| Literature DB >> 34874979 |
Ghulam Raza Sargani1, Yuansheng Jiang1, Deyi Zhou2, Abbas Ali Chandio1, Mudassir Hussain3, Asif Ali4, Muhammad Rizwan5, Najeeb Ahmed Kaleri6.
Abstract
This cross-sectional study sought to identify gender differences in individual behavioral attitudes, personal traits, and entrepreneurial education based on planned behavior theory. The Smart partial least squares (PLS) structural equation model and PLS path modeling were used. A survey design was used to collect data from 309 samples using quantitative measures. The model was tested for validity and reliability and showed variance (full, R2 = 58.9% and split, R2 = 62.7% and R2 = 52.7%) in male and female model predictive power, respectively. Subjective norms (SN), personality traits (PT), and entrepreneurial education (EE) significantly impacted the male sample's intention. Females' intentions toward entrepreneurship was less affected by attitude toward behavior (ATB), subjective norms (SN), perceived behavioral control (PBC), and entrepreneurship education (EE). Further, attitudes, social norms, and behavioral controls as mediation variables indicate a significant and positive role of male and female intentions. These findings imply that behavioral beliefs (ATB, PBC, and SN) influence entrepreneurial intention-action translation. The results significantly supported the designed hypotheses and shed light on individual personality traits (PT) and entrepreneurship education (EE) underpinning enterprise intention. The study determined that EE and PT are the strongest predictors of intention, thus highlighting the role of these motives in the entrepreneurial process. This study contributes to the growing body of knowledge on youth entrepreneurs, expands our understanding of entrepreneurship as a practical career choice, and offers a novel account differentiating male and female PT. The drive to evaluate the effects of entrepreneurial intention among budding disparities in Pakistan requires a more profound knowledge of the aspects that endorse entrepreneurship as a choice of profession and enhances youth incentive abilities to engage in entrepreneurial activities based on exploitation.Entities:
Mesh:
Year: 2021 PMID: 34874979 PMCID: PMC8651106 DOI: 10.1371/journal.pone.0260437
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Study analytic framework extended theory of planned behavior.
Demographic characteristics of the respondents.
| Variable distribution | (N) | (% age) | (M) | (STDEV) | Total | |
|---|---|---|---|---|---|---|
|
| Male | 184 | 59.5 | 0.60 | 0.492 | 309 |
| Female | 125 | 39.5 | ||||
|
| Yes | 202 | 65.4 | 0.65 | 0.477 | 309 |
| No | 107 | 34.6 | ||||
|
| Yes | 193 | 62.5 | 0.62 | 0.485 | 309 |
| No | 116 | 37.5 | ||||
|
| Yes | 175 | 56.6 | 0.57 | 0.496 | 309 |
| No | 134 | 43.4 | ||||
|
| Yes | 179 | 57.9 | 0.58 | 0.494 | 309 |
| No | 113 | 36.6 | ||||
|
| Yes | 196 | 63.4 | 0.63 | 0.482 | 309 |
| No | 75 | 29.4 | ||||
Note: (N) = Number of samples, (% age) = Percentage (M) = Means, (STDEV) = Standard Deviation.
Constructs reliability and validity test measurement.
| Constructs | Items | FL | CA | rho_A | (CR) | (AVE) |
|---|---|---|---|---|---|---|
|
| EI1 | 0.821 | 0.804 | 0.807 | 0.872 | 0.63 |
| EI2 | 0.836 | |||||
| EI3 | 0.863 | |||||
| EI4 | 0.750 | |||||
|
| ATB1 | 0.718 | 0.761 | 0.782 | 0.865 | 0.683 |
| ATB2 | 0.829 | |||||
| ATB3 | 0.920 | |||||
|
| SN1 | 0.791 | 0.609 | 0.627 | 0.776 | 0.537 |
| SN2 | 0.686 | |||||
| SN3 | 0.718 | |||||
|
| PBC1 | 0.739 | 0.689 | 0.703 | 0.824 | 0.610 |
| PBC2 | 0.816 | |||||
| PBC3 | 0.786 | |||||
|
| PT1 | 0.828 | 0.702 | 0.704 | 0.834 | 0.627 |
| PT2 | 0.797 | |||||
| PT3 | 0.749 | |||||
|
| EE1 | 0.821 | 0.813 | 0.923 | 0.878 | 0.706 |
| EE2 | 0.836 | |||||
| EE3 | 0.863 |
Notes: FL = Factor Loadings; CA = Cronbach Alpha CR = Composite Reliability; AVE = Average Variance Extraction.
Measurement of Squared Correlation and Discriminant Validity.
| Constructs | (ATB) | (EE) | (EI) | (PBC) | (PT) | (SN) |
|---|---|---|---|---|---|---|
|
|
| |||||
|
| 0.288 |
| ||||
|
| 0.501 | 0.476 |
| |||
|
| 0.745 | 0.347 | 0.561 |
| ||
|
| 0.555 | 0.287 | 0.696 | 0.697 |
| |
|
| 0.554 | 0.403 | 0.574 | 0.558 | 0.569 |
|
Diagonal values represent the square root of AVE.
Fig 2PLS-SEM Co-efficient path model estimation.
Estimates of Path Co-efficient in a Model.
| Relationships | Male | Female | Full Sample | t- Statistics | p-Value | Hypotheses |
|---|---|---|---|---|---|---|
|
| ||||||
|
| 0.054 | 0.145 | 0.083 | 0.991 | 0.322 | H1a |
|
| 0.171 | 0.135 | 0.146 |
|
| H1b |
|
| 0.026 | -0.100 | -0.035 | 0.384 | 0.701 | H1c |
|
| ||||||
|
| 0.209 | 0.084 | 0.138 |
| 0.004 | H2a |
|
| 0.277 | 0.120 | 0.235 |
| 0.000 | H2b |
|
| 0.171 | 0.230 | 0.159 |
| 0.000 | H2c |
|
| 0.303 | 0.216 | 0.299 |
| 0.000 | H2d |
|
| ||||||
|
| 0.540 | 0.494 | 0.516 |
| 0.000 | H3a |
|
| 0.510 | 0.552 | 0.504 |
| 0.000 | H3b |
|
| 0.666 | 0.590 | 0.654 |
| 0.000 | H3c |
|
| 0.656 | 0.584 | 0.612 |
| 0.000 | H3d |
Notes: Sig
† p < 0.100
* p < 0.050
** p < 0.010
*** p < 0.001 and n.s.* non-significant.
Structural specific indirect effects of the female sample.
| Relationships | Coefficients | Lower Threshold | Upper Threshold | t- Statistics | p-Value | Annotation |
|---|---|---|---|---|---|---|
|
| 0.012 | -0.012 | 0.063 | 0.618 | 0.536 | H4a |
|
| 0.072 | -0.027 | 0.182 | 1.323 | 0.186 | H4b |
|
| 0.016 | -0.026 | 0.062 | 0.757 | 0.449 | H5a |
|
| 0.074 | -0.035 | 0.206 | 1.211 | 0.226 | H5b |
|
| -0.023 | -0.092 | 0.044 | 0.681 | 0.496 | H6a |
|
| -0.059 | -0.208 | 0.125 | 0.698 | 0.485 | H6b |
Note: p- value is significant at the < 0.05. or
*p < 0.05
**p < 0.01and
***p = 0.001, Direct effects and Indirect effects: Bootstrapping: 2000 iterations and 0.95 bias-corrected.
Total indirect effects Male vs. Female.
| Female | Male | |||||
|---|---|---|---|---|---|---|
| Relationships | Coefficients | t- Statistics | p-Value | Coefficients | t- Statistics | p-Value |
|
| 0.005 | 0.142 | 0.887 | 0.063 |
| 0.030** |
|
| 0.087 | 1.496 | 0.135 | 0.133 |
| 0.023** |
Note: p- value is significant at the < 0.05. or
*p < 0.05
**p < 0.01and
***p = 0.001, Indirect effects: Bootstrapping: 2000 iterations and 0.95 bias-corrected.
Multi-group parametric test difference Male vs. Female.
| Constructs | Path Coefficient-diff | t-Value | p-Value |
|---|---|---|---|
|
| 0.084 | 0.531 | 0.710 |
|
| 0.128 | 1.239 | 0.117 |
|
| 0.032 | 0.340 | 0.382 |
|
| 0.055 | 0.594 | 0.724 |
|
| 0.149 | 1.294 | 0.104 |
|
| 0.110 | 0.649 | 0.258 |
|
| 0.045 | 0.455 | 0.332 |
|
| 0.033 | 0.285 | 0.392 |
|
| 0.072 | 0.879 | 0.192 |
|
| 0.035 | 0.357 | 0.638 |
|
| 0.033 | 0.255 | 0.401 |
Note: p-values <0.05 or > 0.95 show significant differences across the two samples.
Fig 3PLS-SEM bootstrapping model significant estimation.
Structural specific indirect effects of the male sample.
| Relationships | Coefficients | Lower Threshold | Upper Threshold | t- Statistics | p-Value | Annotation |
|---|---|---|---|---|---|---|
|
| 0.011 | -0.038 | 0.063 | 0.454 | 0.650 | H4a |
|
| 0.029 | -0.077 | 0.163 | 0.478 | 0.633 | H4b |
|
| 0.047 | 0.003 | 0.099 | 2.040 | H5a | |
|
| 0.087 | 0.006 | 0.192 | 1.812 | 0.070 | H5b |
|
| 0.004 | -0.040 | 0.041 | 0.219 | 0.826 | H6a |
|
| 0.017 | -0.135 | 0.159 | 0.228 | 0.820 | H6b |
Note: p- value is significant at the < 0.05. or
*p < 0.05
**p < 0.01and
***p = 0.001, Direct effects and Indirect effects: Bootstrapping: 2000 iterations and 0.95 bias-corrected.
Permutation test for measuring invariance.
| Compositional Invariance C = 1 | Equal Mean Assessment | Equal Variance Assessment | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
| (CI) | C = 1 | P- Mean | 5% c | p-Values | Partial (MI) Established | Mean Difference | 2.5% | 97.5% | p-Values | Variance Difference | 2.5% | 97.5% | p-Values | Full (MI) Established |
|
| Yes | 0.997 | 0.997 | 0.991 | 0.308 | No | 0.134 | -0.215 | 0.229 | 0.240 | 0.302 | -0.26 | 0.269 | 0.024 | No |
|
| Yes | 0.994 | 0.994 | 0.978 | 0.300 | No | -0.566 | -0.223 | 0.23 | 0.349 | -0.26 | 0.272 | 0.011 | No | |
|
| Yes | 0.998 | 0.999 | 0.997 | 0.144 | No | -0.358 | -0.223 | 0.231 | 0.000 | 0.365 | -0.33 | 0.329 | 0.031 | yes |
|
| Yes | 0.997 | 0.997 | 0.989 | 0.438 | Yes | 0.065 | -0.226 | 0.232 | 0.570 | 0.131 | -0.26 | 0.284 | 0.348 | yes |
|
| Yes | 1.000 | 0.999 | 0.997 | 0.750 | Yes | -0.044 | -0.218 | 0.229 | 0.710 | 0.184 | -0.28 | 0.31 | 0.222 | yes |
|
| Yes | 0.997 | 0.99 | 0.966 | 0.648 | Yes | 0.060 | -0.222 | 0.226 | 0.610 | 0.153 | -0.26 | 0.27 | 0.266 | yes |
Note: Configure Invariance = (CI) measure invariance = (MI), Correlation = (C).