Abstract
Behavioral research in information systems employing quantitative methods has traditionally relied on mainly survey-based approaches to gather subjective user data. With new advances in technology such as mobile computing, wearable devices, and social media, along with computational capabilities, organizations are in a position to leverage objective data in addressing IT issues typically addressed in behavioral research. In this paper, we propose a framework for envisioning how data analytics may be leveraged in conducting behavioral research. Particularly, the framework is explained using examples from extant research along two broad avenues, namely ‘data analytics for data generation’ and ‘data analytics for model generation.’ The article also serves as an introduction to the special issue of the Information Systems Frontiers (ISF) journal on the topic of ‘Data Analytics in Behavioral Research.’
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References
Abbasi, A., Sarker, S., & Chiang, R. H. L. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), 1–32.
Agarwal, R., & Dhar, V. (2014). Editorial–big data, data science, and analytics: The opportunity and challenge for IS research. Information Systems Research, 25(3), 443–448. https://doi.org/10.1287/isre.2014.0546.
Akter, S., & Wamba, S. F. (2016). Big data analytics in e-commerce: A systematic review and agenda for future research. Electronic Markets, 26, 173–194. https://doi.org/10.1007/s12525-016-0219-0.
Albashrawi, M., Kartal, H., Oztekin, A., & Motiwalla, L. (2019). Self-reported and computer-recorded experience in mobile banking: A multi-phase path analytic approach. Information Systems Frontiers, 21(4). https://doi.org/10.1007/s10796-018-9892-1.
Almars, A., Li, X., & Zhao, X. (2019). Modelling user attitudes using hierarchical sentiment-topic model. Data & Knowledge Engineering. https://doi.org/10.1016/J.DATAK.2019.01.005.
Bigsby, K. G., Ohlmann, J. W., & Zhao, K. (2019). The turf is always greener: Predicting decommitments in college football recruiting using twitter data. Decision Support Systems, 116, 1–12. https://doi.org/10.1016/J.DSS.2018.10.003.
Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511–521. https://doi.org/10.1016/J.DSS.2010.11.009.
Chan, F. T. S., & Chong, A. Y. L. (2012). A SEM-neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decision Support Systems, 54(1), 621–630. https://doi.org/10.1016/J.DSS.2012.08.009.
Chang, W.-L. (2018). The impact of emotion: A blended model to estimate influence on social media. Information Systems Frontiers, 1–15. https://doi.org/10.1007/s10796-018-9824-0.
Chapman, L. (2019). VCs plowed a record $9.3 billion into AI startups last year - Bloomberg. Bloomberg News. Retrieved from https://www.bloomberg.com/news/articles/2019-01-08/vcs-plowed-a-record-9-3-billion-into-ai-startups-last-year
Chen, Y.-L., Chang, C.-L., & Yeh, C.-S. (2017). Emotion classification of YouTube videos. Decision Support Systems, 101, 40–50. https://doi.org/10.1016/J.DSS.2017.05.014.
Chiang, R. H. L., Grover, V., Liang, T.-P., & Zhang, D. (2018). Strategic value of big data and business analytics. Journal of Management Information Systems, 35(2), 383–387. https://doi.org/10.1080/07421222.2018.1451950.
Choi, Y., & Lee, H. (2017). Data properties and the performance of sentiment classification for electronic commerce applications. Information Systems Frontiers, 19(5), 993–1012. https://doi.org/10.1007/s10796-017-9741-7.
Dutta, H., Kwon, K. H., & Rao, H. R. (2018). A system for intergroup prejudice detection: The case of microblogging under terrorist attacks. Decision Support Systems, 113, 11–21. https://doi.org/10.1016/J.DSS.2018.06.003.
Freitas, F. A., Peres, S. M., Lima, C. A. M., & Barbosa, F. V. (2017). Grammatical facial expression recognition in sign language discourse: A study at the syntax level. Information Systems Frontiers, 19(6), 1243–1259. https://doi.org/10.1007/s10796-017-9765-z.
Grimmer, J. (2015). We are all social scientists now: How big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(01), 80–83. https://doi.org/10.1017/S1049096514001784.
Gupta, A., Deokar, A. V., Iyer, L., Sharda, R., & Schrader, D. (2018). Big data & analytics for societal impact: Recent research and trends. Information Systems Frontiers, 20(2), 185–194. https://doi.org/10.1007/s10796-018-9846-7.
Hassan Zadeh, A., Zolbanin, H. M., Sharda, R., & Delen, D. (2019). Social media for nowcasting flu activity: Spatio-temporal big data analysis. Information Systems Frontiers, 21(4). https://doi.org/10.1007/s10796-018-9893-0.
Hindman, M. (2015). Building Better Models. The Annals of the American Academy of Political and Social Science, 659(1), 48–62. https://doi.org/10.1177/0002716215570279.
Hofman, J. M., Sharma, A., & Watts, D. J. (2017). Prediction and explanation in social systems. Science (New York, N.Y.), 355(6324), 486–488. https://doi.org/10.1126/science.aal3856.
Johnson, P., Gray, P., & Sarker, S. (2019). Revisiting IS research practice in the era of big data. Information and Organization, 29, 41–56. https://doi.org/10.1016/j.infoandorg.2019.01.001.
Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39–48. https://doi.org/10.1016/J.DSS.2017.02.001.
Kekolahti, P., Karikoski, J., & Riikonen, A. (2015). The effect of an individual’s age on the perceived importance and usage intensity of communications services—A Bayesian network analysis. Information Systems Frontiers, 17(6), 1313–1333. https://doi.org/10.1007/s10796-014-9502-9.
Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., & Prendinger, H. (2018). Deep learning for affective computing: Text-based emotion recognition in decision support. Decision Support Systems, 115, 24–35. https://doi.org/10.1016/J.DSS.2018.09.002.
Lin, Y., & Chen, Y. (2018). Do less active participants make active participants more active? An examination of Chinese Wikipedia. Decision Support Systems, 114, 103–113. https://doi.org/10.1016/J.DSS.2018.08.002.
Luu, B., McDaniel, T., Reitsma, R., & Xiaotong Duan, C. H. (2017). Global Business Technographics® Data & Analytics Survey, 2017: Overview. Retrieved from https://www.forrester.com/report/Global+Business+Technographics+Data+Analytics+Survey+2017+Overview/-/E-RES140551
Müller, O., Junglas, I., vom Brocke, J., & Debortoli, S. (2016). Utilizing big data analytics for information systems research: Challenges, promises and guidelines. European Journal of Information Systems, 25(4), 289–302. https://doi.org/10.1057/ejis.2016.2.
Pir Mohammadiani, R., Mohammadi, S., & Malik, Z. (2017). Understanding the relationship strengths in users’ activities, review helpfulness and influence. Computers in Human Behavior, 75, 117–129. https://doi.org/10.1016/J.CHB.2017.03.065.
Ram, S., & Delen, D. (2018). Introduction to the inaugural issue of journal of business analytics. Journal of Business Analytics, 1(1), 1–1. https://doi.org/10.1080/2573234X.2018.1507527.
Sharma, S. K. (2017). Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling. Information Systems Frontiers, 1–13. https://doi.org/10.1007/s10796-017-9775-x.
Siering, M., Koch, J.-A., & Deokar, A. V. (2016). Detecting fraudulent behavior on crowdfunding platforms: The role of linguistic and content-based cues in static and dynamic contexts. Journal of Management Information Systems, 33(2), 421–455. https://doi.org/10.1080/07421222.2016.1205930.
Siering, M., Deokar, A. V., & Janze, C. (2018a). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52–63. https://doi.org/10.1016/J.DSS.2018.01.002.
Siering, M., Muntermann, J., & Rajagopalan, B. (2018b). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1–12. https://doi.org/10.1016/J.DSS.2018.01.004.
Swain, A. K., & Cao, R. Q. (2017). Using sentiment analysis to improve supply chain intelligence. Information Systems Frontiers, 1–16. https://doi.org/10.1007/s10796-017-9762-2.
Wang, G. A., Liu, X., Wang, J., Zhang, M., & Fan, W. (2015). Examining micro-level knowledge sharing discussions in online communities. Information Systems Frontiers, 17(6), 1227–1238. https://doi.org/10.1007/s10796-015-9566-1.
Xu, Y., Yin, D., & Zhou, D. (2019). Investigating users’ tagging behavior in online academic community based on growth model: Difference between active and inactive users. Information Systems Frontiers, 21(4). https://doi.org/10.1007/s10796-018-9891-2.
Yan, X., Wang, J., & Chau, M. (2015). Customer revisit intention to restaurants: Evidence from online reviews. Information Systems Frontiers, 17(3), 645–657. https://doi.org/10.1007/s10796-013-9446-5.
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393.
Zhang, J., & Piramuthu, S. (2018). Product recommendation with latent review topics. Information Systems Frontiers, 20(3), 617–625. https://doi.org/10.1007/s10796-016-9697-z.
Zhang, L., Yan, Q., & Zhang, L. (2018). A computational framework for understanding antecedents of guests’ perceived trust towards hosts on Airbnb. Decision Support Systems, 115, 105–116. https://doi.org/10.1016/J.DSS.2018.10.002.
Zhu, S., Gupta, A., Paradice, D., & Cegielski, C. (2019). Understanding the impact of immersion and authenticity on satisfaction behavior in learning analytics tasks. Information Systems Frontiers, 21(4). https://doi.org/10.1007/s10796-018-9865-4.
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Motiwalla, L., Deokar, A.V., Sarnikar, S. et al. Leveraging Data Analytics for Behavioral Research. Inf Syst Front 21, 735–742 (2019). https://doi.org/10.1007/s10796-019-09928-8
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DOI: https://doi.org/10.1007/s10796-019-09928-8