Derechos de autor 2020 Investigación e Innovación en Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Aprendizaje automático y PYMES: Oportunidades para el mejoramiento del proceso de toma de decisiones
Corresponding Author(s) : Tomás José Fontalvo Herrera
Investigación e Innovación en Ingenierías,
Vol. 8 Núm. 1 (2020): Enero - Junio
Resumen
Objetivo: Revisar que aprendizaje automático está transformando la forma que las grandes empresas desarrollan las relaciones con sus clientes, diseñan sus productos y gestionan los recursos humanos. Lo anterior como resultado de una transformación digital que consecuentemente generará ventajas competitivas para aquellas empresas que rápidamente puedan adaptar sus estrategias de negocios a los últimos desarrollos tecnológicos. Sin embargo, está demostrado que las PYMES acogen lentamente las innovaciones tecnológicas, asumiendo de esta forma un riesgo de perder las oportunidades que el Aprendizaje Automático les ofrece. Resultados y Conclusiones: Se identificaron y describieron las oportunidades que el Aprendizaje Automático ofrece a las PYMES, relacionando las dimensiones de negocio y tecnología. En primer lugar, se desarrolla una revisión de literatura. Después, se describe en detalle las diferentes perspectivas en las cuales la adopción de un proceso de toma de decisiones mejorado conlleva a un mejoramiento del desempeño organizacional. Finalmente se dan recomendaciones para el desarrollo de procesos de toma decisiones basados en Aprendizaje Automático.
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- X. Parra, X. Tort-Martorell, C. Ruiz Viñals, and F. Álvarez Gómez, “CHROMA: a maturity model for the information-driven decision-making process”, Int. J. Manag. Decis. Mak., vol. 16, n°. 3, pp. 224–242, 2017.
- R. Sharma, S. Mithas, and A. Kankanhalli, “Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations”, Eur. J. Inf. Syst., vol. 23, n°. 4, pp. 433–441, jul. 2014.
- D. A. Shepherd, T. A. Williams, and H. Patzelt, “Thinking about entrepreneurial decision making: Review and research agenda”, J. Manag., vol. 41, n°. 1, pp. 11–46, 2015.
- N. G. Shepherd and J. M. Rudd, “The influence of context on the strategic decision-making process: A review of the literature”, Int. J. Manag. Rev., vol. 16, n°. 3, pp. 340–364, 2014.
- M. Steinrücke and W. Albrecht, “Quantitative decision support for network integration of start-up companies”, Int. J. Glob. Small Bus., vol. 8, n°. 1, p. 73, 2016.
- B. Francioni, M. Cioppi, and F. Musso, “International decision processes within SMEs: the influence of biological sex and stereotypical gender roles”, Int. J. Glob. Small Bus., vol. 9, n°. 4, p. 203, 2017.
- D. Ariely, Predictably irrational. New York: HarperCollins, 2008.
- M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects”, Science, vol. 349, n°. 6245, pp. 255–260, 2015.
- J. Burrell, “How the machine ‘thinks’: Understanding opacity in machine learning algorithms”, Big Data Soc., vol. 3, n°. 1, pp. 1–12, 2016.
- M. Janssen, H. van der Voort, and A. Wahyudi, “Factors influencing big data decision-making quality”, J. Bus. Res., vol. 70, pp. 338–345, 2017.
- S. Erevelles, N. Fukawa, and L. Swayne, “Big Data consumer analytics and the transformation of marketing”, J. Bus. Res., vol. 69, n°. 2, pp. 897–904, 2016.
- C. Jooste, J. Van Biljon, and J. Mentz, “Usability evaluation for Business Intelligence applications: A user support perspective”, South Afr. Comput. J., vol. 53, n°. Special issue 1, pp. 32–44, 2014.
- S. Onyeiwu and A. Ganguly, “Managing competitive pressures in the globalisation era: the case of tooling and machining in Pennsylvania”, Int. J. Glob. Small Bus., vol. 8, n°. 2, p. 158, 2016.
- R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, "Machine learning: An artificial intelligence approach", Springer Science & Business Media, 2013.
- H. Chen, R. H. Chiang, and V. C. Storey, “Business intelligence and analytics: from big data to big impact”, MIS Q., pp. 1165–1188, 2012.
- J.G. Dy and C. E. Brodley, “Feature selection for unsupervised learning”, J. Mach. Learn. Res., vol. 5, pp. 845–889, 2004.
- C. S. Hilas and P. A. Mastorocostas, “An application of supervised and unsupervised learning approaches to telecommunications fraud detection”, Knowl.-Based Syst., vol. 21, n°. 7, pp. 721–726, 2008.
- S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques”, Emerg. Artif. Intell. Appl. Comput. Eng., vol. 160, pp. 3–24, 2007.
- R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning”, Proceedings of the 25th international conference on Machine learning, 2008, pp. 160–167.
- G.B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification”, IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 42, n°. 2, pp. 513–529, 2012.
- A. Chalfin, O. Danieli, A. Hillis, Z. Jelveh, M. Luca., J. Ludwig et al., “Productivity and selection of human capital with machine learning”, Am. Econ. Rev., vol. 106, n°. 5, pp. 124–27, 2016.
- M. Franco and M. Lucas, “Family management of SMEs: an organisational culture perspective”, Int. J. Glob. Small Bus., vol. 8, n°. 1, p. 18, 2016.
- T. H. Davenport and D. J. Patil, “Data Scientist: The Sexiest Job of the 21st Century”, Harvard Business Review, 01-oct-2012. [on line]. Available in: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century. [Consultado: 30-jul-2018].
- W. M. Van der Aalst, “Data scientist: The engineer of the future”, Enterprise interoperability VI, Springer, 2014, pp. 13–26.
- A. De Massis and J. Kotlar, “The case study method in family business research: Guidelines for qualitative scholarship”, J. Fam. Bus. Strategy, vol. 5, n°. 1, pp. 15–29, 2014.
- J. Dean, "Big data, data mining, and machine learning: value creation for business leaders and practitioners", John Wiley & Sons, 2014.
- J. D. Kelleher, B. Mac Namee, and A. D’Arcy, Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press, 2015.
- M. Deloof and M. La Rocca, “Local financial development and the trade credit policy of Italian SMEs”, Small Bus. Econ., vol. 44, n°. 4, pp. 905–924, 2015.
- N. Lee, H. Sameen, and M. Cowling, “Access to finance for innovative SMEs since the financial crisis”, Res. Policy, vol. 44, n°. 2, pp. 370–380, 2015.
- F. Margaretha y N. Supartika, “Factors affecting profitability of small medium enterprises (SMEs) firm listed in Indonesia Stock Exchange”, J. Econ. Bus. Manag., vol. 4, n°. 2, pp. 132–137, 2016.
- P. Quartey, E. Turkson, J. Y. Abor, and A. M. Iddrisu, “Financing the growth of SMEs in Africa: What are the contraints to SME financing within ECOWAS?”, Rev. Dev. Finance, vol. 7, n°. 1, pp. 18–28, 2017.
- S. Mullainathan and J. Spiess, “Machine learning: an applied econometric approach”, J. Econ. Perspect., vol. 31, n°. 2, pp. 87–106, 2017.
- E. Brynjolfsson, L. M. Hitt, and H. H. Kim, “Strength in numbers: How does data-driven decisionmaking affect firm performance?”, 2011.
- S. Ransbotham, D. Kiron, and P. K. Prentice, “Beyond the hype: the hard work behind analytics success”, MIT Sloan Manag. Rev., vol. 57, n°. 3, 2016.
- M. Crawford, T. M. Khoshgoftaar, J. D. Prusa, A. N. Richter, and H. Al Najada, “Survey of review spam detection using machine learning techniques”, J. Big Data, vol. 2, n°. 1, p. 23, 2015.
- M. Zareapoor and P. Shamsolmoali, “Application of credit card fraud detection: Based on bagging ensemble classifier”, Procedia Comput. Sci., vol. 48, pp. 679–685, 2015.
- C. Hines and A. Youssef, “Machine Learning Applied to Point-of-Sale Fraud Detection”, International Conference on Machine Learning and Data Mining in Pattern Recognition, 2018, pp. 283–295.
- E. C. Gbandi and G. Amissah, “Financing options for small and medium enterprises (SMEs) in Nigeria”, Eur. Sci. J. ESJ, vol. 10, n°. 1, 2014.
- X. Y. Leung, B. Bai, and K. A. Stahura, “The marketing effectiveness of social media in the hotel industry: A comparison of Facebook and Twitter”, J. Hosp. Tour. Res., vol. 39, n°. 2, pp. 147–169, 2015.
- K. Yawised, W. O’Donohue, and N. Ann Torugsa, “Exploring social customer relationship management in Australian small and medium enterprises”, Int. J. Glob. Small Bus., vol. 9, n°. 4, p. 222, 2017.
- S. V. Wawre and S. N. Deshmukh, “Sentiment classification using machine learning techniques”, Int. J. Sci. Res. IJSR, vol. 5, n°. 4, pp. 819–821, 2016.
- R. Dubey, A. Gunasekaran, S. J. Childe, S. F. Wamba, and T. Papadopoulos, “The impact of big data on world-class sustainable manufacturing”, Int. J. Adv. Manuf. Technol., vol. 84, n°. 1–4, pp. 631–645, 2016.
- M. Rozhkov, B. C. F. Cheung, and E. Tsui, “Workplace context and its effect on individual competencies and performance in work teams”, Int. J. Bus. Perform. Manag., vol. 18, n°. 1, p. 49, 2017.
- M. Heo and K. J. Lee, “Chatbot as a New Business Communication Tool: The Case of Naver TalkTalk”, Bus. Commun. Res. Pract., vol. 1, n°. 1, pp. 41–45, 2018.
- E. Ascarza, S.A. Neslin, O. Netzer, Z. Anderson, P.S. Fader., S. Gupta, et al., “In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions”, Cust. Needs Solut., vol. 5, n°. 1–2, pp. 65–81, 2018.
- S. Akter y S. F. Wamba, “Big data analytics in E-commerce: a systematic review and agenda for future research”, Electron. Mark., vol. 26, n°. 2, pp. 173–194, 2016.
- D. Larson and V. Chang, “A review and future direction of agile, business intelligence, analytics and data science”, Int. J. Inf. Manag., vol. 36, n°. 5, pp. 700–710, 2016.
- M. Fiore, I. Monasterolo, A. Jones, and F. Contò, “Understanding limits to data informative power for sustainable food policies in transition and post-transition countries”, Int. J. Glob. Small Bus., vol. 7, n°. 3/4, p. 300, 2015.
- W.-C. Lin, S.-W. Ke, and C.-F. Tsai, “Top 10 data mining techniques in business applications: a brief survey”, Kybernetes, vol. 46, n°. 7, pp. 1158–1170, 2017.
- S. Amershi, J. Fogarty, and D. Weld, “Regroup: Interactive machine learning for on-demand group creation in social networks”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2012, pp. 21–30.
Referencias
X. Parra, X. Tort-Martorell, C. Ruiz Viñals, and F. Álvarez Gómez, “CHROMA: a maturity model for the information-driven decision-making process”, Int. J. Manag. Decis. Mak., vol. 16, n°. 3, pp. 224–242, 2017.
R. Sharma, S. Mithas, and A. Kankanhalli, “Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations”, Eur. J. Inf. Syst., vol. 23, n°. 4, pp. 433–441, jul. 2014.
D. A. Shepherd, T. A. Williams, and H. Patzelt, “Thinking about entrepreneurial decision making: Review and research agenda”, J. Manag., vol. 41, n°. 1, pp. 11–46, 2015.
N. G. Shepherd and J. M. Rudd, “The influence of context on the strategic decision-making process: A review of the literature”, Int. J. Manag. Rev., vol. 16, n°. 3, pp. 340–364, 2014.
M. Steinrücke and W. Albrecht, “Quantitative decision support for network integration of start-up companies”, Int. J. Glob. Small Bus., vol. 8, n°. 1, p. 73, 2016.
B. Francioni, M. Cioppi, and F. Musso, “International decision processes within SMEs: the influence of biological sex and stereotypical gender roles”, Int. J. Glob. Small Bus., vol. 9, n°. 4, p. 203, 2017.
D. Ariely, Predictably irrational. New York: HarperCollins, 2008.
M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects”, Science, vol. 349, n°. 6245, pp. 255–260, 2015.
J. Burrell, “How the machine ‘thinks’: Understanding opacity in machine learning algorithms”, Big Data Soc., vol. 3, n°. 1, pp. 1–12, 2016.
M. Janssen, H. van der Voort, and A. Wahyudi, “Factors influencing big data decision-making quality”, J. Bus. Res., vol. 70, pp. 338–345, 2017.
S. Erevelles, N. Fukawa, and L. Swayne, “Big Data consumer analytics and the transformation of marketing”, J. Bus. Res., vol. 69, n°. 2, pp. 897–904, 2016.
C. Jooste, J. Van Biljon, and J. Mentz, “Usability evaluation for Business Intelligence applications: A user support perspective”, South Afr. Comput. J., vol. 53, n°. Special issue 1, pp. 32–44, 2014.
S. Onyeiwu and A. Ganguly, “Managing competitive pressures in the globalisation era: the case of tooling and machining in Pennsylvania”, Int. J. Glob. Small Bus., vol. 8, n°. 2, p. 158, 2016.
R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, "Machine learning: An artificial intelligence approach", Springer Science & Business Media, 2013.
H. Chen, R. H. Chiang, and V. C. Storey, “Business intelligence and analytics: from big data to big impact”, MIS Q., pp. 1165–1188, 2012.
J.G. Dy and C. E. Brodley, “Feature selection for unsupervised learning”, J. Mach. Learn. Res., vol. 5, pp. 845–889, 2004.
C. S. Hilas and P. A. Mastorocostas, “An application of supervised and unsupervised learning approaches to telecommunications fraud detection”, Knowl.-Based Syst., vol. 21, n°. 7, pp. 721–726, 2008.
S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, “Supervised machine learning: A review of classification techniques”, Emerg. Artif. Intell. Appl. Comput. Eng., vol. 160, pp. 3–24, 2007.
R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning”, Proceedings of the 25th international conference on Machine learning, 2008, pp. 160–167.
G.B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification”, IEEE Trans. Syst. Man Cybern. Part B Cybern., vol. 42, n°. 2, pp. 513–529, 2012.
A. Chalfin, O. Danieli, A. Hillis, Z. Jelveh, M. Luca., J. Ludwig et al., “Productivity and selection of human capital with machine learning”, Am. Econ. Rev., vol. 106, n°. 5, pp. 124–27, 2016.
M. Franco and M. Lucas, “Family management of SMEs: an organisational culture perspective”, Int. J. Glob. Small Bus., vol. 8, n°. 1, p. 18, 2016.
T. H. Davenport and D. J. Patil, “Data Scientist: The Sexiest Job of the 21st Century”, Harvard Business Review, 01-oct-2012. [on line]. Available in: https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century. [Consultado: 30-jul-2018].
W. M. Van der Aalst, “Data scientist: The engineer of the future”, Enterprise interoperability VI, Springer, 2014, pp. 13–26.
A. De Massis and J. Kotlar, “The case study method in family business research: Guidelines for qualitative scholarship”, J. Fam. Bus. Strategy, vol. 5, n°. 1, pp. 15–29, 2014.
J. Dean, "Big data, data mining, and machine learning: value creation for business leaders and practitioners", John Wiley & Sons, 2014.
J. D. Kelleher, B. Mac Namee, and A. D’Arcy, Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. MIT Press, 2015.
M. Deloof and M. La Rocca, “Local financial development and the trade credit policy of Italian SMEs”, Small Bus. Econ., vol. 44, n°. 4, pp. 905–924, 2015.
N. Lee, H. Sameen, and M. Cowling, “Access to finance for innovative SMEs since the financial crisis”, Res. Policy, vol. 44, n°. 2, pp. 370–380, 2015.
F. Margaretha y N. Supartika, “Factors affecting profitability of small medium enterprises (SMEs) firm listed in Indonesia Stock Exchange”, J. Econ. Bus. Manag., vol. 4, n°. 2, pp. 132–137, 2016.
P. Quartey, E. Turkson, J. Y. Abor, and A. M. Iddrisu, “Financing the growth of SMEs in Africa: What are the contraints to SME financing within ECOWAS?”, Rev. Dev. Finance, vol. 7, n°. 1, pp. 18–28, 2017.
S. Mullainathan and J. Spiess, “Machine learning: an applied econometric approach”, J. Econ. Perspect., vol. 31, n°. 2, pp. 87–106, 2017.
E. Brynjolfsson, L. M. Hitt, and H. H. Kim, “Strength in numbers: How does data-driven decisionmaking affect firm performance?”, 2011.
S. Ransbotham, D. Kiron, and P. K. Prentice, “Beyond the hype: the hard work behind analytics success”, MIT Sloan Manag. Rev., vol. 57, n°. 3, 2016.
M. Crawford, T. M. Khoshgoftaar, J. D. Prusa, A. N. Richter, and H. Al Najada, “Survey of review spam detection using machine learning techniques”, J. Big Data, vol. 2, n°. 1, p. 23, 2015.
M. Zareapoor and P. Shamsolmoali, “Application of credit card fraud detection: Based on bagging ensemble classifier”, Procedia Comput. Sci., vol. 48, pp. 679–685, 2015.
C. Hines and A. Youssef, “Machine Learning Applied to Point-of-Sale Fraud Detection”, International Conference on Machine Learning and Data Mining in Pattern Recognition, 2018, pp. 283–295.
E. C. Gbandi and G. Amissah, “Financing options for small and medium enterprises (SMEs) in Nigeria”, Eur. Sci. J. ESJ, vol. 10, n°. 1, 2014.
X. Y. Leung, B. Bai, and K. A. Stahura, “The marketing effectiveness of social media in the hotel industry: A comparison of Facebook and Twitter”, J. Hosp. Tour. Res., vol. 39, n°. 2, pp. 147–169, 2015.
K. Yawised, W. O’Donohue, and N. Ann Torugsa, “Exploring social customer relationship management in Australian small and medium enterprises”, Int. J. Glob. Small Bus., vol. 9, n°. 4, p. 222, 2017.
S. V. Wawre and S. N. Deshmukh, “Sentiment classification using machine learning techniques”, Int. J. Sci. Res. IJSR, vol. 5, n°. 4, pp. 819–821, 2016.
R. Dubey, A. Gunasekaran, S. J. Childe, S. F. Wamba, and T. Papadopoulos, “The impact of big data on world-class sustainable manufacturing”, Int. J. Adv. Manuf. Technol., vol. 84, n°. 1–4, pp. 631–645, 2016.
M. Rozhkov, B. C. F. Cheung, and E. Tsui, “Workplace context and its effect on individual competencies and performance in work teams”, Int. J. Bus. Perform. Manag., vol. 18, n°. 1, p. 49, 2017.
M. Heo and K. J. Lee, “Chatbot as a New Business Communication Tool: The Case of Naver TalkTalk”, Bus. Commun. Res. Pract., vol. 1, n°. 1, pp. 41–45, 2018.
E. Ascarza, S.A. Neslin, O. Netzer, Z. Anderson, P.S. Fader., S. Gupta, et al., “In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions”, Cust. Needs Solut., vol. 5, n°. 1–2, pp. 65–81, 2018.
S. Akter y S. F. Wamba, “Big data analytics in E-commerce: a systematic review and agenda for future research”, Electron. Mark., vol. 26, n°. 2, pp. 173–194, 2016.
D. Larson and V. Chang, “A review and future direction of agile, business intelligence, analytics and data science”, Int. J. Inf. Manag., vol. 36, n°. 5, pp. 700–710, 2016.
M. Fiore, I. Monasterolo, A. Jones, and F. Contò, “Understanding limits to data informative power for sustainable food policies in transition and post-transition countries”, Int. J. Glob. Small Bus., vol. 7, n°. 3/4, p. 300, 2015.
W.-C. Lin, S.-W. Ke, and C.-F. Tsai, “Top 10 data mining techniques in business applications: a brief survey”, Kybernetes, vol. 46, n°. 7, pp. 1158–1170, 2017.
S. Amershi, J. Fogarty, and D. Weld, “Regroup: Interactive machine learning for on-demand group creation in social networks”, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2012, pp. 21–30.