Modern Business Research Articles

Cultural Origins of Business Formation in the Context of Kinship and Trust

This paper presents a conceptual framework connecting historical kinship structures and generalized trust into modern business formation. Kinship intensity, defined as the degree of strength in family ties, is shaping the social and economic outlook of societies. Building upon the existing literature, I argue that societies with strong kinship ties often exhibit collectivist characteristics, leading to less trust towards outsiders. This hinders market efficiency and collaborative partnerships, resulting in business formation being largely confined to family-owned enterprises. Conversely, societies with loose kinship structures exhibits higher level of generalized trust that enable to promote engagements beyond family networks. This promotes establishing larger firms with strangers, more efficient markets, and greater cooperation with foreign firms. The paper suggests that kinship and trust behavior are deeply-rooted in culture and influence social and economic development. It is important for policymakers to support initiatives that foster trust beyond family ties in order to promote collaboration among diverse market participants.

An Empirical Study on Advanced Marketing Practices, Sustainability, and EVM Strategies for Business Growth

In the modern business landscape, sustainable marketing strategies have become crucial for success. Companies are increasingly allocating substantial resources to these initiatives. While consumers expect businesses to adopt sustainable practices, their primary motivation remains fulfilling personal needs. This creates a gap between corporate sustainability efforts and consumer expectations. This study proposes a viable framework for sustainability strategies that businesses can implement to bridge this disconnect. Key findings include: First, sustainable marketing significantly enhances brand image development. Second, brand awareness plays a vital role in customer loyalty within the modern marketplace. Third, a strong brand image amplifies the appeal of sustainable purchasing intentions. Fourth, customer retention serves as a critical tool for boosting long-term purchase expectations. Fifth, elevating consumer expectations is essential for driving sustainable buying behaviors. Notably, sustainable marketing acts as an effective mediator between customer engagement and brand perception. Additionally, corporate social responsibility (CSR) strengthens the link between a company's reputation and sustainable purchase intentions. This research provides both a theoretical framework and practical recommendations for the modern business environment, emphasizing the importance of sustainable marketing campaigns as a precursor to organizational success

Can indigenous peasants of Central Kalimantan live viably while conserving their forest lands?

Purpose This study aims to investigate the feasibility of a community-based non-timber forest products (NTFP) enterprise, or CBNE, within the indigenous peasant communities in Manuhing Raya, Central Kalimantan, Indonesia. This includes assessing the potential for generating featured agricultural products from the region. Design/methodology/approach This is a descriptive qualitative study that integrates ethnographic methods into the SLA analysis, to investigate the feasibility of a CBNE approach within an indigenous peasant community. A number of 13 peasant groups, which comprise 251 members, from the seven villages in Manuhing Raya participated in the study. Findings The CBNE approach, when combined with One Village One Product method, is considered appropriate for the determination of featured agricultural products in a local community. In addition, the SLA analysis in this study contributes to identifying vulnerable aspects of CBNE development in the local communities, including weaknesses in social cohesion, leadership and entrepreneurial orientation. Research limitations/implications Efforts to preserve the rainforest lands in Central Kalimantan cannot be excluded from the involvement of the local peasant communities. The best livelihood strategy in this case is to adopt a CBNE approach. This shall be made possible by empowering the peasant communities to revive their local wisdom of Upun Pambelum, which means forest lands are their primary resources for life. Practical implications This study highlights the importance of using ethnographic investigation prior to the implementation of a CBNE development. Originality/value The implementation of the CBNE approach should take into account socioeconomic aspects of the local community, especially the presence of social cohesion, leadership roles and entrepreneurial orientations. In addition, the study demonstrates the potential of integrating traditional ecological knowledge with modern business models in CBNEs.

Leveraging Cloud Native Development for Ecosystem Strategy

This article explores the intersection of cloud-native development and business ecosystem strategies, highlighting how cloud-native technologies create scalable, flexible, and interoperable platforms that foster collaboration and innovation. It examines the composition and dynamics of business ecosystems, the key advantages of cloud-native development for ecosystem strategies, and practical approaches for implementing cloud-native ecosystem platforms. The paper provides insights into the transformative potential of these technologies in shaping modern business ecosystems, supported by market data, case studies, and industry trends.

Superstition and Its Role in Modern Business Management

Book Review: Crossman J. 2024. Superstition, Management and Organisations: Irrationality, Randomness, and Chaos in Decision Making. Palgrave Studies in Workplace Spirituality and Fulfillment. Palgrave Macmillan, Cham, 267 р. DOI: 10.1007/978-3-031-59020-7.

POSSIBILITIES AND PROSPECTS OF USING AR AND VR IN THE MARKETING ACTIVITIES OF BUSINESS STRUCTURES

The relevance of the work is due to the fact that in the conditions of digitization of the economy with the help of AR and VR, businesses can create unique offers for each client, increasing the level of satisfaction and loyalty. The use of AR and VR allows not only to increase customer engagement, but also to create a unique user experience that strengthens the brand and increases sales. The purpose of the article is to research the possibilities and prospects of using augmented reality (AR) and virtual reality (VR) technologies in marketing strategies of modern business. The scientific study includes practical examples of the use of AR and VR in real marketing campaigns. The article presents the distinctive characteristics of virtual reality and augmented reality technologies. The article indicates the potential development trends of AR and VR in the field of marketing and their impact on business competitiveness in the digital economy. The challenges that companies face during the implementation of these technologies are identified, namely: technical challenges (High requirements for technical support, Complexity of content development, Difficult integration with other technologies), economic challenges (High cost of implementation, Limited number of users, High costs of maintenance and updating) and social challenges (Barriers in technology adoption, Privacy and data security, Social inequality). Recommendations for overcoming them have been developed. It is noted that in order to overcome these obstacles, it is necessary to improve technical solutions, reduce costs and work on improving the user experience. At the same time, it is important to take into account the confidentiality and availability of technologies for different categories of the population. AR and VR have been found to be successfully used in various fields to enhance user engagement and create new opportunities for brands. The results of the study can help business structures to better navigate the possibilities of AR and VR, facilitating adaptation to new market realities. Prospects for further research include the development of a method for assessing the effectiveness of AR/VR campaigns.

Current Scenario of the Changing Role of Human Resource in a VUCA Environment: A Case Study of UPRVUNL

The modern business environment is increasingly characterized by Volatility, Uncertainty, Complexity, and Ambiguity (VUCA). In today's VUCA environment, organizations face unprecedented challenges and it is undergoing significant transformations of the role of Human Resources (HR). This paper explores how Human Resources (HR) departments adapt to these conditions, and examines the evolving role of HR in the context of VUCA with a focus on Uttar Pradesh Rajya Vidyut Utpadan Nigam Limited (UPRVUNL), a major power generation company in energy sector in India. Through a detailed case study approach, This research aims to explore the current challenges and strategies adopted by UPRVUNL's HR department in response to VUCA conditions and to understand the evolving role of HR in this context, identifying challenges and strategies that enhance organizational resilience and effectiveness.

Investigation and Assessment the Level of SMES Leanness Using Fuzzy Dematel/Fuzzy AHP/Fuzzy Topsis Integrated Model

Abstract Small and Medium Enterprises (SMEs) have a crucial role in driving economic growth and innovation. In the modern business landscape characterized by uncertainty, the adoption of lean philosophy has gained prominence as a means to continuously improve SMEs’ operational efficiency and competitiveness. A fuzzy assessment model was proposed as comprehensive framework and guide for SMEs during their lean journey that utilizes the Fuzzy Analytic Hierarchy Process (Fuzzy AHP), Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL), and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to serve many objectives; identifying the most important and influence lean dimensions on SMEs leanness; assessing the level of adoption of lean philosophy in SMEs for five essential lean dimensions, namely management, process, supplier, customer, and employee; identifying the interrelationships among them; and identifying the driving dimensions for the improvement process to focus SMEs limited resources on it in improvement process due to its direct influence on the other lean dimensions. The proposed fuzzy model was applied in Iraqi medium-sized enterprise producing healthy water, juice, and soft drinks to identify the most important and influence lean dimension on enterprise leanness, identify the driving dimension of the improvement process, and assess the level of leanness of these five lean dimensions. The results showed that the management dimension has the highest level of importance and influence on SMEs’ leanness, equal to 41.31%, and is distinguished as a driving dimension that has a high influence on improving the driven dimensions of process and customer. Although the level of importance and influence of the process dimension is 19.96%, it is an effect dimension and is considered a driven dimension. Plotting the interrelationship among the five lean dimensions in the diagram shows management has an influence on all lean dimensions and, at the same time, has a mutual relationship with employees and customers. An employee has a mutual relationship with the process dimension. In addition, the results showed that enterprise has deficiency in adopting a lean philosophy, where its level of leanness equal 0.39.

BUILDING DATA WAREHOUSING FOR MACHINE LEARNING AND CLIENT SCORING

In modern business, data is a critical resource that can significantly increase the company's competitiveness. One of the key tasks associated with data is the creation of data warehouses that provide effective storage, processing and analysis of information. In particular, structured data warehouses play a critical role in the development of machine learning and customer scoring systems. In this article, we will look at the stages of building data warehouses, integrating them with machine learning systems, and the importance of structured data for customer scoring. At the end of the article, we will present a mini-project on implementing a data warehouse using SQL and PySpark, as well as an example of machine learning for customer scoring. Keywords: data warehouse, SQL and PySpark, customer scoring, data structuring, structured data warehouses.

AI in business research

AbstractArtificial intelligence (AI) has emerged as a pivotal force in modern business transformation, garnering widespread attention from both practitioners and academics. With a notable exponential increase in AI‐related studies, we provide a research framework aiming to synthesize the existing literature on AI in the business field. We conduct a comprehensive review of AI research spanning from 2010 to 2023 in 25 leading business journals according to this review framework. Specifically, we review the literature from three research perspectives: (i) AI applications, (ii) human perceptions of AI, and (iii) AI behavior. We also identify five principal research questions and offer suggestions for future research directions.

Developing a multidimensional scale for presenteeism: A study on measurement and implications

Presenteeism is among the key factors that negatively affect employee efficiency and, therefore, company organi zational performance. The existing measurement tools for assessing presenteeism highlight health-related issues as the main reason behind this counterproductive behaviour. On the other hand, other factors that influence modern business life and pro cesses are omitted. The paper’s primary purpose is to develop a comprehensive measurement tool suitable for the individual and organizational multi-factor structure of presenteeism that accurately reflects the complex nature of today’s business world. Meth odologically, the research is shaped within the framework of grounded theory. In the study, mixed methods research combining qualitative and quantitative methods was utilized. In this context, firstly, the authors conducted a literature review and focus group interviews with experts in the field in order to reveal the conceptual structure. We analysed the data using content analysis and then moved on to writing 18 scale items. We performed exploratory factor analysis using the SPSS program and confirmatory factor analysis using the AMOS program. As a result, we develop a four-dimensional scale for presenteeism covering work focus, stress management, social loafing, and performance. The verified multidimensional scale fills a vacuum in the literature by offer ing a comprehensive and dependable instrument to gauge presenteeism on organizational and individual dimensions. Among the possible avenues for further research are testing the scale in different cultural contexts and addressing the organizational causes of presenteeism.

Shifting priorities and expectations in the new world of work. Insights from millennials and generation Z

In the contemporary world of work and modern business, the well-being of a company is directly linked with the well-being of its employees, and in this context, the proactive promotion of work-life balance has become more important than ever because it meets the new ideal and is an ongoing challenge through the lens of the younger generation’s age groups. By unpacking worklife balance into five drivers, the present paper aims to explore the recent trends, priorities, and expectations of the modern workforce. The target population was represented by two generations of employees, Millennials and Generation Z, and the research hypotheses were tested using structural equation in SmartPLS. The results of this research aim to enhance the current knowledge base, offering a distinct perspective along with significant findings for employers, managers, recruiters, policymakers, and other nodes of control who can gain insights regarding work-related priorities and expectations.

Closing the Gap: A Holistic Approach to Leadership Development in Times of Change

Effective leadership is essential for organizations to thrive in today’s fast-paced and complex business environment. However, many companies face critical leadership development gaps, particularly in managing change, attracting and retaining top talent, and fostering diversity. This article examines these challenges and provides actionable strategies for addressing them. Organizations often struggle to cultivate leaders prepared to drive transformation, as traditional leadership programs focus on stability rather than agility. Attracting talent is also hindered by the absence of key leadership qualities like emotional intelligence and collaboration. Furthermore, a lack of diversity in leadership pipelines limits innovation and decision-making capabilities. To overcome these gaps, organizations must foster change leadership through hands-on initiatives and mentoring, develop high-performance cultures that empower and engage employees, and create diverse pipelines by addressing unconscious bias and providing sponsorship programs. Companies like Amazon, Google, and Accenture demonstrate how aligning leadership strategies with modern business needs can drive sustained success. Ultimately, leadership development must be a continuous, integrated effort woven into the organization’s culture to meet evolving challenges and ensure long-term growth.

Clean energy challenges and innovation opportunities in Kazakhstan

Abstract Kazakhstan has pledged to transition to a low-carbon economy by implementing national policies and strategies that promote clean energy innovation. However, Kazakhstan is still falling short of its expected targets for energy transition, and there is a lack of knowledge regarding the country’s challenges and opportunities for clean energy development. Towards this end, the current study identifies and assesses the enablers and barriers related to clean energy innovation in Kazakhstan. Using the combination of SWOT analysis, survey data from 41 experts and the DEMATEL decision support tool, we evaluated the key factors affecting Kazakhstan’s clean energy innovation and their implications for energy transition. Assessment results show that the immature business environment, underpinned by technological, institutional, and socioeconomic factors, is perceived as a high-impact constraint for clean energy innovation and green finance deployment in Kazakhstan. Skilled labour shortages, high reliance on hydrocarbons and low retail energy prices are significant challenges to Kazakhstan’s clean energy innovation. The low-profit margin and high investment risk in clean energy projects are identified as transition barriers in the power and energy-intensive industries. In contrast, Kazakhstan’s endowments of resources critical for developing clean energy technologies (rare earth metals, uranium, gas) and the potential of low-carbon investments (e.g. carbon storage) are perceived as prominent enablers of clean energy innovation. Results are consistent across expert subgroups (academia, industry, NGOs, etc). Findings call for policy support to modern and attractive business environments, capacity, and human capital development. The findings can provide helpful insights for countries in Central Asia and beyond with similar socioeconomic structures that aim for a timely energy transition.

The Impact of Knowledge Management on Employee Performance in Knowledge Process Outsourcing Companies in Sri Lanka

Abstract In the modern business world, organizations strive for competitive advantage. This study examines the impact of knowledge acquisition, knowledge creation, knowledge capture and storage, knowledge sharing, and knowledge application on employee performance in Knowledge Process Outsourcing (KPO) companies in Sri Lanka. The study adopted a quantitative approach, employing a structured online questionnaire distributed amongst the KPO industry employees. The data is analyzed using statistical methods such as correlation and multiple regression. According to the findings, knowledge management has a significant impact on employee performance in KPO companies in Sri Lanka. This result indicates a significant relationship between the selected knowledge management components - acquisition, creation, capture and storage, sharing, application and employee performance. However, challenges such as a lack of comprehensive knowledge management strategies, limited employee engagement, and cultural barriers are identified. The findings prove the need for KPO organizations in Sri Lanka to prioritize knowledge management strategies. The study contributes to the literature on knowledge management in the KPO industry.

THE INNOVATIVE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON DIGITAL BUSINESS TRANSFORMATION

In the context of intensifying competition and evolving market dynamics, the deployment of cutting-edge technologies has become not merely a discretionary choice, but an indispensable imperative for any enterprise aspiring to achieve successful growth. Generative artificial intelligence, with its substantial potential for automation, personalisation and optimisation of business processes, is emerging as a highly promising avenue of digital transformation. This study is dedicated to investigating approaches and delineating strategies for aligning generative artificial intelligence with the requirements of digital business transformation. The research examines the development of artificial intelligence, with a focus on symbolic artificial intelligence, machine learning, deep learning and generative artificial intelligence. In addition, it considers the impact of these developments on business processes. The article identifies the potential benefits and challenges associated with the adaptation of generative artificial intelligence to the needs of modern business, in the areas of marketing, sales and data analysis. The utilisation of diverse methodologies and techniques, including prompts, fine-tuning, and the incorporation of interactive guidance systems, can enhance the efficacy and precision of generative AI in a business setting, thereby facilitating optimal outcomes in a multitude of tasks. The authors put forth the proposition of employing generative artificial intelligence technology in conjunction with Retrieval-Augmented Generation, with the objective of enhancing the quality and relevance of responses to user queries. Additionally, they advocate for the utilisation of agents or orchestration tools to provide guidance to models. The successful implementation of generative artificial intelligence hinges on three key factors: the clear definition of objectives, the selection of suitable tools and technologies, and the assurance of managerial and staff support. The implementation of generative artificial intelligence will contribute to increased efficiency through the automation of routine tasks, enhanced competitiveness through personalisation and innovation, optimised cost structures that increase profitability, and expanded opportunities for research and development.

Optimizing Organizational Efficiency: The Strategic Necessity of Modern Industrial Asset Hierarchy Management

Asset Hierarchy Management (AHM) is not just a theoretical concept but a practical tool for optimizing operations in modern businesses, particularly in sectors with intricate infrastructures such as manufacturing, utilities, and healthcare. This research paper explores the significance of AHM in standardizing and enhancing organizational efficiency. It traces AHM's evolution, delineates its benefits, and provides strategies for implementation. By emphasizing the tangible benefits of AHM, the paper aims to instill optimism in the audience about its potential impact. The paper outlines prerequisites and steps for developing a robust asset hierarchy, stressing transparent governance and integration with technology solutions like Enterprise Asset Management (EAM) software. Furthermore, it presents a detailed framework for implementing AHM in manufacturing plants, highlighting the importance of defining clear asset categories, hierarchical levels, and relationships. By adhering to best practices and leveraging advanced tools, organizations can effectively manage assets, drive operational excellence, and achieve sustainable growth.

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.

Theoretical foundations and practical implementation of supply chain optimization a metal fabrication business model

In the dynamic landscape of modern business, effective management of supply chains stands as a cornerstone of organizational success and competitive edge. This study investigates the theoretical principles and practical implementations of optimization models aimed at refining supply chain operations in the metal fabrication business, giving the maximum profit. It begins with a comprehensive survey of optimization theory, encompassing linear programming, integer programming, and stochastic optimization. It proceeds to their application in addressing diverse challenges encountered in supply chain management. The discussion includes deterministic models, such as those optimizing facility location under capacity constraints and transportation logistics, alongside stochastic models designed to manage uncertainties inherent in demand forecasting and inventory control. Advanced methodologies like metaheuristic algorithms and multi-objective optimization techniques are examined for their capacity to navigate the intricate and often conflicting objectives within supply chain networks. Through comprehensive theoretical analysis and a study of the supply chain model developed for the metal fabrication business, this paper contributes to advancing knowledge in mechanical engineering and supply chain management, offering insights pertinent to practitioners and researchers alike. Ultimately, it emphasizes the critical role of optimization models in optimizing efficiency, reducing costs, improving profit margin, reducing CO2 emission, and enhancing competitiveness in modern supply chain operations.

The Role of Automation in Enhancing Cybersecurity: a Technical Analysis

Intelligent automation, a convergence of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), is reshaping modern business operations across various sectors. This article examines the transformative impact of intelligent automation on organizational efficiency, cost structures, and innovation capabilities. Recent industry surveys indicate a significant increase in adoption rates, with 67% of business leaders implementing intelligent automation at scale in 2023, up from 48% in 2020. The integration of AI and ML with RPA has enabled businesses to automate complex, cognitive tasks, leading to an average 35% reduction in operational costs and a 50% improvement in process cycle times. While challenges such as data privacy, ethical considerations, and workforce reskilling persist, the potential benefits of intelligent automation are substantial. This article explores quantifiable impacts across industries, examines real-world case studies, and anticipates future trends including hyperautomation, autonomous decision-making, and integration with emerging technologies like blockchain and edge computing. The article concludes that strategic implementation of intelligent automation solutions will be crucial for organizations to maintain competitiveness and drive innovation in the increasingly digital business landscape.