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Date Published:
24/11/2021
Online Published:
24/11/2021
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Views PDF: 390
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Tran, H. T., Nguyen, D. Q., & Hoang , H. D. (2021). Succeed or fail? A case study of new ventures in Hanoi, Vietnam. Journal of International Economics and Management, 21(3), 47-72. https://doi.org/10.38203/jiem.021.3.0035
Succeed or fail? A case study of new ventures in Hanoi, Vietnam
Main Article Content
Abstract
This paper explores factors of success/failure of new ventures in a startup hub city in an emerging country. The study uses the data from 27 personal interviews with local entrepreneurs in Hanoi, Vietnam. The business model, financial capital, human resources (i.e., human capital, social capital, psychological capital, cultural capital), technology, and the entrepreneurial orientation (i.e., innovative, problem-solving, risk-taking, and proactive) emerge as the factors of success/failure of an entrepreneurial venture. Interestingly, technology is important but not as critical to the business model for the success of new ventures; and proactiveness but not autonomy is also a crucial success factor. The role of cultural capital is also an important input to the model. A conceptual model of success/failure factors of entrepreneurial ventures is developed from the findings, and the implications are discussed.
Article Details
Keywords
Entrepreneurship, Venture, Startup, Success, Failure
References
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Krahel, J.P. and Titera, W.R. (2015), “Consequences of big data and formalization on accounting and auditing standards”, Accounting Horizon, Vol 29 No. 2, pp. 409 - 422.
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Nunnally, J.C. and Bernstein, I.H. (1994), Psychometric theory (3rd Edition), Mcgraw-Hill Books Company.
Pendharkar, P.C. (2005), “A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem”, Computers & Operation Research Journal, Vol. 32 No. 10, pp. 2561 - 2582.
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Richins, G., Stapleton, A., Stratopoulos, T.C. and Wong, C. (2017). “Big data analytics: opportunity or threat for the accounting profession?”, Journal of Information System, Vol. 31 No. 3, pp. 63 - 79.
Sajady, H., Dastgir, M. and Nejad, H.H. (2008), “Evaluation of the effectiveness of accounting information systems”, International Journal of Information Science and Technology, Vol. 6 No. 2, pp. 49 - 59.
Shaqiri, B. (2017), Exploring techniques of improving security and privacy in big data, Ph.D. thesis, University of Information and Technology - Ohrid.
Siebel, T.M. (2019), Digital transformation: survive and thrive in an era of mass extinction, Audible, Inc.
Taber, K. (2017), “ The use of Cronbach’s Alpha when developing and reporting research instruments in science education”, Research in Science Education, Vol. 48 No. 6, pp. 1273 - 1296.
Tiberius, V. and Hirth, S. (2019), “Impacts of digitization on auditing: a Delphi study for Germany”, Journal of International Accounting, Auditing, and Taxation, Vol. 37, 100288.
Tran, T.N.A. (2019), “Tac dong cua cuoc Cach mang Cong nghiep 4.0 den linh vuc ke toan”, Tap chi Tai chinh, Ky 2, Thang 9/2019, tr. 40 - 42.
Yoon, K., Hoogduin, L. and Zhang, L. (2015), “Big data as complementary audit evidence”, Accounting Horizons, Vol. 29 No. 2, pp. 431 - 438.
Zhang, J., Yang, X. and Appelbaum, D. (2015), “Toward effective big data analysis in continuous auditing accounting”, Accounting Horizons, Vol. 29, No. 2, pp. 469 - 476.
Alles, M. (2015), “Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession”, Accounting Horizons, Vol. 29 No. 2, pp. 439 - 449.
Appelbaum, D., Kogan, A. and Vasarhelyi, M.A. (2017), “Big data and analytics in the modern audit engagement: research needs”, Auditing: A Journal of Practice & Theory, Vol. 36 No. 4, pp. 1 - 27.
Bartlett, M.S. (1954), “A note on the multiplying factors for various Chi-square approximation”, Journal of Royal Statistical Society, Vol. 16 Series B, pp. 296 - 298.
Boillet, J. (2018), How artificial intelligence will transform the audit, Earnest & Young Reporting.
Brady, M. (1985), “Artificial intelligence and robotics”, Artificial Intelligence, Vol. 26 No. 1, pp. 79 - 121.
Brown-Liburd, H., Hussein, I.H. and Lombardi, D. (2015), “Behavioral implications of big data’s impact on audit judgment and decision making and future research directions”, Accounting Horizons, Vol. 29 No. 2, pp. 451 - 468.
Brynjolfsson, E. and McAfee, A. (2014), The second machine age: work, progress, and prosperity in a time of brilliant technologies, New York & London: W.W. Norton & Company.
Cao, M., Chychyla, R. and Stewart, T. (2015), “Big data analytics in financial statement audits”, Accounting Horizon, Vol. 29 No. 2, pp. 423 - 429.
Chen, M., Mao, S. and Liu, Y. (2014), “ Big data: a survey”, Mobile Networks and Applications, Vol. 19 No. 2, pp. 171 - 120.
Christidis, K. and Devetsikiotis, M. (2016), “Blockchains and smart contracts for the internet of things”, IEEE Access: Practical Innovations, Open Solutions, Vol. 4, pp. 2292 - 2303.
Constantiou, I.D. and Kallinikos, J. (2015), “New games, new rules: big data and the changing context of strategy”, Journal of Information Technology, Vol. 30 No. 1, pp. 44 - 57.
Cronbach, L.J. (1951), “Coefficient alpha and the internal structure of tests”, Psychometrika, Vol. 16, pp. 297 - 334.
Dai, J. and Vasarhelyi, M.A. (2017), “Toward blockchain-based accounting and assurance”, Journal of Information Systems, Vol. 31 No. 3, pp. 5 - 21.
Elliott, R.K. (2002), “Twenty-first century assurance”, Auditing: A Journal of Practice & Theory, Vol. 21 No. 1, pp. 139 - 146.
Field, A. (2000), Discovering Statistics using SPSS for Windows, London - Thousand Oaks - New Delhi: Sage publications.
Frey, C.B. and Osborne, M.A. (2017), “The future of employment: how susceptible are jobs to computerization?”, Technological Forecasting and Social Change, Vol. 114, pp. 254 - 280.
Fukukawa, H., Mock, T.J. and Srivastava, R.P. (2014), “Assessing the risk of fraud at Olympus and identifying an effective audit plan”, The Japanese Accounting Review, Vol. 4, pp. 1 - 25.
Hair, J.F.J., Anderson, R.E., Tatham, R.L. and Black, W.C. (1998), Multivariate data analysis, 5th edition, Prentice Hall, Upper Saddle River, New Jersey.
International Auditing and Assurance Standards Board. (2014), A Framework for audit quality, International Federation of Accountants.
Kaiser, H. (1974), “An index of factorial simplicity”, Psychometrika, Vol. 39, pp. 31 - 36.
Kempf-Leonard, K. (2005), Encyclopedia of social measurement, Elsevier.
Kokina, J. and Davenport, T.H. (2017), “The emergence of artificial intelligence: how automation is impact of artificial intelligence on accounting and auditing”, Journal of Emerging Technologies in Accounting, Vol. 14 No. 1, pp. 115 - 122.
Krahel, J.P. and Titera, W.R. (2015), “Consequences of big data and formalization on accounting and auditing standards”, Accounting Horizon, Vol 29 No. 2, pp. 409 - 422.
Littley, J. (2012). “Leveraging data analytics and continuous auditing processes for improved audit planning, effectiveness, and efficiency”, Available at http://www.kpmg.com/US/en/ IssuesAndInsights/ArticlesPublications/Documents/data-analytics-continuous-auditing.pdf (Accessed 23 February, 2021).
Lombardi, D., Bloch, R. and Vasarhelyi, M. (2015), “The current state and future of the audit profession”, Current Issues in Auditing, Vol. 9 No. 1, pp. 10 - 16.
Manita, R., Elommal, N., Baudier, P. and Hikkerova, L. (2020), “The digital transformation of external audit and its impact on corporate governance”, Technological Forecasting and Social Change, Vol. 150, 119751.
Minsky, M. (1961), “Steps toward artificial intelligence”, Proceedings of the IRE, Vol. 49 No. 1, pp. 8 - 30.
More, G. (2005), Dealing with Darwin: how grate companies innovate at every phase of their evolution, New York: Penguin.
Nilsson, N. (1980), Principles of artificial intelligence, Palo Alto, California: Tioga Press.
Nguyen, T.H. (2020), “Xu huong thay doi trong linh vuc ke toan kiem toan duoi tac dong cua cong nghe”, Tap chi Tai chinh, Ky 2, Thang 12/2020, tr. 73 - 75.
Nunnally, J.C. and Bernstein, I.H. (1994), Psychometric theory (3rd Edition), Mcgraw-Hill Books Company.
Pendharkar, P.C. (2005), “A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem”, Computers & Operation Research Journal, Vol. 32 No. 10, pp. 2561 - 2582.
Reiff, N. (2020), “Blockchain explained”, Investopedia, Available at https://www.investopedia.com/ terms/b/blockchain.asp (Accessed 05 April, 2021).
Richins, G., Stapleton, A., Stratopoulos, T.C. and Wong, C. (2017). “Big data analytics: opportunity or threat for the accounting profession?”, Journal of Information System, Vol. 31 No. 3, pp. 63 - 79.
Sajady, H., Dastgir, M. and Nejad, H.H. (2008), “Evaluation of the effectiveness of accounting information systems”, International Journal of Information Science and Technology, Vol. 6 No. 2, pp. 49 - 59.
Shaqiri, B. (2017), Exploring techniques of improving security and privacy in big data, Ph.D. thesis, University of Information and Technology - Ohrid.
Siebel, T.M. (2019), Digital transformation: survive and thrive in an era of mass extinction, Audible, Inc.
Taber, K. (2017), “ The use of Cronbach’s Alpha when developing and reporting research instruments in science education”, Research in Science Education, Vol. 48 No. 6, pp. 1273 - 1296.
Tiberius, V. and Hirth, S. (2019), “Impacts of digitization on auditing: a Delphi study for Germany”, Journal of International Accounting, Auditing, and Taxation, Vol. 37, 100288.
Tran, T.N.A. (2019), “Tac dong cua cuoc Cach mang Cong nghiep 4.0 den linh vuc ke toan”, Tap chi Tai chinh, Ky 2, Thang 9/2019, tr. 40 - 42.
Yoon, K., Hoogduin, L. and Zhang, L. (2015), “Big data as complementary audit evidence”, Accounting Horizons, Vol. 29 No. 2, pp. 431 - 438.
Zhang, J., Yang, X. and Appelbaum, D. (2015), “Toward effective big data analysis in continuous auditing accounting”, Accounting Horizons, Vol. 29, No. 2, pp. 469 - 476.