Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal

ilustraciones

Autores:: Pérez Rave, Jorge Iván

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal
dc.title.translated.eng.fl_str_mv	Data/Text Mining–Structural–Multicriteria as a strategic resource in personnel selection
title	Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal
spellingShingle	Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal 620 - Ingeniería y operaciones afines 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Minería de datos Selección de personal Análisis de decisión multicriterio Minería de texto Minería de datos Modelos de ecuaciones estructurales Selección de personal Constructos psicológicos Constructos psicológicos Ciencia de datos Text mining Text analytics Data mining Data analytics Data science
title_short	Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal
title_full	Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal
title_fullStr	Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal
title_full_unstemmed	Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal
title_sort	Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal
dc.creator.fl_str_mv	Pérez Rave, Jorge Iván
dc.contributor.advisor.none.fl_str_mv	Jaramillo Alvarez, Gloria Patricia Juan Carlos, Correa Morales
dc.contributor.author.none.fl_str_mv	Pérez Rave, Jorge Iván
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
topic	620 - Ingeniería y operaciones afines 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Minería de datos Selección de personal Análisis de decisión multicriterio Minería de texto Minería de datos Modelos de ecuaciones estructurales Selección de personal Constructos psicológicos Constructos psicológicos Ciencia de datos Text mining Text analytics Data mining Data analytics Data science
dc.subject.lemb.none.fl_str_mv	Minería de datos Selección de personal
dc.subject.proposal.spa.fl_str_mv	Análisis de decisión multicriterio Minería de texto Minería de datos Modelos de ecuaciones estructurales Selección de personal Constructos psicológicos Constructos psicológicos Ciencia de datos
dc.subject.proposal.eng.fl_str_mv	Text mining Text analytics Data mining Data analytics Data science
description	ilustraciones
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-09-09T15:37:23Z
dc.date.available.none.fl_str_mv	2021-09-09T15:37:23Z
dc.date.issued.none.fl_str_mv	2021-09
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80145
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80145 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Abbe, A., Grouin, C., Zweigenbaum, P., & Falissard, B. (2016). Text mining applications in psychiatry: a systematic literature review. International journal of methods in psychiatric research, 25(2), 86-100. Abuazab, A., Selamat, H., & Yusoff, R. (2017). Challenge of Text Mining in Clinical Decision Support System. Journal of Engineering and Applied Sciences, 12(20), 5261-5273. Aceto, G., Persico, V., & Pescapé, A. (2020). Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration, 18, 100129. Adejuwon, A., & Mosavi, A. (2010). Domain driven data mining–Application to business. IJCSI International Journal of Computer Science Issues, 7(4), 41-44. Aghdaie, M., Zolfani, S., & Zavadskas, E. (2013). A hybrid approach for market segmentation and market segment evaluation and selection: an integration of data mining and MADM. Transformations in Business & Economics, 12(2B), 431-459. Ahmadi, H., Rad, M., Nazari, M., Nilashi, M., & Ibrahim, O. (2014). Evaluating the factors affecting the implementation of hospital information system (HIS) using AHP method. Life Science Journal, 11(3), 202-207. Akins, R. B., Tolson, H., & Cole, B. R. (2005). Stability of response characteristics of a Delphi panel: application of bootstrap data expansion. BMC medical research methodology, 5(1), 1-12. Al-Agili, M. Z., Mamat, M. B., Abdullah, L., & Maad, H. A. (2012). The factors influence students’ achievement in mathematics: A case for Libyan's students. World Applied Sciences Journal, 17(9), 1224-1230. Aldás, J., & Uriel, E. (2017). Análisis multivariante aplicado con R. (2da ed.). Alfacentauro, Parainfo. Al-Moslmi, T., Albared, M., Al-Shabi, A., Abdullah, S., & Omar, N. (2019). A Comparative Study of Co-Occurrence Strategies for Building A Cross-Domain Sentiment Thesaurus. In 2019 First International Conference of Intelligent Computing and Engineering. Hadhramout, Yemen. Alonso, P., Moscoso, S., & Cuadrado, D. (2015). Procedimientos de selección de personal en pequeñas y medianas empresas españolas. Revista de Psicología del Trabajo y de las Organizaciones, 31(2), 79-89. Álvarez-Sánchez, A. (2016). Inconsistencia en la selección de personal en la empresa agrícola del Dr. Omar Paladines de la ciudad de Machala (Bachelor's tesis). Universidad Técnica de Machala. Machala, Ecuador. 137 American Educational Research Association (AERA), American Psychological Association (APA), & National Council on Measurement in Education (NCME). (1999). Standards for educational and psychological testing. American Educational Research Association. Aryee, S., Walumbwa, F. O., Zhou, Q., & Hartnell, C. A. (2012). Transformational leadership, innovative behavior, and task performance: Test of mediation and moderation processes. Human Performance, 25(1), 1-25. Athauda, R., Tissera, M., & Fernando, C. (2009). Data mining applications: Promise and challenges. In Data Mining and Knowledge Discovery in Real Life Applications. InTech. Avolio, B. J., Bass, B. M., & Jung, D. I. (1999). Re‐examining the components of transformational and transactional leadership using the Multifactor Leadership. Journal of occupational and organizational psychology, 72(4), 441-462. Avolio, B., & Bass, B. (1991). The full-range of leadership development. Center for Leadership Studies. Binghamton, NY. Avolio, B. & Bass, B. (2004). Multifactor Leadership Questionnaire (3rd ed.). Manual and Sample Set. University of Nebraska and SUNY Binghamton, CA: Mind Garden. Balan, U., & Mathew, S. (2015). Online word of mouth using text mining: A review of literature and future directions. In 2015 IEEE Computational Intelligence: Theories, Applications and Future Directions (WCI), 1-6. IEEE. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120. Bastida, M., & Moscoso, S. (2015). Steel barrier: Legal implications from a gender equal opportunity perspective. The European Journal of Psychology Applied to Legal Context, 7(1), 13-22. Batista-Foguet, J. M., & Coenders-Gallart, G. (2000). Modelos de ecuaciones estructurales, cuadernos de estadística. La Muralla. Madrid, España. Beier, M., & Ackerman, P. (2012). Time in personnel selection. In N. Schmitt (Ed.), Oxford library of psychology. The Oxford handbook of personnel assessment and selection (721–739). Oxford University Press. Bell, E., Bryman, A., & Harley, B. (2018). Business research methods. Oxford university press. Bentler, P. M. (2007). Covariance structure models for maximal reliability of unit-weighted composites. In Handbook of latent variable and related models (1-19). North-Holland. Bernal-Guerrero, A., Cárdenas-Gutiérrez, A. R., & Montoro-Fernández, E. (2020). Basic business knowledge scale for secondary education students. Development and validation with Spanish teenagers. PloS one, 15(7), e0235681. Binning, J., & Barrett, G. (1989). Validity of personnel decisions: A conceptual analysis of the inferential and evidential bases. Journal of Applied Psychology, 74(3), 478. 138 Boran, F., Genç, S., & Akay, D. (2011). Personnel selection based on intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing & Service Industries, 21(5), 493-503. Campbell, D., & Johansson, C. (1966). Academic interests, scholastic achievements and eventual occupations. Journal of Counseling Psychology, 13(4), 416. Campion, M. C., Campion, M. A., Campion, E. D., & Reider, M. H. (2016). Initial investigation into computer scoring of candidate essays for personnel selection. Journal of Applied Psychology, 101(7), 958. Carless, S. A. (2001). Assessing the discriminant validity of the Leadership Practices Inventory. Journal of Occupational and Organizational Psychology, 74(2), 233-239. Carreras, E. (2006). El pronóstico en modelos estructurales: predecir la satisfacción del cliente. En: Lévy, J., & Varela, J. (2006). Modelización con estructuras de covarianzas en ciencias sociales. Temas esenciales, avanzados y aportaciones especiales, 453. Chavira, D., Lopez, J., Noriega, J., & Retamales, J. (2017). A multicriteria outranking modeling approach for personnel selection. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). IEEE. Chen, C. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9. Cheng, E., & Li, H. (2005). Analytic network process applied to project selection. Journal of construction engineering and management, 131(4), 459-466. Chiavenato, I. (2007). Administración de recursos humanos: el capital humano de las organizaciones. Octava edición, McGraw-Hill. Chien, C., & Chen, L. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with applications, 34(1), 280290. Chomsky, N. (1955). The logical structure of linguistic theory. Manuscript, Harvard University. Published in 1975, New York: Plenum. Colmenero, V. (2015). Nuevas tendencias en selección y reclutamiento de personal: el caso de BBVA (Trabajo fin de grado en relaciones laborales y recursos humanos, Facultad de derecho). Universidad de Almería, http://repositorio.ual.es/bitstream/handle/10835/3701/2584_PROYECTO%20ULTIMORevisado2.pdf?sequence=1 Cooley, R., Mobasher, B., & Srivastava, J. (1997). Web mining: Information and pattern discovery on the world wide web. In Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence (558-567). IEEE. Costa Jr, P. T., & McCrae, R. R. (2008). The Revised NEO Personality Inventory (NEO-PI-R). Sage Publications, Inc. 139 Credé, M., & Harms, P. D. (2015). 25 years of higher‐order confirmatory factor analysis in the organizational sciences: A critical review and development of reporting recommendations. Journal of Organizational Behavior, 36(6), 845-872. Crestani, F. (1997). Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11(6), 453-482. Cronbach, L. (1968). Fundamentos de la exploración psicológica. Ed. Revolucionaría, Instituto cubano del libro. La Habana, Cuba. Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: From intuition to institution. Academy of management review, 24(3), 522-537. Dağdeviren, M. (2010). A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems. Journal of Intelligent manufacturing, 21(4), 451-460. De Bruijn, L., & Martin, J. (2002). Literature mining in molecular biology. In Proceedings of the EFMI Workshop on Natural Language Processing in Biomedical Applications (1-5). De Raad, B. (2000). The big five personality factors: the psycholexical approach to personality. Hogrefe & Huber Publishers. De Soete, B., Lievens, F., & Druart, C. (2013). Strategies for dealing with the diversity-validity dilemma in personnel selection: Where are we and where should we go? Revista de Psicología del Trabajo y de las Organizaciones, 29(1), 3-12. Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The Sage handbook of organizational research methods (p. 671–689). Sage Publications Ltd. Dhliwayo, P., & Coetzee, M. (2020). Cognitive intelligence, emotional intelligence and personality types as predictors of job performance: Exploring a model for personnel selection. SA Journal of Human Resource Management, 18, 13. Díaz-Bravo, L., Torruco-García, U., Martínez-Hernández, M., & Varela-Ruiz, M. (2013). La entrevista, recurso flexible y dinámico. Investigación en educación médica, 2(7), 162-167. Dickens, W., & Flynn, J. (2001). Heritability estimates versus large environmental effects: the IQ paradox resolved. Psychological review, 108(2), 346. Digman, J. M. (1990). Personality structure: emergence of the Five Factors Model. Annual Review of Psychology, 41,417-440. Dinno, A. (2018). paran: Horn's Test of Principal Components/Factors. R package version 1.5.2. https://CRAN.R-project.org/package=paran. Espinosa, K. (2017). An Empirical Study on End-to-End Sentence Modelling. In Proceedings of ACL 2017. Student Research Workshop, 128-135. 140 Esty, K., Griffin, R., & Schorr-Hirsh, M. (1995). Workplace diversity. A managers guide to solving problems and turning diversity into a competitive advantage. Avon, MA: Adams Media Corporation. Evers, A., Anderson, N., & Smit-Voskuijl, O. (2005). The Blackwell handbook of personnel selection (Eds., Vol. 1). John Wiley & Sons. Farahi, S., Abad, M., Farahi, S., & Abadi, M. (2014). Choice theory and effective learning. International Journal of Psychology and Behavioral Research, Special issue, 1(14), 325-329. Farr, J., & Tippins, N. (2017). Handbook of employee selection. Taylor & Francis. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 36–53. Feinerer, I., & Hornik, K. (2018). tm: Text Mining Package. R package version 0.7-6. https://CRAN.R-project.org/package=tm Fenner, C. J., & Piotrowski, C. (2018). Communication theory in leadership research: Which theories predominate. Psychology & Education: An Interdisciplinary Journal, 55(2), 10-18. Firth, J. R. (1957). A synopsis of linguistic theory, 1930-1955. Studies in linguistic analysis, Special Vol., Philological Society, 1-32. Fontana, M. E., & Cavalcante, C. A. V. (2014). Use of Promethee method to determine the best alternative for warehouse storage location assignment. The International Journal of Advanced Manufacturing Technology, 70(9-12), 1615-1624. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18, 39–50. Fteimi, N., Basten, D., & Lehner, F. (2019). Advancing Automated Content Analysis in Knowledge Management Research: The Use of Compound Concepts. International Journal of Knowledge Management (IJKM), 15(1), 53-68. Fyshe, A., Sudre, G., Wehbe, L., Rafidi, N., & Mitchell, T. M. (2019). The lexical semantics of adjective–noun phrases in the human brain. Human brain mapping, 40(15), 4457-4469. Galinium, M. (2017). Integrating data mining technique and AHP in market analysis to propose new product development in real estate. In IOP Conference Series: Materials Science and Engineering, (166(1), 012030). IOP Publishing. García, T. (2003). Etapas del proceso investigador. Instrumentación. El cuestionario como instrumento de investigación/evaluación. España: Almendralejo. Garten, J., Hoover, J., Johnson, K. M., Boghrati, R., Iskiwitch, C., & Dehghani, M. (2018). Dictionaries and distributions: Combining expert knowledge and large-scale textual data content analysis. Behavior Research Methods, 50(1), 344-361. Gibney, R., & Shang, J. (2007). Decision making in academia: A case of the dean selection process. Mathematical and Computer Modelling, 46(7-8), 1030-1040. 141 Goldberg, L. (1993). The structure of phenotypic personality traits. American psychologist, 48(1), 26-34. Golec, A., & Kahya, E. (2007). A fuzzy model for competency-based employee evaluation and selection. Computers & Industrial Engineering, 52(1), 143-161. González, F. (2003). Apuntes acerca de algunos conceptos básicos de investigación cualitativa. Sapiens, Revista Universitaria de Investigación, 4(1), 107-132. Gottfredson, L. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79-132. Gough, H. G. (1979). A creative personality scale for the adjective check list. Journal of personality and social psychology, 37(8), 1398. Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96, 1029–1046. Gutiérrez, D. D. (2015). Machine learning and data science: an introduction to statistical learning methods with R. Technics Publications. Haan, H., Streb, J., Bien, S., & Rösler, F. (2000). Individual cortical current density reconstructions of the semantic N400 effect: Using a generalized minimum norm model with different constraints (L1 and L2 norm). Human Brain Mapping, 11(3), 178-192. Han, S., & Lee, G. (2016). A Preliminary Study on Text Mining-Based Human Resource Allocation in a Construction Project. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (33, 1). Vilnius Gediminas Technical University, Department of Construction Economics & Property. Harris, C. (2017). Finding the Best Job Applicants for a Job Posting: A Comparison of Human Resources Search Strategies. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (189-194). IEEE. Hart, S. (1971). Axiology: Theory of values. International Phenomenological Society, 32, 29–41. Herpertz, S., Nizielski, S., Hock, M., & Schütz, A. (2016). The relevance of emotional intelligence in personnel selection for high emotional labor jobs. PloS one, 11(4), e0154432. Hjørland, B. (2005). Empiricism, rationalism and positivism in library and information science. Journal of Documentation, 61(1), 130-155. Holland, J. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Psychological Assessment Resources. Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185. Hough, L., & Oswald, F. (2000). Personnel selection: Looking toward the future-Remembering the past. Annual review of psychology, 51(1), 631-664. 142 Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1-55. Hua, L. (2016). A data science approach to pattern discovery in complex structures with applications in bioinformatics. Dissertations. 70. Hwang, C., & Yoon, K. (1981). Multiple attribute decision making. Springer-Verlag. Hyun-Park, S., Seon Shin, W., Hyun-Park, Y., & Lee, Y. (2017). Building a new culture for quality management in the era of the Fourth Industrial Revolution. Total Quality Management & Business Excellence, 28(9-10), 934-945. IBM (2012). IBM SPSS Modeler Text Analytics 15 user’s guide. Armonk, N.Y: Author. Illia, L., Sonpar, K., & Bauer, M. W. (2014). Applying co-occurrence text analysis with ALCESTE to studies of impression management. British Journal of Management, 25, 352-372. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (112, 18). New York: springer. Jayaraj, V., & Mahalakshmi, V. (2015). Augmenting efficiency of recruitment process using IRCF text mining algorithm. Indian Journal of Science and Technology, 8(16). Jereb, E., Rajkovic, U., & Rajkovic, V. (2005). A hierarchical multi‐attribute system approach to personnel selection. International Journal of Selection and Assessment, 13(3), 198-205. Jiménez, J. C., Sánchez, J. G., & Aguilar, F. G. (2006). Guía técnica para la construcción de cuestionarios. Odisea Revista electrónica de pedagogía, 3(6). Jockers, M. (2014). Text analysis with R for students of literature. New York: Springer. Joshi, P. (2017). Artificial intelligence with python. Packt Publishing Ltd. Judge, T. (2009). Core self-evaluations and work success. Current Directions in Psychological Science, 18(1), 58-62. Judge, T., & Kammeyer-Mueller, J. (2007). Personality and career success. Handbook of career studies, 59-78. Kaiser, H. (1970). A second generation Little Jiffy. Psychometrika, 35, 401-15. Kazemzadeh, A., Gibson, J., Georgiou, P., Lee, S., & Narayanan, S. (2016). A Socratic epistemology for verbal emotional intelligence. PeerJ Computer Science, 2, e40. Kelemenis, A., & Askounis, D. (2010). A new TOPSIS-based multi-criteria approach to personnel selection. Expert systems with applications, 37(7), 4999-5008. 143 Khwaja, M. G., Mahmood, S., & Zaman, U. (2020). Examining the Effects of eWOM, Trust Inclination, and Information Adoption on Purchase Intentions in an Accelerated Digital Marketing Context. Information, 11(10), 478. Kishton, J. M., & Widaman, K. F. (1994). Unidimensional versus domain representative parceling of questionnaire items: An empirical example. Educational and psychological measurement, 54(3), 757-765. Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26. Kjellström, S., & Golino, H. (2018). Mining concepts of health responsibility using text mining and exploratory graph analysis. Scandinavian journal of occupational therapy, 1-16. Klein, K., Bliese, P., Kozlowshi, S., Dansereau, F., Gavin, M., Griffin, M., . . ., Bligh, M. (2000). Multilevel analytical techniques: Commonalities, differences, and continuing questions. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (512–553). San Francisco, CA: Jossey-Bass. Korayem, M., Crandall, D., & Abdul-Mageed, M. (2012). Subjectivity and sentiment analysis of arabic: A survey. In International conference on advanced machine learning technologies and applications, 128-139. Springer. Berlin, Heidelberg. Koutra, G., Barbounaki, S., Kardaras, D., & Stalidis, G. (2017). A Multicriteria Model for Personnel Selection in Maritime Industry in Greece. In 2017 IEEE 19th Conference on Business Informatics (CBI) (1, 287-294). IEEE. Krause, A. (2016). Reproducible Research in Real Estate: A Review and an Example. Journal of Real Estate Practice and Education, 19(1), 69-85. Kubr, M. (2007). La consultoría de Empresas. México, D.F.: Limusa Kumar, A., & Paul, A. (2016). Mastering text mining with R. Packt Publishing Ltd. Kumari, M. (2012). Data Driven Data Mining to Domain Driven Data Mining. Global Journal of Computer Science and Technology, 11(23). Kumova, S., & Karaoğlan, B. (2011). Measuring collocation tendency of words. Journal of Quantitative Linguistics, 18(2), 174-187. Kwartler, T. (2017). Text mining in practice with R. John Wiley & Sons. Lahitani, A. R., Permanasari, A. E., & Setiawan, N. A. (2016). Cosine similarity to determine similarity measure: Study case in online essay assessment. In 2016 4th International Conference on Cyber and IT Service Management (1-6). IEEE. Lane, G. G. (2000). The Beginner's Guide to the Bootstrap Method of Resampling. Ledolter, J. (2013). Data mining and business analytics with R. John Wiley & Sons, New Jersey. 144 Lévy, J., & Varela, J. (2006). Modelización con estructuras de covarianzas en ciencias sociales. Temas esenciales, avanzados y aportaciones especiales. A Coruña: Netbiblo. Li, D. (2007). Compromise ratio method for fuzzy multi-attribute group decision making. Applied soft computing, 7(3), 807-817. Liddy, E. D. (1998). Enhanced text retrieval using natural language processing. Bulletin of the American Society for Information Science and Technology, 24(4), 14-16. Lievens, F., Van Dam, K., & Anderson, N. (2002). Recent trends and challenges in personnel selection. Personnel Review, 31(5), 580-601. Liu, X. (2016, June). Institutional Dilemmas and the Development of Chief Information Officer System: a case from Shanghai. In Proceedings of the 17th International Digital Government Research Conference on Digital Government Research (pp. 220-225). Lochter, J. V., Zanetti, R. F., Reller, D., & Almeida, T. A. (2016). Short text opinion detection using ensemble of classifiers and semantic indexing. Expert Systems with Applications, 62, 243-249. Loper, E., & Bird, S. (2002). NLTK: the natural language toolkit. Department of Computer and Information Science. arXiv preprint cs/0205028. Lorenz, T., Heinitz, K., Beer, C., & van Woerkom, M. (2021). Adaptation and validation of a German version of the Strengths Use and Deficit Correction (SUDCO) questionnaire. Plos one, 16(1), e0245127. Loughry, W. J., & McDonough, C. M. (2002). Phenotypic variablity within and between litters of nine-banded armadillos. Southeastern Naturalist, 1(3), 287-298. Lozano, J. F. (2011). Qué es la ética de la empresa. Colección saber. Editorial Proteus. Madrid. Lunbeck, E (2017). The allure of Trump’s narcissism. Available at: www.lareviewofbooks.org/article/the-allure-of-trumps-narcissism/#! (accessed 02 April 2021). Marsh, H., Byrne, B., & Craven, R. (1992). Overcoming problems in confirmatory factor analyses of MTMM data: The correlated uniqueness model and factorial invariance. Multivariate Behavioral Research, 27(4), 489-507. Martin, A., Haxby, J. V., Lalonde, F. M., Wiggs, C. L., & Ungerleider, L. G. (1995). Discrete cortical regions associated with knowledge of color and knowledge of action. Science, 270(5233), 102105. Martínez, M., Hernández M., & Hernández, M. (2006). Psicometría. Madrid: Alianza Editorial. Marttunen, M., Lienert, J., & Belton, V. (2017). Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations. European Journal of Operational Research, 263(1), 1-17. Maykut, P., & Morehouse, R. (2002). Beginning qualitative research: A philosophical and practical guide. Routledge. 145 Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In 2013 Proceedings of Workshop at ICLR. https://arxiv.org/pdf/1301.3781.pdf. Moheno, J. M., & Calzada, M. A. H. (2008). Las prácticas de recursos humanos y su relación con el desempeño percibido de las pequeñas empresas. Forum empresarial, 13(2), 23-43. Universidad de Puerto Rico. Moriyama, I.(1968). Indicators of social change. Problems in the measurements of health status. New York: Russell Sage Foundation; 9, 593. Moulin, B. (1992). Conceptual-graph approach for the representation of temporal information in discourse. Knowledge-Based Systems, 5(3), 183-192. Müller, H., Kenny, E., & Sternberg, P. (2004). Textpresso: an ontology-based information retrieval and extraction system for biological literature. PLoS biology, 2(11), e309, 1984-1998. Munier, N., Hontoria, E., & Jiménez-Sáez, F. (2019). Strategic Approach in Multi-Criteria Decision Making. Springer International Publishing. Murphy, K. (2012). Individual differences. In: Schmitt, N. (2012). Personnel assessment and selection (3, 31-47). Oxford, N.Y Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B., & Turaga, D. S. (2017). Learning Feature Engineering for Classification. In IJCAI Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) (2529-2535). Neisser, U., Boodoo, G., Bouchard Jr, T., Boykin, A., Brody, N., Ceci, S., Ha, I., Diane, F., Loehlin, J., Perloff, R., Sternberg, R., & Urbina, S. (1996). Intelligence: Knowns and unknowns. American psychologist, 51(2), 77. Ngai, E., & Lee, P. (2016). A Review of the literature on Applications of Text Mining in Policy Making. In PACIS 2016 Proceedings (343). Noboa, J. E., Zurita, D. G., Barros, V., Arízabal, S. C., & Brito, C. P. (2019). Sistemas estadísticos en la validación de encuestas para levantamiento de datos relacionados a la enseñanza de Física Experimental en Guayaquil utilizando una Hoja de Cálculo. Revista Ibérica de Sistemas e Tecnologias de Informação, (E21), 48-57. Nunkoo, R., & Ramkissoon, H. (2012). Structural equation modelling and regression analysis in tourism research. Current Issues in Tourism, 15(8), 777-802. Nunnally, J. & Bernstein, I. (1995). Teoría psicométrica (3ª ed). México, D.F.: Editorial McGrawHill Latinoamericana. Nunnally, J. (1978). Psychometric theory. New York, NY: McGraw-Hill. Nussbaum, M., Singer, M., Rosas, R., Castillo, M., Flies, E., Lara, R., & Sommers, R. (1999). Decision support system for conflict diagnosis in personnel selection. Information & Management, 36(1), 55-62. 146 Odewade, T. (2013). An Application of a Thesaurus Process Model to Human Resource Business Process Modeling (Master Thesis Harold and Inge Marcus, Department of Industrial and Manufacturing Engineering). The Pennsylvania State University. Ortega López, I. L., Dupotey Varela, N. M., Reyes Hernández, I., Verdecia Rosés, M. E., Veranes Vera, Y., Sagaró Yi, N. D. L. C., ..., & Barroso Barrientos, A. (2017). Content design and validation of a Standard Operating Procedure to provide pharmacotherapy follow-up for the elderly in Cuba. Brazilian Journal of Pharmaceutical Sciences, 53(2). Pandey, S., & Pandey, S. K. (2017). Applying natural language processing capabilities in computerized textual analysis to measure organizational culture. Organizational Research Methods, eprint published December 19, 2017, https://doi.org/10.1177/1094428117745648. Parkar, F., & Irshad, A. (2017). Application of total quality management principles for benchmarking production standards in construction. Patrick, D., Burke, L., Gwaltney, C., Leidy, N. Martin, M., Molsen, E., & Ring, L. (2011). Content validity—establishing and reporting the evidence in newly developed patient-reported outcomes (PRO) instruments for medical product evaluation: ISPOR PRO Good Research Practices Task Force report: part 2—assessing respondent understanding. Value in Health, 14 (8), 978-988. Pellini, A., & Jones, H. (2011). A Study of ADB's Knowledge Taxonomy. Peña, D. (2002). Análisis de datos multivariantes (24). Madrid: McGraw-hill. Peng, R. (2011). Reproducible research in computational science. Science, 334(6060), 1226-1227. Peng, Y., Zhang, Y., Tang, Y., & Li, S. (2011). An incident information management framework based on data integration, data mining, and multi-criteria decision making. Decision Support Systems, 51(2), 316-327. Pennebaker, J., Boyd, R., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin. Pérez-Rave, J. (2012). Revisión sistemática de literatura en ingeniería. Editorial Universidad de Antioquia (1ª edición). Medellín, Colombia. Pérez-Rave, J. (2019). Revisión sistemática de literatura en Ingeniería. Ampliada y actualizada. Idinnov (2ª edición). Medellín, Colombia. Pérez-Rave, J., Jaramillo-Álvarez, G., González-Echavarría, F. (2019). A psychometric data science approach to study latent variables: a case of class quality and student satisfaction. [Manuscript approved for publication], Total Quality Management & Business Excellence. Pérez-Rave, J., Correa-Morales, J., & González-Echavarría, F. (2019). A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes. Journal of Property Research, 36(1), 59-96. Pérez-Rave J, Fernández-Guerrero R, Correa-Morales J (2020) Critical thinking and continuous improvement: a scientific text mining approach. Total Quality Management & Business Excellence, 1-27. 147 Pérez-Rave, J., Jaramillo-Álvarez, G., & Correa-Morales, J. (2020). Desafíos y oportunidades de la minería de datos/texto en la selección de personal. Revista Espacios, 41(22). Pérez-Rave, J., Zapata-Jaramillo, C., & Jaramillo-Álvarez, G. (2020). Mental health in organizations from a text analytics approach: Taxonomic model, trends, and impact of Covid-19. [Manuscript submitted for publication]. Pérez-Rave, J., Zapata-Jaramillo, C., & Jaramillo-Álvarez, G. (2021). Understanding critical thinking in business from an integrative text-driven approach. [Manuscript submitted for publication]. Pérez-Rave, J., Jaramillo-Álvarez, G., & Correa-Morales, J. (2021a). Multi-criteria decision-making leveraged by text analytics and interviews with strategists. Journal of Marketing Analytics, published: 28 July 2021, https://link.springer.com/article/10.1057/s41270-021-00125-8. Pérez-Rave, J., Jaramillo-Álvarez, G., & Correa-Morales, G. (2021b). Psycho-Managerial Text Mining (PMTM) for studying psychological/managerial constructs from a theory/text-driven approach. [Manuscript submitted for publication]. Peruzzini, M., & Stjepandić, J. (2018). Editorial to the special issue “Transdisciplinary analytics in supply chain management”. Journal of Management Analyti
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Jaramillo Alvarez, Gloria Patriciab7746c855dcabe714dff48f3e9721a4b600Juan Carlos, Correa Moralesc92cab7988ca4e0498983d0fe8c4202bPérez Rave, Jorge Iván8e1ffae7647761645574a97d5c796f5f6002021-09-09T15:37:23Z2021-09-09T15:37:23Z2021-09https://repositorio.unal.edu.co/handle/unal/80145Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustracionesEl uso de la Minería de Datos/Texto (M-D/T) en la selección de personal (SP) es un campo prometedor. Sin embargo, deben superarse varios desafíos, entre ellos: (1) Las pocas aplicaciones existentes no están aprovechando el valor de los constructos psicológicos/administrativos. (2) La M-D/T ha prestado poca atención al desarrollo/uso de procedimientos para confirmar la calidad psicométrica de tales constructos. (3) Tampoco suele desarrollar, con criterios de reproducibilidad, la interpretación de patrones y la toma de decisiones (evaluación, ordenación y elección del candidato). Este trabajo desarrolla un marco de M-D/T asistido por modelos de ecuaciones estructurales y análisis de decisión multicriterio para la SP, denominado M-D/T–Estructural– Multicriterio. Dicho marco consta de cuatro procesos (1. Reconocimiento de datos, 2. Descubrimiento de patrones, 3. Confirmación de patrones, 4. Evaluación de alternativas y decisión final) con operaciones semiautomatizadas. El marco se valida usando múltiples/representativos conjuntos de datos (principalmente textos) provenientes de dominios organizativos/individuales, formales/causales, online/offline y estratégicos/operacionales. Las aplicaciones del marco se ilustran para constructos como liderazgo transformacional y pensamiento crítico en las empresas. Se concluye que el marco desarrollado es capaz de asistir la investigación y la práctica de la SP y áreas afines, bajo criterios de fiabilidad, validez, equidad, reproducibilidad y eficiencia. El presente estudio resulta útil para investigadores y profesionales en campos de ciencias de la computación, dirección de organizaciones, psicología organizacional y estadística aplicada (Texto tomado de la fuente)The use of Data/Text Mining (D/T-M) for personnel selection (PS) is a promising field. However, it must overcome several challenges: (1) The few applications of D/T-M are not harnessing the value of psychological/administrative constructs. (2) D/T-M has paid little attention to the development/use of procedures to confirm the psychometric quality of such constructs. (3) D/T-M does not usually develop, with reproducibility criteria, the interpretation of patterns, and decision-making (evaluation, ordering, and candidate selection). This work develops a D/T-M framework assisted by structural equation models and multi-criteria decision analysis for PS. This framework is called D/T-M–Structural–Multicriteria. The framework consists of four processes (1. Data recognition, 2. Pattern discovery, 3. Pattern confirmation, 4. Evaluation of alternatives and final decision) and includes semi-automated operations. The framework performance is examined using multiple/representative data sets (mainly texts) from organizational/individual, formal/casual, online/offline, and strategic/operational domains. The framework applications are illustrated for constructs such as transformational leadership and critical thinking in business. This study concludes that the framework can assist the research/practice of PS and related areas, considering reliability, validity, equity, reproducibility, and efficiency. This study is helpful for researchers and professionals in computer science, organization management, organizational psychology, and applied statistics.DoctoradoDoctor en Ingeniería152 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasDepartamento de la Computación y la DecisiónFacultad de MinasMedellínUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresMinería de datosSelección de personalAnálisis de decisión multicriterioMinería de textoMinería de datosModelos de ecuaciones estructuralesSelección de personalConstructos psicológicosConstructos psicológicosCiencia de datosText miningText analyticsData miningData analyticsData scienceMinería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personalData/Text Mining–Structural–Multicriteria as a strategic resource in personnel selectionTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAbbe, A., Grouin, C., Zweigenbaum, P., & Falissard, B. (2016). Text mining applications in psychiatry: a systematic literature review. International journal of methods in psychiatric research, 25(2), 86-100. Abuazab, A., Selamat, H., & Yusoff, R. (2017). Challenge of Text Mining in Clinical Decision Support System. Journal of Engineering and Applied Sciences, 12(20), 5261-5273. Aceto, G., Persico, V., & Pescapé, A. (2020). Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration, 18, 100129. Adejuwon, A., & Mosavi, A. (2010). Domain driven data mining–Application to business. IJCSI International Journal of Computer Science Issues, 7(4), 41-44. Aghdaie, M., Zolfani, S., & Zavadskas, E. (2013). A hybrid approach for market segmentation and market segment evaluation and selection: an integration of data mining and MADM. Transformations in Business & Economics, 12(2B), 431-459. Ahmadi, H., Rad, M., Nazari, M., Nilashi, M., & Ibrahim, O. (2014). Evaluating the factors affecting the implementation of hospital information system (HIS) using AHP method. Life Science Journal, 11(3), 202-207. Akins, R. B., Tolson, H., & Cole, B. R. (2005). Stability of response characteristics of a Delphi panel: application of bootstrap data expansion. BMC medical research methodology, 5(1), 1-12. Al-Agili, M. Z., Mamat, M. B., Abdullah, L., & Maad, H. A. (2012). The factors influence students’ achievement in mathematics: A case for Libyan's students. World Applied Sciences Journal, 17(9), 1224-1230. Aldás, J., & Uriel, E. (2017). Análisis multivariante aplicado con R. (2da ed.). Alfacentauro, Parainfo. Al-Moslmi, T., Albared, M., Al-Shabi, A., Abdullah, S., & Omar, N. (2019). A Comparative Study of Co-Occurrence Strategies for Building A Cross-Domain Sentiment Thesaurus. In 2019 First International Conference of Intelligent Computing and Engineering. Hadhramout, Yemen. Alonso, P., Moscoso, S., & Cuadrado, D. (2015). Procedimientos de selección de personal en pequeñas y medianas empresas españolas. Revista de Psicología del Trabajo y de las Organizaciones, 31(2), 79-89. Álvarez-Sánchez, A. (2016). Inconsistencia en la selección de personal en la empresa agrícola del Dr. Omar Paladines de la ciudad de Machala (Bachelor's tesis). Universidad Técnica de Machala. Machala, Ecuador. 137 American Educational Research Association (AERA), American Psychological Association (APA), & National Council on Measurement in Education (NCME). (1999). Standards for educational and psychological testing. American Educational Research Association. Aryee, S., Walumbwa, F. O., Zhou, Q., & Hartnell, C. A. (2012). Transformational leadership, innovative behavior, and task performance: Test of mediation and moderation processes. Human Performance, 25(1), 1-25. Athauda, R., Tissera, M., & Fernando, C. (2009). Data mining applications: Promise and challenges. In Data Mining and Knowledge Discovery in Real Life Applications. InTech. Avolio, B. J., Bass, B. M., & Jung, D. I. (1999). Re‐examining the components of transformational and transactional leadership using the Multifactor Leadership. Journal of occupational and organizational psychology, 72(4), 441-462. Avolio, B., & Bass, B. (1991). The full-range of leadership development. Center for Leadership Studies. Binghamton, NY. Avolio, B. & Bass, B. (2004). Multifactor Leadership Questionnaire (3rd ed.). Manual and Sample Set. University of Nebraska and SUNY Binghamton, CA: Mind Garden. Balan, U., & Mathew, S. (2015). Online word of mouth using text mining: A review of literature and future directions. In 2015 IEEE Computational Intelligence: Theories, Applications and Future Directions (WCI), 1-6. IEEE. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120. Bastida, M., & Moscoso, S. (2015). Steel barrier: Legal implications from a gender equal opportunity perspective. The European Journal of Psychology Applied to Legal Context, 7(1), 13-22. Batista-Foguet, J. M., & Coenders-Gallart, G. (2000). Modelos de ecuaciones estructurales, cuadernos de estadística. La Muralla. Madrid, España. Beier, M., & Ackerman, P. (2012). Time in personnel selection. In N. Schmitt (Ed.), Oxford library of psychology. The Oxford handbook of personnel assessment and selection (721–739). Oxford University Press. Bell, E., Bryman, A., & Harley, B. (2018). Business research methods. Oxford university press. Bentler, P. M. (2007). Covariance structure models for maximal reliability of unit-weighted composites. In Handbook of latent variable and related models (1-19). North-Holland. Bernal-Guerrero, A., Cárdenas-Gutiérrez, A. R., & Montoro-Fernández, E. (2020). Basic business knowledge scale for secondary education students. Development and validation with Spanish teenagers. PloS one, 15(7), e0235681. Binning, J., & Barrett, G. (1989). Validity of personnel decisions: A conceptual analysis of the inferential and evidential bases. Journal of Applied Psychology, 74(3), 478. 138 Boran, F., Genç, S., & Akay, D. (2011). Personnel selection based on intuitionistic fuzzy sets. Human Factors and Ergonomics in Manufacturing & Service Industries, 21(5), 493-503. Campbell, D., & Johansson, C. (1966). Academic interests, scholastic achievements and eventual occupations. Journal of Counseling Psychology, 13(4), 416. Campion, M. C., Campion, M. A., Campion, E. D., & Reider, M. H. (2016). Initial investigation into computer scoring of candidate essays for personnel selection. Journal of Applied Psychology, 101(7), 958. Carless, S. A. (2001). Assessing the discriminant validity of the Leadership Practices Inventory. Journal of Occupational and Organizational Psychology, 74(2), 233-239. Carreras, E. (2006). El pronóstico en modelos estructurales: predecir la satisfacción del cliente. En: Lévy, J., & Varela, J. (2006). Modelización con estructuras de covarianzas en ciencias sociales. Temas esenciales, avanzados y aportaciones especiales, 453. Chavira, D., Lopez, J., Noriega, J., & Retamales, J. (2017). A multicriteria outranking modeling approach for personnel selection. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (1-6). IEEE. Chen, C. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9. Cheng, E., & Li, H. (2005). Analytic network process applied to project selection. Journal of construction engineering and management, 131(4), 459-466. Chiavenato, I. (2007). Administración de recursos humanos: el capital humano de las organizaciones. Octava edición, McGraw-Hill. Chien, C., & Chen, L. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with applications, 34(1), 280290. Chomsky, N. (1955). The logical structure of linguistic theory. Manuscript, Harvard University. Published in 1975, New York: Plenum. Colmenero, V. (2015). Nuevas tendencias en selección y reclutamiento de personal: el caso de BBVA (Trabajo fin de grado en relaciones laborales y recursos humanos, Facultad de derecho). Universidad de Almería, http://repositorio.ual.es/bitstream/handle/10835/3701/2584_PROYECTO%20ULTIMORevisado2.pdf?sequence=1 Cooley, R., Mobasher, B., & Srivastava, J. (1997). Web mining: Information and pattern discovery on the world wide web. In Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence (558-567). IEEE. Costa Jr, P. T., & McCrae, R. R. (2008). The Revised NEO Personality Inventory (NEO-PI-R). Sage Publications, Inc. 139 Credé, M., & Harms, P. D. (2015). 25 years of higher‐order confirmatory factor analysis in the organizational sciences: A critical review and development of reporting recommendations. Journal of Organizational Behavior, 36(6), 845-872. Crestani, F. (1997). Application of spreading activation techniques in information retrieval. Artificial Intelligence Review, 11(6), 453-482. Cronbach, L. (1968). Fundamentos de la exploración psicológica. Ed. Revolucionaría, Instituto cubano del libro. La Habana, Cuba. Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: From intuition to institution. Academy of management review, 24(3), 522-537. Dağdeviren, M. (2010). A hybrid multi-criteria decision-making model for personnel selection in manufacturing systems. Journal of Intelligent manufacturing, 21(4), 451-460. De Bruijn, L., & Martin, J. (2002). Literature mining in molecular biology. In Proceedings of the EFMI Workshop on Natural Language Processing in Biomedical Applications (1-5). De Raad, B. (2000). The big five personality factors: the psycholexical approach to personality. Hogrefe & Huber Publishers. De Soete, B., Lievens, F., & Druart, C. (2013). Strategies for dealing with the diversity-validity dilemma in personnel selection: Where are we and where should we go? Revista de Psicología del Trabajo y de las Organizaciones, 29(1), 3-12. Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The Sage handbook of organizational research methods (p. 671–689). Sage Publications Ltd. Dhliwayo, P., & Coetzee, M. (2020). Cognitive intelligence, emotional intelligence and personality types as predictors of job performance: Exploring a model for personnel selection. SA Journal of Human Resource Management, 18, 13. Díaz-Bravo, L., Torruco-García, U., Martínez-Hernández, M., & Varela-Ruiz, M. (2013). La entrevista, recurso flexible y dinámico. Investigación en educación médica, 2(7), 162-167. Dickens, W., & Flynn, J. (2001). Heritability estimates versus large environmental effects: the IQ paradox resolved. Psychological review, 108(2), 346. Digman, J. M. (1990). Personality structure: emergence of the Five Factors Model. Annual Review of Psychology, 41,417-440. Dinno, A. (2018). paran: Horn's Test of Principal Components/Factors. R package version 1.5.2. https://CRAN.R-project.org/package=paran. Espinosa, K. (2017). An Empirical Study on End-to-End Sentence Modelling. In Proceedings of ACL 2017. Student Research Workshop, 128-135. 140 Esty, K., Griffin, R., & Schorr-Hirsh, M. (1995). Workplace diversity. A managers guide to solving problems and turning diversity into a competitive advantage. Avon, MA: Adams Media Corporation. Evers, A., Anderson, N., & Smit-Voskuijl, O. (2005). The Blackwell handbook of personnel selection (Eds., Vol. 1). John Wiley & Sons. Farahi, S., Abad, M., Farahi, S., & Abadi, M. (2014). Choice theory and effective learning. International Journal of Psychology and Behavioral Research, Special issue, 1(14), 325-329. Farr, J., & Tippins, N. (2017). Handbook of employee selection. Taylor & Francis. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 36–53. Feinerer, I., & Hornik, K. (2018). tm: Text Mining Package. R package version 0.7-6. https://CRAN.R-project.org/package=tm Fenner, C. J., & Piotrowski, C. (2018). Communication theory in leadership research: Which theories predominate. Psychology & Education: An Interdisciplinary Journal, 55(2), 10-18. Firth, J. R. (1957). A synopsis of linguistic theory, 1930-1955. Studies in linguistic analysis, Special Vol., Philological Society, 1-32. Fontana, M. E., & Cavalcante, C. A. V. (2014). Use of Promethee method to determine the best alternative for warehouse storage location assignment. The International Journal of Advanced Manufacturing Technology, 70(9-12), 1615-1624. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement errors. Journal of Marketing Research, 18, 39–50. Fteimi, N., Basten, D., & Lehner, F. (2019). Advancing Automated Content Analysis in Knowledge Management Research: The Use of Compound Concepts. International Journal of Knowledge Management (IJKM), 15(1), 53-68. Fyshe, A., Sudre, G., Wehbe, L., Rafidi, N., & Mitchell, T. M. (2019). The lexical semantics of adjective–noun phrases in the human brain. Human brain mapping, 40(15), 4457-4469. Galinium, M. (2017). Integrating data mining technique and AHP in market analysis to propose new product development in real estate. In IOP Conference Series: Materials Science and Engineering, (166(1), 012030). IOP Publishing. García, T. (2003). Etapas del proceso investigador. Instrumentación. El cuestionario como instrumento de investigación/evaluación. España: Almendralejo. Garten, J., Hoover, J., Johnson, K. M., Boghrati, R., Iskiwitch, C., & Dehghani, M. (2018). Dictionaries and distributions: Combining expert knowledge and large-scale textual data content analysis. Behavior Research Methods, 50(1), 344-361. Gibney, R., & Shang, J. (2007). Decision making in academia: A case of the dean selection process. Mathematical and Computer Modelling, 46(7-8), 1030-1040. 141 Goldberg, L. (1993). The structure of phenotypic personality traits. American psychologist, 48(1), 26-34. Golec, A., & Kahya, E. (2007). A fuzzy model for competency-based employee evaluation and selection. Computers & Industrial Engineering, 52(1), 143-161. González, F. (2003). Apuntes acerca de algunos conceptos básicos de investigación cualitativa. Sapiens, Revista Universitaria de Investigación, 4(1), 107-132. Gottfredson, L. (1997). Why g matters: The complexity of everyday life. Intelligence, 24(1), 79-132. Gough, H. G. (1979). A creative personality scale for the adjective check list. Journal of personality and social psychology, 37(8), 1398. Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96, 1029–1046. Gutiérrez, D. D. (2015). Machine learning and data science: an introduction to statistical learning methods with R. Technics Publications. Haan, H., Streb, J., Bien, S., & Rösler, F. (2000). Individual cortical current density reconstructions of the semantic N400 effect: Using a generalized minimum norm model with different constraints (L1 and L2 norm). Human Brain Mapping, 11(3), 178-192. Han, S., & Lee, G. (2016). A Preliminary Study on Text Mining-Based Human Resource Allocation in a Construction Project. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (33, 1). Vilnius Gediminas Technical University, Department of Construction Economics & Property. Harris, C. (2017). Finding the Best Job Applicants for a Job Posting: A Comparison of Human Resources Search Strategies. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (189-194). IEEE. Hart, S. (1971). Axiology: Theory of values. International Phenomenological Society, 32, 29–41. Herpertz, S., Nizielski, S., Hock, M., & Schütz, A. (2016). The relevance of emotional intelligence in personnel selection for high emotional labor jobs. PloS one, 11(4), e0154432. Hjørland, B. (2005). Empiricism, rationalism and positivism in library and information science. Journal of Documentation, 61(1), 130-155. Holland, J. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Psychological Assessment Resources. Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179-185. Hough, L., & Oswald, F. (2000). Personnel selection: Looking toward the future-Remembering the past. Annual review of psychology, 51(1), 631-664. 142 Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1-55. Hua, L. (2016). A data science approach to pattern discovery in complex structures with applications in bioinformatics. Dissertations. 70. Hwang, C., & Yoon, K. (1981). Multiple attribute decision making. Springer-Verlag. Hyun-Park, S., Seon Shin, W., Hyun-Park, Y., & Lee, Y. (2017). Building a new culture for quality management in the era of the Fourth Industrial Revolution. Total Quality Management & Business Excellence, 28(9-10), 934-945. IBM (2012). IBM SPSS Modeler Text Analytics 15 user’s guide. Armonk, N.Y: Author. Illia, L., Sonpar, K., & Bauer, M. W. (2014). Applying co-occurrence text analysis with ALCESTE to studies of impression management. British Journal of Management, 25, 352-372. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (112, 18). New York: springer. Jayaraj, V., & Mahalakshmi, V. (2015). Augmenting efficiency of recruitment process using IRCF text mining algorithm. Indian Journal of Science and Technology, 8(16). Jereb, E., Rajkovic, U., & Rajkovic, V. (2005). A hierarchical multi‐attribute system approach to personnel selection. International Journal of Selection and Assessment, 13(3), 198-205. Jiménez, J. C., Sánchez, J. G., & Aguilar, F. G. (2006). Guía técnica para la construcción de cuestionarios. Odisea Revista electrónica de pedagogía, 3(6). Jockers, M. (2014). Text analysis with R for students of literature. New York: Springer. Joshi, P. (2017). Artificial intelligence with python. Packt Publishing Ltd. Judge, T. (2009). Core self-evaluations and work success. Current Directions in Psychological Science, 18(1), 58-62. Judge, T., & Kammeyer-Mueller, J. (2007). Personality and career success. Handbook of career studies, 59-78. Kaiser, H. (1970). A second generation Little Jiffy. Psychometrika, 35, 401-15. Kazemzadeh, A., Gibson, J., Georgiou, P., Lee, S., & Narayanan, S. (2016). A Socratic epistemology for verbal emotional intelligence. PeerJ Computer Science, 2, e40. Kelemenis, A., & Askounis, D. (2010). A new TOPSIS-based multi-criteria approach to personnel selection. Expert systems with applications, 37(7), 4999-5008. 143 Khwaja, M. G., Mahmood, S., & Zaman, U. (2020). Examining the Effects of eWOM, Trust Inclination, and Information Adoption on Purchase Intentions in an Accelerated Digital Marketing Context. Information, 11(10), 478. Kishton, J. M., & Widaman, K. F. (1994). Unidimensional versus domain representative parceling of questionnaire items: An empirical example. Educational and psychological measurement, 54(3), 757-765. Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26. Kjellström, S., & Golino, H. (2018). Mining concepts of health responsibility using text mining and exploratory graph analysis. Scandinavian journal of occupational therapy, 1-16. Klein, K., Bliese, P., Kozlowshi, S., Dansereau, F., Gavin, M., Griffin, M., . . ., Bligh, M. (2000). Multilevel analytical techniques: Commonalities, differences, and continuing questions. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (512–553). San Francisco, CA: Jossey-Bass. Korayem, M., Crandall, D., & Abdul-Mageed, M. (2012). Subjectivity and sentiment analysis of arabic: A survey. In International conference on advanced machine learning technologies and applications, 128-139. Springer. Berlin, Heidelberg. Koutra, G., Barbounaki, S., Kardaras, D., & Stalidis, G. (2017). A Multicriteria Model for Personnel Selection in Maritime Industry in Greece. In 2017 IEEE 19th Conference on Business Informatics (CBI) (1, 287-294). IEEE. Krause, A. (2016). Reproducible Research in Real Estate: A Review and an Example. Journal of Real Estate Practice and Education, 19(1), 69-85. Kubr, M. (2007). La consultoría de Empresas. México, D.F.: Limusa Kumar, A., & Paul, A. (2016). Mastering text mining with R. Packt Publishing Ltd. Kumari, M. (2012). Data Driven Data Mining to Domain Driven Data Mining. Global Journal of Computer Science and Technology, 11(23). Kumova, S., & Karaoğlan, B. (2011). Measuring collocation tendency of words. Journal of Quantitative Linguistics, 18(2), 174-187. Kwartler, T. (2017). Text mining in practice with R. John Wiley & Sons. Lahitani, A. R., Permanasari, A. E., & Setiawan, N. A. (2016). Cosine similarity to determine similarity measure: Study case in online essay assessment. In 2016 4th International Conference on Cyber and IT Service Management (1-6). IEEE. Lane, G. G. (2000). The Beginner's Guide to the Bootstrap Method of Resampling. Ledolter, J. (2013). Data mining and business analytics with R. John Wiley & Sons, New Jersey. 144 Lévy, J., & Varela, J. (2006). Modelización con estructuras de covarianzas en ciencias sociales. Temas esenciales, avanzados y aportaciones especiales. A Coruña: Netbiblo. Li, D. (2007). Compromise ratio method for fuzzy multi-attribute group decision making. Applied soft computing, 7(3), 807-817. Liddy, E. D. (1998). Enhanced text retrieval using natural language processing. Bulletin of the American Society for Information Science and Technology, 24(4), 14-16. Lievens, F., Van Dam, K., & Anderson, N. (2002). Recent trends and challenges in personnel selection. Personnel Review, 31(5), 580-601. Liu, X. (2016, June). Institutional Dilemmas and the Development of Chief Information Officer System: a case from Shanghai. In Proceedings of the 17th International Digital Government Research Conference on Digital Government Research (pp. 220-225). Lochter, J. V., Zanetti, R. F., Reller, D., & Almeida, T. A. (2016). Short text opinion detection using ensemble of classifiers and semantic indexing. Expert Systems with Applications, 62, 243-249. Loper, E., & Bird, S. (2002). NLTK: the natural language toolkit. Department of Computer and Information Science. arXiv preprint cs/0205028. Lorenz, T., Heinitz, K., Beer, C., & van Woerkom, M. (2021). Adaptation and validation of a German version of the Strengths Use and Deficit Correction (SUDCO) questionnaire. Plos one, 16(1), e0245127. Loughry, W. J., & McDonough, C. M. (2002). Phenotypic variablity within and between litters of nine-banded armadillos. Southeastern Naturalist, 1(3), 287-298. Lozano, J. F. (2011). Qué es la ética de la empresa. Colección saber. Editorial Proteus. Madrid. Lunbeck, E (2017). The allure of Trump’s narcissism. Available at: www.lareviewofbooks.org/article/the-allure-of-trumps-narcissism/#! (accessed 02 April 2021). Marsh, H., Byrne, B., & Craven, R. (1992). Overcoming problems in confirmatory factor analyses of MTMM data: The correlated uniqueness model and factorial invariance. Multivariate Behavioral Research, 27(4), 489-507. Martin, A., Haxby, J. V., Lalonde, F. M., Wiggs, C. L., & Ungerleider, L. G. (1995). Discrete cortical regions associated with knowledge of color and knowledge of action. Science, 270(5233), 102105. Martínez, M., Hernández M., & Hernández, M. (2006). Psicometría. Madrid: Alianza Editorial. Marttunen, M., Lienert, J., & Belton, V. (2017). Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations. European Journal of Operational Research, 263(1), 1-17. Maykut, P., & Morehouse, R. (2002). Beginning qualitative research: A philosophical and practical guide. Routledge. 145 Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In 2013 Proceedings of Workshop at ICLR. https://arxiv.org/pdf/1301.3781.pdf. Moheno, J. M., & Calzada, M. A. H. (2008). Las prácticas de recursos humanos y su relación con el desempeño percibido de las pequeñas empresas. Forum empresarial, 13(2), 23-43. Universidad de Puerto Rico. Moriyama, I.(1968). Indicators of social change. Problems in the measurements of health status. New York: Russell Sage Foundation; 9, 593. Moulin, B. (1992). Conceptual-graph approach for the representation of temporal information in discourse. Knowledge-Based Systems, 5(3), 183-192. Müller, H., Kenny, E., & Sternberg, P. (2004). Textpresso: an ontology-based information retrieval and extraction system for biological literature. PLoS biology, 2(11), e309, 1984-1998. Munier, N., Hontoria, E., & Jiménez-Sáez, F. (2019). Strategic Approach in Multi-Criteria Decision Making. Springer International Publishing. Murphy, K. (2012). Individual differences. In: Schmitt, N. (2012). Personnel assessment and selection (3, 31-47). Oxford, N.Y Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B., & Turaga, D. S. (2017). Learning Feature Engineering for Classification. In IJCAI Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17) (2529-2535). Neisser, U., Boodoo, G., Bouchard Jr, T., Boykin, A., Brody, N., Ceci, S., Ha, I., Diane, F., Loehlin, J., Perloff, R., Sternberg, R., & Urbina, S. (1996). Intelligence: Knowns and unknowns. American psychologist, 51(2), 77. Ngai, E., & Lee, P. (2016). A Review of the literature on Applications of Text Mining in Policy Making. In PACIS 2016 Proceedings (343). Noboa, J. E., Zurita, D. G., Barros, V., Arízabal, S. C., & Brito, C. P. (2019). Sistemas estadísticos en la validación de encuestas para levantamiento de datos relacionados a la enseñanza de Física Experimental en Guayaquil utilizando una Hoja de Cálculo. Revista Ibérica de Sistemas e Tecnologias de Informação, (E21), 48-57. Nunkoo, R., & Ramkissoon, H. (2012). Structural equation modelling and regression analysis in tourism research. Current Issues in Tourism, 15(8), 777-802. Nunnally, J. & Bernstein, I. (1995). Teoría psicométrica (3ª ed). México, D.F.: Editorial McGrawHill Latinoamericana. Nunnally, J. (1978). Psychometric theory. New York, NY: McGraw-Hill. Nussbaum, M., Singer, M., Rosas, R., Castillo, M., Flies, E., Lara, R., & Sommers, R. (1999). Decision support system for conflict diagnosis in personnel selection. Information & Management, 36(1), 55-62. 146 Odewade, T. (2013). An Application of a Thesaurus Process Model to Human Resource Business Process Modeling (Master Thesis Harold and Inge Marcus, Department of Industrial and Manufacturing Engineering). The Pennsylvania State University. Ortega López, I. L., Dupotey Varela, N. M., Reyes Hernández, I., Verdecia Rosés, M. E., Veranes Vera, Y., Sagaró Yi, N. D. L. C., ..., & Barroso Barrientos, A. (2017). Content design and validation of a Standard Operating Procedure to provide pharmacotherapy follow-up for the elderly in Cuba. Brazilian Journal of Pharmaceutical Sciences, 53(2). Pandey, S., & Pandey, S. K. (2017). Applying natural language processing capabilities in computerized textual analysis to measure organizational culture. Organizational Research Methods, eprint published December 19, 2017, https://doi.org/10.1177/1094428117745648. Parkar, F., & Irshad, A. (2017). Application of total quality management principles for benchmarking production standards in construction. Patrick, D., Burke, L., Gwaltney, C., Leidy, N. Martin, M., Molsen, E., & Ring, L. (2011). Content validity—establishing and reporting the evidence in newly developed patient-reported outcomes (PRO) instruments for medical product evaluation: ISPOR PRO Good Research Practices Task Force report: part 2—assessing respondent understanding. Value in Health, 14 (8), 978-988. Pellini, A., & Jones, H. (2011). A Study of ADB's Knowledge Taxonomy. Peña, D. (2002). Análisis de datos multivariantes (24). Madrid: McGraw-hill. Peng, R. (2011). Reproducible research in computational science. Science, 334(6060), 1226-1227. Peng, Y., Zhang, Y., Tang, Y., & Li, S. (2011). An incident information management framework based on data integration, data mining, and multi-criteria decision making. Decision Support Systems, 51(2), 316-327. Pennebaker, J., Boyd, R., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin. Pérez-Rave, J. (2012). Revisión sistemática de literatura en ingeniería. Editorial Universidad de Antioquia (1ª edición). Medellín, Colombia. Pérez-Rave, J. (2019). Revisión sistemática de literatura en Ingeniería. Ampliada y actualizada. Idinnov (2ª edición). Medellín, Colombia. Pérez-Rave, J., Jaramillo-Álvarez, G., González-Echavarría, F. (2019). A psychometric data science approach to study latent variables: a case of class quality and student satisfaction. [Manuscript approved for publication], Total Quality Management & Business Excellence. Pérez-Rave, J., Correa-Morales, J., & González-Echavarría, F. (2019). A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes. Journal of Property Research, 36(1), 59-96. Pérez-Rave J, Fernández-Guerrero R, Correa-Morales J (2020) Critical thinking and continuous improvement: a scientific text mining approach. Total Quality Management & Business Excellence, 1-27. 147 Pérez-Rave, J., Jaramillo-Álvarez, G., & Correa-Morales, J. (2020). Desafíos y oportunidades de la minería de datos/texto en la selección de personal. Revista Espacios, 41(22). Pérez-Rave, J., Zapata-Jaramillo, C., & Jaramillo-Álvarez, G. (2020). Mental health in organizations from a text analytics approach: Taxonomic model, trends, and impact of Covid-19. [Manuscript submitted for publication]. Pérez-Rave, J., Zapata-Jaramillo, C., & Jaramillo-Álvarez, G. (2021). Understanding critical thinking in business from an integrative text-driven approach. [Manuscript submitted for publication]. Pérez-Rave, J., Jaramillo-Álvarez, G., & Correa-Morales, J. (2021a). Multi-criteria decision-making leveraged by text analytics and interviews with strategists. Journal of Marketing Analytics, published: 28 July 2021, https://link.springer.com/article/10.1057/s41270-021-00125-8. Pérez-Rave, J., Jaramillo-Álvarez, G., & Correa-Morales, G. (2021b). Psycho-Managerial Text Mining (PMTM) for studying psychological/managerial constructs from a theory/text-driven approach. [Manuscript submitted for publication]. Peruzzini, M., & Stjepandić, J. (2018). Editorial to the special issue “Transdisciplinary analytics in supply chain management”. Journal of Management Analytics, 5(2), 75-80. Pervin, L. (1980). Personality theory and assessment, Wiley, N.Y Pietraszkiewicz, A., Formanowicz, M., Gustafsson Sendén, M., Boyd, R. L., Sikström, S., & Sczesny, S. (2019). The big two dictionaries: Capturing agency and communion in natural language. European journal of social psychology, 49(5), 871-887. Piotrowski, B. (2009). La axiología y la educación. Educación y educadores, 3, 127-138. Plomin, R., & Rende, R. (1991). Human behavioral genetics. Annual review of psychology, 42(1), 161-190. Ployhart, R., & Schneider, B. (2012). The social and organizational context of personnel selection. In: Personnel assessment and selection (4, 49-67). Oxford, N.Y Ployhart, R., Schneider, B., & Schmitt, N. (2006). Staffing organizations: Contemporary practice and theory (3erd ed.). N.Y, Lawrence Erlbaum & Associates. Ponizovskiy, V., Ardag, M., Grigoryan, L., Boyd, R., Dobewall, H., & Holtz, P. (2020). Development and Validation of the Personal Values Dictionary: A Theory–Driven Tool for Investigating References to Basic Human Values in Text. European Journal of Personality, 34(5), 885-902. Pyburn Jr, K. M., Ployhart, R. E., & Kravitz, D. A. (2008). The diversity–validity dilemma: Overview and legal context. Personnel Psychology, 61(1), 143-151. Qiu, F., Li, Z., & Wang, J. (2016). A data-driven approach to improve wind dispatchability. IEEE Transactions on Power Systems, 32(1), 421-429. 148 Rahaman, H. S., Camps, J., Decoster, S., & Stouten, J. (2020). Ethical leadership in times of change: the role of change commitment and change information for employees’ dysfunctional resistance. Personnel Review, 50(2), 630-647. Randhawa, J., & Sethi, A. (2017). An Empirical Study to Examine the Role Smart Manufacturing in Improving Productivity and Accelerating Innovation. International Journal of Engineering and Management Research (IJEMR), 7(3), 607-615. Reyes, I., Cruzata, Y., Vidal, M. F., Bermúdez, I. B., Nambatya, W., Perrand, M. V., & Dutok, C. M. (2013). Evaluación de un procedimiento para brindar seguimiento farmacoterapéutico a pacientes hospitalizados. Revista mexicana de ciencias farmacéuticas, 44(1), 66-78. Rial, A., Valera, J., Abalo, J., & Lévy, J. (2006). El análisis factorial confirmatorio. En: Lévy, J., & Varela, J. (Dir. y Comp.), Modelización con estructuras de covarianzas en ciencias sociales (119– 143). Editorial Netbiblo S.L. (Editora: Seco, C). La Coruña, España. Robertson, I., & Smith, M. (2001). Personnel selection. Journal of Occupational and Organizational Psychology, 74(4), 441–472. Roque, I. A., Lafargue, B. L. F., Aguirre, C. R., Oliva, E. O., & Marrero, A. G. (2015). Construcción y validación del cuestionario “Satisfacción de usuarios externos con el servicio de salud ocupacional”. Revista Cubana de Salud y Trabajo, 16(3), 55-63. Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. Saaty, T. (1980). Analytic hierarchy Process. N.Y, McGrall Hill. Saaty, T. (1996). Decision making with dependence and feedback: The analytic network process. Pittsburgh. RWS Publications. Salgado, J. F. (2017). Personnel selection. In Oxford Research Encyclopedia of Psychology. Salter, C., Green, M., Hodgson, M., & Joyner, N. (2013). The language of transformational leaders: Communicating to the needs of followers. The Journal of American Academy of Business, 19(1), 60-68. Salter, C., Hargrove, D., Duncan, P. A., Coleman, D., & Woodhull, M. (2017). Do Emergent Leaders Speak Transformational Language: A Study of the Language and Non-Verbal Behavior of Donald Trump and Senator Ted Cruz Announcement for Candidacy Speeches. Journal of Leadership, Accountability and Ethics, 14(1), 58. Sari S., & Adriani, M. (2019). Applications of tf-idf concept to improve monolingual and crosslanguage information retrieval based on word embeddings. In AISS '19: Proceedings of the International Conference on Advanced Information Science and System, 16, 1-6. Schler, J., Koppel, M., Argamon, S., & Pennebaker, J. (2006). Effects of age and gender on blogging. In N. Nicolov, F. Salvetti, M. Liberman, & J. Martin (Eds.), Proceedings of 2006 AAAI spring symposium on computational approaches for analyzing weblogs (199–205). Menlo Park, CA: AAAI Press. 149 Selsaas, L. R., Agrawal, B., Rong, C., & Wiktorski, T. (2015). AFFM: Auto feature engineering in field-aware factorization machines for predictive analytics. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 1705-1709. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin and Company Shehu, M., & Saeed, F. (2016). An adaptive personnel selection model for recruitment using domaindriven data mining. Journal of Theoretical & Applied Information Technology, 91(1), 117-129. Shih, H., Shyur, H., & Lee, E. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8), 801-813. Short, J. C., Broberg, J. C., Cogliser, C. C., & Brigham, K. H. (2010). Construct validation using computer-aided text analysis (CATA) an illustration using entrepreneurial orientation. Organizational Research Methods, 13(2), 320-347. Silge, J., & Robinson, D. (2017). Text Mining with R: A tidy approach. O'Reilly Media, Inc. Sivaram, N., & Ramar, K. (2011). Knowledge Engineering to aid the recruitment process of an Industry by identifying superior selection criteria. ICTACT Journal on soft computing, 138-144. Smith, D., Hanges, P., & Dickson, M. (2001). Personnel selection and the five-factor model: Reexamining the effects of applicant's frame of reference. Journal of Applied Psychology, 86(2), 304. Stapleton, L. M., Yang, J. S., & Hancock, G. R. (2016). Construct meaning in multilevel settings. Journal of Educational and Behavioral Statistics, 41(5), 481-520. Stegmann J (2014) Paradigm shifts. Part II. Reverse Transcriptase. Analysis of reference stability and word frequencies. arXiv preprint arXiv:1412.2416. Strohmeier, S., & Piazza, F. (2013). Domain driven data mining in human resource management: A review of current research. Expert Systems with Applications, 40(7), 2410-2420. Strong Jr, E. (1943). Vocational interests of men and women. Stanford University Press. Stanford. Su, H., & Linderman, K. (2016). An Empirical Investigation in Sustaining High‐Quality Performance. Decision Sciences, 47 (5), 787-819. Swanson, R., & Holton, E. (2005). Research in organizations: Foundations and methods in inquiry. Berrett-Koehler Publishers. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational linguistics, 37(2), 267-307. Tai, W. & Hsu, C. (2006). A realistic personnel selection tool based on fuzzy data mining method. In 9th Joint International Conference on Information Sciences (JCIS-06). Atlantis Press. 150 Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24–54. Teece D, Pisano G, & Shuen A (1997) Dynamic capabilities and strategic management. Strategic management journal, 18(7), 509-533. Teece, D., Pisano, G., & Shuen, A. (1999). Dynamic capabilities and strategic management. Knowledge and strategy, 77-115. Tejeda, M., Scandura, T., & Pillai, R. (2001). The MLQ revisited: Psychometric properties and recommendations. The leadership quarterly, 12(1), 31-52. Tippins, N. (2012). Implementation Issues in employee selection testing. In: Schmitt, N. (2012). Personnel assessment and selection, cap 39, 881-902, Oxford, N.Y Torgerson, C. (2003). Systematic reviews. Bloomsbury Publishing. Torregrosa, J.R. (1985). Ortega y la psicología social histórica. Revista de psicología social, 0, 5563. Ullman, J. (2001). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon. Van Vianen, A. E., Taris, R., Scholten, E., & Schinkel, S. (2004). Perceived fairness in personnel selection: Determinants and outcomes in different stages of the assessment procedure. International Journal of Selection and Assessment, 12(1‐2), 149-159. Vargas, A. (2010). La praxis de Consejos Comunales y la Visión de las Élites sobre la Democracia Participativa. Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28. Vattimo, G. (1992). Metafísica, violencia, secularización. La secularización de la filosofía. Hermenéutica y posmodernidad (63-88). Barcelona: Gedisa. Verma, M., & Rajasankar, J. (2017). A thermodynamical approach towards group multi-criteria decision making (GMCDM) and its application to human resource selection. Applied Soft Computing, 52, 323-332. Vidal, R. V. V. (2013). To be human is to be creative. AI & society, 28(2), 237-248. Wang, Y. M., & Elhag, T. M. (2006). Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert systems with applications, 31(2), 309-319. Weng, Y. (2019). The Nature of Multidimensional Constructs Represented by Item Parcels in Structural Equation Modeling. Chinese Journal of Psychology, 61(1), 25-50. 151 Wenzel, R., & Van Quaquebeke, N. (2018). The double-edged sword of big data in organizational and management research: A review of opportunities and risks. Organizational Research Methods, 21(3), 548-591. Werner, J. (2000). Implications of OCB and contextual performance for human resource management. Human resource management review, 10(1), 3-24. Williams, E. A., Pillai, R., McCombs, K., Lowe, K. B., & Deptula, B. J. (2020). Adaptive and maladaptive narcissism, charisma, and leadership performance: A study of perceptions about the presidential leadership of Donald Trump. Leadership, 16(6), 661-682. Williams, T., Betak, J., & Findley, B. (2016). Text mining analysis of railroad accident investigation reports. In 2016 Joint Rail Conference (V001T06A009-V001T06A009). American Society of Mechanical Engineers. Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the conference on human language technology and empirical methods in natural language processing (347-354). Association for Computational Linguistics. Wirth, R., & Hipp, J. (2000). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (1, 29-39). Springer-Verlag. London, UK. Witten, I., Don, K., Dewsnip, M., & Tablan, V. (2004). Text mining in a digital library. International Journal on Digital Libraries, 4(1), 56-59. Xie, L. (2020). The impact of servant leadership and transformational leadership on learning organization: a comparative analysis. Leadership & Organization Development Journal, 41(2), 220-236. Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. CRC Press. Yaghmaei, F. (2003). Content validity and its estimation. Journal of Medical Education Spring, 3(1), 25-27 Zabaleta, G., & Maurelis, J. (2005). Reclutamiento y selección de recursos humanos. Caso de estudio: LTI Costacaribe Beach Hotel. Universidad de Oriente, Guatamare. http://ri2.bib.udo.edu.ve/bitstream/123456789/4709/2/TESIS-658.311-Z12-2005.pdf Zayas, P. (2010). Los fundamentos teórico-metodológicos de la selección de personal. Edición electrónica. www.eumed.net/libros/2010e/826, 2010. Zhang, S., & Liu, S. (2011). A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection. Expert Systems with Applications, 38(9), 11401-11405. Zhang, Y., Chen, M., & Liu, L. (2015). A review on text mining. In 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS) (681-685). IEEE. 152 Zhou, L., & Zhang, D. (2003). NLPIR: A theoretical framework for applying natural language processing to information retrieval. Journal of the American Society for Information Science and Technology, 54(2), 115-123. Zorrilla, S., & Torres, M. (1992). Guía para elaborar la tesis. Editorial McGRAW–Hill.AdministradoresEstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80145/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL71225056.2021.pdf71225056.2021.pdfTesis de Doctorado en Ingeniería - Ingeniería de Sistemasapplication/pdf3888559https://repositorio.unal.edu.co/bitstream/unal/80145/2/71225056.2021.pdfa7e98596e9c685d3c3ebb8c0bc3d0636MD52THUMBNAIL71225056.2021.pdf.jpg71225056.2021.pdf.jpgGenerated Thumbnailimage/jpeg5132https://repositorio.unal.edu.co/bitstream/unal/80145/3/71225056.2021.pdf.jpg294dbaf5280c67783b9aec2affc3e4ecMD53unal/80145oai:repositorio.unal.edu.co:unal/801452024-07-29 00:00:09.035Repositorio Institucional Universidad Nacional de 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GVyZWNob3MgZGUgYXV0b3IgcXVlIGNvbmxsZXZlIGxhIGRpc3RyaWJ1Y2nDs24gZGUgZXN0b3MgYXJjaGl2b3MgeSBtZXRhZGF0b3MuCkFsIGhhY2VyIGNsaWMgZW4gZWwgc2lndWllbnRlIGJvdMOzbiwgdXN0ZWQgaW5kaWNhIHF1ZSBlc3TDoSBkZSBhY3VlcmRvIGNvbiBlc3RvcyB0w6lybWlub3MuCgpVTklWRVJTSURBRCBOQUNJT05BTCBERSBDT0xPTUJJQSAtIMOabHRpbWEgbW9kaWZpY2FjacOzbiAyNy8yMC8yMDIwCg==

Minería de Datos/Texto–Estructural– Multicriterio como recurso estratégico en la selección de personal

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