Document Type : Original Article
Authors
Department of Industrial Engineering, Payame Noor University, Tehran, Iran.
Abstract
Investigating the impact of business intelligence and innovation on the financial performance of start-ups: the approach of neural network models and structural equations. This research is quantitative in nature, descriptive-correlative in nature, and practical in terms of purpose. There is no up-to-date list of start-ups and start-ups that can be used as examples. To overcome this problem, we reached out to several business growth centers as a means of reaching startups because they work directly with the demographics we want to address. The statistical population of the research is experts and experts available in growth centers and technology parks, 153 people were selected by non-probability sampling method. To test the hypotheses, taking into account the mediating effect of network learning and innovation, the variance-based structural equation method in Smartpls4.0 software and the multilayer perceptron neural network module was used, as well as to test the bias of the measurement tool, Harman's single-factor test was used in Spss27 software package.
The output of the models showed that business intelligence has an impact both directly and indirectly on the financial performance of start-ups and the impact of the variables is 87%. The most important variables in influencing the financial performance of start-ups are innovation and network learning. Also, the multi-layer perceptron neural network approach is more accurate than variance-based structural equation modeling. At the same moment of the formation of start-ups, the discussion of business intelligence and in the real sense of the discussion of innovation according to data analysis, which is one of the tools of illustration of business intelligence, should be used. Business intelligence is usually a capability that companies develop and discover and can influence existing information so that it can be considered an internal organizational variable.
Keywords
[1] Katila, R., Chen, E. L., & Piezunka, H. (2012). All the right moves: How entrepreneurial firms compete effectively. Strategic entrepreneurship journal, 6(2), 116–132. DOI:10.1002/sej.1130
[2] Man, T. W. Y., Lau, T., & Chan, K. F. (2002). The competitiveness of small and medium enterprises: A conceptualization with focus on entrepreneurial competencies. Journal of business venturing, 17(2), 123–142. DOI:10.1016/S0883-9026(00)00058-6
[3] Raghuvanshi, J., Agrawal, R., & Ghosh, P. K. (2017). Analysis of barriers to women entrepreneurship: the DEMATEL approach. Journal of entrepreneurship, 26(2), 220–238. DOI:10.1177/0971355717708848
[4] Guzman, J., & Kacperczyk, A. (2019). Gender gap in entrepreneurship. Research policy, 48(7), 1666–1680. DOI:10.1016/j.respol.2019.03.012
[5] Foster, K., Smith, G., Ariyachandra, T., & Frolick, M. N. (2015). Business intelligence competency center: improving data and decisions. Information systems management, 32(3), 229–233. DOI:10.1080/10580530.2015.1044343
[6] Zahra, S. A., Neubaum, D. O., & El–Hagrassey, G. M. (2002). Competitive analysis and new venture performance: understanding the impact of strategic uncertainty and venture origin. Entrepreneurship theory and practice, 27(1), 1–28. DOI:10.1111/1540-8520.t01-2-00001
[7] Huang, Z. xiong, Savita, K. S., & Zhong-jie, J. (2022). The Business Intelligence impact on the financial performance of start-ups. Information processing and management, 59(1), 102761. DOI:10.1016/j.ipm.2021.102761
[8] Lonial, S. C., & Carter, R. E. (2015). The impact of organizational orientations on medium and small firm performance: A resource-based perspective. Journal of small business management, 53(1), 94–113. DOI:10.1111/jsbm.12054
[9] Wiklund, J., & Shepherd, D. A. (2011). Where to from here? EO-as-experimentation, failure, and distribution of outcomes. Entrepreneurship: theory and practice, 35(5), 925–946. DOI:10.1111/j.1540-6520.2011.00454.x
[10] Villar, C., Alegre, J., & Pla-Barber, J. (2014). Exploring the role of knowledge management practices on exports: A dynamic capabilities view. International business review, 23(1), 38–44. DOI:10.1016/j.ibusrev.2013.08.008
[11] Colombelli, A., Krafft, J., & Quatraro, F. (2013). Properties of knowledge base and firm survival: Evidence from a sample of French manufacturing firms. Technological forecasting and social change, 80(8), 1469–1483. DOI:10.1016/j.techfore.2013.03.003
[12] Wanda, P., & Stian, S. (2015). The secret of my success: an exploratory study of business intelligence management in the Norwegian Industry. Procedia computer science, 64, 240–247. DOI:10.1016/j.procs.2015.08.486
[13] Hoppe, M. (2015). Intelligence as a discipline, not just a practice. Journal of intelligence studies in business, 5(3), 47–54. DOI:10.37380/jisib.v5i3.137
[14] Trieu, V. H. (2017). Getting value from Business Intelligence systems: A review and research agenda. Decision support systems, 93, 111–124. DOI:10.1016/j.dss.2016.09.019
[15] Gerschewski, S., & Xiao, S. S. (2015). Beyond financial indicators: An assessment of the measurement of performance for international new ventures. International business review, 24(4), 615–629. DOI:10.1016/j.ibusrev.2014.11.003
[16] Caseiro, N., & Coelho, A. (2019). The influence of Business Intelligence capacity, network learning and innovativeness on startups performance. Journal of innovation and knowledge, 4(3), 139–145. DOI:10.1016/j.jik.2018.03.009
[17] Hitt, M. A., Ireland, R. D., Camp, S. M., & Sexton, D. L. (2001). Strategic entrepreneurship: entrepreneurial strategies for wealth creation. Strategic management journal, 22(6–7), 479–491. DOI:10.1002/smj.196
[18] Psarras, J. (2006). Education and training in the knowledge-based economy. Vine, 36(1), 85–96.
[19] Wang, Z., & Wang, N. (2012). Knowledge sharing, innovation and firm performance. Expert systems with applications, 39(10), 8899–8908.
[20] Larsson, R., Bengtsson, L., Henriksson, K., & Sparks, J. (1998). The Interorganizational learning dilemma: collective knowledge development in strategic alliances. Organization science, 9(3), 285–305. DOI:10.1287/orsc.9.3.285
[21] Jamal Ali, B., & Anwar, G. (2021). Measuring competitive intelligence network and its role on business performance. International journal of english literature and social sciences, 6(2), 329–345. DOI:10.22161/ijels.62.50
[22] Weerawardena, J., Mort, G. S., Salunke, S., Knight, G., & Liesch, P. W. (2015). The role of the market sub-system and the socio-technical sub-system in innovation and firm performance: a dynamic capabilities approach. Journal of the academy of marketing science, 43(2), 221–239. DOI:10.1007/s11747-014-0382-9
[23] Frank, H., Kessler, A., Mitterer, G., & Weismeier-sammer, D. (2012). Learning orientation of SMEs and Its impact on firm performance. Journal of marketing development and competitiveness, 6(3), 29–42.
[24] Larrañeta, B., Zahra, S. A., & González, J. L. G. (2012). Enriching strategic variety in new ventures through external knowledge. Journal of business venturing, 27(4), 401–413.
[25] Shan, P., Song, M., & Ju, X. (2016). Entrepreneurial orientation and performance: Is innovation speed a missing link? Journal of business research, 69(2), 683–690. DOI:10.1016/j.jbusres.2015.08.032
[26] Paradkar, A., Knight, J., & Hansen, P. (2015). Innovation in start-ups: Ideas filling the void or ideas devoid of resources and capabilities? Technovation, 41, 1–10. DOI:10.1016/j.technovation.2015.03.004
[27] Gunday, G., Ulusoy, G., Kilic, K., & Alpkan, L. (2011). Effects of innovation types on firm performance. International journal of production economics, 133(2), 662–676. DOI:10.1016/j.ijpe.2011.05.014
[28] Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial marketing management, 31(6), 515–524. DOI:10.1016/S0019-8501(01)00203-6
[29] Wang, C. L., & Ahmed, P. K. (2004). The development and validation of the organisational innovativeness construct using confirmatory factor analysis. European journal of innovation management, 7(4), 303–313. DOI:10.1108/14601060410565056
[30] Prajogo, D. I. (2016). The strategic fit between innovation strategies and business environment in delivering business performance. International journal of production economics, 171, 241–249. DOI:10.1016/j.ijpe.2015.07.037
[31] Wiklund, J., & Shepherd, D. (2003). Knowledge-based resources, entrepreneurial orientation, and the performance of small and medium-sized businesses. Strategic management journal, 24(13), 1307–1314.
[32] Lukman, T., Hackney, R., Popovič, A., Jaklič, J., & Irani, Z. (2011). Business intelligence maturity: The economic transitional context within Slovenia. Information systems management, 28(3), 211–222. DOI:10.1080/10580530.2011.585583
[33] AL-Shubiri, F. N. (2012). Measuring the impact of business intelligence on performance: an empirical study. Polish journal of management studies, 6, 162–173.
[34] Adidam, P. T., Banerjee, M., & Shukla, P. (2012). Competitive intelligence and firm’s performance in emerging markets: An exploratory study in India. Journal of business and industrial marketing, 27(3), 242–254. DOI:10.1108/08858621211207252
[35] Hannula, M., & Pirttimaki, V. (2003). Business intelligence empirical study on the top 50 Finnish companies. Journal of american academy of business, 2(2), 593–599.
[36] Venter, P., & Tustin, D. (2012). The availability and use of competitive and business intelligence in South African business organisations. Southern african business review, 13(2), 88–117.
[37] Hoppe, M., Hamrefors, S., & Soilen, K. S. (2009). Competitive intelligence: competing, consuming and collaborating in a flat world. Proceedings of the third european competitive intelligence symposium.
[38] Chang, Y. W., Hsu, P. Y., & Wu, Z. Y. (2015). Exploring managers’ intention to use business intelligence: The role of motivations. Behaviour and information technology, 34(3), 273–285. DOI:10.1080/0144929X.2014.968208
[39] Berndtsson, B. (2015). A Brunn–Minkowski type inequality for Fano manifolds and some uniqueness theorems in Kähler geometry. Inventiones mathematicae, 200(1), 149–200. DOI:10.1007/s00222-014-0532-1
[40] Shollo, A., & Galliers, R. D. (2016). Towards an understanding of the role of business intelligence systems in organisational knowing. Information systems journal, 26(4), 339–367. DOI:10.1111/isj.12071
[41] Bruyat, C., & Julien, P. A. (2001). Defining the field of research in entrepreneurship. Journal of business venturing, 16(2), 165–180. DOI:10.1016/S0883-9026(99)00043-9
[42] Wang, C. L. (2008). Entrepreneurial orientation, learning orientation, and firm performance. Entrepreneurship theory and practice, 32(4), 635–657.
[43] Dhliwayo, S. (2014). Entrepreneurship and competitive strategy: an integrative approach. Journal of entrepreneurship, 23(1), 115–135. DOI:10.1177/0971355713513356
[44] Cepeda-Carrion, G., Cegarra-Navarro, J. G., & Jimenez-Jimenez, D. (2012). The effect of absorptive capacity on innovativeness: Context and information systems capability as catalysts. British journal of management, 23(1), 110–129. DOI:10.1111/j.1467-8551.2010.00725.x
[45] Sangar, A. B., & Iahad, N. B. A. (2013). Critical Factors that Affect the Success of Business Intelligence Systems (BIS) Implementation in an Organization. International journal of scientific & technology research, 2(2), 176–180.
[46] Martini, A., Neirotti, P., & Appio, F. P. (2017). Knowledge searching, integrating and performing: always a tuned trio for innovation? Long range planning, 50(2), 200–220. DOI:10.1016/j.lrp.2015.12.020
[47] Hani, E. H. (2021). The Effect Of Key Business Success Factors On Start-Up Performance. Network interlligence studies, 11(18), 117–129.
[48] Garson, J. . (2018). Smart PLS partial least squares, structural equation and regression models. Mehregan Ghalam Publications.
[49] Ucbasaran, D., Westhead, P., & Wright, M. (2009). The extent and nature of opportunity identification by experienced entrepreneurs. Journal of business venturing, 24(2), 99–115. DOI:10.1016/j.jbusvent.2008.01.008
[50] Kock, N. (2021). Harman’s single factor test in PLS-SEM: Checking for common method bias. Data analysis perspectives journal, 2(2), 1–6.
[51] Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. Handbook of partial least squares, 47–82. DOI:10.1007/978-3-540-32827-8_3
[52] Joreskog, K. G. (1982). The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects. Systems under indirect observation, part i, 263–270.
[53] Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European journal of marketing, 53(11), 2322–2347. DOI:10.1108/EJM-02-2019-0189
[54] Desai, M., & Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical ehealth, 4, 1–11. DOI:10.1016/j.ceh.2020.11.002
)