STRATEGIC DECISION-MAKING AND ORGANIZATIONAL PERFORMANCE IN THE DIGITAL BUSINESS ENVIRONMENT

Strategic Prioritization of Business Initiatives Using the Grey Relational Analysis (GRA) Method

Strategic management is a systematic process that enables organizations to define long-term objectives, make informed decisions, and allocate resources effectively to achieve sustainable growth. It combines strategic analysis, planning, implementation, and evaluation to address competitive challenges while adapting to changing market conditions. By assessing internal strengths and weaknesses alongside external opportunities and threats, organizations can align their mission and vision with strategic goals. Research in strategic management plays a crucial role in enhancing organizational performance by supporting innovation, efficient resource utilization, and competitive advantage. It also promotes ethical practices, sustainability, and responsiveness to emerging technologies and market trends. The strategic management process includes strategy formulation, implementation, and continuous evaluation to ensure organizational adaptability and long-term success. Common strategic approaches include market expansion, product diversification, cost leadership, strategic partnerships, and technological innovation. Performance is often assessed using indicators such as revenue growth potential, market share increase, customer satisfaction, and brand value enhancement, with cost leadership identified as the most effective strategy

Data-Driven Decision Making in Digital Enterprises

The rapid advancement of digital technologies has enabled organizations to generate and analyze vast amounts of data, making Data-Driven Decision Making (DDDM) a critical component of modern digital enterprises. DDDM leverages data analytics, business intelligence, machine learning, and predictive modeling to support evidence-based decision-making, improving operational efficiency, customer satisfaction, innovation, and competitive advantage. Organizations integrate data from enterprise systems, customer platforms, IoT devices, cloud environments, and digital transactions to gain actionable business insights and optimize strategic decisions. Despite its benefits, DDDM implementation faces challenges such as data quality issues, privacy concerns, system integration complexities, and organizational resistance. Successful adoption requires robust data governance, advanced analytical infrastructure, and a data-driven organizational culture aligned with business objectives. This study examines the role of DDDM in digital enterprises by exploring key technologies, implementation strategies, and analytical frameworks that support effective decision-making. A conceptual methodology is proposed to illustrate the transformation of organizational data into actionable business intelligence. The study also presents a quantitative evaluation of decision effectiveness across operational efficiency, customer satisfaction, revenue growth, and risk management. The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning. Furthermore, predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions. The study concludes that DDDM is a key driver of sustainable growth and innovation, with emerging technologies such as artificial intelligence and autonomous analytics expected to further transform enterprise decision-making.

Artificial Intelligence in Supporting Strategic Decision-Making and Its Impact on Organizational Performance

AI capability is a new strategic capability in the organization that goes beyond operational efficiency and can support the quality strategic decision-making, sustainable performance of an organization, and high decision quality. Though AI capability is evolving, current research remains disparate in how to transform an AI capability to a organizational value with the role of governance, leadership, and organizations capability. To solve this, in this study, a integrated conceptual framework grounded in the theory of resource-based view(RBV), dynamic capabilities theory(DCT) and the AI Governance literature is developed and empirically tested. In the model, the sequential relation between AI capability, AI governance, strategic decision quality, organizational agility, and organizational performance was proposed and the moderating role of digital leadership was examined. An explanatory sequential mixed-methods research design was used. The empirical analysis includes two phases. In the first phase, a cross-sectional survey of 446 senior executives and strategic decision makers of public and private organizations was conducted to empirically test the proposed integrated model using Partial Least Squares Structural Equation Modeling (PLS-SEM). In the second phase, qualitative data from 30 semi-structured interviews with senior executives was collected to gain a deep understanding of AI governance, digital leadership and organizational agility practices. Multi-group analysis further revealed differences in the proposed relationships for public and private organizations. Findings revealed that AI capability not only significantly strengthens the AI governance, and consequently the strategic decision quality, but it also improve the organizational agility, resulting in improved performance. Furthermore, digital leadership has a positive effect on reinforcing the association between AI governance and the strategic decision quality. Overall, this study integrates the technology capability, the organizational capability and the leadership capability to establish an AI-enabled strategic decision-making and performance management framework, and provides strategic insights for organizations that aim to realize greater value from their AI investments.

An adaptive organizational model: integrating investment, financial functions, and human resource management in the entertainment industry

This paper analyzes the significance of adaptive organizational models in contemporary business environments, with particular emphasis on their role in financial, strategic, and organizational management. Adaptive organizational models are characterized by their ability to integrate the financial function, human resource management, and strategic decision-making into a unified performance management system. The analysis of the adaptive organizational model is conducted using the case of the Walt Disney Company, which employs a hybrid organizational model based on a combination of centralized strategic management and decentralized business segments. The company's adaptive capabilities are developed through managing diversified business segments, integrating the financial function into the strategic framework, and creating synergies among media content, digital platforms, and theme parks. Furthermore, the paper highlights the need for adaptive organizational structures to encompass human resource management, leadership development, and organizational culture as key determinants of adaptability, as well as the integration of ESG principles into corporate governance. The key characteristics of adaptive management identified in the Walt Disney Company are transferable to organizational structures in other enterprises and may serve as a framework for enhancing organizational adaptability and long term performance.

The Contribution of Business Intelligence to Decision-Making in International Companies

Integrating Strategic Human Resource Analytics with Administrative Decision-Making for Sustainable Organizational Resilience in Emerging Economies

- The growing complexities in today's business environment have underscored the importance of adopting data-driven practices in order to improve decision-making processes and build organizational resilience. Strategic Human Resource Analytics (SHRA) has become a very important skill for any organization, which helps managers convert data about the workforce into valuable information that is used for making administrative decisions, managing the workforce, and ensuring organizational success. This paper analyzes the application of Strategic Human Resource Analytics in administrative decision-making as an approach aimed at fostering sustainable organizational resilience in emerging economies. Based on recent scholarly literature, the study discusses the strategic value of HR analytics, its importance in evidence-based administrative decision-making, as well as how it contributes to workforce planning, talent management, leadership development, employee engagement, and organizational adaptability. In addition, the study will analyze the obstacles that may emerge in using SHRA in emerging economies. The study also highlights the transformative role of digital technologies, including Artificial Intelligence (AI), cloud computing, and business intelligence systems, in strengthening HR analytics capabilities and improving organizational responsiveness. This paper concludes that the use of Strategic Human Resource Analytics in the decision-making process makes an organization more agile, improves decision-making quality, promotes risk management, and increases organizational resilience in volatile business environments. Even though the application of SHRA in practice may face some difficulties, especially in developing countries, companies that focus on digitalization and analytics become better suited for gaining a competitive edge and success. Strategic Human Resource Analytics is not just an innovation but also a strategic capability for resilient and sustainable organizational development.