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The appraisal of corporate reputation based on third-party corporate social responsibility (CSR) indices appears to have been institutionalized. The endorsement of such an approach by sustainability custodians and influencers undermines the uptake of the morality and legitimacy of CSR. This study takes a social realist perspective, which suggests that social phenomena such as CSR and corporate reputation are shaped by social structures and power relations. This study aims to contribute to a deeper understanding of the complex relationship between CSR and corporate reputation and understand ways in which the constructs are influenced by cognitive factors.

This study surveyed 411 respondents across five shopping malls and analyzed the data using path analysis of the structural equation modeling (SEM) technique. The mall-intercept survey sought to critically assess expectations of CSR vis-à-vis evaluation of corporate reputation. Based on a case study of three Johannesburg Stock Exchange listed companies, CSR expectations were measured along the philanthropic, economic, ethical and legal dimensions, while evaluation of corporate reputation was based on product quality, financial performance and social responsibility. SEM path analysis was used to extrapolate the predictive outcomes of CSR on corporate reputation.

Reputation for product quality and social responsibility is underpinned by the fulfillment of ethical CSR expectations, while philanthropic gestures enhance the evaluation of financial performance. Legal CSR significantly influences the reputation for social responsibility and product quality. Fulfillment of economic CSR expectations influences the reputation for product quality. However, no relationship was established between economic performance and social responsibility. Involvement in economic, philanthropic and particularly, legal CSR, are not indicative of the reputation for financial performance. Conversely, companies’ involvement in economic CSR does not suggest a higher propensity for social responsibility.

The predictive outcomes of CSR expectations on corporate reputation can reveal situated understanding of actual perceptions of corporate behavior.

Ethical business conduct is synonymously associated with social responsibility while espoused corporate philanthropy signals strong financial performance. The awareness of consumers’ cognitive evaluation of corporate reputation can offer a pathway to corporate communication professionals, policy makers and agencies to rethink and reposition CSR efforts.

Insensitivity to taken-for-granted cultural prescriptions and reliance on market-based reputational rankings undermine mutually beneficial stakeholder relationships and the social license to operate.

This study brings to the fore, cognitively dominated indicators of consumers’ perceptions of the reputation for CSR, to foster nuanced and halo-removed approaches to social responsibility. The authors show for the first time how companies’ skewed focus on corporate philanthropic giving paradoxically signals a capitalistic notion of social responsibility and unethical business conduct. This study offers a halo-removed orientation to the appraisal of CSR and corporate reputation.

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.