Abstract
Purpose
This study aims to explore the association between the tricycle drivers’ personal attitude and perceived behavioral control and their entrepreneurial intention. Moreover, the study was conducted to determine whether such a relationship is moderated by structural support.
Design/methodology/approach
A total of 349 tricycle drivers in the Philippines were randomly selected and served as the respondents of this study. To determine if there is a significant relationship between the variables, structural equation modeling was used.
Findings
Tricycle drivers denoted that the more positive their perceived behavior control and personal attitude is, the greater their entrepreneurial intention is likely to be. Hence, the relationship of perceived behavioral control and personal attitude on entrepreneurial intention is moderated by perceived structural support.
Originality/value
By learning individuals’ perceived behavioral control, personal attitude and their entrepreneurial intention of specific classes of people, specifically tricycle drivers, it is possible to assess the feasibility of entrepreneurial efforts initiated by the government even before it is implemented, avoiding wastage of both times, resources and effort.
Keywords
Citation
Otchengco Jr., A.M. and Akiate, Y.W.D. (2021), "Entrepreneurial intentions on perceived behavioral control and personal attitude: moderated by structural support", Asia Pacific Journal of Innovation and Entrepreneurship, Vol. 15 No. 1, pp. 14-25. https://doi.org/10.1108/APJIE-08-2020-0124
Publisher
:Emerald Publishing Limited
Copyright © 2021, Alberto Magtoto Otchengco Jr. and Yuri Walter Dulay Akiate.
License
Published in Asia Pacific Journal of Innovation and Entrepreneurship. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
It can be noted that among the irreplaceable drivers of entrepreneurial intention is the government. As the government’s structural support is in the lead for entrepreneurial development, it is assumed that it should provide resources that will help foster entrepreneurship to the best of its abilities. The government intervention toward developing entrepreneurship can start by providing an environment for businesses that will promote entrepreneurship. Given that entrepreneurship is one of the sturdiest bedrocks of society, the researchers said that governments of various countries have historically put in effort and resources toward establishing police intended to uplift entrepreneurship (Obaji and Mercy, 2014).
It is, therefore, undisputed that entrepreneurship is at present a source of socio-economic growth for countries. Entrepreneurship offers goods and services to the community and provides job opportunities (Reynolds et al., 2000). Entrepreneurship based on creativity, flexibility and connectivity (Kim, 2018) likewise plays an irreplaceable part in combatting problems such as unemployment and poverty. Still, in the Philippines, it was found that tricycle driving is the most preferred source of income among unemployed residents (Balaria et al., 2017). The enduring popularity of tricycle driving to earn a living can be credited for various reasons. For one, tricycle driving requires a smaller amount of capital as compared to other businesses. Moreover, tricycle driving often does not require as much risk-taking on the part of Filipinos who are required to provide for their families daily compared to engaging in other businesses. Furthermore, there is a captured customer-base with the steady increase of the commuting population. Tricycle driving is, therefore, a source of livelihood for a good number of Filipinos.
Despite the seeming viability of tricycle driving, tricycle drivers are still faced with severe problems. Balaria et al. (2017), following a computation of income and expenses necessarily included in tricycle operations, found that the daily income of tricycle drivers (assuming that they own the tricycle) amounts to roughly 153 US dollars (7,688 Philippine pesos) monthly. In contrast, the poverty threshold or the amount needed by a family of five to meet their minimum basic food and non-food needs in a month is 209 US dollars (10,481 Philippine pesos). Hence, the oversupply of tricycles will necessarily result in an even lower daily income for the Tricycle drivers due to the excessive supply of tricycles. This is supported by the study of Balaria et al. (2017), which showed that the rate at which driving tricycles earn an income is generally slow because of the drivers’ high waiting time due to excessive competition.
Transport laborers in the metropolitan informal economy share the fundamental conditions that face every single poor workforce: low salary, absence of monetary security, lawful and social insurance, essential health and protection measures and social prohibition and insufficient chances for poverty reduction. Furthermore, numerous transport laborers face unsafe working conditions and badgering day-by-day (Spooner, 2011). Given these growing problems, it is forwarded by the researchers of the present study that entrepreneurship can serve as a possible solution to the worsening plight of tricycle drivers. It is a common trend for governments and non-government organizations to provide programs that assist the development of entrepreneurship in their countries (Yurtkoru et al., 2014).
According to Mutlutürk and Mardikyan (2018), the constantly increasing government support for entrepreneurship only shows its importance in building a nation and one of the prime contributors toward economic growth. Furthermore, with the ever-developing technology and competitiveness in job markets, finding employment has become continuously more difficult. Because of this, entrepreneurs are now in demand. Entrepreneurs are often created through various factors that lead to the enhancement of a factor called entrepreneurial intention. Thus, studies have aimed to examine the factors that make a person into an entrepreneur and the matters linked to the increase of entrepreneurial intention. Wu (2010) found that the more grounded the intention, the more probable an individual will play out a specific behavior. Meanwhile, Botsaris and Vamvaka (2016) likewise concluded that the greater the intensity of intention, the higher the probability of entrepreneurial behavior.
Israr and Hashim (2015) conducted research surveying the available literature on entrepreneurial intention. Among their key conclusions are the following: research studies on entrepreneurial intention have centered on Europe and developed countries in Asia; there is a gap to be filled on entrepreneurial intention research studies on developing and under-developed countries; studies have shown that entrepreneurial intention has been found to be higher in areas with lesser opportunities. This implies that entrepreneurial intention is lower in developed countries and higher in under-developed and developing nations and regarding the framework used by studies. It is the theory of planned behavior (TPB) that is most predominant.
Henceforth, this study aimed to explore the relationship between the tricycle drivers’ PBC, PA and their entrepreneurial intention and determine whether such a relationship is moderated by perceived structural support.
Literature review and hypotheses development
As exemplified by the preceding paragraph, Ajzen (1991) established that TPB is the most frequently used model in analyzing entrepreneurial intention. TPB is also the model that was used in the study. While the model can be used to determine the development of intention to execute a wide range of behavior, Krueger et al. (2000) first used TPB to measure entrepreneurial intentions. At its very core, the model argues that an individual’s behavior results from its intention.
Perceived behavioral control and personal attitude on entrepreneurial intention
Three main factors determine intentions according to the TPB Model: perceived behavioral control (PBC), personal attitude (PA) and subjective norm (SN).
PBC is connected to participants’ confidence that they are capable of executing the behavior being studied (Ajzen, 1991). It is the degree of preparedness, knowledge in business and skills in running the firm. Respondents’ perceptions may vary from the strength of their knowledge to the totality of the business’s entire process. There may be a lower mean in PBC, but it does not affect the idea of a moderate level perception. Any degree of entrepreneurial intent ought to be in like manner processual and strong given the intrinsically processual and conceivably solid nature of entrepreneurship (Valliere, 2015).
On the other hand, PA is a temperament to retort positively and negatively to an event (Ajzen, 1991). This is connected with a term used in the model called attitude toward behavior. Finally, SN is defined as one’s insight that individuals who are significant to him think that they ought or ought not to do the behavior in question. The researchers did not include SN as part of the study, as previous researchers said that SN, in predicting intention, neglects to arrive at statistical significance (Fini et al., 2009). The researchers noted that the SN in the TPB is the component that various research studies have shown to fail in predicting behavioral intentions.
Fini et al. (2009) made a study that attempted to decipher the foundations of entrepreneurial intention. Expanding from TPB, the researchers included other antecedents in their study. The study found that attitude directly predicts entrepreneurial intention. Meanwhile, environmental impact, individual abilities and mental attributes just indirectly affect entrepreneurial intention. Nevertheless, the factors that center on the individual were most apt to predict entrepreneurial intention.
On the other hand, Yurtkoru et al. (2014) conducted a study that likewise used TPB. This study concentrated on the relevant support aspects which the researchers expected affected entrepreneurial intention through PBC and PA. The researchers found that PBC and PA mediate the association between relevant support aspects and entrepreneurial intention. Meanwhile, in terms of support aspects, the researchers concluded that relational support was important in amplifying both PBC and PA.
Koe et al. (2012) also used the TPB by Ajzen to investigate millennials’ entrepreneurial intention. The researchers used ties, experience and knowledge as independent variables in their study while mediating variables used were assertiveness, societal standard, PBC and character qualities. Using the TPB model, the researchers found that PBC and PA projected Millenials’ entrepreneurial intention, but it was PA that had a stronger effect. Hereafter, the study hypothesizes the following:
There is a significant relationship between the tricycle drivers’ PA and their entrepreneurial intentions.
There is a significant relationship between the tricycle drivers’ PBC and their entrepreneurial intentions.
Structural support on entrepreneurial intention
Government policies, accessibility of strategic infrastructure and monetary financiers called structural support certainly impact the improvement of entrepreneurial intention (Fini et al., 2009). Lerner (1999) also stated that governments could help develop entrepreneurship through tax policies, subsidizing plans and other help systems. Additionally, Beck et al. (2005) found that a conducive environment, which has tangible and intangible resources, helps nurture entrepreneurship. Tangible factors include physical infrastructure and corporate physical assets, while intangible factors include human capital and resources.
Perceived structural support is a vital contextual aspect in the development of entrepreneurial intentions. According to Gelard and Saleh (2011), entrepreneurship is molded by economic and political tools regulated by individuals in the non-governmental, private and public sectors. These tools can sometimes prevent the development of entrepreneurial intentions such as when the business environment is threatened with barriers in the form of harsh regulations and governmental controls (Merrill et al., 2008). On the other hand, these tools can likewise help advance entrepreneurial intentions if the conditions are made to be acceptable and encouraging for individuals to start a business. For instance, Malaysia, a Southeast Asian country, has guided various technology funding organizations to give full support in favor of people in business inclined toward technology, including the government of venture capital firms (Ajagbe and Ismail, 2014).
The state recognizes the importance of macro-and micro businesses. The government sector plays a huge role in the structure of arrangements, just as in program execution. There might be components outside the ability to control the business through the open arrangement. This stems from the acknowledgment that the legislature may influence the serious structure of the business condition. Creating strategies that may assist businesses in withholding their seriousness might be imperative for the events’ financial turn.
While positive government intervention can boost entrepreneurial efforts, the government can also be a barrier for most business people. In Nigeria, for example, researchers have noted that different administrations have already made programs geared toward the development of entrepreneurship through developmental and financial assistance instruments (Oni and Daniya, 2012). However, these interventions have not been effective given various factors, including corruption and bribery, bureaucratic processes and insufficient infrastructures (Olaore et al., 2020). Hamedi and Mehdiabadi (2020) also noted that the business environment is weak in Iran because of its lack of political and legal support to its entrepreneurs. These are unfortunate examples of how the government, rather than helping entrepreneurs, instead stymies businesses’ development. Research studies, in fact, show this reality. According to Obaji and Mercy (2014), several studies that have analyzed the role of government programs in entrepreneurship advancement have shown contrasting results. While some researchers were in support of governmental support in favor of entrepreneurship, other researchers have shown that government policies had no effect or negative effect in developing entrepreneurial intention. Obaji and Mercy (2014) noted that a contradiction in government policy’s role toward encouraging entrepreneurship is most prevalent in developing countries. Given these contrasting results, the researchers of this study posit that studying among other factors the impact of government intervention through their structural support in developing entrepreneurial intention in the Philippine setting is an important task.
Studies on entrepreneurial intention have been made worldwide as a basis to support initiatives initiated by governments and non-government organizations to stimulate entrepreneurship in their respective nations. By learning individuals’ PBC, PA and their entrepreneurial intention of specific classes of people, specifically tricycle drivers, it is possible to assess entrepreneurial efforts’ feasibility even before it is implemented, avoiding wastage of both time, resources and effort. This leads to the following hypotheses:
Structural support moderates the relationship between the tricycle drivers’ personal attitude and their entrepreneurial intentions
Structural support moderates the relationship between the tricycle drivers’ perceived behavioral control and their entrepreneurial intentions.
Hence, Figure 1 is the study model developed by the researchers illustrating the four alternate hypotheses.
Methodology
Population sample and profile
The study’s target population was the selected tricycle drivers of Kapisanan ng Fernandino Tricycle Drivers at Operators (KFTODA) in the City of San Fernando, Pampanga, Philippines. The said KFTODA has a total number of 3,756 members. Using a stratified random sampling technique, the study gathered 348 tricycle drivers from August to September of 2019.
As presented in Table 1, the respondents’ ages ranged from as young as 20 to as old as 77 years old, with a mean of 41.9 years. Approximately half of them (51.2%) were 41 years of age and above at the time of survey administration. In terms of sex, all of the respondents are male. This may be explained by gender stereotyping, as tricycle driving is largely considered to be a field for men because of physiological requirements of physical strength and stamina. This finding may also be attributed to the socio-cultural Filipino trait that married men should provide for the family regardless of social status. Length of time as a tricycle driver varied widely from 1 year to 40 years, although the majority have worked as tricycle drivers for 10 years or less. This implies fewer drivers (33%) who maintain tricycle driving as their means of livelihood for more than 10 years, possibly due to the immense heat and air pollution in the area. About three-fourths (75.6%) reached or finished only up to high school level in terms of educational attainment. This finding proves that motorcycle driving is a livelihood option mostly for those who lack the education/credentials needed for other jobs.
Variables description
This study used questionnaires that were designed for obtaining the responses of tricycle drivers. The current study adopted the same instrument used by Kolvereid (1996) to measure PBC and PA. The research also used an entrepreneurial intention questionnaire scale by Liñán and Chen (2009) to measure entrepreneurial intention. The Scale has been used in numerous studies on entrepreneurship intention (Ambad and Damit, 2016; Yurtkoru et al., 2014; Chantson and Urban, 2018). For the Structural factors, Turker and Sonmez Selçuk’s (2009) entrepreneurship support model scale was used. The aforementioned scales have been proven to display high reliability, reportedly having Cronbach’s alpha values have been tested across cultures (Chantson and Urban, 2018).
Construct reliability and validity
The researchers computed the Cronbach’s alpha of the constructs to test its items' reliability, as shown in Table 2. It is observed that all the items are reliable and satisfy the set criteria, as the acceptable Cronbach’s alpha and composite reliability (CR) values are ≥0.70 according to Hair et al. (2011). Likewise, according to the authors, a value of ≥0.5 for the average variance extracted (AVE) requires that the constructs interpret more than half of the variance of its items. As shown in Table 2, the AVE values are ≥0.5. Accordingly, the constructs’ convergent validity is established.
Table 3 shows the Fornell-Larcker criterion, cross-loadings and the heterotrait-monotrait ratio (HTMT) that were also examined to establish the discriminant validity (Hair et al., 2014). Fornell-Larcker criterion, which is the square root of AVE, is met in the current study, as shown in Table 3. On the other hand, it is observed that the cross-loadings are also satisfied, as the loading of each indicator is higher than the loadings of its corresponding indicators. With regard to HTMT, it can be inferred that this criterion is achieved, as all the values are less than 0.85, hence demonstrating that the discriminant validity is established.
Data collection
The mode of data gathering used is the survey method. Each of the respondents was given a close-ended structured set of questions adapted by the researchers. The researchers initially gathered the data by sending a request letter to the President of KFTODA asking permission to conduct the study and secure approval. After gaining approval from the KFTODA President, the researchers then distributed the questionnaire to the KFTODA tricycle drivers and ensured that the data’s fairness and accuracy were gathered. The researchers collected the questionnaire from the respondents and checked whether all the questions were answered.
Data analysis
Structural equation modeling (SEM) was applied in the study. As Ullman (1996) characterized, this is a multivariate technique that permits the investigation of many connections between at least one independent variable and one or more dependent variables.
Descriptive statistics were computed using SPSS version 23, while SEM was performed using the software AMOS.
Results and discussion
Figure 2 shows the path diagram from the SEM procedures, which were performed to investigate the relationships between entrepreneurial intentions and the various predictors considered in this study. Findings are interpreted as follows:
Personal attitude vis-à-vis entrepreneurial intention
As seen in Table 4, PA on entrepreneurial intention has a highly significant affirmative relationship with a p-value that is not only less than 0.05 but also less than 0.001, implying not just 95% confidence but 99.9% confidence with the result. Specifically, for every one-point increase in the Personal Attitude mean score, the Entrepreneurial Intention Scale’s mean score increases by 0.422. This specifies that the further positive the tricycle driver’s PA is, the greater his entrepreneurial intention is likely to be. Similarly, Fini et al. (2009) and Koe et al. (2012) found that attitude directly predicts entrepreneurial intent.
Perceived behavior control vis-à-vis entrepreneurial intention
It can be inferred from Table 5 that PBC also has a highly significant positive association with entrepreneurial intention with a p-value that is less than 0.001. Specifically, for every one-point increase in the PBC mean score, the Entrepreneurial Intention Scale’s mean score increases by 0.208. This shows that the higher the tricycle driver’s PBC, the greater their entrepreneurial intention is likely to be. Koe et al. (2012) had a similar finding; that is, PBC predicted the entrepreneurial intent.
Moderating effect of perceived structural support on the relationship between personal attitude and entrepreneurial intention
As shown from Table 6 that perceived structural support has a moderating effect on the association between PA and entrepreneurial intention as evidenced by its very significant affirmative association with both PA and entrepreneurial intentions, with both p-values being less than 0.001. This means that the outcome of PA on entrepreneurial intention is influenced by perceived structural support. Specifically, the more positive the perceived structural support is, the more positive personal attitude becomes and the greater is its effect on entrepreneurial intention. These findings are similar to those of Turker and Sonmez Selçuk (2009) and Mutlutürk and Mardikyan (2018).
Moderating effect of perceived structural support on the relationship between perceived behavior control and entrepreneurial intention
Established in Table 7, it is surmised that perceived structural support has a moderating effect on the association between PBC and entrepreneurial intention as evidenced by its very significant affirmative association with both PBC and entrepreneurial intentions with both p-values less than 0.001. This means that the outcome of PBC on entrepreneurial intention is influenced by perceived structural support. Specifically, the more positive the perceived structural support is, the more positive PBC becomes and the greater its result is entrepreneurial intention.
These findings underscore the importance of perceived structural support as a factor in the development of entrepreneurial intentions as economic and political tools can either enhance or prevent businesses’ development (Gelard and Saleh, 2011).
Additionally, among the variables establish to have a significant relationship with entrepreneurial intention, PA was found to have the greatest effect on entrepreneurial intention based on the standardized regression weight of 0.327 with a p-value less than 0.001. This is also constant with the results of Koe et al. (2012), who found that PA and PBC predicted entrepreneurial intention, but it was PA that had a more solid influence.
Conclusion
The respondents have a very favorable PA toward entrepreneurship; strong agreement on their PBC in starting and sustaining a business; positive perception of the structural support for entrepreneurship that the local government is providing; and high entrepreneurial intention indicating a generally strong agreement to all the statements. Moreover, the more positive the tricycle driver’s PA is, the greater his EI is likely to be. Likewise, the higher the PBC of the tricycle driver is, the higher his EI is likely to be. The effect of PA on EI is moderated by perceived structural support. Specifically, the more positive the perceived structural support is, the more positive personal attitude becomes and the greater is its effect on entrepreneurial intention. Similarly, the effect of PBC on EI is moderated by perceived structural support. Explicitly, the more positive the perceived structural support is, the more positive PBC becomes and the greater is its effect on EI.
Considering that perceived structural support moderates the association of PA to entrepreneurial intention, perceived the local government might improve structural support by coming up with innovations on policies on different support programs like shortening periods of business processes and simplifying taxation. Moreover, as perceived structural support also moderates the association of PBC to entrepreneurial intention, barangays may be mobilized to do their share in supporting the city’s information dissemination and campaign on the available programs and projects of the government relating to businesses and entrepreneurship. Barangays may also localize such programs subject to the availability of funds or partnerships to private organizations.
This study was conducted only in one city in the Philippines. Future research can be conducted within the study context in other cities or provinces in the country and considering other variables such as outcomes of actual business ventures of tricycle drivers. The national government can use the results of these studies to develop entrepreneurship plans for their constituents.
Figures
Respondents’ profile
| Category | Frequency | (%) |
|---|---|---|
| Age groups | ||
| Below 20 | 3 | 0.9 |
| 21 to 30 years old | 50 | 14.4 |
| 31 to 40 years old | 117 | 33.6 |
| 41 to 50 years old | 103 | 29.6 |
| 51 and above | 75 | 21.6 |
| Sex | ||
| Male | 348 | 100.0 |
| Female | 0 | 0.0 |
| Length of being a tricycle driver | ||
| 10 years or less | 234 | 67.2 |
| 11 to 20 years | 85 | 24.4 |
| 21 to 30 years | 17 | 4.9 |
| 31 to 40 years | 12 | 3.4 |
| Educational attainment | ||
| Elementary level | 34 | 9.8 |
| High school level | 263 | 75.6 |
| Vocational | 43 | 12.4 |
| College undergraduate | 3 | 0.9 |
| College graduate | 5 | 1.4 |
Reliability analysis
| Constructs | Cronbach’s alpha | rho_A | CR | AVE |
|---|---|---|---|---|
| Entrepreneurial intention | 0.942 | 0.942 | 0.954 | 0.776 |
| Perceived behavior | 0.918 | 0.920 | 0.936 | 0.711 |
| Personal attitude | 0.866 | 0.870 | 0.903 | 0.650 |
| Structural support | 0.911 | 0.915 | 0.931 | 0.692 |
Outer model loading and cross-loading
| Variables | EI | PBC | PA | SS |
|---|---|---|---|---|
| Fornell-Larcker criterion | ||||
| EI | 0.881 | |||
| PBC | 0.644 | 0.843 | ||
| PA | 0.645 | 0.612 | 0.806 | |
| SS | 0.646 | 0.704 | 0.579 | 0.832 |
| Cross loadings | ||||
| PA1 | 0.451 | 0.466 | 0.794 | 0.422 |
| PA2 | 0.511 | 0.511 | 0.829 | 0.477 |
| PA3 | 0.476 | 0.472 | 0.787 | 0.464 |
| PA4 | 0.543 | 0.497 | 0.823 | 0.508 |
| PA5 | 0.595 | 0.514 | 0.797 | 0.458 |
| PBC1 | 0.526 | 0.860 | 0.529 | 0.560 |
| PBC2 | 0.492 | 0.815 | 0.512 | 0.525 |
| PBC3 | 0.554 | 0.858 | 0.559 | 0.555 |
| PBC4 | 0.548 | 0.872 | 0.507 | 0.659 |
| PBC5 | 0.527 | 0.844 | 0.474 | 0.611 |
| PBC6 | 0.598 | 0.807 | 0.512 | 0.636 |
| EI1 | 0.868 | 0.592 | 0.550 | 0.599 |
| EI2 | 0.889 | 0.580 | 0.557 | 0.572 |
| EI3 | 0.903 | 0.558 | 0.557 | 0.559 |
| EI4 | 0.857 | 0.564 | 0.571 | 0.588 |
| EI5 | 0.897 | 0.570 | 0.554 | 0.559 |
| EI6 | 0.873 | 0.542 | 0.620 | 0.537 |
| SS1 | 0.475 | 0.534 | 0.414 | 0.816 |
| SS2 | 0.517 | 0.596 | 0.487 | 0.879 |
| SS3 | 0.496 | 0.588 | 0.502 | 0.811 |
| SS4 | 0.574 | 0.661 | 0.554 | 0.875 |
| SS5 | 0.518 | 0.534 | 0.379 | 0.805 |
| SS6 | 0.618 | 0.588 | 0.534 | 0.804 |
| Heterotrait-monotrait ratio (HTMT) | ||||
| EI | − | |||
| PBC | 0.690 | − | ||
| PA | 0.707 | 0.683 | − | |
| SS | 0.691 | 0.764 | 0.645 | − |
Entrepreneurial intentions (EI); perceived behavioral control (PBC); personal attitude (PA); structural support (SS)
Parameter estimates from structural equation modeling
| Dependent variable | Independent variable | Regression weights | |||||
|---|---|---|---|---|---|---|---|
| Unstandardized | Standardized | p-value | |||||
| Entrepreneurial intention | ← | Personal attitude | 0.422 | 0.327 | 0.000 | ||
Personal attitude vis-à-vis entrepreneurial intention
Parameter estimates from structural equation modeling
| Dependent variable | Independent variable | Regression weights | |||
|---|---|---|---|---|---|
| Unstandardized | Standardized | p-value | |||
| Entrepreneurial intention | ← | Perceived behavioral control | 0.208 | 0.241 | 0.000 |
Perceived behavioral control vis-à-vis entrepreneurial intention
Parameter estimates from structural equation modeling
| Dependent variable | Independent variable | Regression weights | |||
|---|---|---|---|---|---|
| Unstandardized | Standardized | p-value | |||
| Personal attitude | ← | Structural support | 0.209 | 0.289 | 0.000 |
| Entrepreneurial intention | ← | Structural support | 0.267 | 0.286 | 0.000 |
Moderating effect of perceived structural support on the relationship between personal attitude and entrepreneurial intention
Parameter estimates from structural equation modeling
| Dependent variable | Independent variable | Regression weights | |||
|---|---|---|---|---|---|
| Unstandardized | Standardized | p-value | |||
| Perceived behavioral control | ← | Structural support | 0.756 | 0.289 | 0.000 |
| Entrepreneurial intention | ← | Structural support | 0.267 | 0.286 | 0.0000 |
Moderating effect of perceived structural support on the relationship between perceived behavioral control and entrepreneurial intention
References
Ajagbe, M.A. and Ismail, K. (2014), “Factors influencing venture capital assessment of high growth companies in Malaysia”, International Journal of Entrepreneurship and Small Business, Vol. 21 No. 4, pp. 457-494.
Ajzen, I. (1991), “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, Vol. 50 No. 2, pp. 179-211.
Ambad, S.N.A. and Damit, D.H.D.A. (2016), “Determinants of entrepreneurial intention among undergraduate students in Malaysia”, Procedia Economics and Finance, Vol. 37 No. 1, pp. 108-114.
Balaria, F.E., Pascual, M.P., Santos, M.D., Ortiz, A.F., Gabriel, A.G. and Mangahas, T. (2017), “Sustainability of e-trike as alternative mode of public transportation system: the case of Cabanatuan City, Philippines”, Open Journal of Civil Engineering, Vol. 7 No. 3, pp. 362-377.
Beck, T., Demirgüç–Kunt, A. and Maksimovic, V. (2005), “Financial and legal constraints to firm growth: does size matter?”, The Journal of Finance, Vol. 60 No. 1, pp. 137-151.
Botsaris, C. and Vamvaka, V. (2016), “Attitude toward entrepreneurship: structure, prediction from behavioral beliefs, and relation to entrepreneurial intention”, Journal of the Knowledge Economy, Vol. 7 No. 2, pp. 433-460.
Chantson, J. and Urban, B. (2018), “Entrepreneurial intentions of research scientists and engineers”, South African Journal of Industrial Engineering, Vol. 29 No. 2, pp. 113-126.
Fini, R., Grimaldi, R., Marzocchi, G.L. and Sobrero, M. (2009), “The foundation of entrepreneurial intention”, Proceedings in Summer Conference 2009, Frederiksberg, pp. 17-19.
Gelard, P. and Saleh, K.E. (2011), “Impact of some contextual factors on entrepreneurial intention of university students”, African Journal of Business Management, Vol. 5 No. 26, pp. 10707-10717.
Hair, J.F., Ringle, C.M. and Sarstedt, M. (2011), “PLS-SEM: indeed a silver bullet”, Journal of Marketing Theory and Practice, Vol. 19 No. 2, pp. 129-151.
Hair, J.F., Sarstedt, M., Hopkins, L. and Kuppelwieser, V.G. (2014), “Partial least squares structural equation modelling (PLS-SEM): an emerging tool in business research”, European Business Review, Vol. 26 No. 2, pp. 106-121.
Hamedi, H. and Mehdiabadi, A. (2020), “Entrepreneurship resilience and Iranian organizations: application of the fuzzy DANP technique”, Asia Pacific Journal of Innovation and Entrepreneurship, Vol. 14 No. 3, pp. 231-247.
Israr, A. and Hashim, N. (2015), “Research on entrepreneurial intention: an academic literature review and classification”, Paradigms, Vol. 9 No. 1, pp. 43-62.
Kim, S. (2018), “Domains and trends of entrepreneurship research”, Management Review: An International Journal, Vol. 13 No. 1, pp. 65-90.
Koe, W.L., Sa’ari, J.R., Majid, I.A. and Ismail, K. (2012), “Determinants of entrepreneurial intention among millenial generation”, Procedia – Social and Behavioral Sciences, Vol. 40 No. 1, pp. 197-208.
Kolvereid, L. (1996), “Organisational employment versus self-employment: reasons for career choice intentions”, Entrepreneurship Theory and Practice, Vol. 20 No. 3, pp. 23-31.
Krueger, N.F., Jr, Reilly, M.D. and Carsrud, A.L. (2000), “Entrepreneurial intentions: applying the theory of planned behavior”, Entrepreneurship and Regional Development, Vol. 5 No. 4, pp. 315-330.
Lerner, J. (1999), “The government as venture capitalist: the long-run impact of the SBIR program”, The Journal of Business, Vol. 72 No. 3, pp. 285-318.
Liñán, F. and Chen, Y.W. (2009), “Development and cross‐cultural application of a specific instrument to measure entrepreneurial intentions”, Entrepreneurship Theory and Practice, Vol. 33 No. 3, pp. 593-617.
Merrill, J., Chambers, T. and Roberts, W. (2008), “Professional associations and barriers to intrapreneurship and entrepreneurship”, Management Review: An International Journal, Vol. 3 No. 1, pp. 4-22.
Mutlutürk, M. and Mardikyan, S. (2018), “Analysing factors affecting the individual entrepreneurial orientation of university students”, Journal of Entrepreneurship Education, Vol. 21 No. 1, pp. 1-15.
Obaji, N. and Mercy, U. (2014), “The role of government policy in entrepreneurship development”, Science Journal of Business and Management, Vol. 2 No. 4, pp. 109-115.
Olaore, G.O., Adejare, B.O. and Udofia, E.E. (2020), “Prospects and challenges of entrepreneurship internationalization on the competitiveness of SMEs”, Asia Pacific Journal of Innovation and Entrepreneurship, Vol. 14 No. 3, pp. 303-315.
Oni, E.O. and Daniya, A. (2012), “Development of small and medium scale enterprises: the role of government and other financial institutions”, Oman Chapter of Arabian Journal of Business and Management Review, Vol. 1 No. 7, pp. 16-29.
Reynolds, P.D., Hay, M., Bygrave, W.D., Camp, S.M. and Autio, E. (2000), “Global Entrepreneurship Monitor 2000. 2001 executive report”, Babson College, London Business School, Kauffman center for Entrepreneurial Leadership.
Spooner, D. (2011), “Transport workers in the urban informal economy: livelihood profile”, International Transportworkers, Vol. 2 No. 1, pp. 1-14.
Turker, D. and Sonmez Selçuk, S. (2009), “Which factors affect entrepreneurial intention of university students?”, Journal of European Industrial Training, Vol. 33 No. 2, pp. 142-159.
Ullman, J.B. (1996), “Structural equation modeling”, in Tabachnick, B.G. and Fidell, L.S. (Eds), Using Multivariate Statistics, 3rd ed., HarperCollins College Publishers, New York, NY, pp. 709-819.
Valliere, D. (2015), “An effectuation measure of entrepreneurial intent”, Procedia - Social and Behavioral Sciences, Vol. 169 No. 1, pp. 131-142.
Wu, J. (2010), “The impact of corporate supplier diversity programs on corporate purchasers’ intention to purchase from women-owned enterprises: an empirical test”, Business and Society, Vol. 49 No. 2, pp. 359-380.
Yurtkoru, S., Kuscu, Z. and Doganay, A. (2014), “Exploring the antecedents of entrepreneurial intention on Turkish university students”, Procedia - Social and Behavioral Sciences, Vol. 150 No. 1, pp. 841-850.