References
- Andriamiarana, V., Kilian, P., Kelava, A., & Brandt, H. (2023). On the requirements of non-linear dynamic latent class SEM: A simulation study with varying numbers of subjects and time points. Structural Equation Modeling. A Multidisciplinary Journal, 30(5), 789-806.https://doi.org/10.1080/10705511.2023.2169698
- Bolton, R. N., McColl-Kennedy, J. R., Cheung, L., Gallan, A., Orsingher, C., Witell, L., & Zaki, M. (2018). Customer experience challenges: Bringing together digital, physical and social realms. Journal of Service Management, 29(5), 776-808. https://doi.org/10.1108/JOSM-04-2018-0113
- Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21(2), 230–258.
- Fan, Y., Chen, J., Shirkey, G., John, R., Wu., R., S., Park, H., & Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecological Processes, 5(19), 1-12.https://doi.org/10.1186/s13717-016-0063-3
- Flores, L. Y., Atilano, R., Suh, H. N., & Navarro, R. L. (2020). A latent growth modeling analysis of the effects of perceived supports, perceived barriers, and coping efficacy on Latina/o engineering students’ life satisfaction. Journal of Career Development, 47(1), 29-43.https://doi.org/10.1177/0894845319826251
- Ghasemy, M., Teeroovengadum, V., Becker, J. M., & Ringle, C. M. (2020). This fast car can move faster: A review of PLS-SEM application in higher education research. Higher education, 80(6), 1121-1152.https://doi.org/10.1007/s10734-020-00534-1
- Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 1-16. https://doi.org/10.1016/j.rmal.2022.100027
- Hamaker, E. L., Asparouhov, T., & Muthén, B. (2021). Dynamic structural equation modeling as a combination of time series modeling, multilevel modeling, and structural equation modeling. The handbook of structural equation modeling (31st ed.). Guilford Press.
- Hidayat-ur-Rehman, I., & Alsolamy, M. (2023). A SEM-ANN analysis to examine sustainable performance in SMEs: The moderating role of transformational leadership. Journal of Open Innovation: Technology, Market, and Complexity, 9(4), 1-15.https://doi.org/10.1016/j.joitmc.2023.100166
- Hu, X., & Lovrich, N. P. (2020). Electronic community-oriented policing: Theories, contemporary efforts, and future directions (1st ed.). Lexington Books. https://books.google.co.tz/books?hl=en&lr=&id=5HT8DwAAQBAJ&oi
- Iskamto, D., & Gunawan, R. I. (2023). Impulse Purchase behaviour on the shopee platform and the role of real-time commerce marketing. Jurnal Manajemen Bisnis, 10(2), 444-461.https://doi.org/10.33096/jmb.v10i2.623
- Juju, U., Arizal, N., & Waldelmi, I. (2023). Changes and determinants of consumer shopping behavior in E-commerce and social media product Muslimah. Journal of Retailing and Consumer Services, 70, 1-10. https://doi.org/10.1016/j.jretconser.2022.103146
- Kim, T., Lee, D., Shin, J., Kim, Y., & Cha, Y. (2022). Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams. Science of The Total Environment, 812, 1-12. https://doi.org/10.1016/j.scitotenv.2021.152520
- Kronemann, B. (2022). The impact of AI on online customer experience and consumer behaviour. An Empirical investigation of the impact of artificial intelligence on online customer experience and consumer behaviour in a digital marketing and online retail context [Dissertation]. University of Bradford.
- Kruschke, J. (2015). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan (2nd ed.). Elsevier.
- Kwasnicka, D., Inauen, J., Nieuwenboom, W., Nurmi, J., Schneider, A., Short, C. E., & Naughton, F. (2019). Challenges and solutions for N-of-1 design studies in health psychology. Health Psychology Review. Health Psychology Review, 13(2), 163-178. https://doi.org/10.1080/17437199.2018.1564627
- Liu, J., Bai, X., & Elsworth, D. (2024). Evolution of pore systems in low-maturity oil shales during thermal upgrading—Quantified by dynamic SEM and machine learning. Petroleum Science, 21(3), 1739-1750.10.1016/j.petsci.2023.12.021
- Maranon, A., Gustafsson, P., & Nilsson, P. (2015). Option framing and Markov chain: A descriptive approach in a state-space modeling of customer behavior. Department of Statistics, Lund University School of Economics and Management, LUSEM.
- McNeish, D., & Hamaker, E. L. (2020). A primer on two-level dynamic structural equation models for intensive longitudinal data in Mplus. Psychological Methods, 25(5), 610-635.https://psycnet.apa.org/buy/2019-78963-001
- Mehedintu, A., & Soava, G. (2022). A Hybrid SEM-Neural network modeling of quality of M-commerce services under the impact of the COVID-19 Pandemic. Electronics, 11(16), 1-8.https://doi.org/10.3390/electronics11162499
- Mueller, R. O., & Hancock, G. R. (2018). Structural equation modeling. The reviewer’ s guide to quantitative methods in the social sciences (2nd ed.). Routledge.
- Napontun, K., & Pimchainoi, K. (2023). The influence of marketing promotion tools on customer satisfaction and repurchase intention: A study on TikTok marketing platform. Service, Leisure, Sport, Tourism & Education, 1(2), 1-25.https://so09.tci-thaijo.org/index.php/BTSMM/article/view/1315
- Natarajan, T., & Veera Raghavan, D. R. (2023). Does service journey quality explain omnichannel shoppers’ online engagement behaviors? The role of customer-store identification and gratitude toward the store. The TQM Journal, 1-13. https://doi.org/10.1108/TQM-07-2023-0217
- Nayyar, V. (2022). Reviewing the impact of digital migration on the consumer buying journey with robust measurement of PLS-SEM and R Studio. Systems Research and Behavioral Science, 39(3), 542-556.https://doi.org/10.1002/sres.2857
- Pazo, M., Gerassis, S., Araújo, M., Antunes, I. M., & Rigueira, X. (2024). Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation. Science of the Total Environment, 927, 1-13.10.1016/j.scitotenv.2024.172340
- Sharma, P. N., Sarstedt, M., Ringle, C. M., Cheah, J. H., Herfurth, A., & Hair, J. F. (2024). A framework for enhancing the replicability of behavioral MIS research using prediction oriented techniques. International Journal of Information Management, 78, 1-9.https://doi.org/10.1016/j.ijinfomgt.2024.102805
- Tao, Y., Mishra, A., Masyn, K. E., & Keil, M. (2022). Addressing change trajectories and reciprocal relationships: A longitudinal method for information systems research. Communications of the Association for Information Systems, 50(1), 1-60. https://doi.org/10.17705/1CAIS.05018
- Thakkar, J. J. (2020). Structural equation modelling. Application for Research and Practice (1st ed.). Springer.https://link.springer.com/
- Thorson, J. T., Andrews III, A. G., Essington, T. E., & Large, S. I. (2024). Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms. Methods in Ecology and Evolution, 15(4), 744-755. https://doi.org/10.1111/2041-210X.14289.
- Wei, C., Lin, W., Liang, S., Chen, M., Zheng, Y., Liao, X., & Chen, Z. (2022). An all-in-one multifunctional touch sensor with carbon-based gradient resistance elements. Nano-micro letters, 14(1), 1-18.https://doi.org/10.1007/s40820-022-00875-9
- Westland, J. C. (2015). Structural equation models. From paths to networks. In J.C. Westeland (Ed.), Studies in Systems, Decision and Control (pp. 1-8). Springer. https://link.springer.com/book/10.1007/978-3-319-16507-3)
- Xu, R., DeShon, R. P., & Dishop, C. R. (2020). Challenges and opportunities in the estimation of dynamic models. Organizational Research Methods, 23(4), 595-619. https://doi.org/10.1177/1094428119842638
- Yu, X., Zaza, S., Schuberth, F., & Henseler, J. (2021). Counterpoint: Representing forged concepts as emergent variables using composite-based structural equation modeling. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 52(SI), 114. The Database for Advances in Information Systems, 52(SI), 114-130.https://doi.org/10.1145/3505639.3505647
- Zhang, Q., Li, W., Zhang, M., Zhao, K., Liu, R., & Ma, C. (2024). Internet altruistic behavior among Chinese early adolescents: Exploring differences in gender and collective efficacy using a latent growth modeling. Current Psychology, 43(6), 5007-5019. https://doi.org/10.1007/s12144-023-04660-8
- Zhi, L., & Ha, H. Y. (2024). The dynamic outcomes of service recovery in tourism services: A latent growth modeling approach. Journal of Hospitality and Tourism Management, 59, 70-80.https://doi.org/10.1016/j.jhtm.2024.03.006
- Zhu, X., Raquel, M., & Aryadoust, V. (2020). Structural equation modelling to predict performance in English proficiency tests. In V. Aryadoust & M. Raquel (Eds.), Quantitative Data Analysis for Language Assessment Volume II: Advanced Methods (pp. 101-126). Routledge.