ORIGINAL_ARTICLE
E-learning Satisfaction during COVID-19 Pandemic Lockdown: Analyzing Key Mediators
An ample of studies have recently been conducted to explore and analyze the predictors of students’ e-learning satisfaction (ELS) during the COVID 19 pandemic lockdown. However, research is scarce on investigating mediating roles of key aspects, such as students’ learning stress (SLS) and students’ willingness to learn (SWL). This research intends to investigate mediating effect of SLS and SWL in relationships among selected IT characteristics factors and ELS during lockdown enforced to curb the COVID 19 pandemic. Selected factors of IT characteristics are IT complexity, IT pace change and IT presenteeism. The data was collected through online questionnaire survey on 470 students in Malaysia selected by employing the convenience sampling during the Movement Control Order period when universities and colleges shifted to online learning platforms. The data was then subjected purification, assessment of normality and reliability. Thereafter, confirmatory factor analysis and validity assessment were conducted. Finally, hypotheses were tested by formulating structural equation model using IBM SPSS AMOS 24.0. The mediation effects were tested by developing parallel mediation structural model. The findings show that SLS fully mediates relationship between IT pace change and ELS. Students’ willingness to study fully mediates relationships between IT presenteeism and IT pace change with ELS.
https://www.ijmae.com/article_139540_7afbd6c02b4fb71a134d7817a217d019.pdf
2021-08-01
542
560
10.5281/zenodo.5731664
E-Learning satisfaction
learning stress
willingness to learn
IT characteristics
lockdown learning
covid 19
Nurhizam
Mohd Satar
nurhizam@ukm.edu.my
1
Research Center for Software Technology and Management (SOFTAM), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
AUTHOR
Omkar
Dastane
omkar.dastane@gmail.com
2
School of Accounting & Business Management, FTMS Global Malaysia, Cyberjaya, Malaysia
LEAD_AUTHOR
Azizan
Morshidi
azizanm@ums.edu.my
3
Faculty of Social Sciences & Humanities, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
AUTHOR
Abidah, A., Hidaayatullaah, H. N., Simamora, R. M., Fehabutar, D., & Mutakinati, L. (2020). The Impact of Covid-19 to Indonesian Education and Its Relation to the Philosophy of “Merdeka Belajar.” Studies in Philosophy of Science and Education, 1(1), 38–49.
1
Ali, M., Alam, N., & Rizvi, S. A. R. (2020). Coronavirus (COVID-19)—An epidemic or pandemic for financial markets. Journal of Behavioral and Experimental Finance, 27.
2
Almaiah, M. A., & Alyoussef, I. Y. (2019). Analysis of the effect of course design, course content support, course assessment and instructor characteristics on the actual use of E-learning system. IEEE Access, 7, 171907–171922.
3
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261-5280.
4
Ames, C. (1984). Achievement attributions and self-instructions under competitive and individualistic goal structures. Journal of Educational Psychology, 76(3), 478.
5
Anderman, E. M., & Maehr, M. L. (1994). Motivation and schooling in the middle grades. Review of educational Research. Review of Educational Research, 64(2), 287–309.
6
Anderson, D., Imdieke, S., & Standerford, N. S. (2011). Feedback please: Studying self in the online classroom. Online Submission, 4(1), 3–15.
7
Awang, Z. (2015). SEM made simple: A gentle approach to learning Structural Equation Modeling. MPWS Rich Publication.
8
Bartik, A. W., Bertrand, M., Cullen, Z. (2020). The impact of COVID-19 on small business outcomes and expectations. Proceedings of the National Academy of Sciences, 117(30), 17656-17666.
9
Benson, A. (2002). Using online learning to meet workforce demand: A case study of stakeholder influence. Quarterly Review of Distance Education, 3(4), 443−452.
10
Carliner, S. (2004). An overview of online learning (2nd ed.). Human Resource Development Press.
11
Chi, X., Becker, B., Yu, Q., et al. (2020). Prevalence and psychosocial correlates of mental health outcomes among chinese college students during the coronavirus disease (COVID-19) pandemic. Frontiers in Psychiatry, 11(1), 803–816.
12
Conrad, D. (2002). Deep in the hearts of learners: Insights into the nature of online community. Journal of Distance Education, 17(1), 1–9.
13
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22.
14
Donni, R., Dastane, O., Haba, H. F., & Selvaraj, K. (2018). Consumer perception factors for fashion M-Commerce and its impact on loyalty among working adults. Business and Economic Research, 8(2), 168. https://doi.org/10.5296/ber.v8i2.13044
15
Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72(1), 218.
16
Ferneini, E. M. (2020). The financial impact of COVID-19 on our practice. Ournal of Oral and Maxillofacial Surgery, 78(7), 1047.
17
Gonzalez, T., de la Rubia, M. A., Hincz, K. P. et al. (2020). Influence of COVID-19 confinement in students performance in higher education. PLOS One, 15(10), 239490.
18
Gorges, J., & Kandler, C. (2012). Adults’ learning motivation: Expectancy of success, value, and the role of affective memories. Learning and Individual Differences, 22(5), 610-617.
19
Haba, H. F., & Dastane, O. (2019). Massive open online courses (MOOCs)–understanding online learners’ preferences and experiences. Journal of Learning, Teaching and Educational Research, 18(8), 227–242.
20
Hair, J.F., Ringle, C.M., Sarstedt, M. . (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Plan, 46(1), 1–12.
21
Harackiewicz, J. M., Durik, A. M., Barron, K. E., et al. (2008). The role of achievement goals in the development of interest: Reciprocal relations between achievement goals, interest, and performance. Journal of Educational Psychology, 100(1), 105.
22
Hashim, N. A., Mukhtar, M., & Safie, N. (2019). Factors affecting teachers’ motivation to adopt cloud-based E-learning system in Iraqi deaf institutions: A pilot Study. In 2019 International Conference on Electrical Engineering and Informatics (ICEEI), 272–277.
23
Helmke, A., & Weinert, F. E. (1997). (n.d.). Bedingungsfaktoren schulischer leistungen. Max-Planck-Inst. für Psychologische Forschung.
24
Hsu, H. H. (2012). (2012). The acceptance of Moodle: An empirical study based on UTAUT. Creative Education, 3(1), 44.
25
Islam, S. D. U., Bodrud-Doza, M., Khan, R. M., et al. (2020). Exploring COVID-19 stress and its factors in Bangladesh: a perception-based study. Heliyon, 6(7), 43–99.
26
Kapasia, N., Paul, P., Roy, A., et al. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116(1), 105194.
27
Kayali, M., Safie, N., & Mukhtar, M. (2019). The effect of individual factors mediated by trust and moderated by IT knowledge on students’ adoption of cloud based e-learning. Int. J. Innov. Technol. Explor. Eng, 9(2).
28
Kinshuk, D., & Yang, A. (2003). Web-based asynchronous synchronous environment for online learning. United States Distance Education Association Journal, 17(2), 5–17.
29
Knowles, M. S., Holton, E., & Swanson, R. (2005). The adult learner: the definitive classic in adult education and human resource development (6th ed.). Elsevier.
30
Krapp, A. (2000). Interest and human development during adolescence: An educational-psychological approach. In J. Heckhausen (Ed.), Motivational psychology of human development: Developing motivation and motivating development (pp. 109–129). Elsevier Science. https://doi.org/https://doi.org/10.1016/S0166-4115(00)80008-4
31
Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: from constructs to theory. Journal of Operations Management, 16(4), 407–425.
32
Manzoor, A. (n.d.). Online teaching and challenges of COVID-19 for inclusion of persons with disabilities in higher education. 2020. https://dailytimes.com.pk/595888/online-teaching-and-challenges-of-covid-19-for-inclusion-of-pwds-in-higher-education/.
33
Martin, F., Ahlgrim-Delzell, L., & Budhrani, K. (2017). Systematic review of two decades (1995 to 2014) of research on synchronous online learning. Journal of Distance Education, 31(1), 3-19.
34
Martins, L. L., & Kellermanns, F. W. (2004). A model of business school students’ acceptance of a web-based course management system. Academy of Management Learning & Education, 3(1), 7–26.
35
Organisation, W. H. (n.d.). No Title. https://www.who.int/news-room/detail/27-04-2020-who-timeline-covid-19
36
Pajarianto, H., Kadir, A., Galugu, N., et al. (2020). Study from home in the middle of the COVID-19 pandemic: Analysis of aeligiosity, teacher, and parents support against academic stress. Journal of Talent Development and Excellence, 12(2), 1791-1807.
37
Palmer, L. (2013). The relationship between stress, fatigue, and cognitive functioning. College Student Journal, 47(2), 312–325.
38
Piccoli, G., Ahmad, R. & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(2), 401-426.
39
Reeve, J., Deci, E. L., & Ryan, R. M. (2004). Self-determination theory: A dialectical framework for understanding socio-cultural influences on student motivation. Big Theories Revisited, 4(1), 31–60.
40
Rehman, U., Shahnawaz, M. G., Khan, N. H., et al. (2020). Depression, anxiety and stress among Indians in times of Covid-19 lockdown. Community Mental Health Journal, 4(1), 1–7.
41
Roy, S., Raju, A., & Mandal, S. (2017). An empirical investigation on E-retailer agility, customer satisfaction, commitment and loyalty. Business: Theory and Practice, 18, 97–108. https://doi.org/10.3846/btp.2017.011
42
Safie, N., & Morshidi, A. (2007). An evaluation of cultural roles and usability attributes in learning management system. Multimedia Communication, 5(1), 55–72.
43
Safie NMS, & Morshidi A. H., Dastane, O. (2020). Success factors affecting e-learning satisfaction during COVID-19 pandemic lockdown. Int. J. Adv. Trends Comput. Sci. Eng, 9(5).
44
Sahu, P. (2020). Closure of universities due to coronavirus disease 2019 (COVID-19): impact on education and mental health of students and academic staff. Cureus, 12(4).
45
Sakib, N., Bhuiyan, A.K.M.I., Hossain, S. et al. (2020). Psychometric validation of the Bangla Fear of COVID-19 Scale: Confirmatory factor analysis and Rasch analysis. International Journal of Mental Health and Addiction, 4(3), 1–12.
46
Samadarshi, S. C. A., Sharma, S., & Bhatta, J. (2020). An online survey of factors associated with self-perceived stress during the initial stage of the COVID-19 outbreak in Nepal. The Ethiopian Journal of Health Development, 34(2).
47
Sandars, J., Correia, R., Dankbaar, M., et al. (2020). Twelve tips for rapidly migrating to online learning during the COVID-19 pandemic. MedEdPublish, 9(1).
48
Satar, N. S. M., Dastane, O., & Ma’arif, M. Y. (2019). Customer value proposition for E-Commerce: A case study approach. International Journal of Advanced Computer Science and Applications, 10(2). https://doi.org/10.14569/ijacsa.2019.0100259
49
Scagnoli NI, Choo J, T. J. (2019). Students’ insights on the use of video lectures in online classes. Br J Educ Technol, 50(1), 399–414.
50
Schunk, D. H., & Zimmerman, B. J. (2012). Motivation and self-regulated learning: Theory, research, and applications. Routledge. Lawrence Erlbaum Associates.
51
Scott, D., Durnell, C., Gauvin, S., et al. (1997). Internet based collaborative learning: an empirical evaluation. Third Australian World Wide Web Conference.
52
Seetharaman, P. (2020). Business models shifts: Impact of Covid-19. International Journal of Information Management, 55(1), 102173.
53
Shahzad, A., Hassan, R., Aremu, A. Y., et al. (2020). Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female. Quality & Quantity, 4(2), 1–22.
54
Simpson, O. (2000). Supporting students in open and distance learning. Kogan Page.
55
Son, C., Hegde, S., Smith, A., et al. (2020). Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. Journal of Medical Internet Research, 22(9), 21279.
56
Strielkowski, W. (2020). COVID-19 pandemic and the digital revolution in academia and higher education. https://doi.org/doi:10.20944/preprints202004.0290.v1
57
UNESCO. (2020). Education: From disruption to recovery. https://en.unesco.org/covid19/educationresponse/
58
Wan, Y. S. (2020). Education during COVID-19. Brief Ideas, 19(2), 3–9.
59
Wang, Y., Di, Y., Ye, J., & Wei, W. (2021). Study on the public psychological states and its related factors during the outbreak of coronavirus disease 2019 (COVID-19) in some regions of China. Psychology, Health & Medicine, 26(1), 13–22.
60
WHO Timeline - COVID-19, April 2020, https://www.who.int/news-room/detail/27-04-2020-who-timeline-covid-19. World Health Organisation
61
Wu, J. H., Tennyson, R. D., Hsia, T. L., & Liao, Y. W. (2008). Analysis of e-learning innovation and core capability using a hypercube model. Computers in Human Behavior, 24(1), 1851–1866.
62
Yang, Z., & Liu, Q. (2007). Research and development of web-based virtual on-line classroom. Computers & Education, 48, 171–184.
63
ORIGINAL_ARTICLE
A Model for Designing and Evaluating LARG-Based Supply Chain Using Axiomatic Design and the Best-Worst Method in a Hesitant Fuzzy Environment
There have been various approaches to the supply chain such as lean, agile, robustness, sustainability, resilient, and green that each one focuses on supply chain from specific aspect. One of the new approaches to the supply chain is an integration of Lean, Agile, Resilient, and Green (LARG) that benefiting from the advantages of different approaches and avoiding their disadvantages. The present study proposes a model to design and evaluate LARG-based supply chain in Iran automotive industry using the concept of Axiomatic Design (AD) in a Hesitant Fuzzy (HF) environment. The study process consisted of two stages: designing stage and evaluating stage. In the first stage, the Functional Requirements (FR) and chain Design Parameters (DP) identified in the LARG supply chain based on the Delphi technique and literature review. Based on independence axiom, it should be considered that whether the satisfaction of one FR by the related DPs affects the quality of the other FR or not, which is examined based on the design matrix. In the second stage an integration of information axiom, the Best-Worst Method (BWM), and hesitant fuzzy logic was used to evaluate four supply chains in Iran automotive industry. The weight of supply chain criteria, the utility rate of desired supply chain criteria, and the current status for each supply chain criteria identified in this stage. The results indicated that the excellent LARG supply chain was consisted of 13 indicators. The model also revealed that the excellent supply chain was contained less information axiom and complexity.
https://www.ijmae.com/article_139541_281942b781cf04edceb54c20ebca654c.pdf
2021-08-01
561
584
10.5281/zenodo.5750767
Axiomatic Design
best-worst method
Hesitant Fuzzy
LARG Supply Chain
Abedin
Eftekhari
a.eftekhari@mehr.pgu.ac.ir
1
Department of Industrial Management, Persian Gulf University, Bushehr, Iran
AUTHOR
Gholamreza
Jamali
gjamali@pgu.ac.ir
2
Department of Industrial Management, Persian Gulf University, Bushehr, Iran
LEAD_AUTHOR
Ali Naghi
Mosleh Shirazi
an_mosleh@yahoo.com
3
Department of Management, Shiraz University, Shiraz, Iran
AUTHOR
Abidah, A., Hidaayatullaah, H. N., Simamora, R. M., Fehabutar, D., & Mutakinati, L. (2020). The Impact of Covid-19 to Indonesian Education and Its Relation to the Philosophy of “Merdeka Belajar.” Studies in Philosophy of Science and Education, 1(1), 38–49.
1
Ali, M., Alam, N., & Rizvi, S. A. R. (2020). Coronavirus (COVID-19)—An epidemic or pandemic for financial markets. Journal of Behavioral and Experimental Finance, 27.
2
Almaiah, M. A., & Alyoussef, I. Y. (2019). Analysis of the effect of course design, course content support, course assessment and instructor characteristics on the actual use of E-learning system. IEEE Access, 7, 171907–171922.
3
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25, 5261-5280.
4
Ames, C. (1984). Achievement attributions and self-instructions under competitive and individualistic goal structures. Journal of Educational Psychology, 76(3), 478.
5
Anderman, E. M., & Maehr, M. L. (1994). Motivation and schooling in the middle grades. Review of educational Research. Review of Educational Research, 64(2), 287–309.
6
Anderson, D., Imdieke, S., & Standerford, N. S. (2011). Feedback please: Studying self in the online classroom. Online Submission, 4(1), 3–15.
7
Awang, Z. (2015). SEM made simple: A gentle approach to learning Structural Equation Modeling. MPWS Rich Publication.
8
Bartik, A. W., Bertrand, M., Cullen, Z. (2020). The impact of COVID-19 on small business outcomes and expectations. Proceedings of the National Academy of Sciences, 117(30), 17656-17666.
9
Benson, A. (2002). Using online learning to meet workforce demand: A case study of stakeholder influence. Quarterly Review of Distance Education, 3(4), 443−452.
10
Carliner, S. (2004). An overview of online learning (2nd ed.). Human Resource Development Press.
11
Chi, X., Becker, B., Yu, Q., et al. (2020). Prevalence and psychosocial correlates of mental health outcomes among chinese college students during the coronavirus disease (COVID-19) pandemic. Frontiers in Psychiatry, 11(1), 803–816.
12
Conrad, D. (2002). Deep in the hearts of learners: Insights into the nature of online community. Journal of Distance Education, 17(1), 1–9.
13
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22.
14
Donni, R., Dastane, O., Haba, H. F., & Selvaraj, K. (2018). Consumer perception factors for fashion M-Commerce and its impact on loyalty among working adults. Business and Economic Research, 8(2), 168. https://doi.org/10.5296/ber.v8i2.13044
15
Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72(1), 218.
16
Ferneini, E. M. (2020). The financial impact of COVID-19 on our practice. Ournal of Oral and Maxillofacial Surgery, 78(7), 1047.
17
Gonzalez, T., de la Rubia, M. A., Hincz, K. P. et al. (2020). Influence of COVID-19 confinement in students performance in higher education. PLOS One, 15(10), 239490.
18
Gorges, J., & Kandler, C. (2012). Adults’ learning motivation: Expectancy of success, value, and the role of affective memories. Learning and Individual Differences, 22(5), 610-617.
19
Haba, H. F., & Dastane, O. (2019). Massive open online courses (MOOCs)–understanding online learners’ preferences and experiences. Journal of Learning, Teaching and Educational Research, 18(8), 227–242.
20
Hair, J.F., Ringle, C.M., Sarstedt, M. . (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Plan, 46(1), 1–12.
21
Harackiewicz, J. M., Durik, A. M., Barron, K. E., et al. (2008). The role of achievement goals in the development of interest: Reciprocal relations between achievement goals, interest, and performance. Journal of Educational Psychology, 100(1), 105.
22
Hashim, N. A., Mukhtar, M., & Safie, N. (2019). Factors affecting teachers’ motivation to adopt cloud-based E-learning system in Iraqi deaf institutions: A pilot Study. In 2019 International Conference on Electrical Engineering and Informatics (ICEEI), 272–277.
23
Helmke, A., & Weinert, F. E. (1997). (n.d.). Bedingungsfaktoren schulischer leistungen. Max-Planck-Inst. für Psychologische Forschung.
24
Hsu, H. H. (2012). (2012). The acceptance of Moodle: An empirical study based on UTAUT. Creative Education, 3(1), 44.
25
Islam, S. D. U., Bodrud-Doza, M., Khan, R. M., et al. (2020). Exploring COVID-19 stress and its factors in Bangladesh: a perception-based study. Heliyon, 6(7), 43–99.
26
Kapasia, N., Paul, P., Roy, A., et al. (2020). Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India. Children and Youth Services Review, 116(1), 105194.
27
Kayali, M., Safie, N., & Mukhtar, M. (2019). The effect of individual factors mediated by trust and moderated by IT knowledge on students’ adoption of cloud based e-learning. Int. J. Innov. Technol. Explor. Eng, 9(2).
28
Kinshuk, D., & Yang, A. (2003). Web-based asynchronous synchronous environment for online learning. United States Distance Education Association Journal, 17(2), 5–17.
29
Knowles, M. S., Holton, E., & Swanson, R. (2005). The adult learner: the definitive classic in adult education and human resource development (6th ed.). Elsevier.
30
Krapp, A. (2000). Interest and human development during adolescence: An educational-psychological approach. In J. Heckhausen (Ed.), Motivational psychology of human development: Developing motivation and motivating development (pp. 109–129). Elsevier Science. https://doi.org/https://doi.org/10.1016/S0166-4115(00)80008-4
31
Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: from constructs to theory. Journal of Operations Management, 16(4), 407–425.
32
Manzoor, A. (n.d.). Online teaching and challenges of COVID-19 for inclusion of persons with disabilities in higher education. 2020. https://dailytimes.com.pk/595888/online-teaching-and-challenges-of-covid-19-for-inclusion-of-pwds-in-higher-education/.
33
Martin, F., Ahlgrim-Delzell, L., & Budhrani, K. (2017). Systematic review of two decades (1995 to 2014) of research on synchronous online learning. Journal of Distance Education, 31(1), 3-19.
34
Martins, L. L., & Kellermanns, F. W. (2004). A model of business school students’ acceptance of a web-based course management system. Academy of Management Learning & Education, 3(1), 7–26.
35
Organisation, W. H. (n.d.). No Title. https://www.who.int/news-room/detail/27-04-2020-who-timeline-covid-19
36
Pajarianto, H., Kadir, A., Galugu, N., et al. (2020). Study from home in the middle of the COVID-19 pandemic: Analysis of aeligiosity, teacher, and parents support against academic stress. Journal of Talent Development and Excellence, 12(2), 1791-1807.
37
Palmer, L. (2013). The relationship between stress, fatigue, and cognitive functioning. College Student Journal, 47(2), 312–325.
38
Piccoli, G., Ahmad, R. & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(2), 401-426.
39
Reeve, J., Deci, E. L., & Ryan, R. M. (2004). Self-determination theory: A dialectical framework for understanding socio-cultural influences on student motivation. Big Theories Revisited, 4(1), 31–60.
40
Rehman, U., Shahnawaz, M. G., Khan, N. H., et al. (2020). Depression, anxiety and stress among Indians in times of Covid-19 lockdown. Community Mental Health Journal, 4(1), 1–7.
41
Roy, S., Raju, A., & Mandal, S. (2017). An empirical investigation on E-retailer agility, customer satisfaction, commitment and loyalty. Business: Theory and Practice, 18, 97–108. https://doi.org/10.3846/btp.2017.011
42
Safie, N., & Morshidi, A. (2007). An evaluation of cultural roles and usability attributes in learning management system. Multimedia Communication, 5(1), 55–72.
43
Safie NMS, & Morshidi A. H., Dastane, O. (2020). Success factors affecting e-learning satisfaction during COVID-19 pandemic lockdown. Int. J. Adv. Trends Comput. Sci. Eng, 9(5).
44
Sahu, P. (2020). Closure of universities due to coronavirus disease 2019 (COVID-19): impact on education and mental health of students and academic staff. Cureus, 12(4).
45
Sakib, N., Bhuiyan, A.K.M.I., Hossain, S. et al. (2020). Psychometric validation of the Bangla Fear of COVID-19 Scale: Confirmatory factor analysis and Rasch analysis. International Journal of Mental Health and Addiction, 4(3), 1–12.
46
Samadarshi, S. C. A., Sharma, S., & Bhatta, J. (2020). An online survey of factors associated with self-perceived stress during the initial stage of the COVID-19 outbreak in Nepal. The Ethiopian Journal of Health Development, 34(2).
47
Sandars, J., Correia, R., Dankbaar, M., et al. (2020). Twelve tips for rapidly migrating to online learning during the COVID-19 pandemic. MedEdPublish, 9(1).
48
Satar, N. S. M., Dastane, O., & Ma’arif, M. Y. (2019). Customer value proposition for E-Commerce: A case study approach. International Journal of Advanced Computer Science and Applications, 10(2). https://doi.org/10.14569/ijacsa.2019.0100259
49
Scagnoli NI, Choo J, T. J. (2019). Students’ insights on the use of video lectures in online classes. Br J Educ Technol, 50(1), 399–414.
50
Schunk, D. H., & Zimmerman, B. J. (2012). Motivation and self-regulated learning: Theory, research, and applications. Routledge. Lawrence Erlbaum Associates.
51
Scott, D., Durnell, C., Gauvin, S., et al. (1997). Internet based collaborative learning: an empirical evaluation. Third Australian World Wide Web Conference.
52
Seetharaman, P. (2020). Business models shifts: Impact of Covid-19. International Journal of Information Management, 55(1), 102173.
53
Shahzad, A., Hassan, R., Aremu, A. Y., et al. (2020). Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female. Quality & Quantity, 4(2), 1–22.
54
Simpson, O. (2000). Supporting students in open and distance learning. Kogan Page.
55
Son, C., Hegde, S., Smith, A., et al. (2020). Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. Journal of Medical Internet Research, 22(9), 21279.
56
Strielkowski, W. (2020). COVID-19 pandemic and the digital revolution in academia and higher education. https://doi.org/doi:10.20944/preprints202004.0290.v1
57
UNESCO. (2020). Education: From disruption to recovery. https://en.unesco.org/covid19/educationresponse/
58
Wan, Y. S. (2020). Education during COVID-19. Brief Ideas, 19(2), 3–9.
59
Wang, Y., Di, Y., Ye, J., & Wei, W. (2021). Study on the public psychological states and its related factors during the outbreak of coronavirus disease 2019 (COVID-19) in some regions of China. Psychology, Health & Medicine, 26(1), 13–22.
60
WHO Timeline - COVID-19, April 2020, https://www.who.int/news-room/detail/27-04-2020-who-timeline-covid-19. World Health Organisation
61
Wu, J. H., Tennyson, R. D., Hsia, T. L., & Liao, Y. W. (2008). Analysis of e-learning innovation and core capability using a hypercube model. Computers in Human Behavior, 24(1), 1851–1866.
62
Yang, Z., & Liu, Q. (2007). Research and development of web-based virtual on-line classroom. Computers & Education, 48, 171–184.
63
ORIGINAL_ARTICLE
Factors Determining Financial Reporting Quality: An Empirical Study on the Publicly Listed Food & Allied Companies of Bangladesh.
In an aim to assess financial reporting quality and its determining factors, this empirical research examined a randomly selected sample from food and allied sector of the Dhaka Stock Exchange (DSE), Bangladesh. As a rapidly growing industry of Bangladesh, the food and allied sector of DSE grabs a significant portion of market capitalization and attentions from security analysts. To make valuable decisions in relevant domains, the financial reporting quality of this sector matters for policymakers, investors and regulators and for corporate managers also. In this research, the financial reporting quality (FRQ) has been measured using the popular model developed by Dechow, Sloan and Sweeney, also known as Modified Jones Model (1995). A documentary analysis of the available audited financial statements and annual reports of randomly selected sample companies for six consecutive years (2015 to 2020) has been used as the primary data sources. Popular statistical tools like correlation studies, regression analyses etc. have been applied to find the statistical significance of the explanatory variables of this research. Fourteen factors have been examined for their effects on the quality of financial reports using a classical linear regression model. This research finds firm size, firm age, foreign ownership and leverage positively significantly determine financial reporting quality while the growth and board independence negatively significantly influential. The findings recommend managers to emphasize their attention on the significant factors to improve their financial reporting quality. Security analysts shall evaluate firms’ value based on the factors found significant in determining the quality of financial reports.
https://www.ijmae.com/article_139542_e3d53245bd72c469b00d14892f6901f3.pdf
2021-08-01
585
628
10.5281/zenodo.5750783
Modified Jones Model (1995)
Firm-specific characteristics
Performance Indicators
Corporate Governance Mechanisms
Discretionary accruals
Abdullah
Masud
aamasudsaad@gmail.com
1
Department of Accounting & Information Systems, University of Dhaka, Dhaka, Bangladesh
LEAD_AUTHOR
Agrawal, A., & Chadha, S. (2005). Corporate Governance and Accounting Scandals. The Journal of Law & Economics, 371-406.
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63
ORIGINAL_ARTICLE
Effects of Development Assistance for Health in Developing Countries
This article assesses the effects of international aid on the health sector in developing countries. We use estimation techniques such as Ordinary Least Squares (OLS), instrumental variables with fixed effects, and the dynamic panel approach. Using the data from various sources over the period 1990 to 2017 covering 126 developing countries, the initial results show that health aid contributes effectively and significantly to improved health outcomes in the developing countries at 1 and 5% of the significance thresholds. These results give the picture that the mobilization of the international community in favour of the health sector in the context of the MDGs through health aid has been more effective in achieving certain health goals from the 2000s onwards than before the Millennium Declaration. This study shows that it is in the interest of development partners, particularly those in the health sector, to significantly improve the survival and health of the populations of developing countries through health aid. It is recommended that development assistance policies be designed to take into consideration the existing institutional framework and how these resource flows interfere with, and therefore change, the incentive structure of recipient countries. The transfer of resources in the form of health aid to meet current needs must be complemented by other additional actions, such as education campaigns and infrastructure improvements, in order to achieve long-term improvement.
https://www.ijmae.com/article_139543_60f700da41183dc11f57249043724925.pdf
2021-08-01
629
646
10.5281/zenodo.5745739
Effects
development assistance for health
Developing countries
MONGBET
Zounkifirou
mongbetzounkifirou@yahoo.fr
1
Faculty of Economics and Management, University of Yaounde II, Yaounde, Cameroon
LEAD_AUTHOR
TOURERE
Zenabou
ze_na_bou@yahoo.fr
2
Department of Economics and management, University of Douala Douala, Cameroon
AUTHOR
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19