The empirical core of the paper is a state-of-the-practice survey, reviewed and approved by the Internal Review Board at the University of Colorado Denver, that asked scholars about the research methods they have used over the past decade, the dependent variables that anchor their studies, the economic theories embedded in doctoral training, and the institutional incentives that shape career decisions. Respondents also answered open-ended questions about the barriers that keep the information systems community from engaging with climate and environmental challenges, and about alternative pathways for doing better. The researchers coded these responses using established qualitative content analysis techniques, with generative AI tools used only to summarize pre-coded themes and select representative quotes, all of which the authors independently verified against the original data.

The picture that emerges is one of a field that is aware of the problem but structurally unprepared to address it. Scholars overwhelmingly recognize the importance of environmental issues, yet the survey reveals that research agendas remain dominated by revenue, innovation, customer, and performance outcomes, while explicit incentives to pursue climate-related work are rare in departments and doctoral programs. Doctoral curricula lean heavily on free-market and mixed economics, leaving graduates with little exposure to the ecological, political, or critical economic traditions that question growth itself. The authors argue that this training pipeline reproduces a narrow view of what information technology is for, treating it primarily as an engine of productivity and profit rather than as a force embedded in biophysical systems.

The technical backdrop to this critique is stark. The information and communication technology sector’s energy and emissions footprint is growing as data volumes soar, and the rise of generative artificial intelligence is accelerating demand for power-hungry data centers, a trend documented in industry analyses from Deloitte and the World Economic Forum. Local communities are beginning to push back, as in the reported rejection of an Amazon-linked data center project amid fears over water use. The authors also cite material flow research showing that the volume of man-made stocks increased twelvefold between 1900 and 2015, with roughly 35 percent of that material now solid waste and 25 percent emitted into the environment. Consumption-based accounting of material footprint shows a consistent rise alongside GDP, raw material extraction, and carbon dioxide emissions since 1970.

Against this backdrop, the paper’s theoretical move is to import the frameworks of ecological economics into information systems research. The field’s intellectual foundations, the authors argue, rest on assumptions inherited from neoclassical economics: that economies can grow indefinitely, that technology will decouple output from resource use, and that market mechanisms will price environmental damage appropriately. Ecological economics starts from a different premise. The Earth is effectively a closed system for materials, open only to incoming solar energy, so no amount of digital innovation can conjure new resources or make waste disappear. Economist Nicholas Georgescu-Roegen argued as early as 1975 that the transition from fossil fuels to solar energy is not merely a policy option but an ultimate necessity for economies to operate within biophysical limits, a point the authors treat as foundational for any credible theory of sustainable computing.

Central to the proposed transformation is Kate Raworth’s Doughnut Economics, whose dual focus on ecological ceilings and social foundations the authors adopt as a template for reorienting the discipline. In this framing, humanity must live below planetary boundaries, such as climate stability and intact ecosystems, while ensuring that everyone has access to the social essentials of a dignified life. The authors suggest that information systems research should be evaluated not by whether it increases firm performance or adoption rates, but by whether it helps societies stay within the doughnut. Alternative indicators already exist to support such a shift: the Genuine Progress Indicator adjusts personal consumption for inequality, household labor, and environmental costs, and long-term data show that GDP and GPI moved together until the late 1970s before diverging, with GDP rising while genuine progress declined.

The survey respondents themselves offered a set of alternative pathways, which the authors organized into four aggregate dimensions: institutional change, leadership, research approach, and other measures. On the institutional front, scholars called for departments to provide explicit financial rewards and promotion criteria for climate-related research, and for doctoral programs to broaden the economic theories taught alongside methodological training. On the research front, respondents pointed toward studying green information systems in organizations, circular economy practices enabled by digital technologies, carbon management systems that promote ecologically responsible behavior, energy informatics, and the detection and reduction of data waste through machine learning. Others emphasized the need to distinguish between artificial intelligence for sustainability and the sustainability of artificial intelligence itself, given the technology’s own growing resource demands.

The authors are careful to note that the barriers are not primarily attitudinal. Information systems scholars already perceive the community as having reasonable awareness of climate and environmental challenges; what is missing, they argue, is domain knowledge, relevant theories, and data sources for measuring climate impacts within the field’s traditional research domains. This gap between awareness and capability is compounded by incentive structures: ranking systems that track publications in the field’s leading journals reward conventional topics, and junior researchers rationally follow the path of least resistance. Without changes to what gets rewarded, the authors warn, individual goodwill will continue to produce only marginal, incremental contributions while the window for meaningful action narrows.

The paper’s recommendations extend to journal editors and institutional leaders, not just individual researchers. Editors are encouraged to welcome work that measures ecological and social outcomes alongside economic ones, and to treat ecological framing as legitimate across the full range of information systems topics rather than confining it to special issues. Institutions are urged to align hiring, promotion, and doctoral education with the reality of the environmental crisis. The authors also acknowledge the practical grounds for optimism: the International Renewable Energy Agency reported in 2025 that solar photovoltaic projects were on average 41 percent cheaper than the lowest-cost fossil fuel alternatives, and onshore wind 53 percent cheaper, meaning that the technological economics increasingly favor the transition that ecological theory demands.

What makes the study notable is its willingness to question the discipline’s own growth-oriented foundations at a moment when the digital sector’s material consequences are becoming impossible to ignore. The authors conclude that, given the urgency of the crisis, ecological perspectives should serve as a guiding meta-framework for designing and conducting general information systems research, shaping research priorities, institutional structures, collaborative practices, and methodological approaches alike. Whether the community takes up that challenge will depend less on the persuasiveness of any single paper than on whether the incentives that govern academic careers can be redesigned to reward work that treats the planet’s limits as a first-order research constraint rather than an afterthought.

Subject of Research: Environmental sustainability and ecological economics in information systems research

Article Title: [State-of-the-Practice-Survey] Environmental Sustainability in Information Systems Research: Beyond Incrementalism Toward a Transformative Path Forward

Article References: Oh, O., Gregg, D. G., & Aghakhani, N. (2026). [State-of-the-Practice-Survey] Environmental Sustainability in Information Systems Research: Beyond Incrementalism Toward a Transformative Path Forward. Information Systems Frontiers. https://doi.org/10.1007/s10796-026-10825-0

Image Credits: AI Generated

DOI: 10.1007/s10796-026-10825-0

Keywords: environmental sustainability, information systems, ecological economics, Doughnut Economics, climate crisis, green information systems, survey research, data centers, generative AI energy use, circular economy, Genuine Progress Indicator, academic incentives

The core premise is straightforward: science produces knowledge, but the way that knowledge circulates is governed by conventions that were established in an era of print journals, small laboratories, and slow communication. Those conventions persist even though the underlying infrastructure has been transformed. Preprint servers, open-access repositories, collaborative platforms, and data-sharing mandates have removed many of the technical barriers to rapid, transparent dissemination. Yet, as the commentary emphasizes, the cultural expectations surrounding credit, career advancement, and publication have not kept pace. Researchers still face powerful incentives to hoard data, to slice findings into the least publishable units, and to prioritize novelty over rigor, because those behaviors are what traditional evaluation systems reward.

Knowledge sharing sits at the center of the argument. The authors contend that openness should not be framed as an additional burden placed on already stretched researchers, but as a redesign of the default workflow. When data, code, protocols, and negative results are shared as a matter of routine, the entire enterprise becomes more efficient and more trustworthy. Other teams can verify findings, reuse materials, and avoid duplicating failed experiments. Systematic reviews and meta-analyses become more complete because the file drawer problem, in which unflattering results disappear from the literature, is mitigated at the source. The commentary suggests that institutions can accelerate this shift by embedding sharing requirements into grant conditions, laboratory onboarding, and graduate training, so that open practices become habitual rather than heroic.

Training emerges as a recurring theme throughout the piece. Cultural change in research, the authors argue, cannot be imposed from the top down alone; it must be cultivated in the next generation of scientists. Doctoral programs and early-career mentoring shape the professional identities of researchers more powerfully than any policy document. If mentorship continues to signal that impact factors and first-author papers in prestigious venues are the only currency that matters, then open-science mandates will remain symbolic. Conversely, when senior researchers model transparent practices, discuss failures openly, and reward careful replication work, junior scientists receive a coherent message about what constitutes good science. The commentary calls for structured programs that teach not only technical skills but also the norms of collaborative, reproducible research.

Evaluation reform receives equally sustained attention. The piece situates its argument within the broader international movement, exemplified by initiatives such as the San Francisco Declaration on Research Assessment and the Leiden Manifesto, that seeks to move institutions away from crude journal-level metrics as proxies for individual quality. The authors argue that evaluation systems function as the immune system of research culture: they determine which behaviors are accepted and which are rejected. As long as hiring, promotion, and funding decisions hinge on publication counts and journal prestige, researchers will rationally optimize for those signals, even at the expense of rigor, openness, and collegiality. Alternative approaches, including narrative curricula vitae, portfolio-based assessment, and explicit weighting of contributions such as peer review, mentoring, data curation, and community service, are presented as practical mechanisms for broadening the definition of scientific merit.

The commentary is careful to acknowledge that evaluation reform is difficult precisely because metrics are convenient. Quantitative indicators allow committees to compare large numbers of candidates quickly and appear objective. Replacing them with qualitative judgment requires time, training, and trust in evaluators. The authors respond that the apparent objectivity of citation counts conceals well-documented distortions, including field differences, self-reinforcing citation networks, and susceptibility to gaming. A more honest system, they suggest, would combine structured qualitative assessment with responsible use of quantitative evidence, applied at the level of individual contributions rather than journal brands. Several funding agencies and universities have already begun experimenting with such frameworks, and the commentary argues that these experiments should be evaluated with the same empirical rigor that scientists demand in their own research.

Community engagement forms the third pillar of the proposed cultural transformation. The authors argue that research culture is not confined to the walls of academia; it extends to how scientists relate to the public, to practitioners, and to the communities affected by their work. Participatory research models, citizen science, and co-design approaches are highlighted as strategies that both improve the relevance of research and redistribute epistemic authority. When patients, teachers, policymakers, or community members help formulate research questions, the resulting studies are more likely to address genuine needs, and the findings are more likely to be trusted and used. Engagement, in this framing, is not a dissemination afterthought but a constitutive part of the scientific process that begins at the stage of question selection.

The piece also addresses the structural conditions that make cultural change possible. Individual researchers, however motivated, operate within systems shaped by institutions, funders, publishers, and learned societies. The commentary therefore advocates coordinated action across these actors: funders can align grant criteria with open practices; institutions can reform promotion guidelines; publishers can support transparent peer review and registered reports; and societies can convene communities to develop shared norms. The authors emphasize that partial, fragmented reforms risk producing cynicism, since researchers asked to adopt new practices without corresponding changes in evaluation will experience openness as a cost with no return. Alignment across the system is presented as the decisive factor separating genuine transformation from superficial compliance.

Importantly, the commentary treats research culture as an empirically tractable subject. Rather than exhortation alone, the authors call for studying cultural interventions themselves: measuring whether particular mentoring programs, assessment reforms, or engagement strategies actually change behavior, improve reproducibility, or increase public trust. This meta-scientific stance reflects the broader movement toward the science of science, in which the research enterprise becomes an object of systematic investigation. By applying the same standards of evidence to cultural interventions that are applied to laboratory experiments, the field can learn which strategies work, under what conditions, and for whom, and can avoid investing in well-intentioned initiatives that fail in practice.

The appearance of this argument in npj Science of Learning is itself significant, since the journal sits at the intersection of education, psychology, and neuroscience, fields that have confronted reproducibility challenges directly and that depend heavily on public trust to justify their societal value. The commentary’s message is ultimately one of cautious optimism: research culture is not an immutable inheritance but a set of practices that scientists themselves created and can therefore remake. By aligning knowledge sharing, evaluation, and community engagement, the authors contend, the research enterprise can become more rigorous, more equitable, and more responsive to the societies that sustain it, provided that institutions, funders, and individual researchers act together rather than waiting for others to move first.

Subject of Research: Strategies for fostering cultural change in the research enterprise through knowledge sharing, research evaluation reform, and community engagement

Article Title: Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies

Article References: Rho, J., Sheu, J.-K., Forbes, A., Tsai, D. P., Alú, A., Li, W., Brongersma, M. L., Choi, J., Garcia de Abajo, F. J., Na Liu, L., Szameit, A., Schloemer, T., Tittl, A., Chemnitz, M., Wang, C., Zhang, J., Kivshar, Y., Cui, T. J., Ma, R.-M., … Matricardi, C. (2026). Fostering cultural change in research through innovative knowledge sharing, evaluation, and community engagement strategies. npj Science of Learning, 11(1), Article 49. https://doi.org/10.1038/s41539-026-00449-z

Image Credits: AI Generated

DOI: 10.1038/s41539-026-00449-z

Keywords: research culture, open science, knowledge sharing, research evaluation, research assessment, community engagement, reproducibility, npj Science of Learning, academic incentives, citizen science, narrative CV, meta-science