Innovative approaches to productivity monitoring: Integrating work sampling and electronic performance monitoring

Organization, Technology and Management in Construction: an International Journal

Volume 17 (2025): Issue 1 (January 2025)

By:

Diego Calvetti and Miguel Luiz Ribeiro Ferreira

Open Access

|Jul 2025

References

Adrian, J. J. (2004). Construction Productivity: Measurement and Improvement. Stipes. Available at https://books.google.pt/books?id=1kJ_QwAACAAJ.
Search in Google Scholar Back to article
Akhavian, R., & Behzadan, A. H. (2016). Smartphone-based construction workers’ activity recognition and classification. Automation in Construction, 71, pp. 198-209. doi: <a href="https://doi.org/10.1016/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.</a> autcon.2016.08.015
Open DOI Search in Google Scholar Back to article
Alder, G. S. (2001). Employee reactions to electronic performance monitoring: A consequence of organizational culture. The Journal of High Technology Management Research, 12(2), pp. 323-342. doi: <a href="https://doi.org/10.1016/S1047-8310(01)00042-6" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S1047-8310(01)00042-6</a>
Open DOI Search in Google Scholar Back to article
Altheimer, J., & Schneider, J. (2024). Smart-watch-based construction worker activity recognition with hand-held power tools. Automation in Construction, 167, p. 105684. doi: <a href="https://doi.org/10.1016/j.autcon.2024.105684" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.autcon.2024.105684</a>
Open DOI Search in Google Scholar Back to article
Aziz, R. F., & Hafez, S. M. (2013). Applying lean thinking in construction and performance improvement. Alexandria Engineering Journal, 52(4), pp. 679-695. doi: <a href="https://doi.org/10.1016/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.</a> aej.2013.04.008
Open DOI Search in Google Scholar Back to article
Calvetti, D., et al. (2022). Construction tasks electronic process monitoring: Laboratory circuit-based simulation deployment. Buildings, 12(8), p. 1174. doi: <a href="https://doi.org/10.3390/buildings12081174" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/buildings12081174</a>
Open DOI Search in Google Scholar Back to article
Calvetti, D., & Ferreira, M. L. R. (2018). Agile methodology to performance measure and identification of impact factors in the labour productivity of industrial workers. U.Porto Journal of Engineering, 4(2), pp. 49-64. doi: <a href="https://doi.org/10.24840/2183-6493_004.002_0005" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.24840/2183-6493_004.002_0005</a>
Open DOI Search in Google Scholar Back to article
Calvetti, D., Goncalves, M., Vahl, F., Meda, P., & Sousa, H. de. (2021a). Labour productivity as a means for assessing environmental impact in the construction industry. Environmental Engineering and Management Journal, 20(5), pp. 781-790. doi: <a href="https://doi.org/10.30638/eemj.2021.073" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.30638/eemj.2021.073</a>
Open DOI Search in Google Scholar Back to article
Calvetti, D., Mêda, P., et al. (2021b) Mechanization of construction tasks: Level assessment and craft workforce awareness. In: 2021 European Conference on Computing in Construction, pp. 342-349. doi: <a href="https://doi.org/10.35490/EC3.2021.172." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.35490/EC3.2021.172.</a>
Open DOI Search in Google Scholar Back to article
Cheng, T., et al. (2013). Automated task-level activity analysis through fusion of real time location sensors and worker’s thoracic posture data. Automation in Construction, 29, pp. 24-39. doi: <a href="https://doi.org/10.1016/j.autcon.2012.08.003" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.autcon.2012.08.003</a>
Open DOI Search in Google Scholar Back to article
Cheng, M.-Y., Khitam, A. F. K., & Tanto, H. H. (2023). Construction worker productivity evaluation using action recognition for foreign labor training and education: A case study of Taiwan. Automation in Construction, 150, p. 104809. doi: <a href="https://doi.org/10.1016/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.</a> autcon.2023.104809
Open DOI Search in Google Scholar Back to article
Cheng, T., & Teizer, J. (2013). Real-time resource location data collection and visualization technology for construction safety and activity monitoring applications. Automation in Construction, 34, pp. 3-15. doi: <a href="https://doi.org/10.1016/j.autcon.2012.10.017" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.autcon.2012.10.017</a>
Open DOI Search in Google Scholar Back to article
Chen, X., & Yu, Y. (2024). Automatic repetitive action counting for construction worker ergonomic assessment. Automation in Construction, 167, p. 105726. doi: <a href="https://doi.org/10.1016/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.</a> autcon.2024.105726
Open DOI Search in Google Scholar Back to article
Costella, M. F., et al. (2018). Proposal and evaluation of a method to implement the lean construction principles. Brazilian Journal of Operations & Production Management, 15(4), pp. 545-557. doi: <a href="https://doi.org/10.14488/BJOPM.2018.v15.n4.a8" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.14488/BJOPM.2018.v15.n4.a8</a>
Open DOI Search in Google Scholar Back to article
Daoud, A. O., El Hefnawy, M., & Wefki, H. (2023). Investigation of critical factors affecting cost overruns and delays in Egyptian mega construction projects. Alexandria Engineering Journal, 83, pp. 326-334. doi: <a href="https://doi.org/10.1016/j.aej.2023.10.052" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.aej.2023.10.052</a>
Open DOI Search in Google Scholar Back to article
Finkler, S. A., Knickman, J. R., Hendrickson, G., Lipkin, Jr., M., & Thompson, W. G. (1993). A comparison of work-sampling and time-and-motion techniques for studies in health services research. Health Services Research, 28(5), pp. 577-597. PMID: 8270422.
Search in Google Scholar Back to article
Ganorkar, A. B., Lakhe, R. R., & Agrawal, K. N. (2019). Methodology for application of Maynard operation sequence technique (MOST) for time-driven activity-based costing (TDABC). International Journal of Productivity and Performance Management, 68(1), pp. 2-25. doi: <a href="https://doi.org/10.1108/IJPPM-06-2017-0156" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1108/IJPPM-06-2017-0156</a>
Open DOI Search in Google Scholar Back to article
Gong, Y., et al. (2022). Wearable acceleration-based action recognition for long-term and continuous activity analysis in construction site. Journal of Building Engineering, 52, p. 104448. doi: <a href="https://doi.org/10.1016/j.jobe.2022.104448" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.jobe.2022.104448</a>
Open DOI Search in Google Scholar Back to article
Gong, J., & Caldas, C. H. (2011). An object recognition, tracking, and contextual reasoning-based video interpretation method for rapid productivity analysis of construction operations. Automation in Construction, 20(8), pp. 1211-1226. doi: <a href="https://doi.org/10.1016/j.autcon.2011.05.005" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.autcon.2011.05.005</a>
Open DOI Search in Google Scholar Back to article
Groover, M. P. (2007). Work Systems and the Methods, Measurement, and Management of Work. Pearson Prentice Hall. Pearson; 1st edition. Available at https://books.google. pt/books?id=ktseAQAAIAAJ
Search in Google Scholar Back to article
Jacobsen, E. L., et al. (2024). Probabilistic forecasting of construction labor productivity metrics. Journal of Information Technology in Construction, 29, pp. 58-83. doi: <a href="https://doi.org/10.36680/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.36680/j.</a> itcon.2024.004
Open DOI Search in Google Scholar Back to article
Jarkas, A. M., & Bitar, C. G. (2012). Factors affecting construction labor productivity in Kuwait. Journal of Construction Engineering and Management, 138(7), pp. 811-820. doi: <a href="https://doi.org/10.1061/(ASCE)CO.1943-7862.0000501" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1061/(ASCE)CO.1943-7862.0000501</a>
Open DOI Search in Google Scholar Back to article
Jesus, C., et al. (2024). Adopting industry 4.0 and lean practices in heavy metalworking: Impact of human factors on productivity. In: Rocha, A., et al. (eds.), Information Systems and Technologies. Springer Nature Switzerland (Lecture Notes in Networks and Systems), Cham, pp. 241-252. doi: <a href="https://doi.org/10.1007/978-3-031-45648-0_24" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/978-3-031-45648-0_24</a>
Open DOI Search in Google Scholar Back to article
Jiang, H., et al. (2015). A labor consumption measurement system based on real-time tracking technology for dam construction site. Automation in Construction, 52, pp. 1-15. doi: <a href="https://doi.org/10.1016/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.</a> autcon.2015.02.004
Open DOI Search in Google Scholar Back to article
Khazen, M., Nik-Bakht, M., & Moselhi, O. (2024). Monitoring workers on indoor construction sites using data fusion of real-time worker’s location, body orientation, and productivity state. Automation in Construction, 160, p. 105327. doi: <a href="https://doi.org/10.1016/j.autcon.2024.105327" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.autcon.2024.105327</a>
Open DOI Search in Google Scholar Back to article
Lee, T. Y., Ahmad, F., & Sarijari, M. A. (2024). Activity sampling in the construction industry: A review and research agenda. International Journal of Productivity and Performance Management, 73(5), pp. 1479-1501. doi: <a href="https://doi.org/10.1108/IJPPM-10-2022-0507" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1108/IJPPM-10-2022-0507</a>
Open DOI Search in Google Scholar Back to article
Lindhard, S. M. (2023). Applying work measurements to identify productivity potentials: The case of prefabricated concrete elements. International Journal of Construction Management, 24(15), pp. 1668-1678. <a href="https://doi.org/10.1080/15623599.202" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1080/15623599.202</a> 3.2286115
Search in Google Scholar Back to article
Lopes Miranda, Jr, H., et al. (2017). The internet of things sensors technologies and their applications for complex engineering projects: A digital construction site framework. Brazilian Journal of Operations & Production Management, 14(4), pp. 567-576. doi: <a href="https://doi.org/10.14488/BJOPM.2017.v14.n4.a12" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.14488/BJOPM.2017.v14.n4.a12</a>
Open DOI Search in Google Scholar Back to article
Meyers, F. E., & Stewart, J. R. (2002). Motion and Time Study for Lean Manufacturing. Prentice Hall. Pearson College Div; Subsequent edition. Available at https://books.google.pt/books?id=c-MoeAQAAIAAJ
Search in Google Scholar Back to article
Nassri, S., et al. (2023). Labor waste in housing construction projects: An empirical study. Smart and Sustainable Built Environment, 12(2), pp. 325-340. doi: <a href="https://doi.org/10.1108/SASBE-07-2021-0108" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1108/SASBE-07-2021-0108</a>
Open DOI Search in Google Scholar Back to article
Nath, N. D., Akhavian, R., & Behzadan, A. H. (2017). Ergonomic analysis of construction worker’s body postures using wearable mobile sensors. Applied Ergonomics, 62, pp. 107-117. doi: <a href="https://doi.org/10.1016/j.apergo.2017.02.007" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.apergo.2017.02.007</a>
Open DOI Search in Google Scholar Back to article
Navon, R. (2005). Automated project performance control of construction projects. Automation in Construction, 14(4), pp. 467-476. doi: <a href="https://doi.org/10.1016/j.autcon.2004.09.006" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.autcon.2004.09.006</a>
Open DOI Search in Google Scholar Back to article
Navon, R., & Goldschmidt, E. (2003a). Can labor inputs be measured and controlled automatically? Journal of Construction Engineering and Management, 129(4), pp. 437-445. doi: <a href="https://doi.org/10.1061/(ASCE)0733-9364(2003)129:4(437)" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1061/(ASCE)0733-9364(2003)129:4(437)</a>
Open DOI Search in Google Scholar Back to article
Navon, R., & Goldschmidt, E. (2003b). Monitoring labor inputs: Automated-data-collection model and enabling technologies. Automation in Construction, 12(2), pp. 185-199. doi: <a href="https://doi.org/10.1016/S0926-5805(02)00043-2" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S0926-5805(02)00043-2</a>
Open DOI Search in Google Scholar Back to article
Niebel, B. W., & Freivalds, A. (2013). Niebel’s Methods, Standards, and Work Design. McGraw-Hill Education. Available at https://books.google.pt/books?id=Pb24LwEACAAJ
Search in Google Scholar Back to article
Nunamaker, Jr, J. F., Chen, M., & Purdin, T. D. M. (1990). Systems development in information systems research. Journal of Management Information Systems, 7(3), pp. 89-106. http://www.jstor.org/stable/40397957. doi: <a href="https://doi.org/10.1080/07421222.1990.11517898" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1080/07421222.1990.11517898</a>
Open DOI Search in Google Scholar Back to article
Panina, D., & Aiello, J. R. (2005). Acceptance of electronic monitoring and its consequences in different cultural contexts: A conceptual model. Journal of International Management, 11(2), pp. 269-292. doi: <a href="https://doi.org/10.1016/j.intman.2005.03.009" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.intman.2005.03.009</a>
Open DOI Search in Google Scholar Back to article
Peffers, K., et al. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), pp. 45-77. doi: <a href="https://doi.org/10.2753/MIS0742-1222240302" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2753/MIS0742-1222240302</a>
Open DOI Search in Google Scholar Back to article
Pereira, L., & Tortorella, G. (2018). Identification of the relationships between critical success factors, barriers and practices for lean implementation in a small company. Brazilian Journal of Operations & Production Management, 15(2), pp. 232-246. doi: <a href="https://doi.org/10.14488/BJOPM.2018.v15.n2.a6" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.14488/BJOPM.2018.v15.n2.a6</a>
Open DOI Search in Google Scholar Back to article
Pérez, C. T., Salling, S., & Wandahl, S. (2022). Using smartwatches to understand the relationship between construction workers’ travelled distance and time spent on direct work. IOP Conference Series: Earth and Environmental Science, 1101(8), p. 082009. doi: <a href="https://doi.org/10.1088/1755-1315/1101/8/082009" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1088/1755-1315/1101/8/082009</a>
Open DOI Search in Google Scholar Back to article
Shamsollahi, D., Moselhi, O., & Khorasani, K. (2024). Data integration using deep learning and real-time locating system (RTLS) for automated construction progress monitoring and reporting. Automation in Construction, 168, p. 105778. doi: <a href="https://doi.org/10.1016/j.autcon.2024.105778" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.autcon.2024.105778</a>
Open DOI Search in Google Scholar Back to article
Shehata, M. E., & El-Gohary, K. M. (2011). Towards improving construction labor productivity and projects’ performance. Alexandria Engineering Journal, 50(4), pp. 321-330. doi: <a href="https://doi.org/10.1016/j.aej.2012.02.001" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.aej.2012.02.001</a>
Open DOI Search in Google Scholar Back to article
Sink, D. S. (1985). Productivity Management: Planning, Evaluation, Control, and Improvement. Wiley; 1st edition. Available at https://books.google.pt/books?id=VQtPAAAAMAAJ
Search in Google Scholar Back to article
Siriwardhana, S., & Moehler, R. (2024). Mastering the skills of construction 4.0: A review of the literature using science mapping. Smart and Sustainable Built Environment, 13(4), pp. 989-1014. doi: <a href="https://doi.org/10.1108/SASBE-03-2023-0045" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1108/SASBE-03-2023-0045</a>
Open DOI Search in Google Scholar Back to article
Teizer, J., Cheng, T., & Fang, Y. (2013). Location tracking and data visualization technology to advance construction ironworkers’ education and training in safety and productivity. Automation in Construction, 35, pp. 53-68. doi: <a href="https://doi.org/10.1016/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.</a> autcon.2013.03.004
Open DOI Search in Google Scholar Back to article
Wandahl, S., et al. (2023). Correlation of construction workers’ movement and direct work rates. Journal of Engineering, Project, and Production Management, 13(2), pp. 125-137. doi: <a href="https://doi.org/10.32738/JEPPM-2023-0013" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.32738/JEPPM-2023-0013</a>
Open DOI Search in Google Scholar Back to article
Wang, K., et al. (2024). From industry 4.0 to construction 4.0: Barriers to the digital transformation of engineering and construction sectors. Engineering, Construction and Architectural Management, 31(1), pp. 136-158. doi: <a href="https://doi.org/10.1108/ECAM-05-2022-0383" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1108/ECAM-05-2022-0383</a>
Open DOI Search in Google Scholar Back to article
Wang, Z., et al. (2025). Sensor adoption in the construction industry: Barriers, opportunities, and strategies. Automation in Construction, 170, p. 105937. doi: <a href="https://doi.org/10.1016/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.</a> autcon.2024.105937
Open DOI Search in Google Scholar Back to article
Xu, L., et al. (2025). Automation in manufacturing and assembly of industrialised construction. Automation in Construction, 170, p. 105945. doi: <a href="https://doi.org/10.1016/j.autcon.2024.105945" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.autcon.2024.105945</a>
Open DOI Search in Google Scholar Back to article
Yang, J., et al. (2015). Construction performance monitoring via still images, time-lapse photos, and video streams: Now, tomorrow, and the future. Advanced Engineering Informatics, 29(2), pp. 211-224. doi: <a href="https://doi.org/10.1016/j.aei.2015.01.011" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.aei.2015.01.011</a>
Open DOI Search in Google Scholar Back to article
Yang, Z., et al. (2019). Assessment of construction workers’ labor intensity based on wearable smartphone system. Journal of Construction Engineering and Management, 145(7), p. 04019039. doi: <a href="https://doi.org/10.1061/(ASCE)CO.1943-7862.0001666" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1061/(ASCE)CO.1943-7862.0001666</a>
Open DOI Search in Google Scholar Back to article
Yap, J. B. H., et al. (2021). Revisiting critical delay factors for construction: Analysing projects in Malaysia. Alexandria Engineering Journal, 60(1), pp. 1717-1729. doi: <a href="https://doi.org/10.1016/j." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.</a> aej.2020.11.021
Open DOI Search in Google Scholar Back to article
Zhang, M., et al. (2018). Research on construction workers’ activity recognition based on smartphone. Sensors, 18(8), p. 2667. doi: <a href="https://doi.org/10.3390/s18082667" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/s18082667</a>
Open DOI Search in Google Scholar Back to article
Zhao, J., et al. (2021). Using real-time indoor resource positioning to track the progress of tasks in construction sites. Frontiers in Built Environment, 7, p. 661166. doi: <a href="https://doi.org/10.3389/fbuil.2021.661166" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3389/fbuil.2021.661166</a>
Open DOI Search in Google Scholar Back to article