In the post-truth era, organisations face a critical vulnerability that we frame under epistemic permeability, a process through which external, radicalised digital regimes infiltrate organisational sensemaking and the workplace. These alternative truths, specifically those originating in the manosphere, challenge the foundations of institutional expertise and Equity, Diversity, and Inclusion frameworks. While traditional knowledge management focuses on internal flows, it often remains epistemically blind to the sophisticated architectures of digital subcultures that compete for authority in the minds of the employees. This study employs a netnographic analysis of the French manosphere on mainstream social media platforms Instagram and TikTok to explore the mechanisms through which ideological claims acquire their authority. Our preliminary findings reveal that these regimes are built through four interconnected mechanisms: the construction of experiential authority via embodied proof, the use of intellectual eloquence and the hijacking of academic rhetoric to provide a facade of analytical rigour, the framing of gender hierarchy as an objective biological fact, and the strategic penetration of organisational vocabularies using corporate palatable concepts. By mapping how these sanitised narratives migrate from the digital fringe into the workplace, we argue that organisations must move beyond conventional codification and compliance. Instead, they should develop resilient governance models centred on epistemic literacy and the critical evaluation of contested truths to safeguard institutional integrity.
This study aims to develop a competence index for teaching librarians in Malaysian higher education academic libraries. Teaching librarians continue to face structural, professional, and pedagogical challenges in delivering effective instructional information literacy, particularly within evolving digital and institutional contexts. Addressing these challenges requires not only identifying relevant competencies but also understanding how professional knowledge is generated, shared, and validated within the librarian community. This qualitative study employed a modified Delphi method as the research design, utilising a structured, double-round process of expert consultation to elicit, evaluate, and refine professional judgements. Beyond consensus building, the Delphi process functioned as a systematic mechanism for knowledge transfer, enabling the exchange of experiential, contextual, and tacit knowledge among experts. Through controlled feedback across successive rounds, panellists were encouraged to reflect on aggregated responses, reassess their positions, and externalise their implicit professional understandings of instructional information literacy practice. Data generated across the Delphi rounds were analysed using thematic analysis to identify recurring patterns, shared meanings, and knowledge structures emerging from expert interactions. The findings resulted in the development of three interrelated constructs: a core competency index, human attributes, and information literacy modules. These constructs reflect not only agreed-upon competencies but also the cumulative professional knowledge that has been transferred and stabilised through the Delphi process. This study offers both practical and theoretical contributions to librarianship and information management. Practically, it provides an empirically grounded competency framework to guide the training, professional development, and performance enhancement of teaching librarians involved in instructional activities. Theoretically, it extends Delphi-based research by demonstrating how the method supports structured knowledge transfer, expert knowledge codification, and the operationalisation of information literacy competencies. By situating the study within a non-Western higher education context, it further broadens the epistemological and contextual scope of information literacy and competency research in academic libraries.
While the human-focused SECI model of Nonaka and his colleagues captures their widely recognised theory of organisational knowledge creation, we live in an era of rapid technological advancements and Artificial Intelligence (AI). AI and in particular Machine Learning (ML), show great potential for organisations to support their learning and to discover and create new knowledge. This leaves the question of how AI and ML impact the SECI model. This study performs a theoretical investigation on integrating human- and machine contributions for organisational knowledge creation. A Design Science Research (DSR) approach is followed to design, develop and propose the conceptual SECI-Machine Partnership (SECI-MaP) model. The SECI-MaP model extends Nonaka’s SECI model and captures a human-machine symbiotic and synergistic partnership for enhanced organisational knowledge creation. It implies that sufficiently mediated and applied combined efforts of humans and machines could be greater than the sum of their individual contributions.
Generative AI (GenAI) systems can support knowledge workers in managing their knowledge, for example, by effectively processing explicit knowledge, such as document location, classification, integration, and summarisation. Document summarisation is especially useful in many cases since it allows users to quickly identify and understand the key information in a written document (a technical report, an academic paper, a user manual, etc.). In other words, effective summation facilitates distilling the essential meaning, ideas, or information from documents. At present, the main used GenAI tools allow document summarisation. However, they provide different performances since they are based on different Large Language models. In this paper, we compare the summarising performance of a sample of the most popular GenAI tools, i.e., ChatGPT, Copilot, Gemini, and Claude. Our analysis compares the summaries of six documents (academic and nonacademic, in English and Italian) provided by the four tools. These summaries were obtained through a prompt engineering process in which we specified the requirements for the summaries. These summaries were then analysed using quantitative metrics, such as ROUGE and BERTScore, and qualitative criteria. By integrating both types of analysis, we achieved a comprehensive evaluation, reducing subjectivity and analysing the summaries across multiple aspects. Our analysis results do not allow us to conclude that there is a “best-in-class” tool regarding the summarisation function. However, we find that the different summaries have specific characteristics, such as the length of the sentences or the number of synonyms used connected with the GenAI model on which each is based. Therefore, our results confirm that a correct understanding of how GenAI tools work is needed to use them consciously, exploit their potential, and reduce their limitations. The study has some limitations. In particular, we compared only a limited number of tools based on a likewise limited number of documents. Moreover, as these tools are constantly evolving, their performance continues to improve over time.
This study explores how hospitality businesses leverage technologies as Knowledge Management (KM) tools to enhance sustainable performance in this sector. By integrating artificial intelligence (AI) and other emerging technological solutions, businesses can mitigate negative environmental impacts, improve employee working conditions, and reduce operational costs. The study highlights the strategic role of technology in facilitating knowledge creation, sharing, and application to drive sustainability in the hospitality sector. The research is based on a narrative analysis of qualitative secondary data obtained from the Hospitality Technology Network and expert panel discussions at a leading industry conference in Poland, focusing on automation and technological advancements in the hospitality sector. The results indicate that AI and other technological innovations serve as effective KM tools, enabling hospitality businesses to achieve economic, social, and environmental benefits. Practitioners view these technologies as critical enablers of sustainable development, process optimization, and long-term competitive advantage. The study underscores the role of technology in transforming knowledge into actionable insights and facilitating sustainable business practices. This paper provides insights into the adoption and effective use of sustainable technologies, particularly for small and medium-sized hospitality enterprises (SMEs). By examining practitioners' experiences with KM-driven technological solutions, the study offers practical recommendations for hospitality managers seeking to enhance sustainability. The findings can serve as a knowledge base for SMEs aiming to integrate technology into their sustainability strategies. This research contributes to the discourse on KM and sustainability in the hospitality industry by identifying specific technologies and systems used by practitioners. By emphasizing the intersection of technology, KM, and sustainable business practices, the study offers a novel perspective on the digital transformation of hospitality businesses. The insights presented provide a foundation for further research and practical applications in the field of sustainable knowledge management.
In an era of rapid innovation and complex business challenges, the integration of knowledge management (KM) and project management (PM) has emerged as a critical key driver of strategic success. Project management is a complex discipline that requires effective decision-making to ensure the successful completion of projects within scope, time, and budget constraints. Traditional decision-making methods often rely on human judgment, which can be subject to biases, inefficiencies, and limitations in data processing. The rapid advancement of Artificial Intelligence (AI) has introduced innovative solutions to enhance decision-making in project management by leveraging machine learning, predictive analytics, and intelligent automation. This paper explores how AI-driven technologies are transforming decision-making processes in project management by improving risk assessment, resource allocation, and project forecasting. AI-powered tools can analyze vast amounts of historical project data to identify patterns, predict potential bottlenecks, and recommend optimal strategies. Additionally, artificial intelligence enables real-time monitoring of project performance, providing project managers with data-driven insights that enhance their ability to make proactive and informed decisions. By integrating AI into project management workflows, organizations can reduce human errors, optimize productivity, and improve overall project success rates. Despite its numerous benefits, AI implementation in project management presents challenges such as data privacy concerns, reliance on high-quality datasets, and the need for organizational adaptation. This paper discusses these challenges and provides recommendations for overcoming them to maximize the benefits of AI-driven decision-making. By analyzing real-world case studies and industry applications, this paper highlights the potential of AI in revolutionizing project management and offers insights into future trends. The findings underscore the importance of AI adoption in modern project environments and emphasize the need for continuous learning and ethical AI deployment. Attendees will gain insights into practical approaches for incorporating KM into project management workflows and fostering a culture of continuous learning and improvement.
Given the benefits and risks associated with GenAI adoption in organizations, many academics and practitioners have stressed the importance of understanding how humans come to trust these technologies and the information and knowledge (e.g., solutions/decisions) they produce. The objective of this paper is to further examine human trust in AI technologies through the lens of a widely accepted organizational trust theory and model developed by Mayer, Davis, and Schoorman. More specifically, this paper focuses on developing a better understanding of perceived factors of GenAI trustworthiness since assessing trustworthiness is a critical determinant of trust. Building on the existing theory and model, it is proposed that an individual's perception of one or more of the following dimensions of trustworthiness - ability, integrity, and benevolence - will determine how trustworthy they find GenAI to be. Ability (or competence) refers to the trustee’s specific skills, knowledge, and expertise required in a specific domain. Integrity reflects the trustee’s sound values or principles (e.g., fairness, consistency, justice). Benevolence is an altruistic loyalty that reflects the trustee’s concern for the welfare, needs, desires, and interests of the individual over organizational or profit motives. Many researchers have proposed assessments related to GenAI ability, but integrity and benevolence are more difficult to assess, as technologies do not intrinsically embody human values or altruistic behaviors. Consequently, other parties within the organizations, such as AI designers and developers, strategic decision-makers, or the organization may be conflated into perceptions of these dimensions. The paper continues by briefly discussing how emotions and organizational culture may influence individuals' perceptions of trustworthiness and concludes by suggesting potential directions and strategies for building and representing each dimension of perceived trustworthiness in the context of GenAI.
In the present situation of the formation of high-quality intellectual capital at scientific institutions in Latvia, the theme of the research is highly topical. The object of the research is the formation of high-quality intellectual capital at scientific institutions in various scientific sectors while the subject of the research is the comparative analysis of the efficiency of high-quality intellectual capital formation at scientific institutions in various scientific sectors of Latvia. The objective of the research is the comparative analysis of the efficiency of high-quality intellectual capital formation at scientific institutions in various scientific sectors of Latvia in the period from 2013 to 2018. The following tasks were determined to reach the objective: to study the formation of high-quality intellectual capital at scientific institutions in various scientific sectors of Latvia; to identify the concept of the efficiency of high-quality intellectual capital formation in various scientific sectors; to calculate main indicators thereof, and, to carry out the comparative analysis of indicators characterising the efficiency of high-quality intellectual capital formation at scientific institutions in various scientific sectors in Latvia. Research methods used in the paper are content analysis, economic analysis, and economic experiment.
The rapid integration of artificial intelligence (AI) into organisational operations has transformed knowledge management (KM) practices, providing opportunities to enhance decision-making, innovation and efficiency. This study explores the adoption and impact of AI on KM practices in Saudi Arabia, focusing on the alignment of these advancements with the Kingdom’s Vision 2030 objectives for economic diversification and innovation-driven growth. Using a quantitative methodology, data were collected from management in private and public organisations to analyse the perceptions, challenges and opportunities of adopting AI for KM. While AI tools significantly enhance knowledge sharing, collaboration and the accuracy of knowledge repositories, the findings reveal that AI adoption is hindered by challenges such as limited technical expertise, high costs and resistance to change. The data underscore a strong expectation that AI-driven KM will become essential for maintaining competitiveness in the next decade. Additionally, organisational managers highlight the role of AI in fostering a culture of innovation and entrepreneurship, crucial for achieving the strategic goals of Vision 2030. This research provides actionable insights for policymakers and business leaders, identifying critical enablers of AI adoption, including investments in digital infrastructure, skill development and change management strategies. By addressing these barriers, organisations can optimise the integration of AI to support sustainable development and innovation. The study contributes to the growing body of literature on AI and KM in emerging economies, offering practical recommendations that bridge the gap between technology-driven initiatives and strategic objectives.
Even though international network building is increasingly recognized to be important for research, the use of seed funding as a means of building researchers’ collaboration networks prior to actual grand writing network building and knowledge sharing has not been addressed. Research is a knowledge creation and knowledge sharing activity. Networking can increase social capital and thus enhance research productivity of HEI’s. International research collaboration networks are important for knowledge production, research productivity, and enhancing innovation capabilities. The enhancement of innovation capabilities is also linked to the mission HEI’s have of collaborating and enhancing the local business community in Finland. Innovation capabilities can be seen as innovation drivers, which improve the competitive advantages of a company or specific region. A number of instruments are in place for pure academic networking; however, these differ from seed funding as seed funding in most cases also includes networking outside of academia, e.g., local SME’s. This research depicts different seed funding sources used to construct international funding application research networks and also highlights the challenges, which should be addressed when considering this approach. The research question the paper addresses is, how do we best utilize seed funding to enhance knowledge sharing. The paper uses a case study approach presenting a number of successful seed funding research collaboration cases led by LAB University of Applied Science, which have also led to successful research funding applications with international partners. The study highlights possible pitfalls and also challenges encountered during the seed funding research collaboration period. Conclusions as to best practices with regards to utilization of seed funding to enhance knowledge sharing and creation are also presented. The findings emphasize the importance of strategic planning and proactive engagement in building effective research networks.
Bibliometric analyses show that industry–academia collaborative research projects tend to achieve higher performance. However, these collaborations often encounter barriers such as connection difficulties, resource constraints, differences in organizational culture, and institutional distance between industry and academia. Effective support from governments, firms, and universities is crucial to address these challenges and enhance the performance of industry–academia collaborative research teams. This study empirically examines the impact of such support on research performance using data from more than 200 collaborative research teams in Japan. This study differs from previous studies in several aspects. First, bibliographic information is matched with survey data, which enables team-level analysis instead of focusing on firms, universities, or regions. Bibliometric analysis provides an objective performance measure, i.e., the citation counts of research papers, whereas survey data offer abundant insights into team dynamics. Second, whereas most prior studies primarily examined financial support, this study considers a broader range of support measures, including the introduction of appropriate collaborators (networking), funding, provision of research equipment, management of intellectual property (IP), and assistance with administrative work. Empirical results reveal that all support measures, except for assistance with administrative work, significantly enhance research-team performance. Notably, assistance with administrative work presents a significant negative impact. Comparing the positive effects of various support measures, soft measures such as networking and IP management exert a greater impact than hard measures such as funding and equipment provision. Further analysis of the unexpected negative impact of administrative assistance indicates that administrative support occasionally results in cultural conflicts between industry and academia. These conflicts partially mediate the negative relationship between administrative support and team performance. However, administrative support from a third party in the presence of cultural conflicts within a team improves team performance. Further research is necessitated to determine the type of administrative support that can effectively bridge the organizational culture gap and enhance team performance.
Sharing knowledge in a community of interest creates additional value to the members of the community. The way to avoid hardly bearable problems and live a better life is to search for and belong to communities of interest. The previous research analyzes the environmental factors for playing a crucial role in shaping knowledge sharing behaviors within virtual communities (Cai and Shi, 2020). The success of open knowledge sharing community requires individuals to involve in and make continuous commitment to a community the one must be accepted by all the members of the community as equally and involved (Wu et al., 2019). Multiple studies highlight the importance of trust in fostering knowledge sharing. For instance, (Lin and Huang, 2013) (Cien et al., 2005) identified affective-based trust as a positive influence on knowledge sharing behavior, while (Lin, Hung and Chen, 2009) found that trust significantly influences knowledge sharing self-efficacy. The norm of reciprocity is frequently examined as an environmental factor for knowledge sharing (Cheung, Lee and Lee, 2013). Reciprocity is mentioned in the selected motherhood community members’ answers. Communities share common goals and create a safe climate where individuals exchange ideas, experiences, and expertise. Such interactions contribute to both individual and group development by accelerating learning, refining skills, and fostering creative solutions to everyday challenges and are the subject to explore. The aim of the research is to identify the factors that contributes to knowledge sharing in communities of Interest for better self-confidence. The research method is case study of community of interest of mothers by conducting qualitative survey. The qualitative methodology was selected on purpose to gain personal reflections on knowledge sharing in communities of interest.
As generative artificial intelligence applications, such as ChatGPT, DeepSeek are widely used, the impacts of generative artificial intelligence on employees’ creativity remains unknown. This study aims to investigate the relationship between generative artificial intelligence applications and employees’ creativity as well as the role of knowledge management activities - namely knowledge acquisition, knowledge sharing, and knowledge application - in this relationship. Based on 350 employees’ valid responses in China, the structural equation modelling was used to test the hypotheses and we find that generative artificial intelligence applications positively impact employees’ creativity. Additionally, knowledge management activities mediate the relationship between generative artificial intelligence applications and employees’ creativity. This study provides insights into the linkage between generative artificial intelligence and employees’ creativity. This study enriches current knowledge by demonstrating how knowledge management activities mediate the relationship between generative artificial intelligence applications and employees’ creativity—an underexplored area in prior research. It also assists us in understanding how generative artificial intelligence applications influence employees’ creativity, thereby informing strategic decisions for organizations to utilize artificial intelligence while developing a creative workforce.
The value of knowledge management (KM) in today's fast-paced world cannot be emphasised. It is a crucial factor that can determine the success or failure of any institution. To guarantee that KM practices are sustainable, institutions must identify and maximise the use of the elements that contribute to KM's success. This identification is especially vital for Higher Education Institutions (HEIs), as effective knowledge sharing is crucial to the institution's growth and development, as well as to enhancing a country's industrial sustainability. This article examines the unique environment of Higher Education Institutions (HEIs) and identifies Critical Success Factors (CSFs) for successful Knowledge Management (KM) implementation, considering external and internal forces and enablers. The research strategy involved a thorough, systematic literature review using academic databases. Key terms related to knowledge management, higher education, strategies, and critical success factors were identified. The search was limited to peer-reviewed journal articles, conference papers, and books published between 1994 and 2025. The study was limited to articles published between 1994 and 2025 to ensure relevance, with 47 of the 96 studies evaluated and deemed significant for the research. The study emphasises the importance of KM in HEIs, highlighting the need for a comprehensive institutionalisation strategy where organisations seek synergy between the management of critical political, economic, technological, legal and social forces and factors such as leadership, governance, technology, infrastructure, trust, people, structure, strategy, finance and culture. The ten-step guideline for institutionalising KM is intended for use by HEIs. While helpful, this guideline oversimplifies the complex nature of KM, particularly concerning KM CSFs in other organisations. Further research is needed to explore variances in KM success factors across industries and the practical implementation of KM in diverse sectors. The research offers a ten-step guideline for the effective institutionalisation of KM in HEI, focusing on resource allocation, collaboration, trust, alignment, and prioritising investments. The research highlights the significance of a holistic perspective to KM institutionalisation in HEIs.
Generative artificial intelligence (GenAI) is a subset of AI capable of autonomously producing new content in the form of text, audio, video and code based on previously learned data patterns. As modern organizations face increasing pressure to align with environmental, social, and governance (ESG) objectives, GenAI, due to its enormous potential is reshaping organizational processes, presents an unprecedented opportunity for fostering sustainable practices related knowledge. In this context, this study aims to critically review the existing literature on the topic with the aim of analysing its potential to support social responsibility. Specifically, this Systematic Literature Review (SLR) examines the intersection of GenAI, Knowledge management (KM), and sustainability, emphasizing the role of Green Knowledge Management (GKM), a framework that integrates environmentally conscious knowledge practices into corporate strategy. Through an extensive review of peer-reviewed articles, four key thematic areas emerge: (1) sector-specific application of GenAI for sustainability, (2) Ethical and regulatory concerns on GenAI implementation, (3) GenAI in promoting organizational knowledge and innovation and (4) The role GenAI in corporate social responsibility (CSR). The result of the review indicate that GenAI is increasingly emerging as a catalyst for sustainability. Its role in promoting green innovation and its capacity to facilitate the exchange of knowledge regarding sustainable practices provide crucial support for firms pursuing ESG compliance. Despite these benefits, considerable concerns remain, especially regarding the ethical use of GenAI. This study contributes to the ongoing discussion on the role of GenAI as key enablers of sustainability. By providing a systematization of the research domain, it not only provides a comprehensive understanding of its current applications but also indicates present gaps and opportunities. In addition, it outlines future directions and serves as foundation for further empirical research on the use of GenAI to promote responsible corporate practices.
People look differently at local and global challenges based on their knowledge and information related to their contexts. Our research focuses on the roles knowledge plays in contemporary times, using the lighthouse metaphor. A lighthouse keeps us on track, reaching our purposes and dealing with changing circumstances. Some follow the lighthouse, and others flow with the waves of the sea. Our journeys depend on our values, motivations, contexts, and knowledge (exchanges). Decisions and knowledge(management) concern the stakeholders' perspectives: are we individually (Me) oriented, do we reason within the context of our organisations (We), or is the societal (All) impact leading? Our paper examines the role of knowledge and information in handling societal challenges. We analyse the aspects in the different contexts and explain why value and motivation matter in our journeys[1]. Value in the lighthouse metaphor, guides us as a lighthouse and ensures self-awareness, giving purpose to our journeys. Motivation is like a ship where the organisational cooperation of the crew shares visions and missions. Context is like the sea, a dynamic, complex, and ever-changing environment that challenges us differently as individuals, organisations, or societies. The article intends to spark discussion. We are not obsessed with scientific proof but aim to initiate a debate on a balance we must find between the Me, We, All contexts and knowledge perspectives in society.
Effective Cyber Threat Intelligence (CTI) exchange is essential for strengthening cybersecurity resilience across critical sectors such as healthcare, energy, and maritime. While theoretical CTI governance models exist, their real-world implementation remains challenging due to issues with trust, compliance, interoperability, and real-time collaboration. This paper aims to address these challenges by proposing a practical knowledge transfer framework for the pilot phase of CTI Exchange governance implementation. Building on two prior research studies that developed a CTI exchange governance model specifically tailored for the DYNAMO platform, this paper focuses on putting that model into practice. By utilizing the insights and methodologies from previous work, the study presents a structured approach to applying, testing, and refining governance principles in real-world settings, ensuring effective operationalization of the model through the DYNAMO platform's capabilities. The DYNAMO project, an EU initiative, offers a comprehensive approach to cyber resilience and business continuity, providing organizations with tools and strategies for threat intelligence generation, analysis, and dissemination. The proposed framework includes strategies for piloting DYNAMO tools with pilot preparation, stakeholder engagement, sector-specific governance adaptations, and evaluation metrics. It also defines clear roles and responsibilities to support consistent application of governance mechanisms, with continuous refinement based on empirical feedback. The framework also emphasizes the importance of cross-sector collaboration, ensuring that various stakeholders, including governmental bodies, private organizations, and technical experts, are actively involved throughout the process. Tailored guidelines for the healthcare, energy, and maritime sectors address sector-specific regulatory and operational challenges. Although the pilot phase has not yet been executed, the guidelines presented here provide a robust roadmap for preparing, launching, and iteratively refining CTI exchange pilots. Ultimately, this work lays the foundation for scalable, secure, and compliant CTI-sharing governance that enhances collaboration and cyber resilience across critical infrastructure environments.
Knowledge hiding (KHi) is the intentional withholding of knowledge from colleagues, often caused by a lack of trust. It takes three forms: rationalized hiding, evasive hiding, and playing dumb, with the latter two fueling ongoing mistrust. To address this, organizations promote sharing both Tacit Knowledge (TK) and Explicit Knowledge (EK), to improve teamwork, problem-solving, and workplace relationships. Recent studies highlight the challenge of distinguishing general knowledge from TK and the difficulty of articulating TK, which adds to its scarcity and value. Examining the intentions and motivations behind KHi and KHo, often driven by fear, provides important insights into organizational knowledge dynamics. This study aimed to identify key factors influencing individuals' decisions to share TK or engage in KHi or KHo behaviors in the workplace. Using an 11-stage Survey Design methodology, data was collected from 285 Knowledge Management (KM) professionals across five countries over 42 days. This comprehensive approach ensured a diverse and representative sample, enhancing the validity and applicability of the findings. Results revealed that participants were aware of and engaged in KHi, KHo, and Knowledge Sharing (KS) behaviors. They recognized that TK holders made deliberate sharing decisions based on trust, sincerity, skillsets, and expertise. This insight underscores the complexity of KM in organizational settings and the need for nuanced approaches to encourage KS. The study highlighted the need for future research to include leadership influences, which significantly impact KHi and KHo behaviors. This finding emphasizes the critical role of leadership in shaping knowledge-sharing cultures within organizations and suggests that effective KM strategies must consider leadership styles and practices. By addressing these complex issues, organizations can develop more effective strategies to promote KS, reduce harmful hiding behaviors, and ultimately enhance their competitive advantage through improved KM practices. The study's findings provide a foundation for future research and practical applications in organizational KM, potentially leading to more efficient and collaborative work environments.
Planning and managing healthcare projects involves making decisions in conditions of high uncertainty due to a complex and turbulent environment, technological transformation, and the use of largely subjective resources of tacit knowledge possessed by project managers as a result of accumulated years of experience. It is desirable to look for solutions that allow for the creation of flexible and agile systems supporting the management of updated knowledge resources, enabling dynamic adaptation to current problems and changing environmental conditions. One possible solution is using intelligent systems that would support adaptive planning. The paper aims to present an AI model for knowledge management in adaptive planning of healthcare projects, which are usually complex, expensive, and require efficiently updated interdisciplinary scientific knowledge. Additionally, such projects often only have generally formulated goals and planned results, which makes their definition, planning, and initial evaluation much more difficult. Thanks to the conclusions drawn from this research, it is possible to reduce the number of errors made during project planning, which usually significantly affect the implementation and success of projects. It is justified to develop interdisciplinary research conducted on the border of healthcare project planning and AI computing systems because expert planning methods dominate, and there is a lack of adequate support. Implementing this type of research and its further continuation allows for the creation of knowledge and methodological solutions related to introducing AI computation to healthcare projects to assist agile decision-making.
Digitization, big data, and follow-on metrics have burgeoned over the last couple of decades. While the knowledge management (KM) community has embraced some of the advances in data-driven decision-making in business, there remain new applications that are relatively unexplored. One of these applications is the use of digital metrics to estimate knowledge holdings or intellectual capital (IC). In a discipline still at the mercy of troublesome metrics, new opportunities to better measure IC would fill a large gap in the existing body of knowledge. Previous work, across several industries and well-known brands, established digital media variables of interest such as volume of mentions, variability of mentions, influencer quality, brand sentiment, and some more platform-specific (X, Facebook, etc.) measures (Erickson, 2023; Erickson & Rothberg, 2023; Erickson, Schmidt & Rothberg, 2020). These indicators were compared with brand equity from a separate source and methodology, establishing apparent links. In short, certain digital media variables seemed related to higher brand equity, a proxy for the intellectual capital component of relational capital. More recent research explored the statistical link between digital metrics and brand equity (relational capital) (Erickson & Rothberg, 2024). On a very small sample, key indicators were shown to predict brand equity values at a very high correlation using different approaches (regression, neural network). This study continues that work, with a larger set of firms. The firms are drawn from two industries, autos and information technology, both of which have a number of companies with available brand equity estimates. Moreover, the study adds price/book value ratios, calculated on an annual basis (like brand equity) but which can also be easily estimated for shorter time periods by altering share price. As a result, in estimating correlation, periodic digital media independent variables can be compared not only with annual brand equity metrics but with matching periodic price/book ratios. Price/book is an effective proxy for not just relational capital but all intellectual capital of the firm, providing a different approach from a new perspective.
