As COVID-19 spreads across the globe, a common public policy response has been to enforce the temporary closure of non-essential business activity. In some countries, governments have underwritten a proportion of the wage income for staff forced to furlough or broadened their welfare systems to accommodate newly laid off workers or small business owners. While these actions are helpful, they do not explicitly address the lack of sales trading activity on business income and cash balances. In commentary, we identify what types of businesses have been increasing their cash holdings in the lead up to COVID-19 as an indication of what types of small and medium-sized enterprises (SMEs) are most at risk if the lockdown extends for a protracted period of time. We find that only 39% of the of businesses were bolstering their cash balances leading up to COVID-19 which suggests that 61% of businesses may run out of cash, including 8.6% that had no retained earnings whatsoever with micro firms at particular risk. The importance of precautionary saving for SMEs is critical to enhance resilience when Black Swan events occur.
Dwi Siti Syarifah Usriani, Selmita Paranoan, Muhammad Din
et al.
Purpose: This study aims to assess the level of compliance of political parties in Palu in reporting their 2024 election campaign funds, with a focus on the transparency and accountability of reports in accordance with applicable regulations.
Methodology/approach: The method used is quantitative descriptive research with a descriptive statistical approach. Data was obtained through documentation, which involved campaign finance reports from 18 political parties. The research sample used a saturated sampling technique.
Findings: Most political parties were compliant in reporting campaign finances, with high compliance rates of 96,3% for RKDK, 95,8% for LADK, 100% for LPSDK, and 97,9% for LPPDK. Despite this progress, stricter supervision by the KPU and Bawaslu and more rigorous enforcement of sanctions are needed to ensure that campaign funds are clearly and transparently accounted for.
Practical and Theoretical Contribution/Originality: This study assists the KPU and Bawaslu of Palu City by demonstrating the effectiveness of existing regulations and increasing public political awareness of the importance of transparency in campaign finance reporting.
Research Limitation: This study is limited to the city of Palu and uses secondary data from audited reports, so it does not describe compliance dynamics throughout Indonesia.
Accounting. Bookkeeping, Business mathematics. Commercial arithmetic. Including tables, etc.
When people begin to aspire and desire to achieve, it is everyone's responsibility to stand hand in hand to help them achieve. The economy is the foundation on which the prosperity of states and societies is based, and in Bahrain we are going through a major economic shift with the implementation of Bahrain's Economic Vision 2030, as Bahrain plans to reduce dependence on oil in the economy and tries to create a more sustainable and efficient economy. And creating a fertile environment for the growth of companies (Bahraini and non-Bahraini) is one of the most effective ways to develop the Bahraini economy, which is what Bahrain has tended to do in recent years as it has provided many facilities to attract international companies to settle in Bahrain. In addition, it encouraged Bahraini startups to grow. Therefore, this research seeks to develop the Bahraini economy and, in particular, help in the development of Bahraini startup companies through applied education and real confrontation with the market, in addition to creating opportunities for them by designing a business accelerator that takes the Bahraini emerging companies to excellence and gives them access to global markets.
Financial reports and earnings communications contain large volumes of structured and semi structured information, making detailed manual analysis inefficient. Earnings conference calls provide valuable evidence about a firm's performance, outlook, and strategic priorities. The manual analysis of lengthy call transcripts requires substantial effort and is susceptible to interpretive bias and unintentional error. In this work, we present a hybrid summarization framework that combines extractive and abstractive techniques to produce concise and factually reliable Reuters-style summaries from the ECTSum dataset. The proposed two stage pipeline first applies the LexRank algorithm to identify salient sentences, which are subsequently summarized using fine-tuned variants of BART and PEGASUS designed for resource constrained settings. In parallel, we fine-tune a Longformer Encoder-Decoder (LED) model to directly capture long-range contextual dependencies in financial documents. Model performance is evaluated using standard automatic metrics, including ROUGE, METEOR, MoverScore, and BERTScore, along with domain-specific variants such as SciBERTScore and FinBERTScore. To assess factual accuracy, we further employ entity-level measures based on source-precision and F1-target. The results highlight complementary trade offs between approaches, long context models yield the strongest overall performance, while the hybrid framework achieves competitive results with improved factual consistency under computational constraints. These findings support the development of practical summarization systems for efficiently distilling lengthy financial texts into usable business insights.
Joann Jasiak, Peter MacKenzie, Purevdorj Tuvaandorj
This paper examines how Canadian firms balance the benefits of technology adoption against the rising risk of cyber security breaches. We merge data from the 2021 Canadian Survey of Digital Technology and Internet Use and the 2021 Canadian Survey of Cyber Security and Cybercrime to investigate the trade-off firms face when adopting digital technologies to enhance productivity and efficiency, balanced against the potential increase in cyber security risk. The analysis explores the extent of digital technology adoption, differences across industries, the subsequent impacts on efficiency, and associated cyber security vulnerabilities. We build aggregate variables, such as the Business Digital Usage Score and a cyber security incidence variable to quantify each firm's digital engagement and cyber security risk. A survey-weight-adjusted Lasso estimator is employed, and a debiasing method for high-dimensional logit models is introduced to identify the drivers of technological efficiency and cyber risk. The analysis reveals a digital divide linked to firm size, industry, and workforce composition. While rapid expansion of tools such as cloud services or artificial intelligence can raise efficiency, it simultaneously heightens exposure to cyber threats, particularly among larger enterprises.
Business process models are essential for the representation, analysis, and execution of organizational processes, serving as orchestration blueprints while relying on (web) services to implement individual tasks. At the representation level, there are two dominant paradigms: procedural (imperative) notations that specify the sequential flows within a process and declarative notations that capture the process as a set of constraints. Although each notation offers distinct advantages in representational clarity and cognitive effectiveness, they are seldom integrated, leading to compatibility challenges. In this paper, we set aside the imperative-declarative dichotomy to focus on orchestrating services that execute the underlying tasks. We propose an execution semantics based on the Continuous Query Language (CQL), where CQL statements respond dynamically to streams of events. As events unfold, these CQL statements update the execution state (tables) and can generate new events, effectively triggering (web) services that implement specific process tasks. By defining all executions around a unified event model, we achieve cross-language and cross-paradigm process enactment. We showcase how industrial process modeling languages, such as BPMN and DCR graphs, can be enacted through CQL queries, allowing seamless orchestration and execution of services across diverse modeling paradigms.
Predicting the success of start-up companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and innovation research. Datasets such as Crunchbase provide both structured information (e.g., funding rounds, industries, investor networks) and unstructured text (e.g., company descriptions), but effectively leveraging this heterogeneous data for prediction remains challenging. Traditional machine learning approaches often rely only on structured features and achieve moderate accuracy, while large language models (LLMs) offer rich reasoning abilities but struggle to adapt directly to domain-specific business data. We present \textbf{CrunchLLM}, a domain-adapted LLM framework for startup success prediction. CrunchLLM integrates structured company attributes with unstructured textual narratives and applies parameter-efficient fine-tuning strategies alongside prompt optimization to specialize foundation models for entrepreneurship data. Our approach achieves accuracy exceeding 80\% on Crunchbase startup success prediction, significantly outperforming traditional classifiers and baseline LLMs. Beyond predictive performance, CrunchLLM provides interpretable reasoning traces that justify its predictions, enhancing transparency and trustworthiness for financial and policy decision makers. This work demonstrates how adapting LLMs with domain-aware fine-tuning and structured--unstructured data fusion can advance predictive modeling of entrepreneurial outcomes. CrunchLLM contributes a methodological framework and a practical tool for data-driven decision making in venture capital and innovation policy.
Generative artificial intelligence (GenAI) is rapidly diffusing into the workplace and is expected to substantially reshape roles, workflows, and skill requirements, particularly for young professionals as early adopters who are highly exposed to these tools. While GenAI is widely regarded as a means to increase productivity, its adoption may simultaneously introduce new challenges, including various forms of technostress. Drawing on 15 semi-structured interviews with young professionals in research and development (R&D), IT, finance, and marketing in organizations piloting or using GenAI, we conducted a structured qualitative content analysis guided by established technostress dimensions. Our findings indicate that classic technostress dimensions remain relevant but manifest differently across sectors and contexts. Moreover, additional GenAI-specific stressors emerged, such as regulatory and compliance ambiguity, data protection and copyright concerns, perceived dependency, potential skill degradation, doubts about the reliability and controllability of AI outputs, and a shift towards more monitoring and conceptual work. At the same time, participants reported techno-eustress in the form of efficiency gains, learning opportunities, and enhanced intrinsic motivation. Overall, the study extends existing technostress frameworks and underscores the importance of AI literacy, clear organizational governance, and supportive work design to mitigate negative technostress while enabling the productive use of GenAI.
The main aim of the article is to present practical examples of HR practices with respect to the quality management system and its standards. A bibliometric analysis was conducted to obtain an overview of research trends in the field of HR management in the context of quality management. The article also presents the connections of HR practices with quality management systems (QMS) with an emphasis on the importance of a process approach in human resource management. The article presents selected human resource management processes such as recruitment, hiring and adaptation. Examples of documents related to human resource management processes are also provided. A standardized job description structure is presented as an output of the job analysis process. An example of a responsibility matrix is provided, defining employee responsibilities, in which their readiness to perform specific work activities, acquired through training, is also recorded.
Ricardo M. Czekster, Alexeis Garcia Perez, Manolya Kavakli-Thorne
et al.
Digital Twin (DT) technologies promise to remove cyber-physical barriers in systems and services and provide seamless management of distributed resources effectively. Ideally, full-fledged instantiations of DT offer bi-directional features for physical-virtual representations, tackling data governance, risk assessment, security and privacy protections, resilience, and performance, to name a few characteristics. More broadly, Federated Digital Twins (FDT) are distributed physical-virtual counterparts that collaborate for enacting synchronisation and accurate mapping of multiple DT instances. In this work we focus on understanding and conceptualising the cyber-physical and business perspectives using FDT in multinational and multimodal transportation systems. These settings enforce a plethora of regulations, compliance, standards in the physical counterpart that must be carefully considered in the virtual mirroring. Our aim is to discuss the regulatory and technical underpinnings and, consequently, the existing operational and budgetary overheads to factor in when designing or operating FDT.
Process (or workflow) execution on blockchain suffers from limited scalability; specifically, costs in the form of transactions fees are a major limitation for employing traditional public blockchain platforms in practice. Research, so far, has mainly focused on exploring first (Bitcoin) and second-generation (e.g., Ethereum) blockchains for business process enactment. However, since then, novel blockchain systems have been introduced - aimed at tackling many of the problems of previous-generation blockchains. We study such a system, Algorand, from a process execution perspective. Algorand promises low transaction fees and fast finality. However, Algorand's cost structure differs greatly from previous generation blockchains, rendering earlier cost models for blockchain-based process execution non-applicable. We discuss and contrast Algorand's novel cost structure with Ethereum's well-known cost model. To study the impact for process execution, we present a compiler for BPMN Choreographies, with an intermediary layer, which can support multi-platform output, and provide a translation to TEAL contracts, the smart contract language of Algorand. We compare the cost of executing processes on Algorand to previous work as well as traditional cloud computing. In short: they allow vast cost benefits. However, we note a multitude of future research challenges that remain in investigating and comparing such results.
Ehsan Valavi, Joel Hestness, Newsha Ardalani
et al.
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of keeping around an infinite supply of older (less relevant) data. In addition, we argue that increasing the stock of data by including older datasets may, in fact, damage the model's accuracy. Expectedly, the model's accuracy improves by increasing the flow of data (defined as data collection rate); however, it requires other tradeoffs in terms of refreshing or retraining machine learning models more frequently. Using these results, we investigate how the business value created by machine learning models scales with data and when the stock of data establishes a sustainable competitive advantage. We argue that data's time-dependency weakens the barrier to entry that the stock of data creates. As a result, a competing firm equipped with a limited (yet sufficient) amount of recent data can develop more accurate models. This result, coupled with the fact that older datasets may deteriorate models' accuracy, suggests that created business value doesn't scale with the stock of available data unless the firm offloads less relevant data from its data repository. Consequently, a firm's growth policy should incorporate a balance between the stock of historical data and the flow of new data. We complement our theoretical results with an experiment. In the experiment, we empirically measure the loss in the accuracy of a next word prediction model trained on datasets from various time periods. Our empirical measurements confirm the economic significance of the value decline over time. For example, 100MB of text data, after seven years, becomes as valuable as 50MB of current data for the next word prediction task.
Blockchain technology truly opened the gate to a wave of unparalleled innovations; however, despite the rapidly growing load of hype, the integration into the business, apart from a few applications, seems to be coming at a slower rate. One reason for that delay may be the need in the real-world applications for the so-called trust model. Trust models are rarely mentioned in blockchain application proposals despite their importance, which creates skepticism about their successful developments. To promote trust model implementation and help practitioners in its redaction, this article provides an outline of what a trust model is, why it is essential, and an example of how it is elaborated. The discussed example comes from a case study of a dairy company that implemented blockchain for the traceability of its products. Despite being tailored on a traceability project, the redaction and elements of the trust model, with few adjustments, could be easily readapted for other applications.
In the infinite servers queue with Poisson arrivals real life practical applications, the busy period and the busy cycle probabilistic study is of main importance. But it is a very difficult task. In this text, we show that by solving a Riccati equation induced by this queue transient probabilities monotony study as time functions, we obtain a collection of service length distribution functions, for which both the busy period and the busy cycle have lengths with quite simple distributions, generally given in terms of exponential distributions and the degenerate at the origin distribution.
Ching-Shih Tsou, Christine Liou, Longsheng Cheng
et al.
Although machine learning for quality prediction of manufacturing processes has attracted attention in the literature, there is a significant lack of case studies from industry, especially in medical sector. This paper proposes a data-driven approach to infer the batch quality of blood glucose test strips. Once the low quality of work in process is detected, unnecessary process waste could be eliminated. Starting from data pre-processing, which consists of Synthetic Minority Over-sampling TEchnique and Random Over-Sampling Example, this project tries to balance the ill-distributed data first. Followed by machine learning aims to classify and predict the quality of blood glucose test strips. Different models are evaluated by the Receiver Operating Characteristic curve and Area Under Curve. Computational results show that the decision tree and random forest after SMOTE perform better than the counterpart of ROSE method. Ensemble learning, such as random forest, out-wins base learner decision tree. To sum up, random forest with SMOTE is the suggested model for accurately predicting the quality of blood glucose test strips. There is a 30% improvement in error rate under random forest and SMOTE for NG class that could be of top concern for prognosis. Several factors, including the direction of applying test reagent onto test strips and the position where the strips are located, that affect quality of test strips have been identified. Explanations in terms of inspection and manufacturing are discussed subsequently. Finally, the prognosis of quality can be attained through big data and statistical machine learning.