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)

Purpose: Despite the rapid growth in the production and utilization of big data and its vast potential for enhancing innovation and improving performance, many Iranian knowledge-based enterprises (KBEs) still face serious challenges in effectively leveraging this strategic resource. If such limitations persist, they are likely to weaken these firms’ competitive positions in today’s turbulent business environment by reducing product commercialization. A review of the existing literature indicates that most prior studies have been conducted in developed countries, and their findings are not necessarily applicable to Iran’s local context. Moreover, much of the research has focused only on partial relationships among the variables, with limited attention given to developing and testing a comprehensive conceptual model that explains the entire value-creation pathway from big data knowledge management capabilities (BDKMC) to business performance (BP). Accordingly, this study seeks to address this research gap and provide empirical evidence within the context of Iranian KBEs. Therefore, this study examines the impact of BDKMC on BP by clarifying the mediating roles of innovation capability (IC), business process innovation (PI), and competitive advantage (CA). This study aims to deliver an integrated and precise picture of how big data can be intelligently harnessed to foster innovation, build CA, and enhance BP.
Methodology: This study was designed and conducted within a positivist paradigm, following a deductive reasoning approach. In terms of purpose, it is categorized as applied research, while methodologically, it is descriptive in nature and implemented as a cross-sectional survey. The population of interest comprised 5,048 knowledge-based companies in Tehran Province. To ensure adequate statistical precision and reduce the likelihood of Type I and Type II errors, the minimum sample size was estimated using G*Power 3. Based on four predictor variables, a significance level of 0.05, an effect size of 0.05, and a statistical power of 0.90, the required sample size was determined to be 313. The unit of analysis was company managers, with one questionnaire administered to each firm in the sample. Sampling was conducted using a simple random sampling procedure using the random sampling function in SPSS. Data were collected using a standardized instrument consisting of 47 items. The research model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 3. In the preliminary stage, factor loadings were inspected to ensure that all exceeded the minimum threshold of 0.40. The model evaluation was conducted in three steps: measurement, structural, and overall models. Within the measurement model, internal consistency reliability was assessed using Cronbach’s alpha, rho_A, and composite reliability (CR) values. Convergent validity was examined using the average variance extracted (AVE), while discriminant validity was assessed using both the Fornell–Larcker criterion and the Heterotrait–Monotrait ratio (HTMT). In the structural model, the predictive capability was first evaluated through the explained variance (R²) and Stone–Geisser’s Q² (predictive relevance). The study hypotheses were then tested. Finally, the overall model fit was assessed using three key indices: the root mean square residual covariance (RMStheta), standardized root mean square residual (SRMR), and goodness-of-fit (GOF) index. Together, these indices ensured the robustness and reliability of our proposed conceptual model.
Findings: The results revealed that BDKMC significantly affected IC (β = 0.329, t = 4.669), BPI (path coefficient = 0.239, t = 3.010), and CA (β = 0.425, t = 6.749). However, the direct effect of BDKMC on BP was not supported (β = 0.052, t = 0.985). Furthermore, IC has significant positive effects on BPI (β = 0.536, t = 9.632), CA (β = 0.443, t = 7.562), and BP (β = 0.299, t = 3.514). In addition, BPI significantly influenced both CA (β = 0.165, t = 2.360) and P (β = 0.146, t = 2.071) in this study. Finally, CA had a strong and significant impact on BP (path coefficient = 0.342, t = 5.517). Regarding the mediation effects, the mediating roles of IC and BPI were not supported (β = 0.098 and 0.035; t = 1.812 and 0.794, respectively). However, the mediating role of CA was confirmed (β = 0.145, t = 2.013).
Research limitations: Despite its theoretical and practical contributions, this study is subject to several limitations, which provide avenues for future research. First, reliance on self-reported data from managers of knowledge-based firms may introduce response bias. Future work could enhance validity by employing multi-method approaches, such as in-depth case studies and semi-structured interviews, to reveal the more nuanced mechanisms of value creation through big data knowledge management. Second, the geographical and industrial scope—focusing on firms in Tehran Province and analyzing heterogeneous industries without differentiation—may limit the generalizability of the findings. Expanding geographical coverage, conducting cross-national comparisons, and pursuing industry-specific studies could address this issue while exploring variations across technological domains and organizational life-cycle stages. Third, the cross-sectional design restricts causal inference and leaves room for reverse or bidirectional effects to be observed. Longitudinal designs can offer stronger causal insights. Fourth, the dynamic nature of environments and the rapid evolution of big data technologies may constrain the measurement validity. Data mining, document and social media content analysis, updated measurement instruments, and novel theoretical perspectives may help mitigate this concern. Fifth, the lack of distinction among firms in terms of size, age, technological focus, and business models may obscure relevant differences. Future research could employ cluster analysis, multigroup analysis, or multilevel modeling to uncover subgroup-specific patterns and test the conceptual framework accordingly. Finally, the insignificant mediating effects highlight the need for further investigation to capture the complexity of relationships and provide a more holistic understanding of the value creation process.
Practical implications: This study’s findings offer several practical insights for managers and policymakers in KBEs. First, they highlight the critical role of BDKMC in enhancing innovation and creating CA, suggesting that firms should strategically invest in knowledge management systems and processes to fully leverage their data resources. Second, the results indicate that IC and the BPI serve as key mechanisms through which the BDKMC impacts BP. Therefore, managers should focus not only on technological adoption but also on fostering a culture of continuous innovation and process improvement to translate data-driven insights into tangible performance results. Third, the confirmed mediating role of CA underscores the importance of aligning innovation and process initiatives with strategic objectives to sustain superior performance in dynamic business environments. Collectively, these insights provide actionable guidance for KBEs aiming to optimize their big data strategies, strengthen their innovation pipelines, and enhance their overall organizational competitiveness and performance.
Originality/value: This study, for the first time, investigates the impact of BDKMC on the performance of Iranian KBEs and provides unique empirical evidence from these companies. The findings address existing gaps in both theoretical and practical literature and offer novel insights into the dynamics of innovation and competitive advantage within this context.