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The authors discuss how artificial intelligence (AI) can facilitate and incentivize reform in corporate social responsibility (CSR), i.e. governance with regard to pledge for socially responsible investments (SRIs).

The current essay examines the impacts of AI systems on the dynamics between corporate governance and financial markets in terms of promoting sustainability. Based on a qualitative review of over 100 pieces of literature, we offer an outlook into the mentioned dynamics through the lens of complex systems. Legal and ethical provisions for safe and robust AI systems are briefly discussed and integrated.

In a closed system, there is a reinforcing feedback loop between SRI and CSR. AI is a moderator to increase SRI and a mediator to incentivize CSR. If the legal and ethical provisions are involved in the AI systems, they could act as catalyst for corporate governance reform towards sustainability.

The findings are based on a solely qualitative assessment of the prior literature in corporate governance, corporate law, AI and data governance, asset pricing, and asset allocation. The scopes and specifics of the relationship are yet to be found through quanatitative or quantitative/qualitative hybrid methods.

This is to certify that to the best of our knowledge, the content of this thesis is our own work. This paper has not been submitted for any degree or other purposes. The authors certify that the intellectual content of this research is the product of the authors’ own work and that all the assistance received in preparing this paper and sources have been acknowledged. Russ McBride Alireza Dastan Poorya Mehrabinia

The authors revisit the inquiry into the primacy of shareholders vis-à-vis stakeholders that has been debated since 19th Century. The authors consider B-business firms as the closest groups of firms that have considerable similarities to stakeholders' firms. The authors model the impact of being certified as stakeholders (B-business) firms in a worldwide environment.

Employing daily returns data of B-corporations in a global setting during 2010–2021, the authors quantify and compare the firms' performance in the pre- and post-certified periods, measure the effect of their environmental social governance (ESG) scores on their performance and gauge the entire results on a standardized approach that yields easy interpretation.

Subject to some caveats arising from limited coverage and the lack of data on proper control variables, the findings, based on the statistical significance of the estimated coefficients, do not indicate any changes in B-corporations' performance in their post-certification dates. Notwithstanding that, market factor appears to be the driving force consistently.

Prior studies on B-corporations are overwhelmingly qualitative. The current study is the first study that evaluate performance of B-corporations' returns at firm level with daily data.

This study proposes the Smart SME Technology Readiness Assessment (SSTRA) methodology which aims to enable practitioners to assess the SMEs Industry 4.0 technology readiness throughout the end-to-end engineering across the entire value chain; the smart product design phase is the focus in this paper.

The proposed SSTRA utilises the analytic hierarchy process to prioritise smart SME requirements, a graphical interface which tracks technologies' benchmarks under Industry 4.0 Technology Readiness Levels (TRLs); a mathematical model used to determine the technology readiness and visual representation to understand the relative readiness of each smart main area. The validity of the SSTRA is confirmed by testing it in a real industrial environment. In addition, the conceptual model for Smart product design development is proposed and validated.

The proposed SSTRA offers decision-makers the facility to identify requirements and rank them to reflect the current priorities of the enterprise. It allows SMEs to assess their current capabilities in a range of technologies of high relevance to the Industry 4.0 area. The SSTRA assembles a readiness profile allowing decision-makers to not only perceive the overall score of technology readiness but also the distribution of technology readiness across the main smart areas. It helps to visualise strengths and weaknesses; whilst emphasising the fundamental gaps that require serious action to assist the program with a well-balanced effort towards a successful transition to Industry 4.0.

The SSTRA provides a step-by-step approach for decision-making based on data collection, analysis, visualisation and documentation. Hence, it greatly mitigates the risk of further Industry 4.0 technology investment and implementation.