Weak Signal Analysis Research Articles

How to identify and interpret weak signals of change in the forest bioeconomy

Weak signals, the first signs of a potentially emerging issue that could disrupt current trends and megatrends, are hidden among disconnected pieces of information. Analyzing weak signals can help decision makers generate preparedness and a rapid-response capacity for possible systemic changes in the forest bioeconomy, where weak signals have not been studied extensively. This exploratory study sought to understand how to identify and interpret weak signals of change that may have an impact on the forest bioeconomy. An international panel of experts in the forest bioeconomy and in foresight provided their views through a two-stage Real-Time Delphi method which utilized the multiple rounds of traditional Delphi and the instantaneous feedback of the Real-Time Delphi method. This study introduces four conceptual frameworks to use when searching for weak signals: Internal/external operating environment, the Multi-level perspective, the Three Horizons Framework, and Complexity theory. These conceptual frameworks were applied in the analysis of collected data with the aim of developing the theory and practice on how to identify and interpret weak signals. According to the results, observations beyond the traditional dominating operating environment and a combination of observations from various sources could help to identify weak signals that may affect the forest bioeconomy. The Real-Time Delphi insights suggest that weak signal analysis becomes stronger via collaboration and interaction between futures specialists and forest bioeconomy experts.

Using corporate foresight to enhance strategic management practices

The ever-increasing environmental complexity makes strategizing a difficult multidimensional task. In this paper, we conducted a corporate foresight case study in an SME in packaging industry in Iran. The case study offers a detailed procedure of implementing corporate foresight (CF) and how it can reshape traditional strategic planning. A multimethodological approach was taken in this case study. Once an intraorganizational team in studied company was formed, archival document analysis, PESTEL and weak signal analysis, importance/uncertainty matrix, cross-impact balanced (CIB) analysis, scenario construction, wind tunneling, robust decision-making, and premortem session were used to create foresight intelligence. This paper presents a detailed description of how CF can be linked to conventional strategizing and reshape it. Key variables, driving forces, critical uncertainties, and 4 plausible scenarios are presented. The case study illustrates that as alternative realities challenged the foresight teams ingrained presuppositions, they found the dialectic between “weight of history” and “pull of future” both revelatory and indigestible. The CF intervention illuminated the fragility of preexisting strategic objectives, the implicit optimism bias underlying them, and an overflowing-plate syndrome of formulating too many strategic objectives. Consequently, studied company decided to revisit their strategic objectives, prepare a contingency plan for worst-case scenarios, and begin developing a crisis-ready culture. The comprehensive case study demonstrates how CF can enhance and contradict traditional strategizing, presents a rich know-how of added value of scenarios, and provides some subtleties and complexities of CF interventions.

Strategizing Digital Transformations: Sensing Threats and Opportunities with the Digital Disruption Analysis

Managers need to navigate the increasing complexity of their business environments due to emerging technological developments and digital disruption. Strategic threats and opportunities to their business must constantly be assessed, but organizations often lack the time and resources to develop a structured approach. With the Digital Disruption Analysis, Voß et al. (2020) published a systematic methodology to assess emerging innovations and to derive digital transformation strategies. In this paper, the Digital Disruption Analysis is tested with technology foresight experts of an established enterprise in the energy sector and improved in an iterative approach. In an initial application, opportunities for improvement are identified through participatory observation and direct feedback. Then, the Digital Disruption Analysis is revised and applied again in the same company to assess the improvements, using the same evaluation method and an additional qualitative questionnaire. The main adaptations of the Digital Disruption Analysis are the integration of a weak signal analysis based on data and text mining approach to identify relevant emerging innovations, the integration of a multidimensional digital disruption scoring model, and a new Trajectory Mapping approach. The experts note improved applicability and overall enhanced functionality of the revised Digital Disruption Analysis. Our findings contribute to future research on applicable management tools for digital transformations and serve as a foundation for understanding the nature of digital disruptions and how they can be identified and monitored.

Detecting emerging technologies and their evolution using deep learning and weak signal analysis

Emerging technologies can have major economic impacts and affect strategic stability. Yet, early identification of emerging technologies remains challenging. In order to identify emerging technologies in a timely and reliable manner, a comprehensive examination of relevant scientific and technological (S&T) trends and their related references is required. This examination is generally done by domain experts and requires significant amounts of time and effort to gain insights. The use of domain experts to identify emerging technologies from S&T trends may limit the capacity to analyse large volumes of information and introduce subjectivity in the assessments. Decision support systems are required to provide accurate and reliable evidence-based indicators through constant and continuous monitoring of the environment and help identify signals of emerging technologies that could alter security and economic prosperity. For example, the research field of hypersonics has recently witnessed several advancements having profound technological, commercial, and national security implications. In this work, we present a multi-layer quantitative approach able to identify future signs from scientific publications on hypersonics by leveraging deep learning and weak signal analysis. The proposed framework can help strategic planners and domain experts better identify and monitor emerging technology trends.

Economic Calculus and Weak Signals: Prevention Against Foggy Bottom

Purpose: Investment decisions are taken based on an economic calculation, considering the analysis of a diverse and partially unpredictable economic environment. Each enterprise's economic calculus is treated as a decision based on rationality and serves as an instrument to support the investment measure's selection. Despite different studies, there is a research gap in the usage of weak signals in economic calculus. Meanwhile, the spectacular collapses of well-recognised firms resulted from weak identification of coming failures. Approach/Methodology/Design: The authors carried out an integrative literature review to generate new knowledge on the usage of weak signals in economic calculus insights in this paper have emerged iteratively. Findings: Based on an integrative literature review, the authors offer the potentially radical generalisation that firms must use weak signal analysis during the process of economic calculus creation. Practical Implications: The article brings several valuable information that can be the base material and reference to further research. Study results can be a starting point of discussion and analysis on each firm, both from the public and private sectors. Originality/Value: The original study aims to initiate discussion and further research weak signal analysis in economic calculus. It is the first such research.

Picking the ICT technology winners - longitudinal analysis of 21st century technologies based on the Gartner hype cycle 2008-2017: trends, tendencies, and weak signals

We analyse longitudinal data of the 2008-2017 Gartner hype cycles and key ICT technologies (151 technologies) in the world. In this study, we have calculated six different index analyses. Our analyses use the following index analyses: 1) ranking index analysis; 2) technology power index analysis; 3) better than other technology power index analysis; 4) triangulation index analysis; 5) weak signal analysis; 6) outlayer analysis. These six analyses are novel analyses. We ranked the information from hype cycles giving a rank for every observed technology. Information of calculations is public and open in the internet publications of the Gartner Ltd. We were able to see the top ten technologies: 1) augmented reality; 2) human augmentation; 3) quantum computing; 4) speech recognition; 5) mobile robots; 6) autonomous vehicles; 7) speech-to-speech translation; 8) mesh network sensor; 9) local-aware applications; 10) idea management.

Weak signal detecting of industry convergence using information of products and services of global listed companies - focusing on growth engine industry in South Korea -

The [World Economic Forum 2016, https://www.weforum.org/events/world-economic-forum-annual-meeting-2016. Accessed 5 Aug 2017] predicted that the world would face the Fourth Industrial Revolution which means innovative changes through convergence of cutting-edge information and communications technologies (ICTs) such as artificial intelligence, IoT, big data and cloud computing with conventional industries. It was forecasted that such innovation would take place across all industries and services. In particular, the Fourth Industrial Revolution has paid attention to changes in conventional traditional industries. The weak signal analysis is a method which can detect the sign of future changes at an early stage. A weak signal is defined as an advanced indicator in such changes. Therefore, its search and monitoring can be an early warning on threat and great opportunity to be prepared. To overcome the limitation of future prediction which is mostly dependent upon an expert group’s intuitive judgment, this study attempted to investigate the weak signal of convergence among industries, using knowledge structure based approaches. As new products and services can lead to convergence between industries, research data has gathered information on the products and services of global listed companies which have been established for the past 11 years. For comparative analysis, they were grouped every 5 years, and product and service trends were compared. And we detected weak signals of convergence with different industries and noticed the changes of existing industries. Historically, the convergence of industries is mainly discussed in relation to science and technology-led industries, so the industrial field that has been studied has chosen Korea’s growth engine industries, a high-tech science and technology industries.

Idea mining for web-based weak signal detection

We investigate the impact of idea mining filtering on web-based weak signal detection to improve strategic decision making. Existing approaches for identifying weak signals in strategic decision making use environmental scanning procedures based on standard filtering algorithms. These algorithms discard patterns with low information content; however, they are not able to discard patterns with low relevance to a given strategic problem. Idea mining is proposed as an algorithm that identifies relevant textual patterns from documents or websites to solve a given (strategic) problem. Thus, it enables to estimate patterns’ relevance to the given strategic problem. The provided new methodology that combines weak signal analysis and idea mining is in contrast to existing methodologies. In a case study, a web-based scanning procedure is implemented to identify textual internet data in the field of self-sufficient energy supply. Idea mining is applied for filtering and weak signals are identified based on the proposed approach. The proposed approach is compared to a further – already evaluated – approach processed without using idea mining. The results show that idea mining filtering improves quality of weak signal analysis. This supports decision makers by providing early and suggestive signals of potentially emerging trends, even with only little expressive strength.

Weak signals analysis, knowledge management theory and systemic socio-cultural transitions

The theoretical goal of this article is to narrow the gap between existing knowledge management theories and theories of weak signal analysis, and partly wild card analysis. The following theories and associated theoretical frameworks are discussed in the article: (1) Environmental Scanning Model, (2) Nonaka's Knowledge Management Theory, (3) Gammelgaard's and Ritter's Knowledge Retrieval Matrix, (4) Boisot's Information Space Model and (5) Vejlgaard's Diamond Shaped Trend Model. These various and nevertheless complementary perspectives are important for the further development of weak signal analysis, knowledge management theory and knowledge management practices in modern organisations as well as for anticipation and decision-making in policy-making arenas. There are still many theoretical and empirical challenges in these fields of scientific knowledge. A general conclusion is that all these frameworks provide interesting new perspectives for modern futures studies as such. Another conclusion is that there are various knowledge management (KM) and scanning frameworks available for implementing weak signal analysis. However, this paper, its observations and conclusions also imply that a more generalised approach to weak signal analysis needs to be developed and that modern KM theories should be used when developing new futures studies/foresight methodologies. According to the theoretical guidelines presented in this article, it is possible to make the suggestion that it would be wise to integrate the latest developments in weak signal analysis into knowledge management theory and vice versa.

Early detection, warnings, weak signals and seeds of change: A turbulent domain of futures studies

This paper marks a milestone in a six year research cycle on weak signal analysis and early detection issues in futures studies. While providing a broad view and discussing a variety of contributions on this topic, the aim of this article is to offer a more constructivist approach to early detection studies than has been typically the case so far. The article starts by positioning the underlying problem of weak signals within the broader field of futures studies. The second section examines the Ansoffian tradition, first on the basis of Ansoff's own contributions, then through key enhancements offered by scholars working in the same perspective. The third section develops the arguments for a constructivist critique of the Ansoffian tradition as a way to renew and enrich scientific debate. The fourth and final section presents the main open issues where research, case studies, methods and applications still need to make significant progress in the vast domain of weak signal analysis and early detection.

The strategic strength of weak signal analysis

Foresight can be seen as a social cognition process involving a complex set of methods and interactive processes intended to assist policy in becoming more adaptive and forward-oriented in unpredictable environments. As a form of foresight raw material, “weak signals” can be thought of as gross, unstructured, fragmented, incomplete and inadvertent environmental data that may be refined into valuable information regarding context and further be articulated into strategically actionable knowledge. As advanced indicators that precede significant discrete one-off events and/or novel developments in the rate and direction of trends, their analysis has the potential to facilitate the real-time alignment between organisational decision-making and changing external circumstances. These predictors of future change pose fundamental problems of identification and interpretation and represent a challenge to established mental models. Thus, the practical significance of weak signals is that they can be transformed into meaningful insight for policy action. Such a value, however, does not materialise automatically. Realising this potential requires a degree of tolerance and fluidity of the collective cognitive frameworks by which weak signals can be apprehended, assessed and acted upon. This paper aims at covering the scope of perceptions and actions typically involved in the tracing and tracking of this shaping process.

Beyond the obvious: Examining ways of consolidating early detection schemes

In the domain of futures studies, the need to develop methods and concepts to identify risks or opportunities “early enough” has become an issue and almost a discrete sub-field with its own debates, specialties and schools of thought. Our examination of the literature in this domain shows that early detection, early warning or weak signal analysis, to mention just a few of the terms being used, tends to convey the idea that finding faint evidence of possible futures is actually rather easy. We, however, believe that it is far from being an off-the-shelf process for whoever wants to detect early signs of changes. At the very least it raises cost/benefit questions, and it may have some shortcomings which, if ignored, could substantially increase the vulnerability of an organization. Our goal in this paper is to explore ways of going beyond an overly optimistic approach and provide a basis for the pragmatic use of weak signals and similar notions in strategic management and policy-making processes. After browsing through the several approaches that propose methods for the “early” tackling of trends, uncertainties, risks or opportunities to come, we will focus on the approaches defined by 1) weak signals and 2) early warnings or equivalents for the analysis of possible futures. We will in particular examine how mainstream claims have been presented then improved in the last 25 years, as well as how some of the problems involved have been insufficiently addressed so far. We will then show how these problems (closely linked to how the early perception claims are configured — how they are framed and modeled but also made affordable and useful) can directly impact the performance and management style of the organizations and societal settings in which they are deployed. The concluding section will propose a pragmatic approach to the problems raised: paradigmatic weaknesses or forms of blindness. This precautionary principle should lead to a real option approach in terms of futures to be considered and evaluated, so as to enhance the chances of avoiding risks, as well as following up promising directions, radical innovations or, more generally, strategic decision-making processes in both the corporate and policy-making arenas.

Making anticipatory systems more robust

PurposeWhen coping with complex, but also possibly disruptive and open‐ended social dynamics, the anticipatory system idea, which was developed by Rosen in the realm of physical and biological system observation, remains a reference framework, but one that may need to be reinforced by other theoretical considerations. This paper aims at using a debate that took place in a specific foresight discussion arena on early detection and weak signal analysis, as a constructive epistemic detour to eventually contribute to such a reinforcement of Rosen's anticipatory system proposal.Design/methodology/approachThe author aims at revisiting Rosen's framework with stimulating inputs drawing upon the early detection debates, by first assessing the original concepts brought up by Ansoff in the 1970s and 1980s and its further enhancements by contemporary scholars. A rather constructivist approach is then developed to weak signal analysis, aiming at emphasising the need, in analytical situations involving social system features, for reflexive stages and capacities. Bearing this requirement in mind, the productive value of the “framing” and “meta‐framing” notions is explored, in order to apply them to Rosen's anticipatory systems and possibly contribute to enriching his original concept.FindingsHow effective the framing and meta‐framing couple can be for a series of anticipatory issues is described in a detailed manner and, then more specifically, Rosen's anticipatory system concept is revisited in the light of those inputs, aiming at putting into perspective new options for research and anticipation activities in general.Research limitations/implicationsThe paper is essentially conceptual and based on a rich but disputable detour by early detection and weak analysis issues so as to emphasise key reflexive references and method. However, most of this material is taken from domains rather untypical of Rosennean debates and in addition would need to be completed by a series of supportive cases, but that is beyond the scope and scale of this paper.Practical implicationsThe paper sets clear distinctions and boundaries for when and when not to apply reflexive steps in a foresight exercise, including in the context of rolling out a Rosen type of approach. Research decision making both in the corporate and policy‐making contexts can benefit from such clues and supportive framework conditions.Social implicationsSocial systems are typically complex and involve multiple perspectives and viewpoints; they concern a series of major challenges to be coped with locally or more globally, at environmental, political, cultural or technological level, and in that category of anticipatory endeavor, the framing/meta‐framing epistemic couple may be of great usefulness.Originality/valueAlthough rather conceptual, the detour proposed by the paper aims at creating a reflexive distance and enriched capability to evaluate one's potential biases and blind spots in anticipatory modelling activities.

Weak signals as a flexible framing space for enhanced management and decision-making

This paper revisits theories of weak signal analysis using the concepts of ‘framing’ and ‘meta-framing’ to provide an enriched methodological environment for management and decision-making in turbulent contexts. This attempt takes place within the effort that futures studies have deployed to cope with the need to be ‘early’ in change processes, and is intended to improve firms’ preparedness regarding uncertainties, threats and opportunities. The weak signal notion is one of the metaphors coined to accomplish that, thanks to the seminal role played by Igor Ansoff in the 1970s and 1980s, and the inputs of more recent continuators who have shaped a wide methodological environment. After over 20 years of weak signal exploration, some pending issues suggest the need to re-open some basic hypotheses as well as the use of a wide-angle lens to review the Ansoff legacy and its relevance for management and decision-making. The concepts of framing and meta-framing are the means that will allow us to do this by adding a more reflexive dimension to weak signal analysis, taking account of analysts’ and commissioning organisations’ own biases, and enhancing firms’ robustness against possibly disruptive futures. Pragmatic managerial orientations of how to make use of methodological blends involving reflexive steps will be outlined, also suggesting diverse tracks for further research on weak signal theories and their contributions to both futures studies and strategic management.