Class imbalance in vehicle payment method classification can cause predictive models to become biased toward the majority. This study aims to build a classification model for automotive consumer payment methods using Extreme Gradient Boosting (XGBoost), with class balancing handled through the Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), and model interpretability performed using SHAP (SHapley Additive Explanations). The dataset consisted of 11,011 records and 13 attributes derived from Toyota vehicle delivery order transactions. Results show that the XGBoost model without balancing achieved 67.37% accuracy but only 0.24 recall for the Cash class. After applying SMOTE, the recall for the Cash class improved to 0.58, while ADASYN produced a similar improvement at 0.59, with overall accuracy maintained at around 61–62% and a stable ROC-AUC of 0.65. Feature importance and SHAP analysis identified c_vehicle_model and c_city as the most influential factors in predicting the payment method. From a business perspective, the improved ability to detect cash customers reduces the risk of misclassification and enables dealers to better segment customer payment preference. This supports more effective marketing campaigns, sales strategies, and financing risk management. The combination of XGBoost, SMOTE, ADASYN, and SHAP has proven effective in handling imbalanced data while offering transparent interpretability of predictions, making it a practical foundation for data-driven decision-making in the automotive industry.
This study investigates generative artificial intelligence (GenAI) usage of university students who study alongside their professional career. Previous literature has paid little attention to part-time students and the intersectional use of GenAI between education and business. This study examines with a grounded theory approach the characteristics of GenAI usage of part-time students. Eleven students from a distance learning university were interviewed. Three causal and four intervening conditions, as well as strategies were identified, to influence the use of GenAI. The study highlights both the potential and challenges of GenAI usage in education and business. While GenAI can significantly enhance productivity and learning outcomes, concerns about ethical implications, reliability, and the risk of academic misconduct persist. The developed grounded model offers a comprehensive understanding of GenAI usage among students, providing valuable insights for educators, policymakers, and developers of GenAI tools seeking to bridge the gap between education and business.
Niklas Schulte, Isaac Henderson Johnson Jeyakumar, Michael Kubach
et al.
Ensuring the timeliness and reliability of master data remains a persistent challenge for many organizations. To mitigate these quality deficits, organizations frequently rely on commercial data brokers. However, this practice creates strategic dependencies and poses significant business risks, particularly as providers typically disclaim liability for the accuracy of the supplied data. In contrast, modern data ecosystems enable the trusted sharing of data assets with strong data sovereignty. In this paper, we address this paradigm shift by deriving a nascent design theory for trustworthy master data management based on self-sovereign identity. The theory is grounded through a hermeneutic literature review combined with industry expert interviews and instantiated through integration into a reference architecture for data spaces. Following an evaluation through additional industry expert interviews, our work provides a framework for a trustworthy master data management in data ecosystems that is reliable, sovereign, and accountable.
We develop an analytically tractable model featuring heterogeneous workers and firms, where labor markets clear through a one-to-many sorting mechanism. Firms determine both the number and composition of their employees, shaping (1) the income distribution among workers and (2) the productivity distribution across firms. We study business cycles driven by market efficiency shocks that disproportionately benefit more productive firms. The model's implications are consistent with empirical regularities on the cyclical behavior of wage and productivity distributions.
REST APIs enable collaboration among microservices. A single fault in a REST API can bring down the entire microservice system and cause significant financial losses, underscoring the importance of REST API testing. Effectively testing REST APIs requires thoroughly exercising the functionalities behind them. To this end, existing techniques leverage REST specifications (e.g., Swagger or OpenAPI) to generate test cases. Using the resource constraints extracted from specifications, these techniques work well for testing simple, business-insensitive functionalities, such as resource creation, retrieval, update, and deletion. However, for complex, business-sensitive functionalities, these specification-based techniques often fall short, since exercising such functionalities requires additional business constraints that are typically absent from REST specifications. In this paper, we present LoBREST, a log-based, business-aware REST API testing technique that leverages historical request logs (HRLogs) to effectively exercise the business-sensitive functionalities behind REST APIs. To obtain compact operation sequences that preserve clean and complete business constraints, LoBREST first employs a locality-slicing strategy to partition HRLogs into smaller slices. Then, to ensure the effectiveness of the obtained slices, LoBREST enhances them in two steps: (1) adding slices for operations missing from HRLogs, and (2) completing missing resources within the slices. Finally, to improve test adequacy, LoBREST uses these enhanced slices as initial seeds to perform business-aware fuzzing. LoBREST outperformed eight tools (including Arat-rl, Morest, and Deeprest) across 17 real-world services. It achieved top operation coverage on 16 services and line coverage on 15, averaging 2.1x and 1.2x improvements over the runner-up. LoBREST detected 108 5XX bugs, including 38 found by no other tool.
Eliott Thomas, Mickael Coustaty, Aurelie Joseph
et al.
Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipelines. While semi-supervised learning (SSL) can leverage unlabeled data, existing methods rely on confidence scores that poorly reflect extraction quality. We propose QUEST, a Quality-aware Semi-supervised Table extraction framework designed for business documents. QUEST introduces a novel quality assessment model that evaluates structural and contextual features of extracted tables, trained to predict F1 scores instead of relying on confidence metrics. This quality-aware approach guides pseudo-label selection during iterative SSL training, while diversity measures (DPP, Vendi score, IntDiv) mitigate confirmation bias. Experiments on a proprietary business dataset (1000 annotated + 10000 unannotated documents) show QUEST improves F1 from 64% to 74% and reduces empty predictions by 45% (from 12% to 6.5%). On the DocILE benchmark (600 annotated + 20000 unannotated documents), QUEST achieves a 50% F1 score (up from 42%) and reduces empty predictions by 19% (from 27% to 22%). The framework's interpretable quality assessments and robustness to annotation scarcity make it particularly suited for business documents, where structural consistency and data completeness are paramount.
Abstract
The purpose of this research is to investigate the role of strategic marketing planning in improving the sustainable competitive advantage of insurance companies through the mediation of marketing knowledge. In terms of purpose, this research is applicable, and of descriptive-correlative type studies. To achieve this goal, this study developed a theoretical framework that captures mediation through data collected randomly from managers and senior marketing experts of insurance companies, with a total population of 60 participants. Data were analyzed using structural equation modeling for model fitting, and SmartPLS software for path analysis; to estimate various direct and indirect effects. The results showed that the ability to plan strategic marketing directly has a significant effect on promoting sustainable competitive advantage, but this effect is limited. The findings provide evidence that marketing knowledge dimensions act as important mediating mechanisms between strategic marketing planning capabilities, and directly promote sustainable competitive advantage. In general, the results of this study showed that strategic marketing planning capability with the mediating role of marketing knowledge has a meaningful and significant effect on promoting sustainable competitive advantage.
Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised server in charge of simultaneously controlling all the vehicles. This study addresses such issues by proposing a novel distributed approach to AIM utilizing multi-agent reinforcement learning (MARL). We show that by leveraging the 3D surround view technology for advanced assistance systems, autonomous vehicles can accurately navigate intersection scenarios without needing any centralised controller. The contributions of this paper thus include a MARL-based algorithm for the autonomous management of a 4-way intersection and also the introduction of a new strategy called prioritised scenario replay for improved training efficacy. We validate our approach as an innovative alternative to conventional centralised AIM techniques, ensuring the full reproducibility of our results. Specifically, experiments conducted in virtual environments using the SMARTS platform highlight its superiority over benchmarks across various metrics.
We describe the methods and technologies underlying the application Grants4Companies. The application uses a logic-based expert system to display a list of business grants suitable for the logged-in business. To evaluate suitability of the grants, formal representations of their conditions are evaluated against properties of the business, taken from the registers of the Austrian public administration. The logical language for the representations of the grant conditions is based on S-expressions. We further describe a Proof of Concept implementation of reasoning over the formalised grant conditions. The proof of concept is implemented in Common Lisp and interfaces with a reasoning engine implemented in Scryer Prolog. The application has recently gone live and is provided as part of the Business Service Portal by the Austrian Federal Ministry of Finance.
Purpose The Public Records (Scotland) Act 2011, implemented in January 2013, celebrated its 10th anniversary this year. This case study aims to examine the implementation of the Act. The Act was born out of the “Historical Abuse Systemic Review: Residential Schools and Children’s Homes in Scotland 1950–1995”, published in 2007. This review identified problems for care leavers and abuse survivors attempting to trace records about themselves, family members or medical issues. It demonstrated an urgent need to take action to preserve historical records and protect the information rights of Scottish citizens, particularly those of the most vulnerable. Scottish Ministers wanted the Act not just to regulate recordkeeping but to change the culture of recordkeeping. Is it doing this? Design/methodology/approach The Act’s guiding principle is continuous improvement. It does not presume records management perfection from public authorities but requires that they assess their arrangements, identify gaps in provision and deliver a commitment to close these gaps over time. This case study draws on the Keeper of the Records of Scotland’s strategy of affecting change through compliance, engagement and advocacy. We can evidence the impact of the legislation through the various tools created to support its administration: scrutiny mechanisms and statutory penalty embedded in the Act; evidence-based compliance under a “Records Management Plan” (RMP); stakeholder surgeries and conferences that address challenges, examine failings, learn from and share successes and explore development opportunities; Progress Update Review mechanism: a self-assessment tool from which we draw evidence of progress or lapses; and webinars and surveys to remain alert to stakeholder issues. Our engagement provides the qualitative and quantitative data required to accurately update Scottish Ministers. Findings Undeniably, the Act is making a difference. It has transformed the recordkeeping landscape in Scotland over the past decade. The legislation has given the Keeper of the Records of Scotland influence and has acted as a national lever for change. For example, an authority employing a records manager and establishing a support unit as a consequence of our address to its Board; and the NHS Scotland Records Management Group, established as a consequence of the Act, now advises NHS senior management. Originality/value There is no doubt about progress on the ground. However, because of the fiscal problems of the 2008 financial crisis, Brexit, COVID and the current cost-of-living crisis, public authority finances are extremely strained. What does this mean for Scottish Ministers’ ambition to change the culture of recordkeeping? What are the challenges for the next decade? Good recordkeeping is not only about efficiencies but about accountable, trustworthy and transparent government. Can the Act meet these challenges?
У статті з’ясовано актуальність дослідження індивідуального споживання, що інтерпретовано як складну систему, яка на основі персоніфікації інтересів споживача, покликана синтезувати відносини між суб’єктами суспільства з метою забезпечення раціональних можливостей щодо відтворення об’єктів споживання. Основним фактором, що визначає рівень індивідуального споживання є дохід індивіда. Рівень та структура індивідуального споживання в Україні є незадовільними, оскільки майже увесь дохід українців спрямовується на придбання лише продовольчих товарів. Така складна ситуація ще більше загострилася після повномасштабного вторгнення Росії, адже суттєво скоротилися грошові доходи населення, збільшився рівень безробіття. Тому з метою вирішення таких проблем й у пошуках стабільності та безпечних місць життєдіяльності відчутними стають міграційні процеси. Одним зі шляхів урегулювання питання щодо індивідуального споживання може стати розробка ефективного механізму його державного регулювання.
Economics as a science, Business records management
In this manuscript, the mean of the study and the auxiliary variable, as well as the rank of the auxiliary variable, were needed to develop a new, improved ratio-in-regression type estimator for population mean. Up to the first order of approximation, expressions for the bias and mean square error of the existing and proposed estimators are computed. The effectiveness and stability of our new, enhanced estimator are evaluated using simulation and two actual data sets. The suggested estimator's superior performance to all other considered estimators is shown both conceptually and numerically. The mean square error is the lowest, and PREs out-performs other known estimators by a factor of more than one hundred. Overall, we draw the conclusion that the suggested new improved estimator outperforms all its predecessors.
Enterprise Resource Planning (ERP) systems are critical to the success of enterprises, facilitating business operations through standardized digital processes. However, existing ERP systems are unsuitable for startups and small and medium-sized enterprises that grow quickly and require adaptable solutions with low barriers to entry. Drawing upon 15 explorative interviews with industry experts, we examine the challenges of current ERP systems using the task technology fit theory across companies of varying sizes. We describe high entry barriers, high costs of implementing implicit processes, and insufficient interoperability of already employed tools. We present a vision of a future business process platform based on three enablers: Business processes as first-class entities, semantic data and processes, and cloud-native elasticity and high availability. We discuss how these enablers address current ERP systems' challenges and how they may be used for research on the next generation of business software for tomorrow's enterprises.
In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of ML approaches for diagnostics and prognostics of industrial systems using open-source datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Reliability Society and summarizes a unified ML framework. This review systematically categorizes and scrutinizes the problems, challenges, methodologies, and advancements demonstrated in these competitions, highlighting the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks related to detection, diagnosis, assessment, and prognosis. Moreover, this paper delves into the common challenges in PHM data challenge competitions by emphasizing data-related and model-related issues and evaluating the limitations of these competitions. The potential solutions to address these challenges are also summarized. Finally, we identify key themes and potential directions for future research, providing opportunities and prospects for next-generation ML-PHM development in PHM domain.
Mohit Singhal, Javier Pacheco, Seyyed Mohammad Sadegh Moosavi Khorzooghi
et al.
Auditing is critical to ensuring the fairness and reliability of decision-making systems. However, auditing a black-box system for bias can be challenging due to the lack of transparency in the model's internal workings. In many web applications, such as Yelp, it is challenging, if not impossible, to manipulate their inputs systematically to identify bias in the output. Yelp connects users and businesses, where users identify new businesses and simultaneously express their experiences through reviews. Yelp recommendation software moderates user-provided content by categorizing it into recommended and not-recommended sections. The recommended reviews, among other attributes, are used by Yelp's ranking algorithm to rank businesses in a neighborhood. Due to Yelp's substantial popularity and its high impact on local businesses' success, understanding the bias of its algorithms is crucial. This data-driven study, for the first time, investigates the bias of Yelp's business ranking and review recommendation system. We examine three hypotheses to assess if Yelp's recommendation software shows bias against reviews of less established users with fewer friends and reviews and if Yelp's business ranking algorithm shows bias against restaurants located in specific neighborhoods, particularly in hotspot regions, with specific demographic compositions. Our findings show that reviews of less-established users are disproportionately categorized as not-recommended. We also find a positive association between restaurants' location in hotspot regions and their average exposure. Furthermore, we observed some cases of severe disparity bias in cities where the hotspots are in neighborhoods with less demographic diversity or higher affluence and education levels.
Ngah, Abdul Hafaz, Kamalrulzaman, Nurul Izni, Ibrahim, Fauzayani
et al.
This study aims to investigate the effect of soft skills and ethics and value on the employers’ willingness to continue recruiting Universiti Malaysia Terengganu (UMT) graduates, together with the moderating effect of knowledge on the relationship between soft skills and the employers’ willingness to continue recruiting UMT graduates. The study’s respondents comprised of 208 employers in Malaysia who responded through an online survey using Google Forms. The survey data was then analyzed using the Partial Least Squares Structural Equation Modelling (PLS-SEM), indicating that soft skills positively affected the employers’ willingness to continue recruiting UMT graduates. Nevertheless, ethics and value were found to be insignificant factors on the employers’ willingness to continue recruiting UMT graduates. It was also revealed that knowledge had the moderating effect on the relationship between soft skills and the employers’ willingness to continue recruiting UMT graduates. Therefore, universities were recommended to invest in soft skills and knowledge education to ensure that graduates met the employers’ professional recruitment standards in areas of expertise.
I develop a simple Schumpeterian agent-based model where the entry and exit of firms, their productivity and markup, the birth of new industries and the social structure of the population are endogenous and use it to study the causes of rising inequality and "declining business dynamism" since the 1980s. My hybrid model combines features of i) the so-called Schumpeter Mark I (centering around the entrepreneur), ii) the Mark II model (emphasizing the innovative capacities of firms), and iii) Cournot competition, with firms using OLS learning to estimate the market environment and the behavior of their competitors. A scenario which is quantitatively calibrated to US data on growth and inequality replicates a large number of stylized facts regarding the industry life-cycle, growth, inequality and all ten stylized facts on "declining business dynamism" proposed by Akcigit and Ates (AEJ:Macro, 2021). Counterfactual simulations show that antitrust policy is highly effective at combatting inequality and increasing business dynamism and growth, but is subject to a conflict of interest between workers and firm owners, as GDP and wages grow at the expense of profits. Technological factors, on the other hand, are much less effective in combatting declining business dynamism in my model.
Accelerating national development requires a certain amount of foreign investment. Meanwhile, the state has an obligation to protect local business actors and workers who are clearly “threatened” by the influx of foreign investment. In fact, the entry of foreign investment is often accompanied by the entry of foreign workers (TKA) as one of the conditions for the entry of the intended investment. Ideally, foreign workers who enter Indonesia have higher qualifications than domestic workers so that they can carry out the process of transferring technology and / or transferring knowledge to local workers. In fact, many foreign workers who enter Indonesia do not have adequate qualifications, causing conflicts with local workers. These conflicts generally lead to a salary or wage gap that is not accompanied by superior competence. Based on this, this research is important to do in order to better understand the dilemma of placing foreign workers from the perspective of Human Resources (HR). This research is a descriptive study which analyzed qualitatively using secondary data and primary data. The results of this study are; disharmony between foreign workers and local workers is due to the lack of training for local workers. Meanwhile, the emergence of narrow nationalism among workers is caused by a lack of understanding of different cultures and values
The study identifies the growth of social and economic divergence between Ukraine and the EU. The purpose of this paper is to fill a gap in the literature on the scale and dynamics of an upward divergence between Ukraine and the EU by comparing integral indices of social and economic development of both areas. Reaching this goal is important for elaborating new guidelines for cohesion policy of the EU and recommendations on Ukraine’s integration strategy. This research differs from the previously published ones by a new approach to measure social and economic integral indices used for estimating the rate of divergence between Ukraine and the EU. A distinctive feature of the author’s methodology of the integral estimation of social and economic development of Ukraine and the EU-28 is the use of the innovative composition of indicators and the multiplicative form of the integral index, the simultaneous normalization and integral assessment indicators and justification of weights based on the principal component analysis (PCA). Priority areas of social and economic development of Ukraine as a prerequisite for the policy of rapprochement and integration with the EU are diversification of foreign economic relations, ensuring the stability and independence of the financial system, improving the parameters of the social security system, involving drivers of economic development, increasing the level of energy efficiency and energy saving.