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To develop a multi‐attribute decision making model for evaluating and selecting among logistic information technologies.

First a multi‐attribute decision making model for logistic information technology evaluation and selection consisting of 4 main and 11 sub criteria is constructed, then a hierarchical fuzzy TOPSIS method is developed to solve the complex selection problem with vague and linguistic data. Sensitivity analysis is presented.

Reviews the literature and provides a structured hierarchical model for logistic information technology evaluation and selection based on the premise that the logistic information technology evaluation and selection problem can be viewed as a product of tangible benefits, intangible benefits, policy issues and resources. Defines tangible benefits as cost savings, increased revenue, and return on investment; intangible benefits as customer satisfaction, quality of information, multiple uses of information, and setting tone for future business; policy issues as risk and necessity level; resources as costs and completion time. Presents a methodology that is developed for the complex, uncertain and vague characteristics of the problem.

Comparisons with other multi‐attribute decision making techniques such as AHP, ELECTRE, PROMETHEE and ORESTE under fuzzy conditions can be done for further research.

This article is a very useful source of information both for logistic managers and stakeholders in making decisions about logistic information technology investments.

This paper addresses the logistic information technology evaluation and selection criteria for practitioners and proposes a new multi‐attribute decision making methodology, hierarchical fuzzy TOPSIS, for the problem.

The objective of this study is to assess quantitatively how feasible blockchain is for various industries, such as logistics and supply chain, health, energy, finance, automotive, pharmaceutical and agriculture and food using a comprehensive list of indicators.

A decision aid was applied to the problem of identifying the feasibility of blockchain in Turkish industries. To this end, first, a set of indicators was identified. Then, the fuzzy AHP and fuzzy TOPSIS were utilized to assess the feasibility comparatively using the data gathered from a group of experts. Finally, a scenario analysis was conducted to ensure the consistency of our evaluation.

The findings of this study suggest that comparatively, logistics and supply chain, finance and health industries are the most feasible industries for blockchain. This study further suggests that blockchain is the least feasible for the automotive industry compared to the rest of the identified industries.

It is cumbersome to find out the respondents who have sufficient knowledge of both blockchain and the identified industries. Even if we took the utmost care in identifying the right respondents, we limited our search to the biggest industrial hubs of Turkey.

The findings of this research may help various decision-makers employed in governments, conglomerates, software and consulting firms and national research institutions make more informed decisions and allocate their resources more effectively.

To this date, the current studies have solely investigated possible research opportunities in blockchain and demonstrated several blockchain applications in stand-alone cases. To the best of our knowledge, however, no single study exists that evaluates the feasibility of blockchain comparatively and holistically among a group of industries using various indicators.