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Navigating Emerging Advancements and Challenges in AI and Big Data...
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Navigating Emerging Advancements and Challenges in AI and Big Data Technologies for Business and Society

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 16091

Special Issue Editor

Prof. Dr. Michael Gerlich

E-Mail Website
Guest Editor
Center for Strategic Corporate Foresight and Sustainability, SBS Swiss Business School, 8302 Kloten, Switzerland
Interests: consumer behavior; AI and big data in marketing; sociology in marketing; change management; leadership; strategic analysis and foresight; changes in society; societal impact of artificial intelligence

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the dynamic landscape of Artificial Intelligence (AI) and Big Data technologies, focusing on both their transformative potential and the challenges they present for businesses and society. As AI and Big Data continue to drive innovation across industries, they raise critical questions about ethics, data privacy, workforce displacement, and equitable access to technological benefits. We invite researchers and practitioners to contribute original insights, methodologies, and case studies that not only highlight advancements in AI and Big Data, but also address the societal, economic, and regulatory challenges arising from their rapid adoption. This Special Issue seeks to create a balanced discourse on the opportunities and complexities of these technologies, fostering a deeper understanding of how organizations and societies can adapt and thrive in this evolving environment.

Prof. Dr. Michael Gerlich
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence (AI)
  • big data technologies
  • business innovation
  • societal impact of AI
  • ethical AI
  • data privacy and governance
  • workforce transformation
  • regulatory challenges in AI
  • sustainable technological development
  • digital transformation in business

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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31 pages, 767 KB  
Article
From Offloading to Engagement: An Experimental Study on Structured Prompting and Critical Reasoning with Generative AI
by Michael Gerlich
Data 2025, 10(11), 172; https://doi.org/10.3390/data10110172 - 30 Oct 2025
Cited by 2 | Viewed by 9341
Abstract
The rapid adoption of generative AI raises questions not only about its transformative potential but also about its cognitive and societal risks. This study contributes to the debate by presenting cross-country experimental data (n = 150; Germany, Switzerland, United Kingdom) on how [...] Read more.
The rapid adoption of generative AI raises questions not only about its transformative potential but also about its cognitive and societal risks. This study contributes to the debate by presenting cross-country experimental data (n = 150; Germany, Switzerland, United Kingdom) on how individuals engage with generative AI under different conditions: human-only, human + AI (unguided), human + AI (guided with structured prompting), and AI-only benchmarks. Across 450 evaluated responses, critical reasoning was assessed via expert rubric ratings, while perceived reflective engagement was captured through self-report indices. Results show that unguided AI use fosters cognitive offloading without improving reasoning quality, whereas structured prompting significantly reduces offloading and enhances both critical reasoning and reflective engagement. Mediation and latent class analyses reveal that guided AI use supports deeper human involvement and mitigates demographic disparities in performance. Beyond theoretical contributions, this study offers practical implications for business and society. As organisations integrate AI into workflows, unstructured use risks undermining workforce decision making and critical engagement. Structured prompting, by contrast, provides a scalable and low-cost governance tool that fosters responsible adoption, supports equitable access to technological benefits, and aligns with societal calls for human-centric AI. These findings highlight the dual nature of AI as both a productivity enabler and a cognitive risk, and position structured prompting as a promising intervention to navigate the emerging challenges of AI adoption in business and society. Full article
(This article belongs to the Special Issue Navigating Emerging Advancements and Challenges in AI and Big Data Technologies for Business and Society)
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21 pages, 2822 KB  
Article
Credit Evaluation of Technology-Based Small and Micro Enterprises: An Innovative Weighting Method Based on Machine Learning and AHP
by Bingya Wu, Zhihui Hu, Zhouyi Gu, Yuxi Zheng and Jiayan Lv
Data 2025, 10(1), 9; https://doi.org/10.3390/data10010009 - 14 Jan 2025
Cited by 3 | Viewed by 3602
Abstract
Technology-based small and micro enterprises play a crucial role in national economic and social development. Managing their credit risk effectively is key to ensuring their healthy growth. This study is based on corporate credit management theory and Wu’s three-dimensional credit theory. It clarifies [...] Read more.
Technology-based small and micro enterprises play a crucial role in national economic and social development. Managing their credit risk effectively is key to ensuring their healthy growth. This study is based on corporate credit management theory and Wu’s three-dimensional credit theory. It clarifies the credit concept and measurement logic of these enterprises, considering their unique development characteristics in China. A credit evaluation system is constructed, and an innovative method combining machine learning with comprehensive evaluation is proposed. This approach aims to assess the credit status of technology-based small and micro enterprises in a thorough and objective manner. The study finds that, first, the credit level of these enterprises is currently moderate, with little variation. Second, financial information remains a key factor in credit evaluation. Third, the ML-AHP (Machine Learning-Analytic Hierarchy Process) combined weighting method effectively integrates subjective experience with objective data, providing a more rational assessment. The findings provide theoretical references and practical guidance for the healthy development of technology-based small and micro enterprises, early credit risk warning, and improved financing efficiency. Full article
(This article belongs to the Special Issue Navigating Emerging Advancements and Challenges in AI and Big Data Technologies for Business and Society)
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Other

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15 pages, 1414 KB  
Data Descriptor
Self-Reported Data for Sustainable Development from People Living in Rural and Remote Areas
by Salem Ahmed Alabdali, Salvatore Flavio Pileggi and Gnana Bharathy
Data 2025, 10(1), 6; https://doi.org/10.3390/data10010006 - 8 Jan 2025
Cited by 1 | Viewed by 1840
Abstract
This paper describes a dataset for the Sustainable Development of remote and rural areas. Version 1.0 includes self-reported data, with a total of 212 valid responses collected in 2024 across different sectors (education, healthcare, and business) from people living in rural and remote [...] Read more.
This paper describes a dataset for the Sustainable Development of remote and rural areas. Version 1.0 includes self-reported data, with a total of 212 valid responses collected in 2024 across different sectors (education, healthcare, and business) from people living in rural and remote areas in Saudi Arabia. The structured survey is understood to support research endeavors and policy making, looking at the peculiar characteristics of those regions. The 40 core questions, in addition to the detailed demographic questions, aim to capture different perspectives and perceptions on innovative and sustainable solutions. Overall, the dataset offers valuable strategic insights to be integrated with other sources of information, as well as the opportunity to incrementally generate extensive and diverse knowledge in the field. The major limitation is inherently related to the local context, as data comes from the most educated persons with access to digital resources. Additionally, the dataset may be considered as relatively small, and there is some gender imbalance due to cultural factors. Full article
(This article belongs to the Special Issue Navigating Emerging Advancements and Challenges in AI and Big Data Technologies for Business and Society)
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