The hidden layers of knowledge management processes: a multidimensional framework for organizational KM excellence

In today’s knowledge-driven economy, knowledge management (KM) is widely acknowledged as a strategic asset and a critical driver of organizational performance. Effective KM strengthens decision-making, accelerates learning, fosters innovation, preserves institutional memory and promotes organizational agility (Alfiero et al., 2025; Bolisani and Bratianu, 2017; Del Giudice et al., 2014; Nakash, 2025; Papa et al., 2020). Conversely, the absence of coherent KM practices – or poorly executed initiatives – introduces significant knowledge risks, including loss, leakage, fragmentation, obsolescence and duplication. These vulnerabilities undermine strategic alignment, erode expertise and weaken resilience, increasingly framed in the literature under knowledge risk management (KRM) (Durst and Khadir, 2025; Nakash and Bouhnik, 2022b; Zieba et al., 2022).

Although KM has evolved from a peripheral function into an integrated organizational capability (Evans et al., 2014; Haslinda and Sarinah, 2009; Heisig, 2009; Nonaka and Takeuchi, 1995; Serenko, 2021), its implementation remains inconsistent and fragmented. Many organizations still struggle to manage knowledge – an inherently abstract, intangible and elusive resource – in ways that create sustainable value, even with knowledge management systems (KMSs) designed to support its capture, sharing and use (Bougoulia and Glykas, 2023; Demchig, 2020; Hislop et al., 2018; Mittal and Kumar, 2019; Wong, 2005). Persistent measurement challenges exacerbate this gap, as KM outcomes are multilayered and context-sensitive, making evaluation complex and frequently misaligned with strategic objectives (Heisig, 2014).

Over thirty years ago, Wiig (1994) underscored the need for coherent and practical frameworks to guide effective KM – a challenge that remains unresolved. Scholars have increasingly called for models that move beyond generic workflows to incorporate strategic alignment, operational routines and value-based assessments (Hislop et al., 2018; Pee and Kankanhalli, 2009). As Dalkir (2005a) observes, there is growing demand for “a more holistic approach to KM” (p.48), one that reflects the field’s evolving complexity and contextual diversity. Demchig (2020) further emphasizes that “the main obstacle is the absence of a universal model at which all essential elements for effective KM are integrated” (p.142).

Addressing this complexity requires systemic approaches that capture KM’s broader impact on adaptability, decision quality and long-term capability development. Without such insight, maturity remains constrained and knowledge-related risks increase (Nakash and Bouhnik, 2022b, 2023). This need for holistic evaluation reflects principles from General Systems Theory (GST), which views organizations as open, interdependent systems requiring integrated feedback and adaptive mechanisms (Forrest et al., 2018; Rousseau, 2015). It also aligns with excellence frameworks such as EFQM 2020 (European Foundation for Quality Management), which advocate systemic assessment and continuous improvement as foundations for resilience and stakeholder value (Bocoya-Maline et al., 2024; Fonseca, 2022).

Underlying these challenges is a deeper epistemological tension: the absence of a unified conceptualization of knowledge. Objectivist and constructivist perspectives remain contested, producing typologies such as tacit-explicit distinctions and categories like know-what, know-how and know-who (Bratianu and Bejinaru, 2019, 2020). While heuristic models such as the DIKW hierarchy (Data-Information-Knowledge-Wisdom) offer conceptual clarity (Rowley, 2007), definitional ambiguity continues to hinder theoretical integration and practical application. Foundational KM frameworks – including the SECI model (Socialization, Externalization, Combination and Internalization) (Nonaka and Takeuchi, 1995), the People-Process-Technology (PPT) model (Pee and Kankanhalli, 2009) and the knowledge-based view (KBV) of the firm (Grant, 1996) – significantly shaped the discipline.

However, these models often adopt a predominantly theoretical orientation and reflect narrow perspectives, limiting their applicability across diverse organizational contexts. Many KM frameworks remain overly abstract, rooted in top-down paradigms or customized for specific settings – factors that constrain their transferability and practical utility (Wang et al., 2018; Wong and Aspinwall, 2004). Consequently, they emphasize isolated dimensions (epistemological, technological or strategic) rather than portraying KM as a dynamic and contextually system (Dalkir, 2005b; Heisig, 2009; Nakash et al., 2022; Wong, 2005).

A comprehensive review reveals that many existing KM models lack a systems-thinking perspective, contributing to persistent implementation challenges and frequent failures (Demchig, 2020). For example, SECI’s spiral of tacit-explicit conversion, while elegant, has been criticized for cultural bias, linear assumptions and limited relevance in digitally mediated environments. Similarly, PPT’s intuitive triadic structure oversimplifies KM by reducing processes to static workflows and neglecting strategic, cultural and evaluative dynamics (Dalkir, 2005a; Hislop et al., 2018). Among its pillars, “process” remains particularly underdeveloped, often treated as mechanistic rather than adaptive (Pee and Kankanhalli, 2009).

Recent scholarship reframes knowledge processes as dynamic capabilities – organizational routines that enable firms to integrate, build and reconfigure knowledge resources in response to changing environments (Bolisani and Bratianu, 2017; Evans et al., 2014). From this perspective, KM is not a static repository but a fluid, adaptive system that evolves continuously to foster resilience in turbulent digital ecosystems (Mele et al., 2024). The rise of artificial intelligence (AI) further complicates this landscape, introducing hybrid human-machine environments that challenge traditional assumptions about knowledge creation and application (Nakash and Bolisani, 2025).

Despite these conceptual advances, the theory-practice gap persists. KM practitioner insights into enabling conditions, tensions and success factors remain underrepresented in academic models, limiting their applicability in real-world contexts (Nakash and Bouhnik, 2021; Syed et al., 2018; Serenko, 2021). Scholars have therefore called for frameworks that are both conceptually rigorous and operationally relevant (Heisig, 2014; Nakash et al., 2022; Serenko, 2021; Wang et al., 2018). This challenge is amplified by the prevalence of positivist epistemologies within the discipline (Ngulube, 2019), underscoring the need for integrative approaches that combine strategic intent, procedural logic, technological enablement, cultural alignment and continuous evaluation (Dalkir, 2005a, 2005b).

This study addresses these gaps by introducing an empirically grounded, multidimensional framework for KM excellence, informed by the perspectives and lived experiences of KM practitioners and enriched by organizational realities. Guided by the overarching question – How do KM professionals experience and conceptualize the framework that supports organizational KM excellence? – the study pursues three objectives:

1. identify the core components of effective KM infrastructure;

2. explore their interdependencies; and

3. develop a context-sensitive conceptual model that bridges theory and practice.

Drawing on qualitative data collected through two complementary strategies – interaction-based methods (in-depth interviews and focus groups) and contextual immersion (ethnographic observations) – the research identifies seven interdependent components that collectively enable KM excellence:

1. Knowledge Strategy.

2. Governance and Leadership.

3. Knowledge Flow Processes.

4. Content and Contextualization.

5. Technological Infrastructure.

6. People and Organizational Culture.

7. Measurement and Evaluation.

The proposed model reconceptualizes the underdeveloped “process” dimension of the PPT framework as a systemic domain, positioning KM as a dynamic capability embedded in organizational routines, infrastructures and cultural norms. In doing so, it bridges academic abstraction and organizational reality, offering a structured yet adaptable roadmap for KM design and implementation across diverse environments.

This study adopted an interpretive-inductive design within a qualitative-constructivist paradigm (Willis et al., 2007), consistent with its exploratory aim of developing a conceptual framework informed by lived experiences and contextual realities of KM. Given KM’s evolving and context-dependent nature, qualitative inquiry was considered most suitable for capturing its complexity and situated meanings. Such an approach is particularly effective for addressing “how” and “why” questions (Yin, 2009) and for revealing the interplay between individual agency, organizational structures and cultural norms (Denzin and Lincoln, 2011). The design incorporated both macro- and micro-level perspectives, enabling a holistic view of KM across strategic orientations, operational routines and contextual constraints (Nakash et al., 2022).

Data were collected between 2020 and 2024 using three complementary qualitative methods: in-depth interviews, focus groups and ethnographic observations. The first two are detailed in Part I, and the latter in Part II below.

A total of 52 KM professionals actively engaged in leading, implementing or advising on KM initiatives participated in this component of the study (see Table 1). They were selected through purposive sampling to ensure a diverse, information-rich group (Creswell and Poth, 2018). Eligibility criteria included demonstrable expertise in KM – whether in methodological, technological or hybrid roles. A screening survey was used to identify approximately 70 potential participants, capturing socio-demographic and professional background data to ensure relevance and depth.

Of these 52 participants, 46 took part in semi-structured in-depth individual interviews, and 18 participated in one of two focus groups (8 in the first, 10 in the second). Twelve participants completed both formats, meaning they attended an individual interview as well as a group discussion, which enabled direct comparison of personal and collective perspectives. All sessions were audio-recorded with participants’ permission and transcribed verbatim.

Participants represented multiple sectors – including technology, media, public administration, finance, healthcare, consumer goods and energy – and included both external KM consultants (offering macro-level views across organizations) and internal KM leaders (providing micro-level insights into their own organizations). The sample consisted of 36 women and 16 men, predominantly mid-career professionals (ages 31–40). Notably, 22 participants held advanced academic degrees in KM, and the average professional experience was 11.46 years (SD = 5.99).

Interviews lasted 60–90 min and were designed to elicit rich narratives and critical reflections on KM practices, challenges and success factors. The guide combined broad thematic prompts with flexible, open-ended questions, such as: What are the ways to make KM successful in organizational environments?; What are the key milestones for long-term KM success?; What are the potential barriers to effective KM?; What risks are associated with poor or absent KM practices?.

Focus groups were conducted to validate and challenge insights emerging from the interviews, thereby operationalizing triangulation and strengthening interpretive depth. Each one-hour session balanced homogeneity and diversity to encourage open discussion and surface contrasting viewpoints. For example, the call in interviews to shift KM performance evaluation from a narrow return on investment (ROI) perspective to a broader value on investment (VOI) approach was strongly reaffirmed in group discussions, with participants expressing these views openly, empowered by the sense of peer alignment. Conversely, the common reliance on KMS login counts as a success metric – initially noted in interviews – was critically contested in the focus groups, highlighting its limited validity and inability to capture KM’s real contribution to organizational learning and innovation. These cases illustrate how focus groups confirmed and challenged emerging themes, enhancing interpretive rigor.

Ethnography served to provide an integrated contextual lens, functioning as an embedded case perspective to strengthen validity through methodological triangulation. Conducted in parallel with interviews and focus groups, fieldwork spanned 18 months across three organizations selected for sectoral diversity and KM relevance (see Table 2). These included:

1. a large financial institution, offering insights into KM in regulated, data-intensive environments;

2. a national government agency, illustrating KM in large-scale bureaucratic systems; and

3. a mid-sized digital services firm, representing agile, customer-facing KM practices.

The researcher adopted an observer-participant role, integrating into the natural organizational setting to experience and document KM practices as they unfolded in real time, while maintaining an analytical stance. Observations were conducted on a rotational basis across multiple units, ensuring exposure to diverse workflows and organizational subcultures. The frequency of observation varied by context, averaging two to three visits per week per site. Only a minority of interview/focus group participants (n = 7, 13.5% of 52) held key roles within the ethnographic sites, meaning this method relied primarily on ongoing informal and unstructured conversations between the researcher and informants during observations, alongside document reviews, systematic field notes and reflective memos recorded in a detailed field diary. This approach enabled immersive engagement with knowledge work, capturing tacit, situated and often invisible dimensions of KM that are difficult to access through formal interviews alone.

Data were analyzed using MAXQDA software, following a thematic analysis approach informed by grounded theory principles (Glaser and Strauss, 1999). To ensure conceptual rigor and transparency, the Gioia et al. (2013) methodology was applied, enabling the systematic development of a multilevel data structure. The analytical process unfolded in three iterative stages:

1. first-order coding, which captured participants’ language and meanings as closely as possible;

2. second-order themes, representing researcher-driven interpretations abstracted from the first-order data; and

3. aggregate dimensions, synthesizing these themes into broader conceptual categories.

From 137 initial codes, 28 themes emerged, culminating in 7 aggregate dimensions. The research design and analytical workflow are summarized in Figure 1, while the mapping of codes to aggregate dimensions is shown in Figure 2.

To ensure rigor, three strategies were employed, following established qualitative research principles (Golafshani, 2003; Lincoln and Guba, 1985). First, methodological triangulation combined interviews, focus groups and ethnographic observations to enhance credibility through cross-validation and case contextualization. This design ensured sectoral diversity, insider perspectives and cross-sector experience, while enabling the integration of organizational context for deeper interpretive insight. Second, inter-rater reliability was evaluated during coding by two independent coders, using thematic meaning units as the unit of agreement. The process achieved 93% agreement and a Cohen’s kappa (κ) of 0.845 (Cohen, 1960), indicating a high level of reliability and construct validity. To assess consistency, approximately 25% of transcripts – strategically selected to capture thematic diversity and contextual variation – were double-coded. Any discrepancies were systematically resolved through collaborative review sessions until full consensus was reached. Third, prolonged engagement in the field, supported by iterative refinement of protocols and detailed field notes, strengthened authenticity and contextual sensitivity. Collectively, these measures reinforced internal validity, analytical transparency and interpretive rigor.

The study adhered to institutional ethical standards and received formal approval from the Human Subjects Institutional Review Board’s ethics committee. All participants provided informed consent and were assured of their right to withdraw at any stage. To safeguard privacy, anonymity and confidentiality were maintained by removing or generalizing personal identifiers and organizational details in transcripts, field notes and publications. Data were stored securely with restricted access, and ethical safeguards were applied throughout all phases of the research – from recruitment and data collection to analysis and dissemination.

The corpus analysis revealed seven interrelated dimensions that collectively support the achievement of KM excellence. These dimensions function as a diagnostic lens, capturing the strategic, structural, procedural, contextual, technological, cultural and evaluative foundations of KM, as observed across the participating organizations. Their presence and integration signal a robust KM environment and reflect a high level of organizational maturity for managing knowledge, whereas their absence or weakness may compromise systemic coherence and heighten exposure to knowledge-related risks. Subsections 4.1–4.7 present each dimension through a consistent format: a concise conceptual framing followed by an in-depth elaboration supported by authentic empirical evidence. Subsection 4.8 synthesizes these findings and outlines actionable recommendations, offering a practical roadmap for organizations seeking to implement or strengthen KM practices in a holistic and sustainable manner.

This dimension refers to the strategic embedding of KM within the organization’s mission, vision and long-term objectives. KM is conceptualized not as an end in itself, but as an essential means to enhance organizational outcomes – such as increased innovation, sustained competitive advantage, improved customer satisfaction, higher workforce productivity and more effective decision-making. Positioned as a cross-functional capability, KM contributes directly to enhanced performance rather than operating as a standalone technical domain, with progress commonly tracked through key performance indicators (KPIs).

Empirical findings revealed four interrelated mechanisms through which organizations strategically integrated KM into their core operations:

1. Mapping the knowledge landscape: Organizations typically began by identifying knowledge gaps and barriers to flow, which frequently manifested as quality deficiencies, such as knowledge that was incomplete, outdated or unreliable. This diagnostic phase surfaced both structural and cultural challenges, including siloed practices and unclear ownership of knowledge assets. In the financial institution, for example, fragmented knowledge flows were found to hinder risk mitigation efforts in the insurance domain. As one participant noted, “We often know what knowledge we need, but not where to find it or how to get it to the right people” (P6).

2. Engaging stakeholders across levels: Following the diagnostic phase, the KM strategy was developed through iterative collaboration with stakeholders at multiple organizational levels. This process ensured that KM initiatives were both strategically aligned and operationally relevant. In the digital services firm, cross-level engagement enhanced responsiveness and adoption. As one interviewee explained, “KM strategy only works when it reflects both the leadership’s vision and the day-to-day realities of those using the knowledge” (P13).

3. Defining KM’s strategic contribution: As part of the strategy formulation, organizations clarified the role of KM in advancing business goals. This included articulating KM’s value proposition and aligning it with the organization’s mission and long-term objectives. In the government agency, this transformation was institutionalized through a multi-year process, culminating in KM’s formal inclusion in policy. As a senior official described, “Managing knowledge became a clear directive; embedded in the organizational agenda, aligned with the vision and positioned as a central response to the broader strategic framework” (P37).

4. Establishing KPIs and implementing pilot initiatives: To reinforce strategic alignment and validate KM practices, organizations combined the definition of clear performance indicators with the launch of targeted pilot initiatives. KPIs served as measurable benchmarks for assessing KM’s impact on outcomes such as knowledge reuse, reduction of duplication and improved decision-making speed. As noted by one focus group participant: “It’s not enough to define general KM goals; they must be translated into concrete targets with clear, specific indicators” (P52). Pilot projects provided a controlled environment to test foundational guidelines, demonstrate early value and refine practices before scaling. In the government agency, successful pilots supported continued investment and policy integration. As one participant described: “Our solution isn’t always going to be something grand and organization-wide. We create a kind of buzz around a central topic […] That kind of incentive can work well to generate interest and commitment” (P3).

This dimension focuses on the structural and relational mechanisms that enable KM to function as a sustained dynamic capability. Effective governance provides the formal scaffolding for KM coordination, while leadership ensures strategic continuity, organizational commitment and cultural integration. KM leaders are not merely administrators; they are influencers who shape policy, mobilize support and embed KM across organizational levels. Without clear ownership and sustained leadership engagement, KM initiatives risk fragmentation and failure.

Empirical findings revealed four interrelated mechanisms through which governance and leadership enable and reinforce organizational KM practices:

1. Designing a governance model with clear roles: Organizations must define a clear governance model – centralized, decentralized or hybrid – alongside well-defined roles and responsibilities. The hybrid model, combining centralized oversight with distributed implementation, was most frequently cited as effective:

Ethnographic data confirmed that this model enables organizational coherence while allowing for contextual adaptation across departments.

2.Appointing and empowering KM leaders: Appointing a dedicated KM leader is essential, but the role must be positioned as strategic rather than administrative. Effective KM leaders possess vision, organizational awareness and the ability to influence across levels. “If there is no ownership of the KM mechanisms, then it simply doesn’t matter what we do – KM will fail” (P13). Participants emphasized that lack of formal leadership and accountability often leads to KM collapse, as illustrated by the case of a telecommunications company that abandoned structured KM governance: “Once no operational structure was defined, KM simply collapsed. That’s it!” (P1).

3.Ensuring leadership continuity across levels:KM must be championed consistently from senior executives to middle managers and direct supervisors. Leadership continuity ensures that KM remains a priority and is embedded into daily operations. “It has to matter at the level of the CEO and their deputies, through middle management and down to the direct supervisor of the employee – for it to matter to the employee as well” (P4). Field observations in the government agency showed that consistent messaging and executive sponsorship were key to sustaining KM momentum.

4.Strategic communication and advocacy for KM: Effective KM leadership extends beyond formal authority to include strategic communication and persuasive advocacy. Leaders actively shape internal narratives that position KM as essential to organizational success, using consistent messaging, executive sponsorship and targeted campaigns to build legitimacy and foster engagement. At the same time, they exercise soft power – mobilizing support through influence, coalition-building and relational approaches that embed KM as a shared priority. “These individuals become key figures who drive and reinforce KM across the organization […] They market KM internally, persuasively communicating its value proposition. When they leave the company, it creates a kind of earthquake in the KM landscape” (P9). Ethnographic observations showed these efforts were pivotal: in the financial institution, strategic messaging institutionalized KM and aligned it with broader goals, while in the service organization, relational advocacy secured cross-functional support and sustained momentum.

This dimension addresses the integration of KM into the everyday work of the organization. KM becomes truly effective when knowledge flows – creation, documentation, sharing and application – are embedded within routine operations, tools and decision-making processes. Rather than functioning as a separate or supplementary activity, KM is conceptualized as a lived organizational practice that supports learning, efficiency and adaptability. As emphasized by the CEO of a KM consulting firm: “KM includes procedural aspects that define who inputs knowledge into the system, who disseminates it, who validates it and when knowledge is pushed to the consumer versus when it’s accessed independently” (P2).

Empirical findings revealed four core mechanisms through which KM was successfully embedded into daily operations:

1. Integrating KM into routine workflows:KM was perceived “as a natural part of work, rather than an external obligation” (P45). To achieve this, KM processes were aligned with existing operational systems and embedded into routine workflows, making them intuitive and sustainable. Clear knowledge flow points were defined, and KM tools were incorporated directly into work processes. As emphasized by one of the CKOs, “KM should not be treated as a discrete or supplementary function, but rather as an intrinsic part of how work is conducted” (P44). Ethnographic observations in the financial institution showed that embedding KM tools into customer service platforms increased usage and normalized KM as part of daily operations.

2. Enabling multidirectional knowledge exchange: Vertical (top-down and bottom-up) and horizontal (peer-to-peer, cross-functional) knowledge exchange was actively supported. Organizations that fostered dynamic flows were better able to surface tacit knowledge and adapt practices in real time. Fieldnotes from the digital services firm highlighted the role of informal peer-sharing sessions and cross-team retrospectives in promoting agility, collective learning and knowledge exchange. As one interviewee stated: “Some of our best ideas come from spontaneous conversations between teams” (P12).

3. Establishing formal validation procedures for knowledge quality: The quality and relevance of knowledge assets were maintained through formal validation and review mechanisms. Participants emphasized the importance of content accuracy, particularly in customer-facing or regulated domains. This approach was especially evident in the government agency, where knowledge transfer was centralized and tightly controlled. “The headquarters insists on being the sole authority… aiming to validate, verify and ensure that the knowledge is accurate, relevant, up-to-date, high-quality” (P8).

4. Monitoring and updating knowledge assets: Ongoing review cycles, informed by usage data and user feedback, were implemented to ensure that knowledge remained current and usable:

This proactive approach ensured that KMSs remained responsive and trusted.

This dimension focuses on how organizational knowledge is structured and contextualized to ensure relevance, clarity and usability. It encompasses the design of content formats, adaptation to audience needs, taxonomy development and the organization of knowledge within KMSs to enhance accessibility and retrieval. While technological platforms such as KMSs are often blamed for the failure of KM initiatives, empirical insights suggest that the root cause typically lies in the content itself – how knowledge is written, structured, presented and tailored to its intended audience. Technology merely facilitates access. When content is well-designed and appropriately framed, it becomes easier to locate, understand and apply – enhancing trust in KMSs and supporting informed action.

Empirical findings revealed four key mechanisms through which the relevance, clarity and usability of organizational knowledge were enhanced:

1. Structuring content for usability: Content was organized using metadata, tagging and workflow-based design to improve intuitive access and retrieval. Participants emphasized the importance of aligning content with employees’ tasks – such as linking related materials and organizing knowledge by process or function – while adopting formats that minimize cognitive load, including visual guides, decision trees and succinct summaries. “There’s nothing quite like seeing organizational knowledge presented in a user-friendly, even visual way – clearly structured and organized according to work stages” (P48). The concept of “KM performance-supporting” (P6) was frequently cited as a design principle for actionable content that supports real-time decision-making.

2. Tailoring knowledge to specific user roles: Content was tailored to the specific needs of distinct user groups, including frontline staff, managers and external stakeholders. Participants emphasized that “effective KM requires not only accurate content, but also thoughtful adaptation to the operational context of each audience” (P10). Field observations in the government agency underscored the importance of presenting regulatory knowledge in audience-specific formats to enhance clarity and support compliance. The same procedural knowledge was reformatted into a checklist for field staff, a dashboard for managers and a policy brief for vendors.

3. Developing and governing shared taxonomies: Unified classification systems were created to support semantic coherence across departments and improve knowledge discoverability. Taxonomy design was described as an iterative process requiring cross-functional input and formal oversight. “We carefully consider how to structure the professional domain within the taxonomy tree […] define the hierarchy levels from macro to micro” (P36). In one of the core units of the financial institution, participant observation revealed that neglecting taxonomy governance led to fragmented content, inconsistent terminology and reduced findability – undermining KMS effectiveness.

4. Optimizing content organization for user experience: User engagement with KMSs was found to depend not only on system functionality, but primarily on the clarity, accessibility and organization of the content it delivers. When users repeatedly fail to locate relevant knowledge items, trust in the system erodes and usage declines. “The level of engagement with a KMS is directly tied to the quality of its content […] if they search the system once and don’t find what they’re looking for, they’re unlikely to return” (P2). These findings underscore the importance of structuring knowledge in a way that supports intuitive navigation, efficient retrieval and confidence in the system’s reliability.

This dimension highlights the enabling role of technology in KM, while cautioning against an overreliance on digital platforms in isolation. KM is conceptualized as a socio-technical practice, requiring alignment between technological systems and organizational workflows, cultural norms and user expectations. While technological infrastructure is essential for supporting knowledge flows, it cannot, on its own, guarantee KM success. As one participant noted:

Empirical findings revealed four interrelated mechanisms through which technological platforms actively enable and enhance organizational KM practices:

1. Positioning technology as a critical enabler: Participants emphasized that digital platforms must be embedded within broader KM methodologies. Overreliance on tools without strategic and cultural integration often led to failure. As one of the attendees in the second focus group explained, “Organizations often gravitate toward the more tangible aspects of KM, but effective KM heavily relies on well-defined methodologies […] They aren’t taken seriously enough” (P39).

2. Integrating KMS with operational systems: KMSs were most effective when seamlessly connected to core enterprise platforms, such as customer relationship management (CRM) platforms. This integration supported continuity, minimized duplication and enabled real-time access to relevant knowledge. In the service organization, the KMS was described as mission-critical, deeply ingrained in the daily routines of customer service representatives: “Without the KMS, we’re blind. We literally can’t function” (P28).

3. Personalization and meaningful engagement: Contemporary KMSs increasingly provide personalized knowledge items delivery aligned with user profiles, roles and preferences, supported by features such as intelligent search, adaptive menus and device-responsive interfaces – mirroring consumer-grade experiences. This personalization is coupled with a shift toward meaningful engagement, prioritizing depth, utility and perceived value over mere interaction volume. As one participant explained: “It’s very much like Netflix or Amazon; highly personalized to the employee’s characteristics, seniority and role” (P2). Participants stressed that high login rates may reflect habitual access rather than genuine benefit: “High usage rates are only meaningful when they reflect genuine engagement and perceived value, rather than superficial or habitual access” (P4). Ethnographic observations confirmed this design shift. In the service organization, usage analytics were complemented by structured feedback mechanisms to assess whether knowledge resources tangibly improved performance – reinforcing the principle that personalization and engagement must operate in tandem to enhance KM effectiveness.

4. Leveraging AI for tagging and taxonomy with human oversight: Participants envisioned KMSs evolving into intelligent environments featuring AI-driven functionalities such as automated tagging, taxonomy generation and proactive content delivery. These capabilities were widely regarded as transformative, yet dependent on human validation to ensure contextual accuracy. “Tagging automation is definitely a real capability, currently underutilized almost […] but it doesn’t eliminate the need for people to tag based on context only they understand” (P2). Another participant emphasized, “AI won’t replace KM – it will transform it” (P1). Ethnographic insights confirmed that while AI enhances efficiency and scalability, human oversight remains essential for maintaining trust and coherence.

This dimension emphasizes the human and social foundations of KM. While technological platforms and formal structures may support KM, their effectiveness ultimately depends on people’s attitudes, behaviors and the cultural norms that shape how knowledge is shared, interpreted and applied. Culture functions as the invisible infrastructure that enables all other KM components to operate. Without a supportive environment – characterized by trust, openness, collaboration and psychological safety – even the most sophisticated KM tools are unlikely to succeed. As one participant succinctly put it: “KM is fundamentally a human and social endeavor” (P42).

Empirical findings revealed four interrelated mechanisms through which people and culture shape KM success:

1. Addressing cultural resistance and knowledge-related fears: Participants described culture as slow to evolve and resistant to top-down interventions. Even when KM tools were available, adoption lagged unless accompanied by a cultural shift that encouraged participation and reduced fear. Barriers such as knowledge hoarding, skepticism and status concerns were common. As one of the interviewees explained: “Our transition to new KM practices required employees to step out of their comfort zones. It’s happening, but the process is very, very slow” (P48). Another added: “Ego and fear for one’s image kill knowledge sharing” (P29). Ethnographic observations confirmed that trust-building and modeling by early adopters were essential to overcoming resistance and preventing knowledge loss.

2. Building trust and engagement: Building a culture of openness was seen as critical to KM success. Participants emphasized that trust cannot be mandated – it must be cultivated through leadership role-modeling, consistent messaging and informal spaces for exchange. As one participant noted: “A KM leader must deeply understand the organizational culture. They need to understand people – because knowledge is, ultimately, human knowledge” (P51). Initiatives such as peer mentoring, knowledge-sharing communities and informal “knowledge cafés” (P6) helped normalize KM in the financial institution as part of everyday interaction.

3. Motivating participation through recognition and gamification: Organizations used recognition programs, gamification and tailored communication to encourage ownership and advocacy. These mechanisms signaled that knowledge was valued as a shared resource. As a director of the KM division explained in: “Change management is absolutely essential, yet managers often skip over it […] We need change agents who can serve as strong ambassadors. KM rises or falls on implementation” (P12). Fieldnotes from the government office showed that public acknowledgment of employee contributions – via newsletters, meetings or symbolic rewards – fostered pride and reinforced KM’s role in organizational identity.

4. Appointing local knowledge champions: The appointment of “knowledge champions” at the team or departmental level was described as an effective strategy for embedding KM into daily routines. These individuals “modeled desired behaviors, supported peers and acted as cultural bridges between formal KM structures and informal practices” (P9). Their presence in the financial institution and the digital services firm helped sustain momentum and reinforced KM as a socially accepted norm.

This dimension focuses on the critical role of measurement in KM, acknowledging the inherent complexity of evaluating a domain that is largely intangible and not reflected in traditional financial statements. KM is often perceived as an abstract organizational capability, making its assessment particularly challenging. Evaluation is not merely a reporting function; it is a dynamic feedback mechanism that legitimizes KM, secures continued investment, and guides continuous improvement. Effective measurement enables organizations to assess what works, identify gaps and adapt KM initiatives in alignment with broader strategic goals.

Empirical findings revealed four interrelated mechanisms through which KM measurement supports value creation:

1. Broadening value beyond ROI to VOI: Participants expressed skepticism toward traditional financial metrics, arguing that KM’s impact cannot be fully captured through ROI alone. Instead, they advocated for broader frameworks such as VOI, which emphasize intangible outcomes like collaboration, learning and organizational resilience. As the CKO of a financial services company noted:

Another participant, CEO of KMS Company, added:

• 2.Combining quantitative and qualitative KPIs: Organizations reported using mixed methods to evaluate KM effectiveness. While KMS usage rates and participation metrics provided baseline data, these were supplemented by qualitative insights from interviews, surveys and observations. As reflected by one of the attendees in the first focus group: “KM measurement is complex […] we will almost always need to combine research methods and examine multiple indicators” (P43). In the service organization, discrepancies between analytics and user sentiment led to redesigns of content formats and training programs.

• 3.Adapting evaluation criteria to evolving needs:KM evaluation was described as iterative and responsive to organizational change. Participants emphasized “the need to adjust indicators based on user feedback, technological developments and strategic priorities” (P38). In the government office, for example, initial metrics focused on login frequency, but later shifted to tracking how knowledge was applied in decision-making, such as referencing shared documents in policy drafts.

• 4.Using measurement as a learning tool for continuous improvement: Measurement was viewed not as a static reporting task but as a strategic compass – a “North Star” (P4) for guiding KM priorities and evolution. Beyond its legitimizing role, participants highlighted its capacity to transform evaluation into an active process of organizational learning and adaptation. As one noted: “Measurement is a learning tool that supports evidence-informed decision-making and long-term value creation” (P24). Rather than serving as proof of impact, measurement was leveraged to make KM outcomes visible and actionable, reinforcing continuous refinement.

Table 3 presents an integrated summary of the seven foundational pillars, highlighting their core focus, strategic contribution, representative insights from practitioners, and a set of actionable recommendations for organizations seeking to implement or enhance KM practices. Indicative KPIs and targets are included as illustrative examples to guide assessment of progress for each pillar.

Building on the seven foundational components identified in the findings, this study proposes an integrative model that reconfigures the essential elements required for a robust and sustainable KM infrastructure. The model is visually represented in Figure 3 as a circular system, symbolizing the dynamic, interdependent and holistic nature of KM in contemporary organizational environments.

Unlike linear or hierarchical models, the circular structure reflects the recursive and nonsequential character of KM practices. Each component is positioned equidistantly around a central core, emphasizing that no single element is dominant or sufficient in isolation. Rather, KM excellence emerges from the synergistic interaction among all components. The absence or weakness of any one element may compromise the coherence, adaptability and long-term viability of the entire system. This systemic interdependence was illustrated, for example, in a leading telecommunications company, as reported by a senior consultant based on prior advisory experience: “They skipped KM governance entirely – no clear roles, no accountability. The effort unraveled. Despite investing in a state-of-the-art KMS, units resisted. Leadership failed to monitor, misread needs and ignored signals regular measurement would have revealed” (P1). This case demonstrates how governance failure cascaded into cultural resistance, technology rejection and lack of evaluative feedback – reinforcing the systemic logic of the model.

At the heart of the model lies the Knowledge Strategy definition, which serves as the organizational compass. It articulates the overarching vision, formal policy framework and guiding principles that shape knowledge-related activities. This central positioning underscores the strategic anchoring of KM and its role in aligning all other components with organizational goals.

Surrounding the core are two concentric rings that distinguish between operational foundations and the enabling environment:

1. The inner ring comprises the operational infrastructure through which KM is enacted in daily practice. It includes: Knowledge Flows Processes, which integrate knowledge flows into routine work; Content and Contextualization, which ensures that knowledge is contextualized, accessible and usable; Technological Infrastructure, which supports the storage, retrieval and dissemination of knowledge assets. These components translate strategic intent into action and embed KM into the organization’s operational fabric.

2. The outer ring represents the broader organizational conditions that enable KM to take root and evolve. It includes: Governance and Leadership, which provide legitimacy, direction and coordination; People and Organizational Culture, which shape the social norms, trust dynamics and behavioral patterns that underpin knowledge sharing; Measurement and Evaluation mechanisms, which offer feedback loops for learning, adaptation and continuous improvement. These elements operate at a systemic level, creating the conditions necessary for KM to be sustained and aligned with evolving organizational realities.

The circular form of the model conveys several key insights. First, it emphasizes that KM is not a discrete function but a living system – structured yet flexible, strategic yet human-centered. Second, it highlights the mutual reinforcement among components: each element both enables and is enabled by the others. Third, the model evokes the metaphor of a navigation system, suggesting that when KM is properly configured, it equips organizational leadership to steer toward innovation, resilience and strategic alignment.

Ultimately, this integrative model offers more than a descriptive framework – it serves as a prescriptive guide for designing, implementing and sustaining KM in complex, dynamic environments. It challenges organizations to move beyond fragmented or ad hoc initiatives and toward a systemic, deliberate approach to KM – one that balances stability with adaptability and formal mechanisms with informal dynamics.

The framework developed in this study offers a significant advance in the conceptualization of KM as a systemic, adaptive capability rather than a fragmented set of practices. Grounded in extensive qualitative evidence, it responds to a long-standing gap in KM literature: the absence of models that capture the complexity and interdependence of KM components as experienced in organizational reality (Escrivão and Silva, 2019; Haslinda and Sarinah, 2009; Nakash et al., 2022; Serenko, 2021). Unlike traditional frameworks that remain largely abstract or mechanistic (Dalkir, 2005b; Demchig, 2020; Mittal and Kumar, 2019; Nakash and Bolisani, 2025; Pee and Kankanhalli, 2009; Wong, 2005), this model integrates diverse dimensions into a coherent architecture, positioning KM as a dynamic enabler of organizational resilience, innovation and alignment.

The theoretical foundation of the framework is firmly anchored in the KBV of the firm (Grant, 1996), which conceives organizations as knowledge-integrating entities whose competitive advantage depends on their ability to mobilize and reconfigure distributed knowledge resources. From this perspective, knowledge is not a static asset but a fluid capability shaped by flows, interactions and environmental contingencies (Bratianu and Bejinaru, 2019, 2020; Mele et al., 2024). The seven pillars identified operationalize this logic by embedding KM into organizational routines and infrastructures while simultaneously functioning as a systemic safeguard against knowledge-related vulnerabilities. In this sense, the framework advances the principles of KRM (Durst and Khadir, 2025; Zieba et al., 2022), mitigating risks such as loss, leakage, fragmentation and obsolescence through integrated mechanisms.

By situating each pillar within established KM theories, the study clarifies its conceptual lineage and contribution:

• Knowledge Strategy extends the KBV (Grant, 1996) by demonstrating how strategic intent translates into operationalization through mechanisms such as KPIs, pilot initiatives and stakeholder engagement; elements that have remained underdeveloped in prior models.

• Governance and Leadership reframes governance (Dalkir, 2005a; Hislop et al., 2018) as both structural, encompassing roles and policies and relational, emphasizing advocacy and legitimacy. This dual perspective highlights the socio-political work of KM leaders in shaping cultural readiness and sustaining organizational commitment, moving beyond the narrow procedural focus of maturity models.

• Knowledge Flow Processes builds on the SECI model (Nonaka and Takeuchi, 1995) and KM life cycle frameworks (Evans et al., 2014), yet departs from their linear assumptions by embedding creation, documentation, sharing and application into existing workflows and systems, thereby normalizing KM as an intrinsic part of organizational life.

• Content and Contextualization expands taxonomic literature and the DIKW hierarchy (Rowley, 2007) by emphasizing content design as a mechanism for reducing cognitive load and ensuring semantic coherence, thus addressing usability challenges that have undermined the effectiveness of many KMS implementations.

• Technological Infrastructure complements socio-technical theory (Nakash and Bouhnik, 2022a) and the PPT model (Pee and Kankanhalli, 2009), illustrating how AI-enabled KMSs act as enablers rather than substitutes for human oversight, and positioning technology as a critical but nondominant component within a broader socio-organizational system.

• People and Organizational Culture reconfirms the centrality of trust, identity and psychological safety as operational levers rather than background conditions, challenging models that treat culture as a static context rather than a dynamic force shaping KM adoption (Wong and Aspinwall, 2004).

• Measurement and Evaluation enriches the literature on KM performance (Alfiero et al., 2025; Nakash and Bouhnik, 2023; Papa et al., 2020) by advancing maturity discourse beyond narrow ROI-based assessments. It shifts the focus to multidimensional VOI and continuous learning, moving beyond simplistic usage metrics to position evaluation as a dynamic feedback mechanism for systemic improvement.

The model also draws on GST (Forrest et al., 2018; Rousseau, 2015), framing KM as an open, adaptive system composed of interdependent subsystems. Each pillar functions as a subsystem whose health depends on reciprocal reinforcement, for example: governance shapes culture; culture influences flow quality; technology enables processes; and measurement informs strategic recalibration. This systemic lens explains why isolated KM interventions often fail and why maturity emerges from integration rather than discrete actions. The framework’s alignment with excellence models such as EFQM 2020 (Bocoya-Maline et al., 2024; Fonseca, 2022) further underscores its relevance for organizations pursuing resilience and stakeholder value through systemic improvement. From an EFQM perspective, the pillars map onto its three core domains: a. Direction (Knowledge Strategy), b. Execution (Knowledge Flow Processes, Content and Contextualization, Technological Infrastructure) and c. Results (Measurement and Evaluation), while Governance and Leadership and People and Organizational Culture span all domains as enabling conditions.

This study advances the PPT model by reframing its “process” dimension as a systemic architecture rather than a narrow set of workflows. In the proposed framework, “process” encompasses five interrelated elements – Knowledge Strategy, Governance and Leadership, Knowledge Flow Processes, Content and Contextualization and Measurement and Evaluation – which together form the structured routines through which KM is enacted, monitored and adapted. Traditionally, KM process has been interpreted as a linear lifecycle or mechanistic workflow (e.g. capture-store-share sequences), a view that lacks conceptual depth and integrative logic (Dalkir, 2005a; Pee and Kankanhalli, 2009). While the expanded perspective presented here moves beyond this narrow interpretation, the term “process” is deliberately retained because it most accurately conveys KM’s structured mechanism: the organized, recurring activities that translate strategic intent into operational practice.

Unlike the “people” and “technology” legs, which serve primarily as enablers, the five elements integrated within the process dimension provide the procedural logic that connects human and technological capabilities into a coherent system. Recasting process as a systemic backbone rather than a static chain of tasks acknowledges its recursive, integrative nature and its role in aligning strategic, cultural and technological dimensions. This reconceptualization transforms process from a technical workflow into a multidimensional capability, offering a more realistic representation of KM as an adaptive, holistic system. To the best of the author’s knowledge, no prior attempts in the literature have extended the process dimension in this way, marking a clear point of originality and addressing a critical gap in maturity and integrative models.

Beyond its theoretical contributions, the framework delivers substantial practical value by translating into a structured yet adaptable roadmap for KM practitioners (see Table 3). It enables organizations to diagnose systemic gaps, prioritize interventions aligned with strategic objectives, and embed KM into operational routines and cultural norms rather than treating it as a discrete function. The model shifts evaluation from narrow ROI metrics to a broader VOI perspective, emphasizing intangible outcomes such as collaboration, learning and resilience. It further provides actionable guidance for leveraging AI-enabled KMSs with human oversight, fostering sustainable leadership and cultivating trust-based cultures that normalize KM as part of everyday work. Owing to its flexibility, the framework is highly relevant for policy development and standardization efforts, offering a coherent approach for embedding KM into governance structures and organizational excellence programs.

While this study provides a robust conceptual and practical foundation, its qualitative design and focus on specific organizational contexts may limit generalizability to some extent. The framework should be regarded primarily as a diagnostic lens for assessing systemic coherence rather than as a predictive model based on complex statistical testing, which creates opportunities for future research to examine causal relationships and predictive validity. As with any qualitative inquiry, the findings reflect participants’ perspectives and it is possible that not all relevant narratives were captured, highlighting the value of complementary approaches in subsequent studies.

Future studies should examine the proposed KM excellence framework across diverse sectors, organizational types and sizes, including highly regulated environments and agile digital firms, to assess scalability and transferability. Longitudinal research is needed to explore how interdependencies among the seven pillars evolve over time and influence KM maturity. Additional directions include evaluating the framework’s contribution to KRM by examining how integrated practices mitigate vulnerabilities such as knowledge loss and fragmentation, and investigating its role in developing dynamic capabilities under technological disruption and organizational change. Further research should also consider the framework’s adaptability in AI-enabled knowledge ecosystems and its utility for policy design and standardization, thereby informing maturity models grounded in systemic integration rather than isolated interventions. Ultimately, these directions will strengthen the evidence base for KM as a systemic capability and advance global standards of KM excellence.