Business Domain Research Articles

Visual Impact: Exploring the Crucial Role of Graphic Design in Contemporary Marketing

Objectives: This paper aims to explore the impacts of graphic design in the context of commercial marketing, with a focus on an in-depth analysis of how meticulous graphic design enhances businesses and facilitates their growth. Moreover, the research provides an opportunity to comprehend how graphic design can play a vital role in improving marketing performance and achieving advancements across various business domains. Methods: In marketing, the focus is on anticipating and satisfying consumer demands. Company owners have long seen marketing as an afterthought, but due to training programs, enterprise growth, and the current push for competitiveness, marketing is once again receiving the attention it deserves. The method used in this paper is qualitative research. Past studies and articles were used to explore the topic. Results: The results showed that graphic design constitutes an exceptional tool in business marketing. When juxtaposed against mere words, well-crafted and visually compelling drawings or images possess the capacity to convey ideas and information with heightened efficacy, consequently culminating in increased sales. Conclusion: Visually stimulating graphic design enhances the effectiveness of advertising messages beyond what words alone can express. When eye-catching visual graphics are used in marketing, professionalism is improved, and the company's reputation is elevated. Effective graphic design in advertising persuades consumers of superior quality, thereby increasing sales.

US sanctions, workforce dynamics, and corporate entrepreneurship: evidence from Iran

Purpose This study aims to explore how political sanctions imposed by the US government have influenced workforce dynamics and corporate entrepreneurship within Iranian listed firms. Design/methodology/approach The study utilizes a difference-in-differences approach to compare the workforce compositions and corporate entrepreneurship of Iranian listed firms subjected to US sanctions with those unaffected by sanctions both before and after the implementation of sanctions. The empirical analysis utilizes data from Iranian listed firms spanning from 2013 to 2023. Findings Our analysis reveals a significant decrease in employment rates among sanctioned firms, indicating a reduction in their workforce size. However, interestingly, we also observe a simultaneous positive effect on the educational qualifications of employees, as evidenced by increases in academic credentials and training hours, coupled with higher research and development (R&D) expenditures. These shifts in human capital investment and R&D spending contribute to a heightened level of entrepreneurship in the subsequent year. Furthermore, our findings suggest that firms enhance their entrepreneurial activities by prioritizing innovation in processes over exploring new business domains. Originality/value Through this investigation, our study elucidates the intricate relationship between sanctions and corporate entrepreneurship, highlighting a complex dynamic wherein sanctions, despite reducing workforce quantity, encourage investments in workforce quality and efficiency, ultimately fostering an environment conducive to enhanced entrepreneurship.

Machine Learning-Based Outlier Detection for Business Intelligence: A Scalable Time Series Analysis Framework

The exponential growth in digital business operations has resulted in an unprecedented volume of time series data generated from diverse business metrics, creating an urgent need for sophisticated anomaly detection systems. This paper presents a comprehensive framework for detecting outliers in business time series data using advanced machine learning techniques, addressing the challenges of scale, accuracy, and real-time processing. We propose a novel hybrid approach that seamlessly integrates statistical methods with deep learning architectures to identify both point anomalies and pattern deviations in multivariate business metrics. The framework incorporates adaptive thresholding mechanisms and contextual awareness, leveraging business domain knowledge to reduce false positives while maintaining high detection accuracy across varying business cycles and seasonal patterns. Our approach addresses the challenges of scalability and real-time processing through a sophisticated distributed computing architecture, making it suitable for enterprise-scale deployments. The framework demonstrates superior performance in handling concept drift, seasonal variations, and complex interdependencies between metrics, while maintaining computational efficiency and interpretability. Keywords—anomaly detection, business intelligence, machine learning, time series analysis, deep learning, statistical methods, distributed computing, real-time processing, adaptive thresholding, feature engineering

Business insights using RAG–LLMs: a review and case study

ABSTRACT As organizations increasingly rely on diverse data sources like invoices and surveys, efficient Information Extraction (IE) is crucial. Natural Language Processing (NLP) enhances IE through tasks such as Named Entity Recognition (NER), Relation Extraction (RE), Event Extraction (EE), Term Extraction (TE), and Topic Modeling (TM). However, implementing these methods requires significant expertise, which smaller organizations often lack. Large Language Models (LLMs), powered by Generative Artificial Intelligence (GenAI), can address this by performing multiple IE tasks without extensive development costs. However, LLMs may struggle with domain-specific accuracy. Integrating Retrieval-Augmented Generation (RAG) with LLMs improves precision by incorporating external data. Despite the potential, research on RAG-LLM applications in the business domain is limited. This article reviews Business IE systems, explores RAG-LLM applications across disciplines, and presents a case study demonstrating how RAG-LLMs can enhance business insights, offering scalable, cost-effective solutions.

The Evolution of On-Demand Platforms: Conceptual Framework, Regulatory Challenges, and Policy Implications in the Digital Economy

Digital platforms currently form the cornerstone of what is referred to as 'platform economics,' progressively assuming a central role in the city's economy, experiential landscape, and governance. On-demand platforms play a pivotal role in elevating the level of digital sophistication among citizens. This article extends the current research on on-demand service markets, aiming to cultivate an understanding of this nascent domain of digital platform business within the broader framework of the smart city. In the first section, we delve into various concepts related to on-demand service markets, outlining their distinctive features in comparison to traditional service markets, and elucidating the principal components of the on-demand digital platform economy. The second part of this paper employs bibliometric analysis to unveil, among other insights, emerging areas of interest, key works, and influential researchers in the field, as well as the countries and institutions where research on on-demand service markets is most advanced. This research endeavour may lay the foundation for the development of a theory pertaining to the provision of on-demand services within the context of the platform economy. Finally, the research extensively discusses regulatory challenges and public policy implications in the context of on-demand platforms, with a focus on worker protection, algorithmic transparency, and data security.

Enterprise Architecture Business Model Planning Using EAP Framework (Case Study: PT. Gempita Cahaya Makmur)

The advancement of technology in a company has an impact on improving business quality. From this observation, architectural planning is a part that is used to build alignment between business strategy and information technology. Architecture within the business domain illustrates how a company conducts business activities and functions to achieve the company's goals. Therefore, the company's business model architecture depicts the current state of architecture by identifying business needs and activities. From this study, the business role of PT. GEMPITA CAHAYA MAKMUR, a company engaged in the procurement of goods and services, especially in the field of wholesale office stationery, printing, photocopier sales, and photocopier and laptop rentals, which has customers from medium-sized companies, large companies, both private and government. The use of the Enterprise Architecture Planning or EAP framework focuses on business architecture. The purpose of this research is expected to produce a blueprint proposal that will be beneficial for PT. GEMPITA CAHAYA MAKMUR to plan the business model architecture that will become the foundation for the design phase of application architecture.

Access scarcity, legislative generalization, and the business-oriented shift of the congressional agenda

Abstract Policy specialization in the U.S. Congress benefits the institution collectively and members individually. Yet members of Congress (MCs) are insufficiently specialized to optimize lawmaking success (Volden and Wiseman 2020). In this paper, we demonstrate the increasing propensity of MCs to generalize legislatively is driven largely by an expansion of MC legislative agendas in business domains. We then offer and test an explanation for this trend whereby business’s increasing demand for congressional attention (Drutman 2015) has outpaced the supply of congressional capacity to serve business needs (Crossen, Furnas, LaPira, and Burgat 2020; McKay 2022). This unmet demand incentivizes MCs to expand their business portfolio, which results in increased campaign contributions from business political action committees (PACs). We provide evidence consistent with this theory, showing that under conditions of access scarcity, MCs benefit financially (in terms of increased business PAC contributions) by broadening the number of business domains they are active in legislatively.

Derechos humanos y empresas: de la fragmentación a la cohesión

The domain of Business and Human Rights (BHR) has experienced significant development in recent years, increasingly leaning towards regulation. As a result, legal instruments have been created at the national, regional, and international levels. This text presents some of these initiatives along with their main elements and shortcomings. The concept of lack of governance is also mentioned to explain why different legal instruments have emerged and their development processes. Furthermore, a recent BHR case study is presented to highlight the significant influence of BHR on international law, the attention it has drawn from the international community, and the scope of its regulation. Additionally, through the examination of this case, two phenomena that currently characterize the international legal system are explained: legal fragmentation and the regime complex.

Analysis of Relationships Between Business Domains and Technical-Skill Requirements for Data Analysts

As growth of the data analytics field continues, more and more academic departments and professional training programs are offering skill training to potential data analysts. However, not all business domains require their data analysts to possess the same sets of technical skills. Based on over 2,500 data-analyst job advertisements posted on LinkedIn and utilizing subject analysis, cross-tabulation analysis, heat map, Kruskal-Wallis test, and Kappa test, this study examined relationships between various business domains and the technical skills required for data analysts within the respective business domains. Among many findings, the study discovered noteworthy disparities among the business domains concerning their requirements of technical-skill sets for data analysts. The findings of this study offer valuable insights to educators and potential data analysts.

Application of machine learning techniques for churn prediction in the telecom business

The telecom business generates a significant amount of data on a daily basis due to its massive client base. Acquiring a fresh client base is more expensive than retaining existing customers, whereas churn refers to customers transitioning from one company to another within a specified timeframe. Telecom managers and analysts investigate why customers cancel their subscriptions and analyze the behavior patterns of customers who have stopped using the services. In this work employs categorization methodologies to determine the instances of leave subscriptions and gathers the rationales behind client leave subscriptions in the telecommunications sector. The primary objective of this work is to examine various machine learning algorithms necessary for creating customer churn prediction (CP) models and identifying the reasons for churn. This work aims to provide retention strategies and plans to address churn. This work utilizes machine learning (ML) technique such as random forests (RF) to collect and classify client data for leave subscriptions. These results compare with other ML algorithm such as support vector machines (SVM), gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and light gradient boosting machines (LGBM), The business model provides a practical analysis of customer churn data, enabling accurate forecasts of customers likely to churn. This allows business management to take timely action to prevent churn and minimize profit loss. In this work obtains an accuracy of 98.1 % by utilizing the random forest classifier for churn prediction. The classifier matrix has obtained a precision of 92.8 % and a recall factor of 92.7 %, resulting in an overall accuracy of 95.6 %. Similarly, our research endeavors enhance churn prediction, encompass additional business domains, and furnish prediction models to retain their current consumers, improve customer service, and efficiently prevent churn.

Precarious love of eSports players in China: work-and-play amidst love and precarity

ABSTRACT The eSports industry has transformed rapidly in China in the last decade to become a flourishing business domain. ESports practitioners have been endorsed by the Chinese government (Wong and Meng-Lewis 2023), which recognized eSports as an official occupation in 2019. However, previous studies have interpreted eSports work as ‘playbour’, neglecting the professional and vocational dimensions of eSports. The field of affects related to eSports work is also understudied. This study employs certain ethnographic methods (participant observation and interview) to demonstrate the dialectical relationship between work and play through the construct of ‘work-and-play’, which emphasizes the new work ethic of ‘do what you love’. Esports players engage professionally in the work of playing games motivated by love, hope and ‘the champion dream’. However, their desire to flourish through eSports work could subject them to precarity and become cruel.

New lessons from biology for economics and business: a systems approach to non-market environments.

Drawing on recent advances in biology, this paper describes a systems approach, 'Systems Public Affairs' (SPA), to integrate non-market strategies in corporate purposes and strategies. Just as the environment of organisms affects and is affected by their development and evolution, so individuals and businesses adjust to and can shape their non-market environment, which we define as 'a historically formed national and social sphere, including laws, regulations, and policies, which supports, maintains and restrains the operation and preservation of markets'. The paper uses cases from South Korea to illustrate this approach. Emergent ideas in biology have great relevance for micro-foundations of business. Traditionally, economic activities are outcomes of individualistic decision-makers, each promoting their distinct objectives within economic markets. In the SPA approach, decision makers in the domains of business, policy and civil society collaborate in shaping non-market environments to align business objectives with public interest. This requires agency to rise to higher levels than that of businesses, policymakers and civil society through collaboration and experimentation in the presence of stochasticity and radical uncertainty. Analogous to the advancement of organism evolution through emergence of nervous systems and learning, so alignment of organizations with their non-market environments accelerates economic and social development.

Reassessing taxonomy-based data clustering: Unveiling insights and guidelines for application

Clustering for taxonomy-based archetype identification has become an established method in Information Systems (IS) research, aiding strategic decision-making across diverse research and business domains. However, the effectiveness of the approach depends critically on the compatibility of clustering methods and algorithms with the specific data characteristics. This study, based on a comprehensive review of 87 articles employing taxonomy-based clustering in IS research, reveals a notable mismatch between the chosen clustering algorithms and the nature of the data, particularly in the context of archetype development from taxonomy-based data. To address these methodological inconsistencies, we introduce a set of clustering guidelines tailored to the unique requirements of archetype development from taxonomy-based data. These guidelines are informed by a computational study involving seven identified datasets from the taxonomy-building literature, ensuring their practical applicability and scientific relevance. Our guidelines are designed to enhance the robustness and scientific validity of insights and decisions derived from taxonomy-based clustering. By improving the methodological rigor of clustering methods, our research addresses a critical mismatch in current practices and contributes to enhancing the quality of decision-making informed by taxonomy-based analysis in IS research.

Utilization of Polymers for the Development of Nanomaterials

AbstractThis study highlights the significance of polymers in the progress of nanoparticles across various areas. The molecules of polymers are highly regarded for their ability to adapt and self‐assemble, making them essential components in the creation of nanomaterials. The variety of polymers include natural polymers such as chitosan, synthetic polymers like polyethylene, and biodegradable polymers like poly(lactic‐co‐glycolic acid) (PLGA). These kinds of polymers possess distinct advantages such as high strength, environmental sustainability, and biocompatibility. Incorporation of nanoscale fillers into polymer matrices, which enhances the mechanical, thermal, and electrical properties of materials, is crucial for the development of nanocomposites. Illustration instances encompass carbon nanotube‐polymer composites and polymer clay hybrids, which find application in the construction, automotive, and aerospace sectors. Composites can employ many synthetic methods to generate nanostructures. Nanofibers have utility in tissue engineering, whereas polymer nanoparticles function as carriers for medical delivery. Also, polymers enhance nanomaterials by modifying their surfaces, a crucial factor for their application in membrane technology, catalysis, and sensing. A collaborative synergy between polymers and nanoparticles fosters a wide range of applications, showcasing the versatility and potential of polymers in altering the characteristics of nanomaterials. The resulting partnership continues to generate pioneering breakthroughs that address complex challenges and unveil unprecedented prospects in the domains of science, technology, and business.

Analytics: What Do Business Majors Need and Where Do They Get It?

Analytical skills are important for all business majors. Because of the statistical underpinnings, statisticians are a natural fit for the delivery of these skills. Unfortunately, there is a lack of common understanding of what skills and competencies are needed. This article has two primary objectives: determine how business analytics course offerings delineate course content (i.e., where statistical and analytical topics are currently offered in business disciplines); and determine the industry analytical needs of various business domains. We performed two phases of analysis. First, we collected course listings and descriptions from the top thirty undergraduate and graduate business programs in the United States to determine what analytics courses are currently offered. Second, we collected industry data to determine what skills are needed across business disciplines based on job postings that identify analytics. Our results indicate there is some overlap of content between current courses. We also find that there are varying analytical skills and competencies needed in other business domains based on job postings. These results can help guide the structure of analytics offerings in business schools.

Big data analytics capabilities and leadership: catalysts of firm performance in telecommunications

PurposeThe proliferation of big data analytics (BDA)-enabled tools and technologies has endowed organizations with the capacity to augment decision-making processes, optimize operational endeavors and foster innovation across diverse business domains. Consequently, BDA has been posited as a catalyst for enhanced customer relationship management, improved risk mitigation strategies and heightened operational efficiencies, all of which converge to augment overall firm performance. Thus, the purpose of this research is to introduce a conceptual framework aimed at explaining the influence of BDA capabilities on the performance of telecommunications firms in Pakistan. Additionally, it examines the potential mediating effect of talent capabilities and moderating effect of top management attitude on firm performance.Design/methodology/approachData from a sample comprising 520 participants were collected via survey questionnaires. The study employed Partial Least Squares-Structural Equation Modeling to empirically evaluate the proposed model.FindingsResults reveal a positive association between BDA technology and information capabilities with both BDA talent capabilities and firm performance. Furthermore, the analysis suggests that BDA talent capabilities mediate the relationship between BDA dynamic capabilities and firm performance, while top management attitude acts as a moderator, enhancing the relationship between BDA talent capabilities and firm performance.Originality/valueThere is a scarcity of research that has examined the relationship of BDA capabilities, top management attitude and firm performance. This study attempts to examine their interrelationships. First, it enhances the extant literature by elucidating the mediating role of BDA talent capabilities in the relationship between BDA technology and information capabilities and firm performance. Second, the study introduces a novel dimension by incorporating top management attitude as a moderator variable. This augmentation adds layers of complexity to comprehending BDA implementation dynamics, emphasizing leadership’s role in fostering an enabling environment for effective utilization of BDA capabilities.

Personalized pricing decisions through adversarial risk analysis

AbstractPricing decisions stand out as one of the most critical tasks a company faces, particularly in today's digital economy. As with other business decision‐making problems, pricing unfolds in a highly competitive and uncertain environment. Traditional analyses in this area have heavily relied on game theory and its variants. However, an important drawback of these approaches is their reliance on common knowledge assumptions, which are hardly tenable in competitive business domains. This paper introduces an innovative personalized pricing framework designed to assist decision‐makers in undertaking pricing decisions amidst competition, considering both buyer's and competitors' preferences. Our approach (i) establishes a coherent framework for modeling competition mitigating common knowledge assumptions; (ii) proposes a principled method to forecast competitors' pricing and customers' purchasing decisions, acknowledging major business uncertainties; and (iii) encourages structured thinking about the competitors' problems, thus enriching the solution process. To illustrate these properties, in addition to a general pricing template, we outline two specifications—one from the retail domain and a more intricate one from the pension fund domain.

Intelligent automation computational modelling for contextual consulting services using Industry 4.0

<p>The methodology towards operational management and service delivery associated with consulting firms, irrespective of any business domain, are less revisited in perspective from the automation-based process management. Adoption of Industry 4.0 has been attempted by various researchers from the business process management; however, there are less evidence of any computational model towards it. Apart from this, existing models are accompanied by various loopholes which makes its further challenging to analyze it on practical environment. Hence, the proposed study introduces a novel computational and analytical framework which is capable of performing the predictive modelling in order to meet the contextual service development and delivery demands in distributed environment. The novelty of this model is its inclusion of contextual data aggregation, contextual constraint analysis, predictive maintenence, and self-adjusting machine which are core attributes of Industry 4.0 automation standards. The study outcome shows that proposed system offers 20% cost reduction and 89% of minimized service delivery time in contrast to existing related work. Same has been also observed in benchmarked outcomes with state-of-art models.</p>

The role of data science in transforming business operations: Case studies from enterprises

Data science has emerged as a pivotal force in transforming business operations across various industries, driving innovation, operational efficiency, and strategic decision-making. This review paper explores the multifaceted role of data science in business, examining key concepts, historical integration, and strategic advantages. It discusses the application of data science in diverse business domains, highlighting techniques such as predictive analytics, sentiment analysis, and optimization that have revolutionized marketing, supply chain management, finance, and customer service. The paper further analyzes data science's tangible and intangible benefits, including cost reduction, improved customer experience, and enhanced productivity, which collectively contribute to a competitive edge in the market. The paper reflects on emerging trends like artificial intelligence, machine learning, ethical data practices, and the integration of blockchain and IoT, which are set to shape the future of data-driven business operations. It offers recommendations for businesses to prepare for this evolving landscape, emphasizing the importance of a data-centric culture, robust infrastructure, and collaboration. The paper concludes by underscoring the critical role of data science in fostering sustained business success in an increasingly data-driven world. Keywords: Data Science, Business Transformation, Predictive Analytics, Operational Efficiency, Artificial Intelligence, Strategic Decision-Making.

Language as a leading light to business cultural insight: A study on expatriates' intercultural communication in central and eastern Europe

The main objective of this research is to examine the complex function of language in promoting intercultural communication within the corporate environment of Central and Eastern Europe (CEE). The process of collecting qualitative data typically include conducting semi-structured interviews and organizing focus groups. The collection of quantitative was conducted by means of online surveys that were distributed through professional networks and business groups in the CEE region. The thorough examination of language, culture, and their impact on business contacts in the CEE region highlights their crucial significance in establishing prosperous economic undertakings. The research emphasized the notable influence of linguistic proficiency and cultural adaptability on the achievement of negotiations, market expansion, and the establishment of relationships in CEE business environments. Additionally, the presence of language hurdles was apparent, impeding the ability to communicate effectively and achieve integration within the business domain. Nevertheless, these obstacles also offered prospects for enhanced cooperation and market growth by strategically harnessing language proficiency and cultural comprehension.