You can find the up-to-date list of Andrea’s publications and citation count on Google Scholar and ResearchGate.
ORCID
0000-0001-9050-5018
SCOPUS 16244425400
2026
Alessia Cantini; Andrea De Mauro
Better Prompts, Better Usefulness: A Systematic Review and Experimental Evaluation of Structured Prompting Techniques in Large Language Models Journal Article
In: Big Data and Cognitive Computing, vol. 10, no. 7, 2026, ISSN: 2504-2289.
@article{bdcc10070224,
title = {Better Prompts, Better Usefulness: A Systematic Review and Experimental Evaluation of Structured Prompting Techniques in Large Language Models},
author = {Alessia Cantini and Andrea De Mauro},
url = {https://www.mdpi.com/2504-2289/10/7/224
https://www.ademauro.com/wp-content/uploads/2026/07/BDCC-10-00224.pdf},
doi = {10.3390/bdcc10070224},
issn = {2504-2289},
year = {2026},
date = {2026-07-06},
urldate = {2026-07-06},
journal = {Big Data and Cognitive Computing},
volume = {10},
number = {7},
abstract = {Large Language Models (LLMs) have rapidly become central components of cognitive computing systems and AI-assisted knowledge work. However, the effectiveness of LLM-generated outputs depends not only on the model’s capabilities but also on the structure of the prompts used to guide them. This study investigates how structured prompting techniques influence perceived output usefulness in business-oriented tasks. First, we conduct a systematic literature review following PRISMA guidelines to identify, classify, and synthesize existing prompt enhancement strategies. The review leads to the development of a taxonomy distinguishing task-alignment techniques (e.g., one-shot and few-shot prompting) from reasoning-transparency techniques (e.g., Chain-of-Thought prompting). Building on this taxonomy, we design a controlled experimental study in which knowledge workers evaluate LLM-generated outputs across analytical and summarization tasks. Using linear mixed-effects modeling, we assess the impact of prompting techniques and the moderating role of Generative AI usage frequency. Results indicate that structured prompting significantly increases perceived usefulness compared to baseline approaches, with the combination of example-based conditioning and explicit reasoning scaffolding yielding the highest evaluations. The moderating effect of usage frequency is not statistically significant, suggesting that the benefits of structured prompt design are robust across different experience levels. These findings position prompt structure as a practical cognitive interface mechanism and provide evidence-based guidelines for enhancing human–AI interaction in cognitive computing environments.},
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Large Language Models (LLMs) have rapidly become central components of cognitive computing systems and AI-assisted knowledge work. However, the effectiveness of LLM-generated outputs depends not only on the model’s capabilities but also on the structure of the prompts used to guide them. This study investigates how structured prompting techniques influence perceived output usefulness in business-oriented tasks. First, we conduct a systematic literature review following PRISMA guidelines to identify, classify, and synthesize existing prompt enhancement strategies. The review leads to the development of a taxonomy distinguishing task-alignment techniques (e.g., one-shot and few-shot prompting) from reasoning-transparency techniques (e.g., Chain-of-Thought prompting). Building on this taxonomy, we design a controlled experimental study in which knowledge workers evaluate LLM-generated outputs across analytical and summarization tasks. Using linear mixed-effects modeling, we assess the impact of prompting techniques and the moderating role of Generative AI usage frequency. Results indicate that structured prompting significantly increases perceived usefulness compared to baseline approaches, with the combination of example-based conditioning and explicit reasoning scaffolding yielding the highest evaluations. The moderating effect of usage frequency is not statistically significant, suggesting that the benefits of structured prompt design are robust across different experience levels. These findings position prompt structure as a practical cognitive interface mechanism and provide evidence-based guidelines for enhancing human–AI interaction in cognitive computing environments.
Ferdinando Sasso; Andrea De Mauro
Perturbation-First Robustness Evaluation of Automated Short Answer Grading Proceedings Article
In: Horvath, Victoria; Deutsch, Nikolett (Ed.): IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers, pp. 1030–1037, IFKAD – Arts for Business, Budapest, Hungary, 2026, ISBN: 978-88-96687-20-8.
@inproceedings{sasso2026perturbation,
title = {Perturbation-First Robustness Evaluation of Automated Short Answer Grading},
author = {Ferdinando Sasso and Andrea De Mauro},
editor = {Victoria Horvath and Nikolett Deutsch},
url = {https://www.ademauro.com/wp-content/uploads/2026/07/SASSO-Pertubated-ASAG.pdf},
isbn = {978-88-96687-20-8},
year = {2026},
date = {2026-07-01},
urldate = {2026-07-01},
booktitle = {IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers},
pages = {1030–1037},
publisher = {IFKAD – Arts for Business},
address = {Budapest, Hungary},
abstract = {Automated Short Answer Grading (ASAG) is increasingly deployed in high-stakes educational contexts, yet the dominant evaluation paradigm focuses primarily on agreement with human raters, overlooking robustness as a complementary quality dimension. Robustness, defined as the consistency of grading behaviour under controlled, meaning-aware input transformations, bears directly on construct validity, the degree to which an assessment measures what it claims to measure, and conditions the trustworthiness of automated decisions in education. We propose a perturbation-first evaluation framework that assesses robustness across three validity-theoretic dimensions: invariance to construct-irrelevant variation, sensitivity to meaning-altering changes, and resistance to adversarial gaming. The framework combines rule-based perturbation generators, two-gate quality validation, and self-referential metrics that compare the grader against its own output rather than against gold labels, isolating robustness from accuracy. A dual-protocol design contrasts within-question evaluation, the optimistic scenario typically reported in the literature, with cross-question evaluation, the realistic deployment scenario, capturing how trained graders degrade under distribution shift. We apply the framework to a benchmark ASAG dataset of science short answers and compare a trained machine-learning baseline with zero-shot Large Language Model (LLM) graders evaluated at two prompting levels, with and without access to a reference answer. Three findings emerge. First, trained graders exhibit substantial robustness degradation under realistic cross-question evaluation, suggesting that standard within-question benchmarks overestimate operational reliability. Second, zero-shot LLM graders exhibit stronger robustness on invariance and sensitivity dimensions while remaining comparatively less vulnerable to adversarial manipulation, although residual sensitivity failures display a stable hierarchy across perturbation types. Finally, providing reference answers introduces a subtle trade-off, with improved consistency on meaning-preserving variations but increased susceptibility to keyword-based gaming, which is invisible to single-metric evaluation. We argue that multi-dimensional robustness evaluation should complement accuracy-based metrics in ASAG systems intended for high-stakes educational use, and we release an open-source framework that operationalises the approach and is freely available to the research community.},
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Automated Short Answer Grading (ASAG) is increasingly deployed in high-stakes educational contexts, yet the dominant evaluation paradigm focuses primarily on agreement with human raters, overlooking robustness as a complementary quality dimension. Robustness, defined as the consistency of grading behaviour under controlled, meaning-aware input transformations, bears directly on construct validity, the degree to which an assessment measures what it claims to measure, and conditions the trustworthiness of automated decisions in education. We propose a perturbation-first evaluation framework that assesses robustness across three validity-theoretic dimensions: invariance to construct-irrelevant variation, sensitivity to meaning-altering changes, and resistance to adversarial gaming. The framework combines rule-based perturbation generators, two-gate quality validation, and self-referential metrics that compare the grader against its own output rather than against gold labels, isolating robustness from accuracy. A dual-protocol design contrasts within-question evaluation, the optimistic scenario typically reported in the literature, with cross-question evaluation, the realistic deployment scenario, capturing how trained graders degrade under distribution shift. We apply the framework to a benchmark ASAG dataset of science short answers and compare a trained machine-learning baseline with zero-shot Large Language Model (LLM) graders evaluated at two prompting levels, with and without access to a reference answer. Three findings emerge. First, trained graders exhibit substantial robustness degradation under realistic cross-question evaluation, suggesting that standard within-question benchmarks overestimate operational reliability. Second, zero-shot LLM graders exhibit stronger robustness on invariance and sensitivity dimensions while remaining comparatively less vulnerable to adversarial manipulation, although residual sensitivity failures display a stable hierarchy across perturbation types. Finally, providing reference answers introduces a subtle trade-off, with improved consistency on meaning-preserving variations but increased susceptibility to keyword-based gaming, which is invisible to single-metric evaluation. We argue that multi-dimensional robustness evaluation should complement accuracy-based metrics in ASAG systems intended for high-stakes educational use, and we release an open-source framework that operationalises the approach and is freely available to the research community.
Laura Nucera; Davide D’Emiliano; Andrea De Mauro
Beyond Marketplace Reviews: Transformer-Based Sentiment Analysis of YouTube Influencer Ecosystems Proceedings Article
In: Horvath, Victoria; Deutsch, Nikolett (Ed.): IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers, pp. 764–771, IFKAD – Arts for Business, Budapest, Hungary, 2026, ISBN: 978-88-96687-20-8.
@inproceedings{nucera2026beyond,
title = {Beyond Marketplace Reviews: Transformer-Based Sentiment Analysis of YouTube Influencer Ecosystems},
author = {Laura Nucera and Davide D'Emiliano and Andrea De Mauro},
editor = {Victoria Horvath and Nikolett Deutsch},
url = {https://www.ademauro.com/wp-content/uploads/2026/07/NUCERA-Beyond-Marketplace-Reviews.pdf},
isbn = {978-88-96687-20-8},
year = {2026},
date = {2026-07-01},
urldate = {2026-07-01},
booktitle = {IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers},
pages = {764–771},
publisher = {IFKAD – Arts for Business},
address = {Budapest, Hungary},
abstract = {Online reviews and influencer-centred social media conversations jointly shape product understanding in digital markets, yet they are still often studied as separate streams. This gap is especially relevant in the beauty sector, where product evaluation depends on subjective experience, perceived efficacy, tolerability, routine fit, and trust in peer-generated accounts. This study develops a cross-platform comparison of Amazon reviews and YouTube comments related to three active skincare products from the same brand, to examine whether the emotional profiles emerging from marketplace reviews differ from those found in influencer-centred comment spaces, and whether YouTube comments reveal needs, doubts, and advice-seeking signals that remain less visible in marketplace environments. The analysis is based on a replicable pipeline that combines data collection, filtering, text preparation, and transformer-based emotion detection using RoBERTa GoEmotions. The findings show that Amazon and YouTube do not produce equivalent emotional profiles. Across the three products, Amazon tends to concentrate emotional expression around admiration and approval, whereas YouTube comments display a broader and more fragmented emotional repertoire. The paper contributes theoretically by integrating review research and influencer ecosystems within a common comparative perspective, methodologically by proposing a transparent cross-platform workflow, and managerially by showing how YouTube-centred social listening can support the detection of consumer concerns that remain underrepresented in marketplace reviews.},
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Online reviews and influencer-centred social media conversations jointly shape product understanding in digital markets, yet they are still often studied as separate streams. This gap is especially relevant in the beauty sector, where product evaluation depends on subjective experience, perceived efficacy, tolerability, routine fit, and trust in peer-generated accounts. This study develops a cross-platform comparison of Amazon reviews and YouTube comments related to three active skincare products from the same brand, to examine whether the emotional profiles emerging from marketplace reviews differ from those found in influencer-centred comment spaces, and whether YouTube comments reveal needs, doubts, and advice-seeking signals that remain less visible in marketplace environments. The analysis is based on a replicable pipeline that combines data collection, filtering, text preparation, and transformer-based emotion detection using RoBERTa GoEmotions. The findings show that Amazon and YouTube do not produce equivalent emotional profiles. Across the three products, Amazon tends to concentrate emotional expression around admiration and approval, whereas YouTube comments display a broader and more fragmented emotional repertoire. The paper contributes theoretically by integrating review research and influencer ecosystems within a common comparative perspective, methodologically by proposing a transparent cross-platform workflow, and managerially by showing how YouTube-centred social listening can support the detection of consumer concerns that remain underrepresented in marketplace reviews.
Davide Madonna; Simone Malacaria; Andrea De Mauro
Contextual–RFM (C-RFM): a Multimodal Framework for the Fusion of Transactional and Semantic Data Through LLMs for Interactive Customer Segmentation Proceedings Article
In: Horvath, Victoria; Deutsch, Nikolett (Ed.): IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers, pp. 1021–1029, IFKAD – Arts for Business, Budapest, Hungary, 2026, ISBN: 978-88-96687-20-8.
@inproceedings{madonna2026contextual,
title = {Contextual–RFM (C-RFM): a Multimodal Framework for the Fusion of Transactional and Semantic Data Through LLMs for Interactive Customer Segmentation},
author = {Davide Madonna and Simone Malacaria and Andrea De Mauro},
editor = {Victoria Horvath and Nikolett Deutsch},
url = {https://www.ademauro.com/wp-content/uploads/2026/07/MADONNA-CRFM.pdf},
isbn = {978-88-96687-20-8},
year = {2026},
date = {2026-07-01},
urldate = {2026-07-01},
booktitle = {IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers},
pages = {1021–1029},
publisher = {IFKAD – Arts for Business},
address = {Budapest, Hungary},
abstract = {This study examines the evolution of customer segmentation from rule-based systems to multimodal AI architectures, emphasizing the necessity for transparent and context-aware CRM strategies. Despite the widespread adoption of RFM models, organizations often struggle to synthesize transactional metrics with multifaceted qualitative feedback, creating a numerical paradox that this research addresses by capturing the authentic voice of the customer within high-dimensional analytical spaces. This paper presents the Contextual-RFM (C-RFM) framework, an innovative multimodal architecture aimed at bridging the representational divergence between quantitative metrics and qualitative feedback. The research operationalizes a comprehensive five-phase pipeline to ensure methodological transparency and replicability: (1) establishment of a statistical baseline, (2) narrative synthesis for tabular serialization, (3) multimodal vectorization utilizing advanced embeddings, (4) semantic analysis through Latent Dirichlet Allocation (LDA) and (5) implementation of the C-RFM Expert chatbot. This entire workflow is implemented using KNIME Analytics and Langflow, providing a robust and replicable process for both scholars and practitioners. A key distinction of the framework is its early-fusion approach. Unlike traditional late-fusion models that integrate data streams only at the final analysis stage, early-fusion retains critical cross-modal correlations during the initial processing phase, effectively addressing the semantic opacity common in traditional numerical-only datasets. Empirical findings indicate that the C-RFM framework adeptly organizes linguistic complexity, evidenced by a significant improvement in structural validity when transitioning from purely transactional environments to topic-validated contexts. Moreover, this study introduces the Discrepancy Detection Rate (DDR) as a novel performance metric to quantify the misalignment between transactional behaviour and qualitative feedback, thereby identifying critical churn risks that conventional quantitative analyses often overlook. The primary contribution of this research lies in the synergy achieved between human expertise and Artificial Intelligence. By deploying dynamic persona chatbots grounded in Retrieval-Augmented Generation (RAG), the framework facilitates a shift from opaque analytical outputs to actionable, customized recovery strategies. This glass-box methodology transforms intricate numerical complexities into auditable business narratives, empowering decision-makers to transcend statistical constructs and foster a deeper, dialogue-based understanding of consumers. The C-RFM framework represents a significant advancement in human-centred AI, promoting strategic marketing intelligence that is not only transparent but also finely tuned to the authentic voices of real-world consumer archetypes, thereby paving the way for the next generation of customer-centric strategies.},
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This study examines the evolution of customer segmentation from rule-based systems to multimodal AI architectures, emphasizing the necessity for transparent and context-aware CRM strategies. Despite the widespread adoption of RFM models, organizations often struggle to synthesize transactional metrics with multifaceted qualitative feedback, creating a numerical paradox that this research addresses by capturing the authentic voice of the customer within high-dimensional analytical spaces. This paper presents the Contextual-RFM (C-RFM) framework, an innovative multimodal architecture aimed at bridging the representational divergence between quantitative metrics and qualitative feedback. The research operationalizes a comprehensive five-phase pipeline to ensure methodological transparency and replicability: (1) establishment of a statistical baseline, (2) narrative synthesis for tabular serialization, (3) multimodal vectorization utilizing advanced embeddings, (4) semantic analysis through Latent Dirichlet Allocation (LDA) and (5) implementation of the C-RFM Expert chatbot. This entire workflow is implemented using KNIME Analytics and Langflow, providing a robust and replicable process for both scholars and practitioners. A key distinction of the framework is its early-fusion approach. Unlike traditional late-fusion models that integrate data streams only at the final analysis stage, early-fusion retains critical cross-modal correlations during the initial processing phase, effectively addressing the semantic opacity common in traditional numerical-only datasets. Empirical findings indicate that the C-RFM framework adeptly organizes linguistic complexity, evidenced by a significant improvement in structural validity when transitioning from purely transactional environments to topic-validated contexts. Moreover, this study introduces the Discrepancy Detection Rate (DDR) as a novel performance metric to quantify the misalignment between transactional behaviour and qualitative feedback, thereby identifying critical churn risks that conventional quantitative analyses often overlook. The primary contribution of this research lies in the synergy achieved between human expertise and Artificial Intelligence. By deploying dynamic persona chatbots grounded in Retrieval-Augmented Generation (RAG), the framework facilitates a shift from opaque analytical outputs to actionable, customized recovery strategies. This glass-box methodology transforms intricate numerical complexities into auditable business narratives, empowering decision-makers to transcend statistical constructs and foster a deeper, dialogue-based understanding of consumers. The C-RFM framework represents a significant advancement in human-centred AI, promoting strategic marketing intelligence that is not only transparent but also finely tuned to the authentic voices of real-world consumer archetypes, thereby paving the way for the next generation of customer-centric strategies.
Gabriele Creta; Andrea De Mauro
Explainable AI as Decision Support in Automated Assessment: An Exploratory Study Proceedings Article
In: Horvath, Victoria; Deutsch, Nikolett (Ed.): IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers, pp. 1141–1148, IFKAD – Arts for Business, Budapest, Hungary, 2026, ISBN: 978-88-96687-20-8.
@inproceedings{creta2026explainable,
title = {Explainable AI as Decision Support in Automated Assessment: An Exploratory Study},
author = {Gabriele Creta and Andrea De Mauro},
editor = {Victoria Horvath and Nikolett Deutsch},
url = {https://www.ademauro.com/wp-content/uploads/2026/07/CRETA-ASAG.pdf},
isbn = {978-88-96687-20-8},
year = {2026},
date = {2026-07-01},
urldate = {2026-07-01},
booktitle = {IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers},
pages = {1141–1148},
publisher = {IFKAD – Arts for Business},
address = {Budapest, Hungary},
abstract = {This paper investigates how rubric-based Explainable Artificial Intelligence (XAI) outputs influence instructor trust and grading behavior in AI-assisted academic assessment. As AI technologies become increasingly integrated into educational evaluation workflows, understanding the cognitive mechanisms through which instructors interact with AI-generated scores is critical for designing effective human–AI collaboration in grading and for the broader governance of knowledge assets in higher education (Khosravi et al., 2022). Two competing hypotheses motivate the study: explanations may either mitigate automation bias by enabling critical evaluation, or amplify it by providing a rationalization for uncritical acceptance of AI recommendations. We present a within-subject pilot experiment in which university-level evaluators (n = 3) assessed 12 short student responses to Computer Science questions under three conditions: (a) AI-only, where only a numerical AI score was displayed; (b) AI + Explanation, where the AI score was accompanied by a four-criterion rubric breakdown and a textual justification; and (c) Anchoring, where evaluators first graded independently and then were exposed to the AI output with an option to revise. AI grading outputs were generated using a Retrieval-Augmented Generation (RAG) pipeline calibrated against three open-access essay scoring datasets (GRE Analytical Writing, DREsS, and ASAP2) and indexed in a FAISS vector store for similarity retrieval. Preliminary results from this pilot are consistent with rubric-based explanations reducing deviation between human and AI grades (mean |delta| = 1.2 vs. 1.9 for AI-only), doubling the observed revision rate in the anchoring condition (67% vs. 33%), and shifting all observed revisions toward the AI score. Qualitative evidence indicates that evaluators cite specific AI observations when revising with explanations but resist adjustment without them. A discrepancy between self-reported and observed AI influence further suggests a metacognitive blind spot consistent with the automation-bias literature. These preliminary findings are consistent with the hypothesis that structured XAI outputs may enhance evaluator reliance on AI grading systems and reshape evaluative judgment through anchoring mechanisms, with implications for the governance of human–AI assessment workflows in higher education.},
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This paper investigates how rubric-based Explainable Artificial Intelligence (XAI) outputs influence instructor trust and grading behavior in AI-assisted academic assessment. As AI technologies become increasingly integrated into educational evaluation workflows, understanding the cognitive mechanisms through which instructors interact with AI-generated scores is critical for designing effective human–AI collaboration in grading and for the broader governance of knowledge assets in higher education (Khosravi et al., 2022). Two competing hypotheses motivate the study: explanations may either mitigate automation bias by enabling critical evaluation, or amplify it by providing a rationalization for uncritical acceptance of AI recommendations. We present a within-subject pilot experiment in which university-level evaluators (n = 3) assessed 12 short student responses to Computer Science questions under three conditions: (a) AI-only, where only a numerical AI score was displayed; (b) AI + Explanation, where the AI score was accompanied by a four-criterion rubric breakdown and a textual justification; and (c) Anchoring, where evaluators first graded independently and then were exposed to the AI output with an option to revise. AI grading outputs were generated using a Retrieval-Augmented Generation (RAG) pipeline calibrated against three open-access essay scoring datasets (GRE Analytical Writing, DREsS, and ASAP2) and indexed in a FAISS vector store for similarity retrieval. Preliminary results from this pilot are consistent with rubric-based explanations reducing deviation between human and AI grades (mean |delta| = 1.2 vs. 1.9 for AI-only), doubling the observed revision rate in the anchoring condition (67% vs. 33%), and shifting all observed revisions toward the AI score. Qualitative evidence indicates that evaluators cite specific AI observations when revising with explanations but resist adjustment without them. A discrepancy between self-reported and observed AI influence further suggests a metacognitive blind spot consistent with the automation-bias literature. These preliminary findings are consistent with the hypothesis that structured XAI outputs may enhance evaluator reliance on AI grading systems and reshape evaluative judgment through anchoring mechanisms, with implications for the governance of human–AI assessment workflows in higher education.
Beren Çay; Mario Miozza; Andrea De Mauro
Retrieval-Augmented Generation in Business Applications: A Systematic Literature Review and Taxonomy Proceedings Article
In: Horvath, Victoria; Deutsch, Nikolett (Ed.): IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers, pp. 1053–1061, IFKAD – Arts for Business, Budapest, Hungary, 2026, ISBN: 978-88-96687-20-8.
@inproceedings{cay2026retrieval,
title = {Retrieval-Augmented Generation in Business Applications: A Systematic Literature Review and Taxonomy},
author = {Beren Çay and Mario Miozza and Andrea De Mauro},
editor = {Victoria Horvath and Nikolett Deutsch},
url = {https://www.ademauro.com/wp-content/uploads/2026/07/CAY-RAG-SLR-1.pdf},
isbn = {978-88-96687-20-8},
year = {2026},
date = {2026-07-01},
urldate = {2026-07-01},
booktitle = {IFKAD 2026 – Intelligent Knowledge for Sustainable Organizations: Book of Papers},
pages = {1053–1061},
publisher = {IFKAD – Arts for Business},
address = {Budapest, Hungary},
abstract = {Retrieval-Augmented Generation (RAG) has become one of the most visible mechanisms for grounding large language model outputs in organizational knowledge sources. While the technical literature on RAG architectures is substantial, the evidence on how RAG is deployed inside organizations, and on the organizational consequences of those deployments, has not been systematically synthesized. This paper addresses that gap by treating RAG as a hybrid knowledge capability pairing a generative model with curated organizational knowledge and human oversight. The study reports a systematic literature review of peer-reviewed work on RAG in business and organizational contexts published between 2023 and 23 February 2026, covering Scopus and Web of Science in line with the PRISMA 2020 reporting framework. From 727 records after database-side filtering, 513 were screened and 41 retained. The studies were synthesized through the empirical-to-conceptual taxonomy development method of Nickerson, Varshney and Muntermann (2013) into a three-dimensional framework linking business application contexts, RAG architectures, and reported organizational consequences. Three findings stand out. First, adoption concentrates in knowledge-intensive support functions: enterprise knowledge retrieval, document intelligence, and decision support together account for around seventy percent of the corpus. Second, Classic RAG remains the most frequent single configuration, while Advanced and Agentic variants together appear in 18 studies and concentrate in deployments with more demanding task structures. Third, the most consistent finding is not a benefit but a limitation: residual output quality issues appear in 26 of 41 studies and recur across every architectural category and every application context, while data-quality dependence is the most frequent implementation challenge. The paper contributes a business-oriented taxonomy empirically distinct from architecture-first surveys, positioning RAG as a socio-technical capability whose organizational value depends on the surrounding data, governance, and oversight infrastructure. For practitioners, it supports deliberate decisions about where RAG delivers value, which architecture fits which task, and which conditions enable successful deployment.},
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Retrieval-Augmented Generation (RAG) has become one of the most visible mechanisms for grounding large language model outputs in organizational knowledge sources. While the technical literature on RAG architectures is substantial, the evidence on how RAG is deployed inside organizations, and on the organizational consequences of those deployments, has not been systematically synthesized. This paper addresses that gap by treating RAG as a hybrid knowledge capability pairing a generative model with curated organizational knowledge and human oversight. The study reports a systematic literature review of peer-reviewed work on RAG in business and organizational contexts published between 2023 and 23 February 2026, covering Scopus and Web of Science in line with the PRISMA 2020 reporting framework. From 727 records after database-side filtering, 513 were screened and 41 retained. The studies were synthesized through the empirical-to-conceptual taxonomy development method of Nickerson, Varshney and Muntermann (2013) into a three-dimensional framework linking business application contexts, RAG architectures, and reported organizational consequences. Three findings stand out. First, adoption concentrates in knowledge-intensive support functions: enterprise knowledge retrieval, document intelligence, and decision support together account for around seventy percent of the corpus. Second, Classic RAG remains the most frequent single configuration, while Advanced and Agentic variants together appear in 18 studies and concentrate in deployments with more demanding task structures. Third, the most consistent finding is not a benefit but a limitation: residual output quality issues appear in 26 of 41 studies and recur across every architectural category and every application context, while data-quality dependence is the most frequent implementation challenge. The paper contributes a business-oriented taxonomy empirically distinct from architecture-first surveys, positioning RAG as a socio-technical capability whose organizational value depends on the surrounding data, governance, and oversight infrastructure. For practitioners, it supports deliberate decisions about where RAG delivers value, which architecture fits which task, and which conditions enable successful deployment.
Valentina Ferreri; Simone Malacaria; Andrea De Mauro
The double edge of generative AI: identifying and addressing gender bias through LLMs Proceedings Article
In: Proceedings of International Conference on AI-Generated Content (AIGC 2025), pp. 268–273, SPIE 2026.
@inproceedings{ferreri2026double,
title = {The double edge of generative AI: identifying and addressing gender bias through LLMs},
author = {Valentina Ferreri and Simone Malacaria and Andrea De Mauro},
url = {https://www.ademauro.com/wp-content/uploads/2026/05/PREPRINT-The-Double-Edge-of-Generative-AI.pdf},
doi = {10.1117/12.3109109},
year = {2026},
date = {2026-04-08},
urldate = {2026-01-01},
booktitle = {Proceedings of International Conference on AI-Generated Content (AIGC 2025)},
volume = {14183},
pages = {268–273},
organization = {SPIE},
abstract = {
Generative AI is changing the way text is created and interpreted, but it also has the potential to reproduce gender stereotypes present in data and speech. This work explores both sides of this paradox to offer a framework for recognizing and addressing gender bias in short texts using large language models. We built a pipeline designed for prompts (Langflow) that assigns a bias score (0 to 4) to each sentence; the result is sorted into binary labels as a measure compared with the human annotation. The approach is validated on complementary datasets (conversational, minimal pairs, and online speech) and stress-tested on an additional synthetically generated set to assess its generalizability. To translate these capabilities into something that can be used routinely, we developed a prototype mobile application that performs realtime analysis, exposes a sensitivity slider, and provides short explanations to support behavioral change, along with inclusive rewrites. The results suggest strong alignment with human judgments on conversational texts, moderate alignment on syntactically minimal examples, and strong detection capability across all language representative of realworld language; revealing both the potential and limitations of LLMs to support sociolinguistic purposes. We explore the implications for responsible AI and offer design principles, transparent criteria, user-centric feedback, and guardrails that allow technical detection to be combined with practical mitigation.},
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Generative AI is changing the way text is created and interpreted, but it also has the potential to reproduce gender stereotypes present in data and speech. This work explores both sides of this paradox to offer a framework for recognizing and addressing gender bias in short texts using large language models. We built a pipeline designed for prompts (Langflow) that assigns a bias score (0 to 4) to each sentence; the result is sorted into binary labels as a measure compared with the human annotation. The approach is validated on complementary datasets (conversational, minimal pairs, and online speech) and stress-tested on an additional synthetically generated set to assess its generalizability. To translate these capabilities into something that can be used routinely, we developed a prototype mobile application that performs realtime analysis, exposes a sensitivity slider, and provides short explanations to support behavioral change, along with inclusive rewrites. The results suggest strong alignment with human judgments on conversational texts, moderate alignment on syntactically minimal examples, and strong detection capability across all language representative of realworld language; revealing both the potential and limitations of LLMs to support sociolinguistic purposes. We explore the implications for responsible AI and offer design principles, transparent criteria, user-centric feedback, and guardrails that allow technical detection to be combined with practical mitigation.
2025
Andrea Sestino; Luigi Nasta; Andrea De Mauro
Taxonomy of Generative Artificial Intelligence (Gen-AI) Use in Marketing Book Chapter
In: Ladhari, Riadh (Ed.): Encyclopedia of Artificial Intelligence in Marketing, pp. 1–11, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-75316-9.
@inbook{Sestino2025,
title = {Taxonomy of Generative Artificial Intelligence (Gen-AI) Use in Marketing},
author = {Andrea Sestino and Luigi Nasta and Andrea De Mauro},
editor = {Riadh Ladhari},
url = {https://www.ademauro.com/wp-content/uploads/2025/12/Taxonomy-of-Generative-Artificial-Intelligence-Gen-AI-Use-in-Marketing-Sestino-Nasta-De-Mauro-2026-PREPRINT.pdf},
doi = {10.1007/978-3-031-75316-9_84-1},
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year = {2025},
date = {2025-12-27},
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publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Generative Artificial Intelligence (AI) is reshaping marketing by enabling hyper-personalized content, real-time consumer engagement, and the automation of marketing processes. While it unlocks substantial opportunities for innovation and efficiency, it also presents challenges related to consumer trust, ethical governance, and organizational preparedness. This entry offers a comprehensive examination of generative Artificial Intelligence in marketing, distinguishing it from broader Artificial Intelligence applications and analyzing its implications for strategy, creativity, and operations. To structure this rapidly evolving field, a taxonomy of generative Artificial Intelligence applications is proposed, distinguishing between consumer-facing and business-facing domains and encompassing key areas such as shopping personalization, consumption experience, decision support, and financial optimization. The entry concludes by identifying key avenues for future research, with particular attention to organizational, ethical, and cultural dimensions that are likely to shape the trajectory of generative Artificial Intelligence in marketing.},
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Generative Artificial Intelligence (AI) is reshaping marketing by enabling hyper-personalized content, real-time consumer engagement, and the automation of marketing processes. While it unlocks substantial opportunities for innovation and efficiency, it also presents challenges related to consumer trust, ethical governance, and organizational preparedness. This entry offers a comprehensive examination of generative Artificial Intelligence in marketing, distinguishing it from broader Artificial Intelligence applications and analyzing its implications for strategy, creativity, and operations. To structure this rapidly evolving field, a taxonomy of generative Artificial Intelligence applications is proposed, distinguishing between consumer-facing and business-facing domains and encompassing key areas such as shopping personalization, consumption experience, decision support, and financial optimization. The entry concludes by identifying key avenues for future research, with particular attention to organizational, ethical, and cultural dimensions that are likely to shape the trajectory of generative Artificial Intelligence in marketing.
2024
Andrea De Mauro; Michele Pacifico
The Financial Times Guide to Data-Driven Transformation: Maximise Business Value with Data Analytics Book
Pearson, 2024, ISBN: 9781292462141.
@book{DataTransformation,
title = {The Financial Times Guide to Data-Driven Transformation: Maximise Business Value with Data Analytics},
author = {Andrea De Mauro and Michele Pacifico},
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isbn = {9781292462141},
year = {2024},
date = {2024-08-28},
publisher = {Pearson},
abstract = {This book will help managers maximise the business value of data to their organisation. Despite data analytics and AI being recognised as critical value drivers for organisations, less than half of the companies report that they have created a data-driven organisation or established a data culture yet. Business leaders and analytics managers are currently struggling to move from theory to practice, and this book is all about solving this problem for them. This indispensable guide provides an actionable path filled with practical tools to navigate and accelerate the journey of data-driven transformation.
• Understand the underlying trends and models driving change and accelerate digital transformation in your company.
• Learn from real-world examples that show how companies of any size and industry have successfully navigated their data journeys.
• Utilise immediately applicable templates and frameworks that cover everything from data governance and technology enablers to organisational setup choices and AI deployment strategies.
• Benefit from straightforward advice and checklists, ensuring you can plan and lead data transformation effectively within your organisation.
Written by recognised experts in the field, this book simplifies complex topics such as artificial intelligence, machine learning, and data management into manageable segments. Whether you're a top manager needing to frame the right questions, a data leader aiming to link technical aspects to business value, or a practitioner looking to enhance your role, this book is designed to help you make a substantial impact on your organisation's data culture and capabilities.},
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This book will help managers maximise the business value of data to their organisation. Despite data analytics and AI being recognised as critical value drivers for organisations, less than half of the companies report that they have created a data-driven organisation or established a data culture yet. Business leaders and analytics managers are currently struggling to move from theory to practice, and this book is all about solving this problem for them. This indispensable guide provides an actionable path filled with practical tools to navigate and accelerate the journey of data-driven transformation.
• Understand the underlying trends and models driving change and accelerate digital transformation in your company.
• Learn from real-world examples that show how companies of any size and industry have successfully navigated their data journeys.
• Utilise immediately applicable templates and frameworks that cover everything from data governance and technology enablers to organisational setup choices and AI deployment strategies.
• Benefit from straightforward advice and checklists, ensuring you can plan and lead data transformation effectively within your organisation.
Written by recognised experts in the field, this book simplifies complex topics such as artificial intelligence, machine learning, and data management into manageable segments. Whether you’re a top manager needing to frame the right questions, a data leader aiming to link technical aspects to business value, or a practitioner looking to enhance your role, this book is designed to help you make a substantial impact on your organisation’s data culture and capabilities.
• Understand the underlying trends and models driving change and accelerate digital transformation in your company.
• Learn from real-world examples that show how companies of any size and industry have successfully navigated their data journeys.
• Utilise immediately applicable templates and frameworks that cover everything from data governance and technology enablers to organisational setup choices and AI deployment strategies.
• Benefit from straightforward advice and checklists, ensuring you can plan and lead data transformation effectively within your organisation.
Written by recognised experts in the field, this book simplifies complex topics such as artificial intelligence, machine learning, and data management into manageable segments. Whether you’re a top manager needing to frame the right questions, a data leader aiming to link technical aspects to business value, or a practitioner looking to enhance your role, this book is designed to help you make a substantial impact on your organisation’s data culture and capabilities.
Mohamad Almgerbi; Andrea De Mauro; Adham Kahlawi; Valentina Poggioni
Data analytics job skills dynamics: a preliminary longitudinal analysis Proceedings Article
In: Fabrizi, Elena; Giambona, Francesca Adele; Marini, Caterina; Marletta, Andrea; Rocca, Antonella (Ed.): Proceedings of ICES 2024 – 2nd Italian Conference on Economic Statistics, pp. 9-13, 2024, ISBN: 9788847629509.
@inproceedings{DataJobs_2024,
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author = {Mohamad Almgerbi and Andrea De Mauro and Adham Kahlawi and Valentina Poggioni},
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abstract = {This study investigates the evolving role of Data Analytics between 2019 and 2023, analyzing the shifting job market demands for professionals in this field. Utilizing a corpus of job postings, we employed Latent Dirichlet Allocation (LDA) for topic modeling to discern patterns in required skills and expertise. Our findings indicate a marked increase in data analytics roles specialization and an increasing prominence of Machine Learning.},
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This study investigates the evolving role of Data Analytics between 2019 and 2023, analyzing the shifting job market demands for professionals in this field. Utilizing a corpus of job postings, we employed Latent Dirichlet Allocation (LDA) for topic modeling to discern patterns in required skills and expertise. Our findings indicate a marked increase in data analytics roles specialization and an increasing prominence of Machine Learning.
2023
Simone Malacaria; Michele Grimaldi; Marco Greco; Andrea De Mauro
Business Talk: Harnessing Generative AI with Data Analytics Maturity Journal Article
In: International Journal on Cybernetics & Informatics, vol. 12, no. 7, pp. 1-10, 2023, ISSN: 2277-548X.
@article{Malacaria_2023,
title = {Business Talk: Harnessing Generative AI with Data Analytics Maturity},
author = {Simone Malacaria and Michele Grimaldi and Marco Greco and Andrea De Mauro},
url = {https://ijcionline.com/paper/12/12723ijci01.pdf},
doi = {10.5121/ijci.2023.120701},
issn = {2277-548X},
year = {2023},
date = {2023-12-30},
urldate = {2023-12-30},
journal = {International Journal on Cybernetics & Informatics},
volume = {12},
number = {7},
pages = {1-10},
abstract = {Generative AI applications offer transformative potential for business operations, yet their adoption introduces substantial challenges. This paper utilizes the CBDAS data maturity model to pinpoint pivotal success factors for seamless generative AI integration in businesses. Through a comprehensive analysis of these factors, we underscore the essentials of generative AI deployment: cohesive architecture, robust data governance, and a data-centric corporate ethos. The study also highlights the hurdles and facilitators influencing its implementation. Key findings suggest that fostering a data-friendly culture, combined with structured governance, optimizes generative AI adoption. The paper culminates in presenting the practical implications of these insights, urging further exploration into the real-world efficacy of the proposed recommendations.},
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Generative AI applications offer transformative potential for business operations, yet their adoption introduces substantial challenges. This paper utilizes the CBDAS data maturity model to pinpoint pivotal success factors for seamless generative AI integration in businesses. Through a comprehensive analysis of these factors, we underscore the essentials of generative AI deployment: cohesive architecture, robust data governance, and a data-centric corporate ethos. The study also highlights the hurdles and facilitators influencing its implementation. Key findings suggest that fostering a data-friendly culture, combined with structured governance, optimizes generative AI adoption. The paper culminates in presenting the practical implications of these insights, urging further exploration into the real-world efficacy of the proposed recommendations.
Simone Malacaria; Andrea De Mauro; Marco Greco; Michele Grimaldi
An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity Journal Article
In: SN Computer Science, vol. 4, no. 5, pp. 696, 2023.
@article{malacaria2023application,
title = {An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity},
author = {Simone Malacaria and Andrea De Mauro and Marco Greco and Michele Grimaldi},
url = {https://link.springer.com/content/pdf/10.1007/s42979-023-02065-9.pdf},
doi = {10.1007/s42979-023-02065-9},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
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publisher = {Springer Nature Singapore Singapore},
abstract = {The aim of this study is to evaluate data maturity of a sample of Italian firms of different sectors and sizes, obtained through an online assessment submitted to 261 professionals and entrepreneurs operating in the data/IT domain. The paper's objective is to assess the relative importance of the factors that determine the success of big data initiatives, according to the company structure and managerial perspective. The questionnaire was digitally submitted to IT professionals and decision-makers in Italy through the LinkedIn platform. The assessment was divided into two sections: the first focused on the assessment of 8 critical success factors for big data, whereas the second assigned weights based on an application of the analytic hierarchy process. The result of this process is a weighted-scores system that reflects the relative importance that managers and employees give to different domains. Respondents agreed to the importance of integrated architecture, data-friendly corporate culture, and integrated organization domains. Once the results consider the weights from the AHP, data friendliness becomes the most sought-after characteristic. The findings provide direction for further development of this assessment system.},
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The aim of this study is to evaluate data maturity of a sample of Italian firms of different sectors and sizes, obtained through an online assessment submitted to 261 professionals and entrepreneurs operating in the data/IT domain. The paper’s objective is to assess the relative importance of the factors that determine the success of big data initiatives, according to the company structure and managerial perspective. The questionnaire was digitally submitted to IT professionals and decision-makers in Italy through the LinkedIn platform. The assessment was divided into two sections: the first focused on the assessment of 8 critical success factors for big data, whereas the second assigned weights based on an application of the analytic hierarchy process. The result of this process is a weighted-scores system that reflects the relative importance that managers and employees give to different domains. Respondents agreed to the importance of integrated architecture, data-friendly corporate culture, and integrated organization domains. Once the results consider the weights from the AHP, data friendliness becomes the most sought-after characteristic. The findings provide direction for further development of this assessment system.
Andrea Sestino; Adham Kahlawi; Andrea De Mauro
Decoding the data economy: A literature review of its impact on business, society and digital transformation Journal Article
In: European Journal of Innovation Management, 2023, ISSN: 1460-1060.
@article{sestino2023decoding,
title = {Decoding the data economy: A literature review of its impact on business, society and digital transformation},
author = {Andrea Sestino and Adham Kahlawi and Andrea De Mauro},
url = {/wp-content/uploads/2023/10/PREPRINT-Decoding-the-Data-Economy-Sestino-Kahlawi-De-Mauro-2023.pdf},
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date = {2023-01-01},
urldate = {2023-01-01},
journal = {European Journal of Innovation Management},
publisher = {Emerald Publishing Limited},
abstract = {Purpose
The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.
Design/methodology/approach
The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.
Findings
The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.
Practical implications
Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.
Social implications
Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.
Originality/value
This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.},
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Purpose
The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.
Design/methodology/approach
The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.
Findings
The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.
Practical implications
Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.
Social implications
Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.
Originality/value
This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.
The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.
Design/methodology/approach
The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.
Findings
The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.
Practical implications
Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.
Social implications
Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.
Originality/value
This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.
2022
Andrea De Mauro; Andrea Sestino; Andrea Bacconi
Machine learning and artificial intelligence use in marketing: a general taxonomy Journal Article
In: Italian Journal of Marketing, vol. 2022, no. 4, pp. 439–457, 2022.
@article{de2022machine,
title = {Machine learning and artificial intelligence use in marketing: a general taxonomy},
author = {Andrea De Mauro and Andrea Sestino and Andrea Bacconi},
url = {https://link.springer.com/content/pdf/10.1007/s43039-022-00057-w.pdf},
doi = {10.1007/s43039-022-00057-w},
year = {2022},
date = {2022-06-24},
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publisher = {Springer International Publishing Cham},
abstract = {The emergence of consumer-generated data and the growing availability of Machine Learning (ML) techniques are revolutionizing marketing practices. Marketers and researchers are far from having a thorough understanding of the broad range of opportunities ML applications offer in creating and maintaining a competitive business advantage. In this paper, we propose a taxonomy of ML use cases in marketing based on a systematic review of academic and business literature. We have identified 11 recurring use cases, organized in 4 homogeneous families which correspond to the fundamentals leverage areas of ML in marketing, namely: shopper fundamentals, consumption experience, decision making, and financial impact. We discuss the recurring patterns identified in the taxonomy and provide a conceptual framework for its interpretation and extension, highlighting practical implications for marketers and researchers.},
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The emergence of consumer-generated data and the growing availability of Machine Learning (ML) techniques are revolutionizing marketing practices. Marketers and researchers are far from having a thorough understanding of the broad range of opportunities ML applications offer in creating and maintaining a competitive business advantage. In this paper, we propose a taxonomy of ML use cases in marketing based on a systematic review of academic and business literature. We have identified 11 recurring use cases, organized in 4 homogeneous families which correspond to the fundamentals leverage areas of ML in marketing, namely: shopper fundamentals, consumption experience, decision making, and financial impact. We discuss the recurring patterns identified in the taxonomy and provide a conceptual framework for its interpretation and extension, highlighting practical implications for marketers and researchers.
Simone Malacaria; Andrea De Mauro; Marco Greco; Michele Grimaldi; Benito Mignacca
Toward the implementation of a Consensual Maturity Model for Big Data in Consumer Goods companies Proceedings Article
In: Proceedings IFKAD 2022, Lugano, Switzerland 20-22 June 2022, Knowledge Drivers for Resilience and Transformation, pp. 2380-2402, 2022, ISBN: 9788896687154.
@inproceedings{Malacaria_2022_conf,
title = {Toward the implementation of a Consensual Maturity Model for Big Data in Consumer Goods companies},
author = {Simone Malacaria and Andrea De Mauro and Marco Greco and Michele Grimaldi and Benito Mignacca},
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abstract = {This paper presents the Consensual Big Data Maturity Assessment System (CBDAS) implementation in a multinational company leader in the Consumer Goods sector. The business case illustrates the objective and the approach which has been taken with the CBDAS initiative. The paper aims to justify the assessment system as a dynamic and flexible system for enterprises operating in the Consumer Good sector. It can be leveraged to understand the maturity stage in the big data domain and guide organizations about their status of advancement in proposing successful big data initiatives. Some results of the first cycle of evaluation by the Senior Managers and IT decision-makers of Procter & Gamble Company are pinpointed to illustrate the advantages and the exchange of good practices following the evaluation.
The paper introduces the CBDAS initiative, implemented on a web application, organized in eight business-relevant domains, comprehensively covering all aspects impacting big data initiatives' success. The assessment contains weights to evaluate the corresponding relevance of a certain domain within the organization's reality.
Company data activities generate value in synergy with other assets. Therefore, to estimate whether it is a priority to intervene, i.e., on the technologies, data strategies, or organizational culture, we isolate the processes and flows deriving from data initiatives in the company, mapping two exemplary processes to intercept priority actions of intervention. Therefore, by determining the type of interventions on processes and maturity levels in each data maturity domain, we derived concrete actions to bridge the existing maturity gap in higher priority areas.},
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This paper presents the Consensual Big Data Maturity Assessment System (CBDAS) implementation in a multinational company leader in the Consumer Goods sector. The business case illustrates the objective and the approach which has been taken with the CBDAS initiative. The paper aims to justify the assessment system as a dynamic and flexible system for enterprises operating in the Consumer Good sector. It can be leveraged to understand the maturity stage in the big data domain and guide organizations about their status of advancement in proposing successful big data initiatives. Some results of the first cycle of evaluation by the Senior Managers and IT decision-makers of Procter & Gamble Company are pinpointed to illustrate the advantages and the exchange of good practices following the evaluation.
The paper introduces the CBDAS initiative, implemented on a web application, organized in eight business-relevant domains, comprehensively covering all aspects impacting big data initiatives’ success. The assessment contains weights to evaluate the corresponding relevance of a certain domain within the organization’s reality.
Company data activities generate value in synergy with other assets. Therefore, to estimate whether it is a priority to intervene, i.e., on the technologies, data strategies, or organizational culture, we isolate the processes and flows deriving from data initiatives in the company, mapping two exemplary processes to intercept priority actions of intervention. Therefore, by determining the type of interventions on processes and maturity levels in each data maturity domain, we derived concrete actions to bridge the existing maturity gap in higher priority areas.
The paper introduces the CBDAS initiative, implemented on a web application, organized in eight business-relevant domains, comprehensively covering all aspects impacting big data initiatives’ success. The assessment contains weights to evaluate the corresponding relevance of a certain domain within the organization’s reality.
Company data activities generate value in synergy with other assets. Therefore, to estimate whether it is a priority to intervene, i.e., on the technologies, data strategies, or organizational culture, we isolate the processes and flows deriving from data initiatives in the company, mapping two exemplary processes to intercept priority actions of intervention. Therefore, by determining the type of interventions on processes and maturity levels in each data maturity domain, we derived concrete actions to bridge the existing maturity gap in higher priority areas.
Andrea Sestino; Andrea De Mauro
Leveraging artificial intelligence in business: Implications, applications and methods Journal Article
In: Technology Analysis & Strategic Management, vol. 34, no. 1, pp. 16–29, 2022.
@article{sestino2022leveraging,
title = {Leveraging artificial intelligence in business: Implications, applications and methods},
author = {Andrea Sestino and Andrea De Mauro},
url = {https://www.researchgate.net/profile/Andrea-De-Mauro-2/publication/349110234_Leveraging_Artificial_Intelligence_in_Business_Implications_Applications_and_Methods/links/602275744585158939907f66/Leveraging-Artificial-Intelligence-in-Business-Implications-Applications-and-Methods.pdf},
doi = {10.1080/09537325.2021.1883583},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Technology Analysis & Strategic Management},
volume = {34},
number = {1},
pages = {16–29},
publisher = {Routledge},
abstract = {The concept of Artificial Intelligence (AI) as a business-disruptive technology has developed in academic and professional literature in a chaotic and unstructured manner. This study aims to provide clarity over the phenomenon of business activation of AI by means of a comprehensive and systematic literature review, aimed at suggesting a clear description of what Artificial Intelligence is today. The study analyses a corpus of 3780 contributions through an original combination of two established machine learning algorithms (LDA and hierarchical clustering). The review produced a structured classification of the various streams of current research and a list of promising emerging trends. Results have shed light on six topics attributable to three different themes, namely Implications, Applications and Methods (IAM model). Our analysis could provide researchers and practitioners with a meaningful overview of the body of knowledge and research agenda, to exploit AI as an effective enabler to drive business value.},
keywords = {},
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The concept of Artificial Intelligence (AI) as a business-disruptive technology has developed in academic and professional literature in a chaotic and unstructured manner. This study aims to provide clarity over the phenomenon of business activation of AI by means of a comprehensive and systematic literature review, aimed at suggesting a clear description of what Artificial Intelligence is today. The study analyses a corpus of 3780 contributions through an original combination of two established machine learning algorithms (LDA and hierarchical clustering). The review produced a structured classification of the various streams of current research and a list of promising emerging trends. Results have shed light on six topics attributable to three different themes, namely Implications, Applications and Methods (IAM model). Our analysis could provide researchers and practitioners with a meaningful overview of the body of knowledge and research agenda, to exploit AI as an effective enabler to drive business value.
Mohamad Almgerbi; Andrea De Mauro; Adham Kahlawi; Valentina Poggioni
A systematic review of data analytics job requirements and online-courses Journal Article
In: Journal of Computer Information Systems, vol. 62, no. 2, pp. 422–434, 2022.
@article{almgerbi2022systematic,
title = {A systematic review of data analytics job requirements and online-courses},
author = {Mohamad Almgerbi and Andrea De Mauro and Adham Kahlawi and Valentina Poggioni},
url = {https://www.ademauro.com/wp-content/uploads/2024/07/PREPRINT-A-systematic-review-of-Data-Analytics-Job-Requirements-and-Online-Courses.pdf},
doi = {10.1080/08874417.2021.1971579},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Journal of Computer Information Systems},
volume = {62},
number = {2},
pages = {422–434},
publisher = {Taylor & Francis},
abstract = {Data analytics’ growing importance in modern business has left many organizations unprepared in terms of human talent. This study sheds light on the intersection between the analytics job skills currently in demand and the offer of massive online open courses for developing them. We have scraped from the web the description of more than 14,000 job posts and 3,600 Data Analytics online courses to systematically capture the need for data skills and available learning opportunities. By using an original combination of topic modeling and text mining algorithms, we provide a systematic mapping of educational offers with business needs, quantifying their presence and identifying gaps. Our study enables both educational providers to improve their offering on Data Analytics and Human Resources professionals to identify skill development opportunities. Additionally, we introduce a general methodology able to produce systematic mappings of job skills and learning opportunities in any domain.},
keywords = {},
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Data analytics’ growing importance in modern business has left many organizations unprepared in terms of human talent. This study sheds light on the intersection between the analytics job skills currently in demand and the offer of massive online open courses for developing them. We have scraped from the web the description of more than 14,000 job posts and 3,600 Data Analytics online courses to systematically capture the need for data skills and available learning opportunities. By using an original combination of topic modeling and text mining algorithms, we provide a systematic mapping of educational offers with business needs, quantifying their presence and identifying gaps. Our study enables both educational providers to improve their offering on Data Analytics and Human Resources professionals to identify skill development opportunities. Additionally, we introduce a general methodology able to produce systematic mappings of job skills and learning opportunities in any domain.
Andrea De Mauro
Data analytics per tutti. Imparare ad analizzare, visualizzare e raccontare i dati Book
Apogeo, 2022, ISBN: 9788850335947.
@book{DataAnalyticsPerTutti,
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Andrea De Mauro
Defining Big Data Book Section
In: Reuter, Martin; Montag, Christian (Ed.): Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics, pp. 443–446, Springer International Publishing Cham, 2022, ISSN: 2196-6613.
@incollection{de2022defining,
title = {Defining Big Data},
author = {Andrea De Mauro},
editor = {Martin Reuter and Christian Montag},
url = {https://www.ademauro.com/wp-content/uploads/2024/07/Defining-Big-Data-De-Mauro-2023-PREPRINT.pdf},
doi = {10.1007/978-3-030-98546-2_26},
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year = {2022},
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booktitle = {Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics},
pages = {443–446},
publisher = {Springer International Publishing Cham},
abstract = {‘Big Data’ has shown to be a trending catchphrase in modern Information Technology. However, it is hard to define a sharp cut between the classic notion of data and the novelties introduced by the arrival of Big Data. This short paper defines the seven structural elements underlying the concept of Big Data, highlighting the features that make it truly different from conventional data.},
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‘Big Data’ has shown to be a trending catchphrase in modern Information Technology. However, it is hard to define a sharp cut between the classic notion of data and the novelties introduced by the arrival of Big Data. This short paper defines the seven structural elements underlying the concept of Big Data, highlighting the features that make it truly different from conventional data.
Simone Malacaria.; Andrea De Mauro.; Marco Greco.; Michele Grimaldi.
An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity Proceedings Article
In: Proceedings of the 24th International Conference on Enterprise Information Systems – Volume 1: ICEIS, pp. 50-61, INSTICC SciTePress, 2022, ISSN: 2184-4992.
@inproceedings{iceis22,
title = {An Application of the Analytic Hierarchy Process to the Evaluation of Companies’ Data Maturity},
author = {Simone Malacaria. and Andrea De Mauro. and Marco Greco. and Michele Grimaldi.},
url = {https://www.scitepress.org/PublishedPapers/2022/110880/110880.pdf},
doi = {10.5220/0011088000003179},
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abstract = { The study reports the data maturity evaluation on a sample of Italian firms of different sectors and sizes, retrieved through an online assessment made by 261 professionals and entrepreneurs operating in the data domain. The paper's objective is to derive the relative importance of the critical factors to impact successful big data initiatives, according to organization reality and manager perspective. The questionnaire was distributed among IT professionals and decision-makers in Italy using the LinkedIn platform. The assessment was divided into two sections: the 1st one contained the assessment of 8 critical success factors for big data, whereas the 2nd section assessed weights based on an application of the analytic hierarchy process. The result of this process is a scoring system that includes the characteristics a company "must-have" to become data-oriented and make data-driven decisions. The application of the weights allows giving more importance to the domains that managers think are more important in a data-driven company. Respondents agreed to the importance of integrated architecture, data-friendly corporate culture, and integrated organization domains. Once the results consider the weights from the AHP, data friendliness becomes the most sought-after characteristic. The findings provide direction for further development of the assessment system.},
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The study reports the data maturity evaluation on a sample of Italian firms of different sectors and sizes, retrieved through an online assessment made by 261 professionals and entrepreneurs operating in the data domain. The paper’s objective is to derive the relative importance of the critical factors to impact successful big data initiatives, according to organization reality and manager perspective. The questionnaire was distributed among IT professionals and decision-makers in Italy using the LinkedIn platform. The assessment was divided into two sections: the 1st one contained the assessment of 8 critical success factors for big data, whereas the 2nd section assessed weights based on an application of the analytic hierarchy process. The result of this process is a scoring system that includes the characteristics a company "must-have" to become data-oriented and make data-driven decisions. The application of the weights allows giving more importance to the domains that managers think are more important in a data-driven company. Respondents agreed to the importance of integrated architecture, data-friendly corporate culture, and integrated organization domains. Once the results consider the weights from the AHP, data friendliness becomes the most sought-after characteristic. The findings provide direction for further development of the assessment system.
2021
Andrea De Mauro
Packt Publishing, 2021, ISBN: 9781801074155.
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Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.
Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.
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Data Analytics Made Easy is an accessible beginner’s guide for anyone working with data. The book interweaves four key elements:
Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.
Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.
Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.
Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.
Data visualizations and storytelling – Tired of people not listening to you and ignoring your results? Don’t worry; chapters 7 and 8 show you how to enhance your presentations and engage with your managers and co-workers. Learn to create focused content with a well-structured story behind it to captivate your audience.
Automating your data workflows – Improve your productivity by automating your data analysis. This book introduces you to the open-source platform, KNIME Analytics Platform. You’ll see how to use this no-code and free-to-use software to create a KNIME workflow of your data processes just by clicking and dragging components.
Machine learning – Data Analytics Made Easy describes popular machine learning approaches in a simplified and visual way before implementing these machine learning models using KNIME. You’ll not only be able to understand data scientists’ machine learning models; you’ll be able to challenge them and build your own.
Creating interactive dashboards – Follow the book’s simple methodology to create professional-looking dashboards using Microsoft Power BI, giving users the capability to slice and dice data and drill down into the results.
Mohamad Almgerbi; Andrea De Mauro; Adham Kahlawi; Valentina Poggioni
Improving Topic Modeling Performance through N-gram Removal Proceedings Article
In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 162–169, 2021.
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abstract = {In recent years, topic modeling has been increasingly adopted for finding conceptual patterns in large corpora of digital documents to organize them accordingly. In order to enhance the performance of topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), multiple preprocessing steps have been proposed. In this paper, we introduce N-gram Removal, a novel preprocessing procedure based on the systematic elimination of a dynamic number of repeated words in text documents. We have evaluated the effects of the utilization of N-gram Removal through four different performance metrics: we concluded that its application is effective at improving the performance of LDA and enhances the human interpretation of topics models.},
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In recent years, topic modeling has been increasingly adopted for finding conceptual patterns in large corpora of digital documents to organize them accordingly. In order to enhance the performance of topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), multiple preprocessing steps have been proposed. In this paper, we introduce N-gram Removal, a novel preprocessing procedure based on the systematic elimination of a dynamic number of repeated words in text documents. We have evaluated the effects of the utilization of N-gram Removal through four different performance metrics: we concluded that its application is effective at improving the performance of LDA and enhances the human interpretation of topics models.
2020
Paola Demartini; Andrea De Mauro
The Impact of Big Data on Board Level Decision Making Proceedings Article
In: International Forum on Knowledge Asset Dynamics (IFKAD) 2020, pp. 1712–1722, 2020, ISSN: 2280-787X.
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title = {The Impact of Big Data on Board Level Decision Making},
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Andrea De Mauro
Big Data per il Business. Guida strategica per manager alle prese con la trasformazione digitale Book
Apogeo, 2020, ISBN: 9788850335367.
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title = {Big Data per il Business. Guida strategica per manager alle prese con la trasformazione digitale},
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2019
Andrea De Mauro
Big Data Analytics. Analizzare e interpretare dati con il machine learning Book
Apogeo, 2019, ISBN: 9788850334780.
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title = {Big Data Analytics. Analizzare e interpretare dati con il machine learning},
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isbn = {9788850334780},
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date = {2019-03-21},
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Andrea De Mauro; Marco Greco; Michele Grimaldi
Understanding Big Data through a Systematic Literature Review: the ITMI model Journal Article
In: International Journal of Information Technology & Decision Making, 2019.
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title = {Understanding Big Data through a Systematic Literature Review: the ITMI model},
author = {Andrea De Mauro and Marco Greco and Michele Grimaldi},
year = {2019},
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2018
Andrea De Mauro; Marco Greco; Michele Grimaldi; Paavo Ritala
In (Big) Data we trust: Value creation in knowledge organizations-Introduction to the special issue Miscellaneous
2018.
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title = {In (Big) Data we trust: Value creation in knowledge organizations-Introduction to the special issue},
author = {Andrea De Mauro and Marco Greco and Michele Grimaldi and Paavo Ritala},
year = {2018},
date = {2018-01-01},
journal = {Information Processing & Management},
volume = {54},
number = {5},
pages = {755–757},
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Andrea De Mauro; Marco Greco; Michele Grimaldi; Paavo Ritala
Human resources for Big Data professions: A systematic classification of job roles and required skill sets Journal Article
In: Information Processing & Management, vol. 54, no. 5, pp. 807–817, 2018.
@article{de2018human,
title = {Human resources for Big Data professions: A systematic classification of job roles and required skill sets},
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year = {2018},
date = {2018-01-01},
journal = {Information Processing & Management},
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2017
Michele Grimaldi; Vincenzo Corvello; Andrea De Mauro; Emanuela Scarmozzino
A systematic literature review on intangible assets and open innovation Journal Article
In: Knowledge Management Research & Practice, vol. 15, no. 1, pp. 90–100, 2017.
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title = {A systematic literature review on intangible assets and open innovation},
author = {Michele Grimaldi and Vincenzo Corvello and Andrea De Mauro and Emanuela Scarmozzino},
year = {2017},
date = {2017-01-01},
journal = {Knowledge Management Research & Practice},
volume = {15},
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publisher = {Palgrave Macmillan UK},
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2016
Andrea De Mauro; Marco Greco; Michele Grimaldi; Giacomo Nobili
Beyond Data Scientists: a Review of Big Data Skills and Job Families Proceedings Article
In: 11th International Forum on Knowledge Assets Dynamics – IFKAD 2016, 2016.
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title = {Beyond Data Scientists: a Review of Big Data Skills and Job Families},
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Andrea De Mauro; Marco Greco; Michele Grimaldi
A formal definition of Big Data based on its essential features Journal Article
In: Library review, vol. 65, no. 3, pp. 122–135, 2016.
@article{de2016formal,
title = {A formal definition of Big Data based on its essential features},
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year = {2016},
date = {2016-01-01},
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2015
Vincenzo Corvello; Andrea De Mauro; Michele Grimaldi; Emanuela Scarmozzino
The role of intangible assets in open innovation processes: a literature review Proceedings Article
In: 10th International Forum on Knowledge Assets Dynamics – IFKAD 2015, 2015.
@inproceedings{corvello2015role,
title = {The role of intangible assets in open innovation processes: a literature review},
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year = {2015},
date = {2015-01-01},
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Andrea De Mauro; Marco Greco; Michele Grimaldi
What is big data? A consensual definition and a review of key research topics Proceedings Article
In: AIP conference proceedings, pp. 97–104, American Institute of Physics 2015.
@inproceedings{de2015big,
title = {What is big data? A consensual definition and a review of key research topics},
author = {Andrea De Mauro and Marco Greco and Michele Grimaldi},
year = {2015},
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booktitle = {AIP conference proceedings},
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number = {1},
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2006
Andrea De Mauro
A Peer-to-peer Network for Real-time Multiple-description Video Communications Using Multiple Paths PhD Thesis
University of Illinois at Chicago, 2006.
@phdthesis{de2006peer,
title = {A Peer-to-peer Network for Real-time Multiple-description Video Communications Using Multiple Paths},
author = {Andrea De Mauro},
year = {2006},
date = {2006-01-01},
booktitle = {2006 IEEE International Conference on Multimedia and Expo},
pages = {921–924},
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school = {University of Illinois at Chicago},
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Andrea De Mauro; Claudio Rossi
A Secure Multiple Path Real-Time Framework for Video Communication over the Internet Proceedings Article
In: SCRA 2006-FIM XIII-Thirteenth International Conference of the Forum for Interdisciplinary Mathematics on Interdisciplinary Mathematical and Statistical Techniques, At Lisbon-Tomar, Portugal, 2006.
@inproceedings{de2006secure,
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