Draft:Knowledge productivity

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Declined by TurboSuperA+ 5 days ago. Last edited by EarwigBot 2 seconds ago.

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Comment: The article has a promotional tone regarding the subject. One of the sources is a link to the submitting editor's webpage. TurboSuperA+_(connect) 08:26, 15 July 2025 (UTC)

Knowledge productivity is defined as the ability of individuals, teams, and organizations to generate, apply, and reuse knowledge for the purposes of process improvement, problem solving, decision making, and innovation. This concept builds on traditional understandings of knowledge use.

Knowledge productivity is one of today’s most pressing challenges, especially if we accept that knowledge is now our primary source of competitive advantage, and intellectual capital is the new currency of wealth. The real challenge lies not just in acquiring knowledge, but in transforming it into value.

It expands upon traditional knowledge management by emphasizing the application of knowledge rather than merely its capture and storage. The term is rooted in organizational learning theory and has gained renewed importance with the rise of artificial intelligence (AI), remote work, and hybrid workplaces.

Origins and definition

The concept of knowledge productivity was formalized by Dutch educational scientist Joseph Kessels, who defined it as "a process that entails signaling, identifying, gathering, absorbing, and interpreting relevant information, using this information to develop new capabilities and to apply these capabilities to incremental improvement and radical innovation of operating procedures, products, and services".^[1]

While early knowledge management (KM) efforts focused on taxonomies, repositories, and document libraries, knowledge productivity focuses on converting that knowledge into real-time, contextual actions that add value to the organization.

Historical context

Knowledge management practices emerged in the late 20th century as organizations recognized the need to manage intellectual capital. Over time, KM evolved through several stages:

First generation KM focused on capturing and sharing existing knowledge within an organization, primarily through technology. This generation of knowledge management relied on “information portals".
Second generation KM practices are more focused on human resource and KM process in general. This is also characterized as demand-side KM by McElroy (2003).^[2] The second generation also highlights the difficulties of the codification of tacit knowledge.
Third generation KM thinking was about knowledge that goes beyond information technology, individuals, and even organizations. This is about connecting people through networks such as communities of practice, social learning and social business tools.
Sixth generation KM, as outlined by Bencsik (2021), is characterized by the integration of artificial intelligence into every step of the KM cycle, including knowledge creation, transfer, and application.^[3]

Knowledge productivity emerged in this context as a response to limitations in static KM models, particularly their inability to support agile and intelligent decision-making in complex environments.

Key concepts

From static KM to dynamic productivity

In contrast to traditional KM, knowledge productivity focuses on:

Shifting from curated knowledge to contextual insight Traditional KM focuses on extensive effort to curate a limited subset of content. In contrast, knowledge productivity is about unblocking unstructured content and co-creating content in context.
Knowledge for the many, not the few: Knowledge access currently depends on knowing the right person or where to find the right files. A productivity-centered model democratizes knowledge creation, sharing and use. Stam (2007) summarizes that knowledge productivity works because it participative, stimulating, action-oriented, collaborative, purposeful and helps to explain the logic of KM.^[4]
Contextual relevance: Relevance creates value and why it important to delivering knowledge tailored by role, geography, or task.
Collective intelligence: Harnessing the input of diverse stakeholders to produce better outcomes.

Related concepts

References

^ Kessels, J.; Poell, R.; Kwakman, K. (2004). "Knowledge Productivity in Organizations: Towards a Framework for Research and Practice" (PDF).
^ McElroy, Mark. (2003). The new knowledge management: Complexity, learning, and sustainable innovation. Inf. Res.. 8.
^ Bencsik, A. (2021). "The sixth generation of knowledge management – the headway of artificial intelligence". Journal of International Studies. pp. 84–101.
^ Stam, C. D. (2007). Knowledge productivity: designing and testing a method to diagnose knowledge productivity and plan for enhancement. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente.

Sources

Deloitte. (2021). The new knowledge management: The human factor activates the collective intelligence
ClearPeople. (2023). "The Knowledge Productivity e-book"

[1] Kessels, J.; Poell, R.; Kwakman, K. (2004). "Knowledge Productivity in Organizations: Towards a Framework for Research and Practice" (PDF).

[2] McElroy, Mark. (2003). The new knowledge management: Complexity, learning, and sustainable innovation. Inf. Res.. 8.

[3] Bencsik, A. (2021). "The sixth generation of knowledge management – the headway of artificial intelligence". Journal of International Studies. pp. 84–101.

[4] Stam, C. D. (2007). Knowledge productivity: designing and testing a method to diagnose knowledge productivity and plan for enhancement. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente.

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