as the value of SPMR is below 0.08, and the value of NFI is above 0.9 (Henseler et al., 2015).

Table 5. Model fit.

	Saturated model	Estimated model
SOME	0.031	0.031
Chi-square	792.498	794.067
NFI	0.923	0.923

Source: Research output.

4.7. Structural model hypothesis testing

Table 6 shows the causal relationships assumed during hypothesis formulation, which was estimated by running a dataset on 2000 bootstrapped samples. The outcome of the SEM analysis leads to the acceptance of the hypothesis (H1–H5) at a 0.05% level of significance. The structural model for the same is shown in Figure 2.

Figure 2. The structural model (Source: Research output).

Table 6. Output bootstrapping-PLS.

Causal hypothesis	Path coefficient (B)	t Statistics	p Values	Result
Attitude -> Green Entrepreneurial Intention	0.144	2.153	0.032	Accepted (H1)
Subjective Norms -> Green Entrepreneurial Intention	0.134	2.425	0.016	Accepted (H2)
Perceived Behavioral Control -> Green Entrepreneurial Intention	0.406	5.812	0.000	Accepted (H3)
Perceived Behavioral Control -> Green Entrepreneurial Behavior	0.377	4.045	0.000	Accepted (H4)
Behavioral Intention -> Green Entrepreneurial Behavior	0.492	5.433	0.000	Accepted (H5)

(Source: Research output).

4.8. Q-square statistic

Additionally, to verify the superiority of the path model, the q-square statistic is estimated. Table 7 shows that the path model has adequate predictive significance as the value of q-square statistics is above 0 (Fornell & Cha, 1994; Silaparasetti et al., 2017).

Table 7. Q Square statistics.

	SO	SSE	Q² (=1-SSE/SSO)
Green Entrepreneurial Behavior	3600	2564.884	0.288
Green Entrepreneurial Intention	3000	2520.633	0.16

(Source: Research output).

4.9. Coefficient of determination (R²)

The predictive ability of the path model was further reconfirmed by using the coefficient of determination (R²) test.

Table 8 illustrates the findings of the R² values of the model. The R² value for buying behavior was 0.571, suggesting that Attitude, SNs can explain 57.1% of the variance, PBC and behavioral intention. Meanwhile, Behavioural Intention (B.I.) was responsible for 29.9% variance in attitude, SNs and PBC. Subsequently, thus, the findings are considered near substantial (Chin, 1998). The R² value is between 0 and 1, and higher levels indicate a greater level of predictive accuracy of the model (Hair et al., 2016)., Thus, a more considerable R² value increases the structural model’s predictive ability, which can be observed in the present path model.

Table 8. R-squares.

	R-square	R-square adjusted
Green entrepreneurial behavior	0.571	0.569
Green entrepreneurial intention	0.299	0.295

(Source: Research Output).

4.10. Effect size (f2)

The effect size (f²) measures the impact of a particular predictor construct on an endogenous construct. The f² values of 0.02, 0.15 and 0.35 represent small, medium and significant effects on an endogenous construct (Bahardin et al., 2020). The findings shown in Table 9 indicate that B.I. has a significant effect size of 0.421 on behavior. However, PBC has a large effect of 0.247 and 0.189 on behavior and B.I. Whereas attitude has a negligible impact of 0.021 on B.I., and SN has a negligible effect on B.I.

Table 9. Effect size (f2).

	Attitude	GEB	GEI	PBC	SN
Attitude	–	–	0.021	–	–
GEB	–	–	–	–	–
GEI	–	0.421	–	–	–
PBC	–	0.247	0.189	–	–
SN	–	–	0.021	–	–

Note: Exogenous constructs (rows), Endogenous constructs (columns).

4.11. Mediating effects

Table 10 shows the mediating effect of behavioral intention in the model. This study indicates that B.I. significantly mediates the relationship between PBC and Behavior (b = 0.2, t = 3.494, p < 0.05). Similarly, B.I. substantially mediates the relationship between SN and Behavior (b = 0.066, t = 2.212, p < 0.05). However, B.I. does not mediate the relationship between attitude and behavior (b = 0.071, t = 1.782, p < 0.05).

Table 10. Specific indirect effect.

	Path Coefficient (B)	t Statistics	p Values	Result	Decision
Perceived Behavioral Control -> Green Entrepreneurial Intention -> Green Entrepreneurial Behavior	0.2	3.494	0.001	Mediation exists	H6 Accepted
Attitude -> Green Entrepreneurial Intention -> Green Entrepreneurial Behavior	0.071	1.782	0.075	No mediation	H7 Rejected
Subjective Norms -> Green Entrepreneurial Intention -> Green Entrepreneurial Behavior	0.066	2.212	0.027	Mediation exists	H8 Accepted

(Source: Research Output).

The direct effect of PBC on GEB has a value of 0.538, while the specific indirect effect via GEI has a value of 0.201. Thus, the total effect has a value of 0.538 + 0.201 = 0.739. Therefore, VAF = 0.538 ÷ 0.739 = 0.731, meaning that about 73.2% of PBC effect on GEB is explained via the GEI mediator. Since the VAF is larger than 20% but smaller than 80%, this situation can be characterized as Partial Mediation (Table 10; Bahardin et al., 2020; Nugroho et al., 2021). The direct effect of SN on GEB has a value of 0.532, while the specific indirect effect via GEI has a value of 0.066. Thus, the total effect has a value of 0.532 + 0.066 = 0.598. Therefore, VAF = 0.432 ÷ 0.598 = 0.722, meaning that about 72.2% of SN effect on GEB is explained via GEI mediator. Since the VAF is larger than 20% but smaller than 80%, this situation can be characterized as Partial Mediation (Table 11; Bahardin et al., 2020; Nugroho et al., 2021).

Table 11. The summary of research hypotheses concerning mediating effects.

Research hypotheses	Result	VAN (%)	Conclusion
Perceived Behavioral Control ->Green Entrepreneurial Intention -> Green Entrepreneurial Behavior	Supported	73.1	Partial mediation
Subjective Norms -> Green Entrepreneurial Intention -> Green Entrepreneurial Behavior	Supported	72.2	Partial mediation

(Source: Research output).

5. Discussion

The study findings emphasize the significant impact of attitude on behavioral intentions in green entrepreneurship, consistent with prior studies by Polas et al. (2020) and Qazi et al. (2020). The modern environment is experiencing a shift in views toward environmentally friendly business practices, which requires an assessment of how they affect people’s intents and behaviors. SNs significantly influence entrepreneurial inclinations, as consistently demonstrated in previous research. Social influence significantly impacts entrepreneurial inclinations (Ham et al., 2015; Wang, 2014).

PBC is a key factor influencing entrepreneurial ambitions, as indicated by previous studies (Liñán et al., 2011; Nowiński & Haddoud, 2019; Sun et al., 2017). This trend is especially noticeable among the younger generation regarding their desire to start environmentally friendly businesses. The research highlights the significant influence of GEIs on real green entrepreneurial actions, aligning with the eagerness of today’s youth to start new businesses. The Theory of Planned Behavior by Ajzen (1991) has been shown in earlier studies by Jiang et al. (2018), Demirel et al. (2019) and Pratono et al. (2019) to effectively encourage GEB.

The study results indicate that PBC strongly influences GEB. Other researches have also reported similar findings (Luu, 2021; Mohiuddin et al., 2018; Ramayah et al., 2019; Ullah & Qaiser Danish, 2020). PBC is strongly linked to GEB. Behavioral control refers to individuals’ focused efforts toward entrepreneurial conduct. The study’s results indicate that there is a growing trend of GEB at present, which is a positive development for sustainability.

6. Implication

The potential of green entrepreneurship is clear and important for various stakeholders, such as governments, entrepreneurs and academia. The various challenges you have r, such as defining the concept accurately, understanding its numerous methodologies and creating a dependable dataset for analysis, require comprehensive investigation and research. Green entrepreneurship must combine with innovation, development and entrepreneurship as a whole. This link is essential for understanding its broader impact on the economy, job creation and technological advancement. It is crucial to address the ambiguity in defining the green economy, sector and jobs to ensure precise and efficient policymaking. Understanding eco-conscious entrepreneurs’ behavior and influence on society is essential, particularly among the younger generation. Their actions can significantly affect perceptions of eco-friendly products, shape societal behavior and set new standards to encourage the use of sustainable practices.

The economic impacts of green entrepreneurship are highly significant. The good benefits on the environment, effective resource use, job creation in green industries and innovation in sustainable technologies all contribute to economic growth and resilience against climate change. It is crucial to effectively communicate eco-friendly initiatives to legislators in order to promote their support and progress. Continuous exploration and analysis in the field of eco-friendly commercial endeavors are essential. The increasing use of eco-friendly practices in many industries, including as manufacturing and service sectors incorporating green HRM and accounting, together with wider societal shifts, highlights the potential positive impacts on both the environment and the economy. Encouraging and supporting the development of eco-friendly business initiatives is anticipated to bring benefits to society, the economy and the environment. Thus, it is an important area for policymakers, researchers and practitioners to invest their time and money.

7. Conclusion, limitations and scope for future research

The global community has shown a growing interest in environmental issues. The solution to the current challenges faced by academia has been identified and is widely acknowledged. Green entrepreneurship is widely seen as a means of ensuring the sustainability of future generations and fostering a promising future. In contemporary times, the concept of environmental sustainability has emerged as a valuable resource for businesses, prompting corporations to seek opportunities to leverage its potential benefits actively. The younger demographic is likely to embrace green entrepreneurship if they recognize its positive impact on global environmental sustainability and its potential to contribute to the well-being of humanity. Appropriate measures and models will be employed to modify individuals’ behavior. A green orientation can be cultivated by fostering a positive attitude, adhering to SNs and enhancing perceived control over behavior. This phenomenon is expected to result in the development of attitudes within the younger demographic, subsequently translating into the adoption of environmentally conscious entrepreneurial practices. GEB is believed to be influenced by multiple factors. In order to foster GEB, it is imperative to direct our attention toward the dimensions outlined in the research model.

In today’s time, it plays a crucial role in various developmental aspects of the country. It is also considered an essential driving force that changes consumer behavior. It has stimulated various things such as innovation, green practices and orientation toward green practices. We are seeing some differences in terms of practices adopted by developed countries and developing countries. Developed countries are now in a position to take advantage of green entrepreneurship, and developing are seeing entrepreneurship as a way out for development. This study has highlighted those GEBs and taking center stage even in developing countries like India. It is to be kept in mind that resources are not sufficient to meet the requirements of people in the country. As evident from the result of the study, green entrepreneurship is the way to provide resources to the people in the most efficient, socially responsible and environmentally friendly. This study has highlighted the significance of green entrepreneurship and how certain factors are helping it and transforming this to GEB.

However, this article has made significant contributions to the existing literature. However, this study has certain limitations too. The sample was taken from the Delhi & NCR in India, confined to national capital regions only. This research can be extended to other parts of the country also. Similar data can be taken from the other sectors, and results can be compared. There may be other important variables that can mediate the relationship between behavioral intention and green behavior. The variables can be entrepreneurial support, sustainable development, government policy, SDG, etc. It may be possible that both quantitative and qualitative methods can be added in future research. Finally, a similar study can be done with other samples as well.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Manoj Kumar Mishra

Mr. Manoj Kumar Mishra is currently working as an Assistant Professor in School of Management at OP Jindal University, Raigarh. He is pursuing his Ph.D from Jagannath University Jaipur, India on ‘Green Entrepreneurship’. He did his MBA from VTU with specialization in marketing and graduation from AKTU. He has presented several research papers in many national and international conferences including presentations at IITs and IIMs. His ten papers are published in various journals of National Repute which includes Scopus indexed, ABDC and UGC – Care Listed Journals. Mr. Mishra has conducted five workshops and twelve FDPs on research methodology, entrepreneurship and green entrepreneurship across different institutes in India.

Nidhi Sharma

Dr. Nidhi Sharma holds a comprehensive work history spanning over 14 years, with 13 years dedicated to teaching and an additional 1 year in the Industry. Presently, she is acting as convener of IQAC and Head of Department BBA at Rukmini Devi Institute Of Advanced Studies. Madam has qualified UGC NET in Management and has obtained her Doctorate from Jagannath University, Jaipur in the area of Organic Foods and Consumer Behavior. Her expertise is in marketing management, sales management and consumer behavior.

Sunil Kumar

Sunil Kumar is an Assistant Professor at GITAM School of Business, GITAM (Deemed to be University) Visakhapatnam, A.P., India. He has over 9 years of teaching experience. He earned his Ph.D from Jamia Millia Islamia (A Central University), New Delhi in the area of NGO Management. He has qualified UGC-NET-JRF in Management. His academic qualifications include BA, MBA, DCA and Ph.D. He has published papers in Journals of repute indexed in Scopus, ABDC and UGC care, etc. He has presented papers at IIT, IIM and other institution of repute in India.

Mohd Asif Shah

Dr. Mohd Asif Shah is working as an Associate Professor of Economics at Kebri Dehar University, Ethiopia. He has completed his bachelor’s (B.A.), master’s (M.A.) and doctorate degree (Ph.D.) in Economics, with more than ten years of teaching and research experience. He has published more good number of research papers (SCI/SCIE/SCOPUS/WOS Indexed) with 460 citations. He has attended more than thirty workshops and faculty development programs sponsored by the Government of India, and has been the keynote speaker for many international conferences and seminars.

 

Annexsure-1

Attitude (AT) Chen (2009)

1. Being an entrepreneur implies more advantages than disadvantages to me.

2. Entrepreneurship would present more up than downsides.

3. A career as entrepreneur is (totally) attractive for me.

4. Being an entrepreneur would entail great satisfactions for me. Perceived behavioral control.

Subjective norms (SN) (Shin et al., 2018)

1. I have access to supporting information to be a green entrepreneur.

2. My closest family members think I should pursue a green entrepreneur career.

3. If I became a green entrepreneur, my close friends would appreciate it.

4. If I were to start my own business, my parents’ opinions would be important to me.

5. If I were to start my own business, my close friends’ opinions would be important to me.

Perceived behavioral control (PBC) (Kang et al., 2012)

1. Starting a firm and keeping it viable would be easy for me.

2. If I wanted to, I could easily pursue a career as entrepreneur.

3. If I tried to start a business, I would have a high chance of being successful.

4. I have skills and capabilities to succeed as an entrepreneur.

5. I am confident that I would succeed if I started my own firm.

6. I can control the creation process of a new firm.

7. The number of events outside my control which could prevent me from being an entrepreneur are very few.

Green entrepreneurial intentions (Zeithaml et al., 1996)

1. I am ready to do anything to be a green entrepreneur.

2. My professional goal is to become a green entrepreneur.

3. I am determined to create a green firm in the future.

4. I was willing to do anything to become a green entrepreneur during my study at university.

5. I will act as a professional manager and get involved in the management of a social enterprise through promotion. Preparation during my study at university.

Green entrepreneurial behaviors (GEBs) (Giampietri et al., 2018)

1. Written a green business plan.

2. Started green product/service development.

3. Attempted to obtain external funding.

4. Purchased material, equipment or machinery for the green business.

5. Registered the green company.