Business Decisions Research Articles

The Use of Generative Artificial Intelligence for Business Decision-Making

The purpose of this study was to develop autonomous artificial intelligence agents capable of working as a cohesive group to solve intricate decision-making processes, ultimately pushing the boundaries of artificial intelligence’s applicability in high-stakes and real-world scenarios. An autonomous multi-agent model was designed and deployed using the Analytic Hierarchy Process (AHP) decision-making framework, with autonomous AI agents, such as CrewAI and AutoGen, orchestrating multiple stages of activities performed by four distinct agents leveraging generative AI (GenAI). These agents were applied to a use case for proof of concept. The agents used a qualitative dataset to generate management recommendations based on cost and market feasibility considerations. The model was tested with publicly available customer feedback data on oatmeal cookies. It synthesized 913 customer reviews, identified common complaints, provided a summary of potential solutions, and generated a summary of market opportunities, along with relevant challenges. Although a subject matter expert's review is necessary to evaluate the practicality and relevance of the recommendations, the results demonstrated the high potential of multi-agent models in synthesizing and distilling large datasets into actionable insights, thereby augmenting decision-making processes in business contexts. The model's modular design allows for enhancements in quality, accuracy, and practicality by incorporating additional datasets and new agents. This study underscores that the integration of GenAI within a multi-agent model empowers businesses to swiftly transform vast amounts of business intelligence data into practical recommendations.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

Redefining global entrepreneurship: shifting focus from China to Central and Eastern Europe

Purpose The purpose of this paper is to explore the viability of shifting foreign direct investment (FDI) from China to Central and Eastern European (CEE) countries in light of recent geopolitical and economic challenges. By analyzing case studies, it argues that CEE nations offer a compelling alternative for Western European businesses, with stronger intellectual property protection, political stability and alignment with European Union (EU) sustainability goals. The paper provides insights for firms and policymakers on mitigating risks and enhancing business operations by pivoting toward the CEE region, offering practical recommendations for adapting to shifting global trade dynamics. Design/methodology/approach The design methodology uses the case study approach to analyze the shift of FDI from China to CEE. This method examines the geopolitical, economic and legal contexts influencing business decisions, using real-world examples of Western European companies that have made this transition. The case studies highlight key factors in decision-making and the benefits of relocating investments to the CEE region. Findings The study identifies several advantages of the CEE region over China for Western European firms. These include geographic proximity, similarities in business values and purposes, environmental accountability, trustworthiness in business, enforceable noncompetition rules, lower risks of counterfeiting, reduced political and administrative risks, lower risks of intellectual property theft and reduced risks of negative publicity. Practical implications The findings suggest that Western European firms should consider redirecting their FDI to the CEE region to mitigate risks associated with investing in China. This move could offer long-term benefits despite short-term complications. Originality/value This paper contributes to the FDI theoretical framework by enhancing the cultural, administrative, geographic and economic (CAGE) distance framework. It provides a unique perspective on the shifting dynamics between Europe and China and highlights the potential of the CEE region as a viable alternative for FDI.

The Role of Digital Accounting for SMEs in Facing Business Challenges in the Digital Era

Research Aims: This research aims to analyze the role of digital accounting in increasing operational efficiency and to examine how digital accounting supports MSMEs in adapting to the challenges of the digital era. Design/methodology/approach: The research employed direct observation and interviews with MSMEs in Malang, encompassing various business types, ages, and education levels. This approach provided a comprehensive understanding of the significance of digital accounting for MSMEs in navigating the business environment in the digital era. Additionally, the researchers conducted digital accounting training for MSMEs to further enhance their understanding. Research Findings: The findings indicate that the implementation of digital accounting offers significant advantages for MSMEs, particularly in improving operational efficiency and enabling quicker, more accurate business decisions. These benefits enhance the competitiveness of MSMEs in the digital era. However, the study also identified key challenges, such as the costs associated with digital accounting implementation and the limited understanding and knowledge among MSMEs regarding its use. Theoretical Contribution/Originality: This study adds to the growing body of literature by emphasizing the critical role of digital accounting in enhancing MSME performance in the digital business landscape. It highlights the need for comprehensive training and support to overcome the barriers associated with digital accounting adoption in MSMEs, offering practical insights for both academia and practitioners.

AI in customer feedback integration: A data-driven framework for enhancing business strategy

The integration of artificial intelligence (AI) into customer feedback systems has emerged as a transformative approach for businesses seeking to enhance their strategies and maintain a competitive edge. This review presents a data-driven framework that leverages AI to analyze, interpret, and act upon customer feedback, providing actionable insights for business decision-making. AI techniques such as natural language processing (NLP), machine learning (ML), and sentiment analysis allow companies to automate the feedback collection process and analyze vast amounts of data from diverse sources, including surveys, reviews, social media, and customer support interactions. The proposed framework facilitates real-time feedback analysis, enabling businesses to identify trends, customer preferences, and potential pain points more efficiently. By integrating AI with existing customer relationship management (CRM) systems, businesses can automate the categorization and prioritization of feedback, allowing for timely responses and more effective problem-solving. Furthermore, predictive analytics tools within the framework can forecast customer needs, allowing businesses to tailor products and services to meet evolving expectations. This framework also supports continuous improvement by enabling businesses to track the impact of changes implemented based on customer feedback. Additionally, AI’s ability to personalize the customer experience by recognizing patterns and individual preferences plays a crucial role in increasing customer satisfaction and loyalty. The data-driven insights generated through AI integration can guide businesses in refining their marketing, product development, and customer service strategies, leading to improved operational efficiency and better alignment with customer expectations. In conclusion, the integration of AI into customer feedback mechanisms represents a significant advancement for data-driven business strategy development. This framework not only enhances feedback accuracy and speed but also empowers businesses to deliver more personalized and customer-centric solutions.

Cultural profiles evaluation and their impact on the performance of production systems in Colombian companies using data analytics

Strategically managing production systems is crucial for creating value and enhancing the competitive capabilities of companies. However, research on organizational culture within these systems is scarce, particularly in the Colombian context. This research aims to evaluate cultural profiles and their impact on the performance of production systems in Colombian firms. The regional focus is vital as cultural and contextual factors can vary significantly between regions, influencing organizational behavior and performance outcomes. To achieve this, we make a study in a sample of Colombian companies, with participation from working students of the Universidad Nacional Abierta y a Distancia (UNAD). We used a data analytics approach to collected data. The results will be relevant to both the scientific community and business practitioners. This research seeks to determine whether the perception of the work environment within a company influences the perceived performance of the company. The findings will provide a deeper understanding of the relationship between organizational culture and production system performance, offering a foundation for business decision-making and enhancing competitiveness in Latin American context.

Portfolio location-related decisions: do proximities matter?

PurposeThis paper aims to examine the relationships between how family owners perceive different aspects of proximity to their territory of origin and the location-related decisions of family business portfolios.Design/methodology/approachBy using the data collected from 112 family business portfolios (FBP), the authors carry out an empirical analysis to test the proposed hypotheses. The results are robust across a battery of robustness analyses performed by the authors.FindingsThe analysis reveals a heterogeneous relationship between the different dimensions of proximity and the maintenance and location of portfolio business units. Specifically, we reveal that entrepreneurial families’ social and institutional proximities to their founding territory have a positive influence on their decisions related to investing and maintaining their business portfolio.Originality/valueThese findings open the black box of how business decisions are affected by how business owners perceive their contexts, providing new insights into how corporate decisions in understudied business organisations, business groups, are shaped by the ownership’s proximity to territories.

Impact of Corporate Entrepreneurship and Market Orientation on the Performance of Small Medium-Sized Enterprises in Ibadan Nigeria

The performance of Small and Medium-Sized Enterprises (SMEs) is pivotal to economic development, particularly in emerging economies like Nigeria. This paper explores the intersection of Corporate Entrepreneurship (CE) and Market Orientation (MO) and their collective impact on the performance of SMEs in Ibadan, Nigeria. Two research questions were raised, and two hypotheses were tested. The study employed the survey research design with five hundred and forty-six (546) respondents as subjects. Representative SMEs were purposively selected from each of the 11 local government areas in Ibadan Metropolis. Primary data was employed using the questionnaire to elicit information from the respondents. Data collected were analysed using Canonical Correlation and Multiple Regression. Findings indicated a combined impact of CE and MO on the performance of SMEs with a Canonical Correlation of 0.510 at p < 0.0. The results also showed that corporate innovation, corporate pro-activeness, risk taking, entrepreneurial culture, intelligence generation, intelligence dissemination and responsiveness using Multiple Regression jointly predicted SMEs performance at F = 19.501; R= .450, R2 = 202, Adj. R2 = .192; P < 000. Based on these results, it is recommended that SMEs should emphasise the practices of CE and put more effort into market research specifically to make confident, progressive business decisions based on market development and market entry opportunities that can lead to sustainable competitive advantage and superior organisational performance.

20 Years of Scientific Study on Business Intelligence and Decision-Making Performance: A Bibliometric Analysis

Background: Business intelligence (BI) is an area in which data analytics is applied to generate crucial information supporting business decision-making and has been a significant domain for over three decades. However, there is uncertainty regarding whether investments can effectively improve organizational outcomes. Objective: This study aimed to provide a comprehensive overview of the knowledge generated and disseminated in previous investigations related to the intricate relationship between BI and decision-making performance (DMP) over the past 20 years. Methods: An R-tool namely bibliometrix, which supports suggested workflow for conducting bibliometrics and includes descriptive as well as knowledge structure analysis was used on a dataset containing 1,484 English-language articles published between 2003 and 2023 and indexed in Web of Science databases. Results: The results showed that field study has stabilized over the past three years, signaling a shift in the focus of scholars. However, only a few studies use decision theory and further investigations are required to fully understand how BI impacts DMP both inside and outside organizational boundaries. Conclusion: Based on the results, BI studies tend to be more application-oriented and there is a need to change the emphasis from focusing only on tools to variables such as the role of effective use and competencies that might improve decision quality. Keywords: Business Intelligence, Decision-Making Performance, Decision Quality, Bibliometrics, R-tool.

A Comprehensive Approach for Selecting Third-Party Logistics Providers

The finest third-party logistics (3PL) supplier is a vital decision for businesses trying to streamline their supply chain operations. In order to free up time for businesses to concentrate on their core skills, third-party logistics providers offer a variety of services, such as distribution management, warehousing, and transportation. However, selecting the best 3PL supplier necessitates a rigorous assessment of variables including sector expertise, technical prowess, service quality, scalability, and cost-effectiveness. This introduction discusses the significance of choosing the best 3PL supplier and emphasizes important factors to take into account when making a choice. It is essential since there is a chance that third-party logistics (3PL) provider selection study may boost the supply chain's effectiveness and competitiveness. Firms may make selections that are tailored to their unique requirements by recognizing and assessing the elements that affect the choosing process. Proper 3PL selection may boost market response, save costs, expand the capacity of operations, and improve customer happiness. As a result, research in this field helps to create best practices and strategies that enhance supply chain management and promote entrepreneurship. A multi-criteria decision-making approach called The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to rank and choose the best option from a list of options. To determine how closely each alternative resembles the ideal answer, TOPSIS takes into account both positive and negative features. Based on the greatest distance from the inverse idealistic and the lowest distance to the positive ideal, the outcome score is calculated. This approach facilitates decision-making processes in a variety of contexts, such as project prioritization, supplier selection, and investment appraisal. Alternative parameters taken as 3PL-1, 3PL-2, 3PL-3, 3PL-4, 3PL-5. Evaluation parameters taken as Delivery Service (%), Quality of the Customer Experience (Quos), Coverage of the EU Territories (%), Flexibility, and Cost (EUR/km). From the result it is seen that 3PL-2 secures the first rank which is acquired by the TOPSIS method. To secure the first place on the table consistency will be the key factor which was maintained by 3PL-2

A Study About Family Businesses in Kanyakumari District

Family Businesses are the oldest form of commercial organization and plays a crucial role in the development of the economy. It is a distinctive organization form with concentrated ownership, deep-rooted tacit knowledge, and lengthy family tenures. These are commercial organizations which are passed on from generations to generations and hence, the members in the family tend to influence the business decision making process (Baltazar et al. 2023). Family business aids in the survival of an individual when there is high unemployment rate and it provides job opportunities as well. However, there are problems involved in family businesses such as inability to incorporate business knowledge to the other family members. This can cause hindrance in the development of the business and even lead to the closure of the business. (Amenah et al. 2023) The research objective of the study is to analyse the issues faced in family businesses and providing the required suggestions. The primary data collection is through convenience sampling, and the sample size is 120. The secondary data collection was through journals, books, websites and databases. The statistical analysis revealed significant differences between age groups and the advantages of family businesses, particularly in areas such as long-term vision, commitment, trust, and agility. Similarly, key challenges like family conflicts, lack of succession planning, and resistance to change were found to significantly impact family businesses, highlighting the need for targeted interventions.

Artificial Intelligence in Strategic Business Decisions: Enhancing Market Competitiveness

Abstract: Integrating Artificial Intelligence (AI) into strategic decision-making is essential for enhancing market competitiveness in today's dynamic business environment. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics optimize operations, personalize customer experiences, and drive product innovation. Machine learning algorithms analyze vast data to uncover patterns, aiding better decision-making. Predictive analytics forecasts market trends and consumer behaviors, allowing companies to anticipate demand and streamline supply chains, reducing risks like overproduction and stockouts. NLP-powered chatbots improve customer interactions by handling routine inquiries, freeing human agents for complex issues, and enabling personalized marketing. AI also accelerates product development by analyzing market data and consumer feedback, simulating scenarios, and predicting outcomes. Operational efficiency is enhanced through automation and optimized workflows, saving costs and increasing productivity. Despite these benefits, challenges such as data privacy, algorithmic bias, significant investment, and a shift to a data-driven culture must be managed. Effective AI integration offers significant competitive advantages, positioning companies to leverage predictive analytics, personalized customer interactions, and operational efficiency for growth and innovation.

An integrated approach using discrete event simulation and design of experiments for working capital management in the fuel retail trade

ABSTRACT This study introduces an innovative approach to Working Capital Management (WCM) in the fuel retail sector, using Discrete Event Simulation (DES) and Design of Experiments (DOE). Efficient WCM is essential for small businesses, particularly in fuel retailing with its tight margins and intense competition. Unlike static financial tools relying on historical data, DES interacts with indicators such as Working Capital Requirement (WCR) and Liquidity Ratios, providing stochastic and accurate information. The study examined three variables (Supplier Payment Deadline, Credit Card Receipt Deadline, and Customer Payment Profile) across 16 experimental scenarios. The Payment Term to Suppliers emerged as the key factor in reducing WCR. Despite lower liquidity potential, the company balanced short-term obligations, identifying optimal WCM scenarios. The study shifts WCM analysis from static data to forecasted, real-time data, supporting dynamic business decision-making. It concludes that DES is a valuable tool for effective WCM in fuel retail, encouraging further research.

Personalized Data Insights: How­­ Machine Learning is Revolutionizing Everyday Business Decisions

The rise of machine learning has fundamentally transformed the way businesses approach decision-making, particularly in the realm of data analytics. In today’s data-driven landscape, organizations are not just gathering information—they’re using advanced algorithms to turn raw data into personalized, actionable insights that drive business strategy. Machine learning enables businesses to go beyond surface-level trends and dig deeper into customer behavior, operational inefficiencies, and market dynamics. This technology is revolutionizing everyday business decisions by delivering real-time, data-driven intelligence that is specifically tailored to each business's unique needs. By leveraging machine learning, businesses can analyze massive datasets, uncover hidden patterns, and make predictions that were previously unimaginable with traditional analytics methods. One of the key benefits of machine learning is its ability to personalize data insights, offering businesses a clearer, more detailed understanding of their customers and operations. It empowers companies to make more accurate decisions in marketing, product development, customer service, and beyond, leading to improved efficiency, customer satisfaction, and overall business performance. This article explores the transformative role of machine learning in generating personalized data insights and its profound impact on everyday business decisions. It highlights how this technology can improve decision-making processes, enhance customer experiences, and drive more effective business strategies through real-time analysis and prediction. Through practical examples, we will demonstrate how businesses across various sectors are already using machine learning to revolutionize their operations and improve outcomes.

Economic and environmental pollutant impact of Maternal-Fetal Telemedicine.

The global issue of greenhouse gas emissions has significant implications for the environment and human health. Telemedicine provides a valuable tool for delivering healthcare while reducing gas emissions by limiting the need for patient travel. However, the environmental effects of telemedicine in high-risk pregnancy populations remain unassessed. The aim of this study was to estimate the economic and environmental impact of an outpatient teleMFM program. This retrospective cohort study examined all visits at 3 teleMFM clinics more than 90 miles away from the nearest in-person MFM office between 10/1/2021 and 6/1/2022. Travel distances and times were calculated for each appointment between the patient's home, telemedicine clinic, and nearest in-person clinics, using zip code data and Google Maps web-based map calculator tools. Travel cost savings and environmental impact were calculated by determining differences in mileage reimbursement rate and emissions between those incurred in attending telemedicine appointments and those that would have been incurred if in-person using inflation-adjusted Internal Revenue Service annual standard mileage reimbursement rate ($0.58 per mile), and the U.S. Environmental Protection Agency Office of Transportation and Air Quality's average annual emissions and fuel consumption for gasoline-fueled passenger vehicles. During the study period a total number of 2,712 appointments were scheduled, of which 2,454 were kept (cancellations removed) and analyzed. Visiting a teleMFM clinic resulted in 204 miles, 200 minutes and 118.32 dollars saved per patient visit compared to visiting the nearest in-person clinic. Over a 7 month period a total of 96.6 metric tons of emissions were saved. This study demonstrates the positive economic and environmental impact of teleMFM utilization in communities remote from in-person care. Given the contribution of greenhouse gas emissions to climate change, such findings may provide strategies for our specialty make informed policy, advocacy, and business decisions.

GOVERNABILIDADE EMPRESARIAL PELA PERSPECTIVA FINANCEIRA ESTRATÉGICA

every company is made up of several sectors, all of unique importance. There is, however, one that stands out for reflecting a more specific nucleus, whose collection of information has an impact on the business decision-making process, that is, the financial one. The movement and administration of the measured capital are the most challenging tasks for the financial manager, so that it goes beyond mere mathematical calculations. It involves the organizational structure in its systemic view, the mapping of the market with its needs and expectations, the determination of objective criteria, regardless of the unpredictable risk factors. These are able to change the decision chain to the point of transforming business strategic planning and its corresponding way of managing. Then the administrator or the financial manager emerges as the protagonist of the corporation's leverage or setback, insofar as the manipulation of the information obtained, some of which are privileged, may assist in the exact allocation of the funds raised, the results of which may give the company notoriety. Corporate governance thus takes a macro perspective, so that financial strategies will be fundamental to mitigate eventual failures in the business endeavor.

Global Metrics for Terrestrial Biodiversity

Biodiversity metrics are increasingly in demand for informing government, business, and civil society decisions. However, it is not always clear to end users how these metrics differ or for what purpose they are best suited. We seek to answer these questions using a database of 573 biodiversity-related metrics, indicators, indices, and layers, which address aspects of genetic diversity, species, and ecosystems. We provide examples of indicators and their uses within the state–pressure–response–benefits framework that is widely used in conservation science. Considering complementarity across this framework, we recommend a small number of metrics considered most pertinent for use in decision-making by governments and businesses. We conclude by highlighting five future directions: increasing the importance of national metrics, ensuring wider uptake of business metrics, agreeing on a minimum set of metrics for government and business use, automating metric calculation through use of technology, and generating sustainable funding for metric production.

Systems Thinking Principles for Making Change

Traditionally, systems thinking support has relied on an ever-increasing plethora of systems tools, methods, and approaches. Arguably though, such support requires something different from, and more accessible than, detailed instruction on somewhat abstract laws and detailed principles and/or constitutive rules associated with conventional systems approaches or systems ‘tools of the trade’. For busy managers and decision makers working in often-stressful conditions, what is perhaps more valued are simple principles for enabling systems thinking in practice. Such principles should acknowledge and build on existing (multi)disciplinary skill sets and expertise, allowing for more meaningful interdisciplinary support amongst professions, as part of a nested transdisciplinary support for addressing wider social challenges. This monograph offers three principles of systems thinking in practice (STiP): relational STiP, perspective STiP, and adaptive STiP. They each have two sets of operational principles applicable to first-order and second-order practice, respectively. The three general principles are nested in an overriding principle of STiP as praxis (theory-informed action or thinking in practice) manifest in the need for being both systemic and systematic. The three principles represent a distilled expression of a systematic literacy of systems thinking, a literacy that speaks to the systemic sensibilities of Inter-relationships, Perspectives, and Boundaries (sometimes referred collectively as IPB), associated with any area of intervention. Drawing on metaphors of bricolage, conversation, and performance, and building on philosophical foundations of boundary critique, the three principles provide for a requisite systems literacy (as an emergent property of systemic sensibilities and systems thinking literacy) for enabling appropriate STiP capabilities to flourish when making a meaningful change.

The Impact of Transport Costs on Business Decision-Making in Rural Areas

Mobility challenges in rural areas can have a negative impact on economic and demographic development. People in these areas often have to move to urban areas to find quality jobs or services, which can lead to a decline in population, and economic activity. In this paper we analyze the factors influencing business decisions in rural areas. Public transport infrastructure based on short energy cycles using solar energy represents a solution to boost business decision management in rural areas.

Metamorphosing forex: advancements in volatility forecasting using a modified fuzzy time series framework

The interplay of exchange rates among nations significantly influences both international and domestic trade, underscoring the pivotal role of the foreign exchange market (Forex) in a country's economic landscape. Forex fluctuations have a significant impact on the everyday lives of both government agencies and the public population, directly influencing a country's prosperity or misfortune. This work proposes an advanced fuzzy time series model that incorporates domain universe sub-partitioning, parameter adjustment optimization methodologies, and interval forecasting methods. We utilized this model to examine annual exchange rate patterns between the Pakistani rupee (PKR) and the US dollar (US$), comparing its forecast accuracy to that of other models. Our proposed methodology outperformed existing methodologies in terms of forecasting precision, providing stakeholders with valuable insights for making informed, data-driven business decisions that benefit both individual firms and the country overall.