1+1 > 2: Integrating Analytical Techniques in the Age of AI
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Abstract
The premise that combining analytical techniques yields results greater than the sum of their parts, captured in the expression 1+1 > 2, has gained renewed urgency in an era marked by the proliferation of artificial intelligence (AI), the explosion of unstructured data, and mounting pressure on researchers to demonstrate both rigor and relevance. However, integrating methods remains more promising than practice in many fields. Survey evidence indicates that while 87 percent of organizations have adopted AI for task automation, only 23 percent employ it in strategic decision-making (McKinsey & Company, 2024; MIT Sloan Management Review, 2024). Similarly, despite decades of methodological pluralism in the social sciences, the dominant mode of inquiry continues to privilege single-technique approaches that sacrifice contextual richness for analytical tractability. This editorial argues that the contemporary landscape demands a different paradigm: one in which hybrid architectures — combining frequentist and Bayesian inference, structured and unstructured data, human judgment, and machine computation — are the norm rather than the exception.
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Limongi, R., Brei, V. A., Almeida, V. de S. e, & Francisco , E. de R. (2025). 1+1 > 2: Integrating Analytical Techniques in the Age of AI. Brazilian Administration Review, 22(4), e250230. https://doi.org/10.1590/1807-7692bar2025250230
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Editorial
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References
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Begley, C. G., & Ellis, L. M. (2012). Drug development: Raise standards for preclinical cancer research. Nature, 483, 531-533. https://www.nature.com/articles/483531a
Bertsimas, D., & Kallus, N. (2019). From predictive to prescriptive analytics. Management Science, 66(3), 1025-1044. https://doi.org/10.1287/mnsc.2018.3253
Boutayeb, A., Lahsen-cherif, I., & Khadimi, A. E. (2024). A comprehensive GeoAI review: Progress, challenges and outlooks. arXiv. https://doi.org/10.48550/arXiv.2412.11643
Camerer, C. F., Dreber, A., Forsell, E., Ho, T.-H., Huber, J., Johannesson, M., Kirchler, M., Almenberg, J., Altmejd, A., Chan, T., Heikensten, E., Holzmeister, F., Imai, T., Isaksson, S., Nave, G., Pfeiffer, T., Razen, M., & Wu, H. (2016). Evaluating replicability of laboratory experiments in economics. Science, 351(6280), 1433-1436. https://doi.org/10.1126/science.aaf0918
Cheng, Q., Lin, P., & Zhao, Y. (2025). Does generative AI facilitate investor trading? Early evidence from ChatGPT outages. Journal of Accounting and Economics, 80(2), 101821. https://doi.org/10.1016/j.jacceco.2025.101821
Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. Journal of Abnormal and Social Psychology, 65(3), 145-153. https://doi.org/10.1037/h0045186
Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997-1003. https://doi.org/10.1037/0003-066X.49.12.997
Favaretto, J. E. R., & Francisco, E. R. (2017). Exploring the archive of RAE-Revista de Administração de Empresas (1961 to 2016) in the light of bibliometrics, text mining, social networks and geoanalysis. RAE-Revista de Administração de Empresas, 57(4), 365-390. https://doi.org/10.1590/S0034-759020170407
Francisco, E. R. (2011). RAE-eletrônica: Exploration of archive in the light of bibliometrics, geoanalysis and social network. RAE - Revista de Administração de Empresas, 51(3), 280-306. https://periodicos.fgv.br/rae/article/view/30994/
Ghasemaghaei, M. (2019). Does data analytics use improve firm decision-making quality? Decision Support Systems, 120, 14-24. https://doi.org/10.1016/j.dss.2019.03.004
Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. The Journal of Strategic Information Systems, 27(1), 101-113. https://doi.org/10.1016/j.jsis.2017.10.001
Hanny, D., Schmidt, S., Gandhi, S., Granitzer, M., & Resch, B. (2025). A multimodal GeoAI approach to combining text with spatiotemporal features for enhanced relevance classification of social media posts in disaster response. Big Earth Data, 1-45. https://doi.org/10.1080/20964471.2025.2572140
Hanssens, D. M., & Pauwels, K. H. (2016). Demonstrating the value of marketing. Journal of Marketing, 80(6), 173-190. https://doi.org/10.1509/jm.15.0417
Hutchins, E. (1995). Cognition in the Wild. MIT Press.
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124
Joshi, S. (2025). The synergy of generative AI and big data for financial risk: Review of Recent Developments. International Journal for Multidisciplinary Research, 7(1). https://doi.org/10.36948/ijfmr.2025.v07i01.35488
Khattak, B., Shafi, I., Khan, A., Flores, E., Lara, R., Samad, M., & Ashraf, I. (2023). A systematic survey of ai models in financial market forecasting for profitability analysis. Ieee Access, 11, 125359-125380. https://doi.org/10.1109/access.2023.3330156
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.
Li, W. (2020). GeoAI: Where machine learning and big data converge in GIScience. Journal of Spatial Information Science, 20, 71-77. https://josis.org/index.php/josis/article/view/116
Li, Y., Zheng, L., Xie, C., & Fang, J. (2024). Big data development and enterprise ESG performance: Empirical evidence from China. International Review of Economics & Finance, 93, 742–755. https://doi.org/10.1016/j.iref.2024.05.027
Licklider, J. C. R. (1960). Man-computer symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1(1), 4-11. https://doi.org/10.1109/THFE2.1960.4503259
Liu, Y., Qin, G., Huang, X., Wang, J., & Long, M. (2024). AutoTimes: Autoregressive time series forecasters via large language models. NeurIPS 2024.
Martín, J., Parra, M. I., Pizarro, M. M., & Sanjuán, E. L. (2025). A new Bayesian method for estimation of value at risk. Empirical Economics, 68(3), 1171-1189. https://doi.org/10.1007/s00181-024-02664-2
McKinsey & Company. (2024). The state of AI in 2024: Generative AI’s breakout year. McKinsey Global Institute.
Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66(1), 195-244. https://doi.org/10.2466/pr0.1990.66.1.195
MIT Sloan Management Review. (2024). AI and strategic decision-making: Bridging the implementation gap.
Mollick, E. (2024). Co-Intelligence: Living and working with AI. Portfolio/Penguin.
Morgan, N. A., Jayachandran, S., Hulland, J., Kumar, B., Katsikeas, C., & Somosi, A. (2022). Marketing performance assessment and accountability. International Journal of Research in Marketing, 39(2), 462-481. https://doi.org/10.1016/j.ijresmar.2021.10.008
Morgan, S. L., & Winship, C. (2014). Counterfactuals and Causal Inference (2nd ed.). Cambridge University Press.
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. https://doi.org/10.1126/science.aac4716
Pearl, J., Glymour, M., & Jewell, N. (2016). Causal inference in statistics: A primer. Wiley.
Pearl, J., & Mackenzie, D. (2018). The book of why. Basic Books.
Popescu, D., Spulbar, C., & Smarandescu, I. (2025). Examining the impact of AI technology on marketing strategies. Annals of Constantin Brâncuşi University.
Porter, A. L., Zhang, Y., & Newman, N. C. (2024). Tech mining: A revisit and navigation. Frontiers in Research Metrics and Analytics, 9, 1364053. https://doi.org/10.3389/frma.2024.1364053
Rai, A., & Tang, X. (2010). Leveraging IT Capabilities and Competitive Process Capabilities for the Management of Interorganizational Relationship Portfolios. Information Systems Research, 21(3), 516-542. https://doi.org/10.1287/isre.1100.0299
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638-641. https://doi.org/10.1037/0033-2909.86.3.638
Singh, S., Singh, S., Kraus, S., Sharma, A., & Dhir, S. (2024). Characterizing generative artificial intelligence applications. Journal of Innovation and Knowledge, 9(3), 100531. https://doi.org/10.1016/j.jik.2024.100531
Song, Y., Zhang, Y., Huang, J., & Yang, A. (2025). Volatility and value-at-risk forecasting using BERT and transformer models incorporating investors’ textual sentiments. Finance Research Letters, 85, 108210. https://doi.org/10.1016/j.frl.2025.108210
Srinivasan, S., & Hanssens, D. M. (2009). Marketing and firm value: Metrics, methods, findings, and future directions. Journal of Marketing Research, 46(3), 293-312. https://doi.org/10.1509/jmkr.46.3.293
Toubia, O., Gui, G. Z., Peng, T., Merlau, D. J., Li, A., & Chen, H. (2025). Database Report: Twin-2K-500: A data set for building digital twins of over 2,000 people based on their answers to over 500 questions. Marketing Science, 44(6), 1217-1459. https://doi.org/10.1287/mksc.2025.0262
Wamba-Taguimdje, S.-L., Wamba, S. F., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of Artificial Intelligence on firm performance. Business Process Management Journal, 26(8), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, process, and purpose. The American Statistician, 70(2), 129-133. https://doi.org/10.1080/00031305.2016.1154108
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413