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A comprehensive and systematic literature review on the employee attendance management systems based on cloud computing

Published online by Cambridge University Press:  28 July 2022

Afshin Ardebili ,
Ahmad Latifian Open the ORCID record for Ahmad Latifian [Opens in a new window] ,
Chya Fatah Aziz ,
Rima H. BinSaeed ,
S. M. Alizadeh  and
Evgeniy V. Kostyrin
Afshin Ardebili
Affiliation:
Department of Management, Universiti Putra Malaysia, Serdang, Malaysia
Ahmad Latifian*
Affiliation:
Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University Of Mashhad (FUM), Mashhad, Iran
Chya Fatah Aziz
Affiliation:
Technical College of Applied Science, Sulaimani Polytechnic University, Kurdistan, Iraq
Rima H. BinSaeed
Affiliation:
College of Business Administration, King Saud University, Riyadh, Saudi Arabia
S. M. Alizadeh
Affiliation:
Petroleum Engineering Department, Australian College of Kuwait, West Mishref, Kuwait
Evgeniy V. Kostyrin
Affiliation:
Bauman Moscow State Technical University, Moscow, Russia
*
*Correspondence to author: Ahmad Latifian, E-mail: latifian@um.ac.ir
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Abstract

Attendance is critical to the success of any business or industry. As a result, most businesses and institutions require a system to track staff attendance. On the other hand, cloud computing technology is being utilized in the human resource management sector. It may be an excellent option for processing and storing large amounts of data and improving management effectiveness to a desirable level. Hence, this paper examines cloud infrastructures for employee attendance management in which the articles are categorized into three groups. The results show that cloud infrastructure has a significant and positive impact on the management of employee attendance systems. Also, the results reveal that the radio frequency identification authentication protocol protects the privacy of tags and readers against database memory. When references operate properly, they help the people concerned and society by making workplaces more efficient and safer.

Keywords

Cloud computingemployee attendancehuman resource managementhuman resourcemodern managementprivacy

Information

Type
Research Article
Information
Journal of Management & Organization , Volume 29 , Issue 4: The Role of New ICT-based Systems in Modern Management , July 2023 , pp. 679 - 696
Copyright
© The Author(s), 2022. Published by Cambridge University Press in association with the Australian and New Zealand Academy of Management

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References

Ahirao, P., & Michael, A. V. (2019). MYP: Digital attendance system using Google Cloud firebase and gradle. Paper presented at the 2nd International Conference on Advances in Science & Technology (ICAST).10.2139/ssrn.3369512CrossRefGoogle Scholar
Al-Shezawi, M. O., Yousif, J. H., & AL-Balushi, I. A. (2017). Automatic attendance registration system based mobile cloud computing. International Journal of Computation and Applied Sciences, 2(3), 116–122.10.24842/1611/0037CrossRefGoogle Scholar
Chen, H., Miao, Y., Chen, Y., Fang, L., Zeng, L., & Shi, J. (2021a). Intelligent model-based integrity assessment of nonstationary mechanical system. Journal of Web Engineering, 20(2), 253–280.Google Scholar
Chen, J., Liu, Y., Xiang, Y., & Sood, K. (2021b). RPPTD: Robust privacy-preserving truth discovery scheme. IEEE Systems Journal, 15, 1–8.Google Scholar
Cheng, C., & Wang, L. (2022). How companies configure digital innovation attributes for business model innovation? A configurational view. Technovation, 112, 102398.10.1016/j.technovation.2021.102398CrossRefGoogle Scholar
Chozzy, H. P. (2020). Synchronization of attendance data between machines with cloud database using rest API. Universitas Mercu Buana Jakarta.Google Scholar
Dalton, D. R., & Enz, C. A. (1987). Absenteeism in remission: Planning, policy, culture.Google Scholar
Dhandapani, V., Majji, S., Udata, K., & Manigandan, M. (2022). Implementation of attendance management system based on text and face recognition. Advances in energy technology (pp. 55–68). Singapore: Springer.10.1007/978-981-16-1476-7_7CrossRefGoogle Scholar
Dkgoi's, C., & Chincholi, S. (2014). RFID authentication protocol for security and privacy maintenance in cloud based employee management system.Google Scholar
Evizal, A. K. (2020). Online classroom attendance system based on cloud computing.Google Scholar
Galton, F. (1889). Personal identification and description. Journal of Anthropological Institute of Great Britain and Ireland, 18, 177–191.10.2307/2842415CrossRefGoogle Scholar
Géczy, P., Izumi, N., & Hasida, K. (2012). Cloudsourcing: Managing cloud adoption. Global Journal of Business Research, 6(2), 57–70.Google Scholar
Golhar, R. V., Vyawahare, P. A., Borghare, P. H., & Manusmare, A. (2016). Design and implementation of android base mobile app for an institute. Paper presented at the 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).10.1109/ICEEOT.2016.7755391CrossRefGoogle Scholar
He, L., Mu, L., Jean, J. A., Zhang, L., Wu, H., Zhou, T., & Bu, H. (2022). Contributions and challenges of public health social work practice during the initial 2020 COVID-19 outbreak in China. The British Journal of Social Work, 52(4).10.1093/bjsw/bcac077CrossRefGoogle Scholar
Jayant, N. K., & Borra, S. (2016). Attendance management system using hybrid face recognition techniques. Paper presented at the 2016 Conference on advances in signal processing (CASP).10.1109/CASP.2016.7746206CrossRefGoogle Scholar
Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 342.10.3390/s20020342CrossRefGoogle ScholarPubMed
Kumar, R. H., Haripriya, D., & Murikipudi, S. (2018). Integrated biometric attendance and informative system using cloud. International Journal of Pure and Applied Mathematics, 118(20), 2807–2819.Google Scholar
Li, M., Tian, T., Zeng, Y., Zhu, S., Lu, J., Yang, J., … Li, G. (2020). Individual cloud-based fingerprint operation platform for latent fingerprint identification using perovskite nanocrystals as eikonogen. ACS Applied Materials & Interfaces, 12(11), 13494–13502.10.1021/acsami.9b22251CrossRefGoogle ScholarPubMed
Lin, P.-H., & Chang, C.-C. (2019). Development of smart positioning attendance management in ERP systems using BLE beacon. International Journal of Organizational Innovation, 12(2), 60–81.Google Scholar
Lv, Z., Tan, Z., Wang, Q., & Yang, Y. (2018). Cloud computing management platform of human resource based on mobile communication technology. Wireless Personal Communications, 102(2), 1293–1306.10.1007/s11277-017-5195-yCrossRefGoogle Scholar
Marín, J. M. M., De Oliveira-Dias, D., Navimipour, N. J., Gardas, B., & Unal, M. (2021). Cloud computing and human resource management: Systematic literature review and future research agenda. Kybernetes, 51(6), 2172–2191.10.1108/K-05-2021-0420CrossRefGoogle Scholar
Maroli, A., Narwane, V. S., & Gardas, B. B. (2021). Applications of IoT for achieving sustainability in agricultural sector: A comprehensive review. Journal of Environmental Management, 298, 113488.10.1016/j.jenvman.2021.113488CrossRefGoogle ScholarPubMed
Masruroh, S. U., Fiade, A., & Julia, I. R. (2018). NFC based mobile attendence system with facial authorization on Raspberry Pi and Cloud Server. Paper presented at the 2018 6th International Conference on Cyber and IT Service Management (CITSM).10.1109/CITSM.2018.8674293CrossRefGoogle Scholar
Mittal, A., Khan, F. S., Kumar, P., & Choudhury, T. (2017). Cloud based intelligent attendance system through video streaming. Paper presented at the 2017 International Conference On Smart Technologies For Smart Nation (SmartTechCon).10.1109/SmartTechCon.2017.8358587CrossRefGoogle Scholar
Naen, M. F., Adnan, M. H. M., Yazi, N. A., & Nee, C. K. (2021). Development of attendance monitoring system with artificial intelligence optimization in cloud. International Journal of Artificial Intelligence, 8(2), 88–98.10.36079/lamintang.ijai-0802.315CrossRefGoogle Scholar
Oo, S. B., Oo, N. H. M., Chainan, S., Thongniam, A., & Chongdarakul, W. (2018). Cloud-based web application with NFC for employee attendance management system. Paper presented at the 2018 International Conference on Digital Arts, Media and Technology (ICDAMT).10.1109/ICDAMT.2018.8376516CrossRefGoogle Scholar
Opris, V. N., Eftimie, S., & Racuciu, C. (2016). Biometric multi-factor authentication scheme in cloud computing. Scientific Bulletin of Naval Academy, 19(1), 472–475.Google Scholar
Othman, M., Arif, M., Abdullah, M. H. A., Yusof, M. M., & Mohamed, R. (2017). Human resource management on cloud. JOIV: International Journal on Informatics Visualization, 1(4–2), 260–263.10.30630/joiv.1.4-2.80CrossRefGoogle Scholar
Parvathy, A., Rajasekhar, B., Nithya, C., Thenmozhi, K., Rayappan, J., Raj, P., & Amirtharajan, R. (2013). RFID in cloud environment for attendance monitoring system. International Journal of Engineering & Technology, 5(3).Google Scholar
Rahmani, A. M., Ali Naqvi, R., Hussain Malik, M., Malik, T. S., Sadrishojaei, M., Hosseinzadeh, M., & Al-Musawi, A. (2021). E-learning development based on internet of things and blockchain technology during COVID-19 pandemic. Mathematics, 9(24), 3151.CrossRefGoogle Scholar
Rajeswari, P., Raju, S. V., Ashour, A. S., & Dey, N. (2017). Multi-fingerprint unimodel-based biometric authentication supporting cloud computing. In Dey, Nilanjan & V. Santhi (Ed.), Intelligent techniques in signal processing for multimedia security (pp. 469–485). Cham: Springer.10.1007/978-3-319-44790-2_21CrossRefGoogle Scholar
Sadrishojaei, M., Jafari Navimipour, N., Reshadi, M., & Hosseinzadeh, M. (2021a). Clustered routing method in the internet of things using a moth-flame optimization algorithm. International Journal of Communication Systems, 34(16), e4964.CrossRefGoogle Scholar
Sadrishojaei, M., Jafari Navimipour, N., Reshadi, M., Hosseinzadeh, M., & Unal, M. (2022). An energy-aware clustering method in the IoT using a swarm-based algorithm. Wireless Networks, 28(1), 125–136.CrossRefGoogle Scholar
Sadrishojaei, M., Navimipour, N. J., Reshadi, M., & Hosseinzadeh, M. (2021b). A new clustering-based routing method in the mobile internet of things using a krill herd algorithm. Cluster Computing, 25, 351–361.CrossRefGoogle Scholar
Salman, H., Uddin, M. N., Acheampong, S., & Xu, H. (2019). Design and implementation of IoT based class attendance monitoring system using computer vision and embedded Linux platform. Paper presented at the Workshops of the International Conference on Advanced Information Networking and Applications.10.1007/978-3-030-15035-8_3CrossRefGoogle Scholar
Shah, H. M., Gardas, B. B., Narwane, V. S., & Mehta, H. S. (2021). The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: A comprehensive review. Kybernetes.Google Scholar
Sharma, T., & Aarthy, S. (2016). An automatic attendance monitoring system using RFID and IOT using Cloud. Paper presented at the 2016 Online International Conference on Green Engineering and Technologies (IC-GET).CrossRefGoogle Scholar
Singh, D., Leavline, E. J., & Vijayan, P. M. (2017). Mobile application for student attendance and mark management system. International Journal of Computational Intelligence Research, 13(3), 425–432.Google Scholar
Soyata, T., Muraleedharan, R., Funai, C., Kwon, M., & Heinzelman, W. (2012). Cloud-vision: Real-time face recognition using a mobile-cloudlet-cloud acceleration architecture. Paper presented at the 2012 IEEE symposium on computers and communications (ISCC).CrossRefGoogle Scholar
Sun, Q., Lin, K., Si, C., Xu, Y., Li, S., & Gope, P. (2022). A secure and anonymous communicate scheme over the Internet of Things. ACM Transactions on Sensor Networks (TOSN), 18(3), 1–21.Google Scholar
Tolba, A., El-Baz, A., & El-Harby, A. (2006). Face recognition: A literature review. International Journal of Signal Processing, 2(2), 88–103.Google Scholar
Wang, Z., Ramamoorthy, R., Xi, X., Rajagopal, K., Zhang, P., & Jafari, S. (2022). The effects of extreme multistability on the collective dynamics of coupled memristive neurons. The European Physical Journal Special Topics, 231, 1–8.CrossRefGoogle Scholar
Yadav, V., & Bhole, G. (2019). Cloud based smart attendance system for educational institutions. Paper presented at the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon).CrossRefGoogle Scholar
Yan, L., Yin-He, S., Qian, Y., Zhi-Yu, S., Chun-Zi, W., & Zi-Yun, L. (2021). Method of reaching consensus on probability of food safety based on the integration of finite credible data on block chain. IEEE Access, 9, 123764–123776.10.1109/ACCESS.2021.3108178CrossRefGoogle Scholar
Yang, J., Xiong, N., Vasilakos, A. V., Fang, Z., Park, D., Xu, X., … Yang, Y. (2011). A fingerprint recognition scheme based on assembling invariant moments for cloud computing communications. IEEE Systems Journal, 5(4), 574–583.10.1109/JSYST.2011.2165600CrossRefGoogle Scholar
Yao, L., Li, X., Zheng, R., & Zhang, Y. (2022). The impact of air pollution perception on urban settlement intentions of young talent in China. International Journal of Environmental Research and Public Health, 19(3), 1080.CrossRefGoogle ScholarPubMed
Zadeh, F. A., Bokov, D. O., Yasin, G., Vahdat, S., & Abbasalizad-Farhangi, M. (2021). Central obesity accelerates leukocyte telomere length (LTL) shortening in apparently healthy adults: A systematic review and meta-analysis. Critical Reviews in Food Science and Nutrition, 61, 1–10.Google Scholar
Zhang, M., Chen, Y., & Lin, J. (2021). A privacy-preserving optimization of neighborhood-based recommendation for medical-aided diagnosis and treatment. IEEE Internet of Things Journal, 8(13), 10830–10842.CrossRefGoogle Scholar
Zhang, H., Feng, X., Liu, H., Guo, P., Krishnamoorthy, S., & Zhang, C. (2019). Cloud-based class attendance record system. Paper presented at the 2019 IEEE 5th International Conference on Computer and Communications (ICCC).CrossRefGoogle Scholar
Zheng, W., Tian, X., Yang, B., Liu, S., Ding, Y., Tian, J., & Yin, L. (2022a). A few shot classification methods based on multiscale relational networks. Applied Sciences, 12(8), 4059.CrossRefGoogle Scholar
Zheng, W., & Yin, L. (2022). Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network. PeerJ Computer Science, 8, e908.CrossRefGoogle ScholarPubMed
Zheng, W., Zhou, Y., Liu, S., Tian, J., Yang, B., & Yin, L. (2022b). A deep fusion matching network semantic reasoning model. Applied Sciences, 12(7), 3416.CrossRefGoogle Scholar
Zhu, B., Zhong, Q., Chen, Y., Liao, S., Li, Z., Shi, K., & Sotelo, M. A. (2022). A novel reconstruction method for temperature distribution measurement based on ultrasonic tomography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(7), 2352–2370.CrossRefGoogle ScholarPubMed