The keyword for the future of academic profession studies is complementarity. Insights from surveys, whether large- or small-scale, differ fundamentally from those from interviews—but together, they can advance the field both theoretically and empirically. Big data should therefore complement surveys and interviews, while macro-level analyses should accompany micro-level studies.

The impact of the increasing globalization and digitalization of science on the higher education research field is potentially high. A business-as-usual approach—in the face of new opportunities and competitive rival fields pursuing similar research agendas—limits the attractiveness of the field to scholarly and policy communities. The increasing availability of digital data on scholarly activities will have a powerful impact on the field.

The Rise of Data Science

Traditional social scientists must now compete with data scientists and computational social scientists, who increasingly focus on issues long explored in higher education research. To remain competitive, the field must embrace new tools and datasets emerging in data-intensive social sciences.

There is growing pressure—both within and outside academia—to use much larger datasets to draw valid conclusions. The pressure to quantify academic careers is increasing, as small-scale academic surveys lose traction in the social sciences. Small sample sizes limit the scope of analyses and weaken policy implications. Low numbers of observations by gender, academic discipline, institutional type, age group, or productivity class reduce analytical power and weaken policy implications. To go beyond standard analyses (in use for several decades now) and to show the ongoing attractiveness of the survey instrument in academic profession studies, future surveys need to use questionnaires returned from larger numbers of scholars. This is the way major competitors to the field—data analysts and computer scientists—currently do their research.

Opportunities for Higher Education Research

The field faces significant opportunities if it understands how globalization-related advances are already used in competing fields for scholarly and policy attention. Digital data on research funding, productivity, collaboration, impact, and mobility can now be explored at an unprecedented scale and with utmost care for detail. The study of the academic profession can be transformed beyond recognition.

However, these opportunities come at a cost: intensified competition. Various fields and subfields now study academics and their institutional settings, making higher education research one of many fields focused on academic careers. Equipped with traditional methods and small datasets, the field risks losing ground to those harnessing big data—especially large bibliometric datasets such as Scopus, Web of Science, or OpenAlex. The question is where future data, interpretation, and knowledge of the academic sector will be located.

Specifically, numerous traditional higher education issues are being increasingly studied in what is termed the “science of science,” quantitative studies, and “research on research.” The social sciences have entered a golden age, with scientists involved in the above data-intensive fields using big data sources and computational power and skills as part of the big data revolution.

Academic profession studies face data science as their primary competitor. Large-scale studies of academic careers, based on hundreds of thousands or millions of individual career and publication portfolios, challenge small-scale, survey-based research in both scholarly and policy terms.

In the contest between the approaches using surveys and those using big digital datasets, the traditional small-scale survey approach is losing. The contest extends far beyond what is more widely read and cited to encompass what is valued in scholarly terms (prestige and status generation) and what is fundable (resource generation). New research from computer scientists examines social stratification, academic career structures, and recognition systems across disciplines and countries.

However, these studies rely heavily on theoretical frameworks developed within higher education research. Our theories from the past five decades remain the foundation on which the field’s future can be successfully built. Today, the field needs to be aware of what the expansionary, competing fields can offer its academic and policy communities, while also not losing its distinct identity. The best way forward is to keep its sophisticated level of theorization, while incorporating new methodological tools and digital datasets for its purposes. That means asking the same fundamental questions that have been asked for decades—i.e., about themes such as productivity, collaboration, impact, and mobility—in addition to new ones, but using new data-intensive approaches, methodologies, and data sources made available by the digital revolution.

The Way Forward

Future studies of the academic profession (and academic careers) may usefully combine bibliometric and survey-based tools, datasets, and methodologies to explore entire populations of academics by combining publication and citation data, large-scale survey data, massive (possibly artificial intelligence-assisted) interviews, and (wherever possible) biographical and career data derived from national registries of scientists and commercial datasets on academic careers. A combination of approaches seems likely to enhance our understanding of the changes and complexity of academic work under powerful political and economic pressures.

The keyword for the field’s future is complementarity. In academic profession studies, big data should complement surveys and interviews; macro-level analyses should accompany micro-level studies. Insights from surveys, whether large- or small-scale, differ fundamentally from those gained through interviews—but together, they can advance the field both theoretically and empirically.

Marek Kwiek is professor and UNESCO Chair in Institutional Research and Higher Education Policy at the University of Poznan, Poland. E-mail: [email protected].