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This study aims to determine whether knowledge creation processes (KCPs) – knowledge exchange and knowledge integration affect digital innovation (DI), including information technology (IT)-enabled capabilities (ITECs) as a mediator and absorptive capacity (AC) as a moderator.

With a survey data set of 390 employees from Pakistani software small- and medium-sized enterprises (SMEs), the current study employed Structural Equation Modeling (SEM) using Smart Partial Least Squares to estimate the structural relationships in the conceptual model.

The results confirm that KCPs – knowledge exchange and knowledge integration positively enhance software SME's DI; ITECs play a partial mediating role in the linkage between KCPs and DI; AC positively moderates the relationship between knowledge integration and ITECs, and ITECs and DI, while AC doesn’t moderate the relationship between knowledge exchange and ITECs. The AC positively moderates the mediating role of ITECs amongst KCPs (knowledge exchange and knowledge integration) and DI, respectively.

This research uniquely integrates the knowledge-based view and dynamic capability theory to present a comprehensive framework that explains the interdependencies between knowledge process, ITECs and AC in driving DI. This approach advances the understanding of how software SMEs can strengthen internal knowledge and IT resources to achieve superior innovation outcomes.

This study investigates the crucial link between sustainable practices and organizational development, focusing on sustainable knowledge management (SKM), green innovation (GI) and corporate sustainable development (CSD) in diverse Pakistani organizations.

This study employs a comprehensive research methodology involving advanced statistical techniques, such as confirmatory factor analysis, structural equation modeling and hierarchical linear modeling. These methods are instrumental in exploring the complex interrelationships between SKM, GI, moderating factors and CSD.

This research generates significant findings and actively contributes to sustainable development. The following sections (Sections 4 and 5) delve into the specific findings and in-depth discussions, shedding light on how industry regulation, organizational sustainability priorities, workplace culture collaboration and alignment between green culture and knowledge management practices influence the relationships between SKM, GI and CSD. These findings provide valuable insights for the research community and organizations striving for sustainability.

The study’s findings have practical implications for organizations seeking to enhance their sustainability efforts and embrace a socially and environmentally conscious approach to organizational growth.

This study contributes to the literature on sustainable practices and organizational development. Researchers and business people can learn a lot from it because it uses advanced econometric models in new ways and focuses on the link between knowledge management, GI and sustainable corporate development.

This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.

Firstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.

The findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.

In this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.

The examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.

The algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.

This study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening.