Determinants of visual analytics adoption in organizations: Knowledge discovery through content analysis of online evaluation reviews
Abstract
Purpose Visual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to support decision-making. Whilst a broad range of visual analytics platforms exists, limited research has been conducted to explore the specific factors that influence their adoption in organizations. The purpose of this paper is to develop a framework for visual analytics adoption that synthesizes the factors related to the specific nature and characteristics of visual analytics technology. Design/methodology/approach This study applies a directed content analysis approach to online evaluation reviews of visual analytics platforms to identify the salient determinants of visual analytics adoption in organizations from the standpoint of practitioners. The online reviews were gathered from Gartner.com, and included a sample of 1,320 reviews for six widely adopted visual analytics platforms. Findings Based on the content analysis of online reviews, 34 factors emerged as key predictors of visual analytics adoption in organizations. These factors were synthesized into a conceptual framework of visual analytics adoption based on the diffusion of innovations theory and technology–organization–environment framework. The findings of this study demonstrated that the decision to adopt visual analytics technologies is not merely based on the technological factors. Various organizational and environmental factors have also significant influences on visual analytics adoption in organizations. Research limitations/implications This study extends the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations. Practical implications This study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals. Originality/value This study is among the first to use the online evaluation reviews to systematically explore the main factors involved in the acceptance and adoption of visual analytics technologies in organizations. Thus, it has potential to provide theoretical foundations for further research in this important and emerging field. The development of an integrative model synthesizing the salient determinants of visual analytics adoption in enterprises should ultimately allow both information systems researchers and practitioners to better understand how and why users form perceptions to accept and engage in the adoption of visual analytics tools and applications.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Before successfully adopting new technologies, organizations must be prepared and have the necessary capabilities.
“However, before successfully adopting new technologies, organizations must be prepared and have the necessary capabilities (Daradkeh, 2019).”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Daradkeh, 2019
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · see citing works
Three Approaches to Qualitative Content Analysis
Hsieh, H.-F., & Shannon, S. E. (2005) · Qualitative Health Research · doi:10.1177/1049732305276687 · Cited by 44,601 · see citing works
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793 · see citing works
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003 · see citing works
What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.Com1
Mudambi, S. M., & Schuff, D. (2010) · MIS Quarterly · doi:10.2307/20721420 · Cited by 2,952 · see citing works
Zhu, F., & Zhang, X. (2010) · Journal of Marketing · doi:10.1509/jmkg.74.2.133 · Cited by 2,372 · see citing works
The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach
McKinney, V. R., Yoon, K., & Zahedi, F. M. (2002) · Information Systems Research · doi:10.1287/isre.13.3.296.76 · Cited by 1,982 · see citing works
Sniehotta, F. F., Scholz, U., & Schwarzer, R. (2005) · Psychology & Health · doi:10.1080/08870440512331317670 · Cited by 1,350 · see citing works
What makes a helpful online review? a study of customer reviews on amazon.com
Mudambi, S. M., & Schuff, D. (2010) · MIS Quarterly · doi:10.5555/2017447.2017457 · Cited by 1,287 · see citing works
Visual Analytics: Definition, Process, and Challenges
Keim, D. A., Andrienko, G., Fekete, J., Görg, C., Kohlhammer, J., & Mélançon, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70956-5_7 · Cited by 1,091 · see citing works
Informational and Normative Social Influence in Buyer Behavior
Burnkrant, R. E., & Cousineau, A. (1975) · Journal of Consumer Research · doi:10.1086/208633 · Cited by 967 · see citing works
Understanding the determinants of RFID adoption in the manufacturing industry
Wang, Y.-M., Wang, Y.-S., & Yang, Y.-F. (2010) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2010.03.006 · Cited by 912 · see citing works
Empirical Studies in Information Visualization: Seven Scenarios
Lam, H., Bertini, E., Isenberg, P., Catherine Plaisant, B., & Carpendale, S. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.279 · Cited by 625 · see citing works
Visual Analytics: Scope and Challenges
Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., & Ziegler, H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-71080-6_6 · Cited by 523 · see citing works
Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.08.017 · Cited by 491 · see citing works
Information Systems Success Measurement
DeLone, W. H., & McLean, E. R. (2016) · Foundations and Trends® in Information Systems · doi:10.1561/2900000005 · Cited by 479 · see citing works
Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features
Li, M., Huang, L., Tan, C., & Wei, K. (2013) · International Journal of Electronic Commerce · doi:10.2753/jec1086-4415170404 · Cited by 361 · see citing works
Wang, Y., Li, H.-T., Li, C.-R., & Zhang, D.-Z. (2015) · Tourism Management · doi:10.1016/j.tourman.2015.09.021 · Cited by 351 · see citing works
SMEs' adoption of enterprise applications
Ramdani, B., Chevers, D. A., & Williams, D. A. (2013) · Journal of Small Business and Enterprise Development · doi:10.1108/jsbed-12-2011-0035 · Cited by 348 · see citing works
A Systematic Review on the Practice of Evaluating Visualization
Tobias Isenberg, A., Isenberg, P., Jian, C., Sedlmair, M., & Möller, T. B. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.126 · Cited by 324 · see citing works
Understanding the Factors Affecting the Organizational Adoption of Big Data
Sun, S., Cegielski, C. G., Jia, L., & Hall, D. J. (2016) · Journal of Computer Information Systems · doi:10.1080/08874417.2016.1222891 · Cited by 306 · see citing works
Ifinedo, P. (2011) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622011004543 · Cited by 277 · see citing works
Chong, A. Y., Ch’ng, E., Liu, M. J., & Li, B. (2015) · International Journal of Production Research · doi:10.1080/00207543.2015.1066519 · Cited by 226 · see citing works
Hameed, M. A., Counsell, S., & Swift, S. (2012) · Information & Management · doi:10.1016/j.im.2012.05.002 · Cited by 206 · see citing works
Sun, G., Wu, Y., Liang, R., & Liu, S.-X. (2013) · Journal of Computer Science and Technology · doi:10.1007/s11390-013-1383-8 · Cited by 197 · see citing works
Adoption of business analytics and impact on performance: a qualitative study in retail
Ramanathan, R., Philpott, E., Duan, Y., & Cao, G. (2017) · Production Planning & Control · doi:10.1080/09537287.2017.1336800 · Cited by 155 · see citing works
Visual analytics in urban computing: An overview
Zheng, Y., Wu, W., Chen, Y., Qu, H., & Ni, L. M. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2586447 · Cited by 141 · see citing works
Solving Problems with Visual Analytics
Kohlhammer, J., Keim, D. A., Pohl, M., et al. (2011) · Procedia Computer Science · doi:10.1016/j.procs.2011.12.035 · Cited by 133 · see citing works
Visualization beyond the Desktop--the Next Big Thing
Roberts, J. C., Ritsos, P. D., Badam, S. K., Brodbeck, D., Kennedy, J., & Elmqvist, N. (2014) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2014.82 · Cited by 127 · see citing works
Too good to be true: the role of online reviews’ features in probability to buy
Masłowska, E., Malthouse, E. C., & Bernritter, S. F. (2016) · International Journal of Advertising · doi:10.1080/02650487.2016.1195622 · Cited by 124 · see citing works
KMS adoption: the effects of information quality
Kuo, R., & Lee, G. (2009) · Management Decision · doi:10.1108/00251740911004727 · Cited by 113 · see citing works
Do Professional Reviews Affect Online User Choices Through User Reviews? An Empirical Study
Zhou, W., & Duan, W. (2016) · Journal of Management Information Systems · doi:10.1080/07421222.2016.1172460 · Cited by 113 · see citing works
Huang, G.-H., & Korfiatis, N. (2015) · International Journal of Electronic Commerce · doi:10.1080/10864415.2015.1029359 · Cited by 104 · see citing works
Racherla, P., Connolly, D. J., & Christodoulidou, N. (2013) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2011.645187 · Cited by 98 · see citing works
SnapShot: Visualization to Propel Ice Hockey Analytics
Pileggi, H., Stolper, C. D., Boyle, J. M., & Stasko, J. (2012) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.263 · Cited by 95 · see citing works
Ali, A. S. B., Anbari, F. T., & Money, W. H. (2008) · Project Management Journal · doi:10.1002/pmj.20041 · Cited by 93 · see citing works
Booshehrian, M., Möller, T. B., Peterman, R. M., et al. (2012) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2012.03116.x · Cited by 90 · see citing works
FinVis: Applied visual analytics for personal financial planning
Rudolph, S., Savikhin, A. C., & Ebert, D. (2009) · 2009 IEEE Symposium on Visual Analytics Science and Technology · doi:10.1109/vast.2009.5333920 · Cited by 85 · see citing works
Leung, D., Lo, A., Fong, L. H. N., & Law, R. (2015) · Tourism Recreation Research · doi:10.1080/02508281.2015.1090152 · Cited by 80 · see citing works
Contextual factors affecting knowledge management diffusion in SMEs
Lin, H. (2014) · Industrial Management & Data Systems · doi:10.1108/imds-08-2014-0232 · Cited by 75 · see citing works
Racherla, P., & Hu, C. (2008) · Journal of Hospitality & Leisure Marketing · doi:10.1080/10507050801978372 · Cited by 75 · see citing works
Visual decision support for business ecosystem analysis
Basole, R. C., Huhtamäki, J., Still, K., & Russell, M. G. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.08.041 · Cited by 75 · see citing works
Patterns for visualization evaluation
Elmqvist, N., & Yi, J. S. (2013) · Information Visualization · doi:10.1177/1473871613513228 · Cited by 63 · see citing works
From Data to Insight: Work Practices of Analysts in the Enterprise
Kandogan, E., Balakrishnan, A., Haber, E. M., et al. (2014) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2014.62 · Cited by 62 · see citing works
Information visualization evaluation in large companies: Challenges, experiences and recommendations
Sedlmair, M., Isenberg, P., Baur, D., et al. (2011) · Information Visualization · doi:10.1177/1473871611413099 · Cited by 59 · see citing works
A new hybrid model for exploring the adoption of online nursing courses
Tung, F.-C., & Chang, S.-C. (2008) · Nurse Education Today · doi:10.1016/j.nedt.2007.06.003 · Cited by 53 · see citing works
Ranking product aspects through sentiment analysis of online reviews
Wang, W., Wang, H.-W., & Song, Y. (2017) · Journal of experimental and theoretical artificial intelligence (Print) · doi:10.1080/0952813x.2015.1132270 · Cited by 47 · see citing works
Understanding and predicting what influence online product sales? A neural network approach
Hou, F., Li, B., Chong, A. Y., Yannopoulou, N., & Liu, M. J. (2017) · Production Planning & Control · doi:10.1080/09537287.2017.1336791 · Cited by 47 · see citing works
DIVE: A Graph-Based Visual-Analytics Framework for Big Data
Rysavy, S. J., Bromley, D., & Daggett, V. (2014) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2014.27 · Cited by 39 · see citing works
Visual Business Ecosystem Intelligence: Lessons from the Field
Basole, R. C. (2014) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2014.104 · Cited by 38 · see citing works
Factors Influencing Perceived Benefits and User Satisfaction in Knowledge Management Systems
Karlinsky-Shichor, Y., & Zviran, M. (2015) · Information Systems Management · doi:10.1080/10580530.2016.1117873 · Cited by 37 · see citing works
Customer knowledge discovery from online reviews
You, W., Xia, M., Liu, L., & Liu, D. (2012) · Electronic Markets · doi:10.1007/s12525-012-0098-y · Cited by 28 · see citing works
Evaluation: A Challenge for Visual Analytics
van Wijk, J. J. (2013) · Computer · doi:10.1109/mc.2013.151 · Cited by 28 · see citing works
FACTORS INFLUENCING BUSINESS INTELLIGENCE SYSTEMS IMPLEMENTATION SUCCESS IN THE ENTERPRISES
Hung, S. Y., Huang, Y., Lin, C. C., et al. (2016) · Pacific Asia Conference on Information Systems · Cited by 26 · see citing works
Scholtz, J. C., Plaisant, C., Whiting, M., et al. (2013) · Information Visualization · doi:10.1177/1473871613490290 · Cited by 24 · see citing works
The impact of trial on technology adoption: the case of mobile TV
Soscia, I., Arbore, A., & Hofacker, C. F. (2011) · Journal of Research in Interactive Marketing · doi:10.1108/17505931111187820 · Cited by 23 · see citing works
Self-Service Business Intelligence Adoption in Business Enterprises
Daradkeh, M. K., & Al-Dwairi, R. M. (2017) · International Journal of Enterprise Information Systems · doi:10.4018/ijeis.2017070105 · Cited by 18 · see citing works
Self-citationRisk Management Usage and Impact on Information Systems Project Success
Reed, A. H., & Angolia, M. (2018) · International Journal of Information Technology Project Management · doi:10.4018/ijitpm.2018040101 · Cited by 18 · see citing works
Knowledge Discovery and Data Visualization
Kasemsap, K. (2017) · International Journal of Organizational and Collective Intelligence · doi:10.4018/ijoci.2017070105 · Cited by 16 · see citing works
Adagha, O., Levy, R., & Carpendale, S. (2015) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-015-1118-5 · Cited by 13 · see citing works
An experimental study of the decision process with interactive technology
Samek, A. S., Hur, I., Kim, S.-H., & Yi, J. S. (2016) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2016.06.004 · Cited by 13 · see citing works
Su, W., Lehto, M. R., Lehto, X. Y., Yi, J. S., Shi, Z., & Liu, X. (2016) · Journal of Quality Assurance in Hospitality & Tourism · doi:10.1080/1528008x.2016.1230035 · Cited by 12 · see citing works
A modular framework for the design of material flow systems
REMBOLD, B., & Tanchoco, J. M. A. (1994) · International Journal of Production Research · doi:10.1080/00207549408956912 · Cited by 11 · see citing works
Ajjan, H., Kumar, R. L., & Subramaniam, C. (2013) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622013400129 · Cited by 11 · see citing works
Building Adoption of Visual Analytics Software
Chinchor, N. A., Cook, K. A., & Scholtz, J. C. (2012) · Springer eBooks · doi:10.1007/978-1-4471-2804-5_29 · Cited by 9 · see citing works
Information visualisation for decision support under risk
Daradkeh, M. K. (2017) · International Journal of Information and Decision Sciences · doi:10.1504/ijids.2017.086786 · Cited by 5 · see citing works
Self-citationDer Markt für Visual Business Analytics
Kohlhammer, J., Proff, D. U., & Wiener, A. (2015) · Springer eBooks · doi:10.1007/978-3-662-47763-2_15 · Cited by 4 · see citing works
Information visualisation for decision support under risk
Daradkeh, M. K. (2017) · International Journal of Information and Decision Sciences · doi:10.1504/ijids.2017.10007807 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Adoption of artificial intelligence (AI) for talent acquisition in IT/ITeS organizations
Pillai, R., & Sivathanu, B. (2020) · Benchmarking: An International Journal · doi:10.1108/bij-04-2020-0186 · Cited by 422 · see citing works
Goswami, M., Jain, S., Alam, T., Deifalla, A. F., Ragab, A. E., & Khargotra, R. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1215706 · Cited by 33 · see citing works
- Authors
- Daradkeh, M. K.
- Affiliations
- Yarmouk University (JO)
- Corresponding author
- Daradkeh, Mohammad Kamel
- Venue
- Information Technology & People
- Publisher
- Emerald
- Published
- 2019-06-03
- Volume
- 32
- Issue
- 3
- Pages
- 668-695
- ISSN
- 0959-3845 (ISSN-L)
- OpenAlex
- W2887549083
- Cited by
- 47
- References
- 69
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 6 · 2023: 4 · 2022: 14 · 2021: 11 · 2020: 2 · 2019: 1
- FWCI
- 5.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Technology and People (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- visual analytics, technology adoption, diffusion of innovations, technology-organization-environment framework, big data analytics, decision support, adoption framework, user acceptance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
