THE BAYESIAN SEM APPROACH ON RELIGIOUS TOURISM AND SME'S ENTREPRENEURIAL OPPORTUNITY INTERRELATION IN RURAL AREA
Economics, social and culture are interrelated fields in developing a country. The social and cultural conditions that grow in an area affect how the economy develops in that area and its surrounding. This study analyzed a causal relationship from 60 nascent entrepreneurs at rural area of religious tourism with Bayesian SEM to handle a small amount of data. Based on the results of the analysis, it was found that entrepreneurial motivation and cultural motivation had a significant effect on rural religious tourism. The latent variable of rural religious tourism and entrepreneurial motivation have a significant effect on SME's entrepreneurial opportunity. The entrepreneurial motivation variable has a correlation with the cultural motivation variable.This characteristics has established the Minangkabau heritage of rural area described on its strong religious tourism aspect into SME's entrepreneurial challenge of nascent entrepreneurs.
D. Barrado-Timón, A. Palacios, and C. Hidalgo-Giralt, “Medium and Small Cities, Culture and the Economy of Culture. A Review of the Approach to the Case of Spain in Light of International Scientific Scholarship," Sustainability, vol. 12, no. 18. p. 7321, 2020, doi: 10.3390/su12187321.
J. Costa, A. C. Rodrigues, and M. R. Ferreira, "Organizational Culture in Social Economy Organizations," J. Sci. Pap. Econ. Sociol., vol. 13, no. 3, pp. 155–170, 2020, doi: doi:10.14254/2071-789X.2020/13-3/10.
F. Eggers, "Masters of disasters? Challenges and opportunities for SMEs in times of crisis," J. Bus. Res., vol. 116, no. 1, pp. 199–208, 2020, doi: https://doi.org/10.1016/j.jbusres.2020.05.025.
M. Jawad, T. Hone, E. P. Vamos, V. Cetorelli, and C. Millett, "Implications of armed conflict for maternal and child health: A regression analysis of data from 181 countries for 2000–2019," PLOS Med., vol. 18, no. 9, p. e1003810, Sep. 2021, [Online]. Available: https://doi.org/10.1371/journal.pmed.1003810.
S. Rambotti and R. L. Breiger, "Extreme and Inconsistent: A Case-Oriented Regression Analysis of Health, Inequality, and Poverty," Socius, vol. 6, p. 2378023120906064, Jan. 2020, doi: 10.1177/2378023120906064.
Z. H. Radhy, "Application of Multiply Regression Linear Model and New Technology Method in Estimating Learning and Education of Students," Int. Electron. J. Math. Educ., vol. 14, no. 1, pp. 87–90, 2019, doi: doi.org/10.12973/iejme/3978.
H. Vaddireddy, A. Rasheed, A. E. Staples, and O. San, "Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data," Phys. Fluids, vol. 32, no. 1, p. 15113, Jan. 2020, doi: 10.1063/1.5136351.
M. Abdelrahman, "Personality Traits, Risk Perception, and Protective Behaviors of Arab Residents of Qatar During the COVID-19 Pandemic," Int. J. Ment. Health Addict., 2020, doi: 10.1007/s11469-020-00352-7.
D. Takagi and T. Shimada, "A Spatial Regression Analysis on the Effect of Neighborhood-Level Trust on Cooperative Behaviors: Comparison With a Multilevel Regression Analysis ," Frontiers in Psychology , vol. 10. p. 2799, 2019, [Online]. Available: https://www.frontiersin.org/article/10.3389/fpsyg.2019.02799.
A. A. Sozinova, "Causal Connections of Formation of Industry 4.0 from the Positions of the Global Economy BT - Industry 4.0: Industrial Revolution of the 21st Century," E. G. Popkova, Y. V Ragulina, and A. V Bogoviz, Eds. Cham: Springer International Publishing, 2019, pp. 131–143.
M. Knoblach, M. Roessler, and P. Zwerschke, "The Elasticity of Substitution Between Capital and Labour in the US Economy: A Meta-Regression Analysis," Oxf. Bull. Econ. Stat., vol. 82, no. 1, pp. 62–82, Feb. 2020, doi: https://doi.org/10.1111/obes.12312.
D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to Linear Regression Analysis. Wiley, 2021.
T. Z. Keith, Multiple Regression and Beyond : An Introduction to Multiple Regression and Structural Equation Modeling, 3rd ed. New York: Routledge, 2019.
F. Yanuar, D. Devianto, S. Marisa, and A. Zetra, "Consistency test of reliability index in SEM model," Appl. Math. Sci., vol. 9, no. 106, pp. 5283–5292, 2015, doi: dx.doi.org/10.12988/ams.2015.56446.
M. K. Cain and Z. Zhang, "Fit for a Bayesian: An Evaluation of PPP and DIC for Structural Equation Modeling," Struct. Equ. Model. A Multidiscip. J., vol. 26, no. 1, pp. 39–50, Jan. 2019, doi: 10.1080/10705511.2018.1490648.
S. Y. Lee, Structural Equation Modeling: A Bayesian Approach. Chichester: John Wiley & Sons, 2007.
R. M. Sirkin, Statistics for the Social Sciences. SAGE Publications, 2006.
K. A. Bollen, Structural Equations with Latent Variables. Toronto: John Wiley & Sons, 1989.
J. B. Ullman, "Structural equation modeling: reviewing the basics and moving forward," J. Pers. Assess., vol. 87, no. 1, pp. 35–50, 2006, doi: 10.1207/s15327752jpa8701_03.
G. E. P. Box and G. C. Tiao, Bayesian Inference in Statistical Analysis. New York: Addison-Wesley Pub. Co., 1992.
R. J. Muirhead, Aspects of Multivariate Statistical Theory. New Jersey: John Wiley & Sons, 2005.
Copyright (c) 2022 Frilianda Wulandari, Dodi Devianto, Ferra Yanuar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.