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• Benner & Tushman: Which two issues continue to be relevant for research ten years later?

  1. The (False) promise for universal "best practices"
  2. The importance of addressing the productivity dilemma

• Benner & Tushman: Two other issues have emerged over the past decade that are directly relevant to exploration and exploitation and the productivity dilemma:

  1. A fundamental change in the phenomenon of innovation
  2. The research stance toward the study of innovation going forward.

• Benner & Tushman: Two secular trends drive the increasing importance of open innovation:

  1. The increasing prevalence and importance of digitization
  2. Modularity associated with task decomposition.

• Fang et al.: Why does a semi-isolated subgroup structure lead to more exploration?

  Seperating the team from the rest of the organization permits it to explore new alternatives, unchained by the demands and norms of the rest of the organization.

• Zott et al. : There are four main streams of research within the e-Business theme:

  1. Description of Generic e-business models and typologies
  2. Components of e-business models
  3. Business model representation
  4. Strategic marketing in e-business

• Zott et al. : There are three main streams of research within the strategic business model theme:

  1. Value creation in networked markets
  2. Business model and firm performance
  3. Strategy and the business model (distinction)

• Zott et al. : Scholar's clarified at least what a business model is not:

  1. The business model does not involve a linear mechanism for value creation from suppliers to the firm to its customers.
  2. The business model is not the same as product market strategy or corporate strategy
  3. The business model cannot be reduced to issues that concern the internal organization of firms.

• Koorn et al. : Some examples of industries using Big Data

  • Retail companies
  • Governments
  • Financial Institutions
  • Travel Industry & Public Transport
  • Insurance Companies

• Koorn et al. : Challenges in dealing with predicting human behavior:

  • Human psychology of cognitive inertia (Resistent to change)
  • Dealing with information overload and quality
  • Ability to make sense of behavioral data

• Koorn et al. : Potentioal Solutions to Big Data Privacy Challenges:

  1. Data Governance and Retention
  2. Compliance
  3. Data Use Cases & Data feed approval
  4. Consent/permission management
  5. Access Management
  6. Anonymization & Pseudonymization
  7. Data Sharing/Third-party Management