Abstract
The aim of this study is to examine the impacts of board size, gender diversity and independence on ESG performance whilst also examining the impact of country-level social trust on such performance. We perform a panel data analysis and the least squares method for a sample of 75 European banks and a time span of 4 years from 2016 to 2019. We find that ESG performance is positively associated with board gender diversity and independence, and negatively associated with board size. Surprisingly, we find a negative relationship between country-level social trust and ESG performance. This is an important finding that we interpret as being related to the loss of confidence in the banking sector in the wake of the 2008 financial crisis. To regain such trust, the banking sector is likely to have suffered higher social pressure to engage in ESG activities in countries where social trust is lower. © 2023 by the authors.

Abstract
Blockchain technology has been the focus of much attention and discussion recently. Its unique characteristics, such as immutability, transparency, and decentralization, create a great potential solution for many industries. Especially the automotive sector, which is known for its complex supply chains, large amounts of data, and wide-reaching infrastructure, can profit in many aspects by incorporating blockchain technology. To illustrate the advantages and potential of blockchain in supply chain management, this chapter includes a case study of one of the leading corporations in the automotive industry that has been increasingly committed to adopting this technology. The analysis uses a set of strategic analysis tools frequently used by managers for strategic planning to highlight the benefits and challenges of this approach. Besides contributing to the literature on blockchain and supply chain management, this chapter offers valuable insights for managers, namely in the automotive sector, who are considering adopting blockchain technology in their operations and processes. © 2023, IGI Global. All rights reserved.

Abstract

Abstract
The recent release of ChatGPT-3 by OpenAI may have been a major disruptive mark in terms of Artificial Intelligence based tools. The testing and rapid user adoption rate of ChatGPT-3 was massive with a worldwide impact. Despite its recent public release ChatGPT-3 is already eliciting a mix of positive reactions revealing outstanding positive aspects as well as some negative ones. A short evaluation of ChatGPT-3 is presented, using the context of genetic algorithms, a topic lectured in introductory artificial intelligence courses. Examples outlining potential advantages of adopting ChatGPT and disadvantages which raise ethical issues and may limit its use are presented.

Abstract

Abstract
Advances in artificial intelligence trigger transformations that make more and more companies enter Industry 4.0 and 5.0 eras. In many cases, these transformations are gradual and performed in a bottom-up manner. This means that in the first step, the industrial hardware is upgraded to collect as much data as possible without actual planning of the utilization of the information. Furthermore, the data storage and processing infrastructure is prepared to keep large volumes of historical data accessible for further analysis. Only in the last step are methods for processing the data developed to improve or gain more insight into the industrial and business processes. Such a pipeline makes many companies face a problem with huge amounts of data, an incomplete understanding of how the existing knowledge is represented in the data, under which conditions the knowledge no longer holds, or what new phenomena are hidden inside the data. We argue that this gap needs to be addressed by the next generation of XAI methods which should be expert-oriented and focused on knowledge generation tasks rather than model debugging. The paper is based on the findings of the EU CHIST-ERA project on Explainable Predictive Maintenance (XPM).