Proceedings IFKAD 2026

PROCEEDINGS e-books

Proceedings IFKAD 2026

Intelligent Knowledge for Sustainable Organizations

Corvinus University of Budapest, Budapest, Hungary 1-3 July 2026
ISBN: 978-88-96687-21-5
ISSN: 2280-787X

List of Included Articles:

Human–LLM Collaboration in Tactical Production Decision-Making: A Literature Review

(186-194)

Ines Ben Atitallah, Remsei Sándor, Heba F. Zaher

Abstract

This research seeks to investigate how large language models contribute to decision-making in manufacturing settings, with special emphasis on how LLM use influences decision effectiveness and the creation of a collaborative decision-making process between humans and LLMs.
The study adopts a literature review following PRISMA 2020 guidelines. A total of 145 papers were sourced from the Scopus and Web of Science databases (2020–2026). After duplicate removal and title and abstract analysis, 53 papers remained for detailed review. Out of these, 38 papers were included in the study. An additional 9 sources were selected through manual searches, bringing the total to 47. The studies were analysed across four dimensions: decision context, LLM involvement, human–LLM collaboration, and decision outcomes.
The results reveal that LLMs are primarily utilised for decision support purposes at the tactical level in production environments, such as planning, scheduling, diagnostics, and coordination. Despite demonstrating significant abilities in data processing and reasoning, the effect of LLMs on decision quality remains uncertain. Current literature mainly emphasises the technical aspects of LLMs rather than the human-machine interaction process, trust-building mechanism, and integration with organisational practices. Studies usually employ a human-in-the-loop framework, where decision-making remains in human hands.
This study focuses on peer-reviewed journal articles published in English and indexed in Scopus and Web of Science. As a result, some relevant research from other sources or languages may not be included. The study offers insights for organisations adopting LLM-based decision-support systems, emphasising the need for transparency, human intervention, and alignment with decision-making contexts and processes. This study provides a clear picture of how current research on LLMs in manufacturing decision-making is structured and highlights the main gaps that still exist. It shows that future research should go beyond focusing solely on technical capabilities and pay more attention to how humans actually interact with LLMs, as well as to how these systems influence decision quality in real-world situations.
The paper makes an original contribution by integrating scattered literature and highlighting significant knowledge gaps in decision-quality evaluation and human-LLM cooperation in tactical production decision-making.

From Cost to Social Capacity: Governing AI Released Time with STRR and S-STRR

(195-201)

Barbara Fasoli Braccini, Federico Gaeta

Abstract

The adoption of generative artificial intelligence and automation technologies is reshaping how organisations create and allocate value. While a growing body of evidence shows that AI can generate measurable productivity gains, support workplace learning, and improve selected aspects of job quality, both research and managerial practice still lack an auditable pathway capable of linking AI-enabled time savings to reinvestment decisions and their internal and societal effects.
Starting from this gap, the paper introduces a fundamental distinction between Created Value and Released Time (RT). Created Value refers to the economic and competitive output generated through organisational activities, whereas Released Time identifies the stock of time, attention, and relational capacity made available by the reduction of routine or low-value tasks. We conceptualise Released Time as a governable resource rather than an automatic source of value.
Building on this foundation, the paper develops an integrated measurement-to-governance architecture. First, Released Time is quantified through Time-Driven Activity-Based Costing (TDABC) time equations, adjusted by a quality–risk–compliance factor to exclude spurious efficiency gains. Second, we introduce the Social Time Reinvestment Ratio (STRR), a micro-level governance indicator (0–1) that captures the share of eligible Released Time intentionally reallocated to work-life balance, learning and coordination, and skills-based volunteering. STRR is designed to be auditable through allocation logs, eligibility rules, documentary evidence, and reconciliation with process accounts.
The framework is then extended to the Societal STRR (S-STRR), which connects reinvested time portfolios to Social Return on Investment (SROI) logic models and proxy maps, incorporating standard adjustments such as deadweight, attribution, displacement, and drop-off. This enables the identification of a “double dividend”: internal human capital strengthening and monetised societal value.
Methodologically, the paper adopts a conceptual-development approach with an explicit early-stage validation protocol. It defines constructs, operational rules, and an evidence framework ensuring reliability (inter-rater agreement on allocation decisions) and integrity (reconciliation between released time and TDABC accounts).
By integrating previously disconnected literatures, namely AI productivity, TDABC, SROI, skills-based volunteering, and AI governance, the paper contributes an auditable measurement-to-governance architecture.

Controversial Advertising: Multicultural and Generational Perspectives on Knowledge and Cultural Meaning- Making

(202-210)

Gintare Kriaučiūnaitė-Lazauskienė

Abstract

Why do some advertisements inspire admiration and stick in people’s minds across cultures, yet cause certain audiences to refuse to buy? This research investigates that puzzle by examining controversial advertising as a site of knowledge diversity. In this research, the perception of advertising is treated as a space in which culturally embedded unspoken knowledge and the collective construction of meaning intertwine with generational identity, thereby shaping how controversial messages are understood, interpreted, and responded to.
Twenty-two international students at Vilnius University of Applied Sciences (VIKO), Lithuania, the majority identifying as Generation Z, participated in a mixed-methods design combining focus group inquiry with a structured Likert-scale questionnaire. The sample was purposefully assembled to maximise cultural heterogeneity, with participants drawn from Morocco, Nigeria, Cameroon, Bangladesh, India, and Lithuania. The stimuli comprised five controversial advertisements drawn from Lithuanian and international contexts.
The main finding highlights what we term the “paradox of advertising in shame-based cultures”. Students from collectivist and shame-oriented cultural backgrounds admitted that controversial advertisements attract attention and are easily memorable. However, they also emphasized that they would not want to purchase products or services from the advertised brands in their home countries. This resistance did not stem from personal indifference but was determined by communal knowledge systems that prioritise group reputation, family honour, and social conformity. Thus, the interpretation of advertising is never culturally neutral (de Mooij, 2019).
To explain this phenomenon, we present the Cultural Knowledge Mediation Model (CKMM). This five-stage system explains why traditional models of advertising effectiveness, such as the ELM, the AIDA hierarchy and the FCB Grid, may fail to adequately explain consumer reactions in collectivist and shame-based cultures. The model also generates three testable propositions (P1–P3) for future research.
For practitioners, the findings underscore that culturally intelligent, ethically responsible communication requires brands to engage with local knowledge ecosystems rather than assuming universal message reception. For knowledge management study, the study positions advertising reception as an under-examined knowledge-intensive phenomenon and challenges the prevailing treatment of Generation Z as a culturally homogeneous cohort.

Unveiling the Unseen: A Meta-Synthesis of Qualitative Informal Female Entrepreneurship Research

(243-250)

Simona Mormile, Roberta Romano, Gabriella Piscopo, Paola Adinolfi

Abstract

Entrepreneurship is widely recognized as a key driver of economic growth, innovation, and social development. However, traditional entrepreneurship research has largely focused on formal business activities, often overlooking the experiences of women operating within the informal economy. Informal female entrepreneurship (IFE) represents a significant yet underexplored phenomenon, particularly in developing contexts where structural barriers, limited employment opportunities, and social norms constrain women’s participation in the formal labour market. Despite its growing relevance, scholarly attention to IFE remains fragmented, and qualitative insights into women’s lived entrepreneurial experiences are still limited.
This study aims to advance the understanding of informal female entrepreneurship by conducting a meta-synthesis of qualitative research. Specifically, the study seeks to identify the key factors influencing women’s engagement in informal entrepreneurial activities and to explore how these factors shape their entrepreneurial trajectories across different socio-economic contexts. To achieve this objective, a qualitative meta-synthesis methodology was adopted. Following a systematic search across major academic databases, 15 peer-reviewed qualitative studies published between 2011 and 2023 were selected according to predefined inclusion criteria. Data were extracted and analyzed using an open coding approach, allowing for the identification of recurring themes and conceptual categories across the selected studies.
The findings highlight four main dimensions shaping informal female entrepreneurship. First, women’s access to labour market resources and entrepreneurial opportunities plays a crucial role in motivating engagement in informal ventures, often driven by limited access to formal employment or financial capital. Second, cultural and social barriers—such as gender norms, domestic responsibilities, and institutional discrimination—significantly influence women’s entrepreneurial choices and growth potential. Third, digital technologies emerge as important enablers, providing new opportunities for networking, market access, and business development. Finally, informal female entrepreneurship generates significant economic and social contributions, including household income generation, community development, and women’s empowerment.
By synthesizing qualitative evidence, this study contributes to the entrepreneurship literature in two main ways. First, it broadens the conceptualization of entrepreneurship by highlighting the diversity of women’s entrepreneurial experiences within informal economic systems. Second, it provides a comprehensive overview of qualitative research on IFE, offering deeper insights into the motivations, strategies, and challenges faced by informal female entrepreneurs. The findings also suggest important implications for policymakers and practitioners, emphasizing the need for supportive policies, access to financial resources, and targeted training programs aimed at fostering women’s entrepreneurial potential and facilitating pathways from informal to formal economic participation.

Innovation Orchestration through Innovation Labs: Evidence from Intellimech

(211-218)

Francesco Santarsiero, Giovanni Schiuma, Alessandro Massaro, Achille Claudio Garavelli

Abstract

Innovation labs are increasingly used to connect firms, universities, technology providers and local institutions around experimentation, knowledge sharing and digital transformation. However, less is known about how innovation labs orchestrate innovation when they operate not as isolated corporate laboratories, but as membership-based collaborative infrastructures serving a network of firms. This paper investigates this issue through an exploratory qualitative case study of Intellimech, an Italian private consortium for mechatronics and industrial innovation located in the Kilometro Rosso innovation district in Bergamo. Drawing on publicly available documentary evidence, including Intellimech’s institutional webpages, statute, project descriptions and published case histories, the paper conceptualizes Intellimech as an innovation lab and intermediary that transforms distributed technological needs into shared learning opportunities, pre-competitive projects, open innovation initiatives and firm-level experimentation. The findings identify five orchestration mechanisms: need sensing and challenge framing; shared knowledge creation and absorptive capacity building; pre-competitive experimentation; boundary spanning across firms, universities and institutional actors; and translation of shared knowledge into firm-specific pilots and organizational routines. Examples such as AIxLAB, the Knowledge Management Cluster, Watchman, Harmony, Shop4cf/Warning and other mechatronics projects show how the lab logic can reduce the cost and uncertainty of innovation for firms while supporting knowledge circulation within a territorial ecosystem. The paper contributes to research on innovation labs, open innovation and knowledge management by showing that innovation orchestration is not limited to coordinating technological projects, but also involves the design of social, cognitive and organizational conditions that allow firms to learn collectively and innovate individually. The study also provides managerial implications for local ecosystems that seek to build sustainable innovation capacity through shared research, mentoring, training, technology scouting and pilot projects. Limitations concern the reliance on secondary public sources; future research should integrate interviews with Intellimech managers and member companies.

Driving Innovation and Digital Transformation in Cultural Tourism Organizations and Local Ecosystems: Final Insights of the T4Y Project

(219-226)

Daniela Carlucci, Antonio Lerro, Francesco Santarsiero

Abstract

Digital transformation is increasingly presented as a necessary pathway for cultural tourism organizations, yet many local and rural ecosystems still struggle to translate digital opportunities into coherent strategies, organizational capabilities, and sustainable business model innovation. This paper presents the final insights of the T4Y research project, which developed and applied models and tools for analysing, mapping and supporting digital transformation and business model innovation in cultural tourism organizations and local ecosystems. The study is grounded in the Tech4You project and adopts an iterative, multi-method and design-oriented research approach. The research process combined an international literature review on digital transformation, digital maturity and business model innovation; an analysis of best practices; the development of a sector-specific Digital Maturity Assessment Model and related assessment tool; qualitative interviews with local stakeholders, operators and institutional actors; survey-based data collection; and participatory workshops in pilot areas in Basilicata, Southern Italy. Findings show that digital transformation in cultural tourism cannot be interpreted as a mere process of technology adoption. Rather, it emerges as a systemic capability involving seven interconnected dimensions: strategy, organization, culture, employees, tourists/customers, operations and technology. The final project insights highlight four main issues. First, digital maturity is uneven and often limited by weak strategic planning, fragmented technological infrastructures, limited use of data and discontinuous collaboration among actors. Second, business model innovation is activated when digital tools are connected to value proposition redesign, stakeholder engagement, experience innovation and territorial identity. Third, participatory tools such as assessment models, SWOT-based reflection, business model mapping and workshops act as boundary objects that transform scattered knowledge into actionable ecosystem learning. Fourth, leadership and relational capabilities are decisive for moving from isolated digital initiatives to coordinated transformation pathways. The paper contributes to knowledge management, digital transformation and tourism innovation research by framing digital maturity as an ecosystem-level knowledge capability and by offering a practical roadmap for cultural tourism organizations and policy makers.

Using Intelligent Knowledge to Offer Digital Services: a Lean Startup Interpretation

(227-234)

Clara Filosa, Lara Agostini, Anna Nosella

Abstract

Digital servitization (DS) enables manufacturing firms to transition from product-centric offerings to digitally enabled product-service systems (PSSs). Despite its strategic potential, DS represents a challenging transformation for incumbent manufacturers, characterized by technological, data-driven, and business model uncertainty. While prior research conceptualizes DS as a strategic and organizational shift, limited attention has been paid to the process mechanisms through which firms generate and leverage knowledge to navigate this transformation. In particular, the role of Intelligent Knowledge (IK) and the mechanisms through which firms act remain insufficiently understood.
This study investigates how IK evolves throughout the DS journey and how the Lean Startup approach helps firms manage uncertainty during this process. To do so, the research draws on a longitudinal multiple-case study of four global B2B manufacturing firms undergoing DS transformations.
Our findings reveal a three-stage pathway through which firms progressively develop and mobilize IK. In the first stage, experimentation enables firms to identify relevant customer problems and achieve problem-solution fit. In the second stage, accumulated insights support refinement and the achievement of PSS-market fit. In the third stage, IK expands to ecosystem-level knowledge, enabling outcome-based services and problem-solution fit.
The study contributes to the DS literature by introducing a knowledge-based perspective on the transformation process and by showing how Lean Startup experimentation functions as an adaptive mechanism to navigate the journey. For managers, the findings provide a structured roadmap for DS, highlighting how staged experimentation and progressive IK development can guide the transition toward scalable PSSs.

Artificial Intelligence as Driver of Sustainable Performance in Entrepreneurial Ecosystems

(235-242)

Antonio Cimino, Vincenzo Corvello, Luca Famà, Ciro Troise

Abstract

Entrepreneurial ecosystems are highly relevant in fostering startup success, as they facilitate access to essential resources and enhance collaboration among diverse actors through the promotion of knowledge and skills exchange. At the same time, the rapid growth in the use of artificial intelligence within organizations is not only profoundly transforming business practices but is also influencing the performance of startups within entrepreneurial ecosystems. Despite the increasing academic and managerial attention to these topics, the literature still presents an important gap regarding how startup behaviors, particularly their dynamic capabilities, influence the adoption of emerging and disruptive technologies such as artificial intelligence, and how such adoption can contribute to improving the sustainability performance of both startups and the entrepreneurial ecosystems in which they operate.
To address this gap, this study proposes a theoretical model that analyzes the role of startups’ dynamic capabilities in the adoption of artificial intelligence. Furthermore, the study examines how the adoption of such technologies influences the sustainable performance of startups, considered a key indicator of the overall sustainability of entrepreneurial ecosystems. The model is empirically tested using Partial Least Squares Structural Equation Modeling (PLS-SEM), based on data collected through a self-administered questionnaire from startups listed in the official Register of Innovative Startups in Italy.
The results show that sensing and transforming capabilities positively influence the adoption of artificial intelligence technologies, while seizing capabilities do not exhibit a significant effect. Moreover, it is found that the adoption of artificial intelligence has a positive impact on the sustainability of entrepreneurial ecosystems. These results contribute to enriching the literature on entrepreneurial ecosystems and provide relevant implications for both researchers and policymakers, as well as managers interested in promoting sustainable innovation.
In particular, they highlight the need to strengthen startups’ capabilities for adaptation and technological monitoring, as well as to develop targeted support policies that facilitate access to financial resources, managerial skills, and training programs, in order to foster more effective adoption of artificial intelligence and, consequently, improve the sustainability and long-term resilience of entrepreneurial ecosystems.

Learning to Scale: What Mentoring Really Does for Startups

(251-256)

Luca Simone Macca, Alain Daou

Abstract

This study examines how entrepreneurial mentoring interaction processes shape founders’ interpretations and enactment of scalability during early venture development. While prior research has established that mentoring positively influences entrepreneurial outcomes, such as self-efficacy, opportunity recognition, and strategic decision-making, it has largely treated mentoring as an input and focused on its effects rather than the interactional mechanisms through which influence unfolds. At the same time, research on scalability has predominantly emphasised structural and organisational dimensions, overlooking how founders come to adopt, or resist, a scalability-oriented mindset. Addressing this gap, the present study conceptualises mentoring as a sensegiving–sensemaking process through which meanings of scalability are constructed, negotiated, and translated into action. The study draws on a qualitative multiple-case design involving five early-stage startups participating in a structured pan-European mentoring program. The analysis is based on twenty mentoring sessions, complemented by follow-up interviews and secondary data. This dataset enables an in-depth examination of how mentoring unfolds over time as an interactional process. The findings show that mentoring introduces a conceptualised scalability evaluative regime, which emerged as a set of externally oriented criteria that redefine what counts as legitimate progress, viable growth, and credible venture development. This regime operates by shifting attention toward investor-oriented metrics, reducing dependence on founder time, enforcing representational clarity, and emphasising narrative coherence and distribution channels. However, findings from the founders’ perspective show that founders do not merely accept these criteria passively. Instead, they engage in boundary work, actively interpreting mentoring inputs through four recurring responses: acceptance, negotiation, identity-protective resistance, and deferral. These interaction dynamics give rise to three distinct scalability trajectories: (1) relabeling, where existing activities are reframed as evidence of scalability; (2) systematising, where ventures are restructured into bounded, repeatable systems; and (3) narrativising, where growth is pursued through strengthened positioning and legitimacy-building channels. The study demonstrates that scalability is not simply a structural capability but an interpretive accomplishment emerging through ongoing interaction. By shifting attention from outcomes to processes, this research contributes to entrepreneurship literature by integrating mentoring, entrepreneurial cognition, and scalability.

AI-Driven Green Knowledge Management Processes and Startup Sustainable Performance: A Multidimensional Conceptual Framework

(257-264)

Furong Cai, Ettore Bolisani, Kathrin Kirchner, Tomas Cherkos Kassaneh, Enrico Scarso, Behrooz Moradi

Abstract

In the face of intensifying environmental challenges and rapid digital transformation, startups are increasingly expected to pursue sustainable development while navigating severe resource constraints and the inherent liability of newness typical of emerging ventures. Although existing literature acknowledges the critical role of Green Knowledge Management (GKM) in driving corporate sustainability, significant theoretical gaps remain. Prior studies frequently conceptualize GKM as a homogeneous construct, thereby masking the complex operational realities of its underlying dimensions. Furthermore, while Artificial Intelligence (AI) is rapidly altering organizational learning, limited academic attention has explored how AI specifically acts as a dynamic catalyst to enhance green knowledge processes within the entrepreneurial context. To address these critical omissions, this conceptual paper develops a comprehensive framework that elucidates how AI-driven GKM processes positively influence Sustainable Performance (SP) in startups. Drawing upon the Resource-Based View, the Knowledge-Based View, and the Dynamic Capabilities View, the proposed framework models GKM as a multidimensional construct comprising five distinct dimensions: green knowledge acquisition, storage, sharing, application, and creation. The model posits that robust GKM routines function as the core operational infrastructure enabling startups to translate environmental awareness into sustainability outcomes, encompassing long-term economic viability, reduced ecological footprints, and enhanced social responsibility. Crucially, this paper conceptualizes AI not as a generic technological solution, but as an advanced dynamic capability. AI is theorized to positively moderate the framework by acting as a digital catalyst that strengthens the relationship between GKM and SP. By providing analytical velocity and reducing prediction costs, AI empowers startups to overcome human capital limitations, rapidly sense subtle ecological shifts, mitigate trial-and-error risks, and efficiently apply internal knowledge assets to changing market demands. Integrating sustainability, knowledge management, and digital capabilities, this research advances theoretical discourse by deconstructing GKM into specific operational routines and refining the conceptual understanding of AI’s moderating role. Ultimately, this conceptual study establishes a foundation for future empirical validation and offers actionable guidance for policymakers and founders to align knowledge routines with AI investments for enduring sustainable performance.

AI-Mediated Knowledge Curation in Platform-Based E-Commerce

(265-271)

Stefano Allega, Daniele Allega, Sara El Matouk, Giovanni Spatola

Abstract

Digital platforms have become entrepreneurial infrastructures rather than simple marketing channels. For start-ups and small and medium-sized enterprises operating in platform-based e-commerce, social media content, online product reviews and customer-service chat transcripts form a distributed textual environment through which opportunities, risks and customer expectations are continuously signalled. This paper develops a conceptual and methodological framework for understanding how artificial intelligence mediates the curation of these multi-source textual assets. The study examines how digital transformation, new technologies and platform dependency reshape entrepreneurial behaviours and knowledge work. Drawing on digital entrepreneurship, the knowledge-based view, platform ecosystem research, the attention economy and emerging literature on AI-mediated knowledge work, the paper argues that AI-enabled analytics operate as a curation layer that extracts, ranks, synthesises and repurposes textual signals. This layer can improve opportunity recognition, experimentation and customer responsiveness, but it also generates a knowledge commoditisation paradox: the same processes that make entrepreneurial knowledge scalable and operationally useful may reduce contextual richness, increase dependency on platform intermediaries and deepen information asymmetries. The paper proposes a mixed-method research design combining semi-structured interviews, digital ethnography, topic modelling, sentiment analysis and intent classification. This design enables researchers to connect entrepreneurs’ interpretive practices with the computational patterns found across social, review and support texts. The paper contributes to digital entrepreneurship by conceptualising multi-source text as entrepreneurial knowledge infrastructure; to organisational learning by clarifying how AI-mediated curation converts feedback into routines; to platform economy scholarship by linking visibility, reputation and data access to knowledge governance; and to AI governance by identifying validation routines for trustworthy curation. Managerially, the framework supports SMEs, consultants and platform managers in developing auditable, cross-source and human-accountable knowledge routines. It also suggests policy directions concerning fake reviews, sustainability claims, data portability and platform transparency.

When Lead-User Signals Mislead: Entrepreneurial Sensemaking in Sport-Tech Innovation

(272-279)

Barbara Bigliardi, Virginia Dolci, Serena Filippelli, Benedetta Pini

Abstract

This study investigates how sport-tech entrepreneurs make sense of heterogeneous lead-user signals in new product development under conditions of uncertainty. While prior research has widely emphasized the role of lead users as sources of valuable and actionable knowledge, it often assumes that user inputs reduce uncertainty by providing clear guidance for innovation. Challenging this assumption, this study draws on sensemaking theory, signaling theory, and paradox theory to conceptualize lead-user feedback as a set of ambiguous and potentially conflicting signals that require interpretation rather than direct translation into product decisions.
An exploratory sequential research design was adopted. In the first phase, survey data from 97 elite and professional athletes were analyzed using k-means cluster analysis to identify distinct configurations of lead-user signals. The analysis revealed two main profiles: (i) cautious/ambivalent users, characterized by moderate perceived usefulness combined with concerns related to usability, comfort, adaptability, and willingness to engage in co-creation; and (ii) receptive/engaged users, characterized by consistently positive evaluations across all dimensions, including performance value, usability, data reliability, and collaborative intent. These findings highlight that lead-user input is not homogeneous, but instead structured in differentiated and sometimes contradictory configurations.
In the second phase, semi-structured interviews with three sport-tech entrepreneurs were conducted to explore how these configurations are interpreted in practice. The qualitative findings show that ambivalent signals are subject to divergent interpretations: they may be framed either as remediable design issues requiring iterative refinement or as indications of deeper misalignment between the product and its target users. Conversely, positive signals are generally interpreted as validation of the product concept, although they do not eliminate uncertainty and are not always associated with immediate readiness for scaling. Across cases, decision-making emerges as an iterative and interpretive process shaped by entrepreneurs’ assumptions, strategic priorities, and technical constraints.
By integrating quantitative and qualitative evidence, this study contributes to lead-user research by reframing user inputs as signals that generate both insight and ambiguity. It also advances research on entrepreneurial decision-making by highlighting the central role of sensemaking in constructing coherence among heterogeneous evaluations. From a managerial perspective, the findings suggest that the key challenge is not merely to collect user feedback, but to develop capabilities to interpret and prioritize conflicting signals in technology-driven innovation contexts.

From Intellectual Capital Statements to AI-Enhanced Strategic Knowledge Management

(280-286)

Manfred Bornemann, Kay Alwert

Abstract

This paper explores the Intellectual Capital Statement (ICS) “made in Germany” as a participatory method for strategic management of intangible resources in knowledge-intensive organizations. The method emerged in the early 2000s and builds on the established categories of Human Capital, Structural Capital, and Relational Capital (Edvinsson and Malone, 1997; Sveiby, 1997; Stewart, 1997). It combines qualitative assessment with moderated workshops and cause-effect modelling (Alwert et al., 2013). ICS links the diagnosis of intangible resources with strategic fields of intervention and implementation measures (Bornemann et al., 2021).
ICS aligns with knowledge management standards such as ISO 30401 (ISO, 2023) and DIN SPEC 91443 (DIN, 2020). These frameworks define system requirements and governance structures. ICS contributes a process perspective that connects assessment with strategy development through moderated dialogue and shared interpretation, which supports organizational sensemaking (Weick, 1995).
A typical ICS application centers on a facilitated workshop. Participants assess mission-critical factors and justify their evaluations. This process externalizes tacit knowledge and supports its transformation into shared understanding (Nonaka and Takeuchi, 1995). The impact matrix visualizes interdependencies between elements of Intellectual Capital and organizational outcomes (Vester, 1984). Empirical evidence from more than 1,000 applications confirms usability and transferability (BMWi, 2010).
Recent applications show how AI supports the ICS process. In the case of SBR, a medium-sized accounting service firm undergoing generational transition and staff turnover, the 2025 ICS workshop was conducted in a hybrid format with automated transcription and GPT-based summarization. AI-supported documentation produced rapid and comprehensive output, reducing cognitive load and enabling deeper reflection on improvement measures In the case of ZM-I, anonymized workshop results were analyzed with large language models to generate draft recommendations, which were refined through structured dialogue. These applications indicate efficiency gains and enhanced analytical depth while interpretation remains anchored in organizational judgement.
ICS develops further in the context of AI-supported knowledge work. Concepts such as real-time dashboards and embedded ICS agents indicate a shift toward continuous observation of Intellectual Capital. These developments require governance structures that ensure transparency and contextual sensitivity. ICS retains its role as a participatory method for organizational sensemaking and strategic transformation (Weick, 1995).

From Unstructured Inputs to Structured Knowledge: A Modular LLM-Based Approach for Enterprise Knowledge Extraction

(287-296)

Umair Ali Khan, Dmitry Kudryavtsev, Akseli Leskinen, Janne Kauttonen, Jukka Remes

Abstract

Enterprises increasingly use generative AI to convert unstructured organizational knowledge into structured outputs that can support reporting, decision-making, analytics, and process automation. However, structured knowledge extraction in real enterprise settings remains difficult because input data often appears in fragmented forms such as speech transcripts, scanned documents, complex forms, and layout-rich business records. In addition, each use case usually requires its own fields, constraints, terminology, and output format, making tailored extraction pipelines costly to build and difficult to maintain.
This paper presents a modular, large language model (LLM)-based knowledge extraction component developed as part of the GAIK toolkit – a generative AI toolkit for supporting organizational knowledge processes. The component enables users to define extraction requirements in plain language, automatically generate a structured schema, and use that schema for the extraction of knowledge from parsed documents or transcribed speech using LLM. The approach is designed to reduce repetitive engineering work while improving consistency, reusability, and adaptability across different enterprise use cases.
The study follows a design science research approach. The artifact was designed, implemented, demonstrated, and evaluated through industry-oriented use cases, with a particular focus on incident reporting and construction site diary creation. The incident reporting case demonstrates how spoken safety observations can be converted into structured reports using a reusable audio-to-structured-data module that combines transcription, transcript enhancement, schema generation, and extraction. A pilot evaluation with 15 incident-reporting audio samples provides preliminary evidence of technical feasibility and shows how schema persistence, human review, and configurable semantic similarity thresholds can support adaptation when business requirements or field definitions change, while maintaining reusable knowledge extraction workflows across organizational contexts.
The paper presents a practical, reusable design for enterprise knowledge extraction using generative AI. It shows how plain-language requirements, dynamic schema generation, and schema-conformant extraction can be combined into configurable building blocks for knowledge capture.

Integrating Farmers’ Behaviors into Agricultural Index-Based Insurance: A Knowledge-Based Theoretical Framework

(297-305)

Giulio Paolo Agnusdei, Donato Longo, Zahra Ardakani, Pier Paolo Miglietta

Abstract

Agricultural index based insurance has emerged as an important mechanism for managing climate related risks; however, existing frameworks largely treat farmers’ behavior as exogenous, overlooking how knowledge driven adaptive responses to early warning signals may dynamically mediate crop losses. This study proposes behavior based index insurance (BBII), a theoretical framework that extends multi hazard index based insurance models by incorporating a behavioral compliance component into actuarial pricing. Specifically, the framework introduces an adaptive behavior index (ABI) as a composite metric that captures farmers’ behaviors in response to early warning signals across three dimensions: compliance with recommended actions, response timing, and intervention intensity. By integrating the ABI into the vulnerability module, BBII allows yield sensitivity to vary as a function of farmers’ behavior, rather than relying on the multi hazard index alone, thereby generating a dynamic tick value that scales payouts per unit of multi hazard index variation according to behavior adjusted yield sensitivity. This mechanism results in a behavior sensitive premium structure in which premiums decrease with higher adaptive behavior and increase under low compliance. The proposed framework reconceptualizes insurance as an active risk governance mechanism rather than a passive compensation mechanism, creating financial incentives for proactive adaptive behavior. By endogenizing adaptive behavior within index insurance design, this framework advances a dynamic, knowledge driven approach to agricultural risk management that aligns pricing incentives with effective climate adaptation.

Artificial Intelligence and Sustainability Management as Innovation Strategies for Sustainable Competitiveness: Evidence from Innovative SMEs

(306-313)

Giovanna Ferraro, Ivana Quinto, Giuseppe Scandurra, Antonio Thomas

Abstract

In recent years, environmental, social, and governance (ESG) practices have progressively become a central component of corporate strategies and investment decisions. Although ESG is often associated with corporate social responsibility (CSR), the two concepts differ in important ways. CSR typically refers to voluntary initiatives oriented toward responsible business conduct, whereas ESG is based on standardized and measurable indicators that enable stakeholders to assess companies’ sustainability performance in a more systematic and comparable manner. The growing relevance respect to ESG practices is driven by increasing societal awareness of sustainability challenges, stronger stakeholder expectations, and the evolution of regulatory frameworks aimed at promoting sustainable development However, the mechanisms that support ESG implementation among small and medium-sized enterprises (SMEs), as well as the potential competitive benefits associated with these practices, remain only partially explored. This study investigates the role of artificial intelligence (AI) and sustainability management (SM) as key drivers of ESG adoption and competitive perspectives in innovative SMEs. The empirical analysis is conducted using Partial Least Squares Structural Equation Modelling (PLS-SEM) on a sample of 163 Italian innovative SMEs located in Lazio Region. Data were collected through a structured online questionnaire administered to company representatives involved in strategic and innovation-related decision-making processes. The results show that both AI and SM significantly contribute to the adoption of ESG practices across environmental, social, and governance dimensions. However, only AI exerts a direct positive effect on competitive perspectives, while SM influences competitiveness indirectly through ESG practices. Furthermore, the ESG dimensions do not equally affect competitive outcomes. The environmental and governance dimensions positively influence competitive perspectives, whereas the social dimension does not exhibit a statistically significant effect. These results provide new insights into the mechanisms through which technological and managerial factors support sustainability-oriented strategies and competitiveness in SMEs, highlighting the importance of integrating digital capabilities and structured sustainability practices.

Artificial Intelligence Ambidexterity and Sustainable Business Models: A Configurational Case Study

(314-322)

Francesca Loia, Irene Fulco, Barbara Aquilani, Marcello Martinez

Abstract

This study explores how artificial intelligence (AI) enables organizational ambidexterity to support sustainability-oriented business models. Building on the knowledge-based view of the firm and organizational ambidexterity theory, it conceptualizes AI as a knowledge-enabling infrastructure that transforms how organizations generate, integrate, and apply knowledge. AI is framed as a dual capability that simultaneously supports knowledge exploitation – through optimization, automation, and efficiency – and knowledge exploration – through experimentation, simulation, and the generation of new insights.
Although prior research on AI and sustainability has expanded, it remains fragmented and often focused on linear applications of AI, overlooking its configurational nature. To address this gap, the study adopts a configurational perspective and investigates how different combinations of AI-enabled exploitation and exploration shape sustainability-oriented business models. A qualitative multiple-case study design is used, based on four multinational firms operating in different industries: Enel, Siemens, Unilever, and Nestlé. Data were collected from secondary sources, including sustainability reports, annual reports, and corporate strategy documents, and analyzed through within-case and cross-case comparison.
The findings identify four distinct archetypes of AI-enabled knowledge ambidexterity: (1) an ambidextrous sustainability transformer characterized by high levels of both exploitation and exploration; (2) an efficiency-oriented sustainability optimizer dominated by exploitation; (3) an innovation-oriented sustainability explorer driven mainly by exploration; and (4) a limited AI adoption configuration with weak integration of both processes. Each configuration corresponds to a different sustainability-oriented business model logic, ranging from system-level transformation to incremental efficiency gains and exploratory but less scalable innovation.
The results show that sustainability outcomes are not determined by AI adoption alone, but by how AI-enabled knowledge processes are configured and aligned with organizational structures and business model logic. The study highlights equifinality, demonstrating that different configurations can lead to sustainability through distinct pathways.
Theoretically, the paper contributes by reframing AI-enabled ambidexterity as a configurational knowledge capability and integrating ambidexterity theory with sustainable business model research. Practically, it suggests that managers should move beyond fragmented AI initiatives and instead design integrated systems that balance exploitation and exploration within coherent organizational and strategic architectures.

Hybrid-Knowledge Management for Sustainable Competitiveness: AI-Human Synergy as Value Enabler

(323-328)

Andrea Gargiulo, Daniele Leone, Salvatore Scognamiglio, Petrella Raffaele Clemente

Abstract

This paper examines how firms can govern hybrid-knowledge management to transform sustainability information into customer-perceived competitive value. The study starts from a practical problem related to sustainability investments, which often fail to generate advantage. This occurs because customers receive fragmented or poorly translated signals. In response, the paper defines hybrid-knowledge management as the deliberate design and governance of customer-facing hybrid sustainability knowledge flows. These exchanges combine AI-generated cues with human-mediated interpretation provided by influencers, frontline employees, and brand communicators. The research asks how sustainable competitiveness can be strengthened through hybrid-knowledge mechanisms involving both humans and AI agents. A qualitative multi-case study design is adopted, involving four cases selected across the food and fashion industries. This setting was chosen because both sectors are highly exposed to sustainability-sensitive consumer choice and feature visible customer journeys in which knowledge is mediated through both digital and human channels. Data collection combined direct observation of customer-facing interfaces and campaign touchpoints with documentary secondary sources, enabling triangulation across firms, channels, and touchpoints. Data were analysed inductively through the Gioia methodology. The findings show that sustainable competitiveness is shaped by three interrelated levers. First, claim integrity, which rests on sustainability cues being auditable, standardised, and accompanied by human explanation. Second, choice guidance, which requires sustainability information to be translated into decision-ready comparisons at the point of choice. Third, relational sensemaking, which depends on customers’ ability to resolve ambiguity through trusted human interaction and receive continuity after purchase. Across the cases, no instance of full alignment was observed. Two-lever configurations generated partial clarity, while one-lever configurations produced sustainability blur, scepticism, or weak conversion. The paper contributes by theorising hybrid-knowledge management as a strategic capability for orchestrating customer perceptions of sustainability, rather than as a purely internal knowledge process. It also offers actionable implications for managers, specifically to align verification, explainability, and human interpretation across touchpoints, and for policymakers seeking stronger traceability and comparability standards in both digital and human communication channels.

Digital ESG Communication in Sustainable Fashion Consumption: Comparing Italy and South Korea

(329-337)

Serena Strazzullo, Sara Ianniello, Jiyeonb Lee, Ilkyeong Moon

Abstract

The fashion industry is increasingly challenged to demonstrate credible sustainability commitments in response to its significant environmental and social impacts, globalised supply chains, and persistent concerns over unsustainable production and consumption patterns. In this context, Environmental, Social, and Governance (ESG) communication has become a central tool by which fashion companies disclose their sustainability efforts and engage consumers. However, the effectiveness of ESG communication in influencing consumer behaviour remains uncertain. This is particularly relevant in digital environments, where sustainability claims are often difficult to verify and where information asymmetry, consumer scepticism, and greenwashing concerns may weaken the credibility of corporate messages. Moreover, consumers’ interpretation of sustainability communication may differ across geographical contexts. Hence, the study adopts a narrative literature review approach, focusing on two main streams of research: ESG communication practices in the fashion industry and consumer behaviour studies related to sustainable consumption, with specific attention to Italy and South Korea, two relevant and contrasting contexts.
Building on signalling theory, the study develops an empirical model that integrates the insights emerging from the narrative review with cross-country evidence. In the proposed model, ESG communication practices are conceptualised as sustainability signals. Sustainability predisposition and digital and social influence are introduced as antecedents, shaping consumers’ interpretation of these signals. In line with the theory, the interpretation of sustainability signals is expected to influence consumers’ behavioural intention, which is linked to actual purchase behaviour, while behavioural barriers are considered as factors that may affect this relation.
In doing so, the study advances a model that can be empirically tested through multi-group analysis to assess whether the proposed relationships operate differently among Italian and South Korean consumers. Generally, the study offers a theoretical and practical foundation for understanding how digital ESG communication becomes meaningful, credible, and behaviourally relevant in different contexts.

Managing Sustainability in a Fashion Supply Chain: The Case of an Italian SME

(338-347)

Sara Ianniello, Livio Cricelli, Serena Strazzullo

Abstract

Sustainability regulations and voluntary standards are increasingly transforming fashion supply chains into information-intensive systems, in which firms are expected to document practices that extend beyond their organisational boundaries. This is particularly relevant in the fashion supply chain, where, although these requirements are usually defined by large focal firms, their implementation often depends on small and medium-sized enterprise (SME) suppliers that collect, interpret, and transmit sustainability information across multiple tiers. However, how SMEs manage sustainability information flows across organisational boundaries remains underexplored. To address this issue, the study draws on knowledge governance literature on supply chains and adopts a single case study based on semi-structured interviews, with an Italian SME operating in a fashion supply chain. Findings show that sustainability information management is mainly driven by customer requests and unfolds as a document-based process of acquisition, internal processing, and dissemination. The SME acts as an intermediary knowledge broker, translating external requirements into operational requests for suppliers, laboratories, and consultants, while sending documentary evidence back to customers. However, the process remains constrained by limited internal verification capabilities and strong dependence on third parties. The analysis identifies a hybrid governance configuration across the fashion supply chain, dominated by contract-based mechanisms, reinforced by market-based pressures, and supported by trust-based coordination, whereas reciprocity-based mechanisms appear marginal. The study contributes to knowledge governance and sustainable supply chain literature by showing how SMEs manage sustainability information under asymmetrical pressure and resource constraints, highlighting both their enabling role and organisational barriers to sustainability implementation. From a managerial perspective, the findings suggest that SMEs should strengthen internal management through simple procedures, clearer allocation of responsibilities, and shared tools for document collection and storage. At the same time, focal firms and policymakers should provide greater support, ensuring that standards, documentary obligations, and reporting tools do not become unsustainable administrative burdens.