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PurposeThe research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education. Design/methodology/approachAs businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis. FindingsAfter revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made. Originality/valueVery limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice. 

Preliminary findings from the national survey on how research administrators use research analytics in data-driven decision making. 

As data-informed decision-making continues to evolve across multiple disciplines in higher education institutions, and as the role of research administration continues to expand from proposal submissions, compliance, and managing research and development expenditures to a profession of an active partnership with investigators to support institutional research, it becomes necessary to have a strategy or model to inform best practices in both strategic planning and business operations within research administration. As numerous research evaluation frameworks and assessment models were proposed to improve the administrative processes of institutional research, this project proposes a data-analytics approach to enhance the services provided by research administrators to minimize risks, maximize efficiencies, and better support researchers and other research and development (R&D) personnel in higher education. This article documents the process of conducting a systematic literature review on leveraging research administration data-analytics in higher education to provide evidence-based practices to inform decision-making regarding strategic advancement in institutional research. The literature review examines current works on how data can be effectively used in process operations, control, and provides new perspectives on how research administration process systems might evolve into a new paradigm of data enhanced operations and control.