Hasil untuk "Management information systems"

LeBlanc Luca, Braun Andreas

Air traffic control is vital in ensuring safe and efficient airspace usage by aircraft. In the event of a strike by air traffic controllers, flights can be delayed, cancelled, and rerouted. While there is previous research in the geospatial analysis of flight data, the approaches and tools used are complex and specialized, leaving a gap for an accessible and easily adaptable practice for the geospatial analysis of flight data, primarily using geographic information systems. We successfully bridged this gap with the methods of this article, increasing the accessibility of future geospatial flight data analysis. The flight data we used is broadcast by aircraft in regular intervals, including both spatial information and supplemental aircraft information. By detecting differences in the air traffic patterns between the day of an air traffic control strike in France and other reference days (used for normalization), we were able to determine both the spatial change of flight density in and around France and the difference in the number of flights that flew through French airspace on the day of the strike. On the day of the strike, the area around French airspace experienced a higher density of flights. In total, French airspace experienced around 2.7 %–7.1 % more air traffic on normal days than on the day of the strike. Mapping flight densities and systematically comparing them between the observed days highlighted corridors within French airspace that experienced significantly higher air traffic, presumably as a consequence of strike management, simplifying the routes that aircraft took in comparison to normal operations. The most prominent corridor consists of a line from Barcelona to western Italy, with another one spanning the distance between the greater Paris area and the border of French and Spanish airspace near the Atlantic coastline.

Anupama Peter Mattathil, Babu George, Tony L. Henthorne

In digital marketplaces, trust in e-commerce platforms has evolved from a protective heuristic into a powerful mechanism of behavioral conditioning. This review interrogates how trust cues such as star ratings, fulfillment badges, and platform reputation shape consumer cognition, systematically displace critical evaluation, and create asymmetries in perceived quality. Drawing on over 47 high-quality studies across experimental, survey, and modeling methodologies, we identify seven interlocking dynamics: (1) cognitive outsourcing via platform trust, (2) reputational arbitrage by low-quality sellers, (3) consumer loyalty despite disappointment, (4) heuristic conditioning through trust signals, (5) trust inflation through ratings saturation, (6) false security masking structural risks, and (7) the shift in consumer trust from brands to platforms. Anchored in dual process theory, this synthesis positions trust not merely as a transactional enabler but as a socio-technical artifact engineered by platforms to guide attention, reduce scrutiny, and manage decision-making at scale. Eventually, platform trust functions as both lubricant and leash: streamlining choice while subtly constraining agency, with profound implications for digital commerce, platform governance, and consumer autonomy.

Wei Wei, Yogi Tri Prasetyo, Omar Paolo Benito et al.

Building Information Modeling (BIM) has become integral to modern construction management in China, yet its successful implementation has been hindered by a shortage of skilled BIM personnel and high turnover rates. This study investigates the key factors influencing turnover intention among BIM workers in China through a structural equation modeling (SEM) approach. Survey data were collected from 558 BIM practitioners and researchers across various regions in China. The SEM results revealed that three work-related factors—management and staff cooperation, working hours, and satisfaction with salary and incentives—significantly affected BIM workers' turnover intentions. Notably, the influence of these factors on turnover intention was fully mediated by two pivotal attitudinal variables: job satisfaction and organizational commitment. Among the predictors, excessive working hours emerged as the most salient driver heightening turnover intention, whereas strong team cooperation and competitive, fair rewards reduce the propensity to leave by enhancing worker satisfaction and commitment. The integrated model developed in this research advances understanding of BIM workforce retention and can be generalized to similar project-based contexts. The findings provide an evidence-based foundation for policymakers and industry leaders to devise strategies (e.g., improving working conditions, strengthening organizational support) aimed at increasing job satisfaction and commitment, thereby mitigating turnover intention among BIM workers.

Wei Wang, Hui Huang, Xuan Fu

Based on data from Chinese listed firms, this study examines how regional artificial intelligence (AI) development affects supply chain efficiency. The results show that regional AI development significantly improves efficiency by promoting digital transformation and reducing information asymmetry, with stronger effects observed in labor-intensive industries and inland regions. This study makes three key contributions. First, it extends the regional innovation systems perspective to the field of supply chain management by highlighting the spillover effects of regional AI ecosystems. Second, it supports the “latecomer advantage” in technology diffusion by analyzing industry and regional heterogeneity. Third, it reveals how external digital infrastructure and internal supply chain collaboration interact through the dual mechanisms of digitalization and information transparency.

Carlos Serôdio, Pedro Mestre, Jorge Cabral et al.

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

Meghan M. Holst, Sabrina Salinas, Waimon T. Tellier et al.

Foodborne outbreak investigations often provide data for public health officials to determine how the environment contributed to the outbreak and on how to prevent future outbreaks. State and local health departments are responsible for investigating foodborne illness outbreaks in their jurisdictions and reporting the data to national-level surveillance systems, including information from the environmental assessment. This assessment is designed to describe how the environment contributed to the outbreak and identifies factors that contributed to the outbreak and environmental antecedents to the outbreak. Environmental antecedents, also referred to as root causes, are specific reasons that allow biological or chemical agents to contaminate, survive, or grow in food. From 2017 to 2019, 24 jurisdictions reported 1,430 antecedents from 393 outbreaks to the National Environmental Assessment Reporting System. The most reported antecedents were lack of oversight of employees/enforcement of policies (89.1%), lack of training of employees on specific processes (74.0%), and lack of a food safety culture/attitude towards food safety (57.5%). These findings highlight the critical role that employees play in restaurant food safety and are heavily influenced by restaurant management, who can exercise active managerial control to manage these antecedents. Identifying antecedents during investigations is essential for understanding the outbreak’s root cause and implementing sustainable corrective actions to stop the immediate outbreak and future outbreaks.

Insung Lee, Duk Kyung Kim

Vehicle-to-everything (V2X) communication is a pivotal technology for advanced driving, encompassing autonomous driving and Intelligent Transportation Systems (ITS). Beyond direct vehicle-to-vehicle (V2V) communication, vehicle-to-infrastructure (V2I) communication via Road Side Unit (RSU) can play an important role for efficient traffic management and enhancement of advanced driving, providing surrounding vehicles with proper road information. To accommodate diverse V2X scenarios, heterogeneous traffic with varied objectives, formats, and sizes needs to be supported for V2X communication. We tackle the challenge of resource allocation for heterogeneous traffic in the RSU-deployed V2X communications, proposing a decentralized Multi-Agent Reinforcement Learning (MARL) based resource allocation scheme with limited shared resources. To reduce the model complexity, RSU is modeled as a collection of virtual agents with a small action space instead of a single agent selecting multiple resources at the same time. A weighted global reward is introduced to incorporate traffic heterogeneity efficiently. The performance is evaluated and compared with random, 5G NR mode 2, and optimal allocation schemes in terms of Packet Reception Ratio (PRR) and communication range. The proposed scheme nearly matches the performance of the optimal scheme and significantly outperforms the random allocation scheme in both underload and overload situations.

I Made Arya Budhi Saputra, Kadek Ardi Juniawan, Ni Nyoman Utami Januhari et al.

The waste problem is a problem that plagues the world. Various programs and methods have been carried out in processing waste. Until now, Bali has become the area with the most waste production in Indonesia, where every day the waste produced reaches 4,281 tons or in other words 1.5 million tons per year. In early 2021, a campaign on the separation of organic and non-organic waste has been initiated in several districts/cities in the province of Bali. The next problem is that people do not know how to process organic waste other than being compost for plants. Black Soldier Fly (BSF) or black soldier fly is an order of diptera whose physical characteristics are similar to wasps. BSF itself has an average life cycle from egg to adult of 45 days. Where the main consumption of BSF itself is organic waste. In Pelaga village, Petang sub-district, Badung district, there is an effort to breed BSF. Where in this effort, it can produce no less than 100 kg of BSF larvae within 15-20 days. The problems encountered by these entrepreneurs are often difficult to predict the amount of BSF larvae production, because around the area there are quite a lot of catfish and chicken farmers who need BSF larvae as feed. The use of linear regression method can overcome these problems. The results obtained between the application of this method include the accuracy value which reaches 62.5% for prediction of sales and 32.5% for prediction of production.

Techane Bosona, Girma Gebresenbet

Due to recurring food quality and safety issues, growing segments of consumers, especially in developed markets, and regulators in agri-food supply chains (AFSCs) require a fast and trustworthy system to retrieve necessary information on their food products. With the existing centralized traceability systems used in AFSCs, it is difficult to acquire full traceability information, and there are risks of information loss and data tampering. To address these challenges, research on the application of blockchain technology (BCT) for traceability systems in the agri-food sector is increasing, and startup companies have emerged in recent years. However, there have been only a limited number of reviews on the application of BCT in the agriculture sector, especially those that focus on the BCT-based traceability of agricultural goods. To bridge this knowledge gap, we reviewed 78 studies that integrated BCT into traceability systems in AFSCs and additional relevant papers, mapping out the main types of food traceability information. The findings indicated that the existing BCT-based traceability systems focus more on fruit and vegetables, meat, dairy, and milk. A BCT-based traceability system enables one to develop and implement a decentralized, immutable, transparent, and reliable system in which process automation facilitates the monitoring of real-time data and decision-making activities. We also mapped out the main traceability information, key information providers, and challenges and benefits of the BCT-based traceability systems in AFSCs. These helped to design, develop, and implement BCT-based traceability systems, which, in turn, will contribute to the transition to smart AFSC systems. This study comprehensively illustrated that implementing BCT-based traceability systems also has important, positive implications for improving AFSC management, e.g., reductions in food loss and food recall incidents and the achievement of the United Nations SDGs (1, 3, 5, 9, 12). This will contribute to existing knowledge and be useful for academicians, managers, and practitioners in AFSCs, as well as policymakers.

Mingyue Wang, Chunhui Feng, Bifeng Hu et al.

Calibration and reconstruction of time series DMSP-OLS nighttime light images are critical for understanding urbanization processes and the evolution of urban spatial patterns from a unique perspective. In this study, we developed an improved stepwise calibration (ISC) method based on numerical constancy to correct and reconstruct the time series of China’s regional nighttime light data, thus eliminating the drawbacks of the invariant target region method. We evaluated the different calibration methods and quantitatively validated the calibrated nighttime light data using gross domestic product (GDP) and electricity consumption (EC) at municipal, provincial, and national scales. The results indicated that the ISC method demonstrated its advantage in screening stable lit pixels and maintaining the temporal variability of multi-year nighttime light variation. The variation curve of reconstructed multi-year nighttime light obtained by the ISC method based on numerical constancy was more consistent with the actual urban development. The ISC method retained the original data’s most abundant and complete information than other calibration methods. Moreover, the significant advantages of this method in the low-light high-variation regions and high-light low-variation regions offered new possibilities for understanding the development of small- and medium-sized nighttime light centers such as towns and villages from a nighttime light perspective. This is an advantage that other calibration methods do not offer. The correlation between the multi-year nighttime light dataset obtained by the ISC method and the socio-economic data was significantly improved. The correlation coefficients with GDP and EC are 0.9695 and 0.9923, respectively. Last but not least, the ISC method is more straightforward to implement. The new framework developed in this study produces a more accurate and reliable long time series nighttime light dataset and provides quality assurance for subsequent research in socio-economic development, urban development, natural disasters, and other fields.

Siti Shahirah Saidin, Adiza Jamadin, Sakhiah Abdul Kudus et al.

Abstract Structural health monitoring (SHM) systems have been developed to evaluate structural responses to extreme events such as natural and man-made hazards. Additionally, the increasing volume of users and vehicle sizes can lead to the sudden damage and collapse of bridge structures. Hence, structural monitoring and dynamic characteristic analyses of bridge structures are critical and fundamental requirements for bridge safety. SHM can overcome the weaknesses of visual inspection practices, such as lack of resolution. However, because of computational limitations and the lack of data analysis methods, substantial quantities of SHM data have been poorly interpreted. In this paper, the SHM of bridges based on dynamic characteristics is used to assess the "health state" of bridge structures. A comprehensive SHM system using vibration-based techniques and modal identification for bridge structures are well defined. Some advanced concepts and applications regarding bridge safety evaluation methods, including damage detection and load-carrying capacity, are reviewed. For the first time, the pros and cons of each vibration technique are comprehensively evaluated, providing an advantage to the authority or structural owner when developing a bridge management database. This information can then be used for continuous structural monitoring to access and predict the bridge structure condition.

Monica Calva, Nelson Piedra

Patient medical information is diverse, extensive and of high value in supporting informed medical decision-making. This information is highly complex, is distributed among different systems, presents high heterogeneity, is stored in different formats, and has different structuring levels. The management of this information poses interoperability challenges in tasks related to data integration and reuse. In this paper, an alternative is presented to face these challenges using semantic technologies. We propose to transform this heterogeneous, distributed, and unstructured information in a way that ensures high interoperability, reuse, and direct processing by machine agents. The pilot of this proposal was developed at the UTPL Hospital.

Teijo Palander, Kalle Kärhä

Forest industry corporations use quality management systems in their wood procurement operations. Spatial quality data are used to improve the quality of wood harvesting and to achieve environmental sustainability. Some studies have proposed new management systems based on LiDAR. The main aim of this study was to investigate how efficiently planning systems can select areas for wood harvesting a priori with respect to avoiding harvesting damage caused by forest machinery. A literature review revealed the possibility of using GISs, and case studies showed the criteria required to predict the required quality levels. Terrestrial LiDAR can be utilized in authorities’ quality control systems, but it is inefficient for preplanning without terrestrial gamma-ray data collection. Airborne LiDAR and gamma-ray information about forest soils can only be used for planning larger regions at the forest level because the information includes too much uncertainty to allow it to be used for planning in small-sized areas before wood harvesting operations involving wood procurement. In addition, airborne LiDAR is not accurate enough, even at the forest level, for the planning of wood procurement systems because wood harvesting remains challenging without field measurements. Therefore, there is a need for the use of manual ground-penetrating radar for determining the peat layer thickness and the depth to the groundwater table.

Wil M.P. van der Aalst

Gordon Dugle, Joseph Kwame Wulifan, John Paul Tanyeh et al.

Abstract Background Health policy and systems research (HPSR) is an inherently cross-disciplinary field of investigation. However, conflicting conceptualisations about inter-, multi- and transdisciplinary research have contributed to confusion about the characteristics of cross-disciplinary approaches in HPSR. This review was conducted to (1) define the characteristic features of context–mechanism–outcome (CMO) configurations in cross-disciplinary HPSR, (2) develop criteria for evaluating cross-disciplinarity and (3) synthesise emerging challenges of the approach. Method The paper is a critical realist synthesis conducted in three phases, as follows: (1) scoping the literature, (2) searching for and screening the evidence, and (3) extracting and synthesising the evidence. Five databases, namely the International Bibliography of the Social Sciences and Web of Science, PubMed central, Embase and CINHAL, and reference lists of studies that qualified for inclusion in the review were searched. The search covered peer-reviewed original research, reviews, commentary papers, and institutional or government reports published in English between January 1998 and January 2020. Results A total of 7792 titles were identified in the online search and 137 publications, comprising pilot studies as well as anecdotal and empirical literature were selected for the final review. The review draws attention to the fact that cross-disciplinary HPSR is not defined by individual characteristics but by the combination of a particular type of research question and setting (context), a specific way of researchers working together (mechanism), and research output (outcome) that is superior to what could be achieved under a monodisciplinary approach. This CMO framework also informs the criteria for assessing whether a given HPSR is truly cross-disciplinary. The challenges of cross-disciplinary HPSR and their accompanying coping mechanisms were also found to be context driven, originating mainly from conceptual disagreements, institutional restrictions, communication and information management challenges, coordination problems, and resource limitations. Conclusion These findings have important implications. First, the CMO framework of cross-disciplinary HPSR can provide guidance for researchers engaging in new projects and for policy-makers using their findings. Second, the proposed criteria for evaluating theory and practice of cross-disciplinary HPSR may inform the systematic development of new research projects and the structured assessment of existing ones. Third, a better understanding of the challenges of cross-disciplinary HPSR and potential response mechanisms may help researchers to avoid these problems in the future.

N. A. Semenov

The international scientific and practical journal “Software products and systems” was organized in 1988 on the initiative of the General Director of the NGO “Centerprogramsystem” prof. V. P. Tikhomirov (Tver) and V. M. Silina (Moscow), head of the Main editorial office of the international journal “Problems of management theory and practice”. In the period from 1988 to 2018, the editor-in-chief of the journal was academician S. V. Yemelyanov. Since 2020, the international editorial Board, which includes well-known scientists in the field of information technologies of the Russian Federation, Belarus, Ukraine, Azerbaijan, Germany, Finland, Mexico, Vietnam and other countries, is headed by academician G. I. Savin. Associate members of the editorial Board are the national research University “MEI” (Moscow), the technological Institute of the southern Federal University (Taganrog), TvSTU and the research Institute “Centerprogramsystem”. The journal is reviewed in the bibliographic databases of RSCI, CrossRef, and is included in the List of leading peerreviewed journals and publications recommended by the higher attestation Commission of the Russian Federation for publication of research results in the defense of candidate and doctoral theses in the fields of computer Science, computer engineering and management (05.13.XX). The journal publishes the results of original research in the field of information technologies, applied artificial intelligence systems, including the development of databases and knowledge bases, expert and training systems, decision support systems and image recognition, multiagent systems, data mining systems, neural networks, robots, and telecommunications systems. However, the subject area of research is not limited. The magazine “Software products and systems” regularly acts as an information sponsor for international and national scientific and technical events in the field of information technology and artificial intelligence.

B. Wilson

D. Jardine

Abraham Munene, David C. Hall

Abstract Background An estimated four million and 43 million people in Canada and the USA use private water supplies. Private water supplies are vulnerable to waterborne disease outbreaks. Private water supplies in Canada and the USA are often unregulated and private water management is often a choice left to the owner. Perceptions of water quality become important in influencing the adoption of private water stewardship practices, therefore safeguarding public health. Methods We conducted a systematic literature review to understand factors that shape perceptions of water quality among private water users. We searched six computer databases (Web of science, Medline, Scopus, EBSCO, PubMed and Agricola). The search was limited to primary peer-reviewed publications, grey literature and excluded conference proceedings, review articles, and non-peer review articles. We restricted the search to papers published in English and to articles which published data on surveys of private water users within Canada and the USA. The search was also restricted to publications from 1986 to 2017. The literature search generated 36,478 records. Two hundred and four full text were reviewed. Results Fifty-two articles were included in the final review. Several factors were found to influence perceptions of water quality including organoleptic preferences, chemical and microbiological contaminants, perceived risks, water well infrastructure, past experience with water quality, external information, demographics, in addition to the values, attitudes, and beliefs held by well owners. Conclusions Understanding the factors that shape perceptions of water quality among private water users is an important step in developing private water management policies to increase compliance towards water testing and treatment in Canada and the USA. As many jurisdictions in Canada and the USA do not have mandatory private water testing or treatment guidelines, delineating these factors is an important step in informing future research and guiding policy on the public health of private water systems.

Yolande E. Chan