Illicit massage businesses (IMBs) masquerade as legitimate massage parlors while facilitating commercial sex and human trafficking. Law enforcement must identify these businesses within a dense population of lawful establishments, but investigative resources are limited and the illicit status of each location is unknown until inspection. Detection methods based on online reviews offer some insight, yet operators can manipulate these signals, leaving covert establishments undetected. IMBs constitute one of the largest segments of indoor sex trafficking in the United States, with an estimated 9,000 establishments. Mobility data offers an alternative to online signals, covering establishments that avoid digital visibility entirely. We derive features from mobility data spanning temporal visitation patterns, dwell times, visitor catchment areas, and demand stability. Because confirmed labels exist only for establishments identified through advertising platforms, we employ positive-unlabeled learning to address the label asymmetry in ground truth. The model achieves 0.97 AUC and 0.84 Average Precision. Four operational signatures characterize high-risk establishments: demand consistency, evening-concentrated visits, compressed service durations, and locally drawn clientele. The model produces risk scores for each business-week observation. Aggregating to the business level, prioritizing the highest-risk 10% of massage establishments captures 53% of known illicit operations, a 5.3-fold improvement over uninformed inspection. We develop a decision-support system that produces calibrated prioritization scores for law enforcement, enabling investigators to concentrate inspections on the highest-risk venues. The operational signatures may resist strategic manipulation because they reflect actual operations rather than online signals that operators can control.
Stephan Sandfuchs, Diako Farooghi, Janis Mohr
et al.
Artificial intelligence (AI) and Machine Learning (ML) have moved from research and pilot projects into everyday business operations, with generative AI accelerating adoption across processes, products, and services. This paper introduces the concept of Responsible AI for organizational practice, with a particular focus on small and medium-sized enterprises. It structures Responsible AI along four focal areas that are central for introducing and operating AI systems in a legally compliant, comprehensible, sustainable, and data-sovereign manner. First, it discusses the EU AI Act as a risk-based regulatory framework, including the distinction between provider and deployer roles and the resulting obligations such as risk assessment, documentation, transparency requirements, and AI literacy measures. Second, it addresses Explainable AI as a basis for transparency and trust, clarifying key notions such as transparency, interpretability, and explainability and summarizing practical approaches to make model behavior and decisions more understandable. Third, it covers Green AI, emphasizing that AI systems should be evaluated not only by performance but also by energy and resource consumption, and outlines levers such as model reuse, resource-efficient adaptation, continuous learning, model compression, and monitoring. Fourth, it examines local models (on-premise and edge) as an operating option that supports data protection, control, low latency, and strategic independence, including domain adaptation via fine-tuning and retrieval-augmented generation. The paper concludes with a consolidated set of next steps for establishing governance, documentation, secure operation, sustainability considerations, and an implementation roadmap.
This study examines whether the tariff policies delivered on promises to revitalize American manufacturing and create jobs. Using county-level business application data from 2018-2025, we analyze the relationship between tariff implementation and new business formation through linear regression analysis. Our findings reveal a statistically significant positive association between US tariffs on China and American business applications. However, when Chinese retaliatory tariffs are included in the analysis, their negative coefficient substantially exceeds the positive US tariff effect, suggesting that retaliatory measures largely offset the benefits of protectionist policies. Control variables including inflation rate, federal funds rate, and government spending show significant positive effects on business formation. These results indicate that while protectionist trade policies may stimulate domestic business formation, their effectiveness is significantly diminished by retaliatory responses from trading partners. The study provides evidence that unilateral tariff measures without diplomatic coordination produce limited net benefits, confirming that trade wars create scenarios where potential gains are neutralized by counteractions.
Previous research has shown that journal article quality ratings from the cloud based Large Language Model (LLM) families ChatGPT and Gemini and the medium sized open weights LLM Gemma3 27b correlate moderately with expert research quality scores. This article assesses whether other medium sized LLMs, smaller LLMs, and reasoning models have similar abilities. This is tested with Gemma3 variants, Llama4 Scout, Qwen3, Magistral Small and DeepSeek R1 on a dataset of 2,780 medical, health and life science papers in 6 fields, with two different gold standards, one novel. Few-shot and score averaging approaches are also evaluated. The results suggest that medium-sized LLMs have similar performance to ChatGPT 4o-mini and Gemini 2.0 Flash, but that 1b parameters may often, and 4b sometimes, be too few. Reasoning models did not have a clear advantage. Moreover, averaging scores from multiple identical queries seems to be a universally successful strategy, and there is weak evidence that few-shot prompts (four examples) tend to help. Overall, the results show, for the first time, that smaller LLMs >4b have a substantial capability to rate journal articles for research quality, especially if score averaging is used, but that reasoning does not give an advantage for this task; it is therefore not recommended because it is slow. The use of LLMs to support research evaluation is now more credible since multiple variants have a similar ability, including many that can be deployed offline in a secure environment without substantial computing resources.
Georg Goldenits, Philip Koenig, Sebastian Raubitzek
et al.
Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructure maintenance. At the same time, proprietary large language models (LLMs) have demonstrated strong performance in phishing-related classification tasks, but their operational costs and reliance on external providers limit their practical adoption in many business environments. This paper investigates the feasibility of small language models (SLMs) for detecting phishing websites using only their raw HTML code. A key advantage of these models is that they can be deployed on local infrastructure, providing organisations with greater control over data and operations. We systematically evaluate 15 commonly used Small Language Models (SLMs), ranging from 1 billion to 70 billion parameters, benchmarking their classification accuracy, computational requirements, and cost-efficiency. Our results highlight the trade-offs between detection performance and resource consumption, demonstrating that while SLMs underperform compared to state-of-the-art proprietary LLMs, they can still provide a viable and scalable alternative to external LLM services. By presenting a comparative analysis of costs and benefits, this work lays the foundation for future research on the adaptation, fine-tuning, and deployment of SLMs in phishing detection systems, aiming to balance security effectiveness and economic practicality.
In a rapidly evolving landscape marked by continuous change and complex challenges, effective cash management stands as a cornerstone for ensuring business sustainability and driving performance. To address these pressing demands, cash managersare increasingly turning to innovative financing solutions such as venture capital, green finance, crowdfunding, advanced services from Pan-African banks, and blockchain technology. These cutting-edge tools are pivotal in bolstering resilience against market volatility, ecological transitions, and the accelerating pace of technological change. The present article aims to examine how such innovative financial approaches can serve as strategic drivers, enabling businesses to transform challenges into opportunities. The analysis underscores that rethinking cash management through innovation is a critical pathway toboost the performance of Moroccan companies. Therefore, embracing these forward-thinking strategies unlocks new avenues for development empowering them to adapt with agility amidst the uncertainties of a shifting environment.
Small and medium-sized businesses are widely recognised as the cornerstone of growth in emerging and middle-class countries, and South Africa is no different. They generate many job possibilities and salaries for many individuals who live in cities, making them an essential component of average national production. The main objective of the research was to examine the level to which Small and Medium-sized businesses in the eThekwini Municipality used financial management. The research project used a quantitative method to collect and analyse data from the field. The survey included 60 participants from various small and medium-sized businesses in eThekwini. Proprietors and managers of small and medium-sized businesses were among those who responded. The rate at which organisations implement systems that effectively organise their money was a crucial component in analysing the financial management engagements of small and medium-sized businesses. This can be assessed in various ways, including distributing closed-ended surveys to responders. As a result, the proprietors of small and medium-sized businesses were prompted to score their companies based on the level to which financial preparation strategies were implemented and the funding sources. The research found that small and medium-sized businesses in the research areas did not develop long-term financial strategies that included investments in non-current assets, shares, stocks, and real estate initiatives. Small and medium-sized businesses must establish trustworthy systems for accounting information, disclose and analyse their financial challenges regularly, and advocate for laws that make it simpler for small and medium-sized businesses to obtain inexpensive financing. Since small and medium-sized businesses are recognised as contributing significantly to national economic growth, they must keep growing.
Purpose of the article. To explore the role of digital marketing outsourcing as an effective tool for cost optimization, enhancing competitiveness, and achieving strategic goals for small and medium-sized enterprises (SMEs). Special attention is given to analyzing the results of a survey of entrepreneurs who have experience using this strategy. Design/Methodology/Approach. The study is exploratory in nature and is based on general scientific methods such as description, comparison, analysis, and specification. A comparative approach was employed to identify key aspects of the digital marketing outsourcing market, including popular areas of transferring functions to external management. Additionally, modern management approaches, such as Agile, Lean, and Data-driven marketing, were analyzed, highlighting their role in ensuring the efficiency of this strategy in the context of global competition. The empirical part of the study is based on a survey of 20 SME entrepreneurs. Research findings. It was found that SME owners believe outsourcing marketing functions (as non-core activities) effectively contributes to cost optimization, provides access to expert knowledge, and improves the achievement of strategic goals. Theoretical value of the research. The study confirmed the feasibility of using digital marketing outsourcing as an effective driver of development within modern management theory. Practical value of the research. Enterprise top managers can use the results of the scenario analysis of outsourcing decision-making to transfer specific functions to external management, achieving scalability, increased efficiency, and competitive advantages in the long term. Originality/Value of the research. The research results include substantiated recommendations on the effectiveness of digital marketing outsourcing in the context of technological progress and a dynamic market environment. Research limitations/Future research. The main conclusions of the study are focused on companies already utilizing marketing function outsourcing. This limits the generalization of the results to companies only planning to adopt such a strategy. Future research could focus on analyzing the integration of artificial intelligence technologies, process automation, and the use of Big Data to enhance the efficiency of outsourcing. Additionally, examining the impact of outsourcing in various economic sectors, such as manufacturing, services, or e-commerce, offers promising opportunities for developing industry-specific recommendations. JEL Classification: M31, M15, L24, O33
The article analyzes the formation and realization of Ukrainian small businesses' potential to increase the labor paid. Based on the results of a comparative analysis of the ratio of productivity growth and real wages for small, medium and large enterprises, as well as taking into account the trends in the share of labor costs in the value added generated by small, medium and large enterprises, we have obtained empirical evidence that if the current trends continue, the redistribution of the total number of employees in favor of small and, in particular, microenterprises will harm the economic conditions for the growth of labor paid and wider involvement the employees to the distribution of benefits, generated by business activity. This may exacerbate the problem of the "forced", exclusively regulatory nature of the enterprises’ motivation to participate in human capital development programs: a problem when the investment of enterprises in increasing the human capital of employees is extremely limited and the willingness to bear additional costs for the preservation of human potential in times of war is determined only by legal requirements or special incentive measures of the government. Such a leading role of state coercion and a subordinate role of commercial motivation significantly reduces the likelihood of the Ukrainian economy approaching a sustainable development trajectory. In addition, it is proven that the traditional grouping of small and medium-sized enterprises in modern economics does not correspond to the trends in the dynamics of productivity, real wages and their share in value added observed in relation to small and medium-sized enterprises in Ukraine.
Juan David Cortes, Jonathan E. Jackson, Andres Felipe Cortes
Purpose – Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts. Design/methodology/approach – The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production. Findings – The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product. Originality/value – The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.
Small and medium-sized businesses, artisans, handicrafts, trades, Business
Jan von der Assen, Muriel F. Franco, Muyao Dong
et al.
Threat modeling has emerged as a key process for understanding relevant threats within businesses. However, understanding the importance of threat events is rarely driven by the business incorporating the system. Furthermore, prioritization of threat events often occurs based on abstract and qualitative scoring. While such scores enable prioritization, they do not allow the results to be easily interpreted by decision-makers. This can hinder downstream activities, such as discussing security investments and a security control's economic applicability. This article introduces QuantTM, an approach that incorporates views from operational and strategic business representatives to collect threat information during the threat modeling process to measure potential financial loss incurred by a specific threat event. It empowers the analysis of threats' impacts and the applicability of security controls, thus supporting the threat analysis and prioritization from an economic perspective. QuantTM comprises an overarching process for data collection and aggregation and a method for business impact analysis. The performance and feasibility of the QuantTM approach are demonstrated in a real-world case study conducted in a Swiss SME to analyze the impacts of threats and economic benefits of security controls. Secondly, it is shown that employing business impact analysis is feasible and that the supporting prototype exhibits great usability.
A combinatorial trade is a pair of sets of blocks of elements that can be exchanged while preserving relevant subset intersection constraints. The class of balanced and swap-robust minimal trades was proposed in [1] for exchanging blocks of data chunks stored on distributed storage systems in an access- and load-balanced manner. More precisely, data chunks in the trades of interest are labeled by popularity ranks and the blocks are required to have both balanced overall popularity and stability properties with respect to swaps in chunk popularities. The original construction of such trades relied on computer search and paired balanced sets obtained through iterative combining of smaller sets that have provable stability guarantees. To reduce the substantial gap between the results of prior approaches and the known theoretical lower bound, we present new analytical upper and lower bounds on the minimal disbalance of blocks introduced by limited-magnitude popularity ranking swaps. Our constructive and near-optimal approach relies on pairs of graphs whose vertices are two balanced sets with edges/arcs that capture the balance and potential balance changes induced by limited-magnitude popularity swaps. In particular, we show that if we start with carefully selected balanced trades and limit the magnitude of rank swaps to one, the new upper and lower bound on the maximum block disbalance caused by a swap only differ by a factor of $1.07$. We also extend these results for larger popularity swap magnitudes.
In order to theoretically understand dust properties in the circum-galactic medium (CGM), we construct a dust evolution model that incorporates the evolution of grain size distribution. We treat each of the galaxy and the CGM as a one-zone object, and consider the mass exchange between them. We take into account dust production and interstellar dust processing for the galaxy based on our previous models, and newly incorporate sputtering in the hot phase and shattering in the cool phase for the CGM. We find that shattering increases the dust destruction (sputtering) efficiency in the CGM. The functional shape of the grain size distribution in the CGM evolves following that in the galaxy, but it is sensitive to the balance between sputtering and shattering in the CGM. For an observational test, we discuss the wavelength dependence of the reddening in the CGM traced by background quasar colors, arguing that, in order to explain the observed reddening level, a rapid inflow from the CGM to the galaxy is favored because of quick dust/metal enrichment. Small grain production by shattering in the CGM also helps to explain the rise of dust extinction toward short wavelengths.
This study aims to examine how digital marketing tactics affect the expansion of Micro, Small, and Medium-Sized Enterprises (MSMEs) in India. Due to the nation's expanding internet and digital technology adoption rates, MSMEs are using more and more digital marketing techniques to expand their consumer base, improve their market presence, and spur corporate expansion. This research will examine the many digital marketing tactics used by MSMEs in India, evaluate their efficacy, and pinpoint the critical elements affecting their uptake and prosperity. The study will use a mixed-methods approach to collect data from a sample of MSMEs operating in various Indian industries, including surveys, interviews, and case studies. The research outcomes will enhance comprehension of the function of digital marketing in MSMEs' expansion and longevity within the Indian setting.
This study examines the impact of entrepreneurial orientation on company performance and competitive advantage in the context of small and medium-sized enterprises (SMEs). Based on 100 SMEs in the culinary sector in Indonesia and using a quantitative approach based on PLS-SEM data analysis, our study reveals that entrepreneurial orientation influences company performance through competitive advantage. This research will help SME owners and managers to deal with the required entrepreneurial orientation without taking excessive risks that could be detrimental to company performance and competitive advantage.
Small and medium-sized businesses, artisans, handicrafts, trades, Business
José Miguel Ortiz García de las Bayonas, María Concepción Parra Meroño, Gonzalo Wandosell Fernández de Bobadilla
The aim of this article is to detect the business model factors that increase firm performance. To carry out this research, a survey was conducted among the CEOs of seventy companies in the Region of Murcia, Spain, belonging to different sectors of economic activity. The empirical evidence obtained indicates that the characteristics of a company's business model affect its performance and future viability. In this sense, the article confirms that the business model factors that contribute to improving the future viability of a firm are mainly innovation, professionalization of the economic-financial area, investment in employees, and strengthening of the commercial area. Therefore, firms that wish to improve their long-term performance should especially strengthen these characteristics of the business model.
Small and medium-sized businesses, artisans, handicrafts, trades, Business
Ugochukwu Orji, Ezugwu Obianuju, Modesta Ezema
et al.
The data revolution experienced in recent times has thrown up new challenges and opportunities for businesses of all sizes in diverse industries. Big data analytics is already at the forefront of innovations to help make meaningful business decisions from the abundance of raw data available today. Business intelligence and analytics has become a huge trend in todays IT world as companies of all sizes are looking to improve their business processes and scale up using data driven solutions. This paper aims to demonstrate the data analytical process of deriving business intelligence via the historical data of a fictional bike share company seeking to find innovative ways to convert their casual riders to annual paying registered members. The dataset used is freely available as Chicago Divvy Bicycle Sharing Data on Kaggle. The authors used the RTidyverse library in RStudio to analyse the data and followed the six data analysis steps of ask, prepare, process, analyse, share, and act to recommend some actionable approaches the company could adopt to convert casual riders to paying annual members. The findings from this research serve as a valuable case example, of a real world deployment of BIA technologies in the industry, and a demonstration of the data analysis cycle for data practitioners, researchers, and other potential users.
Wash trading is a form of market manipulation where the same entity sells an asset to themselves to drive up market prices, launder money under the cover of a legitimate transaction, or claim a tax loss without losing ownership of an asset. Although the practice is illegal with traditional assets, lack of supervision in the non-fungible token market enables criminals to wash trade and scam unsuspecting buyers while operating under regulators radar. AnChain.AI designed an algorithm that flags transactions within an NFT collection history as wash trades when a wallet repurchases a token within 30 days of previously selling it. The algorithm also identifies intermediate transactions within a wash trade cycle. Testing on 7 popular NFT collections reveals that on average, 0.14% of transactions, 0.11% of wallets, and 0.16% of tokens in each collection are involved in wash trading. These wash trades generate an overall total price manipulation, sales, and repurchase profit of \$900K, \$1.1M, and negative \$1.6M respectively. The results draw attention to the prevalent market manipulation taking place and inform unsuspecting buyers which tokens and sellers may be involved in criminal activity.