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.
Adair, W.L. & Brett, J.M. (2005). The negotiation
dance: Time, culture, and behavioral
sequences in negotiation, Organization
Science, 16(1), 33-51.
Bernstein, E. (December, 2016). The Global R&D
Funding Forecast, R&D Magazine, Winter, 1-
Bishop, P., Hines, A., & Collins, T. (2007). The
current state of scenario development: An
overview of techniques. Foresight, (1). 5-25.
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.
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.
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,
, 501-516.
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.
Kajikawa, Y., Takeda, Y., & Matsushima, K.
(2010). Computer-assisted roadmapping: A
case study in energy research. Foresight, (2).
-15.
Kostoff, R.N., & Schaller, R.R. (2001). Science
and technology Roadmaps, IEEE Transactions
on Engineering Management, 48, 132-43.
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,
(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
Pentland, B. T. (2003). Sequential variety in work
processes. Organization Sciences, 14 (5). 528-
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,
, 935-951.
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-
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-
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.
Salvato, C. (2009). Capabilities unveiled: The role
of ordinary activities in the evolution of
product development processes. Organization
Science, 20(2). 384-409.
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/bi
ggest-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),
–849.
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.
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.
Thatchenkery, S. M., Katila, R. & Chen, E. L.
(2012). Sequences of competitive moves and
effects on firm performance. Academy of
Management Annual Meeting Proceedings.
, p1-1
U.S. Federal Government (2016). Retrieved from
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.
Van de Ven, A., & Poole, S. (1990). Methods for
studying innovation development in
Minnesota Innovation Research Program.
Organization Science, 1 (3), 313-335.
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.
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.
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
