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


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.

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This study was sponsored by the German Federal Ministry of Education and Research, grant number 02K16C191.

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Correspondence to Michael Grottke.



To make the tables in this appendix more readable (Tables 5, 67), we will use the following abbreviations for the categories introduced in Sect. 3.3 wherever appropriate:

  1. 1.

    Research discipline (RSD)

    1. (a)

      Weak signal detection (WSD)

    2. (b)

      Emerging trend detection (ETD)

  2. 2.

    Data mining approach (DMA)

    1. (a)

      Text mining (TM)

    2. (b)

      Bibliometric analysis (BA)

    3. (c)

      Joint analysis (JA)

  3. 3.

    Data mining task (DMT), grouping the Data mining methods (DMM)

    1. (a)

      Change and deviation detection (CDD)

    2. (b)

      Clustering (CLU)

    3. (c)

      Classification (CLA)

    4. (d)

      Dependency modeling (DEM)

    5. (e)

      Regression (REG)

    6. (f)

      Summarization (SUM)

  4. 4.

    Data mining process (DMP)

    1. (a)

      Data collection (DAC)

    2. (b)

      Data cleaning and pre-processing (DCP)

    3. (c)

      Data projection and transformation (DPT)

    4. (d)

      Data mining (DAM)

    5. (e)

      Data visualization (DAV)

Table 5 Overview of included papers and their classification
Table 6 Absolute frequencies used in research question 4
Table 7 Absolute frequencies used in research questions 2, 5 and 6

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Mühlroth, C., Grottke, M. A systematic literature review of mining weak signals and trends for corporate foresight. J Bus Econ 88, 643–687 (2018).

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  • Machine learning
  • Weak signal detection
  • Emerging trend detection
  • Corporate foresight
  • Environmental scanning
  • Strategic decision making
  • Big data

JEL Classification

  • C8
  • C88
  • E17
  • M1
  • M19