Business analytics has become an important tool for small and medium-sized enterprises (SMEs) seeking to improve decision-making and strengthen their competitive position. This study examines the influence of business analytics capabilities on the competitive advantage of SMEs, focusing on tangible analytics resources, intangible analytics resources, and human analytical skills. A quantitative research design is proposed using a structured questionnaire administered to SME owners and managers. The collected data will be analyzed using descriptive statistics, reliability and validity testing, correlation analysis, and Partial Least Squares Structural Equation Modeling (PLS-SEM). The study aims to determine whether business analytics capabilities significantly contribute to cost efficiency, innovation, market responsiveness, differentiation, and customer value. The proposed framework emphasizes that analytics technologies alone may not provide competitive advantage; their effectiveness depends on data quality, organizational support, analytical knowledge, and employees' ability to interpret and apply analytical insights. The study is expected to provide practical guidance for SMEs seeking to develop data-driven capabilities and improve their competitiveness in increasingly digital business environments.
Lakshmi Vasanthi Jampani· World Journal of Advanced Re...· 0 citations
The rapid adoption of Artificial Intelligence (AI) and business analytics is transforming organizational decision-making by enabling managers to utilize large volumes of data for timely and informed business decisions. This study examines the role of AI-driven business analytics in improving managerial decision-making and organizational performance. The study proposes an integrated framework in which AI-driven business analytics capability influences organizational performance through enhanced managerial decision-making effectiveness. The framework considers the ability of AI-enabled analytics to provide predictive insights, identify business patterns, support risk assessment, and improve the quality and speed of managerial decisions. A quantitative research approach is proposed, using a structured questionnaire to collect data from managers and executives working in organizations that utilize AI and business analytics. The collected data will be analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the proposed relationships and mediation effects. The study is expected to demonstrate that effective utilization of AI-driven analytics can strengthen managerial decision-making and contribute to improved organizational outcomes. The study contributes to the emerging literature on AI-enabled management by linking analytical capabilities, managerial decision-making, and organizational performance within a unified framework. The findings are expected to provide practical guidance for organizations seeking to develop data-driven and AI-enabled decision-making capabilities.
Lakshmi Vasanthi Jampani· World Journal of Advanced Re...· 0 citations
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