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This article presents a pedagogical model that utilizes students as primary researchers in the identification, interviewing, and then reporting on women entrepreneurs as a major component of a multidisciplinary entrepreneurship course. The purpose of the course is to attract students who may not be familiar with the entrepreneurship concept itself, the role of women in such economic ventures, or the possibilities for people like themselves in such a career avenue. Students are exposed to the accomplishments of women entrepreneurs throughout U.S. history in the broad categories of agriculture and mining; construction; communication; manufacturing; service (both for profit and not-for-profit); transportation; and wholesale and retail trade. This content experience is then enhanced by the studentsʼ own direct interaction with and interviewing of women entrepreneurs. The implementation, potential outcomes, and possible adaptations of the course are described, and this transformational learning process model is illustrated.

The purpose of this study is to examine the role of selected sociodemographic variables in the ethical decision-making (EDM) process of professional accountants in Nigeria.

The study obtained data from 329 professional accountants with the aid of a structured questionnaire containing four dilemmatic ethical vignettes. The data were analysed using Kendall correlation, Kruskal–Wallis and Jonckheere–Terpstra tests.

The results revealed that upbringing, especially parental discipline, and education are significant sociodemographic determinants of EDM. Religion and experience played little or no significant role in predicting accountants’ EDM in the face of ethical dilemmas.

The study used a questionnaire to measure its variables, which may bias and somewhat inflate the findings. Hence, caution should be applied regarding its conclusion.

The evidence in this study could stimulate policy change and review to include a separate ethics course in the accounting education curriculum, which could enhance the ethics training of future accountants. This is important for countries like Nigeria, where no provision is made for a discrete ethics course in the curriculum for accounting under-graduate education.

The study draws attention to the fact that ethical conduct among professionals and in society could be enhanced through proper upbringing and formal education.

The study adds some uniqueness in focusing on professional accountants in Nigeria, a developing country with high corruption profile and weak government institutions, and, as such, contributes to the limited research output on accounting ethics in developing countries.

Ensuring quality is one of the most significant challenges in microtask crowdsourcing tasks. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer, but the existing study seems to be limited to the single-step task. This study aims to look at multiple-step classification tasks and understand aggregation in such cases; hence, it is useful for assessing the classification quality.

The authors present a model to capture the information of the workflow, questions and answers for both single- and multiple-question classification tasks. They propose an adapted approach on top of the classic approach so that the model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. They evaluate their approach with three representative tasks from existing citizen science projects in which they have the gold standard created by experts.

The results show that the approach can provide significant improvements to the overall classification accuracy. The authors’ analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated data sets for the same task. Furthermore, the authors observed interesting patterns in the relationship between the performance of different algorithms and workflow-specific factors including the number of steps and the number of available options in each step.

Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, the proposed method can be applied to a wide range of tasks including both single- and multiple-question classification tasks.