Scalability of generative knowledge management systems: designing for individuals’ and institutions’ mutual benefit

Abstract

Purpose In further conceptualizing a novel generative knowledge management system (KM/KMS), this paper aims to focus on identifying and mitigating the risks related to its envisaged scaling from a prototype to an application with a rapidly growing user base. Design/methodology/approach It follows up on prior publications using design science research (DSR) methodologies in compliance with theory effectiveness, a principle expecting system designs to be purposeful in terms of utility and communication. The KMS perspective taken prioritizes a decentralizing agenda benefiting knowledge workers while also aiming to foster a fruitful co-evolution with conventional organizational KM approaches. Findings The utilization and further extension of the CKDT and a “scalable innovation” heuristic are assisting the detecting of potential scaling risks related to the logics and logistics, generative interoperability, technological capacitating, knowledge dynamics and value chain which further validates the viability of the proposed KM concept and system. Research limitations/implications Although the prototype development is still in progress, the paper conforms to the DSR practice to report on early visions of technology impact on users, organizations and society but also reflects on expectations of viability, desirability and commitment, as well as the system’s prospect as a general-purpose-technology or disruptive innovation. Originality/value In addition to the novel KM-related perspectives, the paper’s practical emphasis on the scaling of more complex systems is rarely dealt with in the literature due to the respective projects’ often large-scale collaborative nature, broad methodological scope and diverse stakeholders’ interests. In this case, the task is eased as prior DSR outputs can be referred to.

Reframing a Novel Decentralized Knowledge Management Concept as a Desirable Vision: As We May Realize the Memex

“…This SICEE sequence has recently been corroborated [84] by empirical knowledgeheritage-research [85] which resulted in extending the C-K-design theory (CKDT) [86]. The five correlating generative types of the extended CKDT are depicted in Figure 6, column 5 (C1-C4, K5).…”

“…As a further extension, a recent article [84] is focusing on the transferring of creation heritages. Since the design of artefacts and services for digital transformation is essential for extending the boundaries of human and organizational capabilities by co-creating and codelivering affordances for citizens and institutions, the concepts of "Creation or Generative Heritages" [85] aim to not only share knowledge resources but also to transfer the capacities or "creative spirits" to utilize them, including, for example, "eliciting generative gains in the form of identifying gaps (turning unknown unknowns into known unknowns, filling gaps [turning known unknowns into known knowns], sharing object structures, progress principles, creative reasoning, usage metrics, value criteria and desires as well as creating new objects and designs)" [36].…”

Gifts, Contexts, Means, and Ends Differing: Informing Task Scenarios to Serve Knowledge Workers’ Needs in Dynamic Complex Settings

“…The novelty of the concept and affordances may also emerge as a dominant design and evolve into an attractive GPT (Figure 3, bottom rows) (Schmitt, 2019c). In addition to the educational services, an appreciation model for potential users as well as a PKM for Development (PKM4D) Framework for users have been devised to attract and retain users (Schmitt, 2016a(Schmitt, , 2020b.…”

“…The risk of successfully scaling a social platform and memetic database also concerns the development of the PKMS-DPE; it has just been focused on in two articles by employing system dynamics, discrete-event, and agent-based modeling as well as the methodologies of C-K-Design Theory, Knowledge Dynamics, and Scalable Innovation (Schmitt, 2020b(Schmitt, , 2020c. Further analysis is necessary to determine how the PKMS-DPE concept compares to and can make use of and add to the advancing semantic web and AI technologies that overlap with some of the novel system's objectives; this includes verifying its central services' potential to generate RDF-statements (resource description framework for knowledge modeling) and ontologies straight from its content and relations repository.…”