Skip to main content
SpringerLink
Log in

Applying an organizational learning perspective to new technology deployment by technological gatekeepers: A theoretical model and key issues for future research

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

Organizations often under-utilize expensive information technology (IT) enabled work processes that automate routines or processes that were previously carried out manually. One reason for this phenomenon may lie in the types of decisions made by technological gatekeepers, who are key individuals charged with deploying new technologies in organizations. From an organizational learning perspective, technological gatekeepers are more likely to perform successfully when they make appropriate decisions about exploring or exploiting the routines associated with a new technology. The factors that influence gatekeepers’ decisions about exploration or exploitation, however, are still largely unexplored. In this study, we present a model based on the basic technology acceptance model (TAM) to examine this issue. We use concepts from the literatures on organizational learning, expertise, and cognitive styles to elaborate on the constructs in our model, and examine how these literatures can inform our understanding of technological gatekeepers’ decisions. The goal of this paper is to accelerate micro-level research on new technology deployment in organizations by identifying some key issues and propositions for future studies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Adams, J. A. (1987). Historical review and appraisal of research on the learning, retention, and transfer of human motor skills. Psychological Bulletin, 101, 41–74.

    Article  Google Scholar 

  • Adelson, B. (1984). When novices surpass experts: the difficulty of a task may increase with expertise. Journal of Experimental Psychology, 10(3), 483–495.

    Google Scholar 

  • Adler, P. (1990). Shared learning. Management Science, 36(8), 938–957.

    Article  Google Scholar 

  • Ahuja, M. K., & Thatcher, J. B. (2005). Moving beyond intentions and toward the theory of trying: effects of work environment and gender on post-adoption information technology use. MIS Quarterly, 29(3), 427–459.

    Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888–918.

    Article  Google Scholar 

  • Allen, T. J. (1984). Managing the flow of technology. Cambridge, MA: MIT.

    Google Scholar 

  • Allen, T. J., & Cohen, S. (1969). Information flow in R&D laboratories. Administrative Science Quarterly, 14, 12–19.

    Article  Google Scholar 

  • Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.

    Article  Google Scholar 

  • Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Antonacoupoulou, E., & Chiva, R. (2007). The social complexity of organizational learning: the dynamics of learning and organizing. Management Learning, 38(3), 277–295.

    Article  Google Scholar 

  • Argyris, C., & Schon, D. (1978). Organizational learning. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Aykin, N. M., & Aykin, T. (1991). Individual differences in human-computer interaction. Computers and Engineering, 20(3), 373–379.

    Google Scholar 

  • Bapuji, H., & Crossan, M. (2004). From questions to answers: reviewing organizational learning research. Management Learning, 35(4), 397–417.

    Article  Google Scholar 

  • Baylor, A. L. (2001). A U-shaped model for the development of intuition by level of expertise. New Ideas in Psychology, 19, 237–244.

    Article  Google Scholar 

  • Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process management: the productivity dilemma revisited. Academy of Management Review, 2, 238–256.

    Article  Google Scholar 

  • Bhattacharjee, A., & Premkumar, G. (2004). Understanding changes in beliefs and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Quarterly, 28(2), 229–254.

    Google Scholar 

  • Burgelman, R. A. (2002). Strategy as vector and the inertia of coevolutionary lock-in. Administrative Science Quarterly, 47, 325–357.

    Article  Google Scholar 

  • Camerer, C. F., & Johnson, E. J. (1991). The process-performance paradox in expert judgment: How can experts know so much and predict so badly? In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise (pp. 195–217). Cambridge: Cambridge University Press.

    Google Scholar 

  • Carlsen, B., & Norheim, O. F. (2003). Introduction of the patient-list system in general practice—changes in Norwegian physicians’ perception of their gatekeeper role. Scandinavian Journal of Primary Health Care, 21(4), 209–213.

    Article  Google Scholar 

  • Chakraborty, I., Hu, P. J. H., & Cui, D. (2008). Examining the effects of cognitive style in individuals’ technology use decision making. Decision Support Systems, 45(2), 228–241.

    Article  Google Scholar 

  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.

    Article  Google Scholar 

  • Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.

    Article  Google Scholar 

  • Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the psychology of human intelligence, vol. 1 (pp. 17–76). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Cohen, M. D. (2007). Reading Dewey: reflections on the study of routine. Organization Studies, 28(5), 773–786.

    Article  Google Scholar 

  • Crossan, M. M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: from intuition to institution. Academy of Management Review, 24(3), 522–537.

    Article  Google Scholar 

  • Davenport, T. H. (1993). Process innovation: Reengineering work through information technology. Boston, MA: Harvard Business School Press.

  • Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology—a comparison of 2 theoretical models. Management Science, 35(8), 982–1003.

    Article  Google Scholar 

  • Duncan, R., & Weiss, A. (1979). Organizational learning: implications for organizational design. Research in Organizational Behavior, 1, 75–123.

    Google Scholar 

  • Easterby-Smith, M. (1997). Disciplines of organizational learning: contributions and critiques. Human Relations, 50(9), 1085–1113.

    Google Scholar 

  • Easterby-Smith, M., Crossan, M., & Nicolini, D. (2000). Organizational learning: debates past, present and future. Journal of Management Studies, 37(6), 783–796.

    Article  Google Scholar 

  • Epple, D., Argote, L., & Devadas, R. (1991). Organizational learning curves: a method for investigating intra-plant transfer of knowledge acquired through learning by doing. Organization Science, 2(1), 58–70.

    Article  Google Scholar 

  • Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. American Psychologist, 49, 709–724.

    Article  Google Scholar 

  • Ericsson, K. A. (2005). Recent advances in expertise research: a commentary on the contributions to the special issue. Applied Cognitive Psychology, 19, 233–241.

    Article  Google Scholar 

  • Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: evidence of maximal adaptation to task constraints. Annual Review of Psychology, 47, 273–305.

    Article  Google Scholar 

  • Ericsson, K. A., & Smith, J. (1991). Prospects and limits of the empirical study of expertise: An introduction. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise. Cambridge: Cambridge University Press.

    Google Scholar 

  • Fiol, C. M., & Lyles, M. (1985). Organizational learning. Academy of Management Review, 10, 799–812.

    Article  Google Scholar 

  • Gupta, A. K., Smith, K. G., & Shalley, C. E. (2006). The interplay between exploration and exploitation. Academy of Management Journal, 49(4), 693–706.

    Article  Google Scholar 

  • Haerem, T., & Rau, D. (2007). The influence of level of expertise and objective task complexity on perceived task complexity and performance. Journal of Applied Psychology, 92, 1320–1331.

    Article  Google Scholar 

  • Hannan, M. T., & Freeman, J. (1977). The population ecology of organizations. American Journal of Sociology, 82, 929–964.

    Article  Google Scholar 

  • Hayes, J., & Allinson, C. W. (1994). Cognitive style and its relevance for management practice. British Journal of Management, 5, 53–71.

    Article  Google Scholar 

  • Hayes, J., & Allinson, C. W. (1998). Cognitive style and the theory and practice of individual and collective learning in organizations. Human Relations, 51(7), 847–871.

    Google Scholar 

  • He, Z., & Wong, P. (2004). Exploration vs. exploitation: an empirical test of the ambidexterity hypothesis. Organization Science, 15(4), 481–494.

    Article  Google Scholar 

  • Hedberg, B. (1981). How organizations learn and unlearn. In P. Nystrom & W. Starbuck (Eds.), Handbook of organizational design, Vol. 1 (pp. 3–27). Oxford: Oxford University Press.

    Google Scholar 

  • Hong, S., Thong, J. Y. L., Moon, J., & Tam, K. (2008). Understanding the behavior of mobile data services consumers. Information Systems Frontiers, 10(4), 431–445.

    Article  Google Scholar 

  • Huber, G. P. (1991). Organizational learning: the contributing processes and the literatures. Organization Science, 2(1), 88–115.

    Article  Google Scholar 

  • Jansen, J. J. P., George, G., Van den Bosch, F. A. J., & Volberda, H. W. (2008). Senior team attributes and organizational ambidexterity: the moderating role of transformational leadership. Journal of Management Studies, 45(5), 982–1007.

    Article  Google Scholar 

  • Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information-technology enabled work systems. MIS Quarterly, 29(3), 525–557.

    Google Scholar 

  • Kane, G. C., & Alavi, M. (2007). Information technology and organizational learning: an investigation of exploration and exploitation processes. Organization Science, 18(5), 796–812.

    Article  Google Scholar 

  • Kanfer, R., & Ackerman, P. L. (1989). Motivation and cognitive abilities: an integrative/ aptitude-treatment interaction approach to skill acquisition. Journal of Applied Psychology, 74, 657–690.

    Article  Google Scholar 

  • Kim, S., & Garrison, G. (2008). Investigating mobile wireless technology adoption: an extension of the technology acceptance model. Information Systems Frontiers, . doi:10.1007/s10796-008-9073-8.

    Google Scholar 

  • Kim, T., & Rhee, M. (2009). Exploration and exploitation: Internal variety and environmental dynamism. Strategic Organization, 7(1), 11–41.

    Article  Google Scholar 

  • Kirton, M. J. (1976). Adaptors and innovators: a description and measure. Journal of Applied Psychology, 61, 622–629.

    Article  Google Scholar 

  • Kirton, M. J. (1977). Manual of the Kirton adaption-innovation inventory. London: National Foundation for Educational Research.

    Google Scholar 

  • Klien, G. (1998). Sources of power. Cambridge, MA: MIT.

    Google Scholar 

  • Kogan, N., & Wallach, M. A. (1964). Risk taking: A study in cognition and personality. New York: Holt, Rinehart, and Winston.

    Google Scholar 

  • Kozhevnikov, M. (2007). Cognitive styles in the context of modern psychology: toward and integrated framework of cognitive style. Journal of Applied Psychology, 133(3), 464–481.

    Google Scholar 

  • Larkin, J., & McDermott, J. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.

    Article  Google Scholar 

  • Lehner, F., & Maier, R. K. (2000). How can organizational memory theories contribute to organizational memory systems? Information Systems Frontiers, 2(3–4), 277–298.

    Article  Google Scholar 

  • Levitt, B., & March, J. G. (1988). Organizational learning. Annual Review of Sociology, 14, 319–340.

    Article  Google Scholar 

  • Lewin, K. (1947). Frontiers in group dynamics: II. Channels of group life; social planning and action research. Human Relations, 1(2), 143–153.

    Article  Google Scholar 

  • Lewin, K. (1951). Field theory in social science: Selected theoretical papers. New York: Harper.

    Google Scholar 

  • Lu, Y. (2007). The human in human information acquisition: understanding gatekeeping and proposing new directions in scholarship. Library & Information Science Research, 29(1), 103–129.

    Article  Google Scholar 

  • MacCrimmon, K. R., & Wehrung, D. A. (1985). A portfolio of risk measures. Theory and Decision, 19, 1–29.

    Article  Google Scholar 

  • MacCrimmon, K. R., & Wehrung, D. A. (1990). Characteristics of risk taking executives. Management Science, 36(4), 422–435.

    Article  Google Scholar 

  • Machina, M. J. (1987). Choice under uncertainty: problems solved and unsolved. Journal of Economic Perspectives, 1, 121–154.

    Google Scholar 

  • March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.

    Article  Google Scholar 

  • Messick, S. (1976). Personality consistencies in cognition and creativity. In S. Messick (Ed.), Individuality in learning (pp. 4–23). San Francisco: Jossey-Bass.

    Google Scholar 

  • Miller, A. (1987). Cognitive styles: an integrated model. Educational Psychology, 7(4), 251–267.

    Article  Google Scholar 

  • Miller, D. (1996). A preliminary typology of organizational learning: synthesizing the literature. Journal of Management, 22(3), 485–505.

    Article  Google Scholar 

  • Miller, K. D., Zhao, M., & Calantone, R. J. (2006). Adding interpersonal learning and Tacit knowledge to March’s exploration-exploitation model. Academy of Management Journal, 49(4), 709–722.

    Article  Google Scholar 

  • Nicholson, N., Soane, E., Fenton-O’Creevy, M., & Willman, P. (2005). Personality and domain-specific risk taking. Journal of Risk Research, 8(2), 157–176.

    Article  Google Scholar 

  • Nicolini, D., & Meznar, M. B. (1995). The social construction of organizational learning: conceptual and practical issues in the field. Human Relations, 48(7), 727–746.

    Article  Google Scholar 

  • Nochur, K. S., & Allen, T. J. (1992). Do nominated boundary spanners become effective technological gatekeepers? IEEE Transactions on Engineering Management, 39(3), 265–269.

    Article  Google Scholar 

  • Patel, V. L., & Groen, G. J. (1991). The general and specific nature of medical expertise: A critical look. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise (pp. 93–125). Cambridge: Cambridge University Press.

    Google Scholar 

  • Pennings, J. M. E., & Smidts, A. (2000). Assessing the construct validity of risk attitude. Management Science, 46(10), 1337–1348.

    Article  Google Scholar 

  • Pfeffer, J. (1981). Power in organizations. Marshfield, MA: Putnam.

    Google Scholar 

  • Schneider, W., & Fisk, A. D. (1982). Dual task automatic and control processing: can it be done without cost? Journal of Experimental Psychology: Learning, Memory, and Cognition, 8, 261–278.

    Article  Google Scholar 

  • Schroeder, H. M. (1989). Managerial competence and style. In M. J. Kirton (Ed.), Adaptors and innovators (pp. 97–124). London: Routledge.

    Google Scholar 

  • Shanteau, J. (1992). Competence in experts: the role of task characteristics. Organizational Behavior and Human Decision Processes, 53, 252–266.

    Article  Google Scholar 

  • Shrivastava, P. (1983). A typology of organizational learning systems. Journal of Management Studies, 20(1), 7–28.

    Article  Google Scholar 

  • Shumsky, R. A., & Pinker, E. J. (2003). Gatekeepers and referrals in services. Management Science, 49(7), 839–856.

    Article  Google Scholar 

  • Siggelkow, N., & Levinthal, D. A. (2003). Temporarily divide to reconquer: centralized, decentralized, and reintegrated organizational approaches to exploration and exploitation. Organization Science, 14(6), 650–669.

    Google Scholar 

  • Simon, M., & Houghton, S. M. (2003). The relationship between overconfidence and the introduction of risky products: evidence from a field study. Academy of Management Journal, 46(2), 139–149.

    Article  Google Scholar 

  • Sitkin, S. B., & Pablo, A. L. (1992). Reconceptualizing the determinants of risk behavior. Academy of Management Review, 17(1), 9–38.

    Article  Google Scholar 

  • Sitkin, S. B., & Weingart, L. R. (1995). Determinants of risky decision-making behavior: a test of the mediating role of risk perceptions and propensity. Academy of Management Journal, 38(6), 1573–1592.

    Article  Google Scholar 

  • Sternberg, R. J. (1988). Mental self-government: a theory of intellectual styles and their development. Human Development, 31, 197–224.

    Article  Google Scholar 

  • Stohr, E. A., & Zhao, J. L. (2001). Workflow automation: overview and research issues. Information Systems Frontiers, 3(3), 281–296.

    Article  Google Scholar 

  • Sveen, F. O., Rich, E., & Jager, M. (2007). Overcoming organizational challenges to secure knowledge management. Information Systems Frontiers, 9(5), 481–492.

    Article  Google Scholar 

  • Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85–92.

    Article  Google Scholar 

  • Taylor, S., & Todd, P. A. (1995a). Assessing IT usage: the role of prior experience. MIS Quarterly, 19(4), 561–570.

    Article  Google Scholar 

  • Taylor, S., & Todd, P. A. (1995b). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144–176.

    Article  Google Scholar 

  • Templeton, G. F., Schmidt, M. B., & Taylor, G. S. (2008). Managing the diffusion of organizational learning behavior. Information Systems Frontiers, . doi:10.1007/s10796-008-9117-0.

    Google Scholar 

  • Uotila, J., Maula, M., Keil, T., & Zahra, A. A. (2009). Exploration, exploitation, and financial performance: analysis of S&P 500 corporations. Strategic Management Journal, 30(2), 221–231.

    Article  Google Scholar 

  • Vandewalle, D. (1997). Development and validation of a work domain goal orientation instrument. Educational and Psychological Measurement, 57(6), 995–1015.

    Article  Google Scholar 

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–205.

    Article  Google Scholar 

  • Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.

    Article  Google Scholar 

  • Venkatraman, N. (1991). IT-induced business reconfiguration. In M. S. Scott Morton (Ed.), The corporation of the 1990 s: Information technology and organizational transformation [Chap. 5]. New York: Oxford University Press.

    Google Scholar 

  • Voss, G. B., Sirdeshmukh, D., & Voss, Z. G. (2008). The effects of slack resources and environmental threat on product exploration and exploitation. Academy of Management Journal, 51(1), 147–164.

    Article  Google Scholar 

  • Wang, H., & Li, J. (2008). Untangling the effects of overexploration and overexploitation on organizational performance: the moderating role of environmental dynamism. Journal of Management, 34(5), 925–951.

    Article  Google Scholar 

  • Weick, K. E. (1991). The nontraditional quality of organizational learning. Organization Science, 2(1), 116–124.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Business, Department of Management, Northern Illinois University, De Kalb, IL, 60115, USA

    Devaki Rau

  2. Dept. of Leadership and Organisational Management, Norwegian School of Management BI, Nydalsveien 37, N-0442, Oslo, Norway

    Thorvald Haerem

  3. Paul Merage School of Business, University of California, Irvine, CA, USA

    Thorvald Haerem

Authors
  1. Devaki Rau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thorvald Haerem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Devaki Rau.

Additional information

Support for this research was provided by Compello Software and Norwegian Research Council of Norway, grant number 162455. Additional support was provided a summer research grant from the Graduate School, Northern Illinois University.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rau, D., Haerem, T. Applying an organizational learning perspective to new technology deployment by technological gatekeepers: A theoretical model and key issues for future research. Inf Syst Front 12, 287–297 (2010). https://doi.org/10.1007/s10796-009-9194-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-009-9194-8

Keywords

Search

Navigation