Strategic Decision-making Process Research Articles

Brand Network Booster: A new system for improving brand connectivity

This paper presents a new decision support system offered for an in-depth analysis of semantic networks, which can provide insights for a better exploration of a brand’s image and the improvement of its connectivity. In terms of network analysis, we show that this goal is achieved by solving an extended version of the Maximum Betweenness Improvement problem, which includes the possibility of considering adversarial nodes, constrained budgets, and weighted networks – where connectivity improvement can be obtained by adding links or increasing the weight of existing connections. Our contribution includes a new algorithmic framework and the integration of this framework into a software system called Brand Network Booster (BNB), which supports brand connectivity evaluation and improvement. We present this new system together with three case studies, and we also discuss its performance. Our tool and approach are valuable to both network scholars and in facilitating strategic decision-making processes for marketing and communication managers across various sectors, be it public or private.

Optimizing risk management in oil and gas trading: A comprehensive analysis

The oil and gas trading sector is inherently volatile, influenced by geopolitical events, market dynamics, and regulatory changes. Effective risk management is crucial for mitigating these uncertainties and ensuring operational stability and profitability. This paper presents a comprehensive analysis of optimizing risk management in oil and gas trading, highlighting strategies, tools, and best practices to manage and mitigate risks. Risk management in oil and gas trading encompasses a broad spectrum of activities, including market risk, credit risk, operational risk, and compliance risk. To optimize risk management, firms must employ a multifaceted approach that integrates advanced analytics, robust risk assessment frameworks, and strategic decision-making processes. Key strategies include the use of financial instruments such as futures, options, and swaps to hedge against price volatility. These instruments enable traders to lock in prices and margins, thereby reducing exposure to market fluctuations. Advanced analytics and data-driven decision-making are essential components of effective risk management. By leveraging big data, machine learning, and artificial intelligence, traders can gain deeper insights into market trends, forecast price movements, and identify potential risks. Predictive analytics allows for proactive risk management, enabling firms to anticipate and respond to market changes swiftly. A comprehensive risk assessment framework is also critical. This involves regular monitoring and evaluation of risk exposure, stress testing, and scenario analysis. Such practices help identify vulnerabilities and ensure that risk mitigation measures are in place. Additionally, fostering a risk-aware culture within the organization is vital. This includes training and educating employees on risk management practices and encouraging transparent communication and reporting of risks. Compliance risk, driven by regulatory changes, is another significant aspect of risk management in oil and gas trading. Staying abreast of regulatory developments and ensuring compliance with international and local regulations is essential to avoid legal penalties and reputational damage. Implementing robust compliance programs and leveraging technology to monitor regulatory changes can help firms stay compliant. In conclusion, optimizing risk management in oil and gas trading requires a holistic approach that combines financial hedging, advanced analytics, comprehensive risk assessment, and compliance management. By adopting these strategies, firms can effectively mitigate risks, enhance decision-making, and achieve sustainable growth in a highly volatile industry. Keywords: Optimizing, Risk Management, Oil and Gas, Trading, Analysis.

The Entente’s Support for the White Armies in Southern Russia (Late 1918–1919)

This article examines a previously underexplored aspect of the Russian Civil War during its critical phase in late 1918–1919: the logistical support provided by the Entente to the White armies in the South, set against the backdrop of the final military interventions. The authors concentrate on the interactions between General A.I. Denikin's regime and the British authorities, who were the primary proponents of the Entente's intervention in Russia, as well as the conditions surrounding the involvement of the French armed forces.The research primarily draws on documents from the Russian State Military Archive, created by officers of the White armies of Southern Russia, and the National Archives of Great Britain. This comprehensive source base enables a reassessment of some long-held views in Russian historiography regarding the Entente's intervention in the Black Sea region. The study aims to delineate the scope and nature of the Entente’s support and to understand why this assistance failed to secure a victory for the White forces.Post-Soviet historiography commonly posits that the Red Army enjoyed an economic advantage in terms of weapons and ammunition supply for most of the Civil War. This article, however, reveals that the support from Entente countries was substantial and that the use of foreign weapons and equipment significantly influenced the late 1918-1919 period. Nonetheless, the critical factor, as argued by the authors, was the timing of deliveries. The peak of the Entente’s military-technical support to the Armed Forces of Southern Russia occurred after the Red Army had already fortified its positions with adequate firepower. The lengthy strategic decision-making processes of the British Cabinet, characteristic of British democratic and bureaucratic systems, resulted in delayed assistance to Denikin’s troops, ultimately undermining its effectiveness.

Assessing The Role of Executive Procedures in Shaping Sharī’ah Governance Commitments in Saudi Banks

This paper investigates the relationship between governance dimensions and the level of commitments to Sharī’ah (Islamic law) governance framework within Saudi banks. To understand the intricacies of governance dimensions in shaping Sharī’ah governance practices in depth, this paper also examines the mediating role of executive procedures. Structural equation modeling is used to test the hypotheses regarding the relationship between studied variables. The findings underscore the role of executive procedures, disclosure and transparency in fostering commitments to Sharī’ah governance framework, with both constructs exhibiting substantial direct effects on Sharī’ah governance commitment level. Additionally, the study underscores the importance of monitoring, auditing, and accountability as crucial determinants of governance in banks, highlighting the importance of robust oversight mechanisms in ensuring adherence to Sharī’ah principles. Meanwhile, organisational structure exhibited a minor decrease in its direct effect on commitment to Sharī’ah governance. The findings of this paper can guide the strategic decision-making process for promoting ethical and sustainable Sharī’ah governance practices in Saudi banks. It also fills the existing gap in literature regarding the relationship between the dimensions of governance and the obligations of Sharī’ah governance framework in Saudi banks.

Exploring Consumer Procurement Patterns: A Comparative Analysis Between H&m And Zara

Embark on a quest to unravel the enigmatic tapestry woven by the whims of Indian youth, where the threads of fashion brands H&M and Zara intertwine. This odyssey delves into the depths of the ever-evolving Indian fashion realm, seeking to illuminate the alignment between the young and these sartorial titans, unveiling the intricate patterns that guide their sartorial pursuits. The journey unfolds through a harmonious fusion of qualitative and quantitative methodologies, a symphony of surveys, interviews, and observational data analysis. Qualitative melodies unveil the intrinsic motivations that orchestrate consumer choices, while quantitative cadences lend statistical validation and broader insights into the rhythms of buying patterns. The fruits of this expedition hold profound implications for H&M, Zara, and all those who navigate the Indian fashion market's currents. By comprehending the nuanced preferences and behaviors of the youth demographic, brands can tailor their product assortments, marketing strategies, and brand positioning efforts with the deft hand of a master artisan. The recommendations born from this odyssey shall serve as beacons, guiding strategic decisions towards heightened brand awareness, expanded market reach, and elevated customer satisfaction. This voyage into the unknown contributes a novel symphony to the annals of consumer behavior within the Indian fashion industry. By focusing on the unique context of youth consumer preferences and behaviors towards H&M and Zara, the study weaves an original tapestry of market trends and consumer dynamics. Furthermore, it illuminates paths for improvement, such as amplifying awareness of collections and catering to diverse income groups, thus enriching the strategic planning and decision-making processes for brands and marketers alike.

Sustainable development goals and corporate strategy: Bridging the gap

Abstract This paper investigates the intricate relationship between corporate strategy and sustainable development goals (SDGs) as outlined by the United Nations. Building upon extensive literature review, the research hypothesizes that corporations can significantly contribute to sustainable development while enhancing their long-term viability through strategic alignment with SDGs. To address this hypothesis, the research poses the following research questions: How do corporations currently integrate sustainable development goals into their strategic planning processes? What are the key challenges and opportunities associated with aligning corporate strategy with sustainable development objectives? What are the most effective strategies and practices that corporations can adopt to maximize their impact on sustainable development while ensuring their own long-term sustainability? The primary objective of this paper is to provide insights and guidance for corporate executives, policymakers, and stakeholders seeking to navigate the complex terrain of sustainable development within the corporate sector. Specifically, the research aims to analyze the current state of corporate strategy with regard to sustainable development goals through a comprehensive literature review, as well as to identify key challenges and opportunities faced by corporations in aligning their strategies with sustainable development objectives. The research proposes actionable strategies and best practices for corporations to effectively integrate SDGs into their strategic decision-making processes. By synthesizing findings from the literature review and empirical research, this paper aims to contribute to both academic discourse and practical understanding of how corporations can play a proactive role in advancing sustainable development goals while ensuring their own long-term success.

Green innovation in family business succession: Exploring decision-making under parent-child co-governance

This study investigates the impact of Parent-Child Co-Governance (PCCG) on green innovation within family businesses (FBs). Analysing Chinese A-share listed FBs from 2012 to 2020, we discover that PCCG FBs perform significantly worse in green innovation compared to non-PCCG FBs. Our findings indicate that FBs under PCCG encounter more substantial financing constraints and display lower risk preferences, both of which hinder green innovation. This study provides fresh insights into the strategic decision-making processes of FBs, particularly in the context of sustainable development. The results not only deepen our understanding of the PCCG model but also offer practical recommendations for FBs aiming to foster green innovation.

Integrating Analytics in Enterprise Systems: A Systematic Literature Review of Impacts and Innovations

Recent advancements in Enterprise Information Systems (EISs) have transitioned from primarily supporting operational and tactical processes to enabling strategic decision-making through integrated analytics. This systematic review critically examines global literature from 2010 to 2023, focusing on the factors influencing the adoption of analytical components in EISs and assessing their impact on strategic decision-making in organizations. Following the PRISMA 2020 guidelines, we meticulously selected and reviewed articles from the Scopus database, employing a robust taxonomy based on the technology–organization–environment (TOE) framework to categorize findings. Our methodology involved a thorough screening of 234 studies, leading to a final analysis of 45 peer-reviewed articles that met our stringent criteria. These studies collectively underscore a significant gap in organizational capabilities, notably in the business ecosystems surrounding EISs, which hampers the effective adoption and utilization of advanced analytics. The results highlight a distinct need for improved understanding and implementation strategies for integrated analytics within EISs to enhance strategic decision-making processes. This review identifies critical factors for integrating analytics into Enterprise Information Systems (EISs), emphasizing technological, organizational, and environmental dimensions. It highlights a significant gap in models guiding ERP systems with Business Intelligence (BI) capabilities and underscores the need for robust research to enhance strategic decision-making through analytics.

Using Big Data and analytics in accounting for strategic decisions

The purpose of this paper is to study the potential of using Big Data and analytics in accounting in order to improve strategic decision-making processes at the enterprise. To achieve the goal, the following tasks were performed in the research process: the concepts, advantages and disadvantages of using Big Data and analytics in accounting were considered; directions for using Big Data in accounting in general and for improving strategic decision-making processes in particular were investigated. The use of advanced technologies in accounting can significantly improve the quality and relevance of accounting information, reduce preparation time and ensure availability of data in real time. Studying the basic principles of accounting organization using information technologies, efficiency, accuracy, security and compliance with regulatory requirements were identified as key. The potential impact of Big Data on the accounting profession and its practices is very significant. As the volume of available information grows, new opportunities appear to improve the accuracy and efficiency of traditional accounting approaches. Big Data analysis can reveal financial trends, patterns, and anomalies that were previously difficult to access or invisible through traditional methods. The use of Big Data and analytics in accounting for making strategic decisions can significantly contribute to the achievement of the goals specified in the modern economic paradigm. Simplified acquisition and analysis of large volumes of data makes it possible not only to increase the accuracy and speed of decision-making in financial management, but also to respond more effectively to changes in macroeconomic conditions, such as diversification of production and exports, improvement of living conditions of the population, and reduction of social inequality. The introduction of high technologies, including Big Data analysis, is becoming a key tool for ensuring sustainable economic development, contributing to the growth of productive forces and optimization of production processes in accordance with modern requirements and challenges of the global market.

Using Artificial Intelligence with Big Data Analytics for Targeted Marketing Campaigns

Artificial intelligence (AI) presents vast potential for revolutionizing marketing practices. This paper explores the pivotal role of AI in enhancing information proliferation, data management, and algorithmic design within marketing domains. We analyze AI's transformative impact on brand-user interactions, highlighting its adaptability to diverse website natures and business types. By leveraging AI capabilities, marketers can prioritize customer-centric approaches, delivering real-time personalized experiences tailored to individual preferences. The integration of AI enables marketers to accurately target content and select channels based on dynamic data insights, fostering increased user engagement and conversion rates. In addition, AI facilitates competitive analysis by scrutinizing competitor campaigns and discerning customer expectations, thereby informing strategic decision-making processes. Within the area of AI, machine learning (ML) emerges as a powerful subset, enabling computers to autonomously analyze and interpret data, thus aiding marketers in efficiently solving complex problems. As ML algorithms continually learn and refine their performance with additional data inputs, they enhance accuracy and effectiveness over time. We sourced relevant articles on AI in marketing from various scholarly platforms such as Scopus, Google Scholar, and Research Gate to conduct this research. Through a comprehensive review of these articles, this paper elucidates the multifaceted applications of AI across diverse marketing segments, highlighting its transformative potential for the marketing sector. We identify and analyze critical applications of AI, offering valuable insights for researchers and practitioners looking to harness its capabilities in marketing contexts

Strategic Decision Making and Strategic Decision Practices in Intelligent Organizations: A Review

Strategic decision-making is a key tool to drive business growth. It helps figure out the best way of achieving a business objective, provided an organization has a decision-making process that involves a well-defined set of policies that must be adhered to by all. Strategic decision making is used in competitive companies and is intended to give a company a competitive advantage by transitioning its scope and the way the company runs its activities. The difference between strategic decision-making and other decision-making processes like administrative and operational is that strategic decision-making is a long-term process which needs to be done very carefully by taking into account all the resources to be utilized and keeping in mind all uncertainties which may occur. Administrative decisions are short-term strategies. Strategic decision-making keeps the company's long-term future in mind, unlike other decision-making processes. The research aims to demonstrate knowledge and understanding of how things work in strategic decision making. The research by reviewing the literature and previous studies the research indicates that the strategic decision-making process plays an important role in the effective performance of the organization. The study also showed that how strategic improvisation is done in intelligent organizations and the interconnection of SDM’S with managerial functions.

Revolutionizing Global Business Excellence Through the Balanced Scorecard Success

This study evaluates PT Berkah Berabu Limpah's performance using the Balanced Scorecard method. It collects qualitative data from primary and secondary sources and analyzes it across Financial, Customer, Internal Business Process, and Learning and Growth perspectives. Results indicate good overall performance but suggest improvements in customer focus and employee training for enhanced competitiveness. Implementing Balanced Scorecard comprehensively is recommended for better performance management. This research contributes to understanding performance evaluation methods for strategic decision-making. Highlight: Integrated Performance Analysis: Evaluates PT Berkah Berabu Limpah holistically using Balanced Scorecard. Customer Centricity Emphasis: Highlights need for enhanced customer focus and satisfaction. Strategic Decision Support: Balanced Scorecard aids in informed, strategic decision-making processes. Keyword: Performance Evaluation, Balanced Scorecard, Qualitative Analysis, Customer Focus, Strategic Decision-making

Leveraging business analytics for competitive advantage: Predictive models and data-driven decision making

This study investigates the transformative potential of predictive analytics in market performance, addressing a significant gap in contemporary business intelligence literature. The primary aim is to evaluate the accuracy, reliability, scalability, and ethical implications of predictive models. Utilizing a qualitative approach, the review incorporates case studies and thematic analysis to capture rich, context-specific insights. Key findings reveal that predictive models significantly enhance market forecasting capabilities, enabling more informed and strategic decision-making processes. The study underscores the robustness of these models across various scenarios, highlighting their efficiency in managing large datasets. Ethical considerations emerge as a crucial theme, emphasizing the need for stringent data privacy measures and transparent practices to build trust and ensure compliance. The conclusions drawn emphasize the substantial competitive advantages that predictive analytics can offer when implemented judiciously, fostering agility and responsiveness in market strategies. However, the study also cautions against potential ethical pitfalls and data privacy issues, advocating for a balanced approach that prioritizes both innovation and integrity. Recommendations include investing in advanced predictive analytics tools while simultaneously strengthening data governance frameworks to enhance predictive accuracy and ensure ethical compliance. Future research should continue exploring the evolving landscape of predictive analytics, particularly focusing on emerging technologies and their potential to reshape market dynamics. Keywords: Predictive Analytics, Market Performance, Business Intelligence, Data Privacy, Qualitative Analysis, Competitive Advantage.

Leveraging employee green entrepreneurial orientation for enhancing environmental performance: The multi-level role of green creativity and green decision comprehensiveness

In view of increasing demands for employees engaging in environmental sustainability, the authors suggest firms can use employee green entrepreneurial orientation (eGEO) to enhance their environmental performance. Relying on the natural resource-based view as the underlying theoretical foundation and the source–position–performance model as the overarching framework, the authors propose a theoretical model across employee and firm levels to examine how eGEO promotes employee and firm environmental performance. Using multi-source surveys and secondary proxy data through multiple waves of data collection from 246 firms, the results indicate that eGEO has a positive indirect effect on employee environmental performance through employee green creativity and a positive indirect effect on firm environmental performance through firm green creativity. The results further indicate that green decision comprehensiveness strengthens these effects. This study contributes to research on green entrepreneurship by focusing on a novel characteristic of eGEO and by underscoring the necessity of treating green creativity as separate dimensions, employee and firm, that relate eGEO and environmental performance. It also contributes to research on strategic decision-making by revealing which strategic decision processes encourage eGEO in both employee and firm green creativity.

Understanding Risk and Uncertainty Management: A Qualitative Inquiry into Developing Business Strategies Amidst Global Economic Shifts, Government Policies, and Market Volatility

This research aims to investigate the management of risk and uncertainty within the context of developing business strategies amidst global economic shifts, government policies, and market volatility. The study employs a qualitative inquiry approach, utilizing systematic literature review and thematic analysis to identify emerging themes and patterns in contemporary business environments. The research findings reveal several key insights regarding adaptive strategies, scenario planning, and technological advancements in risk management. Firstly, adaptive strategies are crucial for organizational competitiveness and sustainability, emphasizing agility, innovation, and dynamic capabilities. Secondly, scenario planning facilitates strategic foresight, resilience, and preparedness by exploring alternative futures and assessing their potential impacts. Thirdly, technological advancements, particularly in AI and data analytics, revolutionize risk assessment capabilities and strategic decision-making processes, enabling proactive risk management and enhanced resilience. The study underscores the importance of fostering a culture of innovation, investing in technological capabilities, and integrating scenario planning into strategic planning processes to navigate uncertainties effectively. Moreover, future research directions include comparative analysis, longitudinal studies, cross-disciplinary research, technological innovation, crisis management, and ethical considerations to deepen understanding and promote effective strategies for organizational resilience and sustainability.

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

Sentiment analysis is a cornerstone of natural language processing. However, it presents formidable challenges due to the intricacies of lexical diversity, complex linguistic structures, and the subtleties of context dependence. This study introduces a bespoke and integrated approach to analyzing customer sentiment, with a particular emphasis on a case study in the Portuguese retail market. Capitalizing on the strengths of SentiLex-PT, a sentiment lexicon curated for the Portuguese language, and an array of sophisticated machine learning algorithms, this research constructs advanced models that encapsulate both lexical features and the subtleties of linguistic composition. A meticulous comparative analysis singles out multinomial logistic regression as the pre-eminent model for its applicability and accuracy within our case study. The findings of this analysis highlight the pivotal role that sentiment data play in strategic decision-making processes such as reputation management, strategic planning, and forecasting market trends within the retail sector. To the extent of our knowledge, this work is pioneering in its provision of a holistic sentiment analysis framework tailored to the Portuguese retail context, marking an advancement for both the academic field and industry application.

The Impact of Production Digitalization Investments on European Companies’ Financial Performance

Businesses investing in production digitalization equipment are supposed to benefit from increased productivity, enhanced efficiency, and revenue growth. Despite the increasing use of digital technologies in business, many companies still struggle to measure and maximize their returns from production digitalization investments. This research assesses the impact of production digitalization investments on companies’ financial performance (operating revenue) for European businesses in the period of 2013 to 2021. To achieve this target, we performed a Fixed Effects Panel Regression analysis, using a sample size of 5706 records from the Orbis database for 30 countries, covering 634 business units. The production digitalization investment in this research is expressed by a calculated variable value, measured as the annual change in a company’s Plant and Machinery value, adjusted with corresponding an annual depreciation value for the assets. The regression output was analyzed by considering the characteristics of the company size and business location. The results suggest that companies in Eastern Europe benefit more from production digitalization than those in Western Europe. The analysis highlights the tendency for the company costs of the employee and intangible fixed asset value to increase as production digitalization investments grow. Additionally, it shows that large companies tend to gain more from such investments than smaller ones. The analysis provides support and guidance for businesses’ production digitalization investment strategic decision-making processes.

Developing industry clusters based on 4As to support agritourism competitiveness

Abstract Competitive agritourism thrives on the pillars of the Attraction, Accessibility, Ancillary, and Amenity (4As), serving as fundamental cornerstones in tourism industry. Nonetheless, most agritourism continue to be entrenched in conventional management practices, exposing them to prevalent agricultural risks. Elevating this landscape necessitates a transformative approach. This article aims to analyze the linkages between the 4As and industry clusters in driving sustainable agritourism. The methodology used in this study is a literature review. Industry clusters in agritourism are important due to their ability to utilize shared inputs, optimize business processes through integration and collaboration, and develop outputs through network expansion. Cooperation, coordination, collaboration, and communication are essential for the successful development and competitiveness of agritourism. The arrangement of accommodation in a particular location encourages collaboration, resource sharing, and economies of scale. Increased accessibility improves supply chain management efficiency and reduces costs through collaboration. The availability of well-designed amenities further enhances the attractiveness and competitiveness of agritourism destinations. From the perspective of attractions, collaboration among agritourism contributes to innovation, product development, and sustainable practices. Understanding these linkages can inform strategic decision-making processes and guide the development of policies that leverage the strengths of the 4As to promote sustainable agritourism development.

A STUDY ON HEALTH, SAFETY AND WELFARE MEASURES AND ITS IMPACT ON EMPLOYEE PRODUCTIVITY

This study aims to examine the influence of health, safety, and welfare measures on employee productivity, with a specific emphasis on BBK Shoes. BBK Shoes stands out for its proactive approach to safety, encompassing rigorous training and regular maintenance to ensure a secure work environment. Additionally, the organization prioritizes the well-being of its workers by providing flexible scheduling alternatives and wellness programs. By recognizing the intrinsic link between well-being and productivity, BBK Shoes fosters a culture of loyalty and engagement among its workforce. Research consistently indicates that organizations prioritizing employee welfare experience higher levels of performance and job satisfaction. The symbiotic relationship between employee well-being and productivity underscores the significance of initiatives undertaken by BBK Shoes. This study aims to provide valuable insights for businesses seeking to enhance productivity. The findings are expected to inform strategic decision-making processes, guiding organizations towards the adoption of practices that prioritize employee well- being. The data was analyzed using SPSS software and nearly 205 responses were collected for the research. Keywords: Employee Productivity, Health, Safety, Welfare.

The Use of Artificial Intelligence and Machine Learning in Forecasting the Financial Growth of Automobile Industries

The automotive industry's financial forecasting is vital for growth and competitiveness in today's rapidly evolving market landscape. Accurate and reliable forecasting is essential for strategic planning, resource allocation, and decision-making processes. However, traditional forecasting methods often struggle to adapt to the dynamic nature of the automotive sector, characterized by fluctuating consumer demands, evolving market trends, and complex supply chain dynamics. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized financial forecasting in the automotive industry, transforming forecasting accuracy, efficiency, and decision-making processes. By leveraging AI algorithms and ML models, automotive companies can gain deeper insights into market dynamics, predict consumer behavior more accurately, optimize production schedules, and streamline distribution processes. This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes. The adoption of AI and ML empowers automotive companies to make data-driven decisions with greater precision and agility, driving innovation, growth, and competitiveness in this dynamic sector. Key Words: Artificial Intelligence, Machine Learning, Financial Forecasting, Automotive Industry, Predictive Maintenance, Hybrid Decision Support Systems, Production Planning and Control Systems, Intelligent Transport Logistics.