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Referring to “behavioural finance” and “normative model” theories, this study explores the relative significance of behavioural heuristic biases in the investment decisions of real estate investment trusts (REITs) when compared with the conventional normative decision factors, with an ultimate aim to identify the significant behavioural factors that should be avoided to ensure rational asset acquisitions and market efficiency.

A triangulation approach was adopted. Qualitative multiple case studies were conducted, with four cases selected from Australian and New Zealand REITs across the industry, to identify what normative and behavioural finance factors are involved in investment decisions. This formed the basis for the subsequent expert review survey to explore how significant the behavioural factors were manifested in the judgement when compared with the normative factors.

Three out of four theoretical behavioural factors manifested themselves in the investment decisions: investor sentiment, anchoring factors and overconfidence. The overall impact of these three behavioural factors was that they were as significant as normative factors in investment decisions. The heuristic availability of information was found to have no significant effect on experienced REIT fund managers.

The findings were based on four multiple cases and an expert review survey of six frontline fund managers, which form a baseline upon which further research can be conducted to widen the scope of research to cover all REITs in Australasia so that the results can become more robust to benefit the entire market in the region.

As behavioural factors are significant in the decision-making process, REIT fund managers should raise awareness to avoid the significant behavioural factors identified, in particular investor sentiment, which was found to be the most significant one.

This study confirms the relative significance of behavioural factors in property investment decisions within the context of Australasian REITs and alerts fund managers to the ways they should follow to ensure rational investments and market efficiency. It also extends the scale of existing studies to cover not only Australia but also New Zealand for the benefit of the entire Australasian market.

This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

This study aims to examine the effect of corporate risk disclosure on investment efficiency. This study also seeks to contribute to existing literature of corporate risk disclosure by investigating voluntary and mandatory risk disclosure and its effect on the investment efficiency.

This study used two measures of corporate risk disclosure, level and quantity of corporate risk disclosure. A content analysis approach is adopted for non-financial Malaysian firms over the period 2010–2018.

The empirical results show that level of corporate risk disclosure leads toward efficient investment, whereas quantity of corporate risk disclosure causes inefficient investment when firms disclose more voluntary risks. Further, categorizing corporate risk disclosure into mandatory and voluntary risk disclosure, this study finds that voluntary risk disclosure tends to have higher investment inefficiency, while no evidence was found for mandatory risk disclosure.

This paper contributes to narrow stream of research investigating corporate risk disclosure through level and quantity contributing to the understanding of the level and quantity of risk disclosure in determining organizational investment efficiency.