The Impact of Big Data Knowledge Management Capabilities on Innovation, Competitive Advantage, and the Performance of Knowledge-Based Enterprises (Case Study: Knowledge-Based Enterprises in Tehran Province)

Aim/Purpose: To design and validate a knowledge management model to improve business performance in knowledge-based enterprises (KBEs). Background: In recent years, KBEs have become essential drivers of economic development, particularly in Iran. These enterprises face unique challenges, such as intense competition and limited access to advanced knowledge management practices. Addressing these challenges through effective management models is critical for enhancing their performance. This study aims to design and validate a knowledge management model tailored to the specific needs of KBEs in Iran, fostering improved business performance and competitiveness. Methodology: This research adopts an exploratory mixed-methods approach, integrating both qualitative and quantitative techniques. The qualitative phase involved in-depth interviews with 21 managers from knowledge-based enterprises in Iran, selected through theoretical sampling. In the quantitative phase, data were collected from 140 experts from these enterprises, chosen using simple random sampling. Data collection was carried out via interviews and questionnaires. The qualitative data were analyzed using grounded theory methodology, while the quantitative data were analyzed using Partial Least Squares (PLS) analysis to validate the proposed knowledge management model. Contribution: This study is the first of its kind in Iran to develop a knowledge management model specifically designed for knowledge-based enterprises and to evaluate its impact on business performance. The results offer valuable insights for policymakers and managers, enabling them to refine competitive strategies and foster sustainable growth. Furthermore, the study introduces a validated paradigmatic model of knowledge management, underscoring its pivotal role in enhancing business performance across financial, marketing, innovation, and social domains. Findings: The findings indicate that the implementation of the knowledge management model can significantly improve the performance of knowledge-based enterprises in Iran. This model focuses on enhancing knowledge repositories, streamlining business processes, and strengthening competitive capabilities. Key factors influencing knowledge management include managerial leadership, knowledge workers, a knowledge-oriented culture, and organizational learning. The knowledge management infrastructure in these enterprises acts as a foundational element, while business support factors and environmental uncertainty serve as mediating variables. Recommendations for Practitioners: Organizations should prioritize the development of robust knowledge management infrastructures to enhance business performance. This includes fostering a knowledge-driven culture, investing in collaborative tools, and ensuring effective leadership to create sustainable competitive advantages. Recommendation for Researchers: Future research should investigate the integration of knowledge management strategies in diverse organizational contexts and evaluate their long-term effects on innovation. Exploring how these practices contribute to organizational agility and resilience is also recommended. Impact on Society: The findings highlight the pivotal role of knowledge management in driving societal and organizational progress, particularly in knowledge-driven economies. By optimizing knowledge flow, organizations can contribute to sustainable development and address complex societal challenges. Future Research: Further research should explore the impact of emerging technologies, such as artificial intelligence (AI) and machine learning, on the enhancement of knowledge management practices. Investigating their potential to automate knowledge processes, optimize decision-making, and facilitate real-time knowledge sharing will be crucial for advancing the field. Additionally, examining the ethical implications of integrating these technologies into knowledge management systems represents a critical area for future inquiry.

Purpose This study investigates the influence of knowledge hiding in organizations (KHOs), knowledge sharing in organizations (KSOs) and big data knowledge management (BDKM) on knowledge management success. Monitoring (MNT) is used as a moderator between KHO and knowledge management success. Besides, knowledge management success and digital business intensity (DBI) are used to attain competitive advantage (CA). Finally, digital business intensity is used as a moderator between knowledge management success and competitive advantage. Design/methodology/approach Data were collected through questionnaires using simple random sampling, and a total of 433 questionnaires were used in the final analysis. Partial least squares structural equation modeling is used to test study hypotheses. Findings Results indicate that KSO, BDKM and monitoring are positive, but KHO is negatively related to knowledge management success. Monitoring moderates the relationship between KHO and knowledge management success. Knowledge management success and digital business intensity significantly attain competitive advantage. Digital business intensity significantly moderates the relationship between knowledge management success and competitive advantage. Practical implications Top managers can use study findings in decision-making if they want to attain a competitive advantage. KHO has a negative influence on knowledge management success, but this relationship can be changed by monitoring the employees. KSO, BDKM, knowledge management success and digital business intensity play a vital role in determining competitive advantage. Originality/value By building knowledge culture using the knowledge-based view, this study contributes to KHO, KSO, BDKM, monitoring, digital business intensity and competitive advantage literature.

Toward Business Process Innovation in the Big Data Era: A Mediating Roles of Big Data Knowledge Management.

Recently, big data analysis (BDA) capabilities and knowledge management are the essential elements for organizational performance, and this aspect needs researchers' and regulators' attention. Hence, the current article investigates the impact of BDA knowledge management, BDA technological, and management capabilities on the firm performance of the oil industry in the UAE. The study also analyzes the mediating role of firms' agility among BDA knowledge management, BDA technological and management capabilities, and firm performance in the oil industry in the UAE. The researchers have gathered the primary data from the BDA of the oil industry using survey questionnaires. The article has employed the smart-PLS to check the data reliability and association among variables. The results indicated that BDA knowledge management, BDA technological, and management capabilities have a positive association with the firm performance of the oil industry in the UAE. The findings also exposed that the firms' agility significantly mediates among BDA knowledge management, BDA technological and management capabilities, and firm performance in the oil industry in UAE. This article guides the regulators in making regulations regarding the improvement of firm performance using BDA knowledge management and BDA technological and management capabilities.

The present study aimed to propose a model for auditing knowledge management in Islamic Azad University, Tehran Province. This study was applied in terms of purpose and cross-sectional in terms of implementation method. The research population consisted of faculty members of Islamic Azad University, Tehran Province during the 2022–2023 academic year. Based on the Krejcie and Morgan table, a sample of 210 individuals was estimated and selected through stratified random sampling. The research instrument was a researcher-made questionnaire for auditing knowledge management in Islamic Azad University, consisting of 116 items. For data analysis, exploratory factor analysis and structural equation modeling methods were employed using SPSS and AMOS software. The findings indicated that all 116 items in the knowledge management audit had factor loadings above 0.40, and the construct comprised 22 components within six dimensions: organizational culture (3 components), strategy and policies (4 components), international communication and collaboration (3 components), information technology and knowledge management tools (3 components), artificial intelligence in knowledge management audit innovation (6 components), and performance evaluation and outcomes (2 components). Moreover, the factor loading of each component and dimension was above 0.05, their average extracted variance exceeded 0.60, and their reliability was estimated at above 0.70. Additionally, the knowledge management audit in Islamic Azad University showed good model fit, and each of the dimensions had a direct and significant effect on the proposed model (P < 0.05). The results demonstrated that the knowledge management audit model designed in this study can be used by higher education experts and administrators to improve the auditing of knowledge management at the university.

A resource-based perspective on knowledge management capability and competitive advantage: an empirical investigation

Background and purpose: Today, institutions compete in a competitive environment; the requirement to survive in such environments is to create a suitable and durable relationship with the surrounding environment to improve organizational performance and productivity. Of course, communicating with the external environment requires formulating a strategy and coordinating other departments to implement it; Therefore, this study examines the relationship between environmental innovation and strategic competitive advantage. Methodology: This research is applied and descriptive. The statistical population of this research is the employees of Pars Oil Company in Tehran province, whose number is about 2800 people. Using Morgan's table, the sample size was determined as 361. A simple random sampling method was used to select the sample size, and finally, 361 questionnaires were selected for analysis. Findings: Based on the results of the structural equation modeling test and the partial least squares method, process innovation and product innovation based on environmental protection have a positive and significant effect on the competitive advantage resulting from differentiation and low costs. Conclusion: The results show that organizational innovation impacts environmental strategies and gaining a competitive advantage, and environmental strategies also impact gaining a competitive advantage.

PurposeThis paper aims to assess the contribution of competitiveness factors in how small and medium-size enterprises (SMEs) would access competitive advantage (CA) by focussing on industry structure and devise a conceptual model thereof.Design/methodology/approachThe enterprises from three industries consisting of knowledge-based, single-use medical device producers and construction stone cutting each with different structures were assessed. The method is qualitative and quantitative where grounded theory and exploratory factor analysis (EFA) are applied. The initial survey involves 36 deep semi-structured interviews with some of the top managers of each of the three selected industries and a questionnaire distributed among 158 individuals with 46 structured questions.FindingsThe findings indicate that the micro-competitiveness factors are more contributive in achieving CA than macro factors as follows: in knowledge-based enterprises, customer relationship management (CRM), goods/services features and knowledge management are the most important variables. As to single-use medical device production industry, the sales force, sales promotion, and CRM are the most effective factors. Regarding construction stone-cutting industry, quality of the stone, sales promotion, and advertisement play the same role. The results of the EFA indicate that the three impressive factors, including capabilities of the enterprises, strategies of the enterprises and macro factors, are the extracted factors.Practical implicationsThe findings here would assist SMEs’ managers in identifying the most essential factors in accessing CA.Originality/valueThe innovation of this study is that although there exist many studies on SMEs and their CAs, this study seeks the models of CA among SMEs in industries with different structures.

The development of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia continues to increase, yet many still face challenges in business management, particularly in implementing knowledge management, fostering innovation capability, and building competitive advantage. This study investigates the influence of knowledge management and innovation capability on MSME performance, with competitive advantage and innovation capability as mediating variables. Data were collected from 329 MSME respondents in Medan City using purposive sampling and analyzed using Structural Equation Modeling (SEM) with AMOS version 26 and Sobel test for mediation analysis. The findings reveal that both knowledge management and innovation capability significantly affect MSME performance. Knowledge management also has a significant impact on both competitive advantage and innovation capability. However, innovation capability does not significantly influence competitive advantage. Furthermore, while competitive advantage directly affects MSME performance, it does not mediate the relationship between knowledge management or innovation capability and performance. In contrast, innovation capability successfully mediates the effect of knowledge management on performance. These results highlight the strategic importance of strengthening knowledge management and innovation capability to enhance MSME sustainability and competitiveness. Keywords: Knowledge Management, Innovation Capability, MSME Performance, Competitive Advantage

Knowledge Capital: How Knowledge-Based Enterprises Really Get Built is an integrated, structured set of conversations with thought leaders and key practitioners in the fields of intellectual capital and knowledge management, who examine-in the form of conversations-the steps necessary for creating and implementing the various dimensions of a knowledge-based enterprise. These are the dimensions that need to be effectively addressed for the organization to successfully make the transition from an activity-based organization to a truly knowledge-based enterprise. The conversations that make up Knowledge Capital are not studies of theory separated from practice or practice without a strong theoretical base. Rather, they are the stories of how knowledge-based enterprises really get built, in the words of the people who built them. While every contributor begins from his or her own unique perspective and background, each moves toward a convergent understanding of the core elements, perspectives, and practices involved. These systemic conversations provide a body of knowledge and experience on how to craft and implement strategies, as well as the how values, learning, performance, relationships, innovation, and change play in the development of usable knowledge environment. These explorations, together, lead to a mapping of what are quickly becoming the foundations of the next stage of the field. Knowledge Capital gives the reader a readily accessible collection of insights and experiences essential for the new era in intellectual capital and knowledge management.

INTRODUCTION: Tehran is always exposed to various dangers due to its high population density. A geographic information system (GIS) can be very useful for reducing the financial and human burden caused by accidents, disasters, and diseases. METHODS: This field study was performed using a practical and descriptive research method. The pre-hospital emergency bases in the east of Tehran province and all the emergency bases covered by Shahid Beheshti University of Medical Sciences in disasters were evaluated as a case study. The population of these cities amounted to 1,149,485 people and included cities in the east of Tehran province, Iran, including Damavand, Firoozkooh, Pakdasht, Pishva, Qarchak, and Varamin. FINDINGS: Rational maps were created and analyzed in the areas where emergency bases were located using ArcGIS software, as well as analysis of regions, distances, point density, and a combination of these factors. Regarding the standards and indicators, it was determined that the Disaster and Emergency Medical Management Center needs to have130 emergency medical technicians, 23 ambulances, and one ambulance bus to equip the exiting emergency bases according to the standard pre-hospital regulations. Other requirements in this regard include the replacement of worn-out ambulances with new ones, construction of two emergency bases in Qarchak, Tehran, Iran, and three emergency bases in Pakdasht, Tehran, Iran, as well as the transfer of bases in the proximity of faults and flood-prone areas to safe places. CONCLUSION: Based on the obtained results, a comprehensive pre-hospital database was designed for the use of managers and officials in the occurrence of accidents, which might be used as a pilot work that can be expanded to other areas of Tehran, Iran, and other provinces in managing disasters and accidents.

Purpose– The current conventional strategic management model is incapable of dealing with various questions on organizational management in a dynamically discontinuous environment. Hence, how an enterprise can effectively apply its knowledge management (KM) capability and develop a uniquely dynamic capability in order to provide quick response to a dynamic environment has become an urgent need. The purpose of this paper is to discuss the above-mentioned issues.Design/methodology/approach– In order to gain best exploration on KM capability, dynamic capability, and organizational performance, the questionnaire and statistical analytical techniques were used.Findings– The results indicate that dynamic capability is an important intermediate organizational mechanism through which the benefits of KM capability are converted into performance effects at the corporate level. That is, KM capability enhances the dynamic capability of organizations. While dynamic capability, in turn, increases organizational performance and provides competitive advantages.Research limitations/implications– This research applied a purposive sampling method and obtained a slightly inadequate number of respondents. Therefore, it is suggested that future research should apply a random sampling method to collect more responses and increase the generalizability.Practical implications– This research aims to investigate KM capability, dynamic capability, and organizational performance, as well as establish and verify the patterns of the aforementioned relationships based on how enterprises implement their KM capabilities and dynamic capabilities to enhance organizational performance.Originality/value– There is still little related literature investigating the relationships among KM capability, dynamic capability, and organizational performance. Hence, this study applies questionnaire methods as the main research tools in order to conduct an in-depth investigation into the influence of KM capability and dynamic capability on organizational performance. Furthermore, this research is expected to provide enterprises with valuable suggestions for management practices.

The paper first takes a look at the state of the art on the overlapping areas of: Enterprise Modelling, Enterprise Integration/Interoperability, Organisational Learning and Knowledge Management. We identify requirements, characterisation and recent contributions for the interoperable and knowledge-based enterprises in the area of production of goods or services. It is then advocated that Enterprise Modelling and Knowledge Representation should combine their efforts to develop reference and reusable core manufacturing enterprise ontologies to support Enterprise Integration and Interoperability on one hand and open the door to Organisational Learning and management of active knowledge for the ‘smart’ enterprises of tomorrow on the other hand. This is illustrated by case studies from projects carried out in industry.

The aim of this paper is to empirically investigate the role of knowledge management capability in the Russian high-tech sector. The high-tech complex plays an important role in the economy of the Russian Federation and accounts for 21% of the gross domestic product. The results of the previous research have shown that among the three selected antecedents the engineering processes have the most impact on knowledge management (KM) capability. However KM capability did not prove the mediating role between these antecedents and enterprise performance. Moreover, the questionnaire used was not clear enough to respondents, and the data processing tool was not appropriate either. This paper presents the next step of the study where an improved questionnaire and a modified data processing tool were used. The main findings are the following: the positive impact of chosen antecedents on KM capability has been confirmed as well as role of KM as a mediator; the peculiarities of knowledge management capability in the Russian high-tech sector have been identified.

The knowledge grey generation process in knowledge-based enterprises consists of the merging, recombination, splitting and deletion of knowledge units, which is the smallest behavior unit of the transformation from internal knowledge to value. This paper establishes a knowledge grey generation model of the knowledge-based enterprises to study the relationship between knowledge grey generation and innovation performance. Through simulation analysis, it concludes that the knowledge grey generation strength can influence the innovation performance differently in the enterprises of different innovation modes. Specifically, in the initial stage of knowledge generation, the innovation performance increases with the knowledge grey generation strength; with time evolution, the innovation performance will be down after it gets the maximum; the rules of the relationship between knowledge grey generation strength and innovation performance are different under different innovation modes.