Impact of COVID-19 on the Behavioral Intention of Job Seekers Towards E-recruitment: A Post-COVID Study
Main Article Content
Abstract
Restrictions on face-to-face social interactions due to COVID-19 made traditional recruitment limited, pushing both employers and job seekers to rely on e-recruitment. This shift highlights the importance of understanding the willingness of job seekers to adopt this technology. The purpose of this paper is to examine the impact of COVID-19 on job seekers’ behavioral intention toward e-recruitment. A modified Technology Acceptance Model (TAM) was used for the purpose of the study. Behavioral intention of the job seekers was thought to be affected by Perceived Ease of Use (PEU), Perceived Usefulness (PU), Subjective Norms (SN), and COVID-19. 441 job seekers were surveyed using a structured questionnaire developed around the model's premise. The collected data were analyzed using structural equation modelling (SEM). The study identified a positive relationship between the dependent and independent variables. COVID-19 was found to have a significant direct impact on the behavioral intention of job seekers toward e-recruitment. The study findings imply that recruiters must consider COVID-19 when designing their online recruitment process. This paper offers guidance for HR professionals, businesses, e-recruitment service providers, and job portals regarding the strategy for designing their online recruitment process, platforms, or websites to attract a large pool of job seekers. This study contributes to the existing literature on job seekers' intentions to use e-recruitment technology in developing countries.
Downloads
Article Details
Section

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
License and Copyright Policy for Bangladesh Journal of MIS (BJMIS)
Articles published in the Bangladesh Journal of MIS from Volume 10, Issue 1 (June 2024) onward are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), except for material explicitly identified as subject to different terms.
Articles published before June 2024 remain subject to their original copyright notices and applicable reuse terms. Free access to an earlier article does not, by itself, grant an open license.
Permitted Use and Attribution
CC BY-NC 4.0 permits users to copy, distribute, and adapt licensed material for noncommercial purposes, provided that they:
-
Give appropriate credit to the author(s) and acknowledge the original publication in Bangladesh Journal of MIS.
-
Provide a link to the license and indicate whether changes were made.
-
Avoid implying endorsement by the author(s) or the journal.
-
Do not impose additional legal or technological restrictions that prevent others from exercising the rights granted by the license.
Commercial reuse requires permission from the relevant copyright holder unless otherwise permitted by law.
Author Rights
For articles published from June 2024 onward:
-
Authors retain copyright in their work.
-
Authors grant BJMIS the right to publish their articles under CC BY-NC 4.0 through the applicable publication agreement.
-
Authors may deposit and share the published version in institutional or subject repositories, on personal websites, or through other platforms without an embargo, in accordance with the license and with appropriate attribution.
Copyright ownership and sharing rights for earlier articles are governed by their original publication agreements and applicable notices.
Third-Party Material and License Scope
Third-party material, including images, tables, and datasets, in articles covered by this policy is included under the article’s CC BY-NC 4.0 license unless a credit line states otherwise.
For material excluded from the license, users must obtain permission from the relevant copyright holder where their intended reuse is not permitted by law. Authors must ensure that material included under the article’s license may lawfully be licensed on those terms.
The article license does not automatically apply to website design or other website material.
How to Attribute
For articles covered by CC BY-NC 4.0, the following attribution format may be used:
Author(s), “Article Title,” Bangladesh Journal of MIS, [Volume, Issue, Year], [DOI or article URL], licensed under CC BY-NC 4.0. [Indicate changes, if any.]
Include a link to the license.
Further Information
The full license terms are available on the Creative Commons CC BY-NC 4.0 license page.
How to Cite
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Akkermans, J., Richardson, J., & Kraimer, M. L. (2020). The Covid-19 crisis as a career shock: Implications for careers and vocational behavior. Journal of Vocational Behavior, 119, 103434. https://doi.org/10.1016/j.jvb.2020.103434
Allden, N., & Harris, L. (2013). Building a positive candidate experience: Towards a networked model of e-recruitment. Journal of Business Strategy, 34(5), 36–47. https://doi.org/10.1108/JBS-11-2012-0072
Asian Development Bank, Hayashi, R., Matsuda, N., & Florida International University. (2020). COVID-19 Impact on Job Postings: Real-Time Assessment Using Bangladesh and Sri Lanka Online Job Portals. Asian Development Bank. https://doi.org/10.22617/BRF200162-2
Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and Test of a Theory of Technological Learning and Usage. Human Relations, 45(7), 659–686. https://doi.org/10.1177/001872679204500702
Brahmana, R. K., & Brahmana, R. (2013). What Factors Drive Job Seekers Attitude in Using E-Recruitment? The South East Asian Journal of Management, 7(2), 123–134. https://doi.org/10.21002/seam.v7i2.2050
Carnevale, J. B., & Hatak, I. (2020). Employee adjustment and well-being in the era of COVID-19: Implications for human resource management. Journal of Business Research, 116, 183–187. https://doi.org/10.1016/j.jbusres.2020.05.037
Chen, S.-C., Shing-Han, L., & Chien-Yi, L. (2011). Recent related research in technology acceptance model: A literature review. Australian Journal of Business and Management Research, 1(9), 124.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Ekanayaka, E., & Gamage, P. (2019). Factors influencing job seeker’s intention to use E-recruitment: L evidence from a State University in Sri Lanka. International Journal of Academic Research & Development (IJAR&D), 50. http://dspace.christcollegeijk.edu.in:8080/jspui/bitstream/123456789/1316/1/International_Journal_of_Academic_Resear%20-%20Jean%20Maria%20George.pdf#page=56
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. https://philarchive.org/archive/FISBAI
Fong, M. W., Gao, H., Wong, J. Y., Xiao, J., Shiu, E. Y. C., Ryu, S., & Cowling, B. J. (2020). Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings—Social Distancing Measures. Emerging Infectious Diseases, 26(5), 976–984. https://doi.org/10.3201/eid2605.190995
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
García‐Izquierdo, A. L., Aguinis, H., & Ramos‐Villagrasa, P. J. (2010). Science–Practice Gap in e‐Recruitment. International Journal of Selection and Assessment, 18(4), 432–438. https://doi.org/10.1111/j.1468-2389.2010.00525.x
García-Milon, A., Olarte-Pascual, C., & Juaneda-Ayensa, E. (2021). Assessing the moderating effect of COVID-19 on intention to use smartphones on the tourist shopping journey. Tourism Management, 87, 104361. https://doi.org/10.1016/j.tourman.2021.104361
Hafeez, M., & Farooq, U. (2017). The Role of E-Recruitment in Attracting Potential Candidates: Evidence from Fresh Graduate Job Seekers. International Conference on Advances in Business, Management and Law (ICABML) 2017, 1(1), 156–171. https://doi.org/10.30585/icabml-cp.v1i1.18
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hochwarter, W. A., Laird, M. D., & Brouer, R. L. (2008). Board Up the Windows: The Interactive Effects of Hurricane-Induced Job Stress and Perceived Resources on Work Outcomes. Journal of Management, 34(2), 263–289. https://doi.org/10.1177/0149206307309264
Holm, A. B. (2012). E-recruitment: Towards an Ubiquitous Recruitment Process and Candidate Relationship Management. German Journal of Human Resource Management: Zeitschrift Für Personalforschung, 26(3), 241–259. https://doi.org/10.1177/239700221202600303
Islam, M. M. (2016). Analysis of E-recruitment Trend and Effectiveness: A case study on some companies in Bangladesh. Management Development, 30(1). http://bim.portal.gov.bd/sites/default/files/files/bim.portal.gov.bd/page/09f3e9a8_0cca_4a71_adb5_ee7082521b3a/Vol%2030%20No%201%262%2C%20January-June%202016_Article_06.pdf
Jayabalan, N., Zafir, M. M., Jenny, M., Nor, A. A. R., & Muhammad, A. A. (2019). E-recruitment technology adoption among generation z job-seekers. International Journal of Engineering and Advanced Technology, 9(2), 1880–1888.
Karim, M. R., Miah, Md. S., & Khatun, A. (2015). E-Recruitment in Practice: A Study on Jobseekers’ Perception in Bangladesh. Global Disclosure of Economics and Business, 4(1), 33–42. https://doi.org/10.18034/gdeb.v4i1.150
Kashi, K., & Zheng, C. (2013). Extending Technology Acceptance Model to the E‐recruitment Context in I ran. International Journal of Selection and Assessment, 21(1), 121–129. https://doi.org/10.1111/ijsa.12022
Khan, N. R., Awang, M., & Ghouri, A. M. (2013). Impact of e-recruitment and job-seekers perception on intention to pursue the jobs. Khan, NR, Awang, M., & Ghouri, AM (2013). Impact of E-Recruitment and Job-Seekers Perception on Intention to Pursue the Jobs. Management & Marketing, 11(1), 47–57.
Kock, N. (2015). Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Lewis, Agarwal, & Sambamurthy. (2003). Sources of Influence on Beliefs about Information Technology Use: An Empirical Study of Knowledge Workers. MIS Quarterly, 27(4), 657. https://doi.org/10.2307/30036552
Liu, I.-F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C.-H. (2010). Extending the TAM model to explore the factors that affect intention to use an online learning community. Computers & Education, 54(2), 600–610.
Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003
Manuell, M., & Cukor, J. (2011). Mother Nature versus human nature: Public compliance with evacuation and quarantine. Disasters, 35(2), 417–442. https://doi.org/10.1111/j.1467-7717.2010.01219.x
Melanthiou, Y., Pavlou, F., & Constantinou, E. (2015). The Use of Social Network Sites as an E-Recruitment Tool. Journal of Transnational Management, 20(1), 31–49. https://doi.org/10.1080/15475778.2015.998141
Moghaddam, H. A., Rezaei, S., & Amin, M. (2015). Examining job seekers’ perception and behavioural intention toward online recruitment: A PLS path modelling approach. J. for Global Business Advancement, 8(3), 305. https://doi.org/10.1504/JGBA.2015.071331
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030
Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. sage. https://books.google.com/books?hl=en&lr=&id=9kB5jE2IjS4C&oi=fnd&pg=PR13&dq=pett+et+al,+2003&ots=y_r9Wznug0&sig=CyqJuPMYYeu4th48Y6s9vAevmFk
Pfieffelmann, B., Wagner, S. H., & Libkuman, T. (2010). Recruiting on Corporate Web Sites: Perceptions of fit and attraction. International Journal of Selection and Assessment, 18(1), 40–47. https://doi.org/10.1111/j.1468-2389.2010.00487.x
Rahman, M. A., Qi, X., & Jinnah, M. S. (2016). Factors affecting the adoption of HRIS by the Bangladeshi banking and financial sector. Cogent Business & Management, 3(1), 1262107. https://doi.org/10.1080/23311975.2016.1262107
Ramayah, T., Rouibah, K., Gopi, M., & Rangel, G. J. (2009). A decomposed theory of reasoned action to explain intention to use Internet stock trading among Malaysian investors. Computers in Human Behavior, 25(6), 1222–1230. https://doi.org/10.1016/j.chb.2009.06.007
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
Seetharaman, P., Mathew, S. K., Sein, M. K., & Tallamraju, R. B. (2020). Being (more) Human in a Digitized World. Information Systems Frontiers, 22(3), 529–532. https://doi.org/10.1007/s10796-020-10020-9
Song, J., Xia, S., Vrontis, D., Sukumar, A., Liao, B., Li, Q., Tian, K., & Yao, N. (2022). The Source of SMEs’ Competitive Performance in COVID-19: Matching Big Data Analytics Capability to Business Models. Information Systems Frontiers, 24(4), 1167–1187. https://doi.org/10.1007/s10796-022-10287-0
Sun, Q., Wang, C., & Cao, H. (2009). An extended TAM for analyzing adoption behavior of mobile commerce. 2009 Eighth International Conference on Mobile Business, 52–56. https://ieeexplore.ieee.org/abstract/document/5169233/
Sylva, H., & Mol, S. T. (2009). E‐Recruitment: A study into applicant perceptions of an online application system. International Journal of Selection and Assessment, 17(3), 311–323. https://doi.org/10.1111/j.1468-2389.2009.00473.x
Tangaza, I. A., Mansor, M. F. B., Abu, N. H., & Nasir, H. (2017). The Moderating Role of User Attitude on the Relationship Between Perceived Ease of Use and E-Recruitment Adoption in Nigerian Civil Service: A Proposed Conceptual Framework. Advanced Science Letters, 23(4), 3226–3228. https://doi.org/10.1166/asl.2017.7724
Taylor, S., & Todd, P. (1995). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19(4), 561. https://doi.org/10.2307/249633
Teo, T. (2010). A path analysis of pre-service teachers’ attitudes to computer use: Applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65–79. https://doi.org/10.1080/10494820802231327
Teoh, W. M.-Y., Tan, S.-C., & Chong, S. C. (2013). Factors influencing perceptions of university students towards internet recruitment. Asian Academy of Management Journal, 18(1), 123.
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wu, Z., & McGoogan, J. M. (2020). Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. JAMA, 323(13), 1239. https://doi.org/10.1001/jama.2020.2648
Yoon Kin Tong, D. (2009). A study of e‐recruitment technology adoption in Malaysia. Industrial Management & Data Systems, 109(2), 281–300. https://doi.org/10.1108/02635570910930145
Zikmund, W. G., Babin, B. J., Griffin, M., & Carr, J. C. (2010). Business Research Methods. South-Western Cengage Learning.
Bangladesh Telecommunication Regulatory Commission. (n.d.). Retrieved February 25, 2024, from http://btrc.portal.gov.bd/site/page/347df7fe-409f-451e-a415-65b109a207f5/http%3A%2F%2Fbtrc.portal.gov.bd%2Fsite%2Fpage%2F347df7fe-409f-451e-a415-65b109a207f5%2F-