Hasil untuk "Business records management"

Jinhui Liu, Ximeng Zhang, Yanbo Ai et al.

Evaluating Text-to-SQL agents in private business intelligence (BI) settings is challenging due to the scarcity of realistic, domain-specific data. While synthetic evaluation data offers a scalable solution, existing generation methods fail to capture business realism--whether questions reflect realistic business logic and workflows. We propose a Business Logic-Driven Data Synthesis framework that generates data grounded in business personas, work scenarios, and workflows. In addition, we improve the data quality by imposing a business reasoning complexity control strategy that diversifies the analytical reasoning steps required to answer the questions. Experiments on a production-scale Salesforce database show that our synthesized data achieves high business realism (98.44%), substantially outperforming OmniSQL (+19.5%) and SQL-Factory (+54.7%), while maintaining strong question-SQL alignment (98.59%). Our synthetic data also reveals that state-of-the-art Text-to-SQL models still have significant performance gaps, achieving only 42.86% execution accuracy on the most complex business queries.

Nicoleta ISAC

In the digital era, companies rely on a strong digital presence to shape their reputation and engage consumers effectively. This study explores the impact of influencers on consumer behavior within the realm of digital marketing. The study employs a comprehensive literature review approach to delve into the dynamic interplay between digital influencers, consumer decisions, and brand promotion strategies. Through content analysis, this research aims to uncover the nuances of consumer interactions with influencers in the digital landscape, shedding light on the evolving nature of online marketing practices. The findings are expected to provide valuable insights for companies seeking to optimize their digital marketing strategies and effectively leverage the influence of digital leaders on consumer behavior.

Febrina Cahyani, Lilik Handajani

This investigation meticulously examines how growth opportunities, firm size, leverage, and liquidity affect the hedging decisions of mining companies listed on the Indonesia Stock Exchange from 2017 to 2022. Out of an initial population of 62 mining enterprises, a purposive sampling method distilled the focus to 14 representative firms, with the collected data subjected to rigorous analysis via SPSS. The research uncovers that growth opportunities do not significantly alter hedging decisions, whereas firm size demonstrates a significant positive association with the likelihood of engaging in hedging practices. In contrast, leverage and liquidity, as indicated by the current ratio, do not show a substantial impact on hedging behavior. This study seeks to illuminate the various determinants shaping hedging strategies within the mining sector, offering crucial insights that could inform future research and enhance the understanding of risk management approaches in this particular industry.

Катерина Крамаренко, Інна Шарко

Актуальність теми зумовлена посиленням фінансово-економічних ризиків внутрішнього та зовнішнього середовища суб’єктів господарювання. Відзначено, що ефективність системи фінансово-економічної безпеки визначається як формальними інституційними механізмами, так і здатністю кожного структурного підрозділу та окремого працівника дотримуватися комплексу законодавчих, етичних норм, внутрішніх стандартів та службових інструкцій, що зумовлює необхідність впровадження підсистеми комплаєнс-менеджменту. Метою дослідження є теоретичне обґрунтування та визначення ролі комплаєнс-менеджменту як складової системи фінансово-економічної безпеки суб’єктів господарювання з урахуванням інституційної специфіки їхньої діяльності. Методологічною основою дослідження є системний підхід до аналізу фінансово-економічної безпеки суб’єктів економіки та ролі комплаєнс-менеджменту в її забезпеченні. Розкрито теоретичні основи та еволюцію концепції комплаєнс-менеджменту. Систематизовано види комплаєнсу відповідно до приналежності економічних суб’єктів до інституційних секторів економіки, які відрізняються цілями, видами діяльності, організаційно-правовими формами тощо. Визначено структурні елементи політики комплаєнсу, а також напрями комплаєнс-програми, розробка і реалізація якої сприятиме попередженню ризиків, що можуть мати негативний вплив на фінансово-економічну безпеку. Зазначено основні загрози та групи ризиків, що має попередити комплаєнс-менеджмент для забезпечення фінансово-економічної безпеки. Доведено, що успіх управління комплаєнс-ризиками залежить від ефективної інтеграції комплаєнс-менеджменту в систему управління фінансово-економічною безпекою. Теоретичне значення результатів проведеного дослідження полягає в розширенні знань про механізми підвищення фінансово-економічної безпеки через комплаєнс-менеджмент. Практичне значення полягає у можливості застосування розроблених рекомендацій для підвищення ефективності управління ризиками. Наукова новизна дослідження полягає в комплексному підході до вивчення ролі комплаєнс-менеджменту як інструменту забезпечення фінансової безпеки. Основні висновки підтверджують важливість інтеграції комплаєнс-менеджменту у стратегії управління фінансово-економічною безпекою суб’єктів господарювання. Дослідження має теоретичний характер. Перспективами подальших досліджень є розробка показників вимірювання ефективності комплаєнс-менеджменту та створення мотиваційних інструментів для його результативної реалізації, а також питання доцільності законодавчого врегулювання мінімальних стандартів комплаєнс-менеджменту для бізнесу та бюджетних установ.

Jannis Kiesel, Jonathan Heiss

Ensuring the integrity of business processes without disclosing confidential business information is a major challenge in inter-organizational processes. This paper introduces a zero-knowledge proof (ZKP)-based approach for the verifiable execution of business processes while preserving confidentiality. We integrate ZK virtual machines (zkVMs) into business process management engines through a comprehensive system architecture and a prototypical implementation. Our approach supports chained verifiable computations through proof compositions. On the example of product carbon footprinting, we model sequential footprinting activities and demonstrate how organizations can prove and verify the integrity of verifiable processes without exposing sensitive information. We assess different ZKP proving variants within process models for their efficiency in proving and verifying, and discuss the practical integration of ZKPs throughout the Business Process Management (BPM) lifecycle. Our experiment-driven evaluation demonstrates the automation of process verification under given confidentiality constraints.

Neda Bagherzadeh, Saeed Setayeshi, Samaneh Yazdani

Ontology is a general term used by researchers who want to share information in a specific domain. One of the hallmarks of the greatest success of a powerful manager of an organization is his ability to interpret unplanned and unrelated events. Tools to solve this problem are vital to business growth. Modern technology allows customers to be more informed and influential in their roles as patrons and critics. This can make or break a business. Research shows that businesses that employ a customer-first strategy and prioritize their customers can generate more revenue. Even though there are many different Ontologies offered to businesses, none of it is built from a cognitive perspective. The objective of this study is to address the concept of strategic business plans with a cognitive ontology approach as a basis for a new management tool. This research proposes to design a cognitive ontology model that links customer measurement with traditional business models, define relationships between components and verify the accuracy of the added financial value.

M. Grosse, K. Honda, C. Spech et al.

Agricultural field operations are generally tracked as work records (WR), incorporating data points such as; work type, machine type, timestamped trajectories and field information. WR data which is automatically recorded by modern machinery equipped with Information and Communication Technologies (ICT) can enable efficient farm management decision making. Globally, farmers often rely on aged or legacy farming machinery and manual data recording, which introduces significant labor costs and increases the risk of inaccurate data input. To address this challenge, a field study in Central Japan was conducted to showcase automated data collection by retrofitting legacy farming machinery with low-cost Internet of Things (IoT) devices. For single-purpose vehicles (SPV), which only carry out single work types such as planting, LTE (Long Term Evolution) and Global Navigation Satellite System (GNSS) units were installed to record trajectory data. For multi-purpose vehicles (MPV), such as tractors which perform multiple work types, the configuration settings of these vehicles had to include implements and attachments data. To obtain this data, industry standard LTE-GNSS Bluetooth gateways were fitted onto MPV and low-cost BLE (Bluetooth Low Energy) beacons were attached to implements. After installation, over a seven-month field preparation and planting period 1,623 WR, including 421 WR for SPV and 1,120 WR for MVP, were automatically obtained. For MPV, the WR included detailed configuration settings enabling detection of the specific work types. These findings demonstrate the potential of low cost IoT GNSS devices for precision agriculture strategies to support management decisions in farming operations.

Sofy AMNONY

This article discusses the relationship between school principals' management styles, the implementation of alternative assessment methods, and their combined effect on student achievement in Israeli high schools. Utilizing a quantitative approach, the research surveyed 30 school principals, 339 teachers, and 337 students across multiple schools. Findings indicate significant correlations between certain management styles of school managers and the successful implementation of alternative assessment methods, which in turn positively influences student academic achievement. The research also revealed that school principals who adopted a more collaborative and integrative leadership style were more likely to encourage and support the implementation of alternative assessment methods. Furthermore, schools with a higher prevalence of alternative assessment methods reported increased student engagement and motivation, leading to improved academic performance. The research highlights the importance of ongoing professional development for principals to enhance their leadership skills and adapt to evolving educational paradigms.

Saumya Kothari, Harsh Shah, Utkarsh Prajapati et al.

Mid-cap companies, generally valued between \$2 billion and \$10 billion, provide investors with a well-rounded opportunity between the fluctuation of small-cap stocks and the stability of large-cap stocks. This research builds upon the long-short equity approach (e.g., Michaud, 2018; Dimitriu, Alexander, 2002) customized for mid-cap equities, providing steady risk-adjusted returns yielding a significant Sharpe ratio of 2.132 in test data. Using data from 2013 to 2023, obtained from WRDS and following point-in-time (PIT) compliance, the approach guarantees clarity and reproducibility. Elements of essential financial indicators, such as profitability, valuation, and liquidity, were designed to improve portfolio optimization. Testing historical data across various markets conditions illustrates the stability and resilience of the tactic. This study highlights mid-cap stocks as an attractive investment route, overlooked by most analysts, which combine transparency with superior performance in managing portfolios.

Aline Werro, Christian Nitzl, Uwe M. Borghoff

Business wargaming is a central tool for developing sustaining strategies. It transfers the benefits of traditional wargaming to the business environment. However, building wargames that support the process of decision-making for strategy require respective intelligence. This paper investigates the role of intelligence in the process of developing strategic foresight. The focus is on how intelligence is developed and how it relates to business wargaming. The so-called intelligence cycle is the basis and reference of our investigation. The conceptual part of the paper combines the theoretical background from military, business as well as serious gaming. To elaborate on some of the lessons learned, we examine specific business wargames both drawn from the literature and conducted by us at the Center for Intelligence and Security Studies (CISS). It is shown that business wargaming can make a significant contribution to the transformation of data to intelligence by supporting the intelligence cycle in two crucial phases. Furthermore, it brings together business intelligence (BI) and competitive intelligence (CI) and it bridges the gap to a company's strategy by either testing or developing a new strategy. We were also able to confirm this finding based on the business wargame we conducted at a major semiconductor manufacturer.

Helle Røer, Olga Korostynska, Frøydis Bjerke et al.

Manual labour in slaughterhouses is hazardous work. Workers suffer from injuries and occupational illnesses resulting from repetitive movements with sharp knives. There is a need for a robotic tool which can perform versatile tasks with a high level of precision. This knife must be able to imitate the same primary cuttings of a professional butcher and produce meat products which are acceptable to the end-market. This paper reports the results of a world-wide assessment of the fresh pork meat cuts as a performance indicator of knives specifically adapted for automated operation. These knives included Victorinox knife, bespoke double bladed Uddeholm knife, vibrating knife and novel smart knife with built-in sensing mechanism that detects in real time the contact with meat and cut depth. Physical appearances of cuts were assessed anonymously by independent responders with different backgrounds. All knives were deemed acceptable in terms of cutting quality. There was also no discernible difference of opinion between manual and robot cuts. This indicates that the new knife for robot-assisted cutting is acceptable by market.

Elham Namavar, Mehrdad Hamrahi, Farrokh Nowrozi

The purpose of this research was to investigate the impact of professional ethics and job integration on the social influence of government organizations in Kazeroon city. According to the nature of the research, the research method is descriptive-survey from the field branch. The statistical population of this research included all the government organizations of Kazeroon city with the number of 727 people. To determine the sample size using Cochran's formula, 252 people were estimated as the sample size and were selected using stratified random sampling method. To collect information, Beigzadeh's professional ethics questionnaire (2003), Shaufli and Becker's (2003) occupation questionnaire, and Yukel's social influence questionnaire (2003) were used. The validity of the questionnaires was confirmed by respected professors and their reliability was also confirmed by the initial implementation in a sample of 30 people. The reliability coefficient of the questionnaires for the variable of professional ethics is estimated at 0.78, 0.89 and social influence at 0.86. Pearson's correlation coefficient and regression analysis were used to test the research hypotheses. The findings of the research indicate that professional ethics and job involvement have an impact on the social influence of government organizations in Kazeroon city.

Soumyadip Sarkar

Portfolio management is an essential component of investment strategy that aims to maximize returns while minimizing risk. This paper explores several portfolio management strategies, including asset allocation, diversification, active management, and risk management, and their importance in optimizing portfolio performance. These strategies are examined individually and in combination to demonstrate how they can help investors maximize alpha and minimize beta. Asset allocation is the process of dividing a portfolio among different asset classes to achieve the desired level of risk and return. Diversification involves spreading investments across different securities and sectors to minimize the impact of individual security or sector-specific risks. Active management involves security selection and risk management techniques to generate excess returns while minimizing losses. Risk management strategies, such as stop-loss orders and options strategies, aim to minimize losses in adverse market conditions. The importance of combining these strategies for optimizing portfolio performance is emphasized in this paper. The proper implementation of these strategies can help investors achieve their investment goals over the long-term, while minimizing exposure to risks. A call to action for investors to utilize portfolio management strategies to maximize alpha and minimize beta is also provided.

J. Middelhuis, R. Lo Bianco, E. Scherzer et al.

Efficient allocation of resources to activities is pivotal in executing business processes but remains challenging. While resource allocation methodologies are well-established in domains like manufacturing, their application within business process management remains limited. Existing methods often do not scale well to large processes with numerous activities or optimize across multiple cases. This paper aims to address this gap by proposing two learning-based methods for resource allocation in business processes to minimize the average cycle time of cases. The first method leverages Deep Reinforcement Learning (DRL) to learn policies by allocating resources to activities. The second method is a score-based value function approximation approach, which learns the weights of a set of curated features to prioritize resource assignments. We evaluated the proposed approaches on six distinct business processes with archetypal process flows, referred to as scenarios, and three realistically sized business processes, referred to as composite business processes, which are a combination of the scenarios. We benchmarked our methods against traditional heuristics and existing resource allocation methods. The results show that our methods learn adaptive resource allocation policies that outperform or are competitive with the benchmarks in five out of six scenarios. The DRL approach outperforms all benchmarks in all three composite business processes and finds a policy that is, on average, 12.7% better than the best-performing benchmark.

Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammed Atiquzzaman

The widespread adoption of Internet of Things (IoT) devices in smart cities, intelligent healthcare systems, and various real-world applications have resulted in the generation of vast amounts of data, often analyzed using different Machine Learning (ML) models. Federated learning (FL) has been acknowledged as a privacy-preserving machine learning technology, where multiple parties cooperatively train ML models without exchanging raw data. However, the current FL architecture does not allow for an audit of the training process due to the various data-protection policies implemented by each FL participant. Furthermore, there is no global model verifiability available in the current architecture. This paper proposes a smart contract-based policy control for securing the Federated Learning (FL) management system. First, we develop and deploy a smart contract-based local training policy control on the FL participants' side. This policy control is used to verify the training process, ensuring that the evaluation process follows the same rules for all FL participants. We then enforce a smart contract-based aggregation policy to manage the global model aggregation process. Upon completion, the aggregated model and policy are stored on blockchain-based storage. Subsequently, we distribute the aggregated global model and the smart contract to all FL participants. Our proposed method uses smart policy control to manage access and verify the integrity of machine learning models. We conducted multiple experiments with various machine learning architectures and datasets to evaluate our proposed framework, such as MNIST and CIFAR-10.

Wing Fung Chong, Runhuan Feng, Hins Hu et al.

This paper introduces a two-pillar cyber risk management framework to address the pervasive challenges in managing cyber risk. The first pillar, cyber risk assessment, combines insurance frequency-severity models with cybersecurity cascade models to capture the unique nature of cyber risk. The second pillar, cyber capital management, facilitates informed allocation of capital for a balanced cyber risk management strategy, including cybersecurity investments, insurance coverage, and reserves. A case study, based on historical cyber incident data and realistic assumptions, demonstrates the necessity of comprehensive cost-benefit analysis for budget-constrained companies with competing objectives in cyber risk management. In addition, sensitivity analysis highlights the dependence of the optimal strategy on factors such as the price of cybersecurity controls and their effectiveness. The framework's implementation across a diverse range of companies yields general insights on cyber risk management.

Zheng Wang, Mingrui Liu, Cheng Long et al.

When users move in a physical space (e.g., an urban space), they would have some records called mobility records (e.g., trajectories) generated by devices such as mobile phones and GPS devices. Naturally, mobility records capture essential information of how users work, live and entertain in their daily lives, and therefore, they have been used in a wide range of tasks such as user profile inference, mobility prediction and traffic management. In this paper, we expand this line of research by investigating the problem of inferring user socioeconomic statuses (such as prices of users' living houses as a proxy of users' socioeconomic statuses) based on their mobility records, which can potentially be used in real-life applications such as the car loan business. For this task, we propose a socioeconomic-aware deep model called DeepSEI. The DeepSEI model incorporates two networks called deep network and recurrent network, which extract the features of the mobility records from three aspects, namely spatiality, temporality and activity, one at a coarse level and the other at a detailed level. We conduct extensive experiments on real mobility records data, POI data and house prices data. The results verify that the DeepSEI model achieves superior performance than existing studies. All datasets used in this paper will be made publicly available.

Khan, Shakeel Ahmad, Ahmed, Alia, Ahmed, Kaleem

Methods for the elimination of police corruption to enhance integrity, usually disregards its roots that are connected to societal elements in light of the fact that police corruption has societal causes and implementing a change of the police needs, to certain degree, transforming the community. In this research, a qualitative approach (semi-structured interviews, focus group meetings and observations) was used the analysis methods from social profiles categorized as per their degree of police corruption utilizing data. Researchers have described and examined the organizational and social determinants of police corruption to help decision-makers establish social and economic policy frameworks to monitor police corruption. Researchers concluded that poor pay, resource shortage, moral economy, and politicization of police are pertinent to police corruption. In addition, research evidence suggests that the government must increasingly strengthen organizational as well as social measures in order to minimize police corruption.

Kazuhiro Seki, Yusuke Ikuta, Yoichi Matsubayashi

This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years' worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient r=0.937) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time.

Rodrigo Rebouças de Almeida, Christoph Treude, Uirá Kulesza

What are the business causes behind tight deadlines? What drives the prioritization of features that pushes quality matters to the back burner? We conducted a survey with 71 experienced practitioners and did a thematic analysis of the open-ended answers to the question: ``Could you give examples of how business may contribute to technical debt?'' Business-related causes were organized into two categories: pure-business and business/IT gap, and they were related to `tight deadlines' and `features over quality', the most frequently cited management reasons for technical debt. We contribute a cause-effect model which relates the various business causes of tight deadlines and the behavior of prioritizing features over quality aspects.