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Wyświetlanie 1-4 z 4
Tytuł:
Decision making, some individual decision-making styles and software for decision making and strategic planning
Autorzy:
Stoyanova Petrova, Elitsa
Ştefănescu, Roxana
Powiązania:
https://bibliotekanauki.pl/articles/23944843.pdf
Data publikacji:
2022
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
decision making
decision-making process
decisional options
styles of decision making
decision making software
Opis:
Objectives: This article presents the eternal relevance of the decision-making process and its inseparable connection with the personality of the decision-maker. Broadly speaking, the subject of consideration is the decision-making process. The main goal of the authors is to review the decision-making process, some styles of decision making and offer information about some types of software for decision making and strategic planning. Methods: The scientific methodology used is a combination of a theoretical review of the issue, a critical scientific review and the presentation of new scientific advances in the world of practice in terms of decision-making through technical means and methods. Results: The conditions that the decisions must meet in order to be useful and accepted, some styles of decision-making processes and also the use of decision-making software are analysed in the article. Conclusions: The authors are aware that this is a repeatedly researched topic and it is in the last part that a novelty of an applied-practical nature is definitely found. Several software for decision making and strategic planning software, which is a category of software critical for organizational leaders who want to ensure more strategic decision-making and implement simpler and more effective reporting are presented in the last part of the article. This software provides a place to manage all strategic elements, in order to achieve high-level organizational structure and long-term goals.
Źródło:
Przegląd Nauk o Obronności; 2022, 7, 15; 1--12
2450-6869
Pojawia się w:
Przegląd Nauk o Obronności
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neuromanagement decision-making and cognitive algorithmic processes in the technological adoption of mobile commerce apps
Autorzy:
Andronie, Mihai
Lăzăroiu, George
Ștefănescu, Roxana
Ionescu, Luminița
Cocoșatu, Mădălina
Powiązania:
https://bibliotekanauki.pl/articles/19322445.pdf
Data publikacji:
2021
Wydawca:
Instytut Badań Gospodarczych
Tematy:
mobile shopping app
mobile commerce platform
mobile payment service
Internet-enabled consumer device
consumer technological adoption
Opis:
Research background: With growing evidence of consumer adoption of mobile shopping apps, there is a pivotal need for comprehending Internet-enabled consumer devices in mobile shopping behavior. Mobile shopping platform features and user technological readiness configure consumers' expectations and demands as regards mobile retailing adoption, leading to acceptance of mobile shopping apps and payment services. Purpose of the article: In this research, prior findings have been cumulated indicating that mobile social apps extend throughout consumer attitudes and behaviors by the widespread adoption of smartphones. We contribute to the literature by showing that cutting-edge technological developments associated with customer behavior in relation to mobile commerce apps have resulted in the rise of data-driven systems. Consumer behavioral intention and adoption intention in relation to mobile shopping apps/websites are developed on perceived risk and trust consequences. Methods: Throughout February and March 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was carried out, with search terms comprising "mobile shopping app", "mobile commerce platform", "mobile payment service", "Internet-enabled consumer device", "consumer technological adoption", and "mobile shopping behavior". As research published between 2018 and 2021 was analyzed, only 330 sources met the suitability criteria. By removing questionable or indeterminate findings (insubstantial/inconsequent data), results unconfirmed by replication, too imprecise content, or having quite similar titles, 66, chiefly empirical, sources were selected. A systematic review of recently published literature was carried out on technological adoption of mobile commerce apps by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. The Systematic Review Data Repository was used, a software program for the gathering, handling, and analysis of data for the systematic review. The quality of the academic articles was determined by harnessing the Mixed Method Appraisal Tool. Findings & value added: The consumer purchase decision-making process in mobile app-based marketing involves consumer engagement and willingness to adopt mobile commerce apps. Further advancements should clarify how technological-based consumer adoption of mobile shopping throughout social commerce can improve the payment for products and services.
Źródło:
Oeconomia Copernicana; 2021, 12, 4; 1033-1062
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence algorithms and cloud computing technologies in blockchain-based fintech management
Autorzy:
Lăzăroiu, George
Bogdan, Mădălina
Geamănu, Marinela
Hurloiu, Lăcrămioara
Luminița, Luminița
Ștefănescu, Roxana
Powiązania:
https://bibliotekanauki.pl/articles/19901187.pdf
Data publikacji:
2023
Wydawca:
Instytut Badań Gospodarczych
Tematy:
artificial intelligence algorithms
cloud computing
blockchain
fintech
green and sustainable finance
banking
Opis:
Research background: Fintech development shapes corporate investment efficiency and economic growth with innovative tools, and can decrease financing constraints of enterprises, enabling direct and indirect financing and furthering inter-bank competition. Crowdfunding- and blockchain-based fintech operations harness deep and maching learning algorithms, augmented and virtual reality technologies, and big data analytics in mobile payment transactions. Purpose of the article: We show that fintechs have reconfigured financial service delivery by harnessing AI-based data-driven algorithms and cloud and blockchain technologies. Fintech optimizes financial organization and services, economic structures and growth, data analysis, and digital banking performance.  Machine learning algorithms can streamline payment operation capabilities and process promptness, ensuring smooth operational flows, assessing risks, and detecting frauds and money laundering by historical data and customer behavior analysis across instant payment networks and infrastructures. Methods: Quality tools: AXIS, Eppi-Reviewer, PICO Portal, and SRDR. Search period: July 2023. Search terms: “fintech” + “artificial intelligence algorithms”, “cloud computing technologies”, and “blockchain technologies”. Selected sources: 40 out of 195. Published research inspected: 2023. Data visualization tools: Dimensions and VOSviewer. Reporting quality assessment tool: PRISMA. Findings & value added: Fintech development enables organizational innovation by mitigating information asymmetry and financing limitations while providing financial assistance and tax incentives in relation to products and services. The fintech growth has influenced the dynamic intermediary function of financial institutions in terms of sustainability and economic development. Fintech and natural resources negatively influence, while green innovations and financial development further, environmental sustainability.
Źródło:
Oeconomia Copernicana; 2023, 14, 3; 707-730
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The economics of deep and machine learning-based algorithms for COVID-19 prediction, detection, and diagnosis shaping the organizational management of hospitals
Autorzy:
Lăzăroiu, George
Gedeon, Tom
Rogalska, Elżbieta
Andronie, Mihai
Frajtova Michalikova, Katarina
Musova, Zdenka
Iatagan, Mariana
Uță, Cristian
Michalkova, Lucia
Kovacova, Maria
Ștefănescu, Roxana
Hurloiu, Iulian
Zabojnik, Stanislav
Stefko, Robert
Dijmărescu, Adrian
Dijmărescu, Irina
Geamănu, Marinela
Powiązania:
https://bibliotekanauki.pl/articles/39832736.pdf
Data publikacji:
2024
Wydawca:
Instytut Badań Gospodarczych
Tematy:
deep and machine learning
COVID 19
prediction
detection
diagnosis
organizational management
hospital
Opis:
Research 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.
Źródło:
Oeconomia Copernicana; 2024, 15, 1; 27-58
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-4 z 4

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