Global uncertainty and digital disruption necessitate human resource management transformation toward human-centric approaches. HRM 5.0 emerges as a strategic paradigm integrating technological advancements with inclusivity, sustainability, and employee wellbeing. Research establishes theoretical foundations for HRM 5.0 and analyzes digital HR ecosystem structure as operational implementation model. The study explores opportunities and barriers for HRM 5.0 deployment within Ukraine's post-crisis context. Qualitative theory-building design employs critical literature review methodology based on analysis of key sources from 2020-2025, addressing core questions regarding HRM evolution, ecosystem structure, technologies, human-centric impact, and implementation challenges. Methodological approach combines narrative synthesis, thematic coding, and comparative analysis. HRM 5.0 represents multidimensional socio-technical transformation beyond technological enhancement. Digital HR ecosystems function as integrated, socially responsible infrastructure supporting human-centered strategy. Research synthesizes global literature with regional perspectives through thematic mapping. Literature classification follows five research tasks with coding encompassing AI applications, ethical HRM, digital wellbeing, and ESG integration. Global insights undergo comparative analysis with regional findings to identify convergence points and gaps. HRM model evolution was systematized, distinguishing HRM 5.0 as post-digital paradigm. Digital HR ecosystem essence, components, and functions were defined through six-level functional architecture. Classification matrix for digital HR technologies was developed based on functionality and interaction modality. Support for sustainability, inclusivity, and wellbeing through specialized digital practices was analyzed. Implementation barriers and strategic opportunities for HRM 5.0 in Ukrainian context were identified. Dual-level implementation matrix was proposed for assessing organizational readiness and external factors. Theoretical significance lies in creating conceptual foundation for understanding HRM 5.0 as integrative socio-technical system. Practical value provides structured tools for organizations aligning digital transformation with human-centered strategies. Scientific novelty encompasses original typology of digital HR tools, ecosystem functions, and implementation challenges considering post-crisis contexts. Future research should focus on empirical validation of conceptual frameworks of digital HR ecosystems. Article type - theoretical.
Economics as a science, Business records management
The purpose of this paper is to provide a comprehensive analysis of the determinants of customer loyalty in the restaurant industry and identify opportunities for future research. A systematic literature review was conducted using the PRISMA protocol, involving a final sample of 33 articles published between 2015 and 2024. The databases used include Scopus journals, with inclusion and exclusion criteria applied to ensure relevance and quality. Key factors influencing restaurant loyalty were identified, including satisfaction, service quality, and food quality, which are crucial for fostering long-term relationships and ensuring repeated patronage. Additional significant factors include brand image, price fairness, sensory experiences, sustainable practices, and customer engagement. Enhancing customer satisfaction through excellent service, quality food, and positive dining experiences is paramount for building a loyal customer base. The study highlights key factors for improving customer loyalty in restaurants, such as satisfaction, service quality, and sustainable practices. It offers valuable insights for both academics and practitioners, suggesting future research on mobile food-ordering apps and sustainability. The findings emphasize the importance of understanding these factors to develop effective strategies for enhancing customer satisfaction and loyalty.
Businesses heavily rely on data sourced from various channels like news articles, financial reports, and consumer reviews to drive their operations, enabling informed decision-making and identifying opportunities. However, traditional manual methods for data extraction are often time-consuming and resource-intensive, prompting the adoption of digital transformation initiatives to enhance efficiency. Yet, concerns persist regarding the sustainability of such initiatives and their alignment with the United Nations (UN)'s Sustainable Development Goals (SDGs). This research aims to explore the integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) as a sustainable solution for Information Extraction (IE) and processing. The research methodology involves reviewing existing solutions for business decision-making, noting that many systems require training new machine learning models, which are resource-intensive and have significant environmental impacts. Instead, we propose a sustainable business solution using pre-existing LLMs that can work with diverse datasets. We link domain-specific datasets to tailor LLMs to company needs and employ a Multi-Agent architecture to divide tasks such as information retrieval, enrichment, and classification among specialized agents. This approach optimizes the extraction process and improves overall efficiency. Through the utilization of these technologies, businesses can optimize resource utilization, improve decision-making processes, and contribute to sustainable development goals, thereby fostering environmental responsibility within the corporate sector.
Cloud computing is continually evolving, enhancing hardware technologies, improving software and enhancing business processes. A payroll management system deployed on the Cloud harnesses on-demand of delivery of computational power and database storage using cloud computing technologies. This project aims to develop and deploy a cloud-based payroll management system. The objectives of this study are: to carry out a study on the existing cloud-based payroll management system, to design a payroll data model for calculating basic salary and enables retrieval of payroll history when needed from the database, to develop and deploy a payroll management system, on the Cloud that generates earning statements, filling the gap between security infrastructure and optimal system performance harnessing cloud computing technologies. The focus was on the design, implementation and deployment, using UML diagrams to illustrate the payroll application and Google App Engine for deployment. The system analysis in comparison of a conventional payroll system and the cloud-based system is endless in terms of speed, processing power, storage capacity, universalization and pricing. The cloud-based payroll has an infinite number of advantages; all conventional payroll system is rendered obsolete as it mends all the cons.
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?
Розвиток технологій четвертої промислової революції стимулює не лише широке запровадження цифрових активів в різні сфери людської життєдіяльності, а й активну появу їх нових видів. Саме завдяки розробці великої кількості різноманітних цифрових активів дедалі чіткіше проявляються їхні характерні особливості, які дають підстави стверджувати про необхідність їхнього трактування як окремої групи господарських засобів. Статтю присвячено виявленню та систематизації унікальних характеристик криптоактивів, як найбільш економічно значущої складової цифрових активів, та обґрунтуванню на цій основі перспектив розробки стандарту, яким регулюватиметься визнання, оцінка і відображення у фінансовій звітності цифрових активів у статусі окремої групи господарських засобів. На основі системного аналізу наукових джерел методами дедукції, аналогії та порівняння у статті проведено структурування класифікації криптоактивів за функціональним призначенням. У результаті для ведення обліку та складання звітності криптоактиви пропонується об’єднати у три групи: криптовалюти; криптодомени та криптогаманці; смарт контракти. Попри унікальні характеристики кожної із зазначених груп, технічні особливості функціонування системи криптоактивів породжують між ними нерозривний взаємозв’язок та унеможливлюють їхнє відокремлене використання. На базі розробленої класифікації визначено перспективи застосування існуючих баз оцінки активів для формування достовірної інформації про різні види криптоактивів у фінансовій звітності. Отримані теоретичні результати можуть слугувати базою цілісного розуміння сутності криптоактивів, що забезпечить їхнє виокремлення як нової групи господарських засобів за формою функціонування та розробку методів оцінки, адекватних їхній економічній природі та особливостям використання у бізнес-практиці. З практичної точки зору статус окремої групи активів забезпечить можливість адекватного відображення усіх складових цифрових активів у фінансовій звітності шляхом розробки спеціалізованого стандарту, в якому їхнє визнання та оцінка ґрунтуватимуться на специфіці життєвого циклу та застосування. Тип статті – теоретична.
Economics as a science, Business records management
Стаття розкриває питання адаптації підприємства в умовах невизначеності зовнішнього середовища, способи компанії налаштувати власні бізнес-моделі для адаптації до змінних економічних умов. Проведено аналіз сучасних підходів до формування бізнес-моделей залежно від динаміки зовнішніх факторів та за різних умов адаптації – цифровізація, криза, військові дії тощо. Розглянуто використання гнучких стратегій управління та інноваційних підходів до розвитку бізнесу. Обґрунтована необхідність постійного оновлення бізнес-моделей для забезпечення стійкості та успішності в умовах швидких змін на ринку і економічних умовах. Розкрито важливість забезпечення гнучкості бізнес-моделі управління компанією для забезпечення її конкурентоспроможності в сучасних умовах. Відмічено, що сьогоднішній бізнес зіштовхується з швидкими змінами технологій, ринковими умовами, законодавством та глобальними трендами. Зміни можуть бути радикальними і виникають дуже швидко, що вимагає постійного оновлення стратегій і моделей бізнесу. Акцентовано, що однією з ключових переваг для підприємства є здатність адаптуватися до нових умов швидше за конкурентів. Доведено, що ефективна адаптація бізнес-моделі дозволяє не лише вижити, а й успішно розвиватися в конкурентному середовищі. Визначено, що адаптація включає в себе впровадження нових інноваційних підходів, що можуть стати новими джерелами прибутку або покращити ефективність операцій підприємства. Водночас, стабільність в умовах змін дозволяє зберігати довгострокові позиції на ринку. Обґрунтовано, що сучасні підходи до адаптації включають в себе не лише реактивні заходи, а й системні стратегії, які враховують потенційні зміни в майбутньому і готують бізнес до них напередодні. Визначено, що проблема адаптації бізнес-моделей до перманентних змін зовнішнього середовища є критично важливою для підтримки конкурентоспроможності, стійкості та успішного функціонування підприємств у сучасній економічній реальності.
Economics as a science, Business records management
Large Language Models (LLMs) are increasingly used for boosting organizational efficiency and automating tasks. While not originally designed for complex cognitive processes, recent efforts have further extended to employ LLMs in activities such as reasoning, planning, and decision-making. In business processes, such abilities could be invaluable for leveraging on the massive corpora LLMs have been trained on for gaining deep understanding of such processes. In this work, we plant the seeds for the development of a benchmark to assess the ability of LLMs to reason about causal and process perspectives of business operations. We refer to this view as Causally-augmented Business Processes (BP^C). The core of the benchmark comprises a set of BP^C related situations, a set of questions about these situations, and a set of deductive rules employed to systematically resolve the ground truth answers to these questions. Also with the power of LLMs, the seed is then instantiated into a larger-scale set of domain-specific situations and questions. Reasoning on BP^C is of crucial importance for process interventions and process improvement. Our benchmark, accessible at https://huggingface.co/datasets/ibm/BPC, can be used in one of two possible modalities: testing the performance of any target LLM and training an LLM to advance its capability to reason about BP^C.
Важливим методичним питанням, яке постає у зв’язку із завданням цілісного аналізу, оцінки та подальших трансформацій соціально-психологічного клімату (СПК) колективу підприємства є визначення його HR-метрик (критеріїв / показників), виражених у певних одиницях виміру. Для оцінки СПК колективу підприємства HR-аналітика пропонує такий інструмент як HR-метрики різних типів (кількісні, якісні, фінансові та метрики результативності). Кількісні HR-метрики оцінки СПК зосереджені на цифрах – характеризують гуманне та інклюзивне робоче середовище на підприємстві (соціальну-демографічну структуру персоналу); метрики руху, стабільності та утримання персоналу; метрики управління скаргами). Якісні HR-метрики оцінки СПК базуються на соціоекономічних технологіях – це метрики ступеня сприятливості СПК колективу; метрики (індекси, рівні, балові оцінки тощо) ставлення працівників до підприємства: задоволеності, лояльності, залученості. Фінансові HR-метрики оцінки СПК зосереджені на вартісному впливі функції HR, дозволяють порівнювати витрати та вигоди HR програм поліпшення СПК колективу – метрики економічності витрат на поліпшення СПК. HR-метрики результативності оцінки СПК зосереджені на тому, щоб показати, чи досягло підприємство поставленої мети – це метрики рівня виконання підприємством поставлених завдань (планів) поліпшення СПК колективу. В сукупності кількісні, якісні, фінансові та HR-метрики результативності відображають узгодженість цілей оцінки соціально-психологічного стану колективу з цілями корпоративного управління. Поліпшення СПК в колективі підприємства на основі використання HR-метрик в HR-аналітиці – засіб досягнення певного бізнес-результату.
Economics as a science, Business records management
Databases are considered to be integral part of modern information systems. Almost every web or mobile application uses some kind of database. Database management systems are considered to be a crucial element from both business and technological standpoint. This paper divides different types of database management systems into two main categories (relational and non-relational) and several sub categories. Ranking of various sub categories for the month of July, 2021 are presented in the form of popularity score calculated and managed by DB-Engines. Popularity trend for each category is also presented to look at the change in popularity since 2013. Complete ranking and trend of top 20 systems has shown that relational models are still most popular systems with Oracle and MySQL being two most popular systems. However, recent trends have shown DBMSs like Time Series and Document Store getting more and more popular with their wide use in IOT technology and BigData, respectively.
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.
Abstract Electrical equipment maintenance is of vital importance to management companies. Efficient maintenance can significantly reduce business costs and avoid safety accidents caused by catastrophic equipment failures. In the current context, predictive maintenance (PdM) is becoming increasingly popular based on machine learning approaches, while its research on electrical equipment such as low-voltage contactors is in its infancy. The failure modes are mainly fusion welding and explosion, and a few are unable to switch on. In this study, a data-driven approach is proposed to predict the remaining useful life (RUL) of the low-voltage contactor. Firstly, the three-phase alternating voltage and current records the life of electrical equipment by tracking the number of times it has been operated. Secondly, the failure-relevant features are extracted by using the time domain, frequency domain, and wavelet methods. Then, a CNN-LSTM network is designed and used to train an electrical equipment RUL prediction model based on the extracted features. An experimental study based on ten datasets collected from low-voltage AC contactors reveals that the proposed method shows merits in comparison with the prevailing deep learning algorithms in terms of MAE and RMSE.
Matyáš Skalický, Štěpán Šimsa, Michal Uřičář
et al.
Information extraction from semi-structured documents is crucial for frictionless business-to-business (B2B) communication. While machine learning problems related to Document Information Extraction (IE) have been studied for decades, many common problem definitions and benchmarks do not reflect domain-specific aspects and practical needs for automating B2B document communication. We review the landscape of Document IE problems, datasets and benchmarks. We highlight the practical aspects missing in the common definitions and define the Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR) problems. There is a lack of relevant datasets and benchmarks for Document IE on semi-structured business documents as their content is typically legally protected or sensitive. We discuss potential sources of available documents including synthetic data.
Geri Skenderi, Christian Joppi, Matteo Denitto
et al.
The fashion industry is one of the most active and competitive markets in the world, manufacturing millions of products and reaching large audiences every year. A plethora of business processes are involved in this large-scale industry, but due to the generally short life-cycle of clothing items, supply-chain management and retailing strategies are crucial for good market performance. Correctly understanding the wants and needs of clients, managing logistic issues and marketing the correct products are high-level problems with a lot of uncertainty associated to them given the number of influencing factors, but most importantly due to the unpredictability often associated with the future. It is therefore straightforward that forecasting methods, which generate predictions of the future, are indispensable in order to ameliorate all the various business processes that deal with the true purpose and meaning of fashion: having a lot of people wear a particular product or style, rendering these items, people and consequently brands fashionable. In this paper, we provide an overview of three concrete forecasting tasks that any fashion company can apply in order to improve their industrial and market impact. We underline advances and issues in all three tasks and argue about their importance and the impact they can have at an industrial level. Finally, we highlight issues and directions of future work, reflecting on how learning-based forecasting methods can further aid the fashion industry.
Xiliang Zhu, David Rossouw, Shayna Gardiner
et al.
Pre-trained contextualized embedding models such as BERT are a standard building block in many natural language processing systems. We demonstrate that the sentence-level representations produced by some off-the-shelf contextualized embedding models have a narrow distribution in the embedding space, and thus perform poorly for the task of identifying semantically similar questions in real-world English business conversations. We describe a method that uses appropriately tuned representations and a small set of exemplars to group questions of interest to business users in a visualization that can be used for data exploration or employee coaching.
The organization has certain characteristics that both man and other living beings have. These include the primary basic types of organization, or organizational configurations, as well as organizational context variables such as environment, power, age, size, business strategy, and technical system. The main research about organizational structures was started by Mintzberg in 1979. After that, many other authors made a research on this topic. Organizational management is first determined by the organization’s configuration and then, by the type of strategy for entering the international market. The paper examines the extent to which the choice of organizational configuration has an impact on the choice of the internationalization strategy or the strategy for entering a foreign market. The research sample consists of 120 export-oriented companies from Bosnia and Herzegovina belonging to one of the four basic types: simple structure, professional bureaucracy, adhocracy, or machine bureaucracy. The basic methods used are the Multinomial Logit Model, the multiple regression model, and the Pearson correlation coefficient. The results of the research indicate that there is a significant correlation between organizational configuration (measured by organizational context variables) and internationalization strategy.
Economic theory. Demography, Business records management
This study aims to investigate the effect of governance in strengthening the organizational immunity in the Greater Madaba municipality. The study includes two variables, an independent variable, governance and four dimensions including organizational justice, accountability, sustainability, and transparency, and the dependent variable is organizational immunity. The researcher designed a questionnaire consisting of (30) items that included the independent variables, organizational justice, accountability, sustainability, and transparency, and the dependent variable, organizational immunity, and it was distributed to employees of the Greater Madaba municipality, where the researcher used the random sample method, as the size of the study population reached (210) employees. The study used a sample size of 136. The study found a set of results, including the level of both organizational immunity and governance in Greater Madaba municipality, was high, according to the viewpoint of the sample members, and the existence of a statistically significant impact at the level of significance (α ≤ 5%) of governance with its combined dimensions, organizational justice, accountability, sustainability, and transparency, on the organizational immunity in the Greater Madaba municipality. Likewise, the absence of a statistically significant impact at the level of significance (α ≤ 5%) for sustainability was one of the governance dimensions in organizational immunity in Greater Madaba, and there was no effect of transparency on regulatory immunity. The study recommends that the Municipality of Madaba focus on providing programs to develop employees and involve them in training courses in institutes, specialized centers, and universities and pay attention to sustainability and transparency.
Due to the significant importance of Big Data analysis, especially in business-related topics such as improving services, finding potential customers, and selecting practical approaches to manage income and expenses, many companies attempt to collaborate with scientists to find how, why, and what they should analysis. In this work, we would like to compare and discuss two different approaches that employed in business analysis topic in Big Data with more consideration on how they utilized Spark. Both studies have investigated Churn Prediction as their case study for their proposed approaches since it is an essential topic in business analysis for companies to recognize a customer intends to leave or stop using their services. Here, we focus on Apache Spark since it has provided several solutions to handle a massive amount of data in recent years efficiently. This feature in Spark makes it one of the most robust candidate tools to upfront with a Big Data problem, particularly time and resource are concerns.
Establishing a new business may involve Knowledge acquisition in various areas, from personal to business and marketing sources. This task is challenging as it requires examining various data islands to uncover hidden patterns and unknown correlations such as purchasing behavior, consumer buying signals, and demographic and socioeconomic attributes of different locations. This paper introduces a novel framework for extracting and identifying important features from banking and non-banking data sources to address this challenge. We present an attention-based supervised feature selection approach to select important and relevant features which contribute most to the customer's query regarding establishing a new business. We report on the experiment conducted on an openly available dataset created from Kaggle and the UCI machine learning repositories.
Catastrophic losses caused by natural disasters receive a growing concern about the severe rise in magnitude and frequency. The constructions of insurance and financial management scheme become increasingly necessary to diversify the disaster risks. Given the frequency and severity of floods in China, this paper investigates the extreme analysis of flood-related huge losses and extreme precipitations using Peaks-Over-Threshold method and Point Process (PP) model. These findings are further utilized for both designs of flood zoning insurance and flooding catastrophic bond: (1) Using the extrapolation approach in Extreme Value Theory (EVT), the estimated Value-at-Risk (VaR) and conditional VaR (CVaR) are given to determine the cross-regional insurance premium together with the Grey Relational Analysis (GRA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The flood risk vulnerability and threat are analyzed with both the geography and economic factors into considerations, leading to the three layered premium levels of the 19 flood-prone provinces. (2) To hedge the risk for insurers and reinsurers to the financial market, we design a flooding catastrophe bond with considerate trigger choices and the pricing mechanism to balance the benefits of both reinsurers and investors. To reflect both the market price of catastrophe risk and the low-correlated financial interest risk, we utilize the pricing mechanism of Tang and Yuan (2021) to analyze the pricing sensitivity against the tail risk of the flooding disaster and the distortion magnitude and the market risk through the distortion magnitude involved in Wang's transform. Finally, constructive suggestions and policies are proposed concerning the flood risk warning and prevention.