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The purpose of this paper is to describe survey findings on onboarding initiatives at the Carnegie Doctoral Research Institutions of Higher Education libraries. The findings would be helpful to libraries that are at the beginning of their own onboarding initiatives or that wish to compare ongoing efforts.

The authors conducted a survey of Carnegie Doctoral Research Institutions of Higher Education libraries using Qualtrics, an online survey software. Link to the survey with a short explanation was e-mailed to the 319 identified contacts and 111 responses were received at the end of the survey.

Survey responses revealed that the most prevalent types of onboarding initiatives are an orientation to campus policies and procedures and meeting with human resources. Half of the respondents introduce new employees to the social/cultural norms of the library informally, with responsibility for onboarding falling on the supervisor. Surprisingly, diversity and inclusion have not been identified as formal components of the onboarding by those who engage in it.

Specific research into the onboarding initiatives of Carnegie Doctoral Research Institutions of Higher Education libraries does not exist.

To develop a method for benchmarking teaching and learning in response to an institutional need to validate a new program in Dentistry at the University of Sydney, Australia.

After a collaborative partner, University of Adelaide, was identified, the areas of teaching and learning to be benchmarked, PBL approach and assessment, were established. A list of quality indicators for these aspects of teaching and learning were first developed conceptually and then validated by the literature. Then, using a quality enhancement framework, levels of achievement for each indicator were developed.

The findings are represented as a set of tables. These were mutually developed with the benchmarking partner and represent an agreed model for a benchmarking project to progress to the next stages of implementation and evaluation.

This model can be adapted for any benchmarking project in all levels of education; primary, secondary, tertiary and continuing.

The issue of benchmarking is high on the educational agenda, especially in higher education. The literature reports on a number of projects but with no clear explanation of a method for benchmarking. The fact that this model is evidence‐based in its approach and that it focuses on learning and teaching, also marks it as original and a significant development in this area.

Due to the popularity of mobile devices and the development of artificial intelligence (AI), AI-powered mobile fitness applications (MFAs) have entered people's daily lives. However, the extant literature lacks empirical investigations that explore users' continuance usage intentions regarding AI-powered MFAs. To fill this research gap, this paper employs goal-setting theory to establish a research model for exploring how AI-enabled features (i.e. intelligence and anthropomorphism) affect users' perceptions of goal difficulties and goal specificities, which in turn affect their MFA continuance usage intentions.

This paper uses a survey method to analyze the research model, and a total of 223 responses are collected. The partial least squares (PLS) technique is utilized for data analysis.

The results show that intelligence and anthropomorphism affect the continuance usage intention of MFA users through their goal difficulty and specificity. Both intelligence and anthropomorphism positively influence goal specificity, whereas they negatively affect goal difficulty. In addition, goal specificity increases users' MFA continuance usage intention, whereas goal difficulty decreases users' continuance usage intention. The findings of this study provide theoretical contributions for AI technology adoption research and offer practical strategies for firms to retain MFA users.

Based on goal-setting theory, this study reveals that as two primary AI features of contemporary mobile fitness apps, intelligence and anthropomorphism, can increase comprehension of users' perceptions regarding goal difficulty and specificity in the context of users' continuance usage intentions toward AI-powered MFAs.