Understanding the Impact of Immersion and Authenticity on Satisfaction Behavior in Learning Analytics Tasks

  • Suning Zhu
  • Ashish Gupta
  • David Paradice
  • Casey Cegielski


As business analytics (BA) applications permeate across various industry sectors, the workforce needs to be trained and upskilled to meet the challenges of understanding and implementing analytics methodologies. To achieve payoffs from the resource investments in BA training, it is critical for enterprises to understand an individual’s learning behavior along with the process and outcome-centric satisfaction associated with a collaborative analytics training task. This study focuses on identifying the factors that influence the process of learning during BA training to entry-level BA users. Drawing on the theories of situated cognition, goal setting, and flow, we propose a model that explains how trainees in a group learn through a process that is influenced by the characteristics of BA training context through context authenticity, the traits of trainees through task motivation and preference towards teamwork. Using an experimental design built on data collection and a unique task of real visits to a historic cemetery, we found that context authenticity and task motivation have significant impact on focused immersion, which in turn significantly impacts process and outcome satisfaction for learning an analytics task. Results of this study extend and validate the theories of situated cognition, goal setting, and flow within the context of business analytics training. Based on these findings, we provide recommendations for practitioners for designing effective analytics tasks for better training outcomes.


Business analytics Focused immersion Context authenticity Satisfaction Behavior 


  1. Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2).Google Scholar
  2. Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly, 665–694.Google Scholar
  3. AMA. (2014). Company see need to build analytical skills in their organizations: a study of analytical skills in the workforce. Retrieved on May 10, 2017 from
  4. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practices: a review and recommended two-step approach. Psychological Bulletin, 103, 411–423.CrossRefGoogle Scholar
  5. Aykol, B., Aksatan, M., & İpek, İ. (2017). Flow within theatrical consumption: the relevance of authenticity. Journal of Consumer Behaviour, 16(3), 254–264.CrossRefGoogle Scholar
  6. Baker, D. S., Underwood III, J., & Thakur, R. (2017). Factors contributing to cognitive absorption and grounded learning effectiveness in a competitive business marketing simulation. Marketing Education Review, 27(3), 127–140.CrossRefGoogle Scholar
  7. Barab, S. A., Squire, K. D., & Dueber, W. (2000). A co-evolutionary model for supporting the emergence of authenticity. Educational Technology Research and Development, 48(2), 37–62.CrossRefGoogle Scholar
  8. Bechky, B. A. (2003). Sharing meaning across occupational communities: The transformation of understanding on a production floor. Organization Science, 14(3), 312–330.CrossRefGoogle Scholar
  9. Bharati, P., & Chaudhury, A. (2018). Assimilation of big data innovation: investigating the roles of IT, social media, and relational capital. Information Systems Frontiers, 1–12.Google Scholar
  10. Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school (expanded ed.). Washington, D.C.: National Academy Press.Google Scholar
  11. Brown, J. S., & Duguid, P. (2001). Knowledge and organization: a social-practice perspective. Organization Science, 12(2), 198–213.CrossRefGoogle Scholar
  12. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.CrossRefGoogle Scholar
  13. Campion, M. A., Medsker, G. J., & Higgs, A. C. (1993). Relations between work group characteristics and effectiveness: implications for designing effective work groups. Personnel Psychology, 46(4), 823–847.CrossRefGoogle Scholar
  14. Chidambaram, L. (1996). Relational development in computer-supported groups. MIS Quarterly, 143–165.Google Scholar
  15. Chin, W. (1998). Commentary: issues and opinion on structural equation modeling. MIS Quarterly, 22(1), Vii–Xvi.Google Scholar
  16. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217.CrossRefGoogle Scholar
  17. Cho, I., & Kim, Y. (2002). Critical factors for assimilation of object-oriented programming languages. Journal of Management Information Systems, 18(3), 125–156.CrossRefGoogle Scholar
  18. Chung, W. (2015). Business analytics in context: Evaluating new curricular modules in an undergraduate statistics course. Paper presented at the Proceedings of The 2015 NSF Workshop on Curricular Development for Computing in Context.Google Scholar
  19. Colquitt, J. A., LePine, J. A., & Noe, R. A. (2000). Toward an integrative theory of training motivation: a meta-analytic path analysis of 20 years of research. Journal of Applied Psychology, 85(5), 678.CrossRefGoogle Scholar
  20. Cook, S. D., & Brown, J. S. (1999). Bridging epistemologies: the generative dance between organizational knowledge and organizational knowing. Organization Science, 10(4), 381–400.CrossRefGoogle Scholar
  21. Covington, M. V. (2000). Goal theory, motivation, and school achievement: an integrative review. Annual Review of Psychology, 51(1), 171–200.CrossRefGoogle Scholar
  22. Cronbach, L. J., & Thorndike, R. L. (1971). Educational measurement. Test validation, 443–507.Google Scholar
  23. Csikszentmihalyi, M. (1990). Flow:The psychology of optimal experience. New York: Harper and Row.Google Scholar
  24. Dartnall, T. (2005). Does the world leak into the mind? Active externalism,“internalism” and epistemology. Cognitive Science, 29(1), 135–143.CrossRefGoogle Scholar
  25. DeSmet, A., McGurk, M., & Schwartz, E. (2010). Getting more from your training programs. The McKinsey Quarterly, 4, 101–107.Google Scholar
  26. Dole, J. A., & Sinatra, G. M. (1998). Reconceptalizing change in the cognitive construction of knowledge. Educational Psychologist, 33(2–3), 109–128.CrossRefGoogle Scholar
  27. Earley, P. C. (1985). Influence of information, choice and task complexity upon goal acceptance, performance, and personal goals. Journal of Applied Psychology, 70(3), 481.CrossRefGoogle Scholar
  28. Elsbach, K. D., Barr, P. S., & Hargadon, A. B. (2005). Identifying situated cognition in organizations. Organization Science, 16(4), 422–433.CrossRefGoogle Scholar
  29. Forfas (2014). Assessing the Demand for Big Data and Analytics Skills, 2013–2020. Retrieved from Assessing_the_Demand_for_Big_Data_and_Analytics_Skills_Full-Publication.pdf.
  30. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 39–50.Google Scholar
  31. Giamellaro, M. (2012). Deep immersion academic learning (DIAL): An analysis of science learning in context. University of Colorado at Denver.Google Scholar
  32. Giamellaro, M. (2014). Primary contextualization of science learning through immersion in Content-Rich Settings. International Journal of Science Education, 36(17), 2848–2871.CrossRefGoogle Scholar
  33. Goel, L., Johnson, N., Junglas, I., & Ives, B. (2010). Situated learning: conceptualization and measurement. Decision Sciences Journal of Innovative Education, 8(1), 215–240.CrossRefGoogle Scholar
  34. Goel, L., Johnson, N. A., Junglas, I., & Ives, B. (2013a). How cues of what can be done in a virtual world influence learning: an affordance perspective. Information Management, 50(5), 197–206.CrossRefGoogle Scholar
  35. Goel, L., Junglas, I., Ives, B., & Johnson, N. (2013b). Decision-making in-socio and in-situ: facilitation in virtual worlds. Decision Support Systems, 52(2), 342–352.CrossRefGoogle Scholar
  36. Green, S. G., & Taber, T. D. (1980). The effects of three social decision schemes on decision group process. Organizational Behavior and Human Performance, 25(1), 97–106.CrossRefGoogle Scholar
  37. Gulikers, J., Bastiaens, T., & Kirschner, P. (2006). Authentic assessment, student and teacher perceptions: the practical value of the five-dimensional framework. Journal of Vocational Education and Training, 58(3), 337–357.CrossRefGoogle Scholar
  38. Gupta, B., Goul, M., & Dinter, B. (2015). Business intelligence and big data in higher education: status of a multi-year model curriculum development effort for business school undergraduates, MS graduates, and MBAs. Communications of the Association for Information Systems, 36, 23.Google Scholar
  39. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.CrossRefGoogle Scholar
  40. Hair, J. F., Sarstedt, M. J., Hopkins, L., & Kuppelwieser, V. J. (2014). Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European Business Review, 26(2), 106–121.CrossRefGoogle Scholar
  41. Heffler, B. (2001). Individual learning style and the learning style inventory. Educational Studies, 27(3), 307–316.CrossRefGoogle Scholar
  42. Huang, S. Y., Lee, C. H., Chiu, A. A., & Yen, D. C. (2015). How business process reengineering affects information technology investment and employee performance under different performance measurement. Information Systems Frontiers, 17(5), 1133–1144.CrossRefGoogle Scholar
  43. Huang, S. C., McIntosh, S., Sobolevsky, S., & Hung, P. C. (2017). Big data analytics and business intelligence in industry. Information Systems Frontiers, 19(6), 1229–1232.CrossRefGoogle Scholar
  44. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 195–204.Google Scholar
  45. IDC. (2017). Big data and business analytics revenues forecast to reach $150.8 billion this year, lead by banking and manufacturing investment, according to IDC. Retrieved on April 24, 2018 from
  46. Jena, R. K. (2016). Measuring business management students’ perceptions towards the business analytics courses. International Journal of Economic Research, 13(8), 3711–3718.Google Scholar
  47. Kampling, H. (2018). The role of immersive virtual reality in individual learning. In Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS 2018). Hawaii, USA.Google Scholar
  48. Kilcourse, B., Rosenblum, P. (2014). Retail analytics moves to the frontline. Retrieved on April 20, 2018 from
  49. Kirkman, B. L., & Shapiro, D. L. (2001). The impact of cultural values on job satisfaction and organizational commitment in self-managing work teams: the mediating role of employee resistance. Academy of Management Journal, 44(3), 557–569.Google Scholar
  50. Kirschner, P. A. (2001). Using integrated electronic environments for collaborative teaching/learning. Learning and Instruction, 10, 1–9.CrossRefGoogle Scholar
  51. Kulturel-Konak, S., D'Allegro, M. L., & Dickinson, S. (2011). Review of gender differences in learning styles: suggestions for STEM education. Contemporary Issues in Education Research, 4(3), 9.CrossRefGoogle Scholar
  52. Lant, T. K. (2002). Organizational cognition and interpretation. In J. Baum (Ed.), Companion to organizations (pp. 344–362). Oxford, UK: Blackwell Publishers Ltd..Google Scholar
  53. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.Google Scholar
  54. Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114.CrossRefGoogle Scholar
  55. Liu, L., Feng, Y., Hu, Q., & Huang, X. (2011). From transactional user to VIP: how organizational and cognitive factors affect ERP assimilation at individual level. European Journal of Information Systems, 20(2), 186–200.CrossRefGoogle Scholar
  56. Locke, E. (2000). Motivation, cognition, and action: an analysis of studies of task goals and knowledge. Applied Psychology, 49(3), 408–429.CrossRefGoogle Scholar
  57. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting and task performance. Prentice Hall: Englewook Cliffs, NJ.Google Scholar
  58. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: a 35-year odyssey. American Psychologist, 57(9), 705.CrossRefGoogle Scholar
  59. Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: a comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.CrossRefGoogle Scholar
  60. Martens, S., & Wyble, B. (2010). The attentional blink: past, present, and future of a blind spot in perceptual awareness. Neuroscience & Biobehavioral Reviews, 34(6), 947–957.CrossRefGoogle Scholar
  61. Maynard, D. C., & Hakel, M. D. (1997). Effects of objective and subjective task complexity on performance. Human Performance, 10(4), 303–330.CrossRefGoogle Scholar
  62. Murray, S. (2016). Why business analytics is a must for MBAs, as Mckinsey, Bain demand big data skills. Retrieved on May 10, 2017 from .
  63. Mustafa, G., Glavee-Geo, R., & Rice, P. M. (2017). Teamwork orientation and personal learning: The role of individual cultural values and value congruence. SA Journal of Industrial Psychology, 43, 13-pages.Google Scholar
  64. Nonaka, I., & Konno, N. (1998). The concept of “ba”: building a foundation for knowledge creation. California Management Review, 40(3), 40–54.CrossRefGoogle Scholar
  65. Pallud, J. (2017). Impact of interactive technologies on stimulating learning experiences in a museum. Information & Management, 54(4), 465–478.Google Scholar
  66. Peng, W., & Hsieh, G. (2012). The influence of competition, cooperation, and player relationship in a motor performance centered computer game. Computers in Human Behavior, 28(6), 2100–2106.CrossRefGoogle Scholar
  67. Petti, B., & Williams, S. (2015). 5 Reasons why your company's analytics program is failing. Retrieved on April, 21, 2018 from
  68. Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2016). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 1–14.Google Scholar
  69. Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344.CrossRefGoogle Scholar
  70. Reinig, B. A. (2003). Toward an understanding of satisfaction with the process and outcomes of teamwork. Journal of Management Information Systems, 19(4), 65–83.CrossRefGoogle Scholar
  71. Rothstein, M. G., Paunonen, S. V., Rush, J. C., & King, G. A. (1994). Personality and cognitive ability predictors of performance in graduate business school. Journal of Educational Psychology, 86(4), 516.CrossRefGoogle Scholar
  72. Seel, N. M. (2001). Epistemology, situated cognition, and mental models: ‘Like a bridge over troubled water’. Instructional Science, 29(4), 403–427.CrossRefGoogle Scholar
  73. Sharda, R., Romano Jr., N. C., Lucca, J. A., Weiser, M., Scheets, G., Chung, J.-M., & Sleezer, C. M. (2004). Foundation for the study of computer-supported collaborative learning requiring immersive presence. Journal of Management Information Systems, 20(4), 31–64.CrossRefGoogle Scholar
  74. Shaw, J. D., Duffy, M. K., & Stark, E. M. (2000). Interdependence and preference for group work: Main and congruence effects on the satisfaction and performance of group members. Journal of Management, 26(2), 259–279.CrossRefGoogle Scholar
  75. Strobel, J., Wang, J., Weber, N. R., & Dyehouse, M. (2013). The role of authenticity in design-based learning environments: the case of engineering education. Computers & Education, 64, 143–152.CrossRefGoogle Scholar
  76. Suh, K. S. (1999). Impact of communication medium on task performance and satisfaction: an examination of media-richness theory. Information Management, 35(5), 295–312.CrossRefGoogle Scholar
  77. Tekleab, A. G., & Quigley, N. R. (2014). Team deep-level diversity, relationship conflict, and team members’ affective reactions: a cross-level investigation. Journal of Business Research, 67(3), 394–402.CrossRefGoogle Scholar
  78. Triandis, H. C., Bontempo, R., Villareal, M. J., Asai, M., & Lucca, N. (1988). Individualism and collectivism: cross-cultural perspectives on self-ingroup relationships. Journal of Personality and Social Psychology, 54(2), 323.CrossRefGoogle Scholar
  79. Van Laer, S., & Elen, J. (2017). In search of attributes that support self-regulation in blended learning environments. Education and Information Technologies, 22(4), 1395–1454.CrossRefGoogle Scholar
  80. Wagner, J. A. (1995). Studies of individualism-collectivism: effects on cooperation in groups. Academy of Management Journal, 38(1), 152–173.Google Scholar
  81. Wang, Y. (2015). Business intelligence and analytics education: Hermeneutic literature review and future directions in is education. Paper presented at the Proceeding of Twenty-First Americas Conference on Information Systems (AMCIS), Puerto Rico.Google Scholar
  82. Williams, E. A., & Castro, S. L. (2010). The effects of teamwork on individual learning and perceptions of team performance: a comparison of face-to-face and online project settings. Team Performance Management: An International Journal, 16(3/4), 124–147.CrossRefGoogle Scholar
  83. Williams, E. A., Duray, R., & Reddy, V. (2008). Teamwork orientation, group cohesiveness, and student learning: a study of the use of teams in online distance education. Journal of Management Education, 30(4), 592–616.CrossRefGoogle Scholar
  84. Wixom, B., Ariyachandra, T., Goul, M., Gray, P., Kulkarni, U., & Phillips-Wren, G. (2011). The current state of business intelligence in academia. Communications of the Association for Information Systems, 29(16), 299–312.Google Scholar
  85. Wold, H. (1985). Partial least squares. In S. Kotz & N. L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 581–591). New York: Wiley.Google Scholar
  86. Wood, R. E., & Locke, E. A. (1990). Goal-setting and strategy effects on complex tasks. Research in Organizational Behavior, 12, 73–109.Google Scholar
  87. Workman, M., & Bommer, W. (2004). Redesigning computer call center work: a longitudinal field experiment. Journal of Organizational Behavior, 317–337.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Systems & Technology, Raymond J. Harbert College of BusinessAuburn UniversityAuburnUSA

Personalised recommendations