How to cite this paper
Nalluri, V., Huynh-Cam, T., Sama, H & Chen, L. (2023). Decision-making model for the effective e-services adoption in the Indian educational organizations.Decision Science Letters , 12(2), 211-224.
Refrences
Adnan, M., & Anwar, K. (2020). Online Learning amid the COVID-19 Pandemic: Students' Perspectives. Online Submission, 2(1), 45-51. http://www.doi.org/10.33902/jpsp.2020261309
Ali, H. (2019). Measurement of e-services quality: an empirical study of University of Bahrain. Education and Information Technologies, 24(3), 1907-1924. https://doi.org/10.1007/s10639-018-9775-6
Al-Shamayleh, h. a. r. e. t. h., Aljaafreh, r. a. s. h. a., Aljaafreh, A., Albadayneh, d. a. r. a., Al-Ali, m. o. h. a. m. m. e. d., Bazin, n. e. n., ... & Khasawneh, a. m. (2015). Measuring the quality of E-services and its impact on students satisfaction at Jordanian universities. Journal of Theoretical and Applied Information Technology, 74(3), 274-285.
Ataburo, H., Muntaka, A. S., & Quansah, E. K. (2017). Linkages among e-service quality, satisfaction, and usage of e-services within higher educational environments. International Journal of Business and Social Research, 7(3), 10-26. http://dx.doi.org/10.18533/ijbsr.v7i3.1040
Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000-3011. https://doi.org/10.1016/j.eswa.2011.08.162
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
Demir, A., Maroof, L., Khan, N. U. S., & Ali, B. J. (2020). The role of E-service quality in shaping online meeting platforms: a case study from higher education sector. Journal of Applied Research in Higher Education. https://doi.org/10.1108/JARHE-08-2020-0253
Featherman, M. S., Miyazaki, A. D., & Sprott, D. E. (2010). Reducing online privacy risk to facilitate e‐service adoption: the influence of perceived ease of use and corporate credibility. Journal of services marketing. https://doi.org/10.1108/08876041011040622
Hassan, H., Shehab, E., & Peppard, J. (2008, May). Adoption of e-service in developing countries: Issues and challenges. In Proceedings of the Cranfield Multi-Strand Conference on Creating Wealth through Research and Innovation, Cranfield, UK (pp. 454-459).
Huang, T. Y., Chen, W. K., Nalluri, V., & Huynh-Cam, T. T. (2022). Evaluating E-Teaching Adoption Criteria for Indian Educational Organizations Using Fuzzy Delphi-TOPSIS Approach. Mathematics, 10(13), 2175. https://doi.org/10.3390/math10132175.
Huynh-Cam, T. T., Nalluri, V., Chen, L. S., & Yang, Y. Y. (2022). IS-DT: A New Feature Selection Method for Determining the Important Features in Programmatic Buying. Big Data and Cognitive Computing, 6(4), 118. https://doi.org/10.3390/bdcc6040118.
Jameel, A. S., Hamdi, S. S., Karem, M. A., & Raewf, M. B. (2021, February). E-Satisfaction based on E-service Quality among university students. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012039). IOP Publishing. DOI 10.1088/1742-6596/1804/1/012039
Joshi, A., Vinay, M., & Bhaskar, P. (2020). Impact of coronavirus pandemic on the Indian education sector: perspectives of teachers on online teaching and assessments. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-06-2020-0087
Kahraman, C., Cebi, S., Onar, SC, Oztaysi, B., Tolga, AC, & Sari, IU (Eds.). (2019). Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019 (Vol. 1029). Springer.
Kahraman, C., Onar, S. C., Oztaysi, B., Sari, I. U., Cebi, S., & Tolga, A. C. (Eds.). (2020). Intelligent and Fuzzy Techniques: Smart and Innovative Solutions: Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21-23, 2020 (Vol. 1197). Springer Nature.
Kaya, B., Behravesh, E., Abubakar, A. M., Kaya, O. S., & Orús, C. (2019). The moderating role of website familiarity in the relationships between e-service quality, e-satisfaction and e-loyalty. Journal of Internet Commerce, 18(4), 369-394. https://doi.org/10.1080/15332861.2019.1668658.
Kundu, A. (2022). An Exploratory Case Study on the Effects of E-Service Quality on Student Satisfaction and Retention. International Journal of Virtual and Personal Learning Environments (IJVPLE), 12(1), 1-18.
Leonnard, O. (2019). Exploring the Relationship among E-service Quality, E-trust, E-satisfaction and Loyalty at Higher Education Institutions. Journal on Efficiency and Responsibility in Education and Science, 12(4), 103-110.
Luo, M. M., Chea, S., & Chen, J. S. (2011). Web-based information service adoption: A comparison of the motivational model and the uses and gratifications theory. Decision Support Systems, 51(1), 21-30. https://doi.org/10.1016/j.dss.2010.11.015
Menezes, L. S., Sellitto, M. A., Librelato, T. P., Borchardt, M., & Pereira, G. M. (2016). Identification and quantification of influent factors in perceived quality of the e-service provided by a university. Business Process Management Journal. https://doi.org/10.1108/BPMJ-07-2015-0100
Nalluri, V., & Chen, L. (2022). Risk assessment for sustainability on telecom supply chain: A hybrid fuzzy approach. Uncertain Supply Chain Management, 10(2), 559-576. DOI: 10.5267/j.uscm.2021.11.007
Nilashi, M., Samad, S., Manaf, A. A., Ahmadi, H., Rashid, T. A., Munshi, A., ... & Ahmed, O. H. (2019). Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Computers & Industrial Engineering, 137, 106005. https://doi.org/10.1016/j.cie.2019.106005
Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of service research, 7(3), 213-233. https://doi.org/10.1177/1094670504271156
Ray, A., & Bala, P. K. (2019). Use of NLP and SEM in determining factors for E-service adoption. In Structural equation modeling approaches to E-service adoption (pp. 38-47). IGI Global. https://doi.org/10.4018/978-1-5225-8015-7.ch003
Ray, A., Bala, P. K., Dasgupta, S. A., & Sivasankaran, N. (2019). Factors influencing adoption of e-services in rural India–perspectives of consumers and service providers. Journal of Indian Business Research. https://doi.org/10.1108/JIBR-11-2018-0295
Rust, R. T., & Lemon, K. N. (2001). E-service and the consumer. International journal of electronic commerce, 5(3), 85-101. https://doi.org/10.1080/10864415.2001.11044216
Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female. Quality & quantity, 55(3), 805-826. https://doi.org/10.1007/s11135-020-01028-z
Solvak, M., Unt, T., Rozgonjuk, D., Võrk, A., Veskimäe, M., & Vassil, K. (2019). E-governance diffusion: Population level e-service adoption rates and usage patterns. Telematics and Informatics, 36, 39-54. https://doi.org/10.1016/j.tele.2018.11.005
Taherdoost, H. (2018). Development of an adoption model to assess user acceptance of e-service technology: E-Service Technology Acceptance Model. Behaviour & Information Technology, 37(2), 173-197. https://doi.org/10.1080/0144929X.2018.1427793
Vinodh, S., Sai Balagi, T. S., & Patil, A. (2016). A hybrid MCDM approach for agile concept selection using fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS. The International Journal of Advanced Manufacturing Technology, 83(9), 1979-1987. https://doi.org/10.1007/s00170-015-7718-6
Wen, Z., Liao, H., Zavadskas, E. K., & Antuchevičienė, J. (2021). Applications of fuzzy multiple criteria decision making methods in civil engineering: A state-of-the-art survey. Journal of Civil Engineering and Management, 27(6), 358-371. https://doi.org/10.3846/jcem.2021.15252
Zhang, X., & Su, J. (2019). A combined fuzzy DEMATEL and TOPSIS approach for estimating participants in knowledge-intensive crowdsourcing. Computers & Industrial Engineering, 137, 106085. https://doi.org/10.1016/j.cie.2019.106085
Ali, H. (2019). Measurement of e-services quality: an empirical study of University of Bahrain. Education and Information Technologies, 24(3), 1907-1924. https://doi.org/10.1007/s10639-018-9775-6
Al-Shamayleh, h. a. r. e. t. h., Aljaafreh, r. a. s. h. a., Aljaafreh, A., Albadayneh, d. a. r. a., Al-Ali, m. o. h. a. m. m. e. d., Bazin, n. e. n., ... & Khasawneh, a. m. (2015). Measuring the quality of E-services and its impact on students satisfaction at Jordanian universities. Journal of Theoretical and Applied Information Technology, 74(3), 274-285.
Ataburo, H., Muntaka, A. S., & Quansah, E. K. (2017). Linkages among e-service quality, satisfaction, and usage of e-services within higher educational environments. International Journal of Business and Social Research, 7(3), 10-26. http://dx.doi.org/10.18533/ijbsr.v7i3.1040
Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000-3011. https://doi.org/10.1016/j.eswa.2011.08.162
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
Demir, A., Maroof, L., Khan, N. U. S., & Ali, B. J. (2020). The role of E-service quality in shaping online meeting platforms: a case study from higher education sector. Journal of Applied Research in Higher Education. https://doi.org/10.1108/JARHE-08-2020-0253
Featherman, M. S., Miyazaki, A. D., & Sprott, D. E. (2010). Reducing online privacy risk to facilitate e‐service adoption: the influence of perceived ease of use and corporate credibility. Journal of services marketing. https://doi.org/10.1108/08876041011040622
Hassan, H., Shehab, E., & Peppard, J. (2008, May). Adoption of e-service in developing countries: Issues and challenges. In Proceedings of the Cranfield Multi-Strand Conference on Creating Wealth through Research and Innovation, Cranfield, UK (pp. 454-459).
Huang, T. Y., Chen, W. K., Nalluri, V., & Huynh-Cam, T. T. (2022). Evaluating E-Teaching Adoption Criteria for Indian Educational Organizations Using Fuzzy Delphi-TOPSIS Approach. Mathematics, 10(13), 2175. https://doi.org/10.3390/math10132175.
Huynh-Cam, T. T., Nalluri, V., Chen, L. S., & Yang, Y. Y. (2022). IS-DT: A New Feature Selection Method for Determining the Important Features in Programmatic Buying. Big Data and Cognitive Computing, 6(4), 118. https://doi.org/10.3390/bdcc6040118.
Jameel, A. S., Hamdi, S. S., Karem, M. A., & Raewf, M. B. (2021, February). E-Satisfaction based on E-service Quality among university students. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012039). IOP Publishing. DOI 10.1088/1742-6596/1804/1/012039
Joshi, A., Vinay, M., & Bhaskar, P. (2020). Impact of coronavirus pandemic on the Indian education sector: perspectives of teachers on online teaching and assessments. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-06-2020-0087
Kahraman, C., Cebi, S., Onar, SC, Oztaysi, B., Tolga, AC, & Sari, IU (Eds.). (2019). Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, Turkey, July 23-25, 2019 (Vol. 1029). Springer.
Kahraman, C., Onar, S. C., Oztaysi, B., Sari, I. U., Cebi, S., & Tolga, A. C. (Eds.). (2020). Intelligent and Fuzzy Techniques: Smart and Innovative Solutions: Proceedings of the INFUS 2020 Conference, Istanbul, Turkey, July 21-23, 2020 (Vol. 1197). Springer Nature.
Kaya, B., Behravesh, E., Abubakar, A. M., Kaya, O. S., & Orús, C. (2019). The moderating role of website familiarity in the relationships between e-service quality, e-satisfaction and e-loyalty. Journal of Internet Commerce, 18(4), 369-394. https://doi.org/10.1080/15332861.2019.1668658.
Kundu, A. (2022). An Exploratory Case Study on the Effects of E-Service Quality on Student Satisfaction and Retention. International Journal of Virtual and Personal Learning Environments (IJVPLE), 12(1), 1-18.
Leonnard, O. (2019). Exploring the Relationship among E-service Quality, E-trust, E-satisfaction and Loyalty at Higher Education Institutions. Journal on Efficiency and Responsibility in Education and Science, 12(4), 103-110.
Luo, M. M., Chea, S., & Chen, J. S. (2011). Web-based information service adoption: A comparison of the motivational model and the uses and gratifications theory. Decision Support Systems, 51(1), 21-30. https://doi.org/10.1016/j.dss.2010.11.015
Menezes, L. S., Sellitto, M. A., Librelato, T. P., Borchardt, M., & Pereira, G. M. (2016). Identification and quantification of influent factors in perceived quality of the e-service provided by a university. Business Process Management Journal. https://doi.org/10.1108/BPMJ-07-2015-0100
Nalluri, V., & Chen, L. (2022). Risk assessment for sustainability on telecom supply chain: A hybrid fuzzy approach. Uncertain Supply Chain Management, 10(2), 559-576. DOI: 10.5267/j.uscm.2021.11.007
Nilashi, M., Samad, S., Manaf, A. A., Ahmadi, H., Rashid, T. A., Munshi, A., ... & Ahmed, O. H. (2019). Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Computers & Industrial Engineering, 137, 106005. https://doi.org/10.1016/j.cie.2019.106005
Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of service research, 7(3), 213-233. https://doi.org/10.1177/1094670504271156
Ray, A., & Bala, P. K. (2019). Use of NLP and SEM in determining factors for E-service adoption. In Structural equation modeling approaches to E-service adoption (pp. 38-47). IGI Global. https://doi.org/10.4018/978-1-5225-8015-7.ch003
Ray, A., Bala, P. K., Dasgupta, S. A., & Sivasankaran, N. (2019). Factors influencing adoption of e-services in rural India–perspectives of consumers and service providers. Journal of Indian Business Research. https://doi.org/10.1108/JIBR-11-2018-0295
Rust, R. T., & Lemon, K. N. (2001). E-service and the consumer. International journal of electronic commerce, 5(3), 85-101. https://doi.org/10.1080/10864415.2001.11044216
Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: the group comparison between male and female. Quality & quantity, 55(3), 805-826. https://doi.org/10.1007/s11135-020-01028-z
Solvak, M., Unt, T., Rozgonjuk, D., Võrk, A., Veskimäe, M., & Vassil, K. (2019). E-governance diffusion: Population level e-service adoption rates and usage patterns. Telematics and Informatics, 36, 39-54. https://doi.org/10.1016/j.tele.2018.11.005
Taherdoost, H. (2018). Development of an adoption model to assess user acceptance of e-service technology: E-Service Technology Acceptance Model. Behaviour & Information Technology, 37(2), 173-197. https://doi.org/10.1080/0144929X.2018.1427793
Vinodh, S., Sai Balagi, T. S., & Patil, A. (2016). A hybrid MCDM approach for agile concept selection using fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS. The International Journal of Advanced Manufacturing Technology, 83(9), 1979-1987. https://doi.org/10.1007/s00170-015-7718-6
Wen, Z., Liao, H., Zavadskas, E. K., & Antuchevičienė, J. (2021). Applications of fuzzy multiple criteria decision making methods in civil engineering: A state-of-the-art survey. Journal of Civil Engineering and Management, 27(6), 358-371. https://doi.org/10.3846/jcem.2021.15252
Zhang, X., & Su, J. (2019). A combined fuzzy DEMATEL and TOPSIS approach for estimating participants in knowledge-intensive crowdsourcing. Computers & Industrial Engineering, 137, 106085. https://doi.org/10.1016/j.cie.2019.106085