Hasil untuk "Business records management"

А. С. Мохненко, Г. С. Остроус

Управління персоналом дедалі більше виступає стратегічним чинником формування конкурентних переваг підприємств в умовах цифрової трансформації та нестабільного ринкового середовища. Зростання значення людського капіталу вимагає від бізнесу нових підходів до організації HRM як складової загальної стратегії. Метою дослідження є розробка інтегрованої моделі управління персоналом, яка забезпечує підвищення конкурентоспроможності підприємства шляхом посилення взаємозв’язку між HR-стратегією та бізнес-результатами. Дослідження ґрунтується на системному підході та структурному моделюванні HR-процесів. Застосовано методи аналізу, синтезу, порівняння та моделювання з метою виявлення чинників ефективного управління персоналом та візуалізації їхнього впливу на ключові показники конкурентоспроможності. Вперше запропоновано авторську графічну модель, яка концептуалізує зв’язок між елементами HRM-системи та результатами діяльності підприємства. Основною гіпотезою дослідження виступає припущення про те, що стратегічно інтегроване управління персоналом прямо впливає на стабільність, адаптивність і інноваційність бізнесу. Методика дослідження включала критичний аналіз наукових джерел, порівняння традиційних і сучасних HR-моделей, оцінку впливу внутрішніх та зовнішніх факторів на HR-стратегію. Побудова графічної моделі дозволила системно представити причинно-наслідкові зв’язки між кадровою політикою та рівнем конкурентоспроможності підприємства. У ході дослідження узагальнено сучасні підходи до HRM, виявлено сильні та слабкі сторони існуючих моделей, систематизовано чинники, що формують ефективну HR-стратегію. Сформульовано концепцію стратегічного HRM як ключового елемента корпоративного розвитку. Розроблено графічну модель, яка охоплює HR-функції, вимірювані результати та зовнішні ефекти. Оцінено потенціал моделі для різних галузей бізнесу, включаючи виробництво й ІТ. Запропоновано практичні рекомендації щодо впровадження моделі в управлінську практику українських підприємств. Теоретичне значення дослідження полягає у формуванні концептуального бачення HRM як багаторівневої системи, що впливає на бізнес-результати. Практична цінність полягає у можливості використання моделі для стратегічного планування HR-політики та удосконалення управлінських рішень у кадровій сфері. Оригінальність роботи виявляється у побудові інтегрованої графічної моделі HRM, адаптованої до умов українського бізнесу. Основні висновки підтверджують ефективність стратегічного HR як джерела довгострокових переваг. Подальші дослідження доцільно спрямувати на емпіричну верифікацію моделі в різних секторах економіки. Обмеженням дослідження є переважно теоретичний характер аналізу без прикладного тестування.

Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi et al.

This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. The primary objective is to analyze operational risk data and evaluate the performance of various machine learning models to develop effective tools for enhancing risk management and minimizing financial losses in banks and financial institutions. Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). Model performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC) to determine the most effective model for risk prediction. The findings indicate that the RF and SVM algorithms outperform other models in predicting operational risk across all scenarios. Furthermore, the results demonstrate the strong predictive capability of machine learning algorithms in assessing operational risk, highlighting their potential as valuable decision-making tools for risk management in the banking sector.Keywords: Risk Prediction, Operational Risk, Risk Management, Machine Learning IntroductionOperational risk is defined as the risk arising from external factors or failures in internal controls or information systems, which may lead to both anticipated and unexpected losses (Crouchy et al., 1998). Lopez (2002) characterizes it as any unquantifiable risk that a bank may encounter. According to the Basel II Agreement, operational risk refers to the probability of loss resulting from deficiencies, breakdowns, or inefficiencies in human resources, processes, technologies, infrastructure, or internal and external events (Pena et al., 2018).To estimate the capital required to cover operational risk, the Basel framework introduces three approaches: the Basic Indicator Approach (BIA), the Standardized Approach (SA), and the Advanced Measurement Approach (AMA) (Mora Valencia, 2010; Mora Valencia et al., 2017). The BIA and SA estimate capital requirements based on annual gross income, with the key distinction being that the SA categorizes a bank’s activities into eight business lines. Under the BIA, an alpha coefficient (α) of 15% is applied, whereas in the SA, each business line has a specific beta coefficient (β) ranging between 12% and 18%. The AMA employs both quantitative and qualitative methods for operational risk modeling, leveraging databases to collect statistical data and utilizing the loss distribution approach (LDA) to model frequency and severity distributions. Capital coverage is then determined based on the cumulative distribution of these variables. Since the LDA is data-driven, the Basel framework (BCBS, 2004) emphasizes the necessity of a robust database for collecting operational risk data. Four key databases are required: internal loss event data, external loss event data, scenario-based analysis data, and a database of business environment and internal control factors.Compared to other banking risks, such as credit and market risks, measuring, monitoring, and managing operational risk is considerably more complex. This risk has gained increasing attention in recent years, as large operational losses have led to the liquidation of financial institutions (Abdymomunov et al., 2020; Afonso et al., 2019). Crisanto and Perino (2017) identify cyber threats and cyber fraud as critical factors influencing operational risk capital estimation. These risks have intensified with the growth of electronic banking services and include illegal access, system disruptions, and the misuse or theft of digital assets for financial gain (BCBS, 2016; Drew & Farrell, 2018). To quantify potential losses in electronic banking transactions, Bouveret (2018) proposed a Bayesian Network (BN) model to estimate operational risk capital requirements in financial institutions.Machine learning has emerged as one of the most promising yet challenging approaches in modern finance (Tsai & Wu, 2008). These methods have transformed the financial industry, with deep learning (DL) being extensively studied and applied due to its adaptability and predictive capabilities (Ivanov, 2019). Pena et al. (2021) employed a fuzzy convolutional deep learning model to estimate the maximum operational risk value at a 99.9% confidence level. Similarly, Zhou et al. (2020) utilized semi-supervised machine learning algorithms to classify operational risks based on financial news, analyzing 5,843 documents from financial articles and newspapers in the Asia-Pacific region between February and March 2019. Their model demonstrated the capability to predict various types of risks in the banking industry. In another study, Akbari and Yazdanian (2023) applied machine learning algorithms to determine optimal thresholds for operational loss severity data, classifying the data and estimating the capital required to cover operational risk by integrating severity and frequency distribution functions with Monte Carlo simulation. Method and DataIn this study, operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including RF, DT, SVM, LR, NB, and KNN. The models' performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and AUC to identify the most effective model for predicting the likelihood of risk occurrence. FindingsThe results indicate that the RF and SVM algorithms exhibit strong performance in predicting operational risk across all scenarios. Specifically, the RF algorithm achieved an accuracy of 0.9690, while the SVM algorithm attained an accuracy of 0.9587 in State 1, making them the most effective models in this setting. Both algorithms demonstrated comparable performance across other modes. Conclusion and DiscussionThis study analyzes and predicts operational risk occurrence in the banking industry using machine learning algorithms. The findings indicate that various algorithms, particularly RF and SVM, demonstrate strong predictive performance. These results have the potential to transform operational risk management in banks, leading to significant reductions in associated costs and losses.A key insight from this study is that leveraging large and diverse datasets can substantially enhance prediction accuracy. Machine learning models can process complex datasets, identify hidden patterns, and facilitate early risk detection, enabling banks to implement preventive measures before risks materialize. Moreover, integrating machine learning into risk management enhances decision-making by providing precise, data-driven predictions, allowing for more effective strategies and efficient resource allocation.Future research could incorporate additional data, such as historical records, economic indicators, and internal process information, to further improve prediction accuracy. With advancements in technology, more sophisticated techniques—such as reinforcement learning methods (e.g., DQN, Q-Learning, DDPG, and Meta-Learning)—could enhance the accuracy and efficiency of operational risk prediction models.

У. Я. Андрусів

Дослідження спрямоване на розробку науково обґрунтованої концептуальної моделі управління організаційними змінами у процесі інтернаціоналізації закладів вищої освіти в умовах глобалізації освітнього простору та посилення міжнародної конкуренції. Методологічною основою слугував системний підхід, що поєднує теоретичний аналіз наукової літератури з питань управління організаційними змінами та інтернаціоналізації університетів, порівняльний аналіз існуючих моделей трансформаційних процесів у різних організаційних контекстах, а також емпіричне дослідження успішних практик міжнародної інтеграції провідних європейських та північноамериканських університетів. Основним науковим результатом є розроблена інтегрована модель управління організаційними змінами в процесі інтернаціоналізації (IUCM), що структурно включає п’ять взаємопов’язаних компонентів: стратегічне планування міжнародної діяльності, оцінку організаційної готовності до трансформацій, структурно-процесні зміни, систематичний розвиток людського капіталу та комплексний моніторинг результатів. Практична реалізація моделі передбачає послідовне проходження чотирьох етапів: ініціювання змін, планування трансформації, безпосереднє впровадження та інституціоналізацію результатів. Розроблено спеціалізований інструментарій діагностики, планування, реалізації та контролю трансформаційних процесів, адаптований до специфічних характеристик академічного середовища. Ідентифіковано критичні фактори успішності впровадження моделі та потенційні бар’єри, включаючи консервативність університетської організаційної культури та обмеженість фінансово-ресурсного забезпечення. Наукова новизна дослідження полягає у створенні теоретично обґрунтованого інтегративного підходу, що синтезує фундаментальні принципи теорії управління організаційними змінами з практичними вимогами процесів інтернаціоналізації закладів вищої освіти.

С. А. Шелудько

Цифровізація банківської діяльності є вагомим чинником забезпечення фінансової безпеки держави в умовах екзистенційних потрясінь (зокрема, пандемії, війни), що підкреслює актуальність цієї теми для України. Дослідження зосереджено на емпіричному аналізі ролі цифрових перетворень у стабільності банківської системи. Метою є визначення характеру впливу цифровізації на фінансову безпеку крізь призму ефективності банківської діяльності в Україні. Аналіз базується на статистичних даних НБУ, поділених на два періоди: до та після лютого 2022 року. Застосовано кореляційний аналіз для виявлення зв’язків між показниками цифровізації та дохідності банків. Тест на причинність за Грейнджером використано для оцінки напряму причинно-наслідкових залежностей. Основна гіпотеза припускає, що цифрові перетворення є рушієм безпеки лише за стабільних умов. Підкреслено активізацію наукових досліджень банківської цифровізації в період пандемії коронавірусу. Визначено два ключові вектори в поглядах науковців на роль цифрових перетворень у забезпеченні фінансової безпеки: як джерело регуляторних ризиків і як драйвер операційної ефективності фінансових установ. Виявлено позитивний вплив безготівкових операцій на дохідність банків до 2022 року. Встановлено послаблення зв’язку між цифровізацією та ефективністю під час війни. Доведено, що у кризові періоди рентабельність активів негативно впливає на обсяг транзакцій. Оцінено двоїсту природу цифровізації залежно від зовнішніх умов. Теоретичні результати розширюють розуміння ролі цифровізації у фінансовій безпеці та можуть бути застосовані для вдосконалення регуляторних підходів. Практичні висновки можуть бути використані для розробки стратегії цифрової трансформації в кризових умовах, а також з метою адаптації макропруденційної політики в сфері цифрових фінансів. Наукова новизна дослідження полягає в отриманні емпіричного підтвердження впливу фактора війни на роль цифровізації у стабільності банківської системи України. Подальші дослідження доцільно спрямувати на розробку комплексного показника цифровізації, верифікацію отриманих результатів на мікрорівні. Тип статті – емпірична.

Luca Attolico

Macroeconomic nowcasting sits at the intersection of traditional econometrics, data-rich information systems, and AI applications in business, economics, and policy. Machine learning (ML) methods are increasingly used to nowcast quarterly GDP growth, but adoption in high-stakes settings requires that predictive accuracy be matched by interpretability and robust uncertainty quantification. This article reviews recent developments in macroeconomic nowcasting and compares econometric benchmarks with ML approaches in data-rich and shock-prone environments, emphasizing the use of nowcasts as decision inputs rather than as mere error-minimization exercises. The discussion is organized along three axes. First, we contrast penalized regressions, dimension-reduction techniques, tree ensembles, and neural networks with autoregressive models, Dynamic Factor Models, and Random Walks, emphasizing how each family handles small samples, collinearity, mixed frequencies, and regime shifts. Second, we examine explainability tools (intrinsic measures and model-agnostic XAI methods), focusing on temporal stability, sign coherence, and their ability to sustain credible economic narratives and nowcast revisions. Third, we analyze non-parametric uncertainty quantification via block bootstrapping for predictive intervals and confidence bands on feature importance under serial dependence and ragged edge. We translate these elements into a reference workflow for "decision-grade" nowcasting systems, including vintage management, time-aware validation, and automated reliability audits, and we outline a research agenda on regime-dependent model comparison, bootstrap design for latent components, and temporal stability of explanations. Explainable ML and uncertainty quantification emerge as structural components of a responsible forecasting pipeline, not optional refinements.

Valdis Vitolins

The thesis discusses topics related to the development of business process management systems. Business process management systems have evolved on the basis of workflow management systems through incremental inclusion of standard information system functions, for example, resource and client management. The application of model driven development is required to deal with the complexity of business management systems and to increase development efficiency. In contrast to conventional information systems, the behavior of business management systems is strongly affected by the business models that they execute. Thus, business process models also can be used for designing and developing business management systems using sequentially applied model transformations that adapt models to a specific execution platform.

milad saeidi, Ali Mansory, omid mahdieh et al.

Abstract This study examines the effect of loyalty to Iranian apparel brands through the paths of satisfaction, trust, and commitment to the brand. In today's competitive environment, brands are considered one of the most valuable assets of organizations, and their ability to attract and retain customers depends heavily on creating satisfaction, trust, and commitment in customers. Therefore, accurate identification of the factors affecting brand loyalty in the apparel industry is of particular importance. This study was conducted using a convenience sampling method on consumers familiar with Iranian apparel brands. Data were collected through a Likert-scale questionnaire that assesses the dimensions of satisfaction, trust, commitment, and loyalty to the brand. After confirming the validity and reliability of the measurement tool, the collected data were analyzed using structural equation modeling (PLS-SEM) in SmartPLS software. The results of this study showed that brand satisfaction plays an effective role in creating brand loyalty and this relationship is strengthened by increasing consumers' trust and commitment to the brand. More precisely, consumers who are satisfied with the brand show a greater tendency to trust and commit, and these factors in turn increase their brand loyalty. Also, trust and commitment, as mediating variables, strengthen the path of satisfaction's effect on loyalty. These findings emphasize the importance of customer-centric strategies in Iranian clothing brands and suggest that brands focus on increasing customer satisfaction and strengthening their trust and commitment in order to create long-term loyalty. Introduction The Iranian clothing industry is facing numerous problems, including a decrease in domestic production, an increase in dependence on imports, a decrease in product diversity, and a decrease in the quality of some products. In addition, the export market of this industry has not grown much due to weakness in competition with global brands and trade restrictions (Khani et al., 2022; Jafari et al., 2023). These challenges require the development of comprehensive strategies in the fields of production, marketing, and export in order to increase the competitiveness of domestic brands. Research in the field of branding and customer loyalty plays an important role in understanding consumer behavior and developing marketing strategies. It is necessary, especially in the Iranian apparel industry, to focus on factors such as brand satisfaction and brand image, and their relationship with customer loyalty. Previous research has shown that trust and brand commitment as mediating variables have a significant impact on this relationship (Saragi et al., 2019; Chinomona, 2013). The results of this study can help manufacturers and marketers improve customer experience, promote brand communication, and increase customer loyalty. In addition, identifying the real needs of consumers will improve the quality and diversity of products and increase the competitiveness of domestic brands in domestic and international markets (Aditer et al., 2020; Medina et al., 2022). Overall, this research can contribute to the sustainable growth of the Iranian apparel industry by providing scientific and practical solutions. Theoretical foundations Definition and importance of brand satisfaction: Brand satisfaction is one of the most important concepts in marketing and customer management strategies that directly affects consumer behavior. Satisfaction is formed when the customer's expectations of the brand are aligned with his or her actual experiences with the brand. Definition and dimensions of brand loyalty: Brand loyalty is a multifaceted characteristic that goes beyond simply repeat purchases and includes the customer's desire to maintain their relationship with the brand through repeated purchases, support for that brand, and even recommend it to others. In addition to purchasing behaviors, this concept also psychologically refers to the customer's positive attitude and emotional commitment to the brand (Chinomona & Dubihlela, 2021). Definition and Importance of Brand Commitment: Brand commitment is defined as a long-term and emotional relationship between the customer and the brand. This concept goes beyond brand loyalty and specifically emphasizes the emotional and psychological interactions of customers with the brand (Meyer et al., 2022). Definition and Impact of Brand Trust: Brand trust is defined as the customer's feeling of confidence in the reliability, quality, and honesty of the brand. This concept is one of the important pillars in the brand-customer relationship, because brand trust reduces the risks perceived by customers and makes customers buy with more confidence (Kim et al., 2021). The interaction and synergy of these factors of satisfaction, commitment, and trust mutually influence each other, and the interaction between these factors creates a comprehensive and positive experience for customers, which ultimately leads to increased brand loyalty. Recent research has shown that when customers feel that the brand best meets their needs, this feeling leads to increased trust and commitment to the brand, which in turn strengthens satisfaction and loyalty. In competitive markets, brands that can strengthen these three factors simultaneously will have a higher competitive advantage (Foroudi et al., 2023). Research Methodology This research is designed as an applicable study, aims to investigate the relationships between different variables in the field of loyalty to Iranian clothing brands. In terms of methodology, this research belongs to the category of descriptive-survey and correlational studies. For sampling, the convenience method was used, which allows researchers to collect data using existing resources. This method is especially common in market and consumer behavior research and provides the required data quickly and effectively. To collect data, a standard questionnaire was used, which is a combination of the questionnaires of Fernandez and Moreira (2019), Bashokooh et al., (2020), and Sang et al., (2019). The target population is all people who use different clothing brands. Also, the applied convenience sampling method is a non-random sampling method in which available people are selected. After distributing the questionnaire link in cyberspace, 157 questionnaires were collected, of which 150 were selected for statistical analysis. Selecting valid and relevant questionnaires increases the quality of data and contributes to the validity of the results. Research findings In this study, the relationships between the variables of brand satisfaction, brand trust, and brand loyalty were analyzed using the structural equation modeling (SEM) method in SMART PLS3 software. T-values ​​were examined to evaluate the significance of the relationships between the variables, so that if the t-value is outside the range of ±1.96, the relationship in question is considered statistically significant. The model includes three latent variables, each measured through different indicators: - Brand satisfaction: four indicators with factor loadings between 14.653 and 34.621. - Brand trust: four indicators with factor loadings between 16.848 and 105.745. - Brand loyalty: four indicators with factor loadings between 17.235 and 124.975. 1- The effect of satisfaction on brand trust: path coefficient of 0.791, indicating a strong and positive effect. 2- The effect of trust on brand loyalty: path coefficient of 0.477, indicating a medium and positive effect. 3- The effect of satisfaction on brand commitment: path coefficient of 0.728, indicating a strong and positive effect. 4- The effect of commitment on brand loyalty: path coefficient of 0.262, indicating a positive but relatively weak effect. 5- The direct effect of satisfaction on loyalty: path coefficient of 0.211, which is weaker than the indirect effect. R² Index and Model Predictive Power: The R² index indicates the model’s ability to predict dependent variables. The results show that the research model has a high ability to explain the relationships between variables: - R² for brand loyalty: 0.764 (very strong) - R² for brand trust: 0.625 (desirable( - R² for brand commitment: 0.531 (adequate) Brand satisfaction directly and indirectly affects brand loyalty through trust and commitment. The indirect effect of satisfaction through trust (0.791 × 0.477) and commitment (0.728 × 0.262) is stronger than its direct effect (0.211). Therefore, organizations should focus on increasing customer satisfaction, strengthening trust, and creating brand commitment to ensure long-term customer loyalty. Discussion and Conclusion In today's competitive world, customer satisfaction and brand loyalty play a key role in the success of businesses. Satisfied customers are less likely to be attracted to competitors and maintain a long-term relationship with the brand. This study, by examining brand satisfaction, brand trust, and brand commitment, shows that customer satisfaction has a direct impact on loyalty, and brand trust and commitment play a mediating role. The results of the study are consistent with previous studies and confirm that customer trust and commitment to the brand transform their satisfaction into loyalty. To improve customer satisfaction, brand trust, and brand commitment, and consequently increase brand loyalty, the following solutions are suggested: Increase customer satisfaction: -Use surveys and digital tools to continuously monitor customer satisfaction. -Make changes and improvements based on customer feedback. -Optimize the customer experience by simplifying the purchasing process and brand interaction. -Provide strong after-sales service and effective support. Strengthening trust and commitment to the brand: -Transparency in communications and providing accurate information about products and services. -Ensuring security in online purchases and maintaining customer privacy. -Providing quality services and responding quickly to customer problems and needs. Increasing brand loyalty: -Designing loyalty programs with points and rewards tailored to customer behavior. -Establishing continuous communication with customers and informing them about brand changes and improvements. -Providing fast and effective support to create a positive and lasting experience. This research focused on customers in the Iranian apparel industry and its results may not be generalized to other industries such as automobiles or home appliances. Also, demographic factors such as age, income, and location affect brand loyalty. These findings indicate that increasing customer satisfaction, creating trust and commitment to the brand, and improving interactions play key roles in strengthening customer loyalty.

Jiajia Jiang, Moting Su, Fengshu Li et al.

As artificial intelligence technology becomes increasingly widespread, AI-generated content (AIGC) is gradually penetrating into many fields. Although AIGC plays an increasingly prominent role in business and cultural communication, the issue of copyright has also triggered widespread social discussion. The current legal system for copyright is built around human creators, yet in the realm of AIGC, the role of humans in content creation has diminished, with the creative expression primarily reliant on artificial intelligence. This discrepancy has led to numerous complexities and challenges in determining the copyright ownership of AIGC within the established legal boundaries. In view of this, it is necessary to meticulously record contributions of all entities involved in the generation of AIGC to achieve a fair distribution of copyright. For this purpose, this study thoroughly records the intermediate data generated throughout the full lifecycle of AIGC and deposits them into a decentralized blockchain system for secure multi-party supervision, thereby constructing a trustworthy AIGC copyright management system. In the event of copyright disputes, auditors can retrieve valuable proof from the blockchain, accurately defining the copyright ownership of AIGC products. Both theoretical and experimental analyses confirm that this scheme shows exceptional performance and security in the management of AIGC copyrights.

Maria-Luiza PASCAL

The paper describes the pathway towards building a unique research infrastructure and the key requirements set by ESFRI in order to enter ESFRI Roadmap and to gain its support along the lifecycle of a research infrastructure. It focuses on the financial matters requested by ESFRI throughout the all phases that METROFOOD-RI has passed through so far, underlining that not only the scientific case and its positioning in the European RIs’ landscape are important while evaluating a future RI, but also how the RI will ensure its long-term financial sustainability. METROFOOD-RI is a distributed pan-European research infrastructure of global interest, in which IBA Bucharest has been an essential partner since its conception in 2015. The document showcases an overview of the architecture and organisation of METROFOOD-RI, its core services, the work performed in preparing a sound cost book, financial plan, cash-flow and business model.

Ivan Sichka

У статті досліджено управління якістю проєктів у готельно-ресторанному бізнесі. Зазначено, що управління якістю проєкту – це процес, який розглядає, як проєкт повинен розвиватися, щоб досягти бажаної якості результатів. Це вимагає постійного вимірювання якості діяльності та процесів. В управлінні якістю проєкту стандарти встановлюються завчасно, щоб оцінити результати, і потрібно вживати заходів протягом усього проєкту, щоб виправити помилки. У готельно-ресторанному бізнесі кінцевою метою управління якістю проєкту є гарантування результатів, які задовольняють потреби та очікування клієнтів. Виділено три компоненти управління якістю проєкту: планування якості; гарантія якості; контроль якості. Охарактеризовано, що на основі управління якістю проєкту можна отримати наступні переваги: вищий рівень задоволеності клієнтів, послуги  кращої якості, підвищена продуктивність, фінансове зростання. Здійснено аналіз інструментів, які можуть підтримувати управління якістю проєкту зважаючи на специфіку готельно-ресторанного бізнесу.

Amin Beheshti, Jian Yang, Quan Z. Sheng et al.

Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is trained on massive amounts of text data and uses deep learning techniques to learn patterns and relationships within the data, enabling it to generate coherent and contextually appropriate text. This position paper proposes using GPT technology to generate new process models when/if needed. We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes. ProcessGPT can be designed by training a generative pre-trained transformer model on a large dataset of business process data. This model can then be fine-tuned on specific process domains and trained to generate process flows and make decisions based on context and user input. The model can be integrated with NLP and machine learning techniques to provide insights and recommendations for process improvement. Furthermore, the model can automate repetitive tasks and improve process efficiency while enabling knowledge workers to communicate analysis findings, supporting evidence, and make decisions. ProcessGPT can revolutionize business process management (BPM) by offering a powerful tool for process augmentation, automation and improvement. Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting data engineers in maintaining data ecosystem processes within large bank organizations. Our scenario highlights the potential of this approach to improve efficiency, reduce costs, and enhance the quality of business operations through the automation of data-centric and knowledge-intensive processes. These results underscore the promise of ProcessGPT as a transformative technology for organizations looking to improve their process workflows.

Kiran Busch, Alexander Rochlitzer, Diana Sola et al.

GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully employed in the business process management (BPM) domain, e.g., for predictive process monitoring and process extraction from text. This, however, typically requires fine-tuning the employed LM, which, among others, necessitates large amounts of suitable training data. A possible solution to this problem is the use of prompt engineering, which leverages pre-trained LMs without fine-tuning them. Recognizing this, we argue that prompt engineering can help bring the capabilities of LMs to BPM research. We use this position paper to develop a research agenda for the use of prompt engineering for BPM research by identifying the associated potentials and challenges.

Sulaiman Olusegun Atiku, Godwin Kaisara, Stewart Kaupa et al.

This study examines the dimensions of learning organization essential in enhancing Human Resources (HR) effectiveness towards the attainment of the strategic objectives of commercial banks operating in Nigeria. This study adopted a survey research design following a quantitative approach for data collection and analysis procedure. The respondents (professional bankers) were selected using a convenience sampling technique. A structured questionnaire was designed and administered to 305 respondents in the participating commercial banks. The data was analysed using a variance-based structural equation modelling via SmartPLS, version 3.2.9. The results showcased specific learning dimensions to consider in designing learning and development interventions for HR effectiveness in commercial banks. There is a dearth of literature on the specific learning dimensions that play a prominent role in ensuring HR effectiveness in the banking industry in developing countries, particularly in Nigeria. The outcomes of this study contribute to the extant literature and assist HR business partners in adding value to commercial banks through HR effectiveness.

Fabian Schueler, Dimitri Petrik

Business economics research on digital platforms often overlooks existing knowledge from other fields of research leading to conceptual ambiguity and inconsistent findings. To reduce these restrictions and foster the utilization of the extensive body of literature, we apply a mixed methods design to summarize the key findings of scientific platform research. Our bibliometric analysis identifies 14 platform-related research fields. Conducting a systematic qualitative content analysis, we identify three primary research objectives related to platform ecosystems: (1) general literature defining and unifying research on platforms; (2) exploitation of platform and ecosystem strategies; (3) improvement of platforms and ecosystems. Finally, we discuss the identified insights from a business economics perspective and present promising future research directions that could enhance business economics and management research on digital platforms and platform ecosystems.

Helena Dudycz, Marcin Hernes, Zdzislaw Kes et al.

The utilization of management control systems in university management poses a considerable challenge because university's strategic goals are not identical to those applied in profit-oriented management. A university's management control system should take into account the processing of management information for management purposes, allowing for the relationships between different groups of stakeholders. The specificity of the university operation assumes conducting long-term scientific research and educational programmes. Therefore, the controlling approach to university management should considerat long-term performance measurement as well as management in key areas such as research, provision of education to students, and interaction with the tertiary institution's socioeconomic environment.This paper aims to develop a conceptual framework of the Intelligent Management Control System for Higher Education (IMCSHE) based on cognitive agents. The main findings are related to developing the assumption, model, and technological basis including the artificial intelligence method.

Uno, Sandiaga Salahuddin, Supratikno, Hendrawan, Ugut, Gracia Shinta S. et al.

This study focuses specifically on the effect of Entrepreneurial Values and Entrepreneurial Orientation on Financial Performance of Indonesian state-owned enterprise and also incorporates Environmental Dynamism (ED) and Resource Availability (RA) as moderating variables which will influence the effect of (i) Entrepreneurial Orientation (EO) on Financial Performance (FP), (ii) Entrepreneurial Values (EV) on Financial Performance (FP), and (iii) Entrepreneurial Values (EV) on Entrepreneurial Orientation (EO). In addition, this study also provides a better understanding about the effect of Financial Performance on Intention to Sustainable Development (ITS) as well as Financial Performance on Intention to Collaboration (ITC) in Indonesian state-owned enterprises. The method of analysis used in this study is PLS-SEM methodology with purposive sampling method. The unit of analysis is all the state-owned enterprises listed which consists of 81 out of 106 state-owned enterprises, while the unit of observation is individuals from those state-owned enterprises (represented by the paired CEO and CFO who are originated from the same company). The findings of this study are that there is a significantly positive effect of: (i) EV on EO; (ii) EO on FP; (iii) FP on ITS; and (iv) FP on ITC. Meanwhile, (i) there is not any significantly positive effect of EV on FP, and (ii) there is no significant moderating effect of ED and RA on the relationship between (1) EV and FP, (2) EO and FP, as well as (3) EV and EO.

Khaddam, Amineh A., Irtaimeh, Hani J., Al-Batayneh, Ahmad Rajaa Salameh et al.

The aim of the study is to investigate the impact of business model innovation (BMI) on firm performance. The sample of the study consisted of 120 managers from Alban Al-youm Company in Jordan, a leading dairy company. Data were collected using a questionnaire administered to managers. Eighty-seven questionnaires were retrieved valid for the purpose of data analysis. BMI was measured using three components: value creation, value proposition and value capture innovations while company performance was assessed via self-rated questions about operational measures of performance. The results accepted the hypotheses that all dimensions of BMI had significant effects on company performance. That being so, the study contributed to the literature on BMI on company performance in the absence of such studies that use samples for Arab countries, particularly, from Jordan in one of the most vital industries, which is a dairy industry.

Phuong Nguyen, Luong Nguyen

In the world, the video game industry has really exploded until about 2000, and since then has achieved great strides, becoming one of the leading forms of the entertainment industry, at least in terms of revenue. The main purpose of this paper is to examine the consumer behavior in the case of video games with three objectives: identify the factors affecting customer satisfaction for video games; analyze these factors to understand how they affect consumer behavior and propose some recommendations to improve the customer satisfaction for video games. Data was collected from 205 Vietnamese gamers addressing the variables of individual, psychological, cultural, and social factors. Regression analysis found that all four factors positively affect consumer behavior, in terms of customer satisfaction, especially cultural factors. The findings of this research analyzed the theoretical foundations of the theory of behavior, based on which investigated the study of consumer behavior of video game services of players in Vietnam by market research, analyze data, thereby helping businesses understand the psychological response, consumer behavior of customers, and can devise appropriate strategies.

Andry Alamsyah, Fariz Denada Sudrajat, Herry Irawan

Online property business or known as e-commerce is currently experiencing an increase in home sales. Indonesia's e-commerce property business has positive trending shown by the increasing sales of more than 500% from 2011 to 2015. A prediction of the property price is important to help investors or the public to have accurate information before buying property. One of the methods for prediction is a classification based on several distinctive property industry attributes, such as building size, land size, number of rooms, and location. Today, data is easily obtained, there are many open data from E-commerce sites. E-commerce contains information about homes and other properties advertised to sell. People also regularly visit the site to find the right property or to sell the property using price information which collectively available as open data. To predict the property sales, this research employed two different classification methods in Data Mining which are Decision Tree and k-NN classification. We compare which model classification is better to predict property price and their attributes. We use Indonesia's biggest property-based e-commerce site Rumah123.com as our open data source, and choose location Bandung in our experiment. The accuracy result of the decision tree is 75% and KNN is 71%, other than that k-NN can explore more data patterns than the Decision Tree.

Michaël Verdonck, Geert Poels