Strategic Decision-making Process Research Articles

Navigating the Cascading Ripples: A Study on the Interplay between the COVID-19 Recession and the Financial Fortunes of Micro Finance Institutions (MFIs)

Purpose: The success of Micro Finance Institutions (MFIs) hinges on effective decision-making amidst challenging economic circumstances, necessitating the ability to anticipate and navigate future business conditions. This research aims to explore the relationship between financial performance and the COVID-19 pandemic's impact on MFIs listed on the National Stock Exchange (NSE) in India. By analyzing financial data and employing statistical techniques, the study seeks to elucidate the resilience of MFIs during the pandemic and the role of financial ratios in predicting earnings per share (EPS). Additionally, the research aims to contribute to a deeper understanding of the interplay between financial indicators and organizational outcomes within the microfinance sector. Design/Methodology/Approach: This research adopts a quantitative approach, utilizing secondary data obtained from quarterly financial reports of selected MFIs listed on the NSE from 2018 to 2022. Statistical analyses such as paired t-tests, correlation, and regression are employed to assess the impact of the COVID-19 pandemic recession on MFIs' profitability and to investigate the relationship between financial ratios and earnings per share (EPS). Findings/Result: Despite the COVID-19 pandemic recession, the profitability of Indian Micro Finance Institutions (MFIs) remained largely unaffected, as indicated by non-significant differences in earnings before interest and taxes (EBIT) before and after the pandemic. Regression analysis highlighted significant influences of financial ratios like the current ratio, total asset ratio, net profit ratio, and gross profit ratio on earnings per share (EPS), underscoring the sector's resilience and the importance of financial analysis in predicting MFI performance. Originality/Value: This research offers novel insights into the resilience of Indian Micro Finance Institutions (MFIs) during the COVID-19 pandemic and underscores the significance of financial analysis in guiding strategic decision-making processes within the microfinance sector. Paper Type: Empirical Research.

Critical Analysis of the Role of Management Information Systems in Optimizing Strategic Decision Making

This journal provides a critical analysis of the role of management information systems (SIM) in optimizing strategic decision making in the current business environment. By exploring related literature and relevant case studies, the authors highlight the importance of SIM integration in the strategic decision-making process. This article highlights the challenges organizations face in effectively implementing SIM as well as the opportunities that exist to improve performance through the use of advanced information technology. This analysis not only provides deep insight into the contribution of SIM to strategic decision making, but also identifies areas where improvements can be made to increase the efficiency and effectiveness of the system. In conclusion, this article emphasizes the importance of SIM as a strategic tool that not only facilitates better decision making, but also provides a competitive advantage to organizations in today's digital era.

Unveiling Agricultural Shifts through Stakeholder Interactions and Strategic Dynamics in Green Rice Production

This study explores the strategic interactions among the government, growers, and the public within the context of green rice production, employing an evolutionary game theory framework. Recognizing the intricate dynamics of agricultural sustainability, we construct a three-party evolutionary game model to investigate the strategic decision-making processes and stability conditions of each stakeholder. The model assesses how various strategies evolve under the influence of economic incentives, regulatory measures, and public engagement. Through analytical and numerical methods, including stability analysis and MATLAB 2020b simulations, we identify the Evolutionarily Stable Strategies (ESS) that signify sustainable practices in green rice production. The results reveal that as government incentives for green production increase and fines for non-green practices are enforced, the likelihood of growers adopting sustainable practices significantly rises. Furthermore, the strategic enhancement of economic incentives and reputational factors not only bolsters governmental regulatory commitment but also reduces the necessity for public supervision, demonstrating a systemic shift towards self-regulation and market-driven sustainability. The simulations demonstrate the system’s evolution towards a stable state where governmental regulation is fully enforced, growers adopt green production universally, and public supervision becomes redundant. The findings underscore the importance of designing policy interventions that harmonize economic and environmental objectives, suggesting that well-structured incentives and regulatory measures can catalyze the transition towards sustainable agricultural practices. Additionally, our study highlights the pivotal role of establishing effective incentive structures that ensure the economic benefits of green production outweigh the costs, facilitating an autonomous regulatory system. This study contributes to the understanding of how strategic interactions shaped by policy and market forces can foster agricultural sustainability.

Navigating business transformation and strategic decision-making in multinational energy corporations with geodata

In the dynamic landscape of multinational energy corporations, navigating business transformation and strategic decision-making requires innovative approaches. Geodata, encompassing spatial and location-based information, emerges as a pivotal asset in this endeavor. This paper outlines the integration of geodata into the fabric of energy corporations, facilitating informed decisions and operational excellence. The introduction elucidates the significance of geodata amidst evolving business paradigms within the energy sector. It explores the transformative potential of geospatial intelligence in fostering agility and resilience, crucial attributes for multinational corporations. Understanding business transformation delves into the fundamental concepts and drivers reshaping the energy industry. Drawing from case studies, it illustrates how corporations leverage strategic shifts to adapt to market dynamics and emerging technologies. The subsequent section emphasizes the pivotal role of geodata in strategic decision-making processes. It elucidates the diverse applications of geospatial information in optimizing operations, mitigating risks, and capitalizing on market opportunities. Integrating geodata into business processes outlines the methodologies and technologies underpinning effective utilization of spatial data. It underscores the importance of robust data management and geospatial analysis techniques for deriving actionable insights. Leveraging geodata for operational excellence explores how energy corporations optimize asset management, streamline supply chains, and enhance risk assessment through geospatial analytics. Case studies and best practices offer real-world examples of successful geodata integration, highlighting key lessons and future trends. Challenges and considerations encompass the complexities of data quality, regulatory compliance, and organizational change management. In conclusion, the paper underscores the transformative potential of geodata in shaping the future trajectory of multinational energy corporations, advocating for a strategic embrace of geospatial intelligence in decision-making processes. Keywords: Business Transformation, Strategic Decision-Making, Multinational Energy Corporations, Geodata.

AN ANALYSIS OF RISK MANAGEMENT STRATEGIES OF ICICI BANK

This study investigates the risk management measures used by ICICI Bank, one of India's largest financial organizations. The banking industry operates in a dynamic environment, continuously confronted with new risks that might have a substantial impact on operations and financial stability. Understanding and controlling these risks are critical for long-term growth and resilience. The goals of this research are twofold: to investigate ICICI Bank's risk management procedures and assess their effectiveness in mitigating various types of risks. To accomplish these goals, a complete study design was used, combining qualitative and quantitative approaches. The data was gathered through a combination of literature reviews, interviews with key stakeholders, and financial report and risk management framework analysis. The study's primary findings shed light on ICICI Bank's multidimensional risk-management approach, which includes credit risk, market risk, operational risk, and compliance risk. The bank uses a variety of tools and procedures, such as risk assessment models, diversification strategies, hedging mechanisms, and strong internal control systems. Furthermore, the report addresses the bank's risk management difficulties, including regulatory compliance, technology improvements, and macroeconomic changes. According to the findings, numerous inferences may be made about the success of ICICI Bank's risk management procedures. Furthermore, the study emphasizes the need to incorporate risk management into strategic decision-making processes and cultivate a risk-aware culture throughout the firm. Following the conclusions, many recommendations for action are made. These include strengthening risk governance structures, improving risk monitoring and reporting systems, investing in advanced analytics and risk modelling skills, and encouraging engagement with regulators and industry peers. Implementing these recommendations would help ICICI Bank strengthen its risk management strategy and efficiently navigate the growing landscape of financial hazards. Overall, this study adds to our understanding of risk management techniques in the banking sector and offers useful insights for practitioners, policymakers, and academics. It underscores the importance of proactive risk management in ensuring the resilience and sustainability of financial institutions like ICICI Bank in an increasingly complex and uncertain environment.

Harnessing business analytics for gaining competitive advantage in emerging markets: A systematic review of approaches and outcomes

In today's rapidly evolving global economy, businesses operating in emerging markets face unique challenges and opportunities. To gain a competitive edge in such dynamic environments, harnessing the power of business analytics has become imperative. This systematic review aims to explore the diverse approaches and outcomes associated with leveraging business analytics for competitive advantage in emerging markets. The study employs a comprehensive review methodology, analyzing a wide range of scholarly articles, case studies, and industry reports. Through this systematic approach, key themes and patterns emerge, shedding light on the multifaceted strategies employed by organizations to leverage business analytics effectively. Findings reveal that successful utilization of business analytics in emerging markets involves several interconnected dimensions. These include data collection and management, advanced analytics techniques such as predictive modeling and data mining, and the integration of analytics into strategic decision-making processes. Moreover, organizational factors such as leadership support, organizational culture, and resource allocation significantly influence the effectiveness of business analytics initiatives. Furthermore, the review highlights the diverse outcomes associated with adopting business analytics in emerging markets. These outcomes encompass enhanced operational efficiency, improved customer insights, better risk management, and the identification of new market opportunities. Additionally, business analytics enables organizations to adapt swiftly to market changes and gain a deeper understanding of customer behaviors and preferences. Overall, this systematic review underscores the critical role of business analytics in enabling organizations to navigate the complexities of emerging markets and achieve sustainable competitive advantage. It provides valuable insights for practitioners, policymakers, and researchers seeking to leverage analytics-driven strategies for success in dynamic business environments.

Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study

The role of critical infrastructures in maintaining the functioning of the economy and society and ensuring national security, particularly their durability in delivering essential services during crises, including natural disasters such as earthquakes, is critical. This work introduces an analytical methodology to quantify potential earthquake damage to power stations and evaluate the cost-effectiveness of measures to enhance their seismic resistance. By employing fragility curves and probabilistic risk analyses, this approach provides a structured framework for the comprehensive assessment of risks and the identification of economically practical mitigation strategies. A detailed examination of strategies to protect critical power station components against seismic activity is presented, revealing that a minor investment relative to the overall project budget for earthquake-proofing measures is economically effective. This investment, representing a marginal fraction of 0.5% of the total project expenditure significantly reduces the seismic risk of power station failure by 36%. Reinforcing essential elements, including switching stations, water treatment facilities, and water tanks, is emphasized to ensure their continued operation during and after an earthquake. This research highlights the critical significance of integrating risk assessment with benefit-to-cost analysis in strategic decision-making processes, supporting the prioritization of investments in infrastructure enhancements. These enhancements promise substantial reductions of risks at minimal costs, thus protecting essential services against the impacts of natural disasters. This research contributes to state-of-the-art research in critical infrastructures resilience.

Insights into Consumer Behavior: A Deep Dive into Online Shopping Trends in Kerala During the COVID-19 Pandemic

The COVID-19 pandemic has catalyzed transformative shifts in consumer behaviour globally, prompting a notable surge in online shopping activities. Kerala, renowned for its diverse cultural landscape and vibrant markets, has witnessed a paradigmatic evolution in consumer preferences during this unprecedented period. This study endeavours to provide a comprehensive analysis of online shopping trends in Kerala amidst the COVID-19 pandemic, offering nuanced insights into the factors influencing consumer behaviour, the emergence of new consumption patterns, and the challenges and opportunities encountered by e-commerce stakeholders. Leveraging a mixed-methods research approach, including surveys and qualitative interviews, the study aims to illuminate the dynamic interplay between socio-economic factors, public health concerns, and technological advancements shaping the e-commerce landscape in Kerala. By elucidating these dynamics, the research seeks to inform strategic decision-making processes for e-commerce businesses, policymakers, and other stakeholders invested in Kerala's retail ecosystem.

The Effect of Green Infrastructure Investment, Capital Structure, and Operational Efficiency on Financial Performance of Companies in Central Java

This study investigates the influence of green infrastructure investment, capital structure, and operational efficiency on the financial performance of companies operating in Central Java. A quantitative research approach was employed, utilizing survey data collected from 173 companies across various industries. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), with hypothesis testing conducted to assess the significance of the relationships between the variables. The results indicate significant positive associations between green infrastructure investment, capital structure, operational efficiency, and financial performance. Specifically, companies that prioritize sustainability initiatives, maintain optimal capital structures, and enhance operational efficiency tend to achieve superior financial performance outcomes. These findings highlight the multidimensional nature of value creation within organizations and underscore the importance of integrating environmental and operational considerations into strategic decision-making processes. The implications of the study extend to policymakers, business leaders, and stakeholders seeking to enhance the financial sustainability and resilience of companies in Central Java.

Improved Decision Support System for College Sports Training Based on Id3 Algorithm

A Decision Support System (DSS) is a computer-based tool designed to assist individuals and organizations in making informed decisions. It utilizes data analysis, modeling techniques, and interactive interfaces to provide users with relevant information and insights. DSSs help streamline decision-making processes by synthesizing large amounts of data from various sources, generating forecasts, and evaluating alternative courses of action. They are particularly valuable in complex and uncertain situations where decisions have significant implications. DSSs can be used in a wide range of domains, including business, healthcare, finance, and logistics, to support strategic, tactical, and operational decision-making processes. This paper introduces an Integrated Hybrid Feature Subset Decision Support System (HFS-DSS) designed to optimize decision-making processes in student performance evaluation within educational institutions. The HFS-DSS combines feature subset selection techniques with decision support algorithms to identify key attributes and make accurate predictions regarding student performance. Through experimental evaluation, the system demonstrates its effectiveness in accurately assessing student performance based on selected features, with predictions closely aligning with actual observations. The results underscore the system's reliability and effectiveness in supporting informed decision-making processes, offering valuable insights for educators and administrators to enhance academic outcomes. The adaptability and versatility of the HFS-DSS make it well-suited for various educational contexts, providing potential applications in curriculum planning, student support services, and performance monitoring. The present paper highlights the significant contributions of the Integrated HFS-DSS towards enhancing decision-making processes and improving academic outcomes in educational institutions.

Tactical Intelligent Decision Modelling in Sports Competitions Based on Reinforcement Learning Algorithms

Tactical intelligence refers to the ability to gather, analyze, and apply information swiftly and effectively to achieve specific goals or objectives in dynamic and often challenging situations. It involves understanding the current environment, assessing available resources, and making informed decisions to gain a competitive advantage or mitigate risks. Tactical Intelligent Decision Modelling in Sports Competitions, grounded in Reinforcement Learning (RL) algorithms, represents a cutting-edge approach to enhancing strategic decision-making processes within athletic domains. This innovative methodology leverages RL's ability to learn optimal actions through trial and error, adapting strategies based on feedback received from the environment. By applying RL algorithms to sports competitions, teams can develop intelligent decision models that dynamically adjust tactics and strategies in response to changing game conditions and opponent behaviors. Such models enable teams to optimize their performance, exploit opponent weaknesses, and capitalize on opportunities during competitions. This paper introduces Predictive Weighted Big Data Reinforcement Learning (PWBDRL), an innovative approach aimed at optimizing decision-making processes and performance outcomes in sports competitions. Leveraging predictive analytics, big data techniques, and reinforcement learning, PWBDRL offers a comprehensive framework for athlete prediction and strategy optimization. The results show a remarkable increase in win rates, with the RL-based decision models achieving an average win rate of 80.5% compared to 65.2% with traditional methods. Additionally, we observe a substantial enhancement in average scores, with the RL-based models achieving an average score of 95.6 compared to 78.3 with baseline approaches. Moreover, the RL-based models exhibit superior adaptability, requiring fewer iterations to converge to optimal strategies, with an average convergence time of 200 episodes compared to 500 episodes for traditional methods.

The Impacts of Digressional Strategies: A Backward Approach for Business Success

This study explores the impacts of digressional strategies as a backward approach for achieving business success. In today's dynamic business environment, organizations constantly face challenges that require innovative and unconventional approaches. The concept of digressional strategies involves stepping back or taking a different path to reach the desired outcome. By examining the effectiveness of such strategies in various business contexts, this research article aims to provide valuable insights into alternative methods for achieving success. Through a combination of theoretical analysis and case studies, the study highlights the potential benefits and risks associated with adopting digressional strategies. The findings contribute to a deeper understanding of strategic decision-making processes and offer practical recommendations for organizations seeking to enhance their competitive advantage through unconventional approaches.

Warehouse site selection for humanitarian relief organizations using an interval-valued fermatean fuzzy LOPCOW-RAFSI model

The selection of warehouse locations for humanitarian organizations represents a critical and strategic decision-making process. It is inherently complex, influenced by uncertainties and conflicting criteria. This study presents an integrated decision-making framework tailored to address this complexity for humanitarian organizations. The framework combined two key methodologies: logarithmic percentage change-driven objective weighting (LOPCOW) and ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (RAFSI). Besides, this model has strengthened by extending with the help of the interval-valued Fermatean fuzzy sets to capture and process highly complex ambiguities. A significant advantage of this proposed model is its high resilience against the rank reversal problem, which is a critical shortcoming and challenge in decision-making methodologies. Additionally, the model boasts a robust, powerful, and practical structure coupled with a straightforward and easily understandable algorithm. Based on the results obtained through the implementation of the model, population density (0.1140) emerges as the most critical factor influencing decisions regarding site selection. Humanitarian organizations aim to rescue and assist as many individuals as possible during and after a disaster, prioritizing their humanitarian needs. Therefore, enhancing logistical efficiency in disaster zones and promptly reaching affected populations becomes feasible when humanitarian aid warehouses are as close as possible to areas with the highest concentration of potential disaster victims. Moreover, according to the study’s findings, the most suitable option identified for the case study was Bağcılar. The conclusions drawn from sensitivity and comparative analyses affirm the model’s reliability, consistency, and resilience.

Managerial rationality, dysfunctional cognition and organizational decision comprehensiveness

PurposeOur study aims to explore the interplay between dysfunctional cognitive schemas and rationality for decision comprehensiveness in organizational strategic decisions.Design/methodology/approachWe used a cross-sectional design in which we evaluated individual decision rationality using an objective decision competence test and dysfunctional cognitive schemas in a sample of 270 managers (145 women with an average age of 41 years old). In addition, we asked managers to rate the decision comprehensiveness of their organization’s strategic decision processes.FindingsOur findings support the detrimental impact of dysfunctional cognition in strategic decision-making in such a way that the association between individual managerial rationality and the comprehensiveness of organizational strategic decisions was positive only when managers reported low dysfunctional cognition, while when managers reported high levels of dysfunctional cognitive schemas, the association between rationality and comprehensiveness was negative.Originality/valueOur study provides initial empirical evidence for the interplay between dysfunctional cognition and managerial rationality in strategic decision processes, and it opens venues for future research to explore the detrimental role of dysfunctional cognitive schemas in strategy processes.

Transactive Memory Systems and Acquisition Performance: A Strategic Decision Making Process Perspective

AbstractDuring the pre‐merger phase of an acquisition, fundamental decisions are made concerning whether to buy, which company to buy, and how much to pay. Further, acquisitions carry significant firm‐wide implications requiring input from multiple different specializations, and hence, they are the product of the judgements, decisions, and social interactions between top managers. We focus our theory development on a pivotal yet under‐researched top management team characteristic, transactive memory system (TMS). TMS is the shared division of cognitive labour with respect to encoding, storing, and retrieving knowledge from individual areas of expertise. We theorize that TMT transactive memory directly influences the strategic decision making process, which in turn determines acquisition performance. We test our hypotheses with a sample of 109 acquisitions, combining survey and archival data. We find that TMT transactive memory increases reliance on expert intuition and procedural rationality, while reducing political behaviour; and each of these three strategic decision processes carries different implications for acquisition performance. Our study advances theory by explaining the team‐level behavioural mechanisms that underlie acquisition performance.

Addressing Environmental Challenges in Technology Adoption within the Digital Supply Chain of Readymade Garments

The study aims to investigate the challenges of technology adoption in the Digital Supply Chain (DSC) of the Readymade Garment (RMG) business, focusing on environmental barriers. The study utilizes the T-O-E framework to examine a sample of 380 participants, consisting of owners and top managers from RMG facilities. The data is analyzed using PLS-SEM Modeling with the aid of SmartPLS4 software. It examines the complex interrelationships between competitive pressure, consumer, external support, stakeholder networks, environmental concerns, and technology adoption within the digital supply chain of the RMG industry. The technology adoption is substantially influenced by environmental conditions, particularly regarding the alignment with customers. The analysis of mediation sheds light on the significance of the environment in both partial and complete mediation, as it exerts influence on competitive pressures, customer involvement, external support, and stakeholder networks. Comprehending this interrelationship is crucial for making well-informed business and policy formulation decisions. organizations must incorporate environmental factors into their strategic decision-making processes, ensuring sustainable technologies are adopted. Policymakers can employ these findings to implement environmentally sustainable policies, promoting innovation within the RMG sector. These measures guarantee the long-term viability of the industry and promote ecological accountability.

Enhancing Transshipment Decision-making in Transportation Companies through Computational Intelligence: A Bayesian Game Model with Data-Driven Simulations at Guangzhou Port

The challenge of port congestion significantly impedes the efficiency of global trade flows and the economic vitality of hinterland regions. Addressing this issue, our study harnesses computational intelligence to construct a sophisticated Bayesian game model between the government and multiple transportation companies, where the transshipment costs, considered as private information to the companies, play a pivotal role. This research integrates data analytics and machine learning techniques to analyze the strategic decision-making processes of transportation companies, which decide on transshipment based on a critical cost threshold influenced by government subsidies and a probabilistic assessment of transshipment costs. Utilizing backward induction, the study outlines how the government can leverage computational models to devise an optimal subsidy strategy for transshipment, taking into account the anticipated responses of the transportation companies. The Bayesian Nash equilibrium identified through our model suggests that companies with costs below a predefined threshold are incentivized by government subsidies to opt for transshipment. This conclusion is further validated through evolutionary game theory analysis, enriched by data-driven simulations. Employing real-world data from Guangzhou Port, we conducted extensive computational simulations to quantify the impact of transshipment subsidies. The findings reveal a substantial alleviation in port congestion, with a 33.7% reduction in congestion levels and a 35% decrease in congestion-related costs, alongside a notable 1.9% increase in government revenue. These simulations, powered by advanced computational algorithms and data analytics, not only underscore the effectiveness of informed subsidy strategies in mitigating port congestion but also demonstrate the potential of integrating computational approaches in logistical and transportation decision-making. This study contributes a novel computational framework to the logistics and transportation literature, offering practical insights for policymakers to tackle the enduring problem of port congestion through data-driven strategies.

Exploring Job Satisfaction in Goa's Hospitality Industry: Insights from the ABCD Framework Perspective

Purpose: The primary objective of this research is to conduct an in-depth exploration and analysis of the role of job satisfaction within Goa's hospitality industry. This study recognizes the pivotal influence of employee contentment in various facets of the sector, notably in enhancing guest services, boosting the industry's contribution to local tourism, and fostering sustainable development practices. The research aims to deliver a holistic appraisal of job satisfaction in Goa, encompassing its positive impacts, potential drawbacks, and strategic significance. This encompasses not only the financial prosperity of the industry but also the responsible management of resources, environmental conservation, cultural preservation, and the creation of a memorable and enriching experience for tourists. Targeted towards stakeholders in Goa's hospitality sector — including business owners, managers, policy makers, and academic researchers — this study seeks to provide valuable insights. These insights are intended to inform and guide strategic decision-making processes, aiming to optimize the benefits of employee satisfaction. Ultimately, this will contribute to achieving the dual goals of sustainable development and promoting Goa as a premier tourism destination. Design/Methodology/Approach: This study adopts a qualitative research methodology to delve into the nuances of job satisfaction within Goa's hospitality industry, focusing on its various impacts including benefits, limitations, and potential challenges. It aims to provide an in-depth understanding of the Goan hospitality landscape, drawing on local Hotel and Hospitality Sector insights. The research methodology incorporates a range of analytical tools, including the ABCD Framework, to explore the link between employee satisfaction and sustainable development in this sector. Data for this study are sourced from diverse online platforms, including websites, academic journals, essays, and digital publications. The approach involves a comparative and evaluative analysis of different facets of the hotel industry, paying special attention to issues pertinent to the sector. By employing a qualitative lens, this research aspires to capture the complex interplay between job satisfaction and its impact on sustainable growth within the unique setting of Goa's hospitality industry. Findings/Results: The findings from this study offer crucial insights into the role of job satisfaction within the Goan hospitality sector. Job satisfaction emerged as a key factor in enhancing guest experiences, contributing to increased repeat visits and favorable reviews. Furthermore, high levels of job satisfaction were found to significantly reduce employee turnover. This results in cost savings for businesses and fosters a stable and experienced workforce, which is instrumental in maintaining quality service and operational efficiency in the hospitality industry. These results underscore the importance of employee contentment as a strategic asset in driving both customer satisfaction and sustainable business practices. Originality/Value: Analyzing the importance of job satisfaction in Goa's hospitality sector from the perspective of the ABCD framework adds a unique and beneficial layer to the area. Paper Type: An industry analysis is the basis for this research case study.

Relationship between entrepreneurial orientation, performance, and environment from the perceptcion of cooperative managers

Entrepreneurial orientation assists in the strategic decision-making process in which managers define organizational purposes, thus seeking to create competitive advantage. The study evaluates the relationship between entrepreneurial orientation (EO), performance, and environment based on the perception of cooperative leaders. For EO the dimensions of Miller (1983) were used, for environment the proposal of Duncan (1972) and performance was measured subjectively. The approach is quantitative, with a descriptive-correlational character. OE and performance were analyzed by factor analysis. The perception of the environment made it possible to perform “Anovas”, which showed significant differences for the second factor of OE. The correlations between OE and performance were positive and significant. The regressions for performance with EO as predictor were not affected by the environment, indicating lack of influence.

Entrepreneurship orientation in family business: Decision process structure & succession planning

Family firms play a significant role in economies worldwide, contributing substantially to wealth generation and employment opportunities. Particularly prevalent in rural and remote areas where larger enterprises may struggle to operate effectively, these firms are crucial for both economic growth and social development. Entrepreneurship, characterized by innovation, courage, and strategic planning, is fundamental for driving economic development and instigating substantial changes within market economies. Previous research has identified various factors influencing entrepreneurial development in response to evolving business environments.This paper aims to investigate the capacity of enduring family firms to maintain an entrepreneurial orientation while also prioritizing strong family ties and effective governance structures. Building upon existing theoretical frameworks in the realms of family business and entrepreneurship, this study examines the prevalence of entrepreneurship within family businesses. Additionally, it explores the significance of entrepreneurial orientation in succession planning and the decision-making processes within family firms.The conceptualization presented here integrates insights from existing literature and proposes a research agenda for further exploration in this field. By reviewing relevant literature and outlining a research methodology, this paper seeks to shed light on the intersection of family business dynamics and entrepreneurial behavior. Understanding the interplay between entrepreneurial orientation, family involvement, and governance structures is crucial for informing strategic decision-making processes within family firms.In summary, this paper contributes to the broader understanding of the role of entrepreneurship within family businesses and its implications for succession planning and decision-making. By elucidating the complex dynamics at play in these firms, it offers insights that can inform both academic research and practical management strategies for sustaining and enhancing the entrepreneurial spirit within family-owned enterprises.