Factors influencing the formation of consumer engagement and consumer satisfaction with e-learning activities
-
DOIhttp://dx.doi.org/10.21511/im.17(2).2021.13
-
Article InfoVolume 17 2021, Issue #2, pp. 137-148
- Cited by
- 871 Views
-
246 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The COVID-19 pandemic that plagued the world has resulted in many e-learning software that drove virtual learning activities to jump sharply and began to replace face-to-face meetings. This paper aims to find out the influence of digital readiness, technical and information quality, instructor quality, e-learning adoption and attitude on consumer engagement, and consumer satisfaction with e-learning performance. The study was conducted in the form of a quantitative survey at Duta Wacana Christian University in the Special Region of Yogyakarta, Indonesia, over the period from June 2020 to September 2020. The study sample using the purposive random sampling technique consisted of 175 students as respondents. Various statistical methods, including descriptive and structural equation modeling, were used to analyze the data and test the hypotheses of the model. Key findings were that there is a statistically direct impact of digital readiness, technical and information quality, e-learning adoption and attitude, and instructor quality on consumer engagement, and thus consumer engagement influences consumer satisfaction positively and significantly. With these results, tutorial activities need to be implemented for the use of popular e-learning software and related technological literacy, because the need for e-learning software will be even more massive in the future.
- Keywords
-
JEL Classification (Paper profile tab)M30, C80
-
References55
-
Tables8
-
Figures2
-
- Figure 1. Model framework
- Figure 2. Model in AMOS graphics
-
- Table 1. Goodness-of-fit criterion
- Table 2. Gender composition
- Table 3. Age of respondents
- Table 4. Respondent occupation
- Table 5. E-learning software
- Table 6. Degree of software usefulness
- Table 7. Goodness-of-fit results
- Table 8. Relationship in the model
-
- Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2), 1-23.
- Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86.
- Al Natour, S., & Woo, C. (2020). The determinants of learner satisfaction with the online video presentation method. Internet Research, 31(1), 234-261.
- Alawamleh, M., Al-Twait, L. M., & Al-Saht, G. R. (2020). The effect of online learning on communication between instructors and students during Covid-19 pandemic. Asian Education and Development Studies.
- Aldholay, A., Abdullah, Z., Isaac, O., & Mutahar, A. M. (2019). Perspective of Yemeni students on use of online learning: Extending the information systems success model with transformational leadership and compatibility. Information Technology and People, 33(1), 106-128.
- Baker, D. McA., & Unni, R. (2018). USA and Asia Hospitality & Tourism Students’ Perceptions and Satisfaction with Online Learning versus Traditional Face-to-Face Instruction. Journal of Business Education & Scholarship, 12(2), 40-54.
- Balaban, I., Mu, E., & Divjak, B. (2013). Development of an electronic Portfolio system success model: An information systems approach. Computers & Education, 60(1), 396-411.
- Bilro, R. G., & Loureiro, S. M. C. (2020). A consumer engagement systematic review: synthesis and research agenda. Spanish Journal of Marketing – ESIC, 24(3), 283-307.
- Boateng, R., Mbrokoh, A. S., Boateng, L., Senyo, P. K., & Ansong, E. (2016). Determinants of e-learning adoption among students of developing countries. International Journal of Information and Learning Technology, 33(4), 248-262.
- Bubou, G. M., & Job, G. C. (2020). Individual innovativeness, self-efficacy and e-learning readiness of students of Yenagoa study centre, National Open University of Nigeria. Journal of Research in Innovative Teaching & Learning.
- Chen, T., Peng, L., Jing, B., Wu, C., Yang, J., & Cong, G. (2020). The impact of the COVID-19 pandemic on user experience with online education platforms in China. Sustainability, 12(18), 7329.
- Cho, M. H., & Tobias, S. (2016). Should instructors require discussion in online courses? Effects of online discussion on community of inquiry, learner time, satisfaction, and achievement. International Review of Research in Open and Distance Learning, 17(2), 123-140.
- Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers and Education, 122, 273-290.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319-339.
- Duran, da C., & Costa, D. (2016). Brazilian Open University System: a public policy on distance learning education in Brazil (2005-2010). CEF, 5, 157-171.
- Duta, N., Panisoara, G., & Panisoara, I.-O. (2015). The Effective Communication in Teaching. Diagnostic Study Regarding the Academic Learning Motivation to Students. Procedia – Social and Behavioral Sciences, 186, 1007-1012.
- Dwivedi, A., Dwivedi, P., Bobek, S., & Sternad Zabukovšek, S. (2019). Factors affecting students’ engagement with online content in blended learning. Kybernetes, 48(7), 1500-1515.
- Eom, S. (2015). The effects of student motivation and self-regulated learning strategies on students’ perceived e-learning outcomes and satisfaction. Journal of Higher Education Theory and Practice, 19(7), 29-42.
- Garcia-Smith, D., & Effken, J. A. (2013). Development and initial evaluation of the Clinical Information Systems Success Model (CISSM). International Journal of Medical Informatics, 82(6), 539-552.
- Gegenfurtner, A., & Ebner, C. (2019). Learning and Satisfaction in Webinar, Online, and Face-to-Face Instruction: A Meta-Analysis. Frontiers in Education, 4(92).
- Giese, J., & Cote, J. (2000). Defining consumer satisfaction. Academy of Marketing Science Review, 2000(1).
- Goh, C. F., Leong, C. M., Kasmin, K., Hii, P. K., & Tan, O. K. (2017). Students’ experiences, learning outcomes and satisfaction in e-learning. Journal of E-Learning and Knowledge Society, 13(2), 117-128.
- Gunesekera, A. I., Bao, Y., & Kibelloh, M. (2019). The role of usability on e-learning user interactions and satisfaction: a literature review. Journal of Systems and Information Technology, 21(3), 368-394.
- Hair, J. F., William C, B., Babin, B. J., & Anderson, R. E. (2014). Multivariate Data Analysis. Pearson.
- Hinson, R., Boateng, H., Renner, A., & Kosiba, J. P. B. (2019). Antecedents and consequences of customer engagement on Facebook: An attachment theory perspective. Journal of Research in Interactive Marketing, 13(2), 204-226.
- Hodge, B., Wright, B., & Bennett, P. (2018). The Role of Grit in Determining Engagement and Academic Outcomes for University Students. Research in Higher Education, 59(4), 448-460.
- Kim, H. J., Hong, A. J., & Song, H. D. (2019). The roles of academic engagement and digital readiness in students’ achievements in university e-learning environments. International Journal of Educational Technology in Higher Education, 16(21).
- Kucuk, S., & Richardson, J. C. (2019). A structural equation model of predictors of online learners’ engagement and satisfaction. Online Learning Journal, 23(2), 196-216.
- Leslie, H. J. (2019). Trifecta of Student Engagement. Journal of Research in Innovative Teaching & Learning, 13(2), 149-173.
- Lin, H. H., Lin, S., Yeh, C. H., & Wang, Y. S. (2016). Measuring mobile learning readiness: scale development and validation. Internet Research, 26(1), 265-287.
- Lin, Y. C., Chung, P., Yeh, R. C., & Chen, Y. C. (2016). An empirical study of college students’ learning satisfaction and continuance intention to stick with a blended e-learning environment. International Journal of Emerging Technologies in Learning, 11(2), 63-66.
- Martins, J. T., & Nunes, M. B. (2016). Academics’ e-learning adoption in higher education institutions: a matter of trust. Learning Organization, 23(5), 299-331.
- Mehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). The Influence of Values on E-learning Adoption. Computers and Education, 141, 103617.
- Monferrer, D., Moliner, M. A., & Estrada, M. (2019). Increasing customer loyalty through customer engagement in the retail banking industry. Spanish Journal of Marketing – ESIC, 23(3), 461-484.
- Mtebe, J. S., & Raphael, C. (2018). Key factors in learners’ satisfaction with the e-learning system at the University of Dar es Salaam, Tanzania. Australasian Journal of Educational Technology, 34(4), 107-122.
- Nortvig, A. M., Petersen, A. K., & Balle, S. H. (2018). A literature review of the factors influencing e-learning and blended learning in relation to learning outcome, student satisfaction and engagement. Electronic Journal of E-Learning, 16(1), 45-55.
- Ozkan, S., & Koseler, R. (2009). Multi-dimensional evaluation of E-learning systems in the higher education context: An empirical investigation of a computer literacy course. 2009 39th IEEE Frontiers in Education Conference, 1-6.
- Papagiannidis, S., Pantano, E., See-To, E. W. K., Dennis, C., & Bourlakis, M. (2017). To immerse or not? Experimenting with two virtual retail environments. Information Technology and People, 30(1), 163-188.
- Parihar, P., Dawra, J., & Sahay, V. (2019). The role of customer engagement in the involvement-loyalty link. Marketing Intelligence and Planning, 37(1), 66-79.
- Pertiwi, W. K. (2020) Penetrasi Internet di Indonesia Capai 64 Persen. In R. K. Nistanto (Ed.), tekno.kompas.com.
- Pransuamitra, P.A. (2020). Nyaris Tembus Rp 14.000/US$, Rupiah The Best di Asia. CNBC Indonesia.
- Purarjomandlangrudi, A., Chen, D., & Nguyen, A. (2016). Investigating the drivers of student interaction and engagement in online courses: A study of state-of-the-art. Informatics in Education, 15(2), 269-286.
- Quesada-Pallarès, C., Sánchez-Martí, A., Ciraso-Calí, A., & Pineda-Herrero, P. (2019). Online vs. Classroom Learning: Examining Motivational and Self-Regulated Learning Strategies Among Vocational Education and Training Students. Frontiers in Psychology, 10.
- Rodríguez, M. Ó., González-Gómez, F., & Guardiola, J. (2019). Do course evaluation systems have an influence on e-learning student satisfaction? Higher Education Evaluation and Development, 13(1), 18-32.
- Ramadiani, Azainil, Haryaka, U., Agus, F., & Kridalaksana, A. H. (2017). User Satisfaction Model for e-Learning Using Smartphone. Procedia Computer Science, 116, 373-380.
- Sary, F. P., & Herlambang, O. (2019). E-learning Program Effectiveness on Students’ Learning Satisfaction at Telkom University Bandung. KnE Social Sciences, 3(14), 271-280.
- Shehzadi, S., Nisar, Q. A., Hussain, M. S., Basheer, M. F., Hameed, W. U., & Chaudhry, N. I. (2020). The role of digital learning toward students’ satisfaction and university brand image at educational institutes of Pakistan: a post-effect of COVID-19. Asian Education and Development Studies, 10(2), 276-294.
- Shokery, N. M. bt A. H., Nawi, N. B. C., Nasir, N. A. B. Md, & Al Mamun, A. (2016). Factors Contributing to the Acceptance of Social Media as a Platform among Student Entrepreneurs: A Review. Mediterranean Journal of Social Sciences, 7(2), 42-51.
- Supriyatna, I. & Djailani, M. F. (2020). Sri Mulyani Kejar Pertumbuhan Ekonomi di Kuartal III-IV 2020. Suara.com.
- Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers and Education, 50(4), 1183-1202.
- Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306-328.
- Ting, D. H., Abbasi, A. Z., & Ahmed, S. (2020). Examining the mediating role of social interactivity between customer engagement and brand loyalty. Asia Pacific Journal of Marketing and Logistics, 33(5), 1139-1158.
- Venkatesan, R. (2017). Executing on a customer engagement strategy. Journal of the Academy of Marketing Science, 45(3), 289-293.
- Wu, C. C., Liao, S. H., Chen, Y. J., & Hsu, W. L. (2011). Service quality, brand image and price fairness impact on the customer satisfaction and loyalty. International Conference on Industrial Engineering and Engineering Management. IEEE International Conference on Industrial Engineering and Engineering Management, 1160-1164.
- Yilmaz, R. (2017). Exploring the role of e-learning readiness on student satisfaction and motivation in flipped classroom. Computers in Human Behavior, 70, 251-260.
-
Effect of work motivation and job satisfaction on employee performance: Mediating role of employee engagement
Setyo Riyanto
,
Endri Endri 
,
Novita Herlisha
doi: http://dx.doi.org/10.21511/ppm.19(3).2021.14
Problems and Perspectives in Management Volume 19, 2021 Issue #3 pp. 162-174 Views: 6792 Downloads: 6802 TO CITE АНОТАЦІЯTechnological developments are things that must be followed by companies to achieve a competitive advantage to improve performance. To achieve and improve performance, companies need active employee engagement by encouraging motivation and fulfilling their job satisfaction. This study aims to analyze the effect of motivation and job satisfaction on performance with employee engagement as a mediating variable. The research sample is Information Technology (IT) companies located in the cities of Jakarta and Bandung, Indonesia. Research respondents are system developers who handle system development activities for a project or part of an ongoing project. By using the convenience sampling technique 103 responses were obtained from IT developers. The research model analysis method uses Partial Least Square (PLS) with SMART PLS Ver 3.0 software. Empirical findings prove that motivation has a positive effect on the performance of IT employees, while job satisfaction is independent. Employee engagement does not directly affect employee performance, but the effect of mediation through motivation and job satisfaction can have a significant effect on employee performance. The research findings have managerial implications, in increasing high employee involvement, motivation needs to be encouraged to be more active and innovative, and facilitate the achievement of the desired results.
Acknowledgment
This study was made possible because of the full support of the Region III Education Service Institute (LL-DIKTI III), the Ministry of Education and Culture of the Republic of Indonesia, and the Research Center at Mercu Buana University, Jakarta. -
Factors affecting service quality at Vietnamese retail banks
Tran Phi Hoang
doi: http://dx.doi.org/10.21511/bbs.13(2).2018.04
Banks and Bank Systems Volume 13, 2018 Issue #2 pp. 39-48 Views: 5962 Downloads: 1226 TO CITE АНОТАЦІЯSustainable growth and development can be the most important lesson that banks have learned in recent decades. Banks would gain many benefits, win the competition and develop sustainably if there have been more and more loyal customers. Customer satisfaction will help build customer loyalty. In order to have loyal customers, service quality must be good. Quality customer service of the bank is to ensure customer satisfaction and take their needs into consideration to improve service quality. High customer service quality will generate value not only to meet customer needs but also the expectations of customers and make banks more prominent than their competitors. This study identifies factors that affect customer satisfaction with the service quality at Vietnamese retail banks. The results are obtained by qualitative and quantitative method. The research model with a questionnaire consisting of 22 variables, 5 scales for a survey of 554 customers, is identified and developed through interviews with experts and group discussions. The findings show that customer satisfaction with the service quality at retail banks is influenced by “reliability”, “responsiveness”, “empathy”, “security”, and “tangibles”. Based on the findings, solutions for improving service quality are proposed for Vietnamese retail banks.
-
Perceived health risk, online retail ethics, and consumer behavior within online shopping during the COVID-19 pandemic
Yuniarti Fihartini ,
Arief Helmi ,
Meydia Hassan ,
Yevis Marty Oesman
doi: http://dx.doi.org/10.21511/im.17(3).2021.02
Innovative Marketing Volume 17, 2021 Issue #3 pp. 17-29 Views: 3938 Downloads: 1523 TO CITE АНОТАЦІЯThe risk of virus contracting during the COVID-19 pandemic has changed consumer preference for online shopping to meet their daily needs than shopping in brick-and-mortar stores. Online shopping presents a different environment, atmosphere, and experience. The possibility of ethical violations is higher during online than face-to-face transactions. Therefore, this study was conducted to investigate the influence of perceived health risk and customer perception of online retail ethics on consumer online shopping behavior during the COVID-19 pandemic, involving seven variables, namely perceived health risk, security, privacy, non-deception, reliability fulfillment, service recovery, and online shopping behavior. The data were collected through an online survey by employing the purposive sampling technique to a consumer who has shopped online during the COVID-19 pandemic in Indonesia. 315 valid responses were obtained and analyzed through quantitative method using SEM-Amos. The results showed that perceived health risk and four variables of online retail ethics including security, privacy, reliability fulfillment, and service recovery affected online shopping behavior. Meanwhile, non-deception was found to have an insignificant effect. The coefficient value proved perceived health risk to be more dominant in influencing online shopping behavior than the variables of online retail ethics. Thus, consumers pay more concern for their health during online shopping. However, positive consumer perceptions of the behavior of online retail websites in providing services also can encourage consumers to shop online during this pandemic.
