International Journal of Knowledge Processing Studies

Abstract This research aimed to design and explain a model of spiritual resilience in start-ups, using a data-driven theoretical approach and highlighting the role of knowledge processing. The present study employed a qualitative approach grounded in a data-driven theory strategy. Data were collected through semi-structured interviews with 15 managers, consultants, and activists in the fields of innovation and startups, and were analyzed using ATLAS.ti software. A total of 173 initial codes were identified, which were subsequently organized into 21 core categories, ultimately resulting in 6 selected categories. The research findings were categorized into five areas: causal conditions, context, intervention, strategy, and outcome. A model consisting of six categories and 21 core codes was developed based on 173 open codes. This model has identified one of the most comprehensive approaches to fostering spiritual resilience in start-ups. The role of knowledge processing—particularly through the analysis of past experiences, ethical decision-making, and tacit knowledge management—is crucial in fostering spiritual resilience. The spiritual resilience model can assist businesses in effectively adapting to technological and environmental developments by fostering flexibility and readiness for change. This approach enables organizations to capitalize on new opportunities and mitigate risks that may arise in a resilient business environment. As a result, the spiritual resilience model can assist start-ups in the ICT sector in achieving success and sustainable growth by fostering positive relationships with spiritual values, promoting flexibility, and enhancing resistance to financial pressures as well as technological and environmental changes.

Abstract In the digital transformation era, customer engagement in online shopping has become a key pillar of success for e-commerce businesses.The researcher analyzed the results and findings of previous researchers using a systematic review and meta-synthesis approach and identified the effective factors by performing the 7 steps of the Sandelowski and Barroso method. Out of 198 articles, 35 articles were selected based on the CASP method. The validity of the analysis was also confirmed by the value of the Holstey coefficient, Scott's p-coefficient, Cohen's kappa index, and Krappendorff's alpha. In this context, in order to measure reliability and quality control, the transcript method was used, and its value was identified at the excellent level of agreement for the identified indicators. The results of analyzing the collected data in MAXQDA software resulted in the identification of model codes. The data consisted of findings from 35 peer-reviewed studies published between 2010 and 2023, selected through purposeful sampling. The findings reveal that customer engagement comprises three main dimensions: cognitive, emotional, and behavioral, each containing key components such as mental focus, enthusiasm, active participation, and enjoyment. Moreover, effective engagement is associated with increased loyalty, repurchase intention, and brand advocacy. The results of this study can serve as a foundation for designing digital marketing strategies, improving user experience, and developing interactive models in online sales platforms. Additionally, the proposed framework offers a basis for further empirical research and advanced modeling in customer engagement.

Abstract This study aims to evaluate knowledge management and its components in knowledge-based project-oriented organizations; knowledge management plays an important role in improving performance, innovation, and sustainable competitive advantage. This applied mixed-method research utilized both qualitative and quantitative approaches. In the qualitative phase, meta-synthesis and the fuzzy Delphi method were used to identify and validate key components. In the quantitative phase, a researcher-developed questionnaire was administered using stratified random sampling.  Data were analyzed with SPSS and model fit was assessed via Smart PLS. The validity and reliability of the instrument were confirmed through expert evaluations, and Cronbach’s alpha exceeded 0.7. Six main dimensions of knowledge management—process, technology, human resources, organizational structure, strategy, and leadership—along with 25 components were identified. The Quantitative results demonstrated that these dimensions were generally in a favorable state. Among them, the process dimension had the highest impact while technology had the lowest. The most influential components included strategy, leadership, and knowledge culture. Evaluating knowledge management enables knowledge-based project-oriented organizations to optimize their resources, enhance productivity, and achieve a sustainable competitive advantage.

Abstract Importance of Knowledge Management (KM) grows in academic libraries, especially in developing countries like Bangladesh. However, academic libraries' role in KM promotion and user perceptions remain unexplored. This study aims to examine the role of academic libraries in promoting KM in Bangladesh, exploring the viewpoints of students, faculty members, and library personnel regarding KM practices, tools, influential factors, and challenges. An online survey was conducted from January to May 2025, involving 236 participants, including students, faculty members, and library personnel from various Bangladeshi universities. Descriptive statistics analyzed demographics and general information. Perception ratings were examined using frequencies, percentages, means, and standard deviations. Cronbach's alpha assessed questionnaire reliability. Chi-square tests determined associations between KM familiarity and demographics. Mann-Whitney U and Kruskal-Wallis tests evaluated differences in KM perceptions based on demographic characteristics. Participants viewed academic libraries' role in KM positively. Collaboration software was observed most important tool for implementing KM. Staff training and institutional support are Key success factors for promoting KM. Lack of awareness, insufficient training, and technological limitations were identified as the main challenges. Positive role of academic libraries in promoting KM and KM's potential for improving library services was widely recognized. Successful implementation and promotion require addressing awareness, training, and technological infrastructure challenges.

Abstract This study explores the application of federated learning in demand forecasting for decentralized supply chains, focusing on enhancing data privacy, forecasting accuracy, and computational efficiency. Federated learning allows multiple nodes, such as retailers and warehouses, to collaboratively train machine learning models without sharing sensitive data. The integration of active learning further improves the model’s accuracy by prioritizing the most informative data points, thereby reducing training time and communication costs.
The results demonstrate that the federated learning model significantly outperforms traditional centralized models in terms of accuracy, with a 45% improvement in Mean Absolute Error (MAE) and a 31% improvement in Root Mean Square Error (RMSE). Moreover, the federated model reduces computational overhead by 35% and enhances privacy, achieving lower epsilon (ε) values, indicating stronger privacy guarantees. These findings suggest that federated learning is a viable and effective solution for real-time demand forecasting in complex and decentralized supply chains.
Future work can build on this approach by integrating additional privacy-preserving techniques and expanding its application to other areas of supply chain management. The study contributes to the growing body of knowledge on the use of artificial intelligence in supply chain optimization, offering a scalable and privacy-preserving alternative to traditional forecasting methods.

Abstract A crucial factor for successful knowledge management is strong support from senior managers across all organizational levels. However, few studies have explored how senior managers' supportive behavior directly influences knowledge management implementation and improvement. This research proposes and tests a model using the Theory of Planned Behavior (TPB) to predict the formation and enhancement of senior managers’ supportive behavior in implementing knowledge management. Data was collected through a questionnaire distributed among 101 senior managers and employees in project-oriented organizations within Iran's Power Plant Industry. The model was analyzed using Structural Equation Modelling (SEM). Results show that the components of mental attitude, social norms, and perceived behavioral control significantly influence the behavioral intentions of senior managers to support knowledge management. Strengthening these components can increase senior managers' support, thereby positively impacting the overall success of knowledge management within the organization. This insight underscores the importance of leadership in driving knowledge management success.