In this paper, we extend Busy Beaver function to a class of higher order Busy Beaver functions based on Turing oracle machine. We prove some results about the relation between decidability of number theoretical formula and higher order Busy Beaver functions, and the relation between computability of max-min partial recursive functions and higher order Busy Beaver functions. We also present some conjectures on higher order Busy Beaver functions.
Enterprise Resource Planning (ERP) systems serve as the digital backbone of modern financial institutions, yet they continue to rely on static, rule-based workflows that limit adaptability, scalability, and intelligence. As business operations grow more complex and data-rich, conventional ERP platforms struggle to integrate structured and unstructured data in real time and to accommodate dynamic, cross-functional workflows. In this paper, we present the first AI-native, agent-based framework for ERP systems, introducing a novel architecture of Generative Business Process AI Agents (GBPAs) that bring autonomy, reasoning, and dynamic optimization to enterprise workflows. The proposed system integrates generative AI with business process modeling and multi-agent orchestration, enabling end-to-end automation of complex tasks such as budget planning, financial reporting, and wire transfer processing. Unlike traditional workflow engines, GBPAs interpret user intent, synthesize workflows in real time, and coordinate specialized sub-agents for modular task execution. We validate the framework through case studies in bank wire transfers and employee reimbursements, two representative financial workflows with distinct complexity and data modalities. Results show that GBPAs achieve up to 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance by enabling parallelism, risk control insertion, and semantic reasoning. These findings highlight the potential of GBPAs to bridge the gap between generative AI capabilities and enterprise-grade automation, laying the groundwork for the next generation of intelligent ERP systems.
Given the busy period and busy cycle major importance in queuing systems, it is crucial the knowledge of the respective distribution functions that is what allows the calculation of the important probabilities. For the M|G|$\infty$ queue system, there are no round form formulae for those distribution functions. But, for the M|D|$\infty$ queue, due the fact that its busy period and busy cycle have both Laplace transform expression round forms, what does not happen for any other M|G|$\infty$ queue system, with an algorithm created by Platzman, Ammons and Bartholdi III, that allows the tail probabilities computation since the correspondent Laplace transform in round form is known, those distribution functions calculations are possible. Here, we will implement the algorithm through a FORTRAN program.
This article presents a curated collection of eight teaching innovations presented at the Association for Business Communication 89th conference Tulsa, Oklahoma, as well as online, in October 2024. These MFA presenters demonstrated teaching ideas specifically on improving students’ writing skills, and this My Favorite Assignment 32nd edition introduces readers to these classroom approaches in teaching business writing. Teaching support materials—instructions to students, stimulus materials, slides, rubrics, frequently asked questions, links, and sample student projects—are downloadable from the Association for Business Communication website.
The confrontation of the laws and languages of different legal systems that occurs in the process of legal translation has naturally inspired interest in comparative law among translation scholars. However, while references to comparative law in legal translation literature are abundant, they tend to be somewhat superfluous and selective, focusing mainly on the traditional functional method and saying little about how exactly legal translators can use comparative law in their practice. Nor is there much in-depth theoretical discussion of how both fields relate. Hence, the present paper aims to discuss the various approaches to comparative law and its role in legal translation adopted by legal translation scholars and to juxtapose them with a comparative account of the goals and processes of legal translation and comparative law. Taking a closer look at the oft-repeated statement that legal translation is an exercise in/of comparative law, the author demonstrates that, despite its rhetorical value, it actually misrepresents both fields. The results of the present research lead to the conclusion that, while comparative law and legal translation are clearly related and potentially useful for each other, the mutual recognition of autonomy could improve understanding between comparatists and legal translation professionals and allow them to learn more from each other.
Business communication. Including business report writing, business correspondence
Extracting key information from documents represents a large portion of business workloads and therefore offers a high potential for efficiency improvements and process automation. With recent advances in Deep Learning, a plethora of Deep Learning based approaches for Key Information Extraction have been proposed under the umbrella term Document Understanding that enable the processing of complex business documents. The goal of this systematic literature review is an in-depth analysis of existing approaches in this domain and the identification of opportunities for further research. To this end, 130 approaches published between 2017 and 2024 are analyzed in this study.
To examine the relation between profitability and business models (BMs) across bank sizes, the paper proposes a research strategy based on machine learning techniques. This strategy allows for analyzing whether size and profit performance underlie BM heterogeneity, with BM identification being based on how the components of the bank portfolio contribute to profitability. The empirical exercise focuses on the European Union banking system. Our results suggest that banks with analogous levels of performance and different sizes share strategic features. Additionally, high capital ratios seem compatible with high profitability if banks, relative to their size peers, adopt a standard retail BM.
In this letter we present a stochastic dynamic model which can explain economic cycles. We show that the macroscopic description yields a complex dynamical landscape consisting of multiple stable fixed points, each corresponding to a split of the population into a large low and a small high income group. The stochastic fluctuations induce switching between the resulting metastable states, and excitation oscillations just below a deterministic bifurcation. The shocks are caused by the decisions of a few agents who have a disproportionate influence over the macroscopic state of the economy due to the unequal distribution of wealth among the population. The fluctuations have a long-term effect on the growth of economic output and lead to business cycle oscillations exhibiting coherence resonance, where the correlation time is controlled by the population size which is inversely proportional to the noise intensity.
Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions -- all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. Thus, we provide a fundamental contribution for making business networks more effective, as we remove a key obstacle to tap the huge potential of learning from data that is scattered throughout the network.
Media hype and technological breakthroughs are fuelling the race to adopt Artificial Intelligence amongst the business community, but is there evidence to suggest this will increase productivity? This paper uses 2015-2019 microdata from the UK Office for National Statistics to identify if the adoption of Artificial Intelligence techniques increases labour productivity in UK businesses. Using fixed effects estimation (Within Group) with a log-linear regression specification the paper concludes that there is no statistically significant impact of AI adoption on labour productivity.
Cristiano André da Costa, Uélison Jean Lopes dos Santos, Eduardo Souza dos Reis
et al.
In this article, we provide an overview of the latest intelligent techniques used for processing business rules. We have conducted a comprehensive survey of the relevant literature on robot process automation, with a specific focus on machine learning and other intelligent approaches. Additionally, we have examined the top vendors in the market and their leading solutions to tackle this issue.
Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of interpretability, which limits their adoption in practice. We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models. Existing solutions focus on giving factual explanations.While factual explanations can be helpful, humans typically do not ask why a particular prediction was made, but rather why it was made instead of another prediction, i.e., humans are interested in counterfactual explanations. While research in explainable AI produced several promising techniques to generate counterfactual explanations, directly applying them to predictive process monitoring may deliver unrealistic explanations, because they ignore the underlying process constraints. We propose LORELEY, a counterfactual explanation technique for predictive process monitoring, which extends LORE, a recent explainable AI technique. We impose control flow constraints to the explanation generation process to ensure realistic counterfactual explanations. Moreover, we extend LORE to enable explaining multi-class classification models. Experimental results using a real, public dataset indicate that LORELEY can approximate the prediction models with an average fidelity of 97.69\% and generate realistic counterfactual explanations.
Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky
The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or short-sighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA.
This study analyses the languages used by companies in their telematic means (website, social media, etc.) in a specific geographical area, the Valencian Community (Spain), where two official languages (Catalan and Spanish) coexist and where there is a large influx of foreign tourists (mainly British). More specifically, the aim of the study is to understand the weight each language has in companies’ telematic communication. The theoretical approach used for the analysis is the centre-periphery model, which is used to analyse national identity and language. Different techniques (use of secondary sources, mystery shopping, content analysis and direct observation) have been used in the quantitative empirical study to obtain a statistically disaggregated data matrix. The results strongly emphasize the peripheral and marginal position of Catalan in this region, and, on an international scale, the resistance of Spanish, which clearly maintains its hegemonic position over English in the telematic communications of these companies.
Business communication. Including business report writing, business correspondence
This paper outlines the pragmaterminological approach of terms in companies and organizations as an efficient way to study situated, dynamic elements conveying sense and meaning for knowledge and communication purposes in the workplace, and making up what we will call company-speak. Broadly speaking, we will define company-speak as the specific sociolect used in a specific company or organization to work and do business and reflecting the ongoing construction of its own knowledge, corporate culture, and identity. Company-speak is truly unique and every single company or organization will develop its own company-speak that competing companies or organizations operating in the same sector or branch of activity cannot and will not use. Particularly, the pragmaterminological approach aims at answering the question of what exactly has to be known to work at micro-level in work communities, and how knowledge must be shared to cope with knowledge asymmetries and ensure cooperation between experts within the company or organization.
Business communication. Including business report writing, business correspondence
Giovanni Quattrone, Antonino Nocera, Licia Capra
et al.
Airbnb is one of the most successful examples of sharing economy marketplaces. With rapid and global market penetration, understanding its attractiveness and evolving growth opportunities is key to plan business decision making. There is an ongoing debate, for example, about whether Airbnb is a hospitality service that fosters social exchanges between hosts and guests, as the sharing economy manifesto originally stated, or whether it is (or is evolving into being) a purely business transaction platform, the way hotels have traditionally operated. To answer these questions, we propose a novel market analysis approach that exploits customers' reviews. Key to the approach is a method that combines thematic analysis and machine learning to inductively develop a custom dictionary for guests' reviews. Based on this dictionary, we then use quantitative linguistic analysis on a corpus of 3.2 million reviews collected in 6 different cities, and illustrate how to answer a variety of market research questions, at fine levels of temporal, thematic, user and spatial granularity, such as (i) how the business vs social dichotomy is evolving over the years, (ii) what exact words within such top-level categories are evolving, (iii) whether such trends vary across different user segments and (iv) in different neighbourhoods.
The sudden onset of the coronavirus (SARS-CoV-2) pandemic has resulted in tremendous loss of human life and economy in more than 210 countries and territories around the world. While self-protections such as wearing mask, sheltering in place and quarantine polices and strategies are necessary for containing virus transmission, tens of millions people in the U.S. have lost their jobs due to the shutdown of businesses. Therefore, how to reopen the economy safely while the virus is still circulating in population has become a problem of significant concern and importance to elected leaders and business executives. In this study, mathematical modeling is employed to quantify the profit generation and the infection risk simultaneously from the point of view of a business entity. Specifically, an ordinary differential equation model was developed to characterize disease transmission and infection risk. An algebraic equation is proposed to determine the net profit that a business entity can generate after reopening and take into account the costs associated of several protection/quarantine guidelines. All model parameters were calibrated based on various data and information sources. Sensitivity analyses and case studies were performed to illustrate the use of the model in practice.
Many companies consider IoT as a central element for increasing competitiveness. Despite the growing number of cyberattacks on IoT devices and the importance of IoT security, no study has yet primarily focused on the impact of IoT security measures on the security challenges. This paper presents a review of the current state of security of IoT in companies that produce IoT products and have begun a transformation towards the digitalization of their products and the associated production processes. The analysis of challenges in IoT security was conducted based on the review of resources and reports on IoT security, while mapping the relevant solutions/measures for strengthening security to the existing challenges. This mapping assists stakeholders in understanding the IoT security initiatives regarding their business needs and issues. Based on the analysis, we conclude that almost all companies have an understanding of basic security measures as encryption, but do not understand threat surface and not aware of advanced methods of protecting data and devices. The analysis shows that most companies do not have internal experts in IoT security and prefer to outsource security operations to security providers.
In this paper, elements of the analysis of the concept of cost that transcend the limits traditionally found between the economic and accounting disciplines are offered. The problem of the weakness of the concept of cost and the need to clarify it and expand its scope in the perspective of current pedagogical, economic and financial needs is evident. Experience and tradition have led to many notions and concepts hibernate without clarity, breadth and accuracy adequate to be understood, learned and taught. One of those concepts is cost. Everything indicates that this concept lacks an adequate theoretical structure capable of constituting a conceptual framework that supports the uses, the daily practices and the multiplicity of applications that can be derived from it.Product of a bibliographic exploration, this research focuses on the interpre-tation of notions, concepts and definitions present in the literature, to provide a conceptual precision that avoids misunderstandings in the use of language and promotes a conceptual and not only functional learning
Business communication. Including business report writing, business correspondence