A multi-stage decision making model for determining a suitable innovation structure using an open innovation approach

Yildirim E., Murat A. R. I., Dabic M., BAKİ B., Peker I.

JOURNAL OF BUSINESS RESEARCH, cilt.147, ss.379-391, 2022 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 147
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.jbusres.2022.03.063
  • Dergi Adı: JOURNAL OF BUSINESS RESEARCH
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, Periodicals Index Online, ABI/INFORM, Business Source Elite, Business Source Premier, CAB Abstracts, INSPEC, Psycinfo, Public Affairs Index, Veterinary Science Database
  • Sayfa Sayıları: ss.379-391
  • Anahtar Kelimeler: Open innovation, Collaboration type, Decision making model, Fuzzy analytic network process, RESEARCH-AND-DEVELOPMENT, INBOUND OPEN INNOVATION, OUTBOUND OPEN INNOVATION, FUZZY ANP, ABSORPTIVE-CAPACITY, PAST RESEARCH, HIGH-TECH, KNOWLEDGE, ADOPTION, MANAGEMENT
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

This study seeks to form an end-to-end analysis of Open Innovation stages, developing a three-part consecutive multi-stage decision making model for a suitable innovation structure by linking the processes used to determine the innovation type (open, closed), the OI type (inbound, outbound, coupled), and the collaboration type (science -based, market-based). The decision-making criteria for the OI stage are determined by academic literature and finalized by Delphi technique. The criteria for relationships are determined through expert evaluation. The Fuzzy Analytic Network Process is applied in order to determine the weights and rankings. The most important criterion to consider when choosing the innovation type is open innovation awareness. For the OI type, the most important criterion is knowledge acquisition and, for collaboration, it is network building. This approach allows us to predict the potential risks or costs of the OI process and the possibility of decision errors can thus be reduced.