Weak signals for strategic intelligence : anticipation tool for managers /

Weak signals for strategic intelligence : anticipation tool for managers /

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Lesca, Humbert
Άλλοι συγγραφείς: Lesca, Nicolas
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : ISTE ; [2011]
Hoboken, N.J. : Wiley, [2011]
Θέματα:
Strategic planning.
Management.
BUSINESS & ECONOMICS > Corporate Governance.
BUSINESS & ECONOMICS > Leadership.
BUSINESS & ECONOMICS > Organizational Development.
BUSINESS & ECONOMICS > Workplace Culture.
Electronic books.
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Contents note continued: 1.4.2. Difference between strategic information and day-to-day management information
  • 1.4.3. Two types of anticipative information
  • 1.5. Weak signals
  • 1.5.1. Definition of a weak signal
  • 1.5.2. An example of weak signal as the trigger to a warning
  • 1.5.3. Should we prefer a "strong" but backward-looking signal, or a "weak" but forward-looking signal?
  • 1.5.4. Conversion, transformation of a weak signal into an early warning signal
  • 1.5.5. Should we refer to a "signal" or a "sign"? Intentionality of the sender
  • 1.5.6. Weak signals ... or decoys, deceptions, and information asymmetry
  • 1.5.7. Characteristics of a weak signal: "Stealthy information"
  • 1.5.8. Sources emitting weak signals: Examples
  • 1.6. Detecting weak signals
  • 1.6.1. Individual intelligence (in the Latin sense of the word): A definition
  • 1.6.2. Cognitive style of a person
  • 1.6.3. Individual cognitive biases
  • 1.6.4. Fear.
  • Contents note continued: 1.7. Interpreting, amplifying and exploiting weak signals to support strategic decision making
  • 1.7.1. Need for collective intelligence (CI) for interpreting weak signals
  • 1.7.2. CM: Justification and definition of the process
  • 1.7.3. Definition of CI as the emergence of CCM
  • 1.7.4. From CCM to knowledge management
  • 1.8. Puzzle® method for the operationalization of CCM
  • 1.8.1. Issue: Why the puzzle metaphor?
  • 1.8.2. Definition of the Puzzle® method
  • 1.8.3. Fundamental hypotheses of the Puzzle® method
  • 1.8.4. Work group and CI
  • 1.9. Global VASIC process for detecting, recognizing and utilizing weak signals
  • 1.9.1. Targeting of anticipative scanning and information sources
  • 1.9.2. Tracking and individual selection of weak signals
  • 1.9.3. Escalating information, collective/centralized selection and storage
  • 1.9.4. Dissemination and preparation of information for CCM sessions
  • 1.9.5. Animation.
  • Contents note continued: 1.9.6. Measurements: Performance indicators of the VASIC process
  • 1.10. Conclusion
  • 1.10.1. Results on completion of Chapter 1
  • ch. 2 Detecting, Recognizing and Corroborating a Weak Signal: Applications
  • 2.1. Recognition of a weak signal: Examples
  • 2.1.1.A lady heading up the purchasing function at a car equipment manufacturer? How bizarre!
  • 2.1.2. When a weak signal is displayed on a sign in the street!
  • 2.1.3.A research center at EADS: Why Singapore?
  • 2.1.4. Danone
  • 2.2. Making a new weak signal reliable
  • 2.2.1. Reliability of the information source
  • 2.2.2.Comparing the weak signal with other information obtained previously
  • 2.2.3. Consulting with an "expert"
  • 2.2.4. Feedback from the animator to the gatekeeper who provided the weak signal
  • 2.3. Conclusion
  • 2.3.1. Result
  • ch. 3 Utilization of Weak Signals, Collective Creation of Meaning: Applications.
  • Contents note continued: 3.1. The Roger case: Should we fear this new entrant to our industry? (the banking sector)
  • 3.1.1. Issues for Roger as a company
  • 3.1.2. Context
  • 3.1.3. Codexi
  • 3.1.4. Information to be used
  • 3.1.5. Conduct of the collective work session
  • 3.1.6. Results
  • 3.2. The case for "valorizing CO2 as a commodity": A preliminary study for the selection of a new strategic direction
  • 3.2.1. The main problem: How to "give birth to an idea" within the Board of Directors (BoD)?
  • 3.2.2. Challenge: Arousing the interest of the BoD
  • 3.2.3. Preparing for the session (which will prove to be the first session)
  • 3.2.4. Background of the experiment (first session)
  • 3.2.5. Conduct of the session (first session)
  • 3.2.6. Second session, three months later
  • 3.2.7. Conclusion and post-scriptum
  • 3.3. The Danone case. The ministry is worried: Are there signs showing that companies will destroy jobs over the next two years? Could Danone leave France?
  • Contents note continued: 3.3.1. The issue at hand
  • 3.3.2. Fresh interest in weak signals
  • 3.3.3. Background: Lack of cross-disciplinarity
  • 3.3.4.Organization and conduct of the experiment
  • 3.3.5. Targeting of a field of study
  • 3.3.6. Selection of Danone as an agent
  • 3.3.7. Conduct of the CCM experiment
  • 3.3.8. Conclusion at the close of the last session: Huge plausible risk on the horizon!
  • 3.4. The Opel case: Initiating collective transversal intelligence to aid strategic decision-making
  • 3.4.1. Issues and background
  • 3.4.2. CI
  • 3.4.3.Organizational context
  • 3.4.4. Preparatory step upstream of the first CCM session
  • 3.4.5. Conduct of the CCM session
  • 3.4.6. Conclusions
  • 3.5. Conclusion
  • 3.5.1. Results
  • ch. 4 Preparation of Weak Signals for Sessions in Collective Creation of Meaning: Applications
  • 4.1. Introduction: Two starting situations
  • 4.2. The Roger case (continued): How are the news briefs used in the Roger CCM session prepared?
  • Contents note continued: 4.2.1. Preparation of the news briefs used in the CCM
  • 4.2.2. The search for raw data: A substantial task
  • 4.2.3. Extraction of news briefs: A time-consuming, delicate task
  • 4.2.4. The Internet trap
  • 4.3.COM2 valorization case: Automatic search for "news briefs"
  • 4.3.1. Guiding idea: "Full text" distillation
  • 4.3.2. Steps in the search for "possible weak signal" news briefs
  • 4.4. The Danone case: Preparation of the weak signals
  • 4.4.1."Manual" search
  • 4.4.2."Manual" extraction
  • 4.4.3. Automatic news briefs search and extraction
  • 4.4.4. Conclusions on the "CO2 valorization" and "Danone" cases using the Approxima prototype
  • 4.5. Software modules for assisting in the automatic search for news briefs
  • 4.5.1. Lookup table of characteristic words for the field being explored. Continuation of the "CO2 valorization" case
  • 4.5.2. Enhancing the anticipative-and characteristic-word bases.
  • Contents note continued: 4.5.3. Semantics problems: Synonyms, polysemes and related matters
  • 4.5.4. Software enabling "event searches"
  • 4.5.5. Integration platform for commercially available software modules
  • 4.6. Conclusion
  • 4.6.1. Result.

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