Respuestas digitales: calidad e impacto en visión estratégica
DOI:
https://doi.org/10.26871/killkana_social.v10i3.1660Palabras clave:
Big data, Calidad de datos, Estudio bibliométrico, Prospectiva estratégicaResumen
La prospectiva estratégica se ha convertido en una herramienta esencial para que las organizaciones y sociedades enfrenten la incertidumbre y los cambios acelerados en los entornos empresariales y sociales. El presente estudio tiene como objetivo analizar la relación entre la calidad de respuesta en cuestionarios digitales y la prospectiva estratégica, entendiendo que la calidad de los datos influye directamente en la generación de escenarios futuros y en la toma de decisiones informadas. La metodología aplicada corresponde a un análisis bibliométrico de artículos indexados en la base de datos Scopus. Se identificaron 34 artículos relevantes, y se aplicó un análisis de co-ocurrencia de palabras clave con el apoyo de Notebook LM. Los resultados evidencian que la calidad de información constituye un aspecto crítico en la prospectiva estratégica, particularmente en el contexto de la proliferación de big data y datos abiertos. A pesar de la disponibilidad creciente de datos, persisten desafíos como el alto grado de manualidad en los procesos de análisis y la dependencia de enfoques tradicionales que limitan la capacidad de automatización y aprendizaje continuo. Se destaca la relevancia de la inteligencia artificial y las técnicas de minería de datos para mejorar la eficiencia, detectar señales débiles y reducir el sesgo humano en las fases iniciales del proceso de prospectiva. Se concluye en la necesidad de fortalecer la calidad de los datos y fomentar la integración de tecnologías avanzadas para potenciar la prospectiva estratégica y, con ello, la capacidad de adaptación organizacional. La aplicación práctica se proyecta en el desarrollo de estrategias más robustas para enfrentar escenarios futuros en sectores como la gestión de riesgos, la innovación tecnológica y la planificación estratégica empresarial.
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Aldulaimi, S. H., Ali, B. J. A., Yas, Q. M., Abdeldayem, M. M., Aswad, A. R., & Hammad, A. M. (2022). Application of Big Data Analysis to Foresight the Future: A Review of Opportunities, Approaches, and New Research Directions. 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2022, 346–354. https://doi.org/10.1109/ICETSIS55481.2022.9888841
Althonayan, A., & Andronache, A. (2019). Resiliency under strategic foresight: The effects of cybersecurity management and enterprise risk management alignment. 2019 International Conference on Cyber Situational Awareness, Data Analytics and Assessment, Cyber SA 2019. https://doi.org/10.1109/CyberSA.2019.8899445
Bharti, A. K., & Dwivedi, S. K. (2011). Design of an analytical and foresight based strategic model for e-governance in public transportation. In Communications in Computer and Information Science: Vol. 250 CCIS. https://doi.org/10.1007/978-3-642-25734-6_105
Brandtner, P., & Mates, M. A. (2021). Artificial intelligence in strategic foresight - Current practices and future application potentials: Current practices and future application potentials. ACM International Conference Proceeding Series, 75–81. https://doi.org/10.1145/3481127.3481177
Canongia, C. (2007). Synergy between Competitive Intelligence (CI), Knowledge Management (KM) and Technological Foresight (TF) as a strategic model of prospecting - The use of biotechnology in the development of drugs against breast cancer. Biotechnology Advances, 25(1), 57–74. https://doi.org/10.1016/j.biotechadv.2006.10.001
Chulok, A. (2021). Bioeconomy in the twenty-first century: Global trends analysis perspective. In Bio Futures: Foreseeing and Exploring the Bioeconomy. https://doi.org/10.1007/978-3-030-64969-2_25
Farrington, T., Henson, K., & Crews, C. (2012). Research foresights. Research Technology Management, 55(2), 26–33. https://doi.org/10.5437/08956308X5502023
Felknor, S. A., Streit, J. M. K., Edwards, N. T., & Howard, J. (2023). Four Futures for Occupational Safety and Health. International Journal of Environmental Research and Public Health, 20(5). https://doi.org/10.3390/ijerph20054333
Geurts, A., Gutknecht, R., Warnke, P., Goetheer, A., Schirrmeister, E., Bakker, B., & Meissner, S. (2022). New perspectives for data-supported foresight: The hybrid AI-expert approach. Futures and Foresight Science, 4(1). https://doi.org/10.1002/ffo2.99
Ghalamallah, I., Loubier, E., & Dousset, B. (2013). Analytical Tools for Competitive Intelligence: From Data Collection to Data Processing. In Competitive Intelligence and Decision Problems. https://doi.org/10.1002/9781118586419.ch2
Gheorghiu, R., Andreescu, L., & Curaj, A. (2016). A foresight toolkit for smart specialization and entrepreneurial discovery. Futures, 80, 33–44. https://doi.org/10.1016/j.futures.2016.04.001
Gibson, E., Daim, T., Garces, E., & Dabic, M. (2018). Technology foresight: A bibliometric analysis to identify leading and emerging methods. Foresight and STI Governance, 12(1), 6–24. https://doi.org/10.17323/2500-2597.2018.1.6.24
Giurca, A., Nichiforel, L., Stăncioiu, P. T., Drăgoi, M., & Dima, D.-P. (2022). Unlocking Romania’s Forest-Based Bioeconomy Potential: Knowledge-Action-Gaps and the Way Forward. Land, 11(11). https://doi.org/10.3390/land11112001
Henden, J.-A., Ims, R. A., Yoccoz, N. G., Asbjørnsen, E. J., Stien, A., Mellard, J. P., Tveraa, T., Marolla, F., & Jepsen, J. U. (2020). End-user involvement to improve predictions and management of populations with complex dynamics and multiple drivers. Ecological Applications, 30(6). https://doi.org/10.1002/eap.2120
Kawai, H., Jatowt, A., Tanaka, K., Kunieda, K., & Yamada, K. (2011). Query expansion and text mining for chronoseeker-search engine for future/past events. IEICE Transactions on Information and Systems, E94-D(3), 552–563. https://doi.org/10.1587/transinf.E94.D.552
Kayser, V., Goluchowicz, K., & Bierwisch, A. (2014). Text mining for technology roadmapping - The strategic value of information. International Journal of Innovation Management, 18(3). https://doi.org/10.1142/S1363919614400040
Keenan, T. P. (2011). Oops - We didn’t mean to do that! - How unintended consequences can hijack good privacy and security policies. In IFIP Advances in Information and Communication Technology: Vol. 352 AICT. https://doi.org/10.1007/978-3-642-20769-3_9
Li, Z., Yu, Z., & Daya, Z. (2018). Strategic foresight and policy interpretation of mass entrepreneurship and innovation (MEI) in China. PICMET 2018 - Portland International Conference on Management of Engineering and Technology: Managing Technological Entrepreneurship: The Engine for Economic Growth, Proceedings. https://doi.org/10.23919/PICMET.2018.8481909
Mühlroth, C., & Grottke, M. (2018). A systematic literature review of mining weak signals and trends for corporate foresight. Journal of Business Economics, 88(5), 643–687. https://doi.org/10.1007/s11573-018-0898-4
Muhlroth, C., & Grottke, M. (2022). Artificial Intelligence in Innovation: How to Spot Emerging Trends and Technologies. IEEE Transactions on Engineering Management, 69(2), 493–510. https://doi.org/10.1109/TEM.2020.2989214
Salih, A. A., Al-Sharayah, J. A., & Abou-Moghli, A. (2024). Unlocking the potential of big data through open innovation on strategic foresight: An empirical analysis. International Journal of Data and Network Science, 8(1), 329–336. https://doi.org/10.5267/j.ijdns.2023.9.021
Sarpong, D., Eyres, E., & Batsakis, G. (2019). Narrating the future: A distentive capability approach to strategic foresight. Technological Forecasting and Social Change, 140, 105–114. https://doi.org/10.1016/j.techfore.2018.06.034
Schlegel, M., Wiederkehr, I., Rapp, S., Koldewey, C., Albers, A., & Dumitrescu, R. (2023a). Future-robust evolution of product portfolios: Need for action from theory and practice. Procedia CIRP, 120, 792–797. https://doi.org/10.1016/j.procir.2023.09.077
Schlegel, M., Wiederkehr, I., Rapp, S., Koldewey, C., Albers, A., & Dumitrescu, R. (2023b). Ontology for Future-robust Product Portfolio Evolution: A Basis for the Development of Models and Methods. Procedia CIRP, 119, 764–769. https://doi.org/10.1016/j.procir.2023.01.017
Shahkooh, K. A., Abdollahi, A., Fasanghari, M., & Azadnia, M. (2009). A foresight based framework for e-government strategic planning. Journal of Software, 4(6), 544–549. https://doi.org/10.4304/jsw.4.6.544-549
Streit, J. M. K., Felknor, S. A., Edwards, N. T., Caruso, D. L., & Howard, J. (2024). Preparing the occupational safety and health workforce for future disruptions. American Journal of Industrial Medicine, 67(1), 55–72. https://doi.org/10.1002/ajim.23548
Tai, C.-F., & Wang, K. (2023). Data Analytics Insight-Driven Organizational Agility. ACM International Conference Proceeding Series, 55–62. https://doi.org/10.1145/3624875.3624885
Trujillo-Cabezas, R. (2020). Integrating Foresight, Artificial Intelligence and Data Science to Develop Dynamic Futures Analysis. Journal of Information Systems Engineering and Management, 5(3). https://doi.org/10.29333/jisem/8428
Uhl, A., Kolleck, N., & Schiebel, E. (2017). Twitter data analysis as contribution to strategic foresight-The case of the EU Research Project “Foresight and Modelling for European Health Policy and Regulations” (FRESHER). European Journal of Futures Research, 5(1). https://doi.org/10.1007/s40309-016-0102-4
van der Laan, L., & Yap, J. (2016). On the Horizon: Asia Pacific Futures. In Management for Professionals: Vol. Part F534. https://doi.org/10.1007/978-981-287-597-6_9
Vasiliki, K., Angelika, K., Alain, O., & Theodoros, T. (2024). A Systematic Literature Review on Developing Job Profiles and Training Content for Open Data-Driven Smart Cities. In Lecture Notes in Business Information Processing: Vol. 502 LNBIP. https://doi.org/10.1007/978-3-031-56481-9_22
Vescent, H., & Blakley, B. (2018). Shifting paradigms: Using strategic foresight to plan for security evolution. ACM International Conference Proceeding Series. https://doi.org/10.1145/3285002.3285013
Volkova, T., & Dominiece-Diasa, B. (2019). Strategic foresight as an enabler of a sustainable future. International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 19(5.3), 705–712. https://doi.org/10.5593/sgem2019/5.3/S21.088
Wang, Z., Liao, H.-T., Lou, J., & Liu, Y. (2020). Making Cyberspace towards Sustainability A Scientometric Review for a Cyberspace that Enables Green and Digital Transformation. ACM International Conference Proceeding Series, 394–400. https://doi.org/10.1145/3444370.3444603
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Derechos de autor 2026 Jorge Andrés Izaguirre Olmedo

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