Purpose The paper proposes an intuitionistic fuzzy TOPSIS multi-criteria decision making (MCDM) method for the selection of start-up businesses in a government venture capital (GVC) scheme. Most GVC funded start-ups fail or underperform compared to those funded by private venture capitals due to a number of reasons including lack of transparency and unfairness in the selection process. By its design, the proposed method is able to increase transparency and reduce the influence of bias in GVC start-up selection processes. The proposed method also models uncertainty in the selection criteria using fuzzy set theory that mirrors the natural human decision making process. Design/methodology/approach The proposed method first presents a set of criteria relevant to the selection of early stage but high potential start-ups in a Government Venture Capital (GVC) financing scheme. These criteria are then analyzed using the TOPSIS method in an intuitionistic fuzzy environment. The intuitionistic Fuzzy Weighted Averaging (IFWA) Operator is used to aggregate ratings of decision makers. A numerical example of how the proposed method could be used in GVC start-up candidates’ selection in a highly competitive government venture capital scheme is provided. Findings The methodology adopted increases fairness and transparency in the selection of start-up businesses for fund support in a government-run venture capital scheme. The criteria set proposed is ideal for selecting start-up businesses in a government controlled venture capital scheme. The decision making framework demonstrates how uncertainty in the selection criteria are efficiently modelled with the TOPSIS method. Practical implications As government venture capital schemes increase around the world, and concerns about failure and underperformance of GVC funded start-ups increase, the proposed method could help bring formalism and ensure the selection of start-ups with high success potential. Originality/value The framework designs relevant sets of criteria for a selection problem, demonstrates the use of extended TOPSIS method in intuitionistic fuzzy sets and apply the proposed method in an area that has not been considered before. Additionally, it demonstrates how intuitionistic fuzzy TOPSIS could be carried out in a real decision making application setting.

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Background: Social media is used in health communication by individuals, health professionals, disease centres and other health regulatory bodies. However, varying degrees of information quality are churned out daily on social media. This review is concerned with the quality of Social Media Health Information (SMHI). Objective: The review sought to understand how SMHI quality issues have been framed and addressed in the literature. Health topics, users and social media platforms that have raised health information quality concerns are reviewed. The review also looked at the suitability of existing criteria and instruments used in evaluating SMHI and identified gaps for future research. Method: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the forward chaining strategy were used in the document search. Data were sourced according to inclusion criteria from five academic databases, namely Scopus, Web of Science, Cochrane Library, PubMed and MEDLINE. Results: A total of 93 articles published between 2000 and 2019 were used in the review. The review revealed a worrying trend of health content and communication on social media, especially of cancer, dental care and diabetes information on YouTube. The review further discovered that the Journal of the American Medical Association, the DISCERN and the Health on the Net Foundation, which were designed before the advent of social media, continue to be used as quality evaluation instruments for SMHI, even though technical and user characteristics of social media differ from traditional portals such as websites. Conclusion: The study synthesises varied opinions on SMHI quality in the literature and recommends that future research proposes quality evaluation criteria and instruments specifically for SMHI.

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