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The Prediction of Opportunity-driven Entrepreneurship Based on XGBoost Algorithm |
Chen Chengmeng1,Huang Yongchun1,2,Wu Shangshuo1,Qian Chunlin1 |
(1.Business School, Hohai University, Nanjing 211100, China;2.Institute of Social Sciences, Hohai University, Nanjing 210098, China) |
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Abstract Opportunity-driven entrepreneurship plays an increasingly prominent role in overcoming the middle-income trap, and realizing economic transformation and high-quality development, especially in the face of an increasingly complex international environment and the impact of the global COVID-19 pandemic. Existing studies have pointed out some influencing factors of opportunity-driven entrepreneurship, but they are relatively fragmented. As an influential behavioral prediction theory in the field of social psychology, the theory of planned behavior can effectively predict entrepreneurial intention and subsequent entrepreneurial behaviors. It provides a comprehensive and powerful explanatory framework for opportunity-driven entrepreneurship. At the same time, entrepreneurial activity is a complex social issue and is subject to the dynamic nature of nonlinear network feedback system. As a result, it is hard to predict entrepreneurial activity. The significance level of traditional regression methods is influenced by the sample size and the coefficient is influenced by the measurement scale, which makes it difficult to assess the importance of influencing factors as well. Opportunity-driven entrepreneurship involves plenty of characteristic variables, and the relationship between each variable and opportunity-driven entrepreneurship may not be limited to a single linear relationship.#br#With the advent of the era of big data and the development of computer information technology, artificial intelligence receives increasing attention in more and more research and application. It is disrupting industries, business management and innovation related to entrepreneurship, but little attention is paid to the field of entrepreneurship. As one of the core algorithms in the field of artificial intelligence, the machine learning algorithm can fit nonlinear relationships well and is suitable for dealing with the prediction of opportunity-driven entrepreneurship. Therefore following the theory of planned behavior, the study selects 12 characteristic variables to construct the influencing factor framework of opportunity-driven entrepreneurship from subject norm, attitudes toward the behavior and perceived behavior control combined with demographic characteristics. The influencing factor framework specifically includes achievement orientation, risk taking, public recognition, media publicity, entrepreneurial self-efficacy, etc. A total of 19 805 samples are obtained from the Global Entrepreneurship Monitor database in 2018. The XGBoost algorithm is used to predict opportunity-driven entrepreneurship and identify key influencing factors, and the results are compared with those of three machine learning algorithms. The results show that the XGBoost algorithm can predict opportunity-driven entrepreneurship better than support vector machine, random forest and logistic regression in accuracy, precision, recall and F1 value. According to the ROC curve, the AUC value can reach 0.94, which again shows that the prediction effect of XGBoost algorithm is good. In the meantime, entrepreneurial self-efficacy, opportunity recognition and relationship perception are important factors affecting opportunity-driven entrepreneurship, while demographic characteristics such as gender are less important.#br#This study makes two contributions.It first applies the theory of planned behavior and demographic characteristics to analyze the influencing factors of opportunity-driven entrepreneurship, which helps make up for the lack of systematicity in the analysis of opportunity-driven entrepreneurship in previous studies, verifies the applicability of planned behavior theory in explaining opportunity-driven entrepreneurial behavior, and deepens the application of the theory in the field of opportunity-driven entrepreneurship. Then the study combines artificial intelligence with opportunity-driven entrepreneurship, applies machine learning methods to the prediction of opportunity-driven entrepreneurship, and establishes a prediction model of “whether individuals participate in opportunity-driven entrepreneurship”. The XGBoost algorithm has the best prediction effect on opportunity-driven entrepreneurship, and can detect the interaction ambiguity and nonlinear effects in the input data, extend the application of machine learning methods in the field of entrepreneurship, and make up for the shortcomings of traditional econometric analysis methods.#br#The study also provides practical insights and references for individuals, government and external investors. First, for government and external investors, they can identify potential opportunity-driven entrepreneurs based on the XGBoost algorithm to facilitate better allocation of resources to potential opportunity-driven entrepreneurs and reduce selection costs to promote opportunity-driven entrepreneurial practice. Second, the government should target to create an environment that fosters opportunity-driven entrepreneurship to continuously improve individual entrepreneurial self-efficacy, enhance individual entrepreneurial relationship perception, and strength individual entrepreneurial opportunity recognition. For individuals, special attention should be paid to enhancing entrepreneurial self-efficacy, focusing on the accumulation of entrepreneurial relationships and actively identifying entrepreneurial opportunities to transform into opportunity-driven entrepreneurial behavior.#br#
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Received: 14 July 2022
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[1] 黄永春,胡世亮,叶子,等.创业还是就业——行为经济学视角下的动态效用最大化分析[J].管理工程学报,2021,35(6):73-86. [2] 刘伟,雍旻,邓睿.从生存型创业到机会型创业的跃迁——基于农民创业到农业创业的多案例研究[J].中国软科学,2018,33(6):105-118. [3] ACS Z. How is entrepreneurship good for economic growth[J]. Innovations:Technology,Goverance,Globalization,2006,1(1):97-107. [4] 陈成梦,黄永春,吴商硕,等.制度环境与创业认知组态如何驱动不同模式创业[J].科技进步与对策,2022,39(13):12-20. [5] SHANE S, VENKATARAMAN S. The promise of entrepreneurship as a field of research[J]. Academy of Management Review, 2000, 25(1): 217-226. [6] BOUDREAUX C J, NIKOLAEV B. Capital is not enough: opportunity entrepreneurship and formal institutions[J]. Small Business Economics,2019,53(3):709-738. [7] 杨婵,贺小刚,李征宇.家庭结构与农民创业——基于中国千村调查的数据分析[J].中国工业经济,2017,34(12):170-188. [8] ANGULO-GUERRERO M J, PREZ-MORENO S, ABAD-GUERRERO I M. How economic freedom affects opportunity and necessity entrepreneurship in the OECD countries[J]. Journal of Business Research,2017,73:30-37. [9] REY-MART A, PORCAR A T, MAS-TUR A. Linking female entrepreneurs' motivation to business survival[J]. Journal of Business Research,2015,68(4):810-814. [10] BRUYAT C, JULIEN P A. Defining the field of research in entrepreneurship[J]. Journal of Business Venturing, 2001,16(2): 165-180. [11] ARIN K P, HUANG V Z, MINNITI M, et al. Revisiting the determinants of entrepreneurship: a bayesian approach[J]. Journal of Management, 2015, 41(2): 607-631. [12] 杜景南,黄德春.中国互联网银行消费者接受度的指标评价研究——基于人工神经网络的实证研究[J].南京社会科学,2018,29(2):27-35. [13] OBSCHONKA M, AUDRETSCH D B. Artificial intelligence and big data in entrepreneurship: a new era has begun[J]. Small Business Economics, 2020, 55(3): 529-539. [14] AJZEN I.The theory of planned behavior[J].Organizational Behavior and Human Decision Processes,1991,50(2):179-211. [15] 王季,耿健男,肖宇佳.从意愿到行为:基于计划行为理论的学术创业行为整合模型[J].外国经济与管理,2020,42(7):64-81. [16] SOUITARIS V, ZERBINATI S, AL-LAHAM A. Do entrepreneurship programmes raise entrepreneurial intention of science and engineering students? the effect of learning, inspiration and resources[J]. Journal of Business Venturing, 2007, 22(4): 566-591. [17] DZSI-BENYOVSZKI A, SZAB T P. Intrapreneurs and entrepreneurs:do they differ in Romania[J]. Acta Oeconomica, 2017, 67(1): 43-61. [18] 朱亚丽,郭长伟.基于计划行为理论的员工内部创业驱动组态研究[J].管理学报,2020,17(11):1661-1667. [19] SHABIR S, ALI J. Determinants of early-stage entrepreneurship in the Kingdom of Saudi Arabia: evidence from the global entrepreneurship monitor database[J]. Managerial and Decision Economics, 2022, 43(5): 1566-1578. [20] LORTIE J, CASTOGIOVANNI G. The theory of planned behavior in entrepreneurship research: what we know and future directions[J]. International Entrepreneurship and Management Journal, 2015, 11(4): 935-957. [21] 张广宁,泄玉珍,孟庆娜.成长型思维与创业行为倾向研究——基于创业失败恐惧的多维度调节作用[J].科技进步与对策,2022,39(10):32-40. [22] BOUDREAUX C J, NIKOLAEV B N, KLEIN P. Socio-cognitive traits and entrepreneurship: the moderating role of economic institutions[J]. Journal of Business Venturing, 2019, 34(1): 178-196. [23] 郑馨,周先波,陈宏辉,等.东山再起:怎样的国家制度设计能够促进失败再创业——基于56个国家7年混合数据的证据[J].管理世界,2019,35(7):136-151,181. [24] 董志强,魏下海,汤灿晴.制度软环境与经济发展——基于30个大城市营商环境的经验研究[J].管理世界,2012,28(4):9-20. [25] 段文婷,江光荣.计划行为理论述评[J].心理科学进展,2008,26(2):315-320. [26] AJZEN I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior[J]. Journal of Applied Social Psychology,2002,32(4):665-683. [27] CHEN C C, GREENE P G, CRICK A. Does entrepreneurial self-efficacy distinguish entrepreneurs from managers[J].Journal of Business Venturing,1998,13(4):295-316. [28] 王言,周绍妮,石凯.国有企业并购风险预警及其影响因素研究——基于数据挖掘和XGBoost算法的分析[J].大连理工大学学报(社会科学版),2021,42(3):46-57. [29] 刘吉祥,肖龙珠,周江评,等.建成环境与青少年步行通学的非线性关系——基于极限梯度提升模型的研究[J].地理科学进展,2022,41(2):251-263. [30] 叶文平,杨学儒,朱沆.创业活动影响幸福感吗——基于国家文化与制度环境的比较研究[J]. 南开管理评论,2018,21(4):4-14. [31] 杨英,李岩,张秀娥,等.正式制度与非正式制度如何驱动社会创业——基于效率驱动型国家的QCA研究[J].科技进步与对策,2021,38(3):21-29. [32] 涂艳,王翔宇.基于机器学习的P2P网络借贷违约风险预警研究——来自“拍拍贷”的借贷交易证据[J].统计与信息论坛,2018,33(6):69-76. [33] 赵振洋,齐舒月,李实秋.科技型中小企业专利证券化质量评价研究[J].科研管理,2021,42(12):56-64. [34] HSU D K, BURMEISTER-LAMP K, SIMMONS S A, et al. “I know I can, but I don’t fit”: perceived fit, self-efficacy, and entrepreneurial intention[J]. Journal of Business Venturing, 2019, 34(2): 311-326. [35] 郭海.管理者的社会关系影响民营企业绩效的机制研究[J].管理科学,2013,26(4):13-24. [36] AUTIO E H, KEELEY R, KLOFSTEN M, et al. Entrepreneurial intent among students in Scandinavia and in the USA[J]. Enterprise and Innovation Management Studies, 2001, 2(2): 145-160.
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