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Ocena ryzyka inwestowania w akcje wybranych spółek sektora motoryzacyjnego notowanych na GPW w Warszawie w latach 2021-2023
Full text linkKażda inwestycja, w tym w akcje spółek, obarczona jest ryzykiem. Dlatego też wskazane, a wręcz konieczne, jest uwzględnianie czynników które to ryzyko kształtują, jak również metod jego szacowania. Ważna jest również właściwa interpretacja wyników w celu dokonania najefektywniejszej alokacji kapitału. W artykule przedstawiono ocenę ryzyka inwestowania w akcje dwóch spółek sektora motoryzacyjnego notowanych na Giełdzie Papierów Wartościowych w Warszawie w okresie od 01.01.2021 r. do 31.12.2023 r., kształtowanie się ich stóp zwrotu oraz indeksu WIG140, a także przyczyny owych tendencji
Bricks or clicks? Factors influencing shopping behavior of Generation Z
No full textResearch background: Generation Z, a consumer base with significant global impact, strongly relies on smart devices, shaping their unique consumption patterns. As this demographic becomes increasingly influential, understanding their shopping behavior is crucial for effective retail marketing strategies.
Purpose of the article: This article aims to evaluate the shopping behavior and habits of Generation Z, particularly focusing on the determinants that influence their choices, with a specific emphasis on the role of smart technology. The objective is to uncover insights that explain the relationship between smart device usage and shopping behavior, offering perspectives for retailers aiming to tailor their strategies to the preferences of this demographic.
Methods: A survey conducted during the 2019/2020 season involved 1,756 respondents from the Czech Generation Z. To estimate the relationships between smart device usage and shopping behavior, the linear probability and the logit models were employed. Additionally, descriptive statistics provided a comprehensive overview of respondents’ preferences and habits.
Findings & value added: The results indicate that while the average time spent on smartphones does not inherently correlate with an increased preference for online shopping, it does enhance the likelihood of engaging in online transactions. Contrary to expectations, brick-and-mortar stores remain competitive and are slightly more preferred than online shops among the Czech young generation. This preference is attributed to the tangible experience of touching products and immersing in the store\u27s ambiance. Furthermore, a relationship emerges between concerns about data security and a reduced frequency of online shopping, emphasizing the need to address such apprehensions in marketing strategies. Overall, these findings provide insights into the nuanced shopping behaviors of Generation Z, with implications that extend beyond regional boundaries, guiding retailers in adapting and optimizing their approaches to meet the needs of this demographic
Digital twin-based cyber-physical manufacturing systems, extended reality metaverse enterprise and production management algorithms, and Internet of Things financial and labor market technologies in generative artificial intelligence economics
No full textResearch background: Generative artificial intelligence (AI) and machine learning algorithms support industrial Internet of Things (IoT)-based big data and enterprise asset management in multiphysics simulation environments by industrial big data processing, modeling, and monitoring, enabling business organizational and managerial practices. Machine learning-based decision support and edge generative AI sensing systems can reduce persistent labor shortages and job vacancies and power productivity growth and labor market dynamics, shaping career pathways and facilitating occupational transitions by skill gap identification and labor-intensive manufacturing job automation by path planning and spatial cognition algorithms, furthering theoretical implications for management sciences. Generative AI fintech, machine learning algorithms, and behavioral analytics can assist multi-layered payment and transaction processing screening with regard to authorized push payment, account takeover, and synthetic identity frauds, flagging suspicious activities and combating economic crimes by rigorous verification processes.
Purpose of the article: We show that edge device management functionalities of cloud industrial IoT and virtual robotic simulation technologies configure plant production and route planning processes across cyber-physical production and industrial automation systems in multi-cloud immersive 3D environments, leading to tangible business outcomes by reinforcement learning and convolutional neural networks. Labor-augmenting automation and generative AI technologies can impact employment participation, increase wage and wealth inequality, and lead to potential job displacement and massive labor market disruptions. The deep learning capabilities of generative AI fintech in terms of adaptive behavioral analytics and credit scoring mechanisms can enhance financial transaction behaviors and algorithmic trading returns, identify fraudulent payment transactions swiftly, and improve financial forecasts, leading to customized investment recommendations and well-informed financial decisions.
Methods: Machine learning-based study selection process and text mining systematic review management software and tools leveraged include Abstrackr, CADIMA, Colandr, DistillerSR, EPPI-Reviewer, JBI SUMARI, METAGEAR package for R, SluRp, and SWIFT-Active Screener. Such reference management systems are harnessed for methodologically rigorous evidence synthesis, study selection and characteristic extraction, predictive document classification, machine learning-based citation and record screening, bias assessment, article retrieval automation, and document classification and prioritization.
Findings & value added: Industrial IoT and 3D augmented reality technologies can create business value by streamlining virtual product and remote asset management across extended reality-based navigation and robotic autonomous systems in smart factory environments by generative AI and machine learning algorithms, articulating business organizational level and theory of management implications. 3D simulation and operational modeling tools can execute and complete complex cognitive task-oriented and knowledge economy jobs, producing first-rate quality outputs swiftly while leading to unemployment spells, labor market disruptions, job displacement losses, and reduced earnings by machine learning clustering and spatial cognition algorithms. Generative AI decentralized finance, interoperable blockchain networks, cash flow management tools, and asset tokenization can mitigate fraud risks, enable digital fund and crypto investing servicing, and automate treasury operations by integrating real-time payment capabilities, routing and configurable workflows, and lending and payment technologies
Generative artificial intelligence marketing, algorithmic predictive modeling, and customer behavior analytics in the multisensory extended reality metaverse
No full textExploring CSR performance as a proxy for competitive ad-vantage across sectors in the Central European countries
No full textResearch background: Corporate social responsibility (CSR) demonstrates that a business should be interested in broader social issues rather than on merely those impacting profit margins. Thus, enterprises across all sectors need to focus on the pillars of CSR, which can ultimately give them a competitive advantage. Previous research papers have focused mainly on the level of CSR in particular industries or how CSR activities are communicated in these industries.
Purpose of the article: The paper focuses on demonstrating the level of CSR performance in the chosen central European countries in the context of corporate earnings and specifying the impact of the economic sectors on the level of CSR performance, which is mapped by the environmental, social, and governance (ESG) score.
Methods: This study used the ESG score, an indicator of the level of CSR performance, and financial and accounting data of 490 publicly traded enterprises from Central Europe. It also applied correlation analysis, the Kruskal–Wallis test and cluster agglomerative hierarchical clustering.
Findings & value added: The results have proved that the CSR performance of central European enterprises is positively associated with the level of corporate earnings in all NACE sectors. This knowledge broadens the existing literature on this topic. The study also revealed statistically significant differences in the development of the CSR concept across the sectors. Then, cluster agglomerative hierarchical clustering identified the groups of sectors with homogenous approaches to CSR. This provides information on the homogeneity or heterogeneity of CSR performance across different industries, which is useful information not only for investors and other stakeholders, but also for researchers
Foreign direct investment in Singapore: the case of Pharmaceutical & Biological Products sector
No full textMotivation: The motivation for the study was the fact that despite of being a small country Singapore is one of the largest recipients of foreign direct investment (FDI) in the world. It is also the location of the largest multinational enterprises (MNEs) in the Pharmaceutical & Biological Products sector.Aim: The objective of this article is to identify and assess the FDI scale and structure in Singapore in the years 1990–2022, with a focus on the Pharmaceuticals & Biological Products sector, and to show Singapore as an FDI location against the background of Asia and the world.Materials and methods: The article uses secondary data from the World Investment Report published annually by the United Nations Conference on Trade and Development (UNCTAD) and statistics published by the Department of Statistics Singapore (DOS). The study uses statistical and economic analysis, comparison, analogies, synthesis, and the method of measuring and aggregating data.Results: Singapore is one of the largest recipients of FDI not only in Asia, but also in the world. In 2022, Singapore’s share of FDI inflows to Asia was 21.3%, and its share of global FDI inflows was 10.9%. From 1998 to 2007, FDI stock in the Pharmaceuticals & Biological Products sector accounted for a significant share of total FDI stock in Singapore and the manufacturing industry, reaching over 40% in 2007. Since then, the share of the analyzed sector has been seeing a downfall. At the end of 2022, the share of FDI in analyzed sector was 4.9% of the FDI in the manufacturing industry and 0.6% of the total FDI
Bitcoin: an alternative to fiat money? A post-Keynesian perspective
No full textMotivation: This paper addresses the ongoing debate surrounding Bitcoin’s potential to function as an alternative to state-issued fiat currencies. With the advent of Bitcoin and its increasing global prominence, it is essential to explore its viability, particularly in light of its formal adoption in El Salvador as legal tender. The limited supply and decentralized nature of Bitcoin present a significant departure from traditional monetary systems, which raises questions regarding its capacity to fulfill the roles of money under Modern Monetary Theory (MMT).Aim: The primary goal of this paper is to critically examine whether Bitcoin can serve as a functional replacement for fiat money. Using a post-Keynesian analytical framework, this study investigates Bitcoin’s ability to meet the requirements of sovereign money, particularly within the theoretical framework of MMT. The analysis focuses on Bitcoin’s decentralized issuance, limited supply, and its role in fiscal policy and credit creation.Materials and methods: This research employs a critical literature review and a case study analysis, using the framework of MMT and post-Keynesian endogenous money theory to assess Bitcoin’s economic implications and its real-world application in El Salvador’s Bitcoin Law. The study analyzes Bitcoin’s economic implications based on the endogenous money supply model and evaluates its impact on government fiscal policy, credit markets, and economic stability in both theoretical and practical contexts, with a special focus on El Salvador’s Bitcoin Law.Results: The analysis reveals significant limitations in Bitcoin’s ability to function as a fiat currency. Its rigid supply and speculative nature hinder its capacity to serve as a medium of exchange, store of value, and unit of account, as envisaged by MMT. The study concludes that Bitcoin’s decentralized nature and deflationary design pose challenges for its broader adoption as state money, particularly in managing aggregate demand and economic crises
Effects of circular economy practices on sustainable firm performance of green garments
No full textResearch background: The concept of sustainable firm performance has gained significant interest within the highly competitive business arena. There has been a significant increase in the adoption and implementation of circular economy practices by industries. If a company can implement an established approach to circular economy practices, it may expedite the attainment of sustainable corporate performance. This research is conducted in the context of ready-made garment organizations that are following green criteria in their business activities. The study focuses on an emerging South Asian country, Bangladesh, as it holds a strong position in the global apparel and garment market; it is imperative to assess and ensure the environmental maintenance of this country’s garment sector.
Purpose of the article: The purpose of this research is to investigate the relationship between circular economy practices and sustainable organizational performance. The study focuses on the contributory relationship of circular economy practices on three-dimensional sustainable performance, i.e. on environmental, financial, and social performance.
Method: This is a quantitative survey-based study; a total of 418 managers were selected to participate. Primary data was collected through a structured questionnaire given to a sample of permanent managers of green garment organizations in Bangladesh. For data input and analysis, SPSS and PLS-SEM software were used.
Findings & value added: The results of our study demonstrate a noteworthy relationship between circular economy practices and sustainable performance. This research enhances our comprehension of the efficacy of circular economy practices in addressing environmental issues. The study examines the potential ramifications of implementing circular economy practices for policymakers in the green garment sector, which is known for its significant labor-intensive activities, and ranks as the country\u27s second-largest contributor. The outcomes provide a distinctive perspective for adding value to the environmental concerns in emerging economies. Thus, through an investigation of circular economy practices, our research provides valuable insights for the market of global garment products concerning the environment, resource maximization, energy saving, and circular production processes
The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals
No full textResearch background: Deep and machine learning-based algorithms can assist in COVID-19 image-based medical diagnosis and symptom tracing, optimize intensive care unit admission, and use clinical data to determine patient prioritization and mortality risk, being pivotal in qualitative care provision, reducing medical errors, and increasing patient survival rates, thus diminishing the massive healthcare system burden in relation to severe COVID-19 inpatient stay duration, while increasing operational costs throughout the organizational management of hospitals. Data-driven financial and scenario-based contingency planning, predictive modelling tools, and risk pooling mechanisms should be deployed for additional medical equipment and unforeseen healthcare demand expenses.
Purpose of the article: We show that deep and machine learning-based and clinical decision making systems can optimize patient survival likelihood and treatment outcomes with regard to susceptible, infected, and recovered individuals, performing accurate analyses by data modeling based on vital and clinical signs, surveillance data, and infection-related biomarkers, and furthering hospital facility optimization in terms of intensive care unit bed allocation.
Methods: The review software systems employed for article screening and quality evaluation were: AMSTAR, AXIS, DistillerSR, Eppi-Reviewer, MMAT, PICO Portal, Rayyan, ROBIS, and SRDR.
Findings & value added: Deep and machine learning-based clinical decision support tools can forecast COVID-19 spread, confirmed cases, and infection and mortality rates for data-driven appropriate treatment and resource allocations in effective therapeutic and diagnosis protocol development, by determining suitable measures and regulations and by using symptoms and comorbidities, vital signs, clinical and laboratory data and medical records across intensive care units, impacting the healthcare financing infrastructure. As a result of heightened use of personal protective equipment, hospital pharmacy and medication, outpatient treatment, and medical supplies, revenue loss and financial vulnerability occur, also due to expenses related to hiring additional staff and to critical resource expenditures. Hospital costs for COVID-19 medical care, screening, treatment capacity expansion, and personal protective equipment can lead to further financial losses while affecting COVID-19 frontline hospital workers and patients
Enhancing hotel employees\u27 well-being and safe behaviors: The influences of physical workload, mental workload, and psychological resilience
No full textResearch background: Despite the dynamically growing cross-sectional academic studies conducted on various aspects related to physical and mental workloads in the context of workplace safety, there is still room for further in-depth analyses of how these workloads affect employees\u27 behavior and well-being. This phenomenon is of particular interest in the case of hospitality, where hotels should recognize the workloads imposed on their employees, since they are considered the most critical and influential stressors in the workplace.
Purpose of the article: Based on the conservation of resources (COR) theory, our study aims to examine how hotel employees\u27 physical and mental workloads affect their well-being and safe behaviors. The study also investigates how employee well-being and psychological resilience play a role in these patterns.
Methods: The study employs an experience-sampling methodology to assess the physical and mental workloads of a group of full-time employees working in luxury hotels in the USA.
Findings & value added: The findings derived from Partial Least Squares Structural Equation Modeling (PLS-SEM) reveal that both physical and mental workloads negatively impact overall well-being and safe behaviors. Additionally, physical workload influences mental workload. Also, employee well-being has been identified as a mediating factor in the relationship between workloads, psychological resilience, and safe behaviors. Notably, psychological resilience has not exhibited a moderating effect. This study expands on the COR theory by examining its impact on the hospitality industry. The study has developed and validated a model for assessing hotel employees\u27 physical workload. Moreover, it emphasizes the significance of employees\u27 well-being and psychological resilience in promoting safe behaviors in hotels. Therefore, this model is a significant step forward toward effectively measuring and maintaining the overall well-being and safe behaviors of employees in the hospitality industry. Furthermore, the value of the research is enhanced by surveying hotel employees directly rather than relying on subjective opinions from management about employee involvement in workplace health and safety. This approach avoids the bias often present in management assessments and provides a more accurate depiction of employee participation