Public dissemination of information on Artificial Intelligence in companies. Design of measurement indicators

Palomino, María Jesús Guillén; Abelaira, Triana Arias; Ariza, Lázaro Rodríguez

doi:10.20396/rdbci.v24i00.8679450

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Article • RDBCI : Rev. Digit. Bibl. e Cienc. Inf. 24 • 2026 • https://doi.org/10.20396/rdbci.v24i00.8679450 linkcopy

Public dissemination of information on Artificial Intelligence in companies. Design of measurement indicators

Authorship SCIMAGO INSTITUTIONS RANKINGS

Introduction: The dissemination of information about artificial intelligence stands as a fundamental pillar to generate trust and encourage the responsible adoption of this technology in the business field.

Objective: The objective of this study is to design a tool that allows measuring and comparing behaviors regarding the dissemination of information about artificial intelligence that companies offer through their websites.

Methodology: To this end, an exploratory study has been carried out on all types of open access documents published in the period 2021-2025, in the area of Business Economics, in the WOS database on information related to different parameters of artificial intelligence.

Results: Therefore, a set of fifty indicators has been established, distributed as follows among the proposed parameters: seven for machine learning, nine for employment, eight for reliability, fourteen for innovation, five for transparency, and seven for sustainability.

Conclusion: It is concluded that this study provides a valuable tool for understanding and improving the way companies communicate about artificial intelligence, which is essential for building trust and promoting its responsible use.

KEYWORDS:
Disclosure of Information; Artificial intelligence; Machine learning; Reliability

Parameter	Search equation	Initial documents	Final documents
Machine learning	“machine learning” AND “Artificial intelligence” AND “Business” AND “Deep learning”	465	153
Employment	“Employment” AND “Artificial intelligence” AND “Business”	59	29
Reliability	“Reliability” AND “Artificial intelligence” AND “Business”	86	18
Innovation	“Innovation” AND “Artificial intelligence” AND “Business”	557	141
Transparency	“Transparency” AND “Artificial intelligence” AND “Business”	74	16
Sustainability	“Sustainability” AND “Artificial intelligence” AND “Business”	248	65
TOTAL

PROPOSED ITEMS
Axis 1: Machine Learning
A1: Reports on the introduction of machine learning models in marketing.	Adapted from Birim et al. (2024); Zhu (2022)
A2: Reports on solving financial problems through machine learning.	Adapted from X. Chen et al. (2021); Y. Liu et al. (2023)
A3: Reports on insider threat detection through machine learning.	Adapted from Abd-Alhalem et al. (2024); J. Wang et al. (2023)
A4: Reports on companies' business alliances through machine learning.	Adapted from D. Lee et al. (2023); Y. Li et al. (2022)
A5: Reports on the use of machine learning models for the assessment of working conditions.	Adapted from X. Fan et al. (2023); Q. Zhang & Zhang (2023)
A6: Reports on the use of machine learning to achieve intelligent decision making.	Adapted from Gandía et al. (2025); Gholami et al. (2023); Khneyzer et al. (2024)
A7: Reports on business-related predictions through machine learning.	Adapted from Ashraf et al. (2024); Belter et al. (2023); Eachempati et al. (2021); Hu et al. (2021); Lombardo et al. (2022)
Axis 2: Employment
E1: Reports on staff replacement due to the growing implementation of AI.	Adapted from Arias-Abelaira et al. (2023); Budhwar et al. (2023); K. Chen et al. (2024); Z. Chen (2023); Georgieff & Hyee (2022); Jazdauskaite et al. (2021); Khalifa et al. (2021); Merola (2022)
E2: Reports on the creation of new markets and job opportunities due to the development of AI technology.	Adapted from Budhwar et al. (2023); Khogali & Mekid (2023); Lazaroiu et al. (2024); Oncioiu et al. (2022)
E3: Reports on the impact of AI on the company's workplace, including productivity, performance, employment, and/or skills.	Adapted from Ashrafi et al. (2023); Drydakis (2024); Khalifa et al. (2021); Lazaroiu et al. (2024); Rožman et al. (2023)
E4: Reports on the comprehensive preparation required for workers to implement AI.	Adapted from Giordano et al. (2024); Grădinaru et al. (2024); Semtner et al. (2024); Uren & Edwards (2023)
E5: Report on job satisfaction assisted by AI.	Adapted from Jia (2022); H. Zhang & Zheng (2022)
E6: Report on improving employee experience with AI adoption.	Adapted from Yu et al. (2023)
E7: Reports on guides and training programs for HR managers on the use of AI.	Adapted from Chowdhury et al. (2023)
E8: Reports on how AI is changing work practices and business organization.	Adapted from Moniz et al. (2022); Yu et al. (2023)
E9: Reports on the implementation of AI in the company's human resources processes.	Adapted from Hajnić & Boshkoska (2021)
Axis 3: Reliability
F1: Reports on whether AI has a certain reliability.	Adapted from Ajah et al. (2024); Almarashda et al. (2021); Alshibly et al. (2024); Alzubaidi et al. (2023); J. Chen (2022); C. Fan et al. (2022); Latorre-Biel et al. (2021); Xiong (2022); Мохаммед & Ваххаб (2024)
F2: Reports on the reliability of the AI-based decision support system for economic management.	Adapted from Akdemir & Bulut (2024); Fonseca et al. (2024); Kumarappan et al. (2024); Zhao (2022)
F3: Reports on the reliability of using ChatGPT in business education.	Adapted from Vecchiarini & Somià (2023)
F4: Reports on objectives and measures to ensure trustworthy AI.	Adapted from AECA (2024); Suárez Giri & Sánchez Chaparro (2024)
F5: Reports on policies that ensure trustworthy AI.	Adapted from AECA (2024); Dahal et al. (2023)
F6: Reports on ethical guidelines for trustworthy AI.	Adapted from AECA (2024)
F7: Reports on the governing bodies responsible for overseeing the responsible development of AI.	Adapted from AECA (2024)
F8: Reports on roles in executing and overseeing AI development.	Adapted from AECA (2024); Talukder et al. (2024)
Axis 4: Innovation
I1: Reports on AI investments to innovate business models.	Adapted from Bustamante et al. (2024); Jorzik et al. (2023); Naeem et al. (2024)
I2: Reports on the use of AI in open innovation management.	Adapted from Bécue et al. (2024); Kuzior et al. (2023)
I3: Reports on whether AI has brought significant innovation to business management.	Adapted from M. Campbell & Jovanović (2024); Cui et al. (2022); Haefner et al. (2021)
I4: Reports on the factors influencing AI in knowledge innovation management.	Adapted from Hussain (2023); Q. Liu (2022)
I5: Reports on the use of AI in technological innovation.	Adapted from Gudigantala et al. (2023); D. Li et al. (2024); J. Liu et al. (2024)
I6: Report on new AI innovations in the supply chain.	Adapted from Braganza et al. (2022); Olan et al. (2022)
I7: Reports on government implications for promoting AI innovation.	Adapted from Beulen et al. (2022); Manta et al. (2024)
I8: Reports on the use of AI to drive business performance in terms of innovation.	Adapted from Dancausa Millán & Millán Vázquez de la Torre (2024); Samadhiya et al. (2024)
I9: Reports on the impact of AI-based innovation on decision-making.	Adapted from Coussement et al. (2024); Rajagopal et al. (2022)
I10: Reports on responsibility in AI innovation.	Adapted from Buhmann & Fieseler (2023); Xing et al. (2023)
I11: Reports on the benefits that AI can bring to the innovation process.	Adapted from Bahoo et al. (2023); Roberts & Candi (2024)
I12: Report on innovative marketing strategies with AI.	Adapted from Awad (2024); Islam et al. (2024)
I13: Reports on AI adoption and links to digital innovation.	Adapted from Saleem et al. (2023); Zeng et al. (2022)
I14: Reports on the benefits of AI in sustainable innovation.	Adapted from Gandía et al. (2025); Govindan (2022); Kolagar et al. (2024)
Axis 5: Transparency
T1: Report on transparency in decision-making with AI.	Adapted from AECA (2024); Booyse & Scheepers (2024); Chowdhury et al. (2023); Grandi et al. (2024; Ryan et al. (2024)
T2: Report on the transparency of high-impact algorithms.	Adapted from AECA (2024); Camilleri (2024); Holmström & Hällgren (2022); Xie (2021)
T3: Reports on whether AI improves the transparency of the investment process.	Adapted from Hajek & Novotny (2022); Sutiene et al. (2024)
T4: Report on whether they are using AI to improve their ESG performance and whether they are being transparent about how they do so.	Adapted from Sklavos et al. (2024); Suárez Giri & Sánchez Chaparro (2024)
T5: Reports on the level of transparency maintained and provided to all supply chain partners.	Adapted from Adamashvili et al. (2024); Manning et al. (2022); Modgil et al. (2022); Singh et al. (2024)
Axis 6: Sustainability
S1: Report on sustainable AI adoption.	Adapted from Balcıoğlu et al. (2024); Bolesnikov et al. (2022); Roumeliotis et al. (2023); Rusch et al. (2023); Sipola et al. (2023)
S2: Reports on the growing importance of environmental sustainability with the adoption of AI.	Adapted from Gao & Liu (2023); Jorzik et al. (2024); Ogbeibu et al. (2022); Sridhar et al. (2023)
S3: Reports on the role of AI in decision-making for sustainability.	Adapted from Ali et al. (2024); Bickley et al. (2024); Pisoni & Molnár (2024)
S4: Reports on the contribution of AI in improving supply chain management.	Adapted from Kazancoglu et al. (2023); Onyeaka et al. (2023); Remondino & Zanin (2022); Zaid et al. (2025)
S5: Report on sustainability in process automation with AI.	Adapted from Aydın & Turan (2023); Rowan et al. (2022); Tairov et al. (2024)
S6: Reports on the implications of AI for achieving several Sustainable Development Goals.	Adapted from Felfernig et al. (2023); Fonseca et al. (2024); Peng et al. (2023)
S7: Report on AI for Sustainable Reporting Analysis.	Adapted from Boloș et al. (2024); Vărzaru (2022)

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