Al-Rahmi, A. M., Shamsuddin, A., Wahab, E., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A. & Almutairy, S. (2022). Integrating the role of UTAUT and TTF model to evaluate social media use for teaching and learning in higher education.
Frontiers in Public Health, 10, 905968.
https://doi.org/10.3389/fpubh.2022.905968
Al-Rahmi, W. M., Al-Adwan, A. S., Al-Maatouk, Q., Othman, M. S., Alsaud, A. R., Almogren, A. S. & Al-Rahmi, A. M. (2023). Integrating communication and task-technology fit theories: The adoption of digital media in learning. Sustainability, 15(10), 8144. https://doi.org/10.3390/su15108144
Ambra, J. D., Wilson, C. S. & Akter, S. (2013). Application of the task-technology fit model to structure and evaluate the adoption of E-books by academics.
Journal of the American Society for Information Science and Technology Archive, 64(1), 48-64.
https://doi.org/10.1002/asi.22757
Annesley T. M. (2010). Bars and pies make better desserts than figures.
Clinical Chemistry, 56(9), 1394–1400.
https://doi.org/10.1373/clinchem.2010.152298
Asamoah, D. (2022). Improving data visualization skills: A curriculum design.
International Journal of Education & Development Using Information & Communication Technology, 18(1), 213–235. Retrieved from
http://ijedict.dec.uwi.edu/viewarticle.php?id=3032
Attié, E. & Meyer-Waarden, L. (2022). The acceptance and usage of smart connected objects according to adoption stages: An enhanced technology acceptance model integrating the diffusion of innovation, uses and gratification, and privacy calculus theories.
Technological Forecasting and Social Change, 176, 121485.
https://doi.org/10.1016/j.techfore.2022.121485
Bagozzi, R. P. & Yi, Y. (1988). On the evaluation of structural equation models.
Journal of the Academy of Marketing Science, 16(1), 74–94.
https://doi.org/10.1007/bf02723327
Brehmer, M. & Munzner, T. (2013). A multi-level typology of abstract visualization tasks.
IEEE Transactions on Visualization and Computer Graphics, 19(12), 2376–2385.
https://doi.org/10.1109/tvcg.2013.124
Bresciani, S. & Eppler, M. (2015). Extending TAM to information visualization: A framework for evaluation.
Electronic Journal of Information System Evaluation, 18(1), 46–58. Retrieved from
https://academic-publishing.org/index.php/ejise/article/view/187/150
Camilleri, M. A. (2024). Factors affecting performance expectancy and intentions to use ChatGPT: Using SmartPLS to advance an information technology acceptance framework.
Technological Forecasting and Social Change, 201, 123247.
https://doi.org/10.1016/j.techfore.2024.123247
Campbell, D.T. & Fiske, D.W. (1995). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105. PMID: 13634291.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology.
MIS Quarterly, 13(3), 319–340.
https://doi.org/10.2307/249008
Dawes, J. (2002). Five point vs eleven point scales: Does it make a difference to data characteristics.
Australasian Journal of Market Research, 10(1), 1-17. Retrieved from
https://www.academia.edu/3570983/Five_point_vs_eleven_point_scales_does_it_make_a_difference_to_data_characteristics
Devaraj, S. & Kohli, R. (2003). Performance impacts of information technology: Is actual usage the missing link?
Management Science, 49(3), 273–289.
https://doi.org/10.1287/mnsc.49.3.273.12736
Dishaw, T. & Strong, M. (1999). Extending the technology acceptance model with task-technology fit constructs.
Information & Management, 36(1), 9–12.
https://doi.org/10.1016/S0378-7206(98)00101-3
Dong, T. & Triche, J. (2020). A longitudinal analysis of job skills for entry-level data analysts.
Journal of Information Systems Education, 31(4), 312-326. Retrieved from
https://jise.org/Volume31/n4/JISEv31n4p312.pdf
Donohoe, D. & Costello, E. (2020). Data visualization literacy in higher education: An exploratory study of understanding of a learning dashboard tool.
International Journal of Emerging Technologies in Learning, 15(17), 115–126.
https://doi.org/10.3991/ijet.v15i17.15041
Duffy, B. & Smith, K. (2003). Comparing data from online and face-to-face surveys.
International Journal of Market Research, 47(6), 615–639.
https://doi.org/10.1177/147078530504700602
Erskine, M. A., Khojah, M. & McDaniel, A. E. (2019). Location selection using heat maps: Relative advantage, task-technology fit, and decision-making performance.
Computers in Human Behavior, 101, 151–162.
https://doi.org/10.1016/j.chb.2019.07.014
Faqih, K. M. S. & Jaradat, M.-I. R. M. (2021). Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: Perspective from a developing country.
Technology in Society, 67, 101787.
https://doi.org/10.1016/j.techsoc.2021.101787
Federer, L. (2018). Defining data librarianship: A survey of competencies, skills, and training.
Journal of the Medical Library Association, 106(3)
, 294-303.
https://doi.org/10.5195/jmla.2018.306
Fornell, C. & Larcker, D.F. (1981). Evaluating structural equation models with unobservable and measurement error. J
ournal of Marketing Research. 18(1), 39-50.
https://doi.org/10.1177/002224378101800104
Fuller, R. M. & Dennis, A. R. (2009). Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks.
Journal Information Systems Research Archive, 20(1), 2-17.
https://doi.org/10.1287/isre.1070.0167
Gefen, D., Straub, D.W. & Boudreau, M.C. (2000). Structural equation modeling and regression: Guidelines for research practice.
Communications of the Association for Information Systems, 4(7), 1-70.
https://doi.org/10.17705/1CAIS.00407
Goodhue, D. L. & Thompson, R. L. (1995). Task-technology fit and individual performance.
MIS Quarterly, 19(2), 213-236.
https://doi.org/10.2307/249689
Goodhue, D. L. (1995). Understanding user evaluations of information systems.
Management Science, 41(2), 1827-1844.
https://doi.org/10.5555/2729947.2729948
Goodhue, D. L., Klein, B. D., & March, S. T. (2000). User evaluations of IS as surrogates for objective performance.
Information & Management, 38(2), 87-101.
https://doi.org/10.1016/S0378-7206(00)00057-4
Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate data analysis. (7th Ed.). Englewood Cliffs, NJ: Prentice Hall
Hau, K.T., Wen, Z. & Chen, Z. (2004). Structural equation model and its applications.
Beijing: Educational Science Publishing House.
Henni, S., Franz, P., Staudt, P., Peukert, C. & Weinhardt, C. (2022). Evaluation of an interactive visualization tool to increase energy literacy in the building sector.
Energy and Buildings, 266, 112116.
https://doi.org/10.1016/j.enbuild.2022.112116
Hogg, D. (2008). Data analysis recipes: Choosing the binning for a histogram. Retrieved
https://doi.org/10.48550/arXiv.0807.4820
Hu, K. (2020). Become competent within one day in generating boxplots and violin plots for a novice without prior R experience.
Methods and Protocols, 3(4), 64.
https://doi.org/10.3390/mps3040064
Huang, X. (2022, October). Data visualization design strategies for promoting exercise motivation in self-tracking applications. In
Proceedings of the 40th ACM International Conference on Design of Communication (pp. 78-89).
https://doi.org/10.1145/3513130.3558981
Isaac, O., Aldholay, A., Abdullah, Z. & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model.
Computers and Education, 136, 113-129.
https://doi.org/10.1016/j.compedu.2019.02.012
Jiang, Y., Yang, X. & Zheng, T. (2023). Make chatbots more adaptive: Dual pathways linking human-like cues and tailored response to trust in interactions with chatbots.
Computers in Human Behavior, 138(C), 107485.
https://doi.org/10.1016/j.chb.2022.107485
Kaiser, H. F. (1960). The application of electronic computers to factor analysis.
Educational and Psychological Measurement, 20(1)
, 141-151.
https://doi.org/10.1177/001316446002000116
Kang, H.-J., Han, J. & Kwon, G. H. (2022). The acceptance behavior of smart home health care services in South Korea: An integrated model of UTAUT and TTF.
International Journal of Environmental Research and Public Health, 19(20), 13279;
https://doi.org/10.3390/ijerph192013279
Kariguh, P. K. (2019). The adoption of data visualization techniques in humanitarian organizations. Master of Science in Information Systems and Technology. United States International University - Africa.
Kim, H. N., & Kim, S.H. (2021). Development on korean visualization literacy assessment test (K-VLAT) and research trend analysis.
Journal of the Korea Institute of Information and Communication Engineering, 25(11), 1696–1707.
https://doi.org/10.6109/jkiice.2021.25.11.1696 [in Korean]
kim, N. W., joyner, S. C., riegelhuth, A. & kim. Y. (2021). Accessible visualization: Design space, opportunities, and challenges.
Computer graphics forum, 40(3)
, 173-188.
https://doi.org/10.1111/cgf.14298
Kim, R.
& Song, H.-D. (2022). Examining the influence of teaching presence and task-technology fit on continuance intention to use MOOCs.
The Asia-Pacific Education Researcher, 31(4), 395–408.
https://doi.org/10.1007/s40299-021-00581-x
Kulas, J. T., Stachowski, A. A. & Haynes, B. A. (2008). Middle-range functioning: Likert responses to personality items.
Journal of Business and Psychology, 22(3), 51-259.
https://doi.org/10.1007/s10869-008-9064-2
Lee, S., Kim, S. H. & Kwon, B. C. (2017). VLAT: Development of a visualization literacy assessment test. IEEE transactions on visualization and computer graphics, 23(1), 551-560.
https://doi.org/10.1109/TVCG.2016.2598920
M Ayyoub, A. A., Abu Eidah, B. A., Khlaif, Z. N., Ahmad El-Shamali, M. & Sulaiman, M. R. (2023). Understanding online assessment continuance intention and individual performance by integrating task technology fit and expectancy confirmation theory.
Heliyon, 9(11), e22068.
https://doi.org/10.1016/j.heliyon.2023.e22068
Marikyan, D. & Papagiannidis, S. (2025). Task-technology fit: A review. In S. Papagiannidis (Ed.), TheoryHub Book. TheoryHub.
Masialeti, M., Mahony, J. & Wang, W. (2024). Acceptance and use of data visualization technologies in mining short interval control systems (SIC).
Issues In Information Systems, 25(1), 71-84.
https://doi.org/10.48009/1_iis_2024_107
Mei, H., Guan, H., Xin, C., Wen, X. & Chen, W. (2020). DataV: Data visualization on large high-resolution displays.
Visual Informatics, 4(3), 12-23.
https://doi.org/10.1016/j.visinf.2020.07.001
Muchenje, G. & Seppänen, M. (2023). Unpacking task-technology fit to explore the business value of big data analytics.
International Journal of Information Management, 69(C), 102619.
https://doi.org/10.1016/j.ijinfomgt.2022.102619
Munzner, T. (2014).
Visualization Analysis and Design. 1
st edition. CRC Press.
https://doi.org/10.1201/b17511
Muth, Ch., L. (2022, September 22).
What to consider when using text in data visualizations. Retrieved from
https://www.datawrapper.de/blog/text-in-data-visualizations
Nguyen, Q. V., Miller, N., Arness, D., Huang, W., Huang, M. L. & Simoff, S. (2020). Evaluation of interactive visualization data with scatterplots.
Visual Informatics, 4(4), 1–10.
https://doi.org/10.1016/j.visinf.2020.09.004
Nunnally, J. C. (1978). Psychometric theory. (2nd ed.). McGraw-Hill.
Odewumi, O. M. (2021). Empowering students’ cognitive learning of creative colors through computer-based concept maps.
International Journal of Education & Development Using Information & Communication Technology, 17(3), 155–165. Retrieved from
http://ijedict.dec.uwi.edu/viewissue.php?id=62
Oliveira, T., Faria, M., Thomas, M. A. & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. I
nternational Journal of Information Management, 34(5), 689-703.
https://doi.org/10.1016/j.ijinfomgt.2014.06.004
Osang, F. & Raj, D. B. (2019). Open educational resources development in Nigeria: Determining task technology fit (TTF) impact on faculty usage, satisfaction, and performance.
International Journal of Human and Technology Interaction, 3(2), 35-46. Retrieved from
https://www.academia.edu/100430184/Open_Educational_Resources_OERs_Development_in_Nigeria_Determining_Task_Technology_Fit_TTF_Impact_on_Faculty_Usage_Satisfaction_and_Performance
Park, C. W., Kim, D., Cho, S. & Han, H. J. (2019). Adoption of multimedia technology for learning and gender differences.
Computers in Human Behavior, 92, 288-296.
https://doi.org/10.1016/j.chb.2018.11.029
Parsons, C. S., Zuiderwijk, A., Orchard, N. A., Oosterhoff, J. H. F. & de Reuver, M. (2025). Task-technology fit of artificial intelligence-based clinical decision support systems: A review of qualitative studies.
BMC Medical Informatics and Decision Making, 25(1), 397.
https://doi.org/10.1186/s12911-025-03237-8
Pastore, M. (2018). Overlapping: a R package for Estimating Overlapping in Empirical Distributions.
The Journal of Open Source Software, 3(32), 1023.
https://doi.org/10.21105/joss.01023
Pedamkar, P. (2023, March 15). Data science vs data visualization | learn 7 best comparisons.
EDUCBA. Retrieved from
https://www.educba.com/data-science-vs-data-visualization/
Phillips, B. (2014).
The relationship between data visualization and task performance. Doctor of Philosophy, University of North Texas. ProQuest Dissertations and Theses Global. Retrieved from
https://digital.library.unt.edu/ark:/67531/metadc699897/m2/1/high_res_d/dissertation.pdf
Radionov, V. (2017). Understanding stacked bar charts: The worst or the best?
Smashing Magazine. Retrieved from
https://www.smashingmagazine.com/2017/03/understanding-stacked-bar-charts/
Ratna, S., Nayati Utami, H., Siti Astuti, E., Wilopo & Muflih, M. (2020). The technology tasks fit, their impact on the use of the information system, performance, and users’ satisfaction.
VINE Journal of Information and Knowledge Management Systems, 50(3). 369-386.
https://doi.org/10.1108/VJIKMS-10-2018-0092
Rogers, M. & Jeffcoat, S. (2024). Data visualization literacy skills of information science students.
Journal of Education for Library and Information Science, 65(4), 410-425.
https://doi.org/10.3138/jelis-2023-0024
Ryan, L., Silver, D., Laramee, R. S. & Ebert, D. (2019). Teaching data visualization as a skill.
IEEE Computer Graphics and Applications, 39(2), 95–103.
https://doi.org/10.1109/mcg.2018.2889526
Safarudin, M. S., Yunesman, Y. & Hermansyah, H. (2023). Task technology fit adoption in the recruitment process using Google Form for IPSM Members.
Jurnal Teknologi Informasi Dan Pendidikan, 16(1), 156-173.
https://doi.org/10.24036/jtip.v16i1.720
Shao, G., Quintana, J. P., Zakharov, W., Purzer, S. & Kim, E. (2021). Exploring potential roles of academic libraries in undergraduate data science education curriculum development.
The Journal of Academic Librarianship, 47(2), 102320.
https://doi.org/10.1016/j.acalib.2021.102320
Skau, D. & Kosara, R. (2016). Arcs, angles, or areas: Individual data encodings in pie and donut charts.
Computer Graphics Forum, 35(3), 121-130.
https://doi.org/10.1111/cgf.12888
Smyth, P. & Wolpert, D. (1997). Stacked density estimation. In
Proceedings of the 10th International Conference on Neural Information Processing Systems, (pp.668–674).
https://dl.acm.org/doi/10.5555/3008904.3008999
Somisetty, S. V. H. V. G., Songa, A., Raavi, S. T., Edara, S., Tetali, S. T. K. R. & Madireddy, B. (2021). A comparative study of various data visualization techniques using COVID-19 data. International Research Journal of Engineering and Technology (IRJET), 8(8), 1306-1328.
Tripathi, S. (2015). Task-technology Fit (TTF) model to evaluate adoption of cloud computing: A multi-case study.
International Journal of Applied Engineering Research, 10(4), 9185-9200. Retrieved from
https://www.ripublication.com/ijaer10/ijaerv10n4_69.pdf
Ulfa, S., Surahman, E., Fatawi, I. & Tsukasa, H. (2024). Task-technology fit analysis: measuring the factors that influence behavioral intention to use the online summary-with automated feedback in a MOOC platform.
Electronic Journal of E-Learning, 22(1), 1.
https://doi.org/10.34190/ejel.22.1.3094
Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view.
MIS Quarterly, 27(3), 425-478.
https://doi.org/10.2307/30036540
Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures. First edition. O’Reilly Media.
Wnuk, A., Kozak, M. & Małgorzata, R. (2009). Mosaic plots help visualize contingency tables. Example questionnaire for a survey on knowledge of and attitudes towards GMOs. Colloquium Biometricum, 39, 137–145.
Zakaria, M. S. (2021). Data visualization as a research support service in academic libraries: An investigation of world-class universities.
The Journal of Academic Librarianship, 47(5), 102397.
https://doi.org/10.1016/j.acalib.2021.102397
Zheng, M., Lillis, D. & Campbell, A. G. (2024). Current state of the art and future directions: Augmented reality data visualization to support decision-making.
Visual Informatics, 8(2), 80-105.
https://doi.org/10.1016/j.visinf.2024.05.001
Zhou, T., Lu, Y. & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption.
Computers in Human Behavior, 26(4), 760-767.
https://doi.org/10.1016/j.chb.2010.01.013
