Background Artificial intelligence (AI) has many applications in various aspects of our daily life, including health, criminal, education, civil, business, and liability law. One aspect of AI that has gained significant attention is natural language processing (NLP), which refers to the ability of computers to understand and generate human language. Objective This study aims to examine the potential for, and concerns of, using AI in scientific research. For this purpose, high-impact research articles were generated by analyzing the quality of reports generated by ChatGPT and assessing the application’s impact on the research framework, data analysis, and the literature review. The study also explored concerns around ownership and the integrity of research when using AI-generated text. Methods A total of 4 articles were generated using ChatGPT, and thereafter evaluated by 23 reviewers. The researchers developed an evaluation form to assess the quality of the articles generated. Additionally, 50 abstracts were generated using ChatGPT and their quality was evaluated. The data were subjected to ANOVA and thematic analysis to analyze the qualitative data provided by the reviewers. Results When using detailed prompts and providing the context of the study, ChatGPT would generate high-quality research that could be published in high-impact journals. However, ChatGPT had a minor impact on developing the research framework and data analysis. The primary area needing improvement was the development of the literature review. Moreover, reviewers expressed concerns around ownership and the integrity of the research when using AI-generated text. Nonetheless, ChatGPT has a strong potential to increase human productivity in research and can be used in academic writing. Conclusions AI-generated text has the potential to improve the quality of high-impact research articles. The findings of this study suggest that decision makers and researchers should focus more on the methodology part of the research, which includes research design, developing research tools, and analyzing data in depth, to draw strong theoretical and practical implications, thereby establishing a revolution in scientific research in the era of AI. The practical implications of this study can be used in different fields such as medical education to deliver materials to develop the basic competencies for both medicine students and faculty members.
Business plan (BP) writing plays a key role in entrepreneurship education by helping learners construct, evaluate, and iteratively refine their ideas. However, conventional BP writing remains a rigid, linear process that often fails to reflect the dynamic and recursive nature of entrepreneurial ideation. This mismatch is particularly challenging for novice entrepreneurial students, who struggle with the substantial cognitive demands of developing and refining ideas. While reflection and meta-reflection are critical strategies for fostering divergent and convergent thinking, existing writing tools rarely scaffold these higher-order processes. To address this gap, we present the Meflex System, a large language model (LLM)-based writing tool that integrates BP writing scaffolding with a nonlinear idea canvas to support iterative ideation through reflection and meta-reflection. We report findings from an exploratory user study with 30 participants that examined the system's usability and cognitive impact. Results show that Meflex effectively scaffolds BP writing, promotes divergent thinking through LLM-supported reflection, and enhances meta-reflective awareness while reducing cognitive load during complex idea development. These findings highlight the potential of non-linear LLM-based writing tools to foster deeper and coherent entrepreneurial thinking.
According to a report by the Spanish Ministry of Agriculture, Fishing and Nutrition (2022), olive oil accounted for a total turnover of 148.39 million euros in 2021, making olive oil Spain’s fifth agricultural product in terms of sales volume. Approximately one third of all production is exported, Germany leading European exports with 258 tons (t) of olive oil. Product descriptions must be translated for foreign markets, and in this case, particular attention must be paid to the terms used to describe olive oil quality, notably the geographical indications and olive oil categories. Terminological research (Ibañez Rodríguez, 2003) is thus a key dimension of translation here: the selected words must be recognisable by potential foreign customers, and they must be in line with the terminology established by the European Commission. In this study, we worked on a corpus of websites of Andalusian small and medium-sized enterprises (SMEs) in both German and Spanish. These SMEs all actively produced and sold olive oil and had an EU geographical indication register. In order to analyse how geographical indications and olive oil categories were translated from Spanish into German and to determine whether EU terminology was applied, we used the Sketch Engine programme. We found a wide range of translations which were potentially creating misunderstandings and raising doubts in the minds of German consumers.
Business communication. Including business report writing, business correspondence
In large-scale software development, understanding the functionality and intent behind complex codebases is critical for effective development and maintenance. While code summarization has been widely studied, existing methods primarily focus on smaller code units, such as functions, and struggle with larger code artifacts like files and packages. Additionally, current summarization models tend to emphasize low-level implementation details, often overlooking the domain and business context that are crucial for real-world applications. This paper proposes a two-step hierarchical approach for repository-level code summarization, tailored to business applications. First, smaller code units such as functions and variables are identified using syntax analysis and summarized with local LLMs. These summaries are then aggregated to generate higher-level file and package summaries. To ensure the summaries are grounded in business context, we design custom prompts that capture the intended purpose of code artifacts based on the domain and problem context of the business application. We evaluate our approach on a business support system (BSS) for the telecommunications domain, showing that syntax analysis-based hierarchical summarization improves coverage, while business-context grounding enhances the relevance of the generated summaries.
Business communication digitisation has reorganised the process of persuasive discourse, which allows not only greater transparency but also advanced deception. This inquiry synthesises classical rhetoric and communication psychology with linguistic theory and empirical studies in the financial reporting, sustainability discourse, and digital marketing to explain how deceptive language can be systematically detected using persuasive lexicon. In controlled settings, detection accuracies of greater than 99% were achieved by using computational textual analysis as well as personalised transformer models. However, reproducing this performance in multilingual settings is also problematic and, to a large extent, this is because it is not easy to find sufficient data, and because few multilingual text-processing infrastructures are in place. This evidence shows that there has been an increasing gap between the theoretical representations of communication and those empirically approximated, and therefore, there is a need to have strong automatic text-identification systems where AI-based discourse is becoming more realistic in communicating with humans.
The article highlights the main methodological aspects of the security principles for the formation of a motivation system for employees in the construction industry during a crisis. The aim of the article is to survay the theoretical and applied security foundations of the motivation system for employees in the construction industry under crisis conditions and to formulate proposals for its harmonious development, adaptation to modern conditions, and efficient improvement. The construction industry is one of the leading sectors in the economy of Ukraine. Current crisis conditions have led to an outflow of qualified staff, including from this sector, and the turnover rate is increasing. A key issue is the danger posed by the ongoing war. Another factor is the desire of staff to earn more, thus, without moral and/or material motivation from management, highly qualified employees change jobs, start their own businesses, emigrate, etc. The methodological basis of the article is the approaches to studying the state of the construction industry in Ukraine and identifying factors that will contribute to the development of the industry, based on the processing of statistical data from the State Statistics Service of Ukraine and analytical reports from expert agencies. The use of system approach along with the application of quantitative and descriptive methods allowed the authors to analyze the structure and dynamics of the volumes of produced goods, completed works, and provided services in the construction industry. The dynamics of the development of the domestic construction industry have been analyzed. Proposals for the efficient development of the construction industry and the retention of qualified workers have been provided. The presented article emphasizes the necessity of a systematic review of motivation systems, integrating the best foreign practices, adapting them to domestic economic conditions, stimulating the younger generation, and so on. A system for the formation and implementation of motivational tools in wartime conditions has been proposed. The implementation and development of the proposed motivational tools (ensuring physical and psychological safety, reassessing rewards, redistributing workloads, forming and applying a system of continuous open communication, etc.) shall contribute to maintaining a high level of employee productivity.
The emergency operations centre (EOC) is a critical emergency response and recovery component that provides information management and resource allocation. EOCs are often used during all hazards; however, after reviewing over 25 after-action reports for active shooter incidents, they are frequently underutilised. Not activating or delaying activation can slow recovery efforts and lead to chaos for the first responders and the public due to a lack of situational awareness. Historical active shooter incidents, such as the San Bernardino attack, Uvalde school shooting and Aurora theatre shooting, highlight both the challenges and successes of EOC activations. Positive examples, including the Los Angeles International Airport (LAX) shooting and Pulse Nightclub attack, demonstrate how timely EOC activation improved resource coordination, victim services and public communications. A specialised active assailant checklist for EOC operations has remained largely absent even though the incidents pose a complex threat. The City of Murrieta and the City of Temecula worked to fill that void. They developed an 'Active Shooter EOC Checklist', informed by lessons learned from previous mass shootings and resources such as the 'United on Guns' protocol. The checklist guides the agencies through emergency operations, ensuring public communication, victim assistance, volunteer and donation management, recovery and other critical functions are not missed. This paper describes how EOC utilisation can streamline response operations, reduce fatalities and support community recovery efforts. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Antonis Papasavva, Shane D Johnson, Ed Lowther
et al.
Fraud is a prevalent offence that extends beyond financial loss, impacting victims emotionally, psychologically, and physically. Advances in online communication technologies continue to create new opportunities for fraud, and fraudsters increasingly using these channels for deception. With the progression of technologies like Generative Artificial Intelligence (GenAI), there is a growing concern that fraud will increase in scale using these advanced methods, with offenders employing deep-fakes in phishing campaigns, for example. However, the application of AI, particularly Natural Language Processing (NLP), to detect and analyse patterns of online fraud remains understudied. This review addresses this gap by investigating the potential role of AI in analysing online fraud using text data. We conducted a Systematic Literature Review (SLR) to investigate the application of AI and Natural Language Processing (NLP) techniques for online fraud detection. The review adhered to the PRISMA-ScR protocol, with eligibility criteria including language, publication type, relevance to online fraud, use of text data, and AI methodologies. Out of 2457 academic records screened, 350 met our eligibility criteria, and 223 were analysed and included herein. We discuss the state-of-the-art AI and NLP techniques used to analyse various online fraud categories; the data sources used for training the AI and NLP models; the AI and NLP algorithms and models built; and the performance metrics employed for model evaluation. We find that the current state of research on online fraud is broken into the various scam activities that take place, and more specifically, we identify 16 different frauds that researchers focus on. Finally, we present the most recent and best-performing AI methods employed for detecting online scams and fraud activities. This SLR enhances academic understanding of AI-based detection methods for online fraud and offers insights for policymakers, law enforcement, and businesses on safeguarding against such activities. We conclude that existing approaches focusing on specific scams are unlikely to generalise effectively, as they will require new models to be developed for each fraud type. Furthermore, we conclude that the evolving nature of scams limits the effectiveness of models trained on outdated data. We also identify that researchers often omit discussions of the limitations of their data or training biases. Finally, we find issues in the consistency with which the performance of models is reported, with some studies selectively presenting metrics, leading to potential biases in model evaluation.
Purpose: Writing has been identified as an important skill. Business writing refers to the form of writing that is used to communicate in formal settings in various corporations and organizations. A number of research studies have identified writing as a crucial skill that needs to be developed by students. The purpose of the study is therefore to understand how an experiential learning module on business writing can improve the email-writing and report-writing skills of management postgraduates. Design/Methodology/Approach: The study uses an experimental research methodology based on experiential learning pedagogy to obtain the results of the intervention on the business writing skills of the management postgraduate students. The module was developed by the researcher and then was taught to the students through the online platform Zoom. Pretest, posttest, and delayed posttest analysis was conducted to find the impact of the intervention. The students were evaluated by an industry expert to avoid bias as they were trained by the researcher. Findings: The results of the study indicated that the intervention had a significant impact on the business writing skills of the participants. The results of the component analysis also indicated a large effect on the content, persuasive abilities, lateral thinking abilities, and the interpersonal skills of the participants in written communication. The analysis of the test scores revealed that an initial training based on the experiential learning methods can have a long-term impact on the improvement of the skills of the students, as the delayed posttest results were more than the posttest results. Originality/value: The study will be beneficial to educators, trainers, as well as students in understanding how experiential learning can impact the business writing skills of the students.
Throughout history, entrepreneurial contributions of women have been largely disregarded and undervalued in the business sector. However, recent decades have witnessed increased visibility of women entrepreneurs striving to challenge gender-based inequities and systemic biases in the workplace. Those who have achieved success are increasingly willing to share their experiences and insights, providing guidance to aspiring women seeking to establish, develop, or advance their enterprises and careers. One notable channel to convey their messages is through women entrepreneurs’ blogs. This paper explores how female entrepreneurs perceive themselves as businesswomen and examines how they construct the position of women in the current business realm. Using a Corpus-Assisted Discourse Analysis approach, a detailed examination of concordances related to selected keywords and key expressions associated with the perception of women entrepreneurs, along with business and societal challenges, unveils typical collocations and contextual usage. The analysis is based on the Women Entrepreneurs’ Blog Corpus, a dataset comprising 329,896 words from 318 unique blog entries in English published between 2019 and 2023. The findings reveal that blogs serve as empowering platforms where women challenge traditional narratives, reshape perceptions of entrepreneurship, and redefine their roles in the business world. The usage of frequently occurring keywords such as female, women, and mompreneur(s), emphasises themes of resilience, motivation, and identity alongside issues like inferiority, lack of confidence, and systemic disadvantages, particularly for women of color. Moreover, keywords like imposter syndrome, self-doubt, glass ceiling, and male-dominated often appear in contexts that underscore overcoming obstacles and the benefits of female entrepreneurship.
Business communication. Including business report writing, business correspondence
Jiale Liu, Yifan Zeng, Malte Højmark-Bertelsen
et al.
Traditional enterprises face significant challenges in processing business documents, where tasks like extracting transport references from invoices remain largely manual despite their crucial role in logistics operations. While Large Language Models offer potential automation, their direct application to specialized business domains often yields unsatisfactory results. We introduce Matrix (Memory-Augmented agent Training through Reasoning and Iterative eXploration), a novel paradigm that enables LLM agents to progressively build domain expertise through experience-driven memory refinement and iterative learning. To validate this approach, we collaborate with one of the world's largest logistics companies to create a dataset of Universal Business Language format invoice documents, focusing on the task of transport reference extraction. Experiments demonstrate that Matrix outperforms prompting a single LLM by 30.3%, vanilla LLM agent by 35.2%. We further analyze the metrics of the optimized systems and observe that the agent system requires less API calls, fewer costs and can analyze longer documents on average. Our methods establish a new approach to transform general-purpose LLMs into specialized business tools through systematic memory enhancement in document processing tasks.
In the field of business data analysis, the ability to extract actionable insights from vast and varied datasets is essential for informed decision-making and maintaining a competitive edge. Traditional rule-based systems, while reliable, often fall short when faced with the complexity and dynamism of modern business data. Conversely, Artificial Intelligence (AI) models, particularly Large Language Models (LLMs), offer significant potential in pattern recognition and predictive analytics but can lack the precision necessary for specific business applications. This paper explores the efficacy of hybrid approaches that integrate the robustness of rule-based systems with the adaptive power of LLMs in generating actionable business insights.
We design a novel, nonlinear single-source-of-error model for analysis of multiple business cycles. The model's specification is intended to capture key empirical characteristics of business cycle data by allowing for simultaneous cycles of different types and lengths, as well as time-variable amplitude and phase shift. The model is shown to feature relevant theoretical properties, including stationarity and pseudo-cyclical autocovariance function, and enables a decomposition of overall cyclic fluctuations into separate frequency-specific components. We develop a Bayesian framework for estimation and inference in the model, along with an MCMC procedure for posterior sampling, combining the Gibbs sampler and the Metropolis-Hastings algorithm, suitably adapted to address encountered numerical issues. Empirical results obtained from the model applied to the Polish GDP growth rates imply co-existence of two types of economic fluctuations: the investment and inventory cycles, and support the stochastic variability of the amplitude and phase shift, also capturing some business cycle asymmetries. Finally, the Bayesian framework enables a fully probabilistic inference on the business cycle clocks and dating, which seems the most relevant approach in view of economic uncertainties.
COVID-19 hits the global supply chains in a non-paradigm manner unfolding new and systemic complexity. Therefore, the unexpected and frequent disruptions forced the concern of preventing or creating supply chain resilience capabilities. This paper aims to provide theoretical and practical reflections on resilience in supply chains of essential goods during pandemics using a systems approach. Documental research was performed in order to characterize business practices in consulting reports and interviews with managers published in business communication media. Thus, a careful content analysis was carried out, including the coding and categorization of the leading practices indicated by these vehicles. We suggest categories of resilience factors as new concepts to face the new normal in the supply chains. These categories are Technology and People, Sourcing, Customer, Ecosystem, and Financial Assets. The systems approach consists of more qualified supply chain management stimulating several inputs and synchronized actions to sense and respond to the external environment dynamics.
The present-day business landscape necessitates novel methodologies that integrate intelligent technologies and tools capable of swiftly providing precise and dependable information for decision-making purposes. Contemporary society is characterized by vast amounts of accumulated data across various domains, which hold considerable potential for informing and guiding decision-making processes. However, these data are typically collected and stored by disparate and unrelated software systems, stored in diverse formats, and offer varying levels of accessibility and security. To address the challenges associated with processing such large volumes of data, organizations often rely on data analysts. Nonetheless, a significant hurdle in harnessing the benefits of accumulated data lies in the lack of direct communication between technical specialists, decision-makers, and business process analysts. To overcome this issue, the application of collaborative business intelligence (CBI) emerges as a viable solution. This research focuses on the applications of data mining and aims to model CBI processes within distributed virtual teams through the interaction of users and a CBI Virtual Assistant. The proposed virtual assistant for CBI endeavors to enhance data exploration accessibility for a wider range of users and streamline the time and effort required for data analysis. The key contributions of this study encompass: 1) a reference model representing collaborative BI, inspired by linguistic theory; 2) an approach that enables the transformation of user queries into executable commands, thereby facilitating their utilization within data exploration software; and 3) the primary workflow of a conversational agent designed for data analytics.
The world of business is constantly evolving and staying ahead of the curve requires a deep understanding of market trends and performance. This article addresses this requirement by modeling business trajectories using news articles data.
Cajetan Ihemebiri, Elochukwu Ukwandu, Lizzy Ofusori
et al.
As several countries were experiencing unprecedented economic slowdowns due to the outbreak of COVID-19 pandemic in early 2020, small business enterprises started adapting to digital technologies for business transactions. However, in Africa, particularly Nigeria, COVID-19 pandemic resulted to some financial crisis that impacted negatively on the sustainability of small and medium-sized (SMEs) businesses. Thus, this study examined the role of social media on selected SMEs in Nigeria in the heat of the COVID-19 pandemic that led to several lock downs in a bid to curtail the spread of the virus. Cross-sectional survey research design was used alongside convenience population sampling techniques. The population was categorised based on selected SMEs businesses, while a quantitative research approach was adopted, and primary data were collected using a questionnaire. The questionnaires were administered to owners and operators of SMEs in Ikotun and Ikeja areas of Lagos State, Nigeria. A total of 190 questionnaires were distributed, where 183 usable responses were analysed. The findings of the study show that SMEs were aware of the usefulness of social media to their businesses as they largely leveraged it in conducting their businesses during the national lockdowns. The study recommended that labour/trade unions should sensitise and encourage business owners on the benefits of continuous use of social media in carrying out their business transactions.
With the continuous development of business process management technology, the increasing business process models are usually owned by large enterprises. In large enterprises, different stakeholders may modify the same business process model. In order to better manage the changeability of processes, they adopt configurable business process models to manage process variants. However, the process variants will vary with the change in enterprise business demands. Therefore, it is necessary to explore the co-evolution of the process variants so as to effectively manage the business process family. To this end, a novel framework for co-evolution between business process variants through a configurable process model is proposed in this work. First, the mapping relationship between process variants and configurable models is standardized in this study. A series of change operations and change propagation operations between process variants and configurable models are further defined for achieving propagation. Then, an overall algorithm is proposed for achieving co-evolution of process variants. Next, a prototype is developed for managing change synchronization between process variants and configurable process models. Finally, the effectiveness and efficiency of our proposed process change propagation method are verified based on experiments on two business process datasets. The experimental results show that our approach implements the co-evolution of process variants with high accuracy and efficiency.
David-Florin Ciocodeică, R. Chivu, Ionuț-Claudiu Popa
et al.
The structural changes in the public communication space through the advent of the Internet and the further development of online commerce culminate today with the explosion of blockchain techniques and social networks. This communication space was quickly taken over by marketing tools, as demonstrated by the many marketing campaigns dedicated to these new communication channels. The development of online commerce and the emergence of social networks have allowed consumers to efficiently search for brands/products/services, compare them, express their point of view on them, and even give them grades. Due to the explosion of relevant data online, the changing business environment needs attention to interpret and extract relevant information. The application of sentiment analysis to public reaction in the online environment provides the researcher with how the authors of the analyzed texts (clients/beneficiaries) express themselves regarding the studied reference (product/service/organization/social theme and a feature of them). Along with the other metrics present in marketing, including digital marketing, the reports in the analysis panels of google analytics and social networks, sentiment analysis instantly provides the general and competitive context in which the product/service/theme evolves. In this article, two types of research have been conducted to highlight the benefits felt, but also the degree of knowledge, implementation, and use of sentiment analysis in online marketing analysis. One of the types of research was qualitative, carried out on 10 participants (specialists in the field of marketing), with the help of an interview guide. Qualitative research aims to find out the level of knowledge of sentiment analysis and the general degree of digitalization of Romanian companies, an indicator considered critical in the new post-pandemic business environment. The second research was quantitative and used to develop analysis by structural equations. For this, a questionnaire applied to a sample of 108 respondents was used. Through the analysis by structural equations, a conceptual model was developed that presents the main factors that are related to others and that contribute to the satisfaction of the users of the analysis of feelings for obtaining marketing data.