Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
PurposeThis research aims to identify the key factors affecting Outbound Open Innovation between Startups and Big organizations using the multiple criteria decision-making analysis (MCDM) approach. The MCDM technique ranks the four key factors identified from the literature study that can help to improve collaboration opportunities with Startups.Design/methodology/approachIdentification of key factors affecting Outbound Open Innovation between Startups and big organizations based on extant literature. A questionnaire is prepared based on these four identified key factors to gather views of the startup's employees, from the designer level to the startup's founder. MCDM techniques are used to evaluate the questionnaire. The ensemble technique is used to rank the key factors coming from three different MCDM methods.FindingsThe findings from the MCDM approach and Ensemble techniques give insight to the big organizations to facilitate outbound Open Innovation effectively. It also provides insight into the requirements of the startups and the kind of support they seek from the big organizations. The ranking can help the big organization close the gaps and make an informed decision to increase the effectiveness of the collaborations and boost innovation.Originality/valueThis is a unique research work where the MCDM approach is used to identify the ranking of key factors affecting outbound open innovation between startups and big organizations. The MCDM technique is followed by the ensemble method to rationalize the findings. Technology Relevance ranks highest, followed by Innovation Ecosystem, Organization commitment and Knowledge Sharing.
PurposeThis research aims to identify the key factors affecting Outbound Open Innovation between Startups and Big organizations using the multiple criteria decision-making analysis (MCDM) approach. The MCDM technique ranks the four key factors identified from the literature study that can help to improve collaboration opportunities with Startups.Design/methodology/approachIdentification of key factors affecting Outbound Open Innovation between Startups and big organizations based on extant literature. A questionnaire is prepared based on these four identified key factors to gather views of the startup's employees, from the designer level to the startup's founder. MCDM techniques are used to evaluate the questionnaire. The ensemble technique is used to rank the key factors coming from three different MCDM methods.FindingsThe findings from the MCDM approach and Ensemble techniques give insight to the big organizations to facilitate outbound Open Innovation effectively. It also provides insight into the requirements of the startups and the kind of support they seek from the big organizations. The ranking can help the big organization close the gaps and make an informed decision to increase the effectiveness of the collaborations and boost innovation.Originality/valueThis is a unique research work where the MCDM approach is used to identify the ranking of key factors affecting outbound open innovation between startups and big organizations. The MCDM technique is followed by the ensemble method to rationalize the findings. Technology Relevance ranks highest, followed by Innovation Ecosystem, Organization commitment and Knowledge Sharing.
The rise of wireless communication has spurred the global adoption of mobile payment services, a trend that is significantly reducing the use of cash. This shift, driven by new technologies and lifestyle changes, not only presents opportunities for businesses but also enhances consumers’ daily activities. Consumers’ and businesses’ willingness to adopt mobile payment services has increased due to factors such as easier access to new technologies, convenience, changing lifestyle choices, and economic conditions. Despite challenges such as limited access to technology, security concerns, and high transaction fees, the potential benefits of mobile payment services are promising. Therefore, this research aims to construct a suitable model for developing a mobile payment service framework that both consumers and businesses are willing to adopt. The proposed model integrates the Delphi method, interpretive structural modeling (ISM), quality function deployment (QFD), an analytic network process (ANP), and fuzzy set theory. To demonstrate the practical application of the model, a case study of developing a mobile payment service framework is presented, showcasing how the model can be used to address real-world challenges and enhance the adoption of mobile payment services. The case study results show that ease of use, system and service quality, and reliability are the most important customer requirements, and encryption, edge computing, authentication, and interoperability are the most important engineering characteristics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.