Hasil untuk "Management information systems"

Elvira Ismagilova, D. L. Hughes, Yogesh Kumar Dwivedi et al.

Abstract Smart cities employ information and communication technologies to improve: the quality of life for its citizens, the local economy, transport, traffic management, environment, and interaction with government. Due to the relevance of smart cities (also referred using other related terms such as Digital City, Information City, Intelligent City, Knowledge-based City, Ubiquitous City, Wired City) to various stakeholders and the benefits and challenges associated with its implementation, the concept of smart cities has attracted significant attention from researchers within multiple fields, including information systems. This study provides a valuable synthesis of the relevant literature by analysing and discussing the key findings from existing research on issues related to smart cities from an Information Systems perspective. The research analysed and discussed in this study focuses on number of aspects of smart cities: smart mobility, smart living, smart environment, smart citizens, smart government, and smart architecture as well as related technologies and concepts. The discussion also focusses on the alignment of smart cities with the UN sustainable development goals. This comprehensive review offers critical insight to the key underlying research themes within smart cities, highlighting the limitations of current developments and potential future directions.

G. Davis

M. Wade, J. Hulland

Wynne W. Chin, J. Cheah, Yide Liu et al.

PurposePartial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.Design/methodology/approachA systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.FindingsThe study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.Originality/valueThis research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

R. Sacks, I. Brilakis, Ergo Pikas et al.

Abstract The concept of a “digital twin” as a model for data-driven management and control of physical systems has emerged over the past decade in the domains of manufacturing, production, and operations. In the context of buildings and civil infrastructure, the notion of a digital twin remains ill-defined, with little or no consensus among researchers and practitioners of the ways in which digital twin processes and data-centric technologies can support design and construction. This paper builds on existing concepts of Building Information Modeling (BIM), lean project production systems, automated data acquisition from construction sites and supply chains, and artificial intelligence to formulate a mode of construction that applies digital twin information systems to achieve closed loop control systems. It contributes a set of four core information and control concepts for digital twin construction (DTC), which define the dimensions of the conceptual space for the information used in DTC workflows. Working from the core concepts, we propose a DTC information system workflow—including information stores, information processing functions, and monitoring technologies—according to three concentric control workflow cycles. DTC should be viewed as a comprehensive mode of construction that prioritizes closing the control loops rather than an extension of BIM tools integrated with sensing and monitoring technologies.

Hoyoung Chung, Jin-Hwi Kim, Junseong Ahn et al.

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops.

Mohamad Ali Saleh Saleh, Mutaz AlShafeey

The transformation to Industry 4.0 has significantly revolutionized manufacturing and production processes, raising important questions about their impact on sustainability. This study aims to explore the interplay between Industry 4.0 and the economic, social, and environmental dimensions of sustainability. The methodological approach includes advanced text-mining, sentiment analysis, and association rule-mining techniques to examine 6,759 abstracts from the Scopus database. The text mining highlighted frequent keywords related to Industry 4.0 and the three sustainability dimensions, characterized by “economic growth,” “circular economy,” “social responsibility,” “education 4.0,” “energy efficiency,” and “waste management.” Sentiment analysis revealed a predominantly positive perspective, with 2,608 positive sentiments out of 2,761 in the economic dimension, 1,604 out of 1,728 in the social dimension, and 1,352 out of 1,527 in the environmental dimension. The association rule mining uncovered the associations between Industry 4.0 and each sustainability dimension. The highest support was observed between Industry 4.0 and economic sustainability, with a support value of 0.444, confidence of 0.855, and a lift of 1.060. These findings highlight the role of Industry 4.0 in promoting resource efficiency and reducing waste through circular economy principles and advanced manufacturing technologies. For the social dimension, the analysis revealed a strong association with Industry 4.0 (support: 0.430, confidence: 0.831, lift: 1.030), emphasizing its role in enhancing worker safety and job satisfaction by automating hazardous tasks and creating new high-tech job opportunities. In the environmental dimension, a significant association was found (support: 0.380, confidence: 0.827, lift: 1.024), showing Industry 4.0′s contribution to sustainability through optimized energy consumption and emissions reduction as the integration of big data and IoT enables real-time monitoring of environmental impacts. The rule combining economic and social aspects with Industry 4.0 (support: 0.219, confidence: 0.87, lift: 1.078) highlights the interconnected nature of these dimensions, suggesting many studies consider economic and social dimensions together in the Industry 4.0 context.

Дмитро АНРДЄЄВ, Олексій ЛИГУН, Андрій ДРОЗД et al.

Critical infrastructures are fundamental to the seamless operation of modern societies, encompassing sectors such as energy, healthcare, transportation, and communications. Ensuring their reliability, performance, continuous operation, safety, maintenance, and protection is a national priority for countries worldwide. The digital twins play a crucial role in critical infrastructure, as they enhance security, resilience, reliability, maintenance, continuity, and operational efficiency across all sectors. Among the benefits offered by digital twins are intelligent and autonomous decision-making, process optimization, improved traceability, interactive visualization, and real-time monitoring, analysis, and prediction. Furthermore, the study revealed that digital twins have the capability to bridge the gap between physical and virtual environments, can be used in combination with other technologies, and can be integrated into various contexts and industries. The use of digital twins was explored as the foundation for developing a modern monitoring system for critical infrastructure facilities enables multi-level assessment of asset conditions in real time, ensuring precise threat detection, anomaly identification, and timely decision-making. Integration with artificial intelligence and big data technologies allows not only the collection and analysis of large volumes of information but also the creation of adaptive behavioral models for systems in emergency situations. Special attention was given to the method of optimizing critical IT infrastructure using digital twins, which combines virtual modeling, predictive algorithms, and automated management. The proposed approach enhances the reliability of digital systems, minimizes downtime, optimizes maintenance costs, and strengthens cybersecurity. This system is especially relevant in the context of growing risks and increasing demands for the stability of strategically important infrastructure assets. The application of digital twins for monitoring and optimizing critical infrastructure demonstrates considerable potential for improving its resilience, safety, and operational efficiency. The approaches discussed in the study confirm the relevance of implementing digital models as tools for timely risk identification, failure prediction, and informed decision-making. By integrating such technologies, organizations can reduce operational costs, minimize downtime, and improve the overall stability of infrastructure operations. Therefore, digital twins represent a vital step toward the digital transformation and modernization of mission-critical systems across various sectors.

Halil Burak Akdeniz, Sinan Levend, Şaban İnam

Global climate change, one of the most important problems of today, and human activities have negative effects on the sustainability of natural resources. It has become necessary to establish planning and management mechanisms for the sustainable use of drinking water basins within the protection-use balance. Beyşehir Basin, Türkiye was chosen as the study area. The aim of this study is to present a new model approach for the use of Analytical Hierarchy Process and Geographic Information Systems, based on the unique topographic, hydrological, and environmental characteristics of the basin, in the determination of the drinking water basin protection zones. Thirteen criteria, which affect the reaching of the pollutants to the water surface and reflect the topographic, hydrological, and environmental characteristics of the basin, were used in the determination of the protection zones. As a result of the study, it was determined that 2.83% of the basin is in the absolute protection zone, 44.97% in the short-range protection zone, 35.93% in the medium-range protection zone and 16.26% in the long-range protection zone. In the last stage, the conservation areas determined by the current legal regulations for the basin and the protection zones determined by the model approach were spatially and areally compared. According to the results of the comparison, it was determined with the proposed protection model that the absolute protection, the short-range protection, and the medium-range protection zones increased areally, and the spatial distributions of these protection zones were shaped according to the structure of the basin.

Jasman Pardede, Desita Nurrohmah

Background: Hepatitis is a contagious inflammatory disease of the liver and is a public health problem because it is easily transmitted. The main factors causing hepatitis are viral infections, disease complications, alcohol, autoimmune diseases, and drug effects. Some hepatitis variants such as B, C, and D can also cause liver cancer if left untreated. Objective: This research aims to determine the effect of Backward Elimination feature selection on the performance of hepatitis disease identification compared to cases where Backward Elimination is not applied. Methods: XGBoost classification, capable of handling machine learning problems, was utilized. Additionally, Backward Elimination was used as a featured selection to increase accuracy by reducing the number of less important features in the data classification process. Results: The results for training XGBoost model with Backward Elimination, and applying Random Search for hyperparameter optimization, achieved an accuracy of 98.958% at 0.64 seconds. This performance was better than using Bayesian search, which produced the same accuracy of 98.958% but required a longer training time of 0.70 seconds. Conclusion: The use of features obtained from Backward Elimination process as well as the use of feature average values for missing value treatment, produced an accuracy of 98.958%.the precision in training XGBoost model with hyperparameter Bayesian search achieved accuracy, recall, and F1 score of 98.934%, 98.934%, and 98.934%, respectively. Consequently, the use of Backward Elimination in XGBoost model led to faster training, improved accuracy, and decreased overfitting.   Keywords: Hepatitis, Backward Elimination, XGBoost, Bayesian Search, Random Search

Milan Zdravković, H. Panetto, G. Weichhart

ABSTRACT This paper considers Enterprise Information Systems functional architecture and carries out review of AI applications integrated in Customer Relationship Management, Supply Chain Management, Inventory and logistics, Production Planning and Scheduling, Finance and accounting, Product Lifecycle Management and Human Resources, with special attention to the manufacturing enterprises. Enhanced capabilities are identified and proposed as AI services. AI-enablement implements improved decision-making or automation by using Machine Learning models or logic-based systems. It is a process of the enterprise transformation leading to the convergence of the four major disruptive technologies, namely Industrial Internet of Things, Agent-based Distributed Systems, Cloud Computing and Artificial Intelligence.

Li Li, Zhaoyun Wu, Liping Lu

The visualization of workshop information can affect production management and efficiency. Information can be presented both graphically and non-graphically (for example, in the form of data lists or tables). Graphical representations are intuitive and clear, but currently, most of them are based on statistical data, which makes it difficult to convey logical linkages between information and cannot help managers make decisions effectively. With the aim of designing the workshop production system with visual processes in small-sized enterprises, the key visualization technologies of the process flow chart, including the visual design of process flow chart, process card management, process flow chart release, process control, and production schedule monitoring, were all addressed in detail. On this basis, the mechanical manufacturing workshop production management system was created using C#.NET as the programming language. The main contribution of the research is that the system designed used the process flow chart as the main line through all functional modules and integrated all process data on the process nodes of the process flow chart to realize the graphical monitoring of workshop production schedule. The visualization technology of the process flow chart makes the system simple to use and easy to understand, which significantly improves information management and work efficiency in the workshop. Additionally, it provides the technical foundation for flow-driven production information transfer in the workshop and can serve as a universal standard for the process module in workshop production management systems.

Fernando García-Ávila, Rita Cabello-Torres, Sergio Iglesias-Abad et al.

Cleaner Production, as an environmental strategy, is integrated into processes, products, and services to increase efficiency and reduce risks to people and the environment. However, Cleaner Production management in water treatment services does not receive much public attention. To address this need, a scientometric analysis of the integration of Cleaner Production in water treatment was developed to investigate the efficacy and limitations of various Cleaner Production strategies applied in water treatment. A search was carried out in the Scopus database using the keywords ``Cleaner production,'' ``Wastewater treatment,'' and ``Drinking water treatment'' between January 1, 2002, and October 31, 2022. Through the scientometric analysis, it was possible to identify that Cleaner Production has been little applied in drinking water treatment. The reason for this was a systematic review of the application of Cleaner Production in drinking water treatment. The data and information obtained were filtered following the guidelines of the Preferred Reporting Declaration of elements for systematic reviews and meta-analyses (PRISMA). The PICO method was used to structure the important components of the research questions. The results of this review showed that there is currently little application of Cleaner Production in drinking water systems. This article concludes that Cleaner Production is a strategy that contributes to reducing the environmental impact generated by the different activities of treatment and distribution of drinking water. However, it is necessary to carry out research that promotes the dissemination and knowledge of the different Cleaner Production strategies, as well as highlighting the social, environmental, and economic benefits that their application could generate. Future research should aim at the application of Cleaner Production strategies such as water recycling and Good Housekeeping. Technological progress in purification processes can offer promising results for saving water, energy, waste reduction, and gaseous emissions.

Nagham N. Abbas, Abbas M. Burhan

One of the most important problems of Iraqi construction projects is the cost variances, so it is important to identify the problems and shortcomings that cause poor cost control. Through the utilization of questionnaires, the study evaluated how project costs were managed and reported. The questionnaire was distributed to 180 professionals working in the Iraqi construction sector, with a response rate of 91%. The results showed that a high percentage of projects are implemented with a difference between real and estimated costs, and the process of documenting cost data needs to be more secure. On the other hand, there is a weakness in providing the necessary work structure information to monitor costs and a lack of processing of the required data regarding mechanisms and equipment and problems. It is related to the accuracy of the estimation and the management and documentation of labor wages. Most of the problems are the lack of appropriate systems to implement cost control appropriately.

Young-Taek Park PhD, Hyeoun-Ae Park PhD, Jae Meen Lee MD et al.

Insufficient information exists on the associations between hospitals’ adoption of mobile-based personal health record (mPHR) systems and patients’ characteristics. This study explored the associations between patients’ characteristics and hospitals’ adoption of mPHR systems in Korea. This cross-sectional study used 316 hospitals with 100 or more beds as the unit of analysis. Previously collected data on mPHR adoption from May 1 to June 30, 2020 were analyzed. National health insurance claims data for 2019 were also used to analyze patients’ characteristics. The dependent variable was mPHR system adoption (0 vs 1) and the main independent variables were the number of patients, age distribution, and proportions of patients with cancer, diabetes, and hypertension among inpatients and outpatients. The number of inpatients was significantly associated with mPHR adoption (adjusted odds ratio [aOR]: 1.174; 1.117-1.233, P < .001), as was the number of outpatients (aOR: 1.041; 1.028-1.054, P  < .001). The proportion of inpatients aged 31 to 60 years to those aged 31 years and older was also associated with hospital mPHR adoption (aOR: 1.053; 1.022-1.085, P  = .001). mPHR system adoption was significantly associated with the proportion of inpatients (aOR: 1.089; 1.012-1.172, P  = .024) and outpatients (aOR: 1.138; 1.026-1.263, P  = .015) with cancer and outpatients (aOR: 1.271; 1.101-1.466, P  = .001) with hypertension. Although mPHR systems are useful for the management of chronic diseases such as diabetes and hypertension, the number of patients, younger age distribution, and the proportion of cancer patients were closely associated with hospitals’ introduction of mPHR systems.

Wenlu Du, Ankan Dash, Jing Li et al.

Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

Jaime Pereira, J. Varajão, Nilton Takagi

ABSTRACT Evaluating the success of projects should be a key process in project management. However, there are only a few studies that address the evaluation process in practice. In order to help fill this gap, this paper presents the results of an exploratory survey with experienced information systems project managers. Results show that opportunities for lessons learned and project management improvement are being missed due to the lack of formal evaluation of success.

Subodha Kumar, V. Mookerjee, A. Shubham

Owing to its multidisciplinary nature, the operations management (OM) and information systems (IS) interface distinguishes itself from the individually focused perspective of both fields. The number and depth of contributions in this department can help both disciplines advance to better address important theoretical and practical challenges of the business world. In this paper, we study the characteristics of problems at the interface between OM and IS, and review past work that has been instrumental in setting the tone and direction of research at this interface. We extend our discussion to provide directions for future research at the OM and IS interface in the domains such as smart city management, healthcare, deep learning and artificial intelligence, fintech and blockchain, Internet of Things and Industry 4.0, and social media and digital platforms.

Khalifa M. Al-Kindi, Saeid Janizadeh

Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj potential in the Nizwa watershed in the Sultanate of Oman (Oman). Nizwa city is a focal point of aflaj that underlies the historical relationship between ecology, economic dynamics, agricultural systems, and human settlements. Three hyperparameter algorithms, grid search (GS), random search (RS), and Bayesian optimisation, were used to optimise the parameters of the XGB model. Sentinel-2 and light detection and ranging (LiDAR) data via geographical information systems (GIS) were employed to derive variables of land use/land cover, and hydrological, topographical, and geological factors. The groundwater aflaj potential maps were categorised into five classes: <i>deficient</i>, <i>low</i>, <i>moderate</i>, <i>high</i>, and <i>very high</i>. Based on the evaluation of accuracy in the training stage, the following models showed a <i>high</i> level of accuracy based on the area under the curve: Bayesian-XGB (0.99), GS-XGB (0.97), RS-XGB (0.96), SVM (0.96), and XGB (0.93). The validation results showed that the Bayesian hyperparameter algorithm significantly increased XGB model efficiency in modelling groundwater aflaj potential. The highest percentages of groundwater potential in the <i>very high</i> class were the XGB (10%), SVM (8%), GS-XGB (6%), RS-XGB (6%), and Bayesian-XGB (6%) models. Most of these areas were located in the central and northeast parts of the case study area. The study concluded that evaluating existing groundwater datasets, facilities, current, and future spatial datasets is critical in order to design systems capable of mapping groundwater aflaj based on geospatial and ML techniques. In turn, groundwater protection service projects and integrated water source management (IWSM) programs will be able to protect the aflaj irrigation system from threats by implementing timely preventative measures.

Erin J. Belval, Karen C. Short, Crystal S. Stonesifer et al.

A severe outbreak of wildfire across the US Pacific Coast during August 2020 led to persistent fire activity through the end of summer. In late September, Fire Weather Outlooks predicted higher than usual fire activity into the winter in parts of California, with concomitant elevated fire danger in the Southeastern US. To help inform the regional and national allocation of firefighting personnel and equipment, we developed visualizations of resource use during recent late season, high-demand analogs. Our visualizations provided an overview of the crew, engine, dozer, aerial resource, and incident management team usage by geographic area. While these visualizations afforded information that managers needed to support their decisions regarding resource allocation, they also revealed a potentially significant gap between resource demand and late-season availability that is only likely to increase over time due to lengthening fire seasons. This gap highlights the need for the increased assessment of suppression resource acquisition and allocation systems that, to date, have been poorly studied.