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With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The purpose of this paper is threefold: to classify wildcards into three particular types sharing similar characteristics; use the Fuzzy TOPSIS as a new method in foresight to turn qualitative ideas into quantitative ones; and apply a combination of Fuzzy TOPSIS and a panel of experts to prioritize weak signals.

In this paper, the authors classify wildcards into three particular types which share similar character: natural wildcards, artificial wildcards (Degree 1) and artificial wildcards (Degree 2). Wildcards point to unexpected and surprising events including important results that can form watershed in the development of a specific trend. In addition, the authors present a Fuzzy TOPSIS model which can be used in various cases to prioritize a number of weak signals and put them in order, so that the most important ones are likely to yield the wildcard in the future

The authors presented a classification of wildcards with the same characteristics being natural wildcards, artificial wildcards (Degree 1) and artificial wildcards (Degree 2). The authors also prioritized the weak signals to deal with the most important ones and take appropriate action in advance so as to minimize possible damages and maximize the benefits of potential wildcards in an uncertain environment.

In this paper, the authors report on the prioritizing of weak signals by applying Fuzzy TOPSIS and classify wildcards. This is significant because, by identifying the most important weak signals, appropriate actions can be taken in the future if necessary. The paper should be of interest to readers in the area of participatory foresight.

This paper introduces a new approach for autonomous internet‐based environmental scanning, which combines concept of weak signals with “information foraging theory”.

Early detection and rapid action with respect to developments in the operating environment is a prerequisite for successful marketing planning. Accordingly, this paper proposes a three‐stage process for overcoming practical obstacles to the detection and use of weak signals from the operating environment, in particular how to identify relevant and useful documents in harsh information environments such as the internet. Its functionality is demonstrated by means of a human‐machine experiment.

A framework based on information foraging theory is well suited to the task of determining the relevance of documents and facilitates the automation of information search processes. A prototype environmental scanning system of this type outperformed human experts in a typical scanning task.

Embedding the detection of weak signals in a formal process permits intelligence gatherers to step beyond anecdotal evidence, and complements the current literature on weak signal detection with formal and systematic procedural guidelines.

The presented methodology facilitates both the selection and the structuring of information sources. This unburdens the managers and leaves time for important tasks such as the development of concrete marketing plans reacting to detected developments.

The paper provides a comprehensive framework for web‐based weak signal detection in business environments, and can be used as a starting point for the development of practicable environmental scanning systems.

The study aims to expand on the concept of an early warning system (EWS) by introducing weak-signal detection, human-in-the-loop (HIL) verification and response tuning as integral parts of an EWS's design.

The authors bibliographically highlight the evolution of EWS over the last 30+ years, discuss instances of EWSs in various types of organizations and industries and highlight limitations of current systems.

Proposed system to be used in the transforming of weak signals to early warnings and associated weak/strong responses.

The authors contribute to existing literature by presenting (1) novel approaches to dealing with some of the well-known issues associated with contemporary EWS and (2) an event-agnostic heuristic for dealing with weak signals.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0513.

This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.

When 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.

The 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.

How 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.

The 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.

The 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 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.

Although 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.

The purpose of this paper is to examine the reasons why the concept of a “strategic early warning system” (SEWS) has not been widely introduced and why it fails to be implemented successfully in German corporations. The aim of such systems is to detect changes in an organizational environment ahead of time by scanning the environment for “weak signals”, which come in the form of trends.

The questions that arise, particularly because the concept of SEWS is not entirely new, are: What are the pitfalls in implementing these systems, why do they occur, and how can they be overcome? To answer these questions, a single case study was conducted.

The study suggests that the failure of SEWS is due mainly to a lack of participation of potential future users in the implementation phase, a lack of joint understanding of the nature of trends, differing and unrevealed requirements of trends by various interested parties, a broad misconception of the “weak signals” concept and trends, an excessively heavy reliance on alleged “hard data”, a lack of interaction among users, and finally a “missing link” to the strategic functions in an organization.

The results of the study are limited due to the fact that they are derived from a single case study.

The identification of pitfalls contributes to the research on implementing SEWS and this can also be linked to “strategic issue management” and the foresight process.

The purpose of this paper is to detect the periodic signal under strong noise background, and estimate its amplitude/phase.

Melnikov method is adopted as calculating the threshold value when chaos occurs, and the detected signal is taken as a system parameter. The system's output state is changed if the parameter has a slight change near the threshold. Meantime, the phase of system's output is recognized to judge whether the output state changes, and the signal parameter is estimated according to the necessary condition.

A small periodic signal in noise can be detected by Duffing oscillator via a transition from chaotic motion to periodic motion.

The paper shows how to calculate the amplitude/phase in low signal‐to‐noise ratios.

The Duffing system is sensitive to the weak periodic signal and has definite immunity to noise, so it is easy to construct a system composed of many oscillators that could process complex signals, even though the environmental noise is intense.

This paper presents a nonlinear method for detecting and extracting the weak signal.

In the controlled environment of a professional business seminar, the paper collects data on the willingness of participants to strategically default on a mortgage that is underwater by varying degrees. By providing the participants with fabricated exogenous strong and weak information signals, the paper is able to examine the effect of the signals on their responses. The purpose of this paper is to find evidence suggesting that gender, moral opposition, level of susceptibility to information, and information signal strength influence herding in business professionals.

The paper adopts the Hirshleifer and Hong Teoh (2003) definition of herding as “any behavior similarity/dissimilarity brought about by the interaction of individuals.” In the controlled environment of a professional business seminar, the paper collects data on the willingness of participants to strategically default on a mortgage that is underwater by varying degrees. By providing the participants with fabricated exogenous strong and weak information signals, the paper is able to examine the effect of the signals on their responses.

The major contribution is that the paper finds evidence suggesting that signal strength does indeed matter. The paper finds that a weak signal is more likely to produce herding when respondents answer questions relating their own decisions and strong signals produce more herding when respondents provide advice to others. The paper also finds that women are less likely to herd and people who report they are susceptible to information influences are more likely to herd. Not surprisingly, participants who are morally opposed to strategic default are less likely to herd in most scenarios.

No other study of which the authors are aware looks specifically at signal strength in a financial setting or uses a sample of business professionals to examine herding of a financial nature.

The purpose of this paper is to study the effectiveness of multiple signals. Specifically, the paper investigates how the individual strength of a marketplace signal varies as a function of whether consumers are exposed to that signal alone or in combination with another signal.

The research uses experimental designs to empirically address the research questions. Hypotheses are formulated primarily based on signaling theory and these hypotheses are tested with laboratory experiments using real consumers.

The key finding is that a signal's stand‐alone credibility largely determines whether its individual strength would be diluted or augmented by the coexistence of another signal. Further, when signals with different stand‐alone strengths coexist, the individual strength of the weaker signal is higher than when that signal is present alone. These effects are observed in brick‐and‐mortar and online shopping media.

Past research reports mixed findings about whether the individual strength of a signal is diluted (dilution effect) or augmented (augmentation effect) by the presence of another signal. This research attempts to resolve this issue, for the first time, by demonstrating that whether dilution effect or augmentation effect occurs depends on the stand‐alone credibility of the individual signals in a mix.