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The purpose of this paper is to examine two channels through which accounting standard differences could affect cross-listing: compliance costs and/or comparability benefits.

The authors use two settings to disentangle the two channels. First, financial reporting requirements are more stringent for cross-listings via direct listings than cross-listings via depositary receipts; as a result, the effect of compliance costs (if any) would be manifested differently in the two venues of cross-listings. Second, some host countries allow foreign firms to report under International Financial Reporting Standards (IFRS) without mandating IFRS for domestic firms; compared to host countries that mandate IFRS for both domestic and foreign firms, these IFRS-permitting countries provide a setting to test the importance of comparability benefits while holding constant compliance costs.

The authors find that prior to IFRS adoption, direct listings decrease with accounting standards differences between two countries while depositary receipts increase with such differences, consistent with the costs of complying with host country’s accounting standards affecting firms’ cross-listing decisions. After the harmonization of accounting standards, the authors find that IFRS-mandating host countries gain cross-listings from other IFRS-mandating jurisdictions, while IFRS-permitting countries do not experience such gains. These combined results suggest that accounting related compliance costs and comparability benefits both influence cross-listing decisions.

The paper employs unique settings that enable an in-depth examination of the role of compliance costs vs that of comparability benefits on cross-listing decisions. The settings employed by the authors allow them to disentangle the two channels and provide an important insight that accounting standard-related compliance costs and comparability benefits both affect cross-listing decisions.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.