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A systematic literature review of mining weak signals and trends for corporate foresight

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  • Christian Mühlroth

    (Chair of Statistics and Econometrics, Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Michael Grottke

    (Chair of Statistics and Econometrics, Friedrich-Alexander-Universität Erlangen-Nürnberg)

Abstract

Due to the ever-growing amount of data, computer-aided methods and systems to detect weak signals and trends for corporate foresight are in increasing demand. To this day, many papers on this topic have been published. However, research so far has only dealt with specific aspects, but it has failed to provide a comprehensive overview of the research domain. In this paper, we conduct a systematic literature review to organize existing insights and knowledge. The 91 relevant papers, published between 1997 and 2017, are analyzed for their distribution over time and research outlets. Classifying them by their distinct properties, we study the data sources exploited and the data mining techniques applied. We also consider eight different purposes of analysis, namely weak signals and trends concerning political, economic, social and technological factors. The results of our systematic review show that the research domain has indeed been attracting growing attention over time. Furthermore, we observe a great variety of data mining and visualization techniques, and present insights on the efficacy and effectiveness of the data mining techniques applied. Our results reveal that a stronger emphasis on search strategies, data quality and automation is required to greatly reduce the human actor bias in the early stages of the corporate foresight process, thus supporting human experts more effectively in later stages such as strategic decision making and implementation. Moreover, systems for detecting weak signals and trends need to be able to learn and accumulate knowledge over time, attaining a holistic view on weak signals and trends, and incorporating multiple source types to provide a solid foundation for strategic decision making. The findings presented in this paper point to future research opportunities, and they can help practitioners decide which sources to exploit and which data mining techniques to apply when trying to detect weak signals and trends.

Suggested Citation

  • Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    • Handle: RePEc:spr:jbecon:v:88:y:2018:i:5:d:10.1007_s11573-018-0898-4
    DOI: 10.1007/s11573-018-0898-4
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    2. Gordon, Adam Vigdor & Ramic, Mirza & Rohrbeck, René & Spaniol, Matthew J., 2020. "50 Years of corporate and organizational foresight: Looking back and going forward," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    3. Amber Geurts & Ralph Gutknecht & Philine Warnke & Arjen Goetheer & Elna Schirrmeister & Babette Bakker & Svetlana Meissner, 2022. "New perspectives for data‐supported foresight: The hybrid AI‐expert approach," Futures & Foresight Science, John Wiley & Sons, vol. 4(1), March.
    4. Christian Mühlroth & Laura Kölbl & Michael Grottke, 2023. "Innovation signals: leveraging machine learning to separate noise from news," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2649-2676, May.
    5. Sick, Nathalie & Bröring, Stefanie, 2022. "Exploring the research landscape of convergence from a TIM perspective: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Marinković, Milan & Al-Tabbaa, Omar & Khan, Zaheer & Wu, Jie, 2022. "Corporate foresight: A systematic literature review and future research trajectories," Journal of Business Research, Elsevier, vol. 144(C), pages 289-311.
    7. Ilya Kuzminov & Pavel Bakhtin & Elena Khabirova & Irina V. Loginova, 2018. "Detecting and Validating Global Technology Trends Using Quantitative and Expert-Based Foresight Techniques," HSE Working papers WP BRP 82/STI/2018, National Research University Higher School of Economics.
    8. Nazemi, Kawa & Burkhardt, Dirk & Kock, Alexander, 2022. "Visual Analytics for Technology and Innovation Management - An Interaction Approach for Strategic Decision Making," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 136215, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
    10. Andreas Pfnür & Benjamin Wagner, 2020. "Transformation of the real estate and construction industry: empirical findings from Germany," Journal of Business Economics, Springer, vol. 90(7), pages 975-1019, August.
    11. Nazemi, Kawa & Burkhardt, Dirk & Kock, Alexander, 2021. "Visual Analytics for Technology and Innovation Management - An Interaction Approach for Strategic Decision Making," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130792, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    12. Ebadi, Ashkan & Auger, Alain & Gauthier, Yvan, 2022. "Detecting emerging technologies and their evolution using deep learning and weak signal analysis," Journal of Informetrics, Elsevier, vol. 16(4).

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    More about this item

    Keywords

    Machine learning; Weak signal detection; Emerging trend detection; Corporate foresight; Environmental scanning; Strategic decision making; Big data;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other

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