Inter-Rater Reliability of a Scale for AI Integration in SME Accounting

Main Article Content

Oscar Bedoya Sánchez
Juan Guzmán Pacheco

Abstract

This article aims to develop and validate an instrument to assess the integration of artificial intelligence (AI) in the accounting processes of SMEs in the city of Ibagué. A quantitative approach was adapted with a non-experimental, cross-sectional, and descriptive design. For the instrument validation, a panel of eleven multidisciplinary experts was consulted to evaluate the relevance and wording of the items using Likert scales. Among the main results, inter-rater reliability was determined through the Average Content Validity Index (CVI = 0.91). Thus, the instrument becomes a reliable tool that facilitates the evaluation of AI implementation in SME accounting, allowing the analysis of benefits and challenges faced in adopting this technology.

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Studies

How to Cite

Bedoya Sánchez, Oscar, and Juan Guzmán Pacheco. 2026. “Inter-Rater Reliability of a Scale for AI Integration in SME Accounting”. Estudios De La Gestión: Revista Internacional De Administración, no. 20 (June): 119-36. https://doi.org/10.32719/25506641.2026.20.6.

References

Arslan, Umit, Kiymet Tunca Caliyurt y Sezer Bozkus Kahyaoglu. 2024. “Financial Statement Anomaly Detection Based on Benford Law and Beneish Model: Case Of A Public Sector Hospital”. EDPACS 69 (1): 69-87. https://doi.org/10.1080/07366981.2024.2312018.

Ato, Manuel, Juan López-García y Ana Benavente. 2013. “Un sistema de clasificación de los diseños de investigación en psicología”. Anales de Psicología 29 (3): 1038-59. https://dx.doi.org/10.6018/analesps.29.3.178511.

Cardozo, Edyamira, Ingrid Velásquez y Carlos Rodríguez. 2012. “Revisión de la definición de PYME en América Latina”. Latin American and Caribbean Conference for Engineering and Technology. https://laccei.org/LACCEI2012-Panama/RefereedPapers/RP031.pdf.

Creswell, Jhon W. 1994. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. SAGE Publications. https://www.ucg.ac.me/skladiste/blog_609332/objava_105202/fajlovi/Creswell.pdf.

García-Vera, Yessica Samari, Fernando Xavier Juca-Maldonado y Vanessa Torres-Gallegos. 2023. “Automatización de procesos contables mediante inteligencia artificial: oportunidades y desafíos para pequeños empresarios ecuatorianos”. Revista Transdisciplinaria de Estudios Sociales y Tecnológicos 3 (3): 68-74. https://doi.org/10.58594/rtest.v3i3.93.

Garzón Castrillón, Manuel Alfonso. 2015. “Modelo de capacidades dinámicas”. Dimensión Empresarial 13 (1): 111-31. http://www.scielo.org.co/scielo.php?pid=S1692-85632015000100007&script=sci_abstract&tlng=es.

González, Jorge, Pedro López y Gregorio Martín de Castro. 2009. “La influencia de las capacidades dinámicas sobre los resultados financieros de la empresa”. Cuadernos de estudios empresariales 19: 105-28. https://dialnet.unirioja.es/servlet/articulo?codigo=3283710.

Haynes, Stephen, David Richard y Edward Kubany. 1995. “Validez de contenido en la valuación psicológica: un enfoque funcional de conceptos y métodos”. Asociación Americana de Psicología 7 (3): 238-47. https://www.researchgate.net/publication/232480869_Content_Validity_in_Psychological_Assessment_A_Functional_Approach_to_Concepts_and_Methods.

Huamán, Pepe, y Cristian Medina. 2022. “Transformación digital en la administración pública: desafíos para una gobernanza activa en el Perú”. Comuni@cción 13 (2): 93-105. http://dx.doi.org/10.33595/2226-1478.13.2.594.

Ko, Ching-Ru, y Hsien-Tsung Chang. 2021. “LSTM-based sentiment analysis for stock price forecast”. PeerJ Computer Science 7: 408. https://doi.org/10.7717/peerj-cs.408.

Koo, Terry, y Mae Li. 2016. “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research”. Journal of Chiropractic Medicine 15 (2): 155-63. https://doi.org/10.1016/j.jcm.2016.02.012.

Lanzagorta, Dioselina, Diego Carrillo y Raúl Carrillo. 2022. “Inteligencia artificial en medicina: presente y futuro”. Gaceta Médica de México 158 (1): 17-21. https://doi.org/10.24875/gmm.m22000688.

Lawshe, Charles. 1975. “A Quantitative Approach to Content Validity”. Personnel Psychology 28 (4): 563-75. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x.

Lynn, Mary. 1986. “Determination and Quantification of Content Validity”. Nursing Research 35 (6): 382-5. https://doi.org/10.1097/00006199-198611000-00017.

Lytras, Miltiadis, y Anna Visvizi. 2021. “Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making”. Sustainability 13 (7): 3598. https://doi.org/10.3390/su13073598.

Mabelane, Keletso, Wilson Tsakane Mongwe, Rendani Mbuvha y Tshilidzi Marwala. 2023. “An Analysis of Local Government Financial Statement Audit Outcomes in a Developing Economy Using Machine Learning”. Sustainability 15 (1): 6-15. https://doi.org/10.3390/

su15010012.

Mohapatra, Susmita, Lopamudra Hota, Sumanta Pyne y Arun Kumar. 2024. “Machine Learning Approaches in Financial Management of Smart Cities”. En Emerging Electrical and Computer Technologies for Smart Cities, editado por Om Prakash Mahela, Baseem Khan y Puneet Kumar Jain. Boca Raton: CRP Press. https://doi.org/10.1201/9781003486930.

Mosteanu, Narcisa, y Alessio Faccia. 2020. “Digital Systems and New Challenges of Financial Management – Fintech, XBRL, Blockchain and Cryptocurrencies”. Quality 21 (174): 159- 66. https://pure.coventry.ac.uk/ws/files/30597575/Binder3_1_.pdf.

Nobanee, Haitham. 2021. “A Bibliometric Review of Big Data in Finance”. Big Data 9 (2): 73-8. https://doi.org/10.1089/big.2021.29044.edi.

Nuñez-Lira, Luis Alberto, Juan Oswaldo Alfaro Bernedo, Aracelli Mónica Aguado Lingan y Erica Rojana González Ponce de León. 2023. “Toma de decisiones estratégicas en empresas: innovación y competitividad”. Revista Venezolana de Gerencia 28 (9): 628-41. https://doi.org/10.52080/rvgluz.28.e9.39.

Ordikhani, Shahin, Sara Habibi y Ahmad Reza Haghighi. 2021. “Identifying Companies’ Bankruptcy Using an Enhanced Neural Network Model: A Case Study Evaluating the Bankruptcy of Iranian Stock Exchange Companies”. International Journal of Industrial and Systems Engineering 38 (4): 503-29. https://doi.org/10.1504/IJISE.2021.116927.

Pérez, Roberto, Luis Hernández, Dagnier Curra, Patricia Zambrano, Enrique Zayas y Julio de la Rosa Melian. 2021. “Model for predicting the specific energy consumption of HSM of AISI 316L using ANN”. En Sinergias en la investigación en STEM, editado por Ana Beltrán y Manuel Félix Angel. https://dialnet.unirioja.es/servlet/articulo?codigo=8434718.

Peng, Xuan, Saeed Mousa, Muddassar Sarfraz, Nassani Abdelmohsen y Mohamed Haffar. 2023. “Improving mineral resource management by accurate financial management: Studying through artificial intelligence tools”. Resources Policy 81: Article 103323. https://doi.org/10.1016/j.resourpol.2023.103323.

Tellez, Jesus, Karamath Ateeq, Aqila Rafiuddin, Haitham Alzoubi, Taher Ghazal, Tariq Ahamed Ahanger, Sunita Chaudhary y G Viju. 2022. “AI-Based Prediction of Capital Structure: Performance Comparison of ANN SVM and LR Models”. Computational Intelligence and Neuroscience. 8334927. https://doi.org/10.1155/2022/8334927.

Tornatzky, Louis, y Mitchell Fleischer. 1990. The Processes of Technological Innovation. https://archive.org/details/processesoftechn0000torn/page/n5/mode/2up.

Tsantekidis, Avraam, Nikolaos Passalis y Anastasios Tefas. 2021. “Diversity-driven knowledge distillation for financial trading using Deep Reinforcement Learning”. Neural Networks 140 (agosto): 193-202. https://doi.org/10.1016/j.neunet.2021.02.026.

Wang, Zhijie. 2021. “TCL stock price prediction model based on LSTM RNN”. Ponencia presentada en IEEE International Conference on Artificial Intelligence and Computer Applications, Dalian, China, 28-30 de junio. https://doi.org/10.1109/ICAICA52286.2021.9497972.

Wu, Jimmy, Zhongcui Li, Norbert Herencsar, Bay Vo y Jerry Chun-Wei Lin. 2023. “A graphbased CNN-LSTM stock price prediction algorithm with leading indicators”. Multimedia Systems 29: 1751-70. https://doi.org/10.1007/s00530-021-00758-w.

Yao, Lu. 2019. “Financial accounting intelligence management of internet of things enterprises based on data mining algorithm”. Journal of Intelligent and Fuzzy Systems 37 (5): 5915- 23. https://doi.org/10.3233/JIFS-179173.

Yao, Wenqing, Yuting Gu, Sanfei Chang, Jing Li, Qingbo Zhao y Fangli Ge. 2022. “Stock price analysis and forecasting based on machine learning”. Proceedings of SPIE - The International Society for Optical Engineering 12506, id. 1250660. https://doi.org/10.1117/12.2662176.

Zeng, Yan. 2022. “Neural Network Technology-Based Optimization Framework of Financial and Management Accounting Model”. Computational Intelligence and Neuroscience, id. 4991244. https://doi.org/10.1155/2022/4991244.