Search

One approach to improve a firm’s Environmental, Social, and Governance (ESG) rating is to acquire a target with a higher relative ESG rating. We explore changes in an acquirer’s ESG rating around merger and acquisition (M&A) announcements and provide empirical evidence of a positive relationship between the change in an acquirer’s ESG rating and the target’s relative ESG rating. Of the three components of an ESG rating, an acquirer’s environmental rating displays the largest increase, with social and governance ratings exhibiting a smaller but still significant post-merger increase. This relationship is weaker for cross-border deals or cross-industry deals. However, deals that are both cross-border and same-industry are associated with a larger increase in an acquirer’s ESG rating. In addition to improved ESG ratings, the acquisition of a firm with a superior ESG rating is also associated with higher bid premiums and improved post-merger financial performance which suggests that acquirers act in shareholders’ best interests. JEL Classification: G14, G34

Purpose: The purpose of this paper is to demonstrate privacy concerns arising from the rapidly increasing advancements and use of artificial intelligence (AI) technology and the challenges of existing privacy regimes to ensure the on-going protection of an individual’s sensitive private information. The authors illustrate this through a case study of energy smart meters and suggest a novel combination of four solutions to strengthen privacy protection. Design/methodology/approach: The authors illustrate how, through smart meter obtained energy data, home energy providers can use AI to reveal private consumer information such as households’ electrical appliances, their time and frequency of usage, including number and model of appliance. The authors show how this data can further be combined with other data to infer sensitive personal information such as lifestyle and household income due to advances in AI technologies. Findings: The authors highlight data protection and privacy concerns which are not immediately obvious to consumers due to the capabilities of advanced AI technology and its ability to extract sensitive personal information when applied to large overlapping granular data sets. Social implications: The authors question the adequacy of existing privacy legislation to protect sensitive inferred consumer data from AI-driven technology. To address this, the authors suggest alternative solutions. Originality/value: The original value of this paper is that it illustrates new privacy issues brought about by advances in AI, failings in current privacy legislation and implementation and opens the dialog between stakeholders to protect vulnerable consumers.