This study examines the relationship between the adoption of blockchain technology and environmental efficiency by using a sample of US firms over the 2015-2019 period. Our results indicate that the adoption of blockchain technology is positively and significantly associated with environmental efficiency, suggesting that blockchain improves environmental sustainability. In further analyses, we determine that the relationship between blockchain and environmental efficiency is more pronounced for firms in financial and technological industries than for those in other industries.Our findings are also robust to other methods that control for endogeneity, including difference in difference regressions and propensity score matching. Overall, we provide empirical evidence to incentivize business leaders and policymakers to adopt innovative technologies, such as blockchain.

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We examine the impact of three business strategies separately and in combination on the tendency for firms to engage in corruption. Using a sample of 56,827 firm-year observations for small-and medium-sized enterprises (SMEs) over the 2006-2018 period, we find that firms with business group affiliations are more likely to engage in corrupt practices in countries with low business freedom. However, those in countries with high business freedom are less likely to do so. We also find that firms that engage the services of external auditors and adopt international standards are less likely to be corrupt, especially in countries with weak financial reporting standards. Our results also show that corruption intensity reduces even more for firms that employ the three strategies, whether we consider institutional factors or not. This result holds when we use a three-way interaction term. We conclude that the three strategies are mutually reinforcing and that firm-level and country-level efforts complement each other in mitigating corruption.

The conclusions of any quantitative research must be supported by appropriate data. This chapter discusses the methods of collecting quantitative data for research. It begins by giving an overview of the nature of quantitative research. It also discusses the two major sources of collecting quantitative data. For primary data collection, issues such as sampling, measurement and surveys are discussed. Examples and sources of obtaining secondary data are also presented. Finally, some ideas are provided for how to evaluate quantitative data in order to ensure it is appropriate for analysis.

We investigate the informativeness of earnings announcements in African stock markets and examine whether, conditional on the level of synchronicity and liquidity of stocks, market reactions are influenced by earnings characteristics. Normalized volatility indicates that earnings announcements are informative across the sample. The results are driven by less frequently traded stocks and informativeness manifests more clearly at announcement and in the post‐announcement window. There is little evidence of leakage. Informativeness is also present for highly traded stocks, notably after announcement. Cross‐sectional tests provide evidence of an effect of both earnings fundamentals and investor behaviour on stock returns around earnings announcements.

We investigate whether corruption in host countries drives the different routes of Chinese economic engagement with Africa. Using data from 49 African countries for 2000–2018, we find that corruption affects each route of China’s engagement with Africa differently. Corruption in Africa is significantly negatively associated with FDI from China, but significantly positive with both trade and construction. These relationships are moderated by the availability of natural resources but do not change after the implementation of the Xi Jinping anti-corruption campaign. By disaggregating China–Africa financial engagement into its different routes, we demonstrate that the relationship between corruption and China’s presence in Africa varies with the nature of the engagement. JEL Classification: F21, F23

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PurposeThis study investigates the level of stock return synchronicity in African markets with the aim of establishing whether, contrary to conventional wisdom, stock return synchronicity can be low in countries with relatively weak information environments.Design/methodology/approachThe authors use a sample of five African countries (Botswana, Ghana, Kenya, Nigeria and South Africa) and a total of 616 firms over the period 2005–2015. This study's main measure of synchronicity is the R2 from a regression of stock returns on index returns. The authors also carry out regression analysis to investigate the main firm-level drivers of synchronicity.FindingsOn average, firms in African markets do not exhibit high levels of stock return synchronicity, providing support for the view that stock return synchronicity can be low in markets with relatively weak transparency. The authors, however, observe an increase in the level of synchronicity during the global financial crisis, notably for Ghana and Kenya. In the regression analysis, the main firm-level driver of synchronicity is firm size, while contrary to some previous studies, ownership structure has no impact. The authors also find evidence of the impact of changes in accounting regulation, notably the mandatory adoption of IFRS, on the level stock synchronicity.Originality/valueThis study contributes to the understanding of stock return synchronicity and how price discovery can vary between different information environments. The authors argue that stock returns in African countries may not always fit the stereotypical view that they are synchronous. The level of synchronicity among firms suggests that corporate events may carry some stock price implications.