A new model for identifying emerging technologies
DOI:
https://doi.org/10.37380/jisib.v7i1.217Keywords:
Big data analytics, competitive intelligence, emerging technology, open innovation, technology sequence analysisAbstract
Today, the complexity of so many emerging technologies requires an understanding of adjacent technologies often originating from multiple industries. Technology sequence analysis has been used by organizations, governments and industries to help make sense of the many variables impacting the evolution of technologies. This technique relies heavily on the input of experts who can offer perspectives on the status of current technologies while also highlighting the potential opportunities in the future. However, the volume and speed at which scientific research is accelerating is making it nearly impossible for even the most knowledgeable expert to stay current with research in their own industries. Today however, the use of big data search tools can help identify emerging trends around disruptive technologies well before many of the experts have fully grasped the impact of these technologies. Despite the fear of many in the intelligence community that these tools will make their jobs obsolete, we expect that the value of the intelligence expert will increase given their unique knowledge of relevant data sources and how to connect the data in meaningful ways to derive value for the firm. We propose a new forecasting model that incorporates a combination of technology sequencing analysis and big data tools within the organization while also leveraging experts from across the open innovation spectrum. This new model, informed by current client engagements, has the potential to create significant competitive advantages for organizations as they benefit from expanded search breadth, search depth and search speed all while leveraging a range of internal and external experts to make sense of the rapidly changing technological landscape confronting their environment.
References
Abbott, A. (1990). A primer on sequence methods. Organization Science, (4), 375-392. DOI: https://doi.org/10.1287/orsc.1.4.375
Adair, W.L. & Brett, J.M. (2005). The negotiation dance: Time, culture, and behavioral sequences in negotiation, Organization Science, 16(1), 33-51. DOI: https://doi.org/10.1287/orsc.1040.0102
Bernstein, E. (December, 2016). The Global R&D Funding Forecast, R&D Magazine, Winter, 1-36.
Bishop, P., Hines, A., & Collins, T. (2007). The current state of scenario development: An overview of techniques. Foresight, (1). 5-25. DOI: https://doi.org/10.1108/14636680710727516
Cheng, C.C. & Huizingh, E.K. (2014). When is open innovation beneficial? The role of strategic orientation. Journal of Product Innovation Management, (6), 1235-1253. DOI: https://doi.org/10.1111/jpim.12148
Chesbrough, H., (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston, MA.
Christenson, C. (2000). The Innovator’s Dilemma: The Revolutionary Book That Will Change the Way You Do Business, HarpersCollins Publisher. New York, New York.
Cohen, W.M. & Levinthal, D.A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, (1), 128-152. DOI: https://doi.org/10.2307/2393553
European Union Open Data Portal. (2016). Retrieved from https://data.europa.eu/euodp/en/data/
Gassmann, O., & Enkel, E. (2004). Towards a theory of open innovation: Three core process archetypes. Proceeding of R&D Management Conference, Lisbon, Portugal, July.
Gilad, B. (1996). Early warning: Using Competitive Intelligence to Anticipate Market Shifts, Control Risk, and Create Powerful Strategies, AMACOM, New York, NY.
Greco, M., Grimaldi, M., & Cricelli, L. (2016). An analysis of the open innovation effect on firm performance. European Management Journal, 34, 501-516. DOI: https://doi.org/10.1016/j.emj.2016.02.008
Herzog, P. (2008). Open and Closed Innovation. Different Cultures for Different Strategies.
Gabler, Wiesbaden. Hwang, J. & Lee, Y. (2010). External Knowledge search, innovative performance and
productivity in the Korean ICT sector. Telecommunications Policy, 34 (10), 562-571.
Inauen, M. & Schenker-Wicki, A. (2012). Fostering radical innovations with open innovation. European Journal of Innovation Management, 15 (2), 212-231. DOI: https://doi.org/10.1108/14601061211220986
Kajikawa, Y., Takeda, Y., & Matsushima, K. (2010). Computer-assisted roadmapping: A 4-15. DOI: https://doi.org/10.1108/14636681011035726
Kostoff, R.N., & Schaller, R.R. (2001). Science and technology Roadmaps, IEEE Transactions on Engineering Management, 48, 132-43. DOI: https://doi.org/10.1109/17.922473
Perks, H. & Roberts, D. (2013). A Review of Longitudinal Research in the Product Innovation Field, with Discussion of Utility and Conduct of Sequence Analysis. Journal of Product Innovation Management, Nov. 2013, 30 (6), 1099-1111
Parida, V., Westerberg, M., & Frishammar, J., (2012). Inbound open innovation activities in high-tech SMEs: the impact on innovation performance. Journal of Small Business Management, 50 (2), 283–309 DOI: https://doi.org/10.1111/j.1540-627X.2012.00354.x
Pentland, B. T. (2003). Sequential variety in work processes. Organization Sciences, 14 (5). 528-540. DOI: https://doi.org/10.1287/orsc.14.5.528.16760
Perks, H., Gruber, T., & Edvardsson, B. (2012). Co-creation in radical service innovation: A systematic analysis of micro-level processes. Journal of Product Innovation Management, 29, 935-951. DOI: https://doi.org/10.1111/j.1540-5885.2012.00971.x
Perks, H. & Roberts, D. (2013). A review of longitudinal research in the product innovation field, with discussion of utility and conduct of sequence analysis. Journal of Product Innovation Management, 30 (6), 1099-1111. DOI: https://doi.org/10.1111/jpim.12048
Park, A., Kim, J., Lee, H., Jang, D., & Jum, S. (2016). Methodology of technological evolution for three-dimensional printing. Industrial Management & Data Systems, 116 (1), 122-146. DOI: https://doi.org/10.1108/IMDS-05-2015-0206
Powell, W.W., Koput, K., Smith-Doerr, L., (1996). Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology. Administrative Science Quarterly, 41(1), 116–145. DOI: https://doi.org/10.2307/2393988
Salvato, C. (2009). Capabilities unveiled: The role of ordinary activities in the evolution of product development processes. Organization Science, 20(2). 384-409. DOI: https://doi.org/10.1287/orsc.1080.0408
Schneider, C. (2016, May 25). The biggest data challenges that you might not even know you have, IBM Blog, Retrieved from https://www.ibm.com/blogs/watson/2016/05/biggest-data-challenges-might-not-even-know/
Sisodiya, S.R., Johnson, J.L., Grégoire, Y., (2013). Inbound open innovation for enhanced performance: enables and opportunities. Industrial Marketing Management. 42(5), 836–849. DOI: https://doi.org/10.1016/j.indmarman.2013.02.018
Smith, J.E. & Saritas, O. (2010). Science and technology foresight baker’s dozen: A pocket primer of comparative and combined foresight methods. Foresight, 13(2), 79-96. DOI: https://doi.org/10.1108/14636681111126265
Tether, B. S., & Tajar, A. (2008). Beyond industry university links: sourcing knowledge for innovation from consultants, private research organizations and the public science-base. Research Policy, 37(6), 1079-1095. DOI: https://doi.org/10.1016/j.respol.2008.04.003
Thatchenkery, S. M., Katila, R. & Chen, E. L. (2012). Sequences of competitive moves and effects on firm performance. Academy of Management Annual Meeting Proceedings. 2012, p1-1 DOI: https://doi.org/10.5465/AMBPP.2012.203
U.S. Federal Government (2016). Retrieved from https://www.data.gov/
U.S. Economic & Statistics Administration. (2016). Retrieved from http://www.esa.doc.gov/reports/fosteringinnovation-creating-jobs-driving-betterdecisions-value-government-data
Un, C. A., Cuervo-Cazurra, A., & Asakawa, K. (2010). R&D collaborations and product innovation. Journal of Product Innovation Management, 27(5), 673-689. DOI: https://doi.org/10.1111/j.1540-5885.2010.00744.x
Van de Ven, A., & Poole, S. (1990). Methods for studying innovation development in Minnesota Innovation Research Program. Organization Science, 1 (3), 313-335. DOI: https://doi.org/10.1287/orsc.1.3.313
Vanian, J. (2016, July 15). Why Data Is The New Oil. Fortune, Retrieved from http://fortune.com/2016/07/11/data-oilbrainstorm-tech/
Vaseashta, A. (2014). Advanced sciences convergence based methods for surveillance of emerging trends in science, technology and intelligence. Foresight, (1), 17-36. DOI: https://doi.org/10.1108/FS-10-2012-0074
Wang, C.H., Chang, C.H., & Shen, G.C. (2015). The effect of inbound open innovation on firm performance: Evidence from high-tech industry. Technological Forecasting & Social Change, 99, 222-230. DOI: https://doi.org/10.1016/j.techfore.2015.07.006
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