DIGITAL TRANSFORMATION AND KNOWLEDGE MANAGEMENT IN SOCIAL ENTERPRISES: DATA-DRIVEN DECISION-MAKING

In the present dynamic and knowledge-driven business environment, Knowledge Management (KM) has emerged as a critical factor for enhancing organizational performance, fostering innovation, and ensuring long-term sustainability. Organizations increasingly rely on effective KM practices to systematically create, store, share, and apply knowledge, thereby improving decision-making processes, strengthening collaboration, and achieving strategic objectives. KM serves as a catalyst for managing intellectual assets and transforming them into valuable organizational outcomes, contributing significantly to competitiveness and growth . Despite its growing importance, the implementation of effective KM practices remains a significant challenge across organizations. Various barriers such as cultural resistance, technological limitations, lack of resources, and inadequate organizational support hinder the successful adoption of KM systems. Cultural dimensions, including organizational hierarchy, power distance, and communication patterns, play a crucial role in influencing knowledge-sharing behavior and the overall effectiveness of KM practices. In many cases, rigid organizational structures and resistance to change restrict open communication and limit the free flow of knowledge within organizations . Furthermore, the increasing emphasis on sustainability and digital transformation has highlighted the need for robust and adaptive KM frameworks. Emerging concepts such as Green Knowledge Management (GKM) emphasize the role of KM in achieving environmental, social, and economic sustainability. Organizations are increasingly integrating KM practices with sustainability goals to promote resource efficiency, environmental responsibility, and long-term development. At the same time, advancements in digital technologies, including artificial intelligence (AI), data analytics, and information systems, are transforming traditional KM processes by enhancing knowledge creation, storage, and dissemination capabilities . Although significant progress has been made in KM research, several gaps still exist in understanding its application across diverse organizational and industrial contexts. Existing KM frameworks often fail to adequately address cultural and contextual differences, which are critical for successful implementation. Moreover, while KM has been extensively studied in large organizations, its application in small and medium-sized enterprises (SMEs) remains relatively underexplored. SMEs face unique challenges such as limited resources, lack of structured KM systems, and informal management practices, which affect the effectiveness of KM initiatives . In addition, there is a need for more empirical and longitudinal studies to evaluate the long-term impact of KM practices on organizational performance, innovation, and sustainability. Many existing studies are conceptual in nature and lack practical validation across different industries and cultural settings. The integration of KM with emerging technologies and its role in supporting innovation and decision-making processes also require further exploration . This study adopts a literature review-based approach to examine the current landscape of Knowledge Management research, with a focus on its interrelationship with culture, innovation, and sustainability. By synthesizing findings from various studies, the research aims to provide a comprehensive understanding of KM practices, identify key challenges and research gaps, and suggest directions for future research. The study contributes to the existing body of knowledge by highlighting the importance of context-specific KM frameworks and the need for integrating technological, cultural, and sustainability dimensions in KM practices.

The Digital Transformation phenomenon always tends to be complicated, ambiguous, challenging, and non-routine managerial tasks for organizations. The success rate of such digital transformation is very low due to rapid changes in technologies. Digital transformation through these technologies demands a fundamental change in organization processes, technology and behavior of the people. This broad change results in different socio-technical issues and challenges. The primary objective of the study is to discover issues, challenges and impact/benefits during digital transformation, investigated by various researchers. For this purpose, a systematic literature review (SLR) as prescribed by Levy and Ellis (2005) helped in identifying the challenges of digital transformation. The articles published from 2008 to 2018 were selected and analyzed. The findings of this research reflect the importance of developing an effective digital transformation strategy in organizations. The proactive strategy regarding people, process, technology, and most importantly their alignment in the organization is found critical in executing such transformation initiative in the organization. The planned efforts regarding knowledge management also played an important role in executing and sustaining such transformation initiatives in the organizations. Moreover, value creation, operational efficiency, competitive advantage, customer relationship, and new business model emerged as a vital motivational factor and outcome for digital transformation. Keywords: Digital transformation, digital transformation issues and challenges, systematic literature review, digital transformation benefits/impact.

This paper considers the current problems of knowledge and information systems management, which are solved with the help of digital institutes for the purpose of the development of high-tech production in the conditions of Industry 4.0. The paper identifies the role of knowledge in the innovative development of industrial production, characterises the specifics of the influence of digital technologies on the technological development of companies and production processes, and focuses on the sensitivity of high-tech production to the level of intellectual support of production processes. Emphasis is made on the necessity of using comprehensive digital institutes, which can expand the boundaries of production companies and use the potential of digital platforms and systems in full. The research methodology combines traditional and new scientific directions and institutional theories in disciplines that study the research problem in the context of an increase in the intellectualisation of labour in the conditions of Industry 4.0. The research methodology covers general scientific methods of synthesis, analysis, the monographic method, observation, generalisation, and comparison. The paper contains important thoughts regarding the features of digital institutes from the position of their formation and influence on knowledge management in high-tech companies. It considers the structure and functions of the digital institutes of knowledge and information systems management in the conditions of Industry 4.0 and characterises selected institutes in the context of their influence on the digital transformation of the economy and the improvement of the state of knowledge management.

This paper presents a Knowledge Management Capability framework based upon an empirical case study conducted at a CMM Level 5 software project organisation. The paper discusses the development of the organisation’s knowledge management (KM) initiative from its initial state, to an organisational state where the KM practices are institutionalised and embedded within the daily activities and work methods of the organisation. The organisation’s KM initiative is analysed through the development of two KM capabilities, namely infrastructure and processes, which were examined in depth while conducting the case study, and form the basis for the KM Capability Framework. The resulting framework helps organisations to analyse any imbalance that may exist in their KM initiative and needs to be addressed. In doing so, the framework benefits organisations in making corrections and restoring balance between their KM infrastructure and process capabilities, thereby improving the path of successful KM implementation towards a state of organisational KM capability.

PurposeThe purpose of this paper is to distinguish and describe knowledge management (KM) technologies according to their support for strategy.Design/methodology/approachThis study employed an ontology development method to describe the relations between technology, KM and strategy, and to categorize available KM technologies according to those relations. Ontologies are formal specifications of concepts in a domain and their inter‐relationships, and can be used to facilitate common understanding and knowledge sharing. The study focused particularly on two sub‐domains of the KM field: KM strategies and KM technologies.Findings”KM strategy” has three meanings in the literature: approach to KM, knowledge strategy, and KM implementation strategy. Also, KM technologies support strategy via KM initiatives based on particular knowledge strategies and approaches to KM. The study distinguishes three types of KM technologies: component technologies, KM applications, and business applications. They all can be described in terms of ”creation” and ”transfer” knowledge strategies, and ”personalization” and ”codification” approaches to KM.Research limitations/implicationsThe resulting framework suggests that KM technologies can be analyzed better in the context of KM initiatives, instead of the usual approach associating them with knowledge processes. KM initiatives provide the background and contextual elements necessary to explain technology adoption and use.Practical implicationsThe framework indicates three alternative modes for organizational adoption of KM technologies: custom development of KM systems from available component technologies; purchase of KM‐specific applications; or purchase of business‐driven applications that embed KM functionality. It also lists adequate technologies and provides criteria for selection in any of the cases.Originality/valueAmong the many studies analyzing the role of technology in KM, an association with strategy has been missing. This paper contributes to filling this gap, integrating diverse contributions via a clearer definition of concepts and a visual representation of their relationships. This use of ontologies as a method, instead of an artifact, is also uncommon in the literature.

Corporate digital transformation and financing constraints: The moderating effect of institutional investors

DESIGNING A CONCEPTUAL PERFORMANCE MEASUREMENT FRAMEWORK OF KNOWLEDGE MANAGEMENT INITIATIVES

Small- and medium-sized enterprises (SMEs) are increasingly embracing digital transformation (DT) to remain competitive; however, the enabling role of knowledge management (KM) remains underexplored. This systematic literature review investigates how KM supports DT in SMEs, focusing on strategic processes, tools, barriers, and policy contexts. A structured search was conducted in Google Scholar, Scopus, and Web of Science using the string: (“knowledge management” OR “KM”) AND (“digital transformation” OR “DT”) AND (“small and medium enterprises” OR “SME”). The search yielded 32,547 results, from which 19 studies met the eligibility criteria (English, 2020–2025, KM–DT focus, clear methodology). Results indicate that KM supports DT primarily through change management (31.58%), innovation enablement (21.05%), as well as improved decision-making and agility (15.79%). The most cited tools include KM systems, AI/analytics, and collaborative platforms. Major barriers include limited resources, lack of digital skills, and poor KM culture. Critical success factors identified are leadership commitment (26.32%) and strategic alignment (21.05%). Theoretical foundations are dominated by the Resource-Based View and Dynamic Capabilities Theory. While KM is proven to be a strategic driver of DT in SMEs, more empirical and policy-grounded studies are needed. This review provides a framework to guide future research and inform SME practitioners and policymakers.

Public organizations are increasingly investing in knowledge management (KM) initiatives. However, most KM concepts, practices, and tools have mainly been developed and studied in the private sector and there is a lack of understanding of KM in public organizations. This paper addresses the gap by discussing the differences between public and private organizations in their KM endeavors. Specifically, KM drivers in the context of public organizations are first identified. Salient KM enablers and how they may differentially affect the success of KM initiatives in public and private organizations are then discussed. Finally, tools for assessing KM performance in public organizations are suggested, in view of their unique organizational objectives.

The concept of Knowledge Management concerns the creation of structures that combine the most advanced elements of technological resources and the indispensable input of human response and decision making. This chapter addresses a very interesting topic—Knowledge Management (KM) and e-business initiatives within global organizations. It compares and contrasts the experiences of two global corporations as they have conceived and implemented KM initiatives. After in-depth secondary research on the subject, both companies developed their own KM in e-business strategy. The two case studies discussed in this chapter highlight Business-to-Business (B2B), Business-to-Consumer (B2C), and Business-to-Employee (B2E) initiatives at these two global organizations. This provides concepts and viewpoints related to the drivers of KM, the hurdles to KM in an organization, the elements of KM philosophy and process, how KM can come to be understood as a critical part of a company’s competitive strategy, and how to deploy a sustainable KM system that suits the business needs of an organization.

The objective of this study is to contribute to the increasing database regarding the effects of school leadership on teachers' data usage by investigating the relationship between the technological leadership behaviors of school principals and data-driven decision-making at their schools based on the mediating and moderating effects of the knowledge management variable. 408 teachers from 14 provinces of Turkey in the 2020-2021 academic year were included in the study. The School Principal Technological Leadership Competency Scale, the Knowledge Management Scale, and the Data-Driven Decision-Making in Schools Scale were used as data collection tools in this cross-sectional quantitative study. Descriptive statistics, correlation, and structural equation modeling (SEM) were used in data analysis. The study results demonstrate the school principals' practices of technological leadership, knowledge management at schools, and data-driven decision-making to be high. It has been found out in the study that knowledge management implementations play a mediating role in data-driven decision-making at schools by increasing the technological leadership competency levels of school principals, while not having a moderating effect on the relationship between technological leadership and data-driven decision-making. In addition, technological leadership and knowledge management have been identified to be significant and positive predictors of data-driven decision-making. Based on the study results, suggestions have been made to improve the technological leadership behaviors and knowledge management implementations of school principals at schools.

In the era of the digital economy, digital transformation is an inevitable choice for enterprises to enhance their competitiveness; however, vague strategic positioning often leads to poor transformation results. Data-driven decision-making based on business analytics can solve the empirical dilemma of strategy positioning. This study focuses on how business analytics can help strategic positioning amid corporate digital transformation while clarifying its core mechanism and corresponding practice models. This study suggests that business analytics plays a crucial role throughout the whole process of data-driven decision-making: in the initial stage, it lays a strategic foundation by digital maturity assessment along with internal and external analysis; In the middle stage, the strategic positioning will be transformed into a set of quantifiable indicator systems; In the later stage, data-driven method is used to ensure the synchronization between strategy and environment. Regarding practice models of different enterprises, large enterprises can cooperate through unified analysis platforms, while small and medium-sized enterprises (SMEs) rely on lightweight tools to build advantages. To cope with key challenges during transition, including ambiguous strategic goals and insufficient digital foundation, enterprises are advised to acquire systematic and self-generated digital capabilities, such as strengthened data governance, improved organizational digital literacy, and an integrated decision loop. This research promotes business analytics from a mere tool to a strategic decision enabler, providing enterprises with a feasible path to convert their strategic positioning from experience-driven to data-driven, which is of guiding significance for reducing risks and optimizing allocation.

Globally, enterprises are undergoing significant transformation in line with developments based on industrial revolution by leveraging extensive computing resources, data capture technologies, information processing systems, and advanced data science models that span analytics, optimization, and algorithmic intelligence. The global market's increasing demand, diverse resource supply options, global competition, and environmental protection needs are driving organizations to adopt sustainable strategies. These involve utilizing information technology and analytics more effectively, innovating manufacturing and service support systems, and employing novel problem-solving methods. The awareness of these challenges and opportunities inspired the theme of the joint event: 56th Annual Convention of ORSI (2023-ORSI) and the 10th International Conference on Business Analytics and Intelligence (2023-ICBAI), held at Indian Institute of Science, Bangalore, India, from December 18 to 20, 2023. The Operational Research Society of India (ORSI) Karnataka, the Department of Management Studies, IISc Bangalore, and the Analytics Society of India (ASI), DCAL, IIM Bangalore jointly organized this event. The joint event aimed to establish a premier platform for knowledge sharing among distinguished practitioners, academics, and researchers from industry and academia, focusing on the current applications of Operations Research (OR), Business Analytics (BA), and Business Intelligence (BI). The conference received over 655+ paper submissions, with 455 selected for presentation. 66 papers were deemed particularly interesting, and authors of 13 promising articles were invited to submit extended versions for a special issue of International Journal of Mathematical, Engineering and Management Sciences (IJMEMS). After rigorous peer review, eight papers were accepted for publication in this special issue, addressing common challenges in Operations Research, Business Analytics, and Business Intelligence. This Special Issue of the IJMEMS explores recent developments in Operations Research, Business Analytics, and Business Intelligence. It presents cutting-edge trends and substantial contributions to key areas such as Scheduling problems, Transshipment problems, E-commerce, Nanofluids, Blockchain Technology, Generative AI, Augmented Analytics, Machine Learning, and real-time Anomaly Detection. This Special Issue delves into following eight topics: Unmasking Content Clarity: Advancements in Defining, Measuring and Enhancing Readability: The authors present a novel method using natural language processing and Generative AI to quantitatively evaluate readability and comprehension. This approach surpasses traditional readability indices, offering substantial benefits for content creation and knowledge management in fields like education, business, technical support, and policy platforms. Strategic Insights into Blockchain Adoption in Automotive Supply Chains: A Comparative AHP-TOPSIS and TISM-MICMAC Analysis The authors explore blockchain adoption in the automotive industry using a multidisciplinary approach involving AHP, TOPSIS, TISM, and MICMAC analyses. This study identifies key enablers and their relationships, offering actionable insights and practical recommendations for automotive managers considering blockchain adoption. Avoid Maximum Cost Method for Solving Linear Fractional Transshipment Problem: The authors introduce a mathematical model for the linear fractional transshipment problem (LFTP) and suggest “Avoid Maximum Cost Method” to obtain an initial basic feasible solution for LFTP. This study conducts a comparative analysis with existing methods to demonstrate the efficiency of the proposed approach. Mathematical Study of Dispersion of Nano Biosensors in an Artery with Multiple Stenosis: This work examines nano-biosensors in a diseased artery with multiple stenoses, determining the temperature, velocity of nanofluid, and transport coefficients. The results lay the groundwork for developing nano-biosensors to diagnose, treat, and manage cardiovascular disease. The mathematical model had possible scope for target detection and drug delivery at stenosed sites. Integrating Generative AI in Business Intelligence: A Practical Framework for Enhancing Augmented Analytics: This study offers a practical framework for integrating generative AI (Gen AI) into Business Intelligence (BI). By adopting it, businesses can maximize GenAI and BI's potential, enhancing analytics, operations, and fostering a collaborative, data-driven culture. Data Monetization Through Cross Industry Collaboration in Retail Banking: This paper examines how data sharing between banks and e-commerce platforms, facilitated by data monetization, can improve banking customer experiences. This study proposes a framework using propensity models to identify promising customers and offer personalized products and promotions. Development of Dispatching Rule based Heuristic Algorithms for Real-Time Dynamic Scheduling of Non-identical Parallel Burn-in Ovens with Machine Eligibility Restriction: This study tackles a realistic problem in semiconductor manufacturing by scheduling non-identical parallel Burn-in ovens. The study proposes 25 heuristic algorithms for real-time dynamic scheduling with machine eligibility restrictions. Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. Retraining and false positives were minimized, outperforming traditional methods in experiments. Challenges and Future Directions 1. Enhanced Decision-Making: Addressing uncertainty and complexity in decision-making processes to improve outcomes. 2. Regulatory Frameworks: Conducting further research to establish flexible yet robust regulatory structures that can effectively adapt to the rapid evolution of blockchain technology. 3. Optimization Algorithms: Developing efficient algorithms to solve linear fractional transshipment problem with multi-objective linear fractional functions. 4. AI in Business Intelligence: Investigating long-term impacts of AI-enabled Business Intelligence tools on data-driven decision-making and organizational performance. 5. Ethical AI Considerations: Addressing ethical concerns such as data privacy, fairness, and biases in AI systems to ensure responsible use. 6. Digital Collaboration Frameworks: Developing frameworks that integrate digital footprints and cross-industry collaboration data to enhance strategic partnerships. 7. Advanced Scheduling Algorithms: Creating advanced meta-heuristic algorithms using efficient dispatching rule-based heuristics for dynamic scheduling of non-identical parallel batch processing machines with eligibility constraints. 8. Hybrid Anomaly Detection: Proposing a hybrid framework that combines multivariate extension of Hierarchical Temporal Memory with multivariate Sequential Probability Ratio Test for enhanced anomaly detection. 9. Advanced Text Generation: Utilizing advanced text generation techniques like prompt engineering and fine-tuning to produce more readable and engaging content. We extend our sincere appreciation to all contributing authors for their significant contributions and anonymous reviewers for their dedication and sincere evaluation of submissions. Their timely and excellent responses have been truly gratifying. Additionally, we would like to express our heartfelt thanks to Professor Mangey Ram, Editor-in-Chief of the International Journal of Mathematical, Engineering and Management Sciences, for his support in accepting this special issue and providing unwavering backing from its inception. Guest Editors

This paper seeks to contribute to the on-going research in knowledge management (KM) by presenting a study conducted in six public service agencies in Singapore. The study was guided by three research foci, namely, (1) to elucidate the nebulous nature of KM initiatives, (2) to uncover the motivation behind KM measurement and (3) to identify the various elements of a KM initiative that can be measured. Data collected from the public service agencies revealed that KM initiatives were generally top-down and technology-focused. Project management and the need to quantify the value of KM initiatives drove KM measurement. The measurement indicators adopted by the agencies encompassed four elements of measurement: activities, knowledge assets, organizational processes and business outcomes. In conclusion, this paper highlights two practical implications for the design of a KM measurement regime and suggests a number of possible directions for further research.

This article is an overview of the Systematic Literature Review (SLR) on Knowledge Management (KM), Digital Transformation (DT), and Industry 4.0, which defines the interactions, bonds, and interdependencies of these new research streams. Playing an important role in a progressive discipline, our research summarizes the state of the art past literature using a rigorous methodological approach. The researchers adopted the Scopus database in their analysis and used the Bibliometrix R package. The analysis revealed 761 peer-reviewed English articles. This study shows several research clusters: KM and DT; KM and innovation ecosystem; KM and border technology; and KM, decision making, and Industry 4.0. In addition, this article contributes to identifying growing areas of research that have uncovered previously unknown and interesting relationships between KM, DT, and the public sector. Therefore, the article emphasizes the important role of DT in the development of KM,, discusses future research perspectives as quantitative analysis and combines academics and practitioners.