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In early 2014, recent Stanford University graduate Tyler Shultz was in a quandary. He had been working at Theranos, a blood-diagnostic company founded by Elizabeth Holmes, a Stanford-dropout wunderkind, for almost a year. Shultz had learned enough about the company to realize that its practices and the efficacy of its much-touted finger-prick blood-testing technology were questionable and that the company was going to great lengths to hide this fact from the public and from regulators.

Theranos and Holmes were Silicon Valley darlings, enjoying positive press and lavish attention from potential investors and technology titans alike. Just as companies like PayPal had revolutionized the stagnant payments industry and Uber had upended the for-hire transportation sector, Theranos had been positioned as the latest technology firm to substantially disrupt yet another mature sector: the medical laboratory business. By the start of 2014, the company had raised more than $400 million in funding, and had an estimated market valuation of $9 billion.

Shultz's situation was exacerbated by the fact that his grandfather, the highly respected former US Secretary of State George Shultz, was on the Theranos board and was one of Elizabeth Holmes's biggest supporters.

But Tyler Shultz worried about the customers he was convinced were receiving highly unreliable and often inaccurate blood-test results. With so much at stake, Shultz wondered how he should proceed. Should he raise his concerns with the firm's investors? Blow the whistle externally? Report to industry regulators? Go away quietly?

This case and its subsequent four brief follow-up cases are based largely on interviews with Tyler Shultz, and outline the dilemma he faced and the various steps he would take both to extricate himself from his unsavory position and let the public know the full extent of the deception at Theranos.

Five optional handouts are available to instructors to further discussion after the case has been debriefed. The handouts serve as additional decision points for the students if your class time permits.

This bibliometric study aims to analyze the latest trends and patterns of big data applications in librarianship from 2000 to 2022. By conducting a comprehensive examination of the existing literature, this study aims to provide valuable insights into the emerging field of big data in librarianship and its potential impact on the future of libraries.

This study employed a rigorous four-stage process of identification, screening, eligibility and inclusion to filter and select the most relevant documents for analysis. The Scopus database was utilized to retrieve pertinent data related to big data applications in librarianship. The dataset comprised 430 documents, including journal articles, conference papers, book chapters, reviews and books. Through bibliometric analysis, the study examined the effectiveness of different publication types and identified the main topics and themes within the field.

The study found that the field of big data in librarianship is growing rapidly, with a significant increase in publications and citations over the past few years. China is the leading country in terms of publication output, followed by the United States of America. The most influential journals in the field are Library Hi Tech and the ACM International Conference Proceeding Series. The top authors in the field are Minami T, Wu J, Fox EA and Giles CL. The most common keywords in the literature are big data, librarianship, data mining, information retrieval, machine learning and webometrics.

This bibliometric study contributes to the existing body of literature by comprehensively analyzing the latest trends and patterns in big data applications within librarianship. It offers a systematic approach to understanding the state of the field and highlights the unique contributions made by various types of publications. The study’s findings and insights contribute to the originality of this research, providing a foundation for further exploration and advancement in the field of big data in librarianship.

This study aims to identify how objective indicators of destination country risk differentiate business study abroad programs from those in other academic disciplines.

The authors trained a neural network model on six years of student-initiated inquiries about study abroad programs at a large US university. The model classified business versus nonbusiness study abroad programs using objective measures of destination country risk as the primary inputs.

The model correctly classifies business and nonbusiness study abroad programs with over 70% accuracy. Business programs were found to be 20% less likely to include destinations where the Centers for Disease Control and Prevention recommend nonroutine vaccinations and favor countries with higher Global Peace Index scores.

These results underscore the need to consider destination country risk in the design and administration of study abroad programs. An understanding of student preferences for lower risk destinations can contribute to improved planning, execution and student experiences in these programs.

Better planning and management of study abroad programs based on understanding of destination country risk can lead to enhanced student safety and experiences.

This study offers a unique perspective on understanding study abroad programs by focusing on objective measures of destination country risk rather than risk perceptions. It also is, to the best of the authors’ knowledge, the first to use a neural network to classify study abroad programs as business versus nonbusiness using objective measures of country-specify risk indicators.