Conceptual System of Technology Weak Signals Detection

Document Type : Research Paper

Authors

1 Researcher at SND University, Tehran, Iran

2 Professor at SND University, Tehran, Iran

Abstract

Purpose: The purpose of this research is to study the systematic dimensions of "weak Signals (WS)" detection. based on theoretical framework of socio-technical systems (STS) and by combining the soft systems methodology (SSM) and Viable systems model (VSM), conceptual design of a system for WS detection in the field of technology in Iranian governmental organizations is explored.
Method: This research is a qualitative research in terms of methodology. according to the case of study in this research, it is applied-developmental research. Based on Minger’s approach, SSM-VSM model is used to design the expected socio-technical system of WS detection. The Methods including Library method, in-depth Interview and Expert Panel and Charrette approach, as an evolutionary approach in data analysis and design are used.
Findings: The importance and strategic position of WS is neglected in Iranian organizations. Iranian managers has less interest in identifying WS and analyzing their consequences to make strategic decisions. In order to detect WS, Iranian organizations need to develop and strengthen socio-technical infrastructures. for this, the structural, functional and procedural conceptual design of the system are presented in this research.
Conclusion: Identifying and exploring WS of change in addition to machine approaches, including Artificial Intelligence (AI) and Deep learning, fundamentally needs expert networks developments including official and virtual networks in Iranian organizations. Technology Watching abilities needs the development and strengthening of systematic infrastructures, especially Socio-Technical systems.

Keywords

References

Ahlqvist, Toni; & Uotila, Tuomo. (2020). Contextualising weak signals: Towards a relational theory of futures knowledge. Futures, 119, 102543. https://doi.org/10.1016/j.futures.2020.102543
Ansoff, H. Igor. (1975). Managing Strategic Surprise by Response to Weak Signals. California Management Review, 18(2), 21-33.
Ansoff, H. Igor. (1980). Strategic issue management. Strategic Management Journal, 1(2), 131-148. https://doi.org/https://doi.org/10.1002/smj.4250010204
Beer, Stafford. (1984). The Viable System Model: Its Provenance, Development, Methodology and Pathology. Journal of the Operational Research Society, (35), 7-25. https://doi.org/https://doi.org/10.1057/jors.1984.2
Carins, George; & Wright, George. (2018). Scenario thinking: Preparing your organization for the future in an unpredicatble world. Springer International Publishing.
Decker, Reinhold; Wagner, Ralf; & Scholz, Sören W. (2005). An internet‐based approach to environmental scanning in marketing planning. Marketing Intelligence & Planning. https://doi.org/10.1108/02634500510589930
Espejo, Raul; & Reyes, Alfonso. (2011). Organizational Systems Managing Complexity with the Viable System Model. Springer.
Garcia-Nunes, Pedro Ivo; & da Silva, Ana Estela Antunes. (2019). Using a conceptual system for weak signals classification to detect threats and opportunities from web. Futures, 107, 1-16. https://doi.org/10.1016/j.futures.2018.11.004
Garcia-Nunes, Pedro Ivo; Rodrigues, Pedro Artico; Oliveira, Kaulitz Guimarães; & da Silva, Ana Estela Antunes. (2020a). A computational tool for weak signals classification – Detecting threats and opportunities on politics in the cases of the United States and Brazilian presidential elections. Futures, 123, 102607. https://doi.org/10.1016/j.futures.2020.102607
Garcia-Nunes, Pedro Ivo; Rodrigues, Pedro Artico; Oliveira, Kaulitz Guimarães; & da Silva, Ana Estela Antunes. (2020b). A computational tool for weak signals classification – Detecting threats and opportunities on politics in the cases of the United States and Brazilian presidential elections. Futures, 123, 102607. https://doi.org/10.1016/j.futures.2020.102607
Gheorghiu, Radu; Andreescu, Liviu; & Curaj, Adrian. (2016). A foresight toolkit for smart specialization and entrepreneurial discovery. Futures, 80, 33-44. https://doi.org/10.1016/j.futures.2016.04.001
Gokhberg, Leonid; Kuzminov, Ilya; Khabirova, Elena; & Thurner, Thomas. (2020a). Advanced text-mining for trend analysis of Russia’s Extractive Industries. Futures, 115, 102476. https://doi.org/10.1016/j.futures.2019.102476
Gokhberg, Leonid; Kuzminov, Ilya; Khabirova, Elena; & Thurner, Thomas. (2020b). Advanced text-mining for trend analysis of Russia’s Extractive Industries. Futures, 115, 102476. https://doi.org/10.1016/j.futures.2019.102476
Griol-Barres, Israel; Milla, Sergio; Cebrián, Antonio; Fan, Huaan; & Millet, Jose. (2020). Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing. Sustainability, 12(19), 7848. https://doi.org/10.3390/su12197848
Halaweh, Mohanad. (2013). Emerging Technology: What is it. Journal of technology management & innovation, 8(3), 108-115. https://doi.org/10.4067/S0718-27242013000400010
Hiltunen, Elina. (2008). The future sign and its three dimensions. Futures, 40(3), 247-260. https://doi.org/10.1016/j.futures.2007.08.021
Ilmola, Leena; & Kuusi, Osmo. (2006). Filters of weak signals hinder foresight: Monitoring weak signals efficiently in corporate decision-making. Futures, 38(8), 908-924. https://doi.org/10.1016/j.futures.2005.12.019
Kayser, Victoria; & Bierwisch, Antje. (2016). Using Twitter for foresight: An opportunity? Futures, 84, 50-63. https://doi.org/10.1016/j.futures.2016.09.006
Kim, Jieun; & Lee, Changyong. (2017). Novelty-focused weak signal detection in futuristic data: Assessing the rarity and paradigm unrelatedness of signals. Technological Forecasting and Social Change, 120, 59-76. https://doi.org/10.1016/j.techfore.2017.04.006
Kim, Seonho; Kim, You-Eil; Bae, Kuk-Jin; Choi, Sung-Bae; Park, Jong-Kyu; Koo, Young-Duk; Park, Young-Wook; Choi, Hyun-Kyoo; Kang, Hyun-Moo; & Hong, Sung-Wha. (2013). NEST: A quantitative model for detecting emerging trends using a global monitoring expert network and Bayesian network. Futures, 52, 59-73. https://doi.org/10.1016/j.futures.2013.08.004
Lee, Changyong; Kwon, Ohjin; Kim, Myeongjung; & Kwon, Daeil. (2018). Early identification of emerging technologies: A machine learning approach using multiple patent indicators. Technological Forecasting and Social Change, 127, 291-303. https://doi.org/10.1016/j.techfore.2017.10.002
Li, Xin; Xie, Qianqian; Jiang, Jiaojiao; Zhou, Yuan; & Huang, Lucheng. (2019a). Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology. Technological Forecasting and Social Change, 146, 687-705. https://doi.org/10.1016/j.techfore.2018.06.004
Li, Xin; Xie, Qianqian; Jiang, Jiaojiao; Zhou, Yuan; & Huang, Lucheng. (2019b). Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology. Technological Forecasting and Social Change, 146, 687-705. https://doi.org/10.1016/j.techfore.2018.06.004
Mariani, Manuel Sebastian; Medo, Matúš; & Lafond, François. (2019). Early identification of important patents: Design and validation of citation network metrics. Technological Forecasting and Social Change, 146, 644-654. https://doi.org/10.1016/j.techfore.2018.01.036
Mayer, Jörg H. (2011). Managing the Future—Six Guidelines for Designing Environmental Scanning Systems. In DESRIST 2011 (pp. 276-290). Retrieved from https://link.springer.com/chapter/10.1007/978-3-642-20633-7_20
Melnikovas, Aleksandras. (2018). Towards an Explicit Research Methodology: Adapting Research Onion Model for Futures Studies. Joiurnal of Futures Studies, 23(2), 29-44. https://doi.org/DOI:10.6531/JFS.201812_23(2).0003
Mendonça, Sandro; Pina e Cunha, Miguel; Kaivo-oja, Jari; & Ruff, Frank. (2004). Wild cards, weak signals and organisational improvisation. Futures, 36(2), 201-218. https://doi.org/10.1016/S0016-3287(03)00148-4
Moreira, A; Hayashi, T; Coelho, G; & Silva, A. (2015). A Clustering Method for Weak Signals to Support Anticipative Intelligence. Computer Science, International Journal of Artificial Intelligence and Expert Systems(6).
Moro, Alberto; Joanny, Geraldine; & Moretti, Christian. (2020). Emerging technologies in the renewable energy sector: A comparison of expert review with a text mining software. Futures, 117, 102511. https://doi.org/10.1016/j.futures.2020.102511
Pe´rez Rı´os, Jose´. (2012). Design and Diagnosis for Sustainable Organizations. Springer.
Rousseau, Pauline; & Camara, Daniel. (2021). Weak signal detection and identification in large data sets: a review of methods and applications. researchgate.
Rowe, Emily; Wright, George; & Derbyshire, James. (2017). Enhancing horizon scanning by utilizing pre-developed scenarios: Analysis of current practice and specification of a process improvement to aid the identification of important “weak signals.” Technological Forecasting and Social Change, 125, 224-235. https://doi.org/10.1016/j.techfore.2017.08.001
Schoemaker, Paul J. H.; Day, George S.; & Snyder, Scott A. (2013). Integrating organizational networks, weak signals, strategic radars and scenario planning. Technological Forecasting and Social Change, 80(4), 815-824. https://doi.org/10.1016/j.techfore.2012.10.020
Schoemaker, Paul J.H.; & Day, George S. (2009). How to Make Sense of Weak Signals. MIT Sloan Management Review. Retrieved from https://sloanreview.mit.edu/article/how-to-make-sense-of-weak-signals/
Schwarz, Jan Oliver; Kroehl, Rixa; & von der Gracht, Heiko A. (2014). Novels and novelty in trend research — Using novels to perceive weak signals and transfer frames of reference. Technological Forecasting and Social Change, 84, 66-73. https://doi.org/10.1016/j.techfore.2013.09.007
Sensuse, Dana Indra; & Ramadhan, Arief. (2012). The Relationships of Soft Systems Methodology (SSM), Business Process Modeling and e- Government. International Journal of Advanced Computer Science and Applications, 3(1). https://doi.org/http://dx.doi.org/10.14569/IJACSA.2012.030128
Song, Chie Hoon; Elvers, David; & Leker, Jens. (2017). Anticipation of converging technology areas — A refined approach for the identification of attractive fields of innovation. Technological Forecasting and Social Change, 116, 98-115. https://doi.org/10.1016/j.techfore.2016.11.001
Thorleuchter, D.; & Van den Poel, D. (2015). Idea mining for web-based weak signal detection. Futures, 66, 25-34. https://doi.org/10.1016/j.futures.2014.12.007
Thorleuchter, Dirk; Scheja, Tobias; & Van den Poel, Dirk. (2014). Semantic weak signal tracing. Expert Systems with Applications, 41(11), 5009-5016. https://doi.org/10.1016/j.eswa.2014.02.046
Thorleuchter, Dirk; & Van den Poel, Dirk. (2013). Weak signal identification with semantic web mining. Expert Systems with Applications, 40(12), 4978-4985. https://doi.org/10.1016/j.eswa.2013.03.002
van Veen, Barbara L.; & Ortt, J. Roland. (2021). Unifying weak signals definitions to improve construct understanding. Futures, 134, 102837. https://doi.org/10.1016/j.futures.2021.102837
Xu, Haiyun; Winnink, Jos; Yue, Zenghui; Zhang, Huiling; & Pang, Hongshen. (2020). Multidimensional Scientometric indicators for the detection of emerging research topics. Technological Forecasting and Social Change, 120490. https://doi.org/10.1016/j.techfore.2020.120490

Files

Share

How to cite

Statistics