Appendix 8.3 Comparison between NextGen.LX and Conventional Knowledge Management

Basic Paradigm

Dimension	Conventional Knowledge Management	Social Learning Design
Concept of knowledge	Knowledge as an object that is stored, transferred and applied	Knowledge as an emergent process that is continuously co-constructed in social interactions.
Understanding of learning	Individual knowledge acquisition through content acquisition	Collective intelligence development through structured social learning processes.
Central mechanism	Codification and distribution of explicit knowledge	Orchestration of productive social dynamics for knowledge generation.
Theoretical foundation	Information processing, taxonomies, best practice archiving (Hansen et al., 1999)	SECI model (Nonaka), conversational framework (Laurillard), systems theory (Luhmann).

Architecture and Infrastructure

Dimension	Conventional Knowledge Management	Social Learning Design
Technological role	Repository systems (intranets, wikis, document management)	AI as a social operating system, infrastructure for collective intelligence
Primary function	Storing, searching and finding documented knowledge	Space for uncertainty, conflict and emergent thinking
Quality criterion	Completeness, timeliness and accessibility of the knowledge base	Epistemic value of contributions, productivity of social dynamics
Structural principle	Hierarchical categorization, taxonomies, tags	Functional decoupling of status and contribution, temporal exposure

Organisational Logic

Dimension	Conventional Knowledge Management	Social Learning Design
Statusbehandlung	Expertise-based authority determines knowledge validity	Status becomes functionally invisible; contributions weighted according to learning value
Fehlerkultur	Errors as knowledge deficits, to be avoided through better documentation	Errors as an epistemic resource, algorithmically valorized
Unsicherheit	Problem to be solved through more information	Functional resource that is structurally maintained
Skalierungs-mechanismus	More content, broader distribution, better search functions	Replicable design patterns for learning processes, internalized process competence

Psychological Safety

Dimension	Conventional Knowledge Management	Social Learning Design
Treatment	Cultural aspiration, not addressed or treated as a separate HR issue	System property, architecturally operationalized
Mechanism	Normative appeals, dependent on leadership	Structural design principles,  technologically guaranteed
Vulnerability	Moral virtue (often implicitly expected, rarely enabled)	Functionally necessary and structurally rewarded
Voice	Voluntary contributions in forums/communities	Epistemically weighted participation as a  system norm

Learning Processes

Dimension	Conventional Knowledge Management	Social Learning Design
Learning unit	Documents, modules, best practices	Learning activities, visually composed in storyboards
Learning mode	Acquisition (reading, completing e-learning courses)	Construction (collaborative design, dialogic learning)
Knowledge transfer	Push (distribution) or pull (search)	Conversion between tacit and explicit (SECI)
Design responsibility	Central knowledge managers or L&D department	Teams themselves, supported by visual storyboard tools
Reuse	Copy-paste of documents and templates	Adaptation and remix of learning process patterns

Role of AI

Dimension	Conventional Knowledge Management	Social Learning Design
AI function	Intelligent search, recommendation algorithms, chatbots for FAQs	Social operating system: evaluation of epistemic value, synthesis of polyphony, productive conflict mediation
Optimization goal	Relevance of search results, content personalization	Quality of collective sensemaking processes
Interaction mode	Human asks, AI answers (tutorial)	AI provides space for human collective reasoning and planning
Value criterion	Accuracy, precision, recall	Epistemic contribution, diversity of perspectives, productive tension

Leadership Role

Dimension	Conventional Knowledge Management	Social Learning Design
Primary function	Knowledge provision, demonstration of expertise	Shaping learning conditions, narrative leadership
Authority	Hierarchical (who knows the most?)	Curatorial (who connects learning with effectiveness?)
Core competence	Conveying specialist knowledge, establishing best practices	Storyboarding, adaptive moderation, hybrid intelligence
Relationship to uncertainty	Minimizing through expertise	Maintaining productivity through structural safety

Metrics and Success

Dimension	Conventional Knowledge Management	Social Learning Design
Primary metrics	Number of documents, download rates, usage statistics	Innovation output, decision-making quality, time-to-competency, transformation success
Success indicator	‘All relevant information is documented and retrievable’	‘Teams can learn in a self-organized manner even in uncertain situations’
ROI calculation	Time savings through faster information retrieval	Productivity gains through structural psychological safety (12-27% innovation, 25-40% problem-solving speed)
Quality criterion	Completeness of the knowledge base	organizational learning ability as a strategic resource

Dynamics of Scaling

Dimension	Conventional Knowledge Management	Social Learning Design
Scaling logic	Linear: More content → More users → Higher costs	Exponential: Design patterns become organizational resources
Bottleneck	Centralized content production, curation, updating	Initial design competence development (then internalized)
Dependency	By knowledge managers, taxonomy architects, content creators	From structural architecture (once implemented, self-sustaining)
Cost structure	Rising operating costs as growth increases	Declining marginal costs through internalization

Organizational Outcomes

Dimension	Conventional Knowledge Management	Social Learning Design
Primary benefits	Efficiency through access to information	Effectiveness through collective intelligence
Learning speed	Limited by content production and distribution	Limited by structural learning ability (significantly faster)
Adaptability	Reactive (document first, then apply)	Proactive (learning while doing)
Competitive advantage	Short-term, imitable	Long-term, competence as a strategic asset
Transformability	Limited (70% failure rate, McKinsey 2023)	Increased (3.5 times higher success rate)

Limitations

Dimension	Conventional Knowledge Management	Social Learning Design
Main weakness	Tacit knowledge remains unrecorded; social barriers to learning remain unaddressed	Requires courage and commitment to cultural shift and leadership’s future skills development
Typical failure	‘Knowledge graveyards’: no one uses the repositories, social transfer to the working world is insufficient	Risk of superficial implementation without deep structural anchoring
Prerequisites	Technical infrastructure, discipline for documentation	Commitment to psychological safety, diverse and inclusive teams
What is not resolved	Social costs of learning, status dynamics, voice withholding, organizational innovation and transformation	External knowledge & skill-acquisition via standardized training still necessary

Integration with traditional knowledge management

Important to note: Social learning design does not replace conventional knowledge management , but rather addresses its blind spots. The ideal integration is traditional KM for explicit, codifiable knowledge (compliance, facts) and social learning design for tacit knowledge conversion, i.e. for complex problem solving, process modelling and innovation under uncertainty. This combination enables efficient management of consolidated knowledge (KM), rapid generation of new knowledge under uncertainty, and the structural transfer of emerging insights into documented knowledge.

These tables illustrate that social learning design is not an incremental update of knowledge management, but a fundamental paradigm shift from managing knowledge to enabling learning.

Additional References

Hansen, M. T., Nohria, N., & Tierney, T. (1999). What’s your strategy for managing knowledge? Harvard Business Review, 77(2), 106–116.

Nonaka, I., & Konno, N. (1998). The concept of “Ba”: Building a foundation for knowledge creation. California Management Review, 40(3), 40–54. https://doi.org/10.2307/41165942