Hasil untuk "Small and medium-sized businesses, artisans, handicrafts, trades"

Alassane Bah, Timothée Brochier

The North-West African coast is enriched by the Canary current, which sustain a very produc- tive marine ecosystem. The Senegalese artisanal fishing fleet, the largest in West Africa, ben- efit from this particularly productive ecosystem. It has survived the ages with remarkable adaptability, and has great flexibility allowing it to react quickly to changes, in particular by changing fishing gear and performing migrations. However, since the 1980s, the increasing fishing effort led to a progressive fish depletion, increasing fisher's migration distances to access new fishing grounds. Since 2007 many fishers even started to navigate to Canary archi- pelago in order to find a more lucrative job in Europe, carrying candidate to emigration in their canoes. This phenomenon further increased since 2022 due to a new drop in fishery yields, consecutive to the development of fishmeal factories along the coast that amplified overfishing. Climate change may also impact fish habitat, and by consequence the distribution of fishing grounds. The question addressed in this research was how climate change, fishing effort and socio-economic parameters interact and determine the artisanal fishery dynamics. An interdisciplinary approach allowed us to collect data and qualitative information on cli- mate, fishing effort and socio-economic parameters. This served as a basis to build a multi- agent model of the mobility of Senegalese artisanal fishing. We implemented a first version of the model and presented some preliminary simulations with contrasted fishing effort and climate scenario. The results suggested that first, climate change should have only a slight impact on artisanal fishing, even in the most extreme climate scenario considered. Second, if fishing effort was maintained at current levels, we found a collapse of the fishery with massive fishers migrations whatever the climate scenario. Third, with reduced fishing effort, a sustain- able fishery equilibrium emerges in which Senegal's artisanal fishery catches ~250,000 tons of fish a year mostly in Senegal, approaching the 2000s catches records. This sustainable equi- librium maintained with the two-climate change scenario tested. Fishers migrations provide clues of the fish populations state and have implications for the sustainable exploitation of fishing resources. Senegalese artisanal fishers' migrations impact the regional distribution of the fishing effort, therefore must be taken into account in regional development and planning policies for this sector, particularly in a context of increasing infrastructure and spatial man- agement measures (e.g. marine protected areas). This work lays the foundations of a computer simulation tool for decision support.

Jiazhu Xie, Bowen Li, Heyu Fu et al.

Large Language Model (LLM)-based question-answering systems offer significant potential for automating customer support and internal knowledge access in small businesses, yet their practical deployment remains challenging due to infrastructure costs, engineering complexity, and security risks, particularly in retrieval-augmented generation (RAG)-based settings. This paper presents an industry case study of an open-source, multi-tenant platform that enables small businesses to deploy customised LLM-based support chatbots via a no-code workflow. The platform is built on distributed, lightweight k3s clusters spanning heterogeneous, low-cost machines and interconnected through an encrypted overlay network, enabling cost-efficient resource pooling while enforcing container-based isolation and per-tenant data access controls. In addition, the platform integrates practical, platform-level defences against prompt injection attacks in RAG-based chatbots, translating insights from recent prompt injection research into deployable security mechanisms without requiring model retraining or enterprise-scale infrastructure. We evaluate the proposed platform through a real-world e-commerce deployment, demonstrating that secure and efficient LLM-based chatbot services can be achieved under realistic cost, operational, and security constraints faced by small businesses.

Quentin Romero Lauro, Aakash Gautam, Yasmine Kotturi

Generative AI can help small business owners automate tasks, increase efficiency, and improve their bottom line. However, despite the seemingly intuitive design of systems like ChatGPT, significant barriers remain for those less comfortable with technology. To address these disparities, prior work highlights accessory skills -- beyond prompt engineering -- users must master to successfully adopt generative AI including keyboard shortcuts, editing skills, file conversions, and browser literacy. Building on a design workshop series and 15 interviews with small businesses, we introduce BizChat, a large language model (LLM)-powered web application that helps business owners across digital skills levels write their business plan -- an essential but often neglected document. To do so, BizChat's interface embodies three design considerations inspired by learning sciences: ensuring accessibility to users with less digital skills while maintaining extensibility to power users ("low-floor-high-ceiling"), providing in situ micro-learning to support entrepreneurial education ("just-in-time learning"), and framing interaction around business activities ("contextualized technology introduction"). We conclude with plans for a future BizChat deployment.

Mark Clague

Written as a concise guide for students and working professionals, this article examines and critiques the “portfolio career” in arts professional development to offer an alternative conceptual strategy to forge a sustainable life. Eight modes of arts work are explored (performing, teaching, creating, writing, healing, manufacturing, distributing, and administering). The “Platform Career” is proposed as an extension of and possible solution to the shortcomings of the portfolio career. In the platform model, one professional activity serves as a financial base for the artist’s panoply of creative work, providing health insurance and other employment benefits plus additional financial stability to reduce financial and emotional precarity.

Thomas Gaertner, Christoph Lippert, Stefan Konigorski

In response to the growing demand for accurate demand forecasts, this research proposes a generalized automated sales forecasting pipeline tailored for small- to medium-sized enterprises (SMEs). Unlike large corporations with dedicated data scientists for sales forecasting, SMEs often lack such resources. To address this, we developed a comprehensive forecasting pipeline that automates time series sales forecasting, encompassing data preparation, model training, and selection based on validation results. The development included two main components: model preselection and the forecasting pipeline. In the first phase, state-of-the-art methods were evaluated on a showcase dataset, leading to the selection of ARIMA, SARIMAX, Holt-Winters Exponential Smoothing, Regression Tree, Dilated Convolutional Neural Networks, and Generalized Additive Models. An ensemble prediction of these models was also included. Long-Short-Term Memory (LSTM) networks were excluded due to suboptimal prediction accuracy, and Facebook Prophet was omitted for compatibility reasons. In the second phase, the proposed forecasting pipeline was tested with SMEs in the food and electric industries, revealing variable model performance across different companies. While one project-based company derived no benefit, others achieved superior forecasts compared to naive estimators. Our findings suggest that no single model is universally superior. Instead, a diverse set of models, when integrated within an automated validation framework, can significantly enhance forecasting accuracy for SMEs. These results emphasize the importance of model diversity and automated validation in addressing the unique needs of each business. This research contributes to the field by providing SMEs access to state-of-the-art sales forecasting tools, enabling data-driven decision-making and improving operational efficiency.

Teng Ye, Jingnan Zheng, Junhui Jin et al.

While small businesses are increasingly turning to online crowdfunding platforms for essential funding, over 40% of these campaigns may fail to raise any money, especially those from low socio-economic areas. We utilize the latest advancements in AI technology to identify crucial factors that influence the success of crowdfunding campaigns and to improve their fundraising outcomes by strategically optimizing these factors. Our best-performing machine learning model accurately predicts the fundraising outcomes of 81.0% of campaigns, primarily based on their textual descriptions. Interpreting the machine learning model allows us to provide actionable suggestions on improving the textual description before launching a campaign. We demonstrate that by augmenting just three aspects of the narrative using a large language model, a campaign becomes more preferable to 83% human evaluators, and its likelihood of securing financial support increases by 11.9%. Our research uncovers the effective strategies for crafting descriptions for small business fundraising campaigns and opens up a new realm in integrating large language models into crowdfunding methodologies.

Lihu Chen, Gaël Varoquaux

Large Language Models (LLMs) have made significant progress in advancing artificial general intelligence (AGI), leading to the development of increasingly large models such as GPT-4 and LLaMA-405B. However, scaling up model sizes results in exponentially higher computational costs and energy consumption, making these models impractical for academic researchers and businesses with limited resources. At the same time, Small Models (SMs) are frequently used in practical settings, although their significance is currently underestimated. This raises important questions about the role of small models in the era of LLMs, a topic that has received limited attention in prior research. In this work, we systematically examine the relationship between LLMs and SMs from two key perspectives: Collaboration and Competition. We hope this survey provides valuable insights for practitioners, fostering a deeper understanding of the contribution of small models and promoting more efficient use of computational resources. The code is available at https://github.com/tigerchen52/role_of_small_models

Margaret J. Wyszomirski

Cases on Arts Entrepreneurship (Tonelli, M., & Heise, A. [Eds.]. [2023]. Cases on Arts Entrepreneurship. Edward Elgar Publishing) presents a collection of thirteen case studies that will be much appreciated by professors who teach and students who are studying entrepreneurship in the arts. Each case study exhibits a similar framework: a descriptive narrative that runs between seven and sixteen pages, including references; Teaching Notes that include an abstract, learning outcomes, keyword topics, discussion questions, a seventy-five-minute class plan, and supplemental readings (if any). The editors provide a brief introduction to the book’s purpose as well as a seven-page concluding chapter that identifies five themes that emerge across chapters: exposure to the arts at a young age, network and relationship building, kindness and collaboration, financial management, and balancing multiple income streams.

Golshan Javadian, Anil Nair, David Ahlstrom et al.

Oscar R. Navarrete-Parra, Victor Uc-Cetina, Jorge Reyes-Magana

In this paper, we propose a methodology to align a medium-sized GPT model, originally trained in English for an open domain, to a small closed domain in Spanish. The application for which the model is finely tuned is the question answering task. To achieve this we also needed to train and implement another neural network (which we called the reward model) that could score and determine whether an answer is appropriate for a given question. This component served to improve the decoding and generation of the answers of the system. Numerical metrics such as BLEU and perplexity were used to evaluate the model, and human judgment was also used to compare the decoding technique with others. Finally, the results favored the proposed method, and it was determined that it is feasible to use a reward model to align the generation of responses.

Alexander Barbie, Wilhelm Hasselbring

The transformation to Industry 4.0 changes the way embedded software systems are developed. Digital twins have the potential for cost-effective software development and maintenance strategies. With reduced costs and faster development cycles, small and medium-sized enterprises (SME) have the chance to grow with new smart products. We interviewed SMEs about their current development processes. In this paper, we present the first results of these interviews. First results show that real-time requirements prevent, to date, a Software-in-the-Loop development approach, due to a lack of proper tooling. Security/safety concerns, and the accessibility of hardware are the main impediments. Only temporary access to the hardware leads to Software-in-the-Loop development approaches based on simulations/emulators. Yet, this is not in all use cases possible. All interviewees see the potential of Software-in-the-Loop approaches and digital twins with regard to quality and customization. One reason it will take some effort to convince engineers, is the conservative nature of the embedded community, particularly in SMEs.

Shun-Yang Lee, Julian Runge, Daniel Yoo et al.

Research has investigated the impact of the COVID-19 pandemic on business performance and survival, indicating particularly adverse effects for small and midsize businesses (SMBs). Yet only limited work has examined whether and how online advertising technology may have helped shape these outcomes, particularly for SMBs. The aim of this study is to address this gap. By constructing and analyzing a novel data set of more than 60,000 businesses in 49 countries, we examine the impact of government lockdowns on business survival. Using discrete-time survival models with instrumental variables and staggered difference-in-differences estimators, we find that government lockdowns increased the likelihood of SMB closure around the world but that use of online advertising technology attenuates this adverse effect. The findings show heterogeneity in country, industry, and business size, which we discuss and is consistent with theoretical expectations.

Dylan Cromhout, Rodney Duffett

A number of SMEs lack the essential marketing skills, knowledge, tools and resources, and financial access to ensure the survival of their businesses. Service learning could be used as an effective pedagogy for assisting SMEs with vital marketing communication (MC) strategies via the development of tools and resources that may increase business growth and sustainability. The primary research objective was to evaluate SMEs’ satisfaction regarding performance factors, and student developed MC tools and resources that were implemented via a MC service learning programme (in the form of student-run agencies). The inquiry utilized the triad service learning model and quality assurance cycle to apply an evaluation research design that was substantiated by the expectancy-disconfirmation paradigm. A survey was conducted among 107 SME owners and managers via a structured questionnaire. The student developed MC tools and resources and their perceived usefulness resulted in a positive influence on a number of performance factors among SMEs. MC tools and resources such as a customer database, email address, and Facebook page had the largest influence on performance factors. Performance factors such as an increase in sales, new customers, brand awareness, competitive advantage, business efficiency, and motivation of employees were found to have a positive influence SME satisfaction. Further inquiry could replicate the study via various marketing-related service learning programmes in different countries that have divergent cultures, economics and contexts.

Ana Luisa Foguel, Eduardo S. Fraga

We investigate the impact of a presumed axion-like-particle (ALP) emission in a core-collapse supernova explosion on neutrino luminosities and mean energies employing a relatively simple analytic description. We compute the nuclear Bremsstrahlung and Primakoff axion luminosities as functions of the protoneutron star (PNS) parameters and discuss how the ALP luminosities compete with the neutrino emission, modifying the total PNS thermal energy dissipation. Our results are publicly available in the python package ARtiSANS, which can be used to compute the neutrino and axion observables for different choices of parameters.

Fernando Gimeno-Arias

Within the distribution channels of fast-moving consumer goods (FMCG), the negotiating of agreements with official suppliers is critical for the performance of small and medium-sized (SME) distributors. These distributors are limited by their size and negotiating power, which is significantly lower than that of their suppliers, leading them to seek alternative supply sources, such as that provided by the gray market. The participation of SME distributors in the gray market is not only conditioned by the negotiations with their official suppliers, but also by the role played by the size of the gray market and by the relationship with their suppliers. The literature shows very few studies into SMEs within this area of the distribution channel, so this article contributes an explanatory model of this phenomenon. Based on a sample of 181 Spanish distribution companies, our results confirm that negotiation is a favorable element, while granting limited importance to the role of the relationship. In addition, we find evidence of the key role of commitment between parties in a situation as peculiar as that of parallel marketing channels.

Stefano Chiaradonna, Nicolas Lanchier

As cyber attacks have become more frequent, cyber insurance premiums have increased, resulting in the need for better modeling of cyber risk. Toward this direction, Jevtić and Lanchier (2020) proposed a dynamic structural model of aggregate loss distribution for cyber risk of small and medium-sized enterprises under the assumption of a tree-based local-area-network topology that consists of the combination of a Poisson process, homogeneous random trees, bond percolation processes, and cost topology. Their model assumes that the contagion spreads through the edges of the network with the same fixed probability in both directions, thus overlooking a dynamic cyber security environment implemented in most networks, and their results give an exact expression for the mean of the aggregate loss but only a rough upper bound for the variance. In this paper, we consider a bidirectional version of their percolation model in which the contagion spreads through the edges of the network with a certain probability moving toward the lower level assets of the network but with another probability moving toward the higher level assets of the network, which results in a more realistic cyber security environment. In addition, our mathematical approach is quite different and leads to exact expressions for both the mean and the variance of the aggregate loss, and therefore an exact expression for the insurance premiums.

Nicola Mori

The Generic Geant4 Simulation (GGS) is a package designed to speed-up the realization and deployment of Monte Carlo simulation software based on Geant4, for small- and medium-sized high-energy experiments. For many common use cases, the task of setting up a full-featured simulation of the detector is reduced to the definition of the detector geometry, by providing generic and reusable implementations of the mandatory Geant4 user classes (particle generation, scoring, output etc.). Extensibility is provided by a simple plugin system that allows replacing of the generic implementations distributed with GGS with custom ones. These features make it especially suitable for cases where limited manpower, like during preliminary detector design studies, can severely limit the scope of an R\&D program.

Haoxue Wang, Liexin Cheng

Loan risk for small businesses has long been a complex problem worthy of exploring. Predicting the loan risk can benefit entrepreneurship by developing more jobs for the society. CatBoost (Categorical Boosting) is a powerful machine learning algorithm suitable for dataset with many categorical variables like the dataset for forecasting loan risk. In this paper, we identify the important risk factors that contribute to loan status classification problem. Then we compare the performance between boosting-type algorithms(especially CatBoost) with other traditional yet popular ones. The dataset we adopt in the research comes from the U.S. Small Business Administration (SBA) and holds a very large sample size (899,164 observations and 27 features). In order to make the best use of the important features in the dataset, we propose a technique named "synthetic generation" to develop more combined features based on arithmetic operation, which ends up improving the accuracy and AUC of the original CatBoost model. We obtain a high accuracy of 95.84% and well-performed AUC of 98.80% compared with the existent literature of related research.

Javier Heredia Yzquierdo, Antonio Sánchez-Bayón

The Europe 2020 Strategy is aimed at making the EU into a smart, sustainable and inclusive economy by 2020. This Strategy has to promote environmental policies and economic opportunities. Back in 2007 Italy was performing slightly below average and way below the most advanced EU Member States as far as percentage of green energy of the total energy produced in Italy. Measures were taken and though the Italian regulation around green energies has been hectic though effective. Italian legislation recently passed will put emphasis on the relevance of a Green Power strategy by guarantying an attractive minimum price per Kw produced through clean and environmental friendly sources, notably from Wind energy sources. Within the sector a new area is grafting attention: the mini wind farms. The Trentino Alto Adige region in Northern Italy has taken particularly profit of the national legal framework and has develop a further regional frame that has placed the region on top of the Italian green energy production charts. The local idiosyncrasy is making of the mini wind farms a case study

Paul A. Smith, Chiara Bocci, Nikos Tzavidis et al.

Small area (or small domain) estimation is still rarely applied in business statistics, because of challenges arising from the skewness and variability of variables such as turnover. We examine a range of small area estimation methods as the basis for estimating the activity of industries within the retail sector in the Netherlands. We use tax register data and a sampling procedure which replicates the sampling for the retail sector of Statistics Netherlands' Structural Business Survey as a basis for investigating the properties of small area estimators. In particular, we consider the use of the EBLUP under a random effects model and variations of the EBLUP derived under (a) a random effects model that includes a complex specification for the level 1 variance and (b) a random effects model that is fitted by using the survey weights. Although accounting for the survey weights in estimation is important, the impact of influential data points remains the main challenge in this case. The paper further explores the use of outlier robust estimators in business surveys, in particular a robust version of the EBLUP, M-regression based synthetic estimators, and M-quantile small area estimators. The latter family of small area estimators includes robust projective (without and with survey weights) and robust predictive versions. M-quantile methods have the lowest empirical mean squared error and are substantially better than direct estimators, though there is an open question about how to choose the tuning constant for bias adjustment in practice. The paper makes a further contribution by exploring a doubly robust approach comprising the use of survey weights in conjunction with outlier robust methods in small area estimation.