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Influencing turnover intention among building information modeling (BIM) Workers in China: A structural equation modeling approach

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Acta Psychologica 2025年 257卷

作者： Wei Wei Yogi Tri Prasetyo Omar Paolo Benito Maela Madel L. Cahigas Reny Nadlifatin Ma. Janice J. Gumasing School of Industrial Engineering and Engineering Management Mapúa University Manila Philippines. 658 Muralla St. Intramuros Manila 1002 Philippines School of Graduate Studies Mapúa University Manila Philippines. 658 Muralla St. Intramuros Manila 1002 Philippines Hulunbuir University. No. 26 Middle Genghis Khan Road Hulunbuir Nei Mongol China International Bachelor Program in Engineering Yuan Ze University 135 Yuan-Tung Road Chung-Li 32003 Taiwan Department of Industrial Engineering and Management Yuan Ze University 135 Yuan-Tung Road Chung-Li 32003 Taiwan Department of Information Systems Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Surabaya 60111 Indonesia Department of Industrial and Systems Engineering Gokongwei College of Engineering De La Salle University Manila 1004 Philippines

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.

关键词： Chinese Job satisfaction Organizational commitment Management and staff cooperation Working hours Salary and incentives Turnover

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Multi-step ahead Bitcoin Price Forecasting Based on VMD and Ensemble Learning Methods

Multi-step ahead Bitcoin Price Forecasting Based on VMD and ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ramon Gomes da Silva Matheus Henrique Dal Molin Ribeiro Naylene Fraccanabbia Viviana Cocco Mariani Leandro dos Santos Coelho Industrial & Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering Federal University of Parana (UFPR) Curitiba Parana Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bitcoin is the leading currency in the cryptocurrency market capturing attention worldwide. Forecasting the Bitcoin price as accurate as possible is essential, but due to its high volatility this task is challenging. Many researchers try, through the years, to develop efficient models for predicting the Bitcoin price using several different data-driven approaches. The objective of this paper is to develop a novel decomposition-ensemble learning model that combines Variational Mode Decomposition (VMD) and Stacking-ensemble learning (STACK) with machine learning algorithms to forecast the Bitcoin price multi-step ahead. The algorithms are k-Nearest Neighbors, Support Vector Regression with Linear kernel, Feed-forward Artificial Neural Network with single-layer perceptron, Generalized Linear Model, and Cubist. Correlation matrix (CORR), principal component analysis (PCA), and Box-Cox transformation (BOXCOX) were used as data preprocessing techniques. Estimating the performance of the proposed models (namely VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) using relative root mean square error, symmetric mean absolute percentage error, and absolute percentage error measures, defined that for one-day-ahead forecast VMD-STAK-BOXCOX model presented the better performance, and for two and three-days-ahead forecast VMD-STACK-CORR model was chosen, compared to VMD, STACK, and machine learning algorithms models' performance. Diebold-Mariano statistical test was conducted to evaluate a reduction in forecasting errors. Therefore, the proposed models (VMD-STACK-CORR, VMD-STACK-PCA, and VMD-STACK-BOXCOX) indeed forecast accurately Bitcoin price and outperformed the compared models (VMD, STACK, and machine learning models).

关键词： Predictive models Biological system modeling Data models Bitcoin Training Machine learning algorithms

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Analyzing the Implications of New Technologies to the Management of Operations – Protocol Proposal and Application Illustration

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Procedia Manufacturing 2019年 39卷 904-912页

作者： Paulo Henrique Brunheroto Cassiano Souza Beller Fernando Deschamps Manufacturing Engineering Graduate Program Department of Mechanical Engineering Federal University of Parana Curitiba PR Brazil Industrial and Systems Engineering Graduate Program Polytechnic School Pontifícia Universidade Católica do Paraná Curitiba PR Brazil

The increasing adoption of new automation and information technologies are transforming production systems into cyber-physical systems (CPS). Although consulting companies have developed management frameworks to better characterize the implications of these technologies to how operations are managed, mainly focusing on new business models, efficiency, and effectiveness, the real impacts on decision areas (e.g., quality management) and performance objectives (e.g., flexibility) are still unclear. How are management systems being transformed? Are new managerial approaches emerging that address the particularities of new technologies? What are the critical success factors for conducting this transformation? To address these questions, a more systematic approach needs to be undertaken. This paper contributes to this by presenting a protocol to be used in systematic studies of the implications of the adoption of new technologies to the management of operations, and by illustrating its application to the analysis of the adoption of a cognitive vision system in an automotive manufacturer primarily deployed for improving product quality. The analysis protocol is based on Pettigrew’s dimensions of context, content, and process for change initiatives and is further subdivided into information requirements, variables to be analyzed and its potential sources of data. The case shows that the application of the protocol was able to reveal that besides expected effects on quality and productivity, new forms of interaction among workers and between workers and supervisors emerged that need further investigation. From the results of the case, future applications of the protocol are discussed.

关键词： management of operations transformation quality management cognitive vision system

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Macroscopic Fundamental Diagrams According to the Different Flood Depths on Probe Vehicle Data in Urban Bangkok

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Journal of Traffic and Transportation engineering 2020年第1期8卷 20-28页

作者： Piyapong Suwanno Prasan Jitpat Rattanaporn Kasemsri Bethala Aditya Atsushi Fukuda Program of Transportation Systems Engineering Graduate School of Science and TechnologyNihon UniversityChiba 274-8501Japan Research Unit of Technology and Innovation on Civil Engineering College of Industrial Technology and ManagementRajamangala University of Technology SrivijayaNakhon Si Thammrat 80210Thailand School of Civil Engineering Suranaree University of TechnologyNakhon Ratchasima 30000Thailand Department of Transportation Systems Engineering College of Science and TechnologyNihon UniversityChiba 274-8501Japan

Impact of flood depth on traffic volume in two different zones of the Bangkok road network was investigated using traffic data obtained from probe vehicle trajectories.A Macroscopic Fundamental Diagram(MFD)was utilized to compare traffic flow rates across two road network zones in the city for a variety of flood depths,namely 0-5 cm,5-10 cm,10-15 cm,15-30 cm,and more than 30 *** of empirical analysis over an observation period of one year as 2019 showed that flood depths had a strong correlation with the MFD parameters of free-flow speed,maximum flow,and traffic jam *** particular,road floods greatly reduced average maximum flow across the inner city road network in *** floods had a significant impact on traffic characteristics of urban road networks.

关键词： Road flood probe vehicle MFD

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Classification of the Structural Integrity by the General Vibration Method

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Procedia Computer Science 2019年 157卷 382-387页

作者： Fergyanto E. Gunawan Benfano Soewito Nico Surantha Tuga Mauritsius Nobumasa Sekishita Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Information Systems Information Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia 11480 Department of Mechanical Engineering Toyohashi University of Technology Toyohashi Aichi 441-8580 Japan

Traditionally, for the purpose of classifying the integrity of an engineering structure, one may use the information of the natural frequencies or mode shapes or some other measures derived from the two. In this research, we propose a classification method on the basis of the general vibration model. The general vibration model is essentially a set of differential equations describing the dynamics of the structure under consideration. For the purpose of damage detection, a deviation of the general vibration model from the dynamic equilibrium point marks the occurrence of the damage. To demonstrate the effectiveness of the method, we study two simplest dynamical systems consisting of one- and two-concentrated masses subjected to a prescribed dynamic load. The structural damages are introduced artificially by reducing the stiffness of spring in the structures. We find that the general vibration model is more sensitive to damages than the traditional methods.

关键词： General Vibration Model Structural Integrity Structural Health Monitoring Power Spectral Density Damage Index

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A Study of the Sensitivity of the Fuel Consumption to Driving Strategy by Micro Simulation

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Procedia Computer Science 2019年 157卷 375-381页

Global warming due to excessive emission greenhouse gases is a significant issue faced by humanity, and collectively, cars and trucks contribute about 20% to the entire global-warming gases. For a reason, recently, many attempts have been endured to reduce the emission and to increase fuel efficiency, including vehicle platooning, better driving strategy, and increasing engine efficiency. This study intends to provide a more detail assessment regarding the effects of the driving strategy to fuel consumption. For the purpose, the vehicle dynamics are quantified by employing the car-following model based on the optimal velocity model. The fuel consumption is estimated from the regression model of Ahn 18 . The result suggests that the braking distance strongly affects fuel consumption.

关键词： Optimal Velocity Model Micro Simulation Car-Following Model Fuel Efficiency

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A Machine Learning Method for Vehicle Classification by Inductive Waveform Analysis ⁎

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IFAC-PapersOnLine 2020年第2期53卷 13928-13932页

作者： Bruno R. Vasconcellos Marcelo Rudek Marcelo de Souza Pontifícia Universidade Católica do Paraná - PUCPR Control and Automation Egineering Curitiba PR 80215-901 Pontifícia Universidade Católica do Paraná - PUCPR Industrial and Systems Engineering Graduate Program Curitiba PR 80215-901 Fiscaltech Curitiba PR 81290-270

The classification of vehicles is a matter of great importance for traffic control and management, helping with traffic surveillance as well as in statistical data collection. Among the several vehicular classification techniques, the most popular uses inductive loop sensors, because they achieve high accuracy rate at low cost. This paper proposes 5 different vehicle classification models by inductive waveform analysis: KNN, SVC, Decision Tree, Random Forest, and Voting Classifier. A brief introduction to the mathematical basis of these models and the main forms of vehicle detection are also presented. The obtained results reached an accuracy of 94% and showed how inductive waveform analysis is still a valid option for vehicle classification.

关键词： Machine learning Intelligent transportation systems Information processing Decision support

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Dynamic Multi-criteria Classifier Selection for Illegal Tapping Detection in Oil Pipelines

Dynamic Multi-criteria Classifier Selection for Illegal Tapp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Victor Henrique Alves Ribeiro Pedro Henrique Domingues Paulo Rodrigo Cavalin Gilberto Reynoso-Meza Helon Vicente Hultmann Ayala Luís Fernando Alzuguir Azevedo Industrial and Systems Engineering Graduate Program Pontifícia Universidade Católica do Paraná Curitiba Brazil IBM Research Rio de Janeiro Brazil Department of Mechanical Engineering Pontifícia Universidade Católica do Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Illegal tapping of fuel pipelines has recently become one of the most relevant safety problems faced by the industry. Hundreds of illegal interventions have been reported around the world, causing a significant number of deaths, relevant impacts on the environment, and capital loss. Therefore, it is important to develop systems that are able to detect such scenarios at an early stage, enabling a fast counteract. To this end, machine learning algorithms can train models on available data for detecting future issues. Most recently, ensemble learning and dynamic classifier selection (DCS) techniques have been achieving promising results in supervised learning tasks. Such models are usually trained based on a single criterion. However, it is desirable to take into account both the number of false positives (FP) and false negatives (FN) for the illegal tapping detection task, since they are conflicting and both lead to financial losses and/or accidents. Therefore, this work proposes a novel DCS technique based on multiple criteria, namely overall local class-specific accuracy (OLCA), which employs multi-criteria decision making for dynamically selecting the best classifier for a new sample given the local true positive and negative ratios. A numerical experiment is conducted for assessing the generalization performance of the proposed method in an oil pipeline, with the goal of detecting illegal taping using pressure transient signals. Results show that OLCA is able to reduce the number of both FP and FN when dynamically selecting the classifiers of a baseline Random Forest ensemble.

关键词： Pipelines Leak detection Oils Decision making Feature extraction Biological system modeling Task analysis

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A Comparative Analysis between Particle Swarm Optimization and Mathematical programming for Optimal Location of Maintenance Facilities

A Comparative Analysis between Particle Swarm Optimization a...

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2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019

作者： Filho, Paulo M. O. Borba, Bruno S. M. C. Ferreira, Vitor H. Tavares, Rafael V. Queiroga, Eduardo Silva, João Marcos P. Uchoa, Eduardo Bergiante, Níssia C. R. Salgado Moreira, Bernardo Coelho MacHado, Pedro Henrique Universidade Federal Fluminense Electrical Engineering Department Niterói Brazil Universidade Federal Fluminense Computing Institute Niterói RJ Brazil Univeridade Federal da Paraíba Industrial Engineering Graduate Program João Pessoa PB Brazil Universidade Federal Fluminense Industrial Engineering Department Niterói RJ Brazil Energisa Distribuição Cuiabá MT Brazil Energisa Tocantins Palmas TO Brazil

ISBN: (纸本)9781538695678

This paper addresses the problem of maintenance facilities (depot) location in electric power distribution systems with a focus on strategic planning. Power interruptions can be harmful for both society and distribution companies. Determining the optimal number and sites of depot location to handle power interruptions in the long term can improve the quality of service. Through a good depot location, the distance from depots to work orders (WO) is minimized, operational costs can be minimized and customer interruption duration is reduced, which is a good indicator of the reliability of the system. The problem was solved by a Particle Swarm Optimization (PSO) algorithm and a Mixed Integer Linear programming (MILP) model. In addition to the real instance, some synthetic ones were generated to compare both approaches in large-sized scenarios. The computational results show that the two approaches are complementary strategies, being presented the scenarios where each proposal could provide better results. © 2019 IEEE.

关键词： Electric power distribution

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Electricity energy price forecasting based on hybrid multi-stage heterogeneous ensemble: Brazilian commercial and residential cases

Electricity energy price forecasting based on hybrid multi-s...

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International Joint Conference on Neural Networks (IJCNN)

作者： Matheus Henrique Dal Molin Ribeiro Ramon Gomes da Silva Cristiane Canton Naylene Fraccanabbia Viviana Cocco Mariani Leandro dos Santos Coelho Industrial & Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Mathematics Federal Technological University of Parana (UTFPR) Pato Branco Parana Brazil Pato Branco University Center (UNIDEP) Pato Branco Parana Brazil Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil Department of Electrical Engineering Federal University of Parana (UFPR) Curitiba Parana Brazil

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The development of accurate models to forecast electricity energy prices is a challenge due to the number of factors which can affect this commodity. In this paper, a hybrid multi-stage approach is proposed to forecast multi-stepahead (one, two and three-month-ahead) Brazilian commercial and residential electricity energy prices. The proposed data analysis combines the pre-processing named complementary ensemble empirical mode decomposition (CEEMD) in the first stage coupled with the coyote optimization algorithm (COA) to define the CEEMD's hyperparameters, aiming to deal with time series non-linearities and enhance the model's performance. On the next stage, four machine learning models named extreme learning machine, Gaussian process, gradient boosting machine, and relevance vector machine are employed to train and predict the CEEMD's components. Finally, in the final stage, the results of the previous step are directly integrated to compose a heterogeneous ensemble learning of components to obtain the final forecasts. In this case, a grid of models is obtained. The best model is one that has better generalization out-of-sample. Through developed comparisons, results showed that combining COA-CEEMD with a heterogeneous ensemble learning can develop accurate forecasts. The modeling developed in this paper is promising and can support decision making in electricity energy price forecasting.

关键词： Predictive models Biological system modeling Forecasting Data models Optimization Empirical mode decomposition

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