The moderating effect of entrepreneurial motivation on the relationship between entrepreneurial intention and behaviour: An extension of the theory of planned behaviour on emerging economy

Attitude towards entrepreneurship (ATE) and entrepreneurial intention (EI)

According to Ajzen (1991) theory, a person's attitude, whether affective/experiential (feelings of joy or satisfaction) or instrumental/cognitive (beliefs, thoughts, or rational arguments), is the key determinant in initiating any action. Ajzen (1991, 2002) and Kolvereid (1996) research suggests that an individual's ATE reflects their personal evaluation of its desirability, indicating the degree to which they hold a positive or negative view towards being an entrepreneur. An individual's attitude is a predisposition that influences their positive or negative response towards an object, person, institution, or event. This attitude plays a significant role in shaping the individual's behavioural intentions (Ajzen and Driver, 1992; Bell and Cui, 2023; Thoudam et al., 2021). The assessment of an individual's ATE indicates their perception of the positive or negative consequences associated with engaging in entrepreneurial activities (Esfandiar et al., 2019). When considering different career options, an individual considers various potential consequences such as financial gain, risk factors, and level of autonomy (Douglas and Shepherd, 2002; Gibson et al., 2021). According to Lumpkin and Dess (1996), entrepreneurship involves various types of risks such as personal, social, and psychological, while independence pertains to the level of autonomy in decision-making, and income encompasses both material and non-material benefits of entrepreneurship. Although some studies (Gultom et al., 2020; Zahid and Haji Din, 2019; Zainuddin and Mukhtar, 2022) have examined the relationship between ATE and EI, we aim to test this relationship in the Indian context to confirm its validity, as previous research has not extensively examined this relationship in India. Thus, the following hypothesis is proposed:

Subjective norm (SN) and entrepreneurial intention (EI)

Subjective norm (SN), a construct associated with TPB, is believed to predict intentions, as it comprises an individual's beliefs such as whether others think they should perform a particular behaviour (Conner and Armitage, 1998). Regarding entrepreneurship, SN measures the perceived social pressure an individual feels to become an entrepreneur, which can come from significant people like family members, friends, teachers, and financiers. The strength of an individual's motivation to comply with these perceptions also plays a role (Krueger et al., 2000; Yasir et al., 2023).

There needs to be more consensuses among scholars in the existing literature on the impact of SN on EI. While some studies suggest that SN has a weaker influence compared to ATE and PBC (Autio et al., 2001; Echchabi, Ayedh and Omar, 2020; Ruizalba Robledo et al., 2015), other studies have demonstrated a significant relationship between SN and EI (Gultom et al., 2020; Khan et al., 2020; Kolvereid, 1996; Ruiz-Dotras and Lladós-Masllorens, 2022). According to Hofstede (2011) research, the results regarding the relationship between SN and EI are divided into socialistic and individualistic countries. The study found a positive correlation between SN and EI in socialistic countries like Russia, Ghana, and Southeast Asian countries. However, in the context of the USA and EU, a negative correlation or no significant correlation was observed between SN and EI. Considering the communal lifestyle, the limited job opportunities available in the public sector, and the perceived difficulties associated with entrepreneurship in an industrially underdeveloped region with corrupt practices related to setting up a business (Panda, 2000), the investigators are encouraged to reconsider the correlation between SN and EI in the Indian context. Drawing on the literature review results, the investigators have formulated Hypothesis H2 in the following manner.

Perceived behavioural control (PBC) and entrepreneurial intention (EI)

PBC, the third construct of the TPB, plays a critical role in assessing the magnitude of both EI and entrepreneurial behaviours, according to Boyd and Vozikis (1994) research. Ajzen (1991) defines PBC as an individual's perception of the level of difficulty or ease in performing behaviour, taking into account anticipated obstacles and challenges, as well as previous experiences. According to Ajzen (2002), PBC is a more comprehensive construct than self-efficacy, as it considers not only internal factors such as knowledge, skills, and confidence but also external factors like resources, opportunities, and potential barriers. PBC predicts behaviour directly, while self-efficacy only predicts intentions (Armitage and Conner, 2001). Boyd and Vozikis (1994) suggest that self-efficacy, as a control belief, serves as the foundation for an individual's perception of behavioural control. This perception is based on preconceived notions about the availability or unavailability of necessary resources and opportunities (Ajzen, 2001). In addition to differences in conceptualization and operationalization, the construct has become controversial due to the lack of consistent empirical evidence supporting its impact on intention (Yap, Othman and Wee, 2013). Although the positive impact of PBC on EI has been extensively studied in other countries (Giovanni Di Stefano et al., 2023; Krueger and Carsrud, 1993; Ruizalba Robledo et al., 2015), authors here aim to revisit its impact in the specific context of India, where factors such as perceived government support, family support, entrepreneurship development programs, and service quality of entrepreneurship education may affect EI with varying degrees of influence. Drawing on the aforementioned considerations, authors have formulated Hypothesis H3 in the following manner.

Entrepreneurial alertness (EA) and entrepreneurial intention (EI)

EA is an essential trait for an entrepreneur. It refers to the ability of individuals to identify and recognize entrepreneurial opportunities. Several earlier studies have reported a positive relationship between EA and EI (Tang et al., 2012; Hu et al., 2018; Neneh, 2019). EA has garnered significant attention in entrepreneurship, as it facilitates the identification of suitable career paths and enables the exploitation of entrepreneurial opportunities (Tang, 2008; Short et al., 2009; Xin and Ma, 2023). According to Montiel Campos (2017), EA begins with an individual's capability to identify potential opportunities, followed by continuous personal growth necessary to transform those opportunities into actual outcomes. McMullen and Shepherd (2006) suggest that EA should lead to entrepreneurial action. Lu and Wang (2018) found that there is a direct relationship between EA and EI. This is because EA helps individuals recognize business opportunities and make informed judgments, which in turn influences their intent to pursue entrepreneurship. In addition, several scholars have pointed out that alertness is an essential skill for entrepreneurs to anticipate and recognize opportunities (Ardichvili and Cardozo, 2000; Mannino and Schiera, 2017; Shamsudeen, Keat and Hassan, 2017). Thus, it can be inferred that individuals who possess a higher level of alertness are more likely to identify favourable opportunities and embark on an entrepreneurial career. Based on this observation, the study will propose the following hypothesis.

Need for achievement (NACH) and entrepreneurial intention (EI)

McClelland (1961) proposed the need for achievement, a personality trait characterized by a strong aspiration to establish and sustain elevated performance levels. People with a high need for achievement are highly motivated to succeed, set higher goals, take calculated risks, and select innovative and moderately tricky tasks that are challenging yet achievable. Entrepreneurial careers allow for greater control over outcomes, entail moderate risk-taking, and offer immediate feedback on performance. It is logical to anticipate that individuals with a strong need for achievement would be inclined to pursue entrepreneurship as a profession. McClelland (1965) conducted a longitudinal study, which revealed that a greater number of individuals with a high need for achievement scores were engaged in entrepreneurial occupations compared to those with lower scores on the same trait. Mukesh, Pillai and Mamman (2019) found that NACH was a significant predictor of EI among engineering students in India. Other studies, such as Chaudhary (2017) and Littunen (2000), have also reported that NACH has a stronger link with entrepreneurship than any other trait.

In contrast, Davidsson and Wiklund (1999) argued that the need for achievement traits has limited significance in predicting entrepreneurial behaviour. The conflicting results highlight the necessity for further investigation into the relationship between the need for achievement traits and entrepreneurial intention. Consequently, the authors propose a hypothesis.

Risk Tolerance (RT) and Entrepreneurial Intention

Risk-taking propensity is often considered an important trait for entrepreneurs, as it involves the willingness to take risks in the face of uncertainty. Entrepreneurs face many uncertain situations and must make decisions without complete information, and a high tolerance for risk can help them navigate these challenges. Brockhaus (1980) was one of the first to suggest that risk-taking propensity is an important trait for entrepreneurs. Since then, many studies have reported a positive relationship between risk-taking propensity and entrepreneurial intentions or behaviours (Liñán and Chen, 2009; Rauch and Frese, 2007). However, it's important to note that not all entrepreneurs are high-risk takers, and there is some debate over the extent to which risk-taking propensity is a necessary trait for entrepreneurship (Busenitz and Lau, 1996; Mitchell et al., 2002; Sobaih and Elshaer, 2023). Therefore, authors hypothesize that:

Moderating role of entrepreneurial motivation (EM) on the relationship between entrepreneurial intention (EI) and entrepreneurial behaviour (EB)

There will be significant differences between intention and behaviour of real start-ups. Although scholars advise longitudinal studies to assess the rate of entrepreneurial intention to actual behaviour (Farooq et al., 2018), the difference cannot be determined using conventional research methods. The objective of research is to transform intentions into action. In the 1980s, Sexton, Donald and Smilor, Raymond (1986) and Smilor and Kuhn (1986) conducted preliminary research on EM from theoretical and practical viewpoints. After that, research on entrepreneurs' characteristics took the lead, and EM research could no longer attract scholars.

Bird (1989), and Krueger and Carsrud (1993) stressed the importance of transforming intention into behaviour in realising the complete process of entrepreneurship. The attitude-intention and intention-behaviour links define the relationship between attitude and behaviour. Scholars have not focused on empirical studies on the relationship between entrepreneurial motivation and behaviour (Kuratko et al., 2017); nevertheless, a previous study by Carsrud, Olm and Thomas (1989) addressed motivation and behaviour in the context of business performance. According to Carsrud and Brännback (2010) EM produces the relationship between intention and action. Motivators are impulses that ultimately drive action in pursuit of a goal. Carsrud and Brännback (2010) further found that scholars have researched motivation to explain different reactions of persons to the same stimuli of motivation and choice of diverse personal behaviour.

The push motivation and pull motivation theories are the two types of motivation theories. The pull factors are the ones that motivate individuals to engage in entrepreneurial endeavours (CantúCavada, Bobek and Maček, 2017; Iqbal et al., 2020). The pull factors include the ability to employ others, social status, the opportunity to use one's education and experience, the support and encouragement of one's family, independence, the potential to learn new skills, market opportunities, financial independence, more negotiating power at home, and more control over household decisions (Chhabra, Raghunathan and Rao, 2020; Lockyer and George, 2012). The push factors are the ones which motivate individuals to engage in entrepreneurial activities (Zgheib, 2018). Push factors include lack of job satisfaction, low household earnings, insufficient pay and necessity (Dawson and Henley, 2012).

The goal is essential in motivational studies (Lockyer and George, 2012). Goals are intangible, representing future results and motivating people to keep working hard (Chhabra, Raghunathan and Rao, 2020). Also, motivation acts as a link between intention and behaviour. The capacity of individuals to adapt to changing environments originates from their ability to modify their motivations and goals (Chaudhary, 2017).

According to Ryan and Deci (2000), motivation is influenced by a combination of an individual's cognitive processes and natural and social factors. Motivational initiatives set the direction with purpose and determination. Therefore, the pursuit of motivation, which is driven by an individual's goals and motives, serves as a crucial factor in bridging the gap between intention and behaviour. Earlier research studies suggest that there is a considerable time lag between the formation of intentions in individuals and their actual manifestation in behaviour (Helmreich et al., 1986). Ajzen (1991) TPB posits that an individual's behavioural intention is influenced by their ATB, SN, and PBC, which in turn leads to the actual manifestation of the behaviour. A link between intention-behaviour through motivation may exist, but the same has not been tested in terms of extension of the TPB (Carsrud and Brännback, 2010). Scholars have highlighted the lack of research on this particular aspect of entrepreneurship, which has been addressed in the present study that aims to elucidate engineering students' intentions, motivations, and behaviours. As a result, the following hypothesis is developed:

Figure 1 shows a conceptual framework developed based on the hypotheses discussed above.

Figure 1. Conceptual framework for the study.

Common method bias (CMB)

CMB needs to be assessed when both independent and dependent variables are assessed using the same survey instrument. CMB was assessed using Harman’s single-factor analysis (Lee et al., 2014). The result of this test shows that a single factor explains 34.99% of the total variance. As this value is significantly less than 50%, it is safe to assume that there does not exist any one dominant factor in the data set. Hence, it is proved that the CMB issue does not exist with the collected samples.

Research design

This study used a survey questionnaire as the research instrument (see Extended data; Nayak, 2023b). All the items used to measure the constructs were adopted from previous studies. The attitude towards entrepreneurship, subjective norms and entrepreneurial intention were measured using a 5-item, 3-item, and 4-item scale adapted from Liñán et al. (2010). Perceived behavioural control and risk tolerances were measured using a 5-item and 7-item scale adapted from Chatterjee, Das and Srivastava (2019). The need for achievement was measured using an 8-item scale developed and adapted by Chatterjee, Das and Srivastava (2019) and Dinis et al. (2013). Entrepreneurial alertness was measured using a 4-item scale developed and adopted by Tang et al. (2012). The entrepreneurial motivation was measured using a 4-item scale developed and adapted by Barba-Sánchez and Atienza-Sahuquillo (2012). Entrepreneurial behaviour was measured using a 4-item scale developed and adopted by Li et al. (2020). All items were measured on a 5-point Likert scale, with 1 indicating “strongly disagree” and 5 indicating “strongly agree”.

Sampling design

A quantitative approach was applied to accomplish the research objectives and test the proposed research model, and a cross-sectional descriptive research design was used in the study. Data were collected from final-year engineering students studying in several colleges across India, using a structured questionnaire which was adapted from earlier studies. The questionnaires were personally distributed to the students, and they were informed that participation in the survey was purely on a volunteer basis. They were also assured that their response would be used only for academic purposes and kept confidential. This study distributed 2000 hard copies of the survey questionnaire; finally, 1564 usable questionnaires were further processed for data analysis, yielding a response rate of 78.2%.

Statistical analysis

The proposed research model was analyzed using the SEM supported by AMOS and the SPSS software program. According to Hair et al. (2010), the analysis of moments structures model (AMOS) is one of the latest software. Developed and available in the market, this software is used to assist researchers in performing analysis of the inter-relationships and make models for such inter-relationships within constructs that possess multiple indicators in an efficient, accurate and effective manner. Confirmatory factor analysis was used to evaluate the reliability and validity of each construct in the model. The criteria to consider the model achieve overall fit with actual data when CFI, GFI, TLI, and IFI are all greater than 0.9 and RMSEA is less than 0.08 (Hu and Bentler, 1999). The factor loadings of items within each construct are greater than 0.5, showing that the constructs in the model achieve convergent validity. The constructs achieve reliability when composite reliability (CR) and Cronbach's Alpha are greater than 0.6 and the average variance extracted (AVE) is greater than 50% (Fornell and Larcker 1981; Lee et al., 2013). To test discriminant validity between constructs in the model, we used criteria comparing the square root value of AVE and correlation coefficients in the model or using a 95% confidence interval of correlation coefficients (Fornell and Larcker, 1981). If the square root of AVE values of each construct is greater than the correlation of constructs, or the 95% confidence interval of the correlation coefficient does not contain one value indicating that the constructs reach discriminant validity. We used structural equation modeling to test hypotheses with criteria statistically significant at a level of 5%.

Assessment of measurement model

We used confirmatory factor analysis (CFA) to test the properties of our measures with the saturated model (final model). The results analysis showed that the model achieved overall fits with the actual data: CFI = 0.958; GFI = 0.949; TLI = 0.929; IFI = 0.947, all were larger than 0.9 and RMSEA = 0.041 was less than 0.08 (Nayak, 2023a).

All the constructs had factor loadings higher than the benchmark level of 0.05, which indicated that the constructs achieved convergent validity. The Cronbach's alpha and composite reliability coefficients of all constructs exceed the 0.7 benchmarks, and all AVEs were larger than 0.5 (Table 1). These tests showed that our measures for constructs have achieved internal consistency and reliability.

The analysis result indicated that all constructs have the square root of AVE values of all the constructs was greater than the inter-construct correlations (Table 2 and Figure 2). Therefore, based on the Fornell–Larcker criterion, it was proved that adequate levels of discriminant validity exist in the measurement model.

Figure 2. Structural model analysis.

Assessment of structural model

After successfully validating the measurement model, researchers proceeded to the next stage of their analysis to evaluate the structural model to test their hypotheses about the relationships between latent variables. To assess the appropriateness of the proposed model, researchers examined the R-squared value of the structural model. The results showed that the constructs of ATE, SN, PBC, NACH, RT and EA explained 81% of the variance in EI. Additionally, the impact of EI on behaviour was also measured and found to be 59%, respectively.

Table 3 shows the results of the hypotheses testing. The results showed that all constructs are significant predictors of EI. Furthermore, SN (Path Coefficients = 0.252, CR = 9.993, P = 0.000) and EA (Path Coefficients = 0.246, CR = 8.044, P = 0.000) show a relatively strong effect on EI, followed by PBC (Path Coefficients = 0.208, CR = 8.020, P = 0.000) and ATE (Path Coefficients = 0.198, CR = 7.482, P = 0.000). However, NACH (Path Coefficients = 0.127, CR = 6.203, P = 0.000) and RT (Path Coefficients = 0.054, CR = 2.073, P = 0.038) show the lowest influence on EI.

Moderating effect of entrepreneurial motivation (EM)

According to Table 4, the direct influence of entrepreneurial motivation (EM) on entrepreneurial behaviour (EB) is not statistically significant, indicating that EM alone may not significantly impact EB. Nevertheless, the study revealed that the interaction between entrepreneurial intention (EI) and EM (EI x EM) is significant, with a p-value of less than 0.05. This outcome supports the hypothesis that the relationship between EI and EB is moderated by EM, which implies that the influence of EI on EB is reliant on the level of EM. To examine this relationship further, the researchers created a plot illustrating the three variables at three levels of both EI and EM based on their means and standard deviations. These levels include low, medium, and high, providing a better understanding of how the interaction between EI and EM affects EB at varying levels.

The results obtained from simple slope analyses, as shown in Figure 3, suggest that there is a positive relationship between entrepreneurial intention (EI) and entrepreneurial behaviour (EB) among the engineering students who participated in the study. The plot further reveals that higher levels of entrepreneurial motivation (EM) correspond to higher values of EB for a given level of EI, indicating that motivation amplifies the impact of EI on EB. Moreover, as the level of EI increases, the difference between the plots for various levels of EM also increases, implying that the effect of motivation on EB is more pronounced at higher levels of EI. Thus, the findings suggest that students with high levels of EI and EM are more likely to exhibit entrepreneurial behaviour.

Figure 3. Moderating effect of EM on the relation between EI and EB.

Practical implications

Unemployment is a significant problem in many developing nations, particularly India, where many educated individuals graduate from various academic institutions each year, but employment opportunities are scarce. To tackle this issue effectively, policymakers and educators must put more effort into recognising and cultivating potential entrepreneurs. In this direction, the outcomes of this study have essential practical applications for policymakers and educators in emerging economies.

The study found that ATE significantly influences students' EI, which supports Hypothesis 1. Policymakers can use this information to develop policies and programs encouraging a positive ATE among engineering students, such as creating an entrepreneurial ecosystem on campuses that fosters innovation and creativity (Dubey, 2022). This can include mentorship and networking programs, competitions, and other experiential learning opportunities that expose students to entrepreneurship. Academicians can incorporate entrepreneurship education into engineering curricula to foster a positive ATE. This can include courses covering topics such as opportunity recognition, business planning, marketing, and financial management, as well as experiential learning such as internships, competitions, and incubation programs (Mukesh et al., 2020).

SN is the most significant determinant of students' EI, which supports Hypothesis 2. This has practical implications for policymakers and academicians. For policymakers, this result suggests that efforts to promote entrepreneurship among engineering students should address the social and cultural norms surrounding entrepreneurship (Boutaky and Sahib Eddine, 2022). This could involve creating awareness campaigns highlighting successful entrepreneurs' impact on society and the benefits of entrepreneurship for individuals and communities. Additionally, policymakers could work towards establishing a supportive environment for entrepreneurs by providing access to financing, infrastructure, and other necessary resources for successful startups. For academicians, the finding emphasizes the importance of developing programs and curricula that address social and cultural norms related to entrepreneurship. Universities and engineering institutes could incorporate case studies and guest lecturers that showcase successful entrepreneurs and their contributions to society (Bazkiaei et al., 2021). Also, establishing mentorship programs that connect students with successful entrepreneurs in their field could be useful. Academics could also work towards identifying and overcoming any cultural or social barriers to entrepreneurship among engineering students by providing resources and support to tackle these challenges.

The study supports Hypothesis 3, indicating that PBC significantly influences students' EI. Therefore, policymakers and academicians can develop and promote programs that enhance engineering students' PBC towards entrepreneurship. This can include providing opportunities for students to participate in entrepreneurship training and education programs that focus on building their knowledge, skills, and self-efficacy regarding starting and managing a business (Ambad, 2022; Lopez et al., 2021). To increase students' confidence in their ability to start and run a business, policymakers and academicians can collaborate with industry partners to offer hands-on experience and exposure to real-world entrepreneurial settings (Marmat, 2021).

The study supports Hypothesis 4, indicating that engineering students' EA significantly and positively influences their EI. Policymakers and academicians can take practical steps to develop and enhance students' EA by providing training programs and workshops that expose them to industry experts and successful entrepreneurs. These programs may include case studies and exercises designed to improve students' ability to identify entrepreneurial opportunities and take calculated risks. Additionally, academic institutions can encourage collaboration between students, faculty, and industry to foster an entrepreneurial culture on campus and provide students with more opportunities to hone their entrepreneurial skills (Lim, Lee and Mamun, 2021). Policymakers can also promote innovation and entrepreneurship in the engineering sector by offering funding opportunities for startups and incentivizing businesses to invest in research and development (Neneh, 2019). By creating an environment that values entrepreneurship and innovation, policymakers and academicians can contribute to developing the next generation of successful entrepreneurs in the engineering field.

The NACH significantly influences students' EI. The result supports hypothesis 5. To encourage more students to pursue entrepreneurship, policymakers can consider creating an environment fostering achievement and success. This can be done by promoting and supporting entrepreneurial events and competitions, offering financial incentives and support to young entrepreneurs, and developing policies encouraging entrepreneurship (Biswas and Verma, 2021). By creating an environment that rewards achievement, policymakers can help increase the number of engineering students who desire to start their own businesses. Academicians can also take note of this finding and use it to design and implement programs that foster a sense of achievement among engineering students. For example, entrepreneurship courses and programs can be developed that emphasize the importance of goal-setting, hard work, and perseverance in achieving success as an entrepreneur. These programs also offer opportunities for students to work on real-world entrepreneurial projects, which can help them to build their confidence and sense of achievement. By providing engineering students with the tools and resources they need to succeed as entrepreneurs, academicians can help increase the number of students pursuing entrepreneurial careers.

The RT significantly influences students' EI, which supports hypothesis 6. Policymakers can use this information to design policies and programs that encourage and support risk-taking behaviours among engineering students, such as providing access to funding, mentoring, and incubation programs that can help students mitigate and manage risks associated with entrepreneurship (Dubey, 2022). Additionally, policymakers can explore ways to incentivize industry collaborations and startup partnerships that expose engineering students to real-world experiences that can increase their risk tolerance. Academicians can use this finding to design and implement educational and training programs that help students develop and improve their risk-taking abilities (Ilevbare et al., 2022). This can include integrating experiential learning opportunities into the engineering curriculum, such as business plan competitions and hackathons, where students can practice identifying and mitigating risks in a low-stakes environment (Ibidunni, Mozie and Ayeni, 2020). It can also offer courses and workshops on risk management and decision-making to help students develop the skills and knowledge necessary to evaluate and manage risks associated with entrepreneurship.

Limitations and scope for future research

The limitations of this study suggest several areas for future research. Researchers could expand the theoretical model used in this study to include environmental and external factors that may influence entrepreneurial intentions and behaviour. Furthermore, researchers could use qualitative or mixed-method approaches to gain a more in-depth understanding of the factors that contribute to entrepreneurial intentions and behaviour. Future studies could also include samples from other countries, such as those in South Asia, to provide cross-country data on the effectiveness of entrepreneurship education. Additionally, future research could explore the generalizability of the findings beyond India by including students from other nationalities. Longitudinal studies could be conducted better to understand the entrepreneurial journey of higher education students as they transition to becoming entrepreneurs. These studies could provide insights into the factors that influence the development and success of entrepreneurs, as well as the challenges they face along the way.