The development and integration of social media platforms into everyday life began to gain momentum around the mid-2000s. That period can be considered the start of the social media “learning curve”—a phase in which both users and developers began to understand the potential and limitations of these platforms. Over the following decade and a half, social media technologies progressed rapidly, evolving from experimental communication tools into mature and widely adopted digital infrastructures. Today, social media is no longer an emerging technology; it is a stable, consolidated element of both personal and business communication strategies.
Although platforms like TikTok have gained remarkable popularity, their core innovation lies less in the underlying information system architecture and more in their business models and user engagement techniques. TikTok, for instance, emphasizes video content and streamlines the multimedia consumption experience. However, it does not fundamentally alter the technological principles established by earlier platforms such as Facebook, Twitter, or Instagram. The novelty is primarily in the way content is presented and consumed—through short, engaging video formats and algorithm-driven personalization—rather than in any radical shift in the structure of information systems.
From a technical perspective, social media platforms rely on complex backend infrastructures, including distributed databases, real-time content delivery networks, and data-driven recommendation algorithms. These technologies have matured significantly, allowing for high scalability, global reach, and seamless user experiences. Nonetheless, the most recent innovations in this space often pertain to business intelligence, marketing analytics, and monetization strategies rather than purely technical breakthroughs.
Knowledge and Information Systems
The concept of knowledge management began to gain traction in academic and business environments during the late 2000s, following the rise of social media and digital communication systems. However, the theoretical foundation of knowledge management predates these technologies and is rooted in earlier discussions about data, information, and wisdom.
A fundamental framework in information theory describes a hierarchy often referred to as the DIKW model:
- Data: Raw facts and figures without context or meaning.
- Information: Data that has been organized or structured to provide context and meaning.
- Knowledge: Information that has been processed, understood, and applied to decision-making.
- Wisdom: The ability to make sound judgments and decisions based on knowledge, often involving ethical considerations or long-term thinking.
The Role of Engineers in Bridging Technology and Organizational Knowledge
In the context of computer engineering and information systems, knowledge plays a critical role. Engineers are responsible for designing and maintaining the technical systems that enable organizations to store, process, and utilize data effectively. These systems are not merely technological artifacts—they are components of larger socio-technical systems that support both operational processes and strategic decision-making.
The modern engineer operates within a complex and dynamic environment where technical knowledge must be aligned with business strategy and organizational processes. This means that computing professionals must be conversant not only with technical concepts such as machine learning, artificial intelligence, and data analytics, but also with how these tools are applied in real-world contexts to generate value.
Example
For example, if a supplier proposes a solution involving AI-based optimization, the engineer must be capable of critically evaluating the proposal—understanding its feasibility, underlying models, and potential business impact. This capability stems from a solid grounding in both the technical aspects of the solution and the broader organizational needs it is intended to address.
This technical confidence gives engineers a unique advantage. When confronted with new technologies, they are not easily overwhelmed or misled because they possess the conceptual tools to understand or learn what is required. This is not always the case for professionals from non-technical backgrounds, who may struggle to evaluate the implications of technological changes or understand how they can contribute meaningfully to the innovation process.
Knowledge Definition
Despite its centrality to organizational success, knowledge is an inherently abstract concept, and many definitions exist depending on disciplinary perspective.
Definition
Some definitions frame knowledge as a state of mind, emphasizing the internalization of justified beliefs that enable individuals or systems to perform effective actions. Others see knowledge as a process—the application of expertise to solve problems—or as an object, something that can be stored, retrieved, and manipulated, particularly in knowledge-based systems.
From a technical standpoint, these definitions can be operationalized in the design of information systems. For instance, knowledge management systems (KMS) aim to capture and codify organizational knowledge so that it can be reused and shared. These systems often integrate databases, content management tools, and collaborative platforms to support knowledge creation, dissemination, and application.
Knowledge becomes particularly valuable when it helps address organizational challenges. If a piece of information contributes to solving a problem or supports decision-making, it transitions into knowledge. In this light, the effectiveness of an information system is not measured solely by its ability to store data, but by its capacity to generate actionable insights—this is where knowledge management intersects with business strategy.
Some researchers have proposed a reverse perspective, arguing that meaningful data is only preserved when it supports knowledge generation. From this viewpoint, knowledge precedes data collection, guiding what data is worth storing and analyzing. This approach highlights the interpretive and contextual nature of data, which gains value only through its relevance to knowledge production.
Organizational Knowledge
Organizational knowledge is a multifaceted concept that lacks a universally agreed-upon definition. Despite the numerous interpretations available in literature and practice, a common thread can be identified: knowledge goes beyond the mere possession of information. This distinction is critical from an engineering and software design standpoint, where knowledge must be embedded into systems in meaningful, context-aware ways.
To begin with, it’s essential to differentiate between information and knowledge.
Definition
- Information is often viewed as a collection of facts, data points, or signals that can be processed and analyzed. It is typically structured and can be stored in databases or transmitted through various channels. Information can exist independently of context and may not necessarily lead to actionable insights.
- Knowledge, on the other hand, is a more complex construct. It involves the interpretation and application of information within a specific context. Knowledge is not just about having access to data; it is about understanding how to use that data effectively to make informed decisions or solve problems.
The concept of context plays a pivotal role.
Context
Context includes the situational factors surrounding an action or decision—such as location, timing, user needs, and objectives—that influence how information should be interpreted and acted upon.
Consider the use of augmented reality (AG) in tourism. A tourist using an AR-enabled smartphone app can point the device at a historical landmark and instantly receive overlaid information about its history, significance, and nearby amenities. If the user mentions having only 45 minutes available, the system should not recommend a destination that takes an hour to reach. Instead, it should factor in temporal and spatial constraints to offer relevant suggestions—perhaps a nearby restaurant that matches the user’s dietary preferences and time budget. This is an example of contextual knowledge application, where systems must understand not just “what” information is needed, but also “when,” “where,” and “why” it is needed.
This shift in how we interact with technology has revitalized interest in knowledge management (KM), especially within mobile and ubiquitous computing environments. Modern KM systems must support context-aware computing, which implies the integration of sensors, user profiles, and decision logic to deliver timely, relevant knowledge.
Knowledge Management
In organizational contexts, knowledge management has long been a priority, but the mobile revolution has introduced new challenges and opportunities. Businesses must not only disseminate information but also ensure it is actionable within the diverse and changing contexts in which employees operate. This involves a deeper understanding of how knowledge is structured and shared across the organization.
Knowledge can be categorized along several dimensions.
- Internal vs. External: Internal knowledge resides within the organization, while external knowledge comes from outside sources.
- Personal vs. Collective: Personal knowledge is held by individuals, built through personal experience and learning. Collective knowledge is developed and shared through group interaction, collaboration, and established practices, relevant in interdisciplinary teams where diverse expertise must converge to achieve a common goal.
- Tacit vs. Explicit: Tacit knowledge is often unarticulated and difficult to formalize, while explicit knowledge can be documented and easily shared. Technically, knowledge management systems (KMS) must support both the codification of explicit knowledge and the facilitation of tacit knowledge transfer. This dual approach involves a combination of knowledge repositories, decision support tools, communication platforms, and real-time context-aware services.
Assessing the Success of KM Initiatives
Evaluating the success of knowledge management initiatives requires a multidimensional approach. This is because the impact of KM is often distributed across strategic, technological, operational, and behavioral domains. Organizations typically assess KM efforts through several distinct but interrelated frameworks:
- Project-Oriented Evaluation focuses on whether the specific KM initiative met its predefined objectives, such as the successful deployment of a new knowledge-sharing platform or the completion of training programs.
- System-Oriented Evaluation examines the performance of the KMS itself, such as system uptime, number of active users, frequency of access, and volume of contributions. This may involve both quantitative metrics and qualitative user feedback.
- Efficiency Evaluation measures how the KM initiative improves internal processes. For example, it might track reductions in task completion time, fewer repeated errors, or improved cross-department communication.
- Financial Evaluation considers the economic return of KM projects, including cost savings, increased productivity, or even revenue growth attributable to enhanced innovation and faster decision-making.
A powerful and often overlooked KPI in this domain is KM Project Survival. This metric refers to the system’s long-term adoption and sustained usage. Many KM projects fail not due to technical flaws, but because of a lack of user engagement over time. Initially, a new KMS might receive positive reception and active usage. However, without continuous motivation, governance, and value reinforcement, the system may gradually fall into disuse. When this happens, knowledge repositories become outdated, and the system’s perceived relevance diminishes—leading to a full cycle of abandonment.
Other Key Performance Indicators (KPIs) include:
- User reach and engagement: Number of unique users, frequency of login, and average session duration.
- Knowledge utilization rates: Frequency with which documents, templates, or procedures are accessed or reused.
- Survey-based usefulness scores: Perceived utility of the KM tools as reported by users through feedback instruments.
- Cycle time reduction: Improvements in process execution times.
- Customer satisfaction: Indirect outcomes observable through reduced errors or better service response.
Resource-Based View (RBV)
The modern approach to organizational theory has undergone a profound transformation, especially in how it conceptualizes resources and competitive advantage. One of the most influential developments in this field is the Resource-Based View (RBV) of the firm, which focuses on the internal resources of a company as the key determinants of its performance and competitive standing.
Definition
Knowledge management (KM) refers to the systematic approach of capturing, structuring, distributing, and effectively using knowledge to improve organizational performance and maintain competitive advantage.
Within this framework, knowledge is increasingly recognized not just as a generic asset, but as a strategic resource—one that, if properly managed, can lead to long-term, sustainable competitive advantage. From a technical and managerial standpoint, this process involves identifying the most valuable types of knowledge, ensuring their preservation, and facilitating their diffusion across different areas of the enterprise.
There are two primary categories of knowledge that require special attention:
- Personal or tacit knowledge: This type of knowledge is often embedded in the skills and experiences of individuals. It is difficult to formalize and can be lost when employees leave the organization.
- Organizational best practices: These are repeatable processes, routines, and methods that have been proven to lead to successful outcomes. They are often tied to key performance indicators (KPIs) and critical success factors.
The RBV posits that not all resources are equally valuable. Organizations should identify and prioritize critical resources, particularly those that are unique and difficult to replace. In this view, knowledge becomes central only when it meets certain strategic criteria. It’s not necessary for a company to retain every piece of knowledge; instead, it must focus on the subset of knowledge that holds the highest value in terms of business impact. Often referred to as the 80/20 principle, this idea suggests that roughly 20% of the knowledge base typically generates 80% of the value. This key portion of knowledge is what should be carefully safeguarded and developed.
Critical Success Factors of RBV
Determining what constitutes key knowledge involves assessing whether it contributes to what are known as Critical Success Factors (CSFs)—the essential areas in which a company must excel in order to outperform competitors. CSFs are deeply tied to what makes a company distinctive in the market. They are the reasons customers choose one provider over another. The knowledge that enables these CSFs—be it in product innovation, operational efficiency, or customer service excellence—is the type that offers sustained competitive advantage.
A sustained competitive advantage refers to an edge that is not easily eroded over time by competitors. It is enduring and often becomes a central pillar of the firm’s long-term strategy. To assess whether a specific type of knowledge qualifies as such, the RBV provides four key evaluative dimensions:
-
Value: This dimension asks whether the knowledge contributes to efficiency, effectiveness, or innovation in a way that improves the firm’s market position. For example, expert-level coding skills in a software company may enable the development of complex systems that competitors struggle to replicate. The market often assigns tangible economic value to such capabilities, which is reflected in the high salaries of elite programmers.
-
Rarity: Rarity assesses how uncommon or difficult it is to acquire a specific type of knowledge. If a skill set or body of knowledge is widely available in the labor market, it may be valuable but not rare. Conversely, if only a handful of individuals or firms possess this knowledge, it becomes a rare resource, which enhances its strategic importance.
-
Imperfect Imitability: This criterion evaluates how difficult it is for competitors to replicate the knowledge. Imitation may be hindered by complexity, tacit understanding, or path dependency.
-
Non-Substitutability: This measures whether the knowledge can be replaced by alternative means or technologies. If no viable substitutes exist, the knowledge is considered non-substitutable and thus highly strategic. For instance, a firm that relies on a cutting-edge Large Language Model (LLM) may face the risk of rising costs or licensing changes. If the LLM cannot be substituted by older technology or internally developed solutions without a significant loss in performance, then the knowledge surrounding its use becomes vital.
When making strategic decisions—especially in IT architecture or knowledge-intensive sectors—managers often engage in knowledge prioritization exercises based on these four dimensions. A common method involves assigning scores (e.g., low/medium/high or numeric values such as 0/1/2) across each attribute for various knowledge domains or technological options. By summing the scores, decision-makers can rank the relative importance and strategic value of different knowledge assets. These rankings inform decisions about which knowledge to develop internally, which to acquire externally, and which to protect as a core competency.
Additionally, organizations may carry out risk analysis to evaluate how dependent they are on particular knowledge resources and what the impact would be if these were lost, became obsolete, or prohibitively expensive. This involves estimating the exposure to knowledge-related risks and devising strategies to mitigate them—whether through succession planning, documentation, training, or diversification of expertise.
Managing Knowledge within Organizations
Beyond individual practices, companies deploy Knowledge Management Systems (KMSs) to support knowledge-sharing and decision-making. However, it is critical to understand that a KMS is not limited to a specific type of software. Rather, any information system—including enterprise resource planning (ERP) systems, customer relationship management (CRM) tools, or even collaborative platforms—can serve as a KMS if it is designed or adapted to deliver relevant information in the right context to support decisions.
Each KM initiative may be classified according to three main approaches:
- Technocratic: This approach focuses on the technical aspects of knowledge management, including the design and implementation of systems that facilitate knowledge sharing and retrieval. It emphasizes the importance of having robust IT infrastructures that can support the flow of information.
- Economic: This perspective emphasizes the need to demonstrate the value of knowledge management initiatives. It focuses on quantifying the benefits derived from KM practices, such as improved efficiency, reduced costs, or enhanced innovation.
- Behavioral: This approach centers on the human aspects of knowledge management, including the culture and practices that encourage knowledge sharing and collaboration among employees. It recognizes that technology alone cannot drive KM success; it requires a supportive organizational culture.
An effective KM strategy often requires integrating all three approaches. Systems must be technically sound, economically justified, and supported by a collaborative organizational culture. As information systems evolve, the design of KMSs becomes increasingly user-centric and context-aware, prioritizing actionable insights over raw data. This is a fundamental shift from traditional monolithic systems to adaptive, modular KM infrastructures that align more closely with the real-world decision-making processes of users.
KM Technocratic Strategy
In knowledge-intensive organizations, one of the primary strategies for managing knowledge is the technocratic strategy.
Definition
This approach focuses on building and leveraging technical systems to formalize, store, and disseminate knowledge across the organization. The core idea behind this strategy is that knowledge can and should be encoded into structured formats—such as documents, databases, and software tools—making it widely accessible and reusable.
Technocratic strategies aim to convert tacit knowledge, which is embedded in individual experience, into explicit knowledge that is formally documented and can be shared across the organization. A fundamental component of this strategy is the creation of technological infrastructures that facilitate knowledge sharing. These include knowledge portals, internal databases, directories of expertise, repositories of past projects, and collaborative platforms.
Example
For instance, a consulting firm might deploy a knowledge portal that includes detailed profiles of employees, outlining their roles, brief CVs, and summaries of past projects. This allows employees to identify colleagues who have worked on similar topics or technologies, enabling collaboration and avoiding redundancy—what is often described as not reinventing the wheel. If someone is assigned a new project involving a specific technology or client sector, they can consult the portal to find relevant internal experts and access documented best practices or lessons learned.
It is essential to understand that while external sources like the web can offer basic, general-purpose knowledge, internal knowledge platforms are often richer in context-specific expertise that is not publicly available. Professionals rarely share critical or competitive knowledge online because it may diminish their personal market value or compromise their employer’s advantage. Within an organization, however, the incentives are different: sharing knowledge helps the company operate more efficiently and enhances its competitive edge.
The technocratic strategy assumes that providing the tools for knowledge sharing is only part of the solution. For these systems to work effectively, people must be motivated to use them. In other words, simply having a knowledge management system (KMS) in place is not sufficient; there must also be a cultural and incentive structure that encourages knowledge contribution and reuse. Unlike casual sharing on social media, employees within organizations may require different types of rewards, such as recognition, promotion opportunities, or direct financial incentives.
The technocratic strategy also supports critical success factors (CSFs), those essential areas where satisfactory performance is crucial for the organization to thrive. The implementation of technical systems must be aligned with the organization’s CSFs and associated KPIs.
Example
For instance, a KPI might track how often employees consult the knowledge portal or how frequently content is uploaded and used. This data can then be integrated into executive dashboards, supported by enterprise systems like ERP or decision-support tools, enabling managers to monitor and improve knowledge flows.
KM Economic Strategy
While the technocratic strategy focuses on internal systems and platforms for knowledge dissemination, the economic strategy addresses how knowledge can be monetized and protected as a valuable organizational asset. In this approach, knowledge is seen not just as a resource for internal use, but as a strategic asset that can generate competitive advantage and financial returns.
The economic strategy involves identifying valuable knowledge within the organization and seeking ways to protect and exploit it commercially. One common method is through intellectual property rights, particularly patents.
Definition
A patent is a legal instrument that simultaneously discloses and protects an invention. By obtaining a patent, a company publicly registers ownership of a particular idea, technology, or process, securing the exclusive right to exploit it economically.
However, a patent’s value depends on enforcement. Without legal follow-through, a patent is little more than a publication. Companies must actively monitor for infringements and be prepared to engage legal action when necessary. This is why intellectual property management often involves collaboration between engineers, legal teams, and business strategists.
Beyond patents, other protective mechanisms include copyrights and non-disclosure agreements (NDAs), which are contractual tools used to control the flow of sensitive information. NDAs are especially common during early stages of collaboration between firms, allowing parties to exchange proprietary knowledge without fear of it being publicly disclosed or misused. These agreements can be unilateral or bilateral, and their legal strength varies depending on the clauses they contain, such as pre-agreed penalties for breach.
KM Behavioral Strategy
Definition
Communities of practice are informal groups of people who share a specific domain of expertise, work on similar issues, and are genuinely interested in what they do. These groups are characterized by a strong inclination to discuss, exchange opinions, and build collective knowledge through continuous interaction.
A key aspect concerns the nature of these communities: they are not necessarily confined within a single organization. On the contrary, many communities of practice are public and open to professionals from different companies.
However, participating in public communities comes with implications: employees may be tempted to share their knowledge and experiences, but they must also consider the potential risks. For example, sharing too much information could lead to intellectual property theft or competitive disadvantage. Employees may inadvertently disclose sensitive information about their employer, which could have legal or reputational consequences.
Within the corporate environment, alternative and safer solutions exist to promote knowledge sharing: namely, the creation of internal communities of practice. The main challenge here lies in building trust among members of the organization and encouraging behavior that supports open sharing.
A key component of this strategy is the creation of a knowledge-sharing culture, which emphasizes the importance of collaboration, open communication, and mutual support. This culture must be actively cultivated by leadership and reinforced through policies, practices, and incentives that encourage employees to share their knowledge and expertise.
Example
A practical example of this is the introduction of knowledge-sharing platforms within organizations. These platforms can take various forms, such as internal wikis, forums, or collaborative tools that allow employees to share documents, insights, and best practices. By providing a structured environment for knowledge exchange, organizations can facilitate the flow of information and foster a culture of collaboration.
This philosophy, however, contrasts sharply with strictly hierarchical corporate structures, where information flows only vertically and sharing is limited to interactions between subordinates and superiors. An open and horizontal environment, by contrast, promotes spontaneous and cross-level interactions, breaking down organizational barriers.
Another important tool is the physical office layout: open spaces and offices with open doors signal availability and openness. In contrast, closed or overly compartmentalized environments signal detachment and reduce the likelihood of collaboration.
Finally, to ensure the success of knowledge-sharing practices, it’s essential to explicitly legitimize them. Employees must be encouraged to share documents, ideas, and resources across teams, and they need clear reassurance from leadership that such behavior is welcome and aligned with company values.
Common Challenges and Open Issues in KM and IT Integration
A recurring question in both academic and corporate environments is: Why do so many knowledge management projects fail, despite strong technical infrastructure and organizational investment?
The answer typically lies in governance and behavioral alignment rather than in technology itself. In many cases, KM initiatives fail because they do not address the actual knowledge-sharing needs of the organization.
Example
For example, a system might be built to promote sharing across departments that do not naturally collaborate, or it may include content that is too broad or irrelevant to users’ daily work. When users feel that the shared knowledge is not useful, or that it is drowned in noise, their motivation to engage declines.
Another failure point is the overestimation of technology’s ability to drive behavior change. Organizations often assume that simply providing a sophisticated platform will automatically result in active participation. In reality, user adoption requires cultural change, leadership endorsement, incentives, and user-friendly design. If these elements are missing, even the most feature-rich platform may remain underutilized.
The lifecycle of a failed KM initiative often follows a recognizable pattern:
- Initial excitement: The organization invests in a new KMS, and there is a surge of enthusiasm as employees are introduced to the platform.
- Early adoption: A core group of users begins to engage with the system, contributing content and sharing knowledge.
- Decline in usage: Over time, the initial excitement wanes, and usage declines. Employees revert to old habits or find the system cumbersome.
- Abandonment: Eventually, the system becomes a digital graveyard, filled with outdated or irrelevant content, and is no longer seen as a valuable resource.
A common issue is resistance to changing communication habits. Employees are often attached to familiar tools like email or messaging apps (e.g., WhatsApp) and may resist adopting new platforms like Slack, or Microsoft Teams, even if these are better suited for structured knowledge sharing.
Technical Complexity and Strategic Implications
From a technical perspective, designing an effective KMS involves several complex challenges.
- The system architecture must be robust enough to handle large volumes of data and user interactions while remaining flexible to adapt to changing organizational needs.
- Data modeling and metadata management are crucial for ensuring that knowledge is properly categorized, indexed, and searchable. This requires a deep understanding of the organization’s knowledge landscape and user needs.
- User analytics and feedback loops are essential for understanding how users interact with the system and identifying areas for improvement. This involves collecting data on user behavior, preferences, and pain points to inform ongoing development.
- Alignment with organizational strategy, leadership, and change management is critical for ensuring that the KMS is integrated into the organization’s broader goals and objectives. This includes establishing clear governance structures, defining roles and responsibilities, and providing ongoing training and support to users.
- Incentive structures must be carefully designed to encourage knowledge sharing and collaboration. This includes both extrinsic motivators (e.g., monetary rewards) and intrinsic motivators (e.g., recognition, career advancement).
Incentives in Knowledge Management Systems (KMS)
Knowledge sharing is not a natural behavior in organizations, especially when it requires effort like changing habits or learning new tools without clear personal benefit. While extrinsic motivators like monetary rewards are often used, their design and implementation require careful consideration of human behavior and system dynamics.
Example
For instance, Vodafone implemented a KMS for call center operators to capture customer insights. Operators could submit suggestions via a structured form, subject to peer and managerial review. Accepted contributions earned a small monetary reward (e.g., €10). The idea was to incentivize operators to share actionable knowledge, enriching organizational intelligence.
Initially, such systems often see a surge in activity driven by financial incentives. High engagement volume might appear successful. However, without proper quality controls, the system quickly becomes saturated with:
- Redundant content
- Low-value information
This makes it difficult to find useful knowledge, decreasing system utility and leading users to abandon it despite the large amount of input data. Downloads and engagement subsequently drop sharply.
This illustrates a major pitfall of monetary incentives: they can prioritize quantity over quality. Participants may “game” the system, for example, through reciprocal endorsements, similar to behavior seen on social media or professional networks where endorsements lack genuine value. If the monetary incentive is removed, the user base often disappears, revealing that the platform lacked intrinsic value for its users.
To build a sustainable KMS, focus should be on intrinsic motivation and task relevance. Employees are more likely to contribute meaningfully when the KMS helps them solve real problems or improve their workflow. Introducing a KMS as a solution to existing issues (e.g., troubleshooting, onboarding) provides clear personal benefits, fostering engagement and habitual use, making it easier to expand the platform later.
Another psychological principle explaining knowledge sharing is the theory of editoriality. Individuals often see themselves as curators of expertise and desire recognition as subject matter experts. This can be formalized through individual KPIs integrated into the KMS, such as:
- Number of document downloads
- Citations in internal reports
- Requests for expertise
In larger organizations, the territoriality of knowledge can confer informal power. Employees recognized as experts are often consulted on strategic decisions, enhancing their influence. They have an incentive to publish high-quality content on the KMS, especially if it is widely consumed. Additionally, sharing valuable insights can foster networked reciprocity, where sharing today creates an expectation of receiving knowledge in return later.
From a technical perspective, KMS must support both content validation and reputation mechanisms. Features like:
- Content ranking algorithms
- Peer review
- Advanced search (semantic search, NLP tagging)
- Highlighting trending or highly-rated contributions help manage information overload and improve discoverability and perceived usability.
Example
Organizations like NASA exemplify these principles. Facing the loss of tacit knowledge from retiring employees, NASA developed programs to capture expertise through interviews, structured templates, and post-retirement roles. This approach acknowledged the difficulty of transferring deep, experiential knowledge and created a system that respected the effort involved in meaningful knowledge capture.