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The introductory chapter includes how to design-in good practices in theory, data collection procedures, analysis, and interpretations to avoid these bad practices. Given that bad practices in research are ingrained in the career training of scholars in sub-disciplines of business/management (e.g., through reading articles exhibiting bad practices usually without discussions of the severe weaknesses in these studies and by research courses stressing the use of regression analysis and structural equation modeling), this editorial is likely to have little impact. However, scholars and executives supporting good practices should not lose hope. The relevant literature includes a few brilliant contributions that can serve as beacons for eliminating the current pervasive bad practices and for performing highly competent research.

This paper tests the information efficiency of the Chinese stock market to judge if it contravenes the Fama (1965) efficient market hypothesis. Following China’s stock market reforms the market has grown in international importance, however many find information difficult to obtain and interpret.

The relative importance of earnings announcements is quantified using cross-sectional regressions of calendar-year annual returns R_i on returns in the four quarterly earnings announcement windows return R_j. The metric developed by Ball and Shivakumar (2008) and Basu et al. (2013) is used to determine the level of new information contained in earnings announcements.

Analysis reveals earnings announcements contain little new information of value, thus demonstrating the Chinese markets are not informationally efficient. Therefore, investors cannot automatically assume assets are always correctly priced, as they are in other established markets.

Major structural changes were made to Chinese equity markets in 2004, with the introduction of the Qualified Foreign Institutional Investors scheme. At the same time, firms were required to make earnings announcements on a quarterly basis. This paper is the first to test the impact of these changes on the information value of earnings announcements since the changes.

In many security domains, the ‘human in the system’ is often a critical line of defence in identifying, preventing and responding to any threats (Saikayasit, Stedmon, & Lawson, 2015). Traditionally, such security domains are often focussed on mainstream public safety within crowded spaces and border controls, through to identifying suspicious behaviours, hostile reconnaissance and implementing counter-terrorism initiatives. More recently, with growing insecurity around the world, organisations have looked to improve their security risk management frameworks, developing concepts which originated in the health and safety field to deal with more pressing risks such as terrorist acts, abduction and piracy (Paul, 2018). In these instances, security is usually the specific responsibility of frontline personnel with defined roles and responsibilities operating in accordance with organisational protocols (Saikayasit, Stedmon, Lawson, & Fussey, 2012; Stedmon, Saikayasit, Lawson, & Fussey, 2013). However, understanding the knowledge that frontline security workers might possess and use requires sensitive investigation in equally sensitive security domains.

This chapter considers how to investigate knowledge elicitation in these sensitive security domains and underlying ethics in research design that supports and protects the nature of investigation and end-users alike. This chapter also discusses the criteria used for ensuring trustworthiness as well as assessing the relative merits of the range of methods adopted.