importance of research in culture

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The New Analytics of Culture

• Matthew Corritore,
• Amir Goldberg,
• Sameer B. Srivastava

Culture is easy to sense but hard to measure. The workhorses of culture research—employee surveys and questionnaires—are often unreliable.

Studying the language that employees use in electronic communication has opened a new window into organizational culture. New research analyzing email, Slack messages, and Glassdoor postings are challenging prevailing wisdom about culture.

Some of the findings are (1) cultural fit is important, but what predicts success most is the rate at which employees adapt as organizational culture changes over time, (2) cognitive diversity helps teams during ideation but hinders execution, and (3) the best cultures encourage diversity to drive innovation but are anchored by shared core beliefs.

What email, Slack, and Glassdoor reveal about your organization

Idea in Brief

The problem.

Culture is easy to sense but difficult to measure. The workhorses of culture research—employee surveys and questionnaires—are often unreliable.

A New Approach

Studying the language that employees use in electronic communication has opened a new window into organizational culture. Research analyzing email, Slack messages, and Glassdoor postings is challenging prevailing wisdom about culture.

The Findings

• Cultural fit is important, but what predicts success most is the rate at which employees adapt as organizational culture changes over time.
• Cognitive diversity helps teams during ideation but hinders execution.
• The best cultures encourage diversity to drive innovation but are anchored by shared core beliefs.

A business’s culture can catalyze or undermine success. Yet the tools available for measuring it—namely, employee surveys and questionnaires—have significant shortcomings. Employee self-reports are often unreliable. The values and beliefs that people say are important to them, for example, are often not reflected in how they actually behave. Moreover, surveys provide static, or at best episodic, snapshots of organizations that are constantly evolving. And they’re limited by researchers’ tendency to assume that distinctive and idiosyncratic cultures can be neatly categorized into a few common types.

• MC Matthew Corritore is an assistant professor of strategy and organization at McGill’s Desautels Faculty of Management.
• AG Amir Goldberg is an associate professor of organizational behavior at Stanford’s Graduate School of Business. He and Sameer B. Srivastava codirect the Berkeley-Stanford Computational Culture Lab.
• SS Sameer B. Srivastava is an associate professor and the Harold Furst Chair in Management Philosophy and Values at the University of California, Berkeley’s Haas School of Business. He and Amir Goldberg codirect the Berkeley-Stanford Computational Culture Lab.

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The Importance of Cultural Context: Expanding Interpretive Power in Psychological Science

In 1995, psychological scientists Betty Hart and Todd R. Risley made a splash with their influential book Meaningful Differences in the Everyday Experience of Young American Children , in which they estimated that by age 4, poor children heard 32 million fewer words than wealthy children did. Furthermore, they argued that the number of words children hear early in life predicts later academic outcomes, potentially contributing to socioeconomic educational disparities. Interventions encouraging low-income parents to talk to their children gained traction even at the highest levels of US government. The Obama administration, for example, launched a campaign to raise awareness about the “30-million word gap.”

Twenty-three years after Hart and Risley’s book appeared, however, Douglas E. Sperry (Saint Mary-of-the-Woods College), Linda L. Sperry (Indiana State University), and Peggy J Miller (University of Illinois at Urbana-Champaign) published analyses of five studies that called in question the existence and magnitude of a “word gap”. Using Hart and Risley’s measurement of words spoken to a child by a primary caregiver, Sperry and colleagues found inconsistent support for a word gap among a more diverse sample of wealthy and poor families.

This publication incited widespread debate. Some critiqued Sperry and colleagues’ measurement and conclusions, while others focused on the initial study’s limitations. Many suggested Hart and Risley conflated race and social class, as a majority of the poor families were Black while a majority of the wealthy families were White. Others questioned their methodology, speculating that the anxiety of being observed by educated White researchers could cause poor Black parents to speak less to their children than they normally would. Others argued Hart and Risley’s narrow focus on words spoken by a primary caregiver to a child reflected White, middle-class cultural norms. Children in other cultural contexts hear a great deal of language from other caregivers (e.g., siblings, extended family) and their ambient environments, but Hart and Risley excluded this language. Thus, in cultural contexts in which extended family plays a large role in child rearing, focusing on the primary caregiver’s language may result in an incomplete representation of the richness of a child’s linguistic environment. In fact, using more expansive measurements of words children heard at home, Sperry and colleagues found that children in some lower-income communities heard more words than wealthy children did.

While psychological scientists surely have something to learn from both iterations of the “word gap” study, we have equally as much to learn from the debate itself. The criticisms raised illustrate a problem that we suggest results from a lack of interpretive power in psychological science. Interpretive power refers to the ability to understand individuals’ experiences and behaviors in relation to their cultural contexts. It requires understanding that cognition, motivation, emotion, and behavior are shaped by individuals’ cultural values and norms. The same behavior takes on different meanings in diverse cultural contexts, and different cultural contexts promote divergent normative responses to the same event.

To accurately understand human behavior, psychological scientists must understand the cultural context in which the behavior occurs and measure the behavior in culturally relevant ways. When they lack this interpretive power, they risk drawing inaccurate conclusions about psychological processes and thus building incomplete or misguided theories.

Failures of interpretive power take many forms, including:

• failing to acknowledge that culture shapes psychological processes, even if scientists do not fully understand how;
• failing to consider whether a measure or methodology captures a psychological process as it unfolds for the population studied;
• assuming findings generalize to other cultural contexts unless otherwise demonstrated; and
• not understanding how researchers’ own cultural experiences shape their assumptions, decisions, and conclusions.

To build stronger theories, psychological scientists can leverage interpretive power. The burden rests not just on individual researchers, but on the field as a whole to implement practices that attend to cultural influences. Using the culture cycle framework, we describe changes at four key levels of psychological science — ideas, institutions, interactions, and individuals — that can help the field build interpretive power

Figure: The Culture Cycle Framework (adapted from Markus & Conner, 2013)

Developing Culture-Conscious Research Questions

One of the key problems underlying psychology’s lack of interpretive power is the fact that a majority of research is conducted by people from WEIRD (Western, Educated, Industrialized, Rich, Democratic) contexts and relies on WEIRD samples. Developing interpretive power involves recognizing that many psychological theories describe human behavior in these particular cultural contexts, and that we know less about processes in non-WEIRD contexts. We must embrace the idea that culture shapes human experiences and reject the notion that any one group or context represents “normative” human functioning.

Scientific institutions (e.g., journals, universities, professional organizations) can play a powerful role in promoting attention to culture. For example, journals can showcase research with non-WEIRD samples to communicate the possibilities and importance of conducting research with diverse populations. Journals can also encourage greater transparency regarding studies’ cultural limitations by requiring researchers to specify the cultural contexts from which they recruited subjects and to which they expect findings to generalize. Critically, generalizability should not determine whether research is published. Studies that include small, difficult to recruit, or culturally specific samples should be considered potentially informative so long as they use sound methodologies.

Given that research with non-WEIRD populations is often more expensive and time consuming than research with WEIRD samples, institutions also have a responsibility to support and incentivize non-WEIRD research. Universities can account for the time, expense, and potential impact of non-WEIRD research when making tenure decisions, and professional organizations can create competitive awards to support this work. Perhaps most critically, universities can recruit psychological scientists from diverse backgrounds to join and lead departments.

Cross-cultural interactions also provide an avenue for increasing interpretive power. Both psychological institutions and individual scientists can build trusting, mutually beneficial relationships with diverse communities, many of which the field has historically mistreated, misunderstood, or ignored. In building these relationships, psychological scientists can work to reserve judgment and design research to address the communities’ concerns and needs.

On an individual level, building interpretive power requires exposure to different cultures and perspectives. Seeking diverse collaborators can render more nuanced and informed research questions. APS William James Fellow Hazel Markus and APS Fellow Shinobu Kitayama, for example, generated their influential theory of cultural models of self by comparing their own cultural experiences. Psychological scientists can also engage with the theoretical frameworks and knowledge about non-WEIRD cultures that are abundant in other academic disciplines (e.g., sociology, history, anthropology) to generate more culturally-informed research questions.

Using Culture-Conscious Research Design

In psychological science, hypothesis testing is the gold standard, yet many of our research designs are developed by and tested among people from WEIRD cultural contexts. Furthermore, a priori hypotheses often stem from researchers’ own experiences and thus often regard WEIRD processes. Embracing hypothesis generating methodologies can reduce WEIRD bias in research design. Ethnographic observations, focus groups, case studies, content analyses, and archival analyses all provide means of gaining insight about non-WEIRD cultural contexts that can inspire further experimental work. Leveraging interpretive power in research design means placing greater value on such methodologies.

To a great extent, scientific institutions serve as gatekeepers of “high-quality” research design. Journals, for instance, dictate which methodologies are acceptable for publication, with the most prestigious journals valuing — or even requiring —hypothesis testing. Because WEIRD samples are often most feasible for these designs, non-WEIRD populations and processes remain underrepresented in high-impact journals. To build interpretive power, journals can make space for a wider range of methodologies. They can recognize that, given the dearth of non-WEIRD research, exploratory work is often most helpful in advancing understanding of these cultural contexts. Journals can also make space for non-WEIRD findings that diverge from previous research with WEIRD populations. These findings can be considered not as “failures to replicate”, but as information about how psychological processes might differ cross-culturally.

Interactions with experts inside and outside of the field can also expand psychological scientists’ methodological repertoires and lead to more culture-conscious research design. Disciplines that use information-rich methodologies provide examples of how to thoroughly document qualitative and quantitative non-experimental findings. By drawing inspiration from research that probes different levels of society and uses diverse means of gathering and integrating data, we will find more generative methodologies to build interpretive power in our own field.

Finally, just as psychological scientists conduct a priori statistical power analyses, they can also conduct a priori interpretive power analyses. They can examine whether their methodology has been tested with non-WEIRD populations and learn about the cultural influences likely to shape the processes they study. Simultaneously, researchers can reflect on how their own cultural values and assumptions shape their empirical approach. Many fields encourage positionality statements, wherein researchers describe their own experiences in relation to their subject. This practice can help psychological scientists identify how cultural biases or misunderstandings might enter their research.

Implementing Culture-Conscious Analysis and Interpretation

Many of the statistical analyses psychological scientists use to test hypotheses treat unexplained variance as noise. Some of these variations reflect divergent cultural processes, but they are often averaged out by the majority or dismissed as outliers. Psychological researchers can commit to supplementing these analyses with practices that better illustrate variations and provide opportunities to explore potential cultural influences.

Journals can encourage psychological scientists to explore and report cultural variations. Journals can also encourage researchers to use online supplements to identify outliers and report information that may explain their variation.

Increased cross-lab communication also provides opportunities for better understanding cultural variation. Although any given dataset may include only a handful of participants from a particular culture, researchers exploring similar phenomena can pool data to create larger, more diverse samples for testing hypotheses about how and why psychological phenomena manifest differently across cultures.

Finally, psychological scientists can make a concerted effort to explore variation in their own data. Scatterplots, histograms, and spaghetti plots, for example, illustrate the diversity of effects across subjects. Rather than focusing on average effects, researchers can examine the percentage of participants for whom the hypothesized effect occurred and the percentage for which no effect or an opposite effect occurred. These small changes can elucidate cultural variation.

Stronger Theories, Better Understanding

Debates over “failed replications” such as the “30-million word gap” research can leave psychological scientists feeling anxious and unmotivated. However, they also point to the truth that our science has room for improvement, and they offer important critiques that can help our field grow. By leveraging interpretive power to understand a diversity of human experiences, psychology can build stronger theories and a more comprehensive understanding of human behavior. Perhaps more importantly, we will be better positioned to contribute our expertise to alleviate problems facing communities across the globe.

Hart, B., & Risley, T. R. (1995). Meaningful differences in the everyday experience of young American children . Baltimore, MD: Brookes.

Shankar, M. (2014, June 25). Empowering our children by bridging the word gap . Retrieved from https://obamawhitehouse.archives.gov/blog/2014/06/25/empowering-our-children-bridging-word-gap .

Brady, L. M., Fryberg, S. A., & Shoda, Y. (2018). Expanding the interpretive power of psychological science by attending to culture. Proceedings of the National Academy of Sciences, 115 (45), 11406-11413. doi:10.1073/pnas.1803526115

Rosebery A. S., Warren B., Tucker-Raymond E. (2016). Developing interpretive power in science teaching. Journal of Research in Science Teaching, 53 (10), 1571–1600. doi:10.1002/tea.21267

Markus, H. R., & Conner, A. (2013). Clash! 8 cultural conflicts that make us who we are . New York: Hudson Street Press.

Henrich, J., Heine, S., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33 (2-3), 61-83. doi:10.1017/S0140525X0999152X

Markus, H. R., & Kitayama, S. (1991). Culture and the self: Implications for cognition, emotion, and motivation. Psychological Review, 98 (2), 224-253. doi:10.1037/0033-295X.98.2.224

Whitsett, D. D., & Shoda, Y. (2014). An approach to test for individual differences in the effects of situations without using moderator variables. Journal of Experimental Social Psychology, 50 , 94-104. doi:10.1016/j.jesp.2013.08.008

I applaud the authors efforts but they missed step 1: make an anthropologist a part of your research team. We’ve been studying these things and theorizing them for over 100 years. There’s even a subfield of psychological anthropology!

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About the Authors

Laura Brady, Stephanie Fryberg, and APS Fellow Yuichi Shioda are all research scientists at the University of Washington. They can be reached by contacting [email protected] .

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“When families are informed about the importance of conversational interaction and are provided training, they become active communicators and directly contribute to reducing the word gap (Leung et al., 2020).”

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Expanding Research Integrity: A Cultural-Practice Perspective

Govert valkenburg.

1 Centre for Science and Technology Studies (CWTS), Faculty of Social and Behavioral Sciences, Leiden University, Leiden, Netherlands

4 Present Address: Department of Interdisciplinary Studies of Culture, Faculty of Humanities, Norwegian University of Science and Technology, Trondheim, Norway

5 Center for Higher Education Policy Studies (CHEPS), Faculty of Behavioural, Management and Social Sciences (BMS), University of Twente, P.O. Box 217, 7500 AE Enschede, the Netherlands

Joeri Tijdink

2 Department of Ethics, Law and Humanities, Amsterdam University Medical Center, Location VUmc, Amsterdam, Netherlands

3 Department of Philosophy, VU University, Amsterdam, Netherlands

Sarah de Rijcke

Research integrity (RI) is usually discussed in terms of responsibilities that individual researchers bear towards the scientific work they conduct, as well as responsibilities that institutions have to enable those individual researchers to do so. In addition to these two bearers of responsibility, a third category often surfaces, which is variably referred to as culture and practice . These notions merit further development beyond a residual category that is to contain everything that is not covered by attributions to individuals and institutions. This paper discusses how thinking in RI can take benefit from more specific ideas on practice and culture. We start by articulating elements of practice and culture, and explore how values central to RI are related to these elements. These insights help identify additional points of intervention for fostering responsible conduct. This helps to build “cultures and practices of research integrity”, as it makes clear that specific times and places are connected to specific practices and cultures and should have a place in the debate on Research Integrity. With this conceptual framework, practitioners as well as theorists can avoid using the notions as residual categories that de facto amount to vague, additional burdens of responsibility for the individual.

Introduction

Research Integrity (RI) 1 as an umbrella concept captures a collection of qualities that researchers and research institutions must possess, to ensure that research produces valid and reliable scientific knowledge, in a way that is societally desirable, and with a proper positioning of scientists in society. The concept designates two primary subjects that are “to do” integrity: the researcher and the research institution. We argue that alongside these two subjects, a third one merits further attention: the culture or practice in which researchers do their work. Giving further substance to these concepts enables actors to target interventions that can help build RI more specifically.

The current literature predominantly addresses institutions and individuals as relevant subjects in integrity work that can be accountable and responsible for promoting RI. For example, most advice on RI, such as the 11 recommendations in the report by the Committee on Assessing Integrity in Research Environments ( 2002 ), consists of standards of good research behaviour that the researcher should live up to, or of structural measures that institutions have to provide. In addition, a review of a decade of empirical research on research integrity revealed that empirical analysis is skewed towards measures that target individual researchers, and pays less attention to the effect of institutional governance and policy (Aubert Bonn and Pinxten 2019 ). What is more, the authors remind us that individual researchers are likely to act differently in specific situations, as their perceptions and expectations will be different. This diversity has so far been poorly addressed in existing research.

On the one hand, values can be thought of as essentially individual duties (Meriste et al. 2016 ; Steneck 2006 ; Shamoo and Resnik 2015 ), as they often refer to “doing good.” Thereby, they almost tautologically appear as a trait that should be internal to the researcher as a person. Also, surfacing mishaps are usually judged as a failure of individuals to comply with obvious norms of integrity—the proverbial “rotten apples” that spoil the bunch. In addition, the individualization of such responsibilities is reflected by the fact that courses on RI are typically offered to individuals. We never send an academic hospital to ethics class—in a manner of speaking. And finally, the responsibility for realizing more specific values constituting RI are often attributed to individual researchers by research codes of conduct, even though values such as transparency, respect, and responsibility could equally as well be seen as part of the responsibility of a decent institution, and such attributions are indeed often ambiguous (Valkenburg et al. 2020 ). Only until recently, the revised Dutch Code of Conduct addresses the responsibility of an institution and refer to it as institutional duties of care (p. 20).

On the other hand, considerable attention is directed at arranging organizational structures in such a way that integrity is safeguarded independently of the actions of individual persons (DeMets et al. 2017 ; Israel and Drenth 2015 ; Committee on Assessing Integrity in Research Environments 2002 ; Kaiser 2014 ; Jordan 2013 ). Research institutions are expected to put in place rules and regulations regarding integrity and responsible conduct. They are typically supposed to have committees and boards that assess allegations of misconduct. Universities and medical schools offer ethical training for staff at all levels. And institutions as well as professional societies issue Research Integrity codes of conduct that their employees or members are supposed to follow. RI has thus become an object of governance: something that institutions are called to account for, and that they seek to marshal their employees into.

Reality is obviously much more complex than a simple divide between individual and institutional responsibilities for the realization of values, even though for example the Committee on Assessing Integrity in Research Environments ( 2002 ) does phrase advice along these sharp lines. 2 Arguably, individuals and institutions constitute a complex adaptive system (National Academies of Sciences Engineering and Medicine 2017 , p. 14), and the boundary between the two categories is not that clear-cut.

The interrelations between actors and their contexts, i.e. how actors respond differently to the signals provided by their environment, has been argued to be a less central topic in the literature (Aubert Bonn and Pinxten 2019 ). Nonetheless, there are several studies that do focus on the ways in which individuals are situated in their respective institutional contexts. To start with, there are studies that focus on perceptions of the organization and on environmental stressors, where these “perceptions” are in some literatures reckoned the constituents of “research climate”. Similarly, individuals are less able to resist illegitimate coping strategies when they cannot realize personal ideals or receive persistent negative feedback (Martinson et al. 2010 , 2006 ).

In addition, there are studies that identify mentoring as an important site for novices to learn and incorporate the norms of the professional community. Anderson et al. ( 2007a ) show that problematic behaviour can be related to mentoring on research ethics, securing of funding, and academic survival. It is interesting that they observe that mentoring on different topics leads to different levels of RI promotion. Notably, early-career researchers seem more (rather than less!) likely to engage in problematic behaviour when they are mentored on financial issues seems to increase the likelihood of problematic behaviour in early-career researchers.

Finally, there are studies that direct attention at patterns of action. For example, the National Academies of Sciences Engineering and Medicine ( 2017 ) list “a larger pattern of social deviance” as a source of problematic behaviour that may compromise individuals’ research integrity. The report draws on Reason ( 2000 ) in arguing that the most reliable organizations—think of nuclear power plants and air traffic control rooms—are those that build structural safety measures that circumvent such human fallibility. In contrast to how such organizations are organized, the marketization and commercialization of current research institutions leads to competition that compromises researchers’ integrity by prioritizing their own interests above those of the scientific community (Anderson et al. 2007c , 2007b ).

This body of work has in common the suggestion that RI is linked to less tangible things than individual responsibilities and organizational rules and regulations. Carrying this idea further, we make an additional step of conceptualizing the relation between the individual and the collective. We do so, first, by thinking through “culture” and “practice” as twin notions that mediate the relation between individuals and the institutional contexts in which they act. These two notions merit further scrutiny, in particular how they are different from both “the institution” and “the individual”. Using insights from social practice theory and cultural theory, we hold that advanced notions of culture and practice should be central to RI debates and interventions.

In the section thereafter, we link our notions of culture and practice to the norms and values that typically appear in discussions on RI. We divide them in two broader categories. On the one hand, there are sanctionable norms and values that lead to individual and organizational responses when researchers do not live up to them. These values include the avoidance of falsification, fabrication and plagiarism (FFP), fair credit, transparency and human dignity. On the other hand, there are aspirational values that are less easily sanctioned by others. These values have to do with things that are good to do and that make one a good or better scientist if one holds them dear, but where there is no way to formally enforce them. We then show that both categories of values take us beyond the individual and institutional levels of analysis and intervention. In fact, we suggest that a vast proportion of what makes up research integrity is negotiated and constructed at these levels of culture and practice.

In the final section, we conclude with recommendations for how RI measures can benefit from further developed notions of practice and culture and how implementing RI can be targeted better at specific sites of intervention.

Thinking through Culture and Practice

Culture, practice, and climate in ri literature.

With our claim that culture and practice need further development we do not mean to argue that these notions are not discussed at all. In fact, references to notions such as “culture”, “practice” and “research climate” abound. For example: the term “practice” is used to place a practice-based ethics in opposition to a principle-based ethics (Nia et al. 2019 ; Fuerholzer et al. 2019 ; Clegg et al. 2007 ). “Culture” emerges in notions such as a “peer review culture” (Atkinson 2001 ), a “culture of publish-or-perish” (Genova and de la Vara 2019 ), or “national culture” (Antes et al. 2018 ). The contribution by Meyers ( 2004 ) effectively equates culture with what we have earlier defined as the institutional level, i.e. the norms and standards set by leadership and enshrined in rules and regulations. Also, in explicit relation to research integrity, it is broadly recognized that “culture” is crucially important to promoting research integrity (e.g. Bouter 2015 ; Martinson et al. 2005 ; DeMets et al. 2017 ). Ann Nichols-Casebolt ( 2012 , p. 16) substantiates a “culture of integrity” as making sure ideas of integrity are part and parcel of education, discussions, having clear mission statements, setting specific requirements, setting policies for reporting misconduct, and setting good examples. Ellis ( 2015 ) identifies research culture as the realm where perverse publication incentives compromise integrity, notably through specific reward structures. And finally, in Anderson et al. ( 2007a ), the notion of culture emerges chiefly as a normative ideal of science, that is handed over to the individual through mentorship and education.

While these accounts of culture can roughly be understood as notions of “what culture does”, accounts have also been given of “what culture consists of”. For example, the report by the National Academies of Sciences Engineering and Medicine ( 2017 ) presents a number of elements through which culture can be operationalized for research. These include a range of what could be called “good behaviours,” including proper data handling, publication, correcting errors, collaboration, and peer review. The report also mentions incentives that run counter responsible research, such as publication pressure and the need to acquire funding, which arguably make up a bad culture.

The report by the Committee on Assessing Integrity in Research Environments ( 2002 ) defines (p. 60) culture as shared norms, values, beliefs, and assumptions, and climate as the prevailing moral beliefs. Yet, its operationalization (p. 54) is limited to what we prefer to regard as institutional: clear definitions of roles and responsibilities, proper policies and procedures, and thoughtful decision-making practices. To the question how this is to be effectuated in practice and in the actual actions of researchers, only “leadership”, “supervision” and “socialization” are mentioned. This calls for further development beyond giving merely conceptual advice.

In some RI literatures, climate is explicitly distinguished from culture. Research climate has been operationalized as individual and shared perceptions of the research culture (Crain et al. 2013 ; Martinson et al. 2016 ). This definition singles out climate as the more tangible and observable correlate of culture. In this conceptualization, climate is split into categories such as visible ethical leadership, openness to ethical discussion, conformity to policies, and the awareness that ethical behaviour is expected. Martinson et al. ( 2016 ) mention that this conceptualization of climate is more subjective in the sense that it engages with the perception that individuals have of the research culture. This at least potentially opens up the hazard of implicitly rendering all responsibility to the individual level: after all, it is the individual who has to act on these impressions. This calls for complementary thought of how such cultures operate more independently of how people perceive them. Even if we assume that cultures can only operate to the extent of what people make from them, it is not necessarily the case that these people have an explicit or even coherent account of how they perceive them, nor is their perception necessarily in congruence with how it actually works out. For example, people may think of their work sphere as very much conducive to plagiarism and corner cutting, while in fact neither they themselves nor their colleagues actually commit this transgression.

In our view, these notions of culture (and climate) and practice leave some of the potential of these concepts unrealised. They merit further development, and the question should be asked explicitly what culture and practice can (help to) explain that cannot be explained at the level of individuals performing well or poorly, nor at the level of institutions being arranged properly or improperly.

Clifford Geertz possibly offers the most foundational and widespread notion of culture. He defines culture as “an historically transmitted pattern of meanings embodied in symbols, a system of inherited conceptions expressed in symbolic forms by means of which men [sic] communicate, perpetuate, and develop their knowledge about and attitudes toward life” (Geertz 1973 , p. 89). Swidler ( 1986 ) adds that culture also appears as ritual in the literature and in general conversations: the recurrent social processes through which behaviour is shared. Following Keesing ( 1974 ) and Hannerz ( 1969 ), Swidler ( 1986 ) adds that there is something public and explicit about how meaning circulates as the constituent of culture. The primary importance of culture to RI is in that it is what orients people’s actions (Eckstein 1997 ).

Relating more specifically to research settings, Knorr Cetina ( 1999 , p. 10) has argued that cultures, at the level of research practices, engender specific styles of knowledge production, and therefore need to be attended to when explaining the production of scientific knowledge. She posits that three properties can be attributed to such research cultures. The first is that they are not uniform but may differ across practices and disciplines. Second, culture comes with a certain richesse of what matters to courses of affairs , including instrumental, linguistic, theoretical and organizational frameworks. And third, it relates to the patterns of meaning through which people communicate, which are hand over to next generations (cf. Geertz 1973 ). This is why, according to Sismondo ( 2008 ), research outcomes are heavily marked by the research context in which they come about.

To operationalize culture further as an orientation of people’s actions with respect to RI, it seems meaningful to split this orientation tentatively in four parts. First, it may be thought to predispose people to do particular things: following routines and habits, copying behaviour et cetera. Second, it may predispose people to value things in a particular way: what is important, what is right or wrong, and what is it that a good researcher typically does. Third, it may predispose how people know things, including but not limited to the disciplinary curricula that we consider part of the theoretical frameworks mentioned by Knorr Cetina. And fourth, it may pre-structure distributions of responsibility and accountability: who does a given task belong to, who or what can we expect to solve a problem, and who can we summon in case things go wrong. We will use these dimensions of culture and practice to assess a number of values below.

The notion of practice directs attention at an empirically existing situation in which people operate, in this case the practice of research. As follows from the foundational text by Pickering ( 1992 ), studying scientific research practice makes properties of science into explanandum , rather than seeing for example different disciplines as explanans of scientific outcomes. That is to say: we cannot use scientific knowledge or its nature as the explanation of why science happens to be done the way it is, but rather we must look at how science is actually done, if we want to understand the nature of scientific knowledge. In the context of RI, such a reversal would lead us to asking not so much what a good scientific conduct is and derive the answer from ethical and other normative principles, but rather to asking how such standards have been put in place, and the negotiations needed to both define and enact such ideas of integrity.

In its most basic form, a practice is any unit of coordinated human action. Reckwitz ( 2002 , p. 249) defines the basis of practice as a “routinized type of behaviour which consists of several elements, interconnected to one other: forms of bodily activities, forms of mental activities, “things” and their use, a background knowledge in the form of understanding, know-how, states of emotion and motivational knowledge.” MacIntyre ( 1981 ) considers it vital that there is some shared understanding of a good that the practice pursues (see also Schatzki 1996 , p. 89), but not all notions of practice are that strict in the necessity of aspiring to a shared good for a practice to exist. In the case of scientific research practices, the production of valuable knowledge could be surmised to be such a shared good, but we do not take this to be an essential or defining property for the current argument.

Social practice theory has been positioned primarily as an alternative level of analysis to more structuralist social theories, and builds on influential authors such as Bourdieu, Giddens, Taylor and the later Foucault (see Reckwitz 2002 ). Schatzki ( 1996 ) already elaborated that a level of analysis between the individual and any sort of “social totality” had so far been lacking in social theory. In between these two levels, various versions of practice theory offer an alternative level of explanation of what determines human action, as opposed to explanation from either mere principles or mere goal orientation (Reckwitz 2002 ).

Despite their diversity, notions of practice do share a number of elements. One is that practices are situated in space and time . The place aspect is that practices are connected to specific sites, and spatial proximity is vital for people to become performers of the practice. The time aspect refers to their repetitiveness and path-dependency in the sense that what has happened before is of constituting significance to what happens now. According to Pickering ( 1992 ), this temporal aspect is in fact where practice is complementary to culture. Only by focusing on specific times and places can meaningful observation take place, and becomes apparent how peoples’ actions are (also) driven by routines, workplace facilities, colleagues, etc. (Schatzki 1996 , p. 89). Practices differ across time and place- also within overarching institutions. This resonates with the work by Knorr Cetina ( 1999 ), pointing towards the differences that exist between how different scientific disciplines produce knowledge in different ways.

Thinking of human action as happening in practices and to a smaller or larger extent determined by those practices, also means a move away from seeing actions as purely individual phenomena. This is not a mere reduction of human action to “structure” or any other concept located outside the individual. The level of practice connects those actions to the context in which the individual is situated (Schatzki 1996 , p. 97), and members of a practice also take part in the production of those practices through their performance (Shove 2014 ; Douglas 1986 , p. 45). The constituting relationship between practice and human action is thus bidirectional.

Apart from time and space, a second element that different notions of practice share, is the articulation of how technologies and other material arrangements affect people’s actions. The working of devices cannot be seen apart from the actions of human beings, and this is where skills come in as an essential element of practices: what people are capable of, both mentally and physically. This is also pivotal in setting the boundaries of the practice: mastering specific skills to engage with relevant devices becomes a condition for being admitted as a member of the practice (Shove 2014 , 2017 ). Skills are importantly connected to the repetitiveness of a practice. Many skills are transferred from masters to apprentices, and often implicitly so, by performing them time and again. Practices are thus among the primary sites where mentor-apprentice relationships emerge.

This point generalizes to the idea that practices have a scheme of membership : not just everybody takes part in any given practice, and it requires a degree of socialization to become accepted as “one of them”. Defining who is “in” and “out” is vital for the practice to survive, and the accompanying process of socialization is an important mechanism through which the practice and its culture predispose members to do, value, know and account in specific ways.

Research-Integrity Values in Practice

Continuing our argument of splitting the workings of culture and practice into four dispositions of doing, valuing, knowing and accounting, we propose to distinguish between two main categories of norms and values. On the one hand, we discern those norms and values that are sanctionable. One typically experiences unfavourable consequences if one does not live up to them. On the other hand, we discern those norms and values that are aspirational: things that are good to do and that possibly make you into a bad scientist if you don’t hold them dear, but where there is no formal way to enforce them. The rationale behind this tentative classification is that sanctionability naturally places an issue at the institutional level: it is literally enshrined in rules, regulations, procedures and formal responsibilities of offices how sanctions are shaped and executed. Thus, if the world only consisted of institutional and individual responsibilities, sanctionability would be an informed guess of where the boundary is. This rationale thus guides our inquiry into culture: as a working hypothesis, sanctionable values are a concern of institutions and management, whereas aspirational values are a concern of research scientists and the research communities they work in.

One important proxy question to this boundary condition is who or what suffers in case the value is breached, which provides a direct answer to the question of accountability. With aspirational values, the consequences of breaching are primarily for the researchers; they will typically suffer a loss in reputation. Conversely, when breached, sanctionable values lead to liability for the institution, damage for eventual patients or research subjects, or a corruption of the body of scientific knowledge (see Shaw 2019 for a treatment of this last point). Thus, the question whether a value is aspirational or sanctionable also depends on the distribution of benefits, ownership and liability, and hence power, between the researcher and the institution.

The distinction between sanctionable and non-sanctionable values is compatible with the observation by Horbach and Halffman ( 2017 ), who show that sanctionable values are more the language of policy makers and journalists, whereas aspirational values appear more in the language used by scientists themselves. Similarly, Israel and Drenth ( 2015 ) note that aspirational values fall behind in terms of their effectuation in practice. Finally, it resonates with the observation by Davies ( 2019 ) of a tension between ideals of good science that researchers aspire to, and the abstract, principle-based codes that seem not to capture these ideals. The exact distinction between sanctionable and aspirational values remains contingent, and consequential for what practice will prevail, which is exactly why this level of practice merits further explanation in RI theory.

Sanctionable Values

Sanctionable values are in a way the “hard boundaries” of what gets defined as proper scientific research. According to Plemmons et al. ( 2006 ), knowledge of these principles is successfully conveyed in RI courses. One could think of the avoidance of fabrication, falsification and plagiarism. Other clear examples are the proper use of informed consent in case of medical research, and the principle in the engineering sciences not to accept assignments for which one lacks the proper qualifications. Also, we may somehow expect these hard boundaries to play out in explicit ways in who is included in the practice or not.

In the following, we highlight four values that circulate primarily as sanctionable. The list is not exhaustive and even to some extent arbitrary. The items are merely intended to exemplify how such sanctionable values can be thought to connect to substantiated notions of practice and culture.

Avoidance of Falsification, Fabrication and Plagiarism

Falsification, fabrication and plagiarism (FFP) count as the epitomes of a lack of RI. Through plagiarism, credit is withheld from the people who have actually done the research. And through fabrication and falsification statements enter the scientific knowledge base that are in fact untrue (Shamoo and Resnik 2015 , p. 38). Such cases are typically resolved through institutional measures, but it is worth asking how FFP can emerge, in light of the above definitions of practice and culture. Perhaps there are circumstances that at least enable people to “give it a try” to get away with improper behaviour—even though today, most institutions and publishers have access to some form of plagiarism check (Luparenko 2014 ). Though these automated checks are not (and probably will not very soon be) perfect, it requires skills and intricate knowledge of the whole chain of scientific knowledge production to get away with plagiarism. These chains of knowledge production are discipline-specific and practice-specific. Hence, in order to stand a reasonable chance at successful plagiarism, one has to be a member of the practice in the first place.

A similar argument can be made about falsification and fabrication. If researchers want made-up knowledge to appear credibly, they need intricate knowledge of how their claims will be assessed in the peer-review process. This knowledge is only available in the practice itself, and can only be learnt in the same way other skills are transferred in practice: through mentoring, practising, and various forms of teaching.

This means that apart from the obvious sanctioning of FFP-related misconduct, the ways in which the practice itself makes such conduct possible in the first place, could be subject to further reflection. In a way, the usual training is a perfect preparation to actually commit the transgression. Carrying this to a conclusion on a substantiated notion of practice, it could be suggested that the master-apprentice relationships in which the skills are transferred, could do with more reflection on how such skills can (and should not) be abused. Similarly, the repetitiveness of practices could be taken as an object of reflection in case misconduct emerges: what where the patterns of action that led to the misconduct, or at least have failed to eliminate it? Has anything been lacking in those patterns that could over time have served as an additional safeguard against the mishap taking place? And, to relate to the different roles that “things” can have in a practice: is there any way in which the infrastructure of automated plagiarism checks could have been used or arranged differently, so as to improve its performance (possibly combined with additional human skills), to prevent plagiarism?

In terms of the questions of culture and practices, it is clear that even if a practice does not force a person to “do” FFP, it at least enables them to. At the same time, material entities like plagiarism checks counter this ability to some extent. Also, the practice expresses an ambiguous valuation of cutting corners: it should not be done, but if successful, it may help one’s career move on.

Fair Credit

Closely related to the problem of plagiarism is the fact that scientists are assumed to be fair about what is their own merit, and what is the work of others. Authorship should be attributed to the people who have actually deserved it through their work. People should also be credited through other means, for instance by citing their work (Plemmons et al. 2006 ). Consoli ( 2006 ) shows however that the category of “author” is far from unproblematic: notions such as “responsibility for the output” and “relative contribution to the output” are hard to quantify or compare to some sort of threshold. Also, it is clear that many aspects of the incentives and rewards for authorship that define the landscape in which publishing takes place (Martinson 2017 ), are in fact such that fair credit is in fact not always an attractive way to go. In addition, there are very clear power relations between seniors and juniors that disturb practicing fair credit (Shah et al. 2018 ). Thus, the meaning of the category “author” is not self-evident and univocal, which means that it will receive different specifications in different contexts. Taking and giving credit corresponds directly to the distribution of accountability and responsibility. This is thus in fact a mechanism through which culture may play a more important role than institutional relations or individual virtue, and socialization into a practice reproduces it.

Thus, the intricacies of fair credit and the diversity of practical implementations of it, clearly form a clue towards where a culture may prioritize a specific valuation over others. Also, who is accountable for the exact acknowledgement of credit will differ between practical situations: in some disciplines, hierarchy is such that research leaders are co-author by default, and others are not. At exactly this point, Thornton ( 2013 ) argues that entitlements are dominantly shaped by masculine and neoliberal norms.

Transparency

Research should be transparent, or so the consensus can be assumed to be. An editorial in Nature ( 2017 ) provides 5 steps to substantiate transparency: pre-registration or publication of a research protocol prior to conducting the research; pre-publishing a draft before final submission of the paper; releasing the data analysis plan; releasing the analysis code; and publishing the data set. It needs saying that these steps are deeply ingrained with a biomedical and natural science approach, and generalizing them to other fields, notably social sciences and humanities, might involve some critical and problematic translation. What such steps would look like in a strictly theoretical exercise like mathematics, or for example in anthropology where anonymity and confidentiality are key to the production of data in the first place, remains to be debated (see also Penders et al. 2019 ; Irwin 2018 ). For example, Spier ( 2006 , p. 189) emphasizes the need for rigor in method and reporting. It is at this point already instructive, as Consoli ( 2006 ) argues in reference to the US Federal Policy on Research Misconduct , that the presentation and publication of proper facts is- in that policy—considered a more important responsibility than the exact conduct in the lab that precedes that very publication. Remarkably, in a large-scale study on how scientists conceive of good research practice, Hangel and Schickore ( 2017 ) argue that especially the reporting of method often remains notoriously obscure. They also show that transparency of primary material is often obfuscated, for example by working with numerical codes that nobody can decipher.

Regarding transparency, the answers to the questions of action, value, knowledge and accountability are ambivalent. It makes an individual accountable, but forces to give up any competitive edge related to knowledge ownership, which is a particular way of valuing. Also, the elegant presentation of e.g. methodology is a skill that requires training, which likely comes with mentorship and jargon, and the membership that is constructed through those. Transparency is thus ambiguous, and it is crucial here that this ambiguity cannot be resolved by clearer (institutional) rules, nor by (individual) moral deliberation, which thus makes the accountability in fact ambiguous. Thus, even if transparency appears sanctionable, it depends on the practice context how it unfolds exactly.

Human Dignity

Perhaps the most ambiguous value in the category of sanctionable values is that of human dignity. On the one hand, it emerges as strongly sanctionable, from historical failures such as the Tuskegee experiment (Brandt 1978 ; Daugherty-Brownrigg 2013 ) and the atrocities of research in Nazi concentration camps (Baumschlag 2005 ). At the same time, standing definitions do not help us very much. For example, Drenth ( 2006 , p. 17) defines dignity as the safeguarding of all individuals’ autonomy and freedom of choice, which in the case of participation to research is chiefly shaped as informed consent, and the rejection of every intent to commercialise the human body. Similarly, Spier ( 2006 , p. 191) defines it as the avoidance of any intended negative effects on the environment and society, both for current and future generations. In a general sense, dignity has been observed to be a term that is utterly vague, and usually captured to defend very particular interests (Macklin 2003 ; Pinker 2008 ).

Hence, in addition to the aforementioned procedural implementations, it seems that dignity importantly remains a matter of “good intuition”. While this may be more open to individual moral insight, compared to for example transparency, it is also a matter of how the Tuskegee and Nazi stories circulate in courses and mentorship relations. Thus, this is a matter of how people “know” things, including knowing in a particular way how their research relates to the obvious atrocities. Also, the translation of these stories to concrete decisions on the work floor is dependent on the “doing” and “valuing” at specific times and places, in ways that cannot be reduced to institutional rules nor individual qualities.

From Sanction to Practice

Even though we started the present set of examples as a tentative list of sanctionable values, in all cases there are sides to them that are not resolved by sanctioning or other institutional arrangements. The realization of these values depends on how routines circulate, how actions are valued, how responsibilities are distributed between people, and between people and the institution. It also, in some cases, depends on the practice-based skills with respect to research devices as well as (working around) plagiarism checks. In all the values listed here, we see that the responsibility for their realization is not reducible to either the individual person, or the institution.

Aspirational Values

Starting from the assumed sanctionability of the values above, we observed that there are in fact more cultural and practice-related aspects to them then might be suggested by their initial appearance as sanctionable and the according institutional responsibility to secure them. What does it look like if we start from the other end of the spectrum, i.e. values that appear as aspirational and hence connected to individual responsibility? One could think of honesty, scrupulousness, independence and responsibility (KNAW 2018 ). These are said to be less successfully conveyed in RI courses (Plemmons et al. 2006 ). Shamoo and Resnik ( 2015 , p. 283) argue that beyond avoiding harm, scientific research should be aimed at furthering the public good and public knowledge. They conclude that little substantiation has been given thereto so far, which we take to be a hint at their substantiation taking place in practice. Following this line of thought, we discuss four such aspirational values and how this substantiation can be understood.

It may appear circular to discuss “integrity” as a constituting value if it is also the overarching goal. Clearly, the sanctionable values above are part of it. Nonetheless, notions of integrity proper do circulate in much the same way as aspirational values do. For example, Becker ( 1998 , p. 157) as quoted in Breit and Forsberg ( 2016 , p. 15), understands integrity as “the principle of being principled, practicing what one preaches regardless of emotional or social pressure, and not allowing any irrational consideration to overwhelm one’s rational convictions”. A lack of integrity (ibid.) consists of lack of principles; lack of consistency in moral principles; and behaviour influenced by social pressures. In other words, integrity is the capacity to act in accordance with moral principles, but those moral principles themselves are not further substantiated, or at least not within this definition.

The substantiation that such openness calls for is by no means essentially the responsibility of the individual, the institution, nor essentially the product of culture and practice. Rather, it will be a combination of those, and the balance may be tipped differently in different cases. Nevertheless, discussing the value of integrity here is instructive: it offers a clear example where limiting the analysis to individuals and institutions would overlook the importance of how knowing, doing and valuing are predisposed in practice.

Inquisitiveness

Many sources mention inquisitiveness and curiosity as primary virtues for scientists (Shamoo and Resnik 2015 ; Drenth 2006 ; Gläser et al. 2002 ). At face value, this appears as an predominantly personal trait. Yet, Shamoo and Resnik ( 2015 , p. 61) argue that choice of research topics, so what exactly the scientist practices curiosity on, is inextricably tied up with the resources that are available for doing research. This renders them ambiguous as a personal responsibility: it is equally the institution’s responsibility to provide resources. Thus, institutional responsibilities clearly extend beyond the prevention of problematic behaviour. Also, this choice depends upon the research objects that are available. These objects thus become at once explanans and explanandum, given the effort that goes into constructing those objects in the first place (Knorr Cetina 1999 ).

The contextual character of inquisitiveness becomes even clearer if we think of what it takes to develop oneself as an inquisitive researcher: not only should the institutional atmosphere in some way be conducive to that, it also requires that one is trained into recognizing the interesting scientific challenges. What is more, curiosity can only persist if there is a legitimacy to trying out possible dead ends and failures. It has in fact been demonstrated that current levels of competition and the pressure of acquiring scarce resources lead researchers to avoiding such risks (Moore et al. 2017 ). Thus, the realization of the value of inquisitiveness is dependent on infrastructures such as funding and research agendas that enable it, but also on how the local practice allows and even values failure. The extent to which a researcher is free to be inquisitive, depends on the hierarchical position one is placed in, and how such hierarchies work in a specific practice. And what is valued as an interesting research problem is similarly inscribed not in the rules, but rather in the unwritten value schemes that circulate in the practice. To see inquisitiveness merely as aspirational would be to disregard this complexity. And to explain this contextual complexity, it is not enough to only look at the institutional arrangements.

Reflexivity

Consoli ( 2006 ) argues that scientists should have reflexivity , or the capacity to think about their own work from an external perspective, in view of the broader context to which their work connects. This reflexivity is needed to be able to deal with the moral complexities that research work inevitably comes with. To a large extent, along the lines of Consoli’s discussion, the moral thinking that reflexivity requires can be delivered by an individual person. Nonetheless, it is also self-evident that moral thinking can be supported by training as well as peer-discussion, and both depend on what is done and not done in the direct research environment, and how such critique is valued. Are the customs of the practice such that there is space—in terms of time and place, but also psychological safety—to conduct such reflection? Are the meanings that circulate in the practice sufficiently open-ended to make engagement sensible, or are they rather fixed and hostile to reflection? These are clearly questions of collectiveness, practice and culture, not (merely) individual or institutional matters.

Collegiality and Trust

The need for a good collegial context and the duty to preserve that context is often mentioned. In fact, this is exactly one of the guises in which the unspecific notions of culture often appear from which this analysis started. This lack of specificity may contribute to the seemingly self-evident appearance as not-institutional and hence aspirational, but such a conclusion cannot be drawn before looking in more detail to the constituting values.

One thing that its slightly more specific is the value of trust . The German Research Foundation DFG (1998) emphasizes the need for trust in the relations scientists build within their community, where building trust consists of maintaining clear and transparent procedures, accuracy in attribution and citation, and accessibility of securing facilities such as counselling and report. It also posits trust to be a necessary condition for any self-regulation of science to emerge. Yet, in contrast, Stroebe et al. ( 2012 ) have argued that such self-regulation, chiefly based on principles of peer-review and replication, are insufficient to prevent fraud, and have indeed failed so in notorious cases.

Remarkably, both understandings are elaborated as more or less “manageable” issues, i.e., through procedures. Alternatively, in view of our discussion above of the concepts of practices and culture, trust could be seen as a relation between persons and groups of persons, that consists of the belief that the other party in that relation is truthful and well-meaning. The extent to which such belief can emerge, depends on how people behave in daily practice, the narratives they repeat about what they think is important, and the responsibilities they avow to take. In some contexts, trust will primarily be conferred to one’s equals, and in other contexts more along hierarchical lines up and down. Or it may in some contexts more than others be connected to merit and the credit one person has acquired with the other.

One specific guise in which collegiality appears is in the duty of peer review. It is mentioned widely as a core aspect of preserving the quality of scientific knowledge (Spier 2006 ; Hangel and Schickore 2017 ). In order to contribute to the progress of science, peer review should be done in a critical but fair and constructive way. Ripley et al. ( 2012 ) argue that teaching peer review is generally recognized as an important element of mentorship. Interestingly, they also argue that such mentorship could do with further training support for the mentors. Several sources (Bohannon 2013 ; Ioannidis 2005 ) show that peer review in practice drops the ball quite often, and fails to single out all instances of bad science. It is also biased, notably against interdisciplinarity and against diversity and inclusion (Rafols et al. 2012 ; Moore et al. 2017 , p. 3).

A problem such as the bias against interdisciplinary research can only be understood as defects of the research culture: along the lines discussed, it reproduces itself independently of both the positions of single researchers and institutional rules. Trying to resolve this through further rules and regulations seems futile, and also it seems not a matter of individual peer reviewers having bad intentions. Rather, it requires active reflections on how things are done and valued, and how responsibility is distributed.

From Aspiration to Culture

We started from the working hypothesis that aspirational values are more open to interpretation and more difficult to manage than sanctionable values, and therefore more likely to end up as individual responsibilities. However, in the exemplary values discussed here, it becomes clear that this attribution of responsibility is again complex, and by no means maps onto the individual-institution dichotomy. For their substantiation, the aspirational values are dependent on the practice and how people act, know and value within it. At the same time, it appears that this dependency is less clear than with the sanctionable values, and the elements related to culture and practice are less tangible.

Building Responsible Cultures and Practices

Our analysis started with an articulation of some elements that relate to RI in culture and practice. We subsequently explored how values that are central to RI can be thought to map onto such a field of culture and practice halfway the scale between individuals and institutions. What does this imply for achieving RI? What interventions are opened up by these insights? Where should they be developed, and by whom? What does it mean to build cultures and practices of research integrity? The current analysis makes clear that the specific times and places that are connected to practices, and the specific content of cultures, are important objects on which this integrity work is to operate, rather than the abstract notions of culture and practice themselves.

Gunsalus ( 1993 ) already articulated that achieving RI is not only about having the appropriate regulations in place, but also about the leadership of an institution “walking the talk” (see also Mejlgaard et al. 2020 for a recent reflection on this) and expressing the value of acting ethically. However, this idea of “walking the talk” solves a different problem than does introducing the idea of integrity work as presented by Breit and Forsberg ( 2016 ): the former is about identifying leadership examples as specific normative sources, the latter is about recognizing the dynamics of different normative sources and different types of normative source, and the fact that they are never “finished” and permanently in need of attention. Or put in the terms developed in this article, some of the ethics of RI needs to be about “caring for the research practice” (cf. Davies 2019 , who reports that researchers do in fact recognize and articulate this need). It involves taking into account how certain social and cultural processes may become institutionalised and thus normalised and taken for granted (cf. Powell and DiMaggio 1991 ).

First, the elements of practice are themselves direct points of interventions. Skills are important in the makeup of practices, and we have seen that much of RI depends on them (see above: peer review, methodology and its presentation, etc.). Also, mentorship has been identified as a vital mechanism of transferring skills, but it needs attention how what is transferred in mentorship circulates further through the practice. And even though “technological fixes” for moral problems such as automated plagiarism checks have historically proven naïve (Johnston 2018 ; Sarewitz and Nelson 2008 ), there might nonetheless be realistic pointers to technical or procedural interventions.

Second, there is the issue of where the interventions are to be made. Given that human action is influenced by more complex sources than (individual) ethics and (institutional) rules and regulations, the question of “how to achieve better integrity” will hardly be answered by “more ethics” or “more rules and regulations”. Being articulate about practice and culture in this differentiated way might refine future interventions. One important site of intervention that does emerge from the vocabulary of practices and culture, and which has so far received little attention in the RI discourse, is the realm of meaning . While it is far from straightforward how meaning can be an object of intervention, we also cannot do without it. The questions of what authorship means and what trustability means are vital. While asking these questions could be part of the reflexivity that Consoli ( 2006 ) calls for, our analysis adds that this reflexivity should not only concern individual conduct and motivations, but also the practice at large, including the elements of which it is built: skills and routines by which people do things, technologies and other contextual arrangements that allow people to do some things and not others, value systems that are in place in both formal and informal ways, and the written and unwritten hierarchies with ensuing distributions of accountability and responsibility. That is: how things are done and known, how they are valued, and how people account for their actions and the conduct of scientific research.

This requires both a philosophical sensitivity among research practitioners and a sociological sensitivity on the part of research administrators and mentors. They need to be able to articulate and convey what is to be done, what is good research, as well as supply this knowledge with an account of how this is both a matter of individual duties and of collectively maintaining the practice as such. This goes beyond institutional provision of training. Krstic ( 2015 ) argues that such efforts should be both aimed at, and arranged by, early career academics. This group is at once most vulnerably positioned in power relations, and in the epistemic position to identify those vulnerabilities. The vocabulary of cultures and practices developed here is seems then appropriate to articulate those relations.

The notion of integrity work (Breit and Forsberg 2016 ) must in our view be situated at exactly this level of culture and practices. Breit and Forsberg use the word “work” to literally refer to activities undertaken either by individuals or by institutions to get integrity in place: e.g. making ethical decisions, organizing integrity courses, and offering resistance against pressures towards compromising of integrity. In light of the current analysis, this idea merits further expansion: it is not only the actions that matter, but also what kind of world is both reflected and constructed through these actions. Culture and practice do need a similar approach to be part of the integrity work that they are referring to. The current analysis thus also responds to the call by Clegg et al. ( 2007 ) for a further development of the notion of “ethics as practice” to reflect the contextual and dynamic nature of research integrity, and the fact that rule-based ethics typically fails to capture the intricacies of making choices on the work floor.

Conclusion and Reflection

Starting from the observation that in discussions on RI, notions of “culture” and “practice” are underdeveloped, the current discussion provides a further conceptualization of these notions. Connecting them to values that typically circulate in RI discussions, these conceptualizations of practice and culture were carried towards possible implementations of integrity work .

Implementation of culture starts with awareness of the role of culture. Interestingly, a quantitative survey recently found that culture plays a considerable role in the occurrence of questionable research practices (Haven et al. 2020 ) and this potentially gives more reason to start working on other interventions that may foster a responsible research culture. This should entail RI-training, training of supervisors and mentors of PhD students, foster reflexivity at research departments; interventions that specifically address research culture. The current analysis provides further advice on the direction in which such development can be sought.

One question to reflect upon at this point is whether culture is a meaningful and necessary addition to thinking through research integrity. Indeed, Gläser et al. ( 2015 ) argue that culture is superfluous as an explanatory factor. However, in the current argument, the term is not used as an explanation ( explanans ), but rather as the thing that needs to be explained ( explanandum ) and something that can serve as a site of intervention. Combined with the mapping that was provided to values that are central to RI, the notions of practice and culture have been elevated well above triviality. We believe that this is a valuable contribution to the RI repertoire and should be part of discussions about RI.

It is likely that people will keep using the notions of culture and practice in an unreflexive and sweeping way, i.e. using it exactly as an explanans and not as an explanandum. The concepts are vulnerable to such usage, because they can easily be captured: no one will contest the general statement that a “good practice” and a “culture of safety” are desirable (even if such usage only serves very particular interests, as observed in medical-ethical debates on "human dignity", see Macklin 2003 ; Pinker 2008 ). This is one additional reason why formal training in research integrity needs to address what we mean when we use philosophical and practice-theoretical terms such as culture and practice. In concrete cases of such sweeping usage, it seems there are two possible remedies. If the usage is in fact correct, it may require specification in the terms developed above. If it is incorrect and the usage in fact obfuscates ambiguous or unspecific policy, it should be dropped in favour of concrete measures and clear and unambiguous attributions of responsibility.

In conclusion, cultural and practice theory can enrich the discourse on RI. Policy as well as training should pay attention to measures that can help foster a responsible research culture, which includes paying attention to the important values that constitute culture, to how these values influence practices of researchers, and to how they can be targeted for interventions. It helps to look at how values relate to the way people know things in practice, how they value them, how they do things, and how they attribute responsibility. In any case, it is high time to abandon vague references to culture. Our framework provides the initial tools to do so. However, future research should further explore how the notions of culture and practice can take a more prominent role in the debate on Research Integrity.

Fundig was provided by ZonMw (Grant No. 445001010).

1 RI is often equated with Responsible Conduct of Research (RCR), even though the former is strictly a somewhat more moral and abstract concept, while the latter would refer to a more positive and concrete specification of what “is and is not done”. Also, both RI and RCR are often just assumed to amount to the absence and active elimination of Questionable Research Practices (QRPs) (Tijdink et al. 2014 ; Fanelli 2009 ; Steneck 2006 ), Sloppy Science (Harris 2017 ), and fraud understood as Falsification, Fabrication and Plagiarism (FFP) (ALLEA 2017 , p. 8; Breit and Forsberg 2016 , p. 3; Al-Amad 2017 ). Also, Research Ethics has been suggested as differing from Research Integrity by its focus on building a good reputation for science, while the latter focuses on the commitments and responsibilities of the individual (Spier 2006 , p. 190). While all these notions highlight slightly different problems and coping strategies, we reckon them part of the same overarching discourse of RI, which we will thus use as the main term throughout. Unless stated otherwise, our use of RI comprehensively refers to all these issues.

2 The boundary between the two categories is in reality not that clear cut. see also Shaw ( 2019 ), who shows that the notion of individual responsibility is already problematic at the philosophical level.

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• Feature Article

Research Culture: Setting the right tone

• Elizabeth Adams
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Improving the research culture of an institution may lead to a fairer, more rewarding and successful environment, but how do you start making changes?

The University of Glasgow was founded more than 550 years ago and currently welcomes over 5000 researchers working in a wide range of subjects across the sciences and the humanities. Feedback suggests that our research culture is already good, but we think that it could be even better. As the Head of Research Policy (TC) and the Researcher Development Manager (EA), we have spent the past few years working to update research culture at Glasgow. Based on our experiences, our advice to anyone trying to change the culture of their institution is to be practical, consistent, and to aim for progress, not perfection. Start even if you cannot see the end. The project is big, slow, fragmented: and yes, it is a fantasy to imagine that a university has, or should have, a single culture.

The recent research culture survey by the Wellcome Trust has highlighted what many of us would not dispute: that the pursuit of a narrow definition of research excellence, and of excellence at any cost, has limited the research endeavour and had an adverse impact on the wellbeing of researchers as well as the quality and reliability of the research they undertake. It is not too late to fix this issue, but solutions will emerge only once research organisations, funders, publishers and government coordinate their efforts to identify practical actions that can be implemented consistently across the research community.

Meanwhile, the complexity of the problem should in no way stop us from implementing changes within our own institutions. At Glasgow, we focus on fostering a positive research culture . To do so, we develop policies, guidance, communications, training and related initiatives that support the success of researchers at all stages of their career.

With the support of our senior management, we have introduced several initiatives that we hope will make our institution an inspiring place in which to develop a career — whether it is academic or administrative, operational or technical, or indeed something different altogether. Some of these initiatives are summarised in this post ; in this article we will also share the lessons we learned along the way that might be useful to others.

Start from what you know

Research culture is a hazy concept, which includes the way we evaluate, support and reward quality in research, how we recognise varied contributions to a research activity, and the way we support different career paths.

Of all the things you could do to improve research culture, start from the priorities that you think matter most to your organisation; those that reflect its values, fit with what your community really cares about, or align to the activities that are already in progress. If you can, line up your agenda to an external driver. In our situation, two prominent drivers are the UK Research Excellence Framework (an exercise that assesses the quality of research, including the research environment, at all UK universities), and the Athena Swan awards (which evaluate gender equality at institutional and local levels). Our research culture initiatives also work alongside everyday drivers from research funders and other bodies, such as concordats on research integrity , career development and open research data .

Even better, align your initiative to more than one agenda. For example, we are supporting transparency, fairness, accountability (and therefore quality, career development, and collaboration) by requesting that research articles deposited in our institutional repository follow the CRediT taxonomy , whereby the roles and responsibilities of each authors are laid down explicitly.

Once you know what you mean by culture, write it down and let people know. This will aid communication, keep everyone focussed, and avoid the misunderstanding that culture is a solution to all our problems (“The car parking is a nightmare. I thought we had a culture agenda!”).

At Glasgow we define a positive research culture as one in which colleagues (i) are valued for their contributions to a research activity, (ii) support each other to succeed, and (iii) are supported to produce research that meets the highest standards of academic rigour. We have then aligned our activities to meet these aims, for example by redesigning our promotion criteria to include collegiality, and creating a new career track for research scientists (see Box 1 ).

Changing promotion criteria and career trajectories to foster a different research culture

At the University of Glasgow, academic promotion criteria are based on a 'preponderance approach': candidates need only meet the necessary criteria in four of the seven dimensions used to assess staff for promotion (academic outputs; grant capture; supervision; esteem; learning and teaching practice; impact; leadership, management and engagement). For the 2019–2020 promotions round, the University has also introduced a requirement to evidence collegiality as well as excellence in each of the four qualifying dimensions. The criteria recognise not only the achievement of the individual but also how that individual has supported the careers of others.

From 2019–2020 onwards, promotion criteria for the academic track also explicitly state that one of the four qualifying criteria should be either academic outputs or impact. By ‘impact’ we mean the evidenced benefits to society that have resulted from the research – these could be economic, societal, cultural, or related to health and policy. The new criteria therefore formally acknowledge that societal impact holds as much value to the institution as outputs, and that generating and evidencing impact takes time. It also ensures that staff does not feel under pressure to ‘do everything’. We will be monitoring the effect of these changes in mid 2020.

In addition, Glasgow has recently introduced a career pathway for research scientists: this track recognises and rewards the contributions made by researchers who have specialist knowledge and skills, such as bioinformaticians. The contributions and intellectual leadership provided by these roles are often not reflected in the traditional promotion criteria, which depend on lead or senior authorships. Research scientists can instead progress in their careers by demonstrating specialist work stream, as well as team contributions.

Practice, not policy

Success will not come from issuing policies, but by making practical changes that signal “the way we do things around here”. Even if university policies are read, they will be forgotten unless the principles are embedded in standard practice. And if we are not serious about our practices, then we are not credible about our intentions.

Over 1500 organisations have signed DORA and have committed not to use unreliable proxies such as journal impact factors in research evaluation. Yet, even purging references to journal impact factors from all paperwork is no guarantee that these or other metrics will not be used. If we are serious about fair evaluation mechanisms, then we need to provide evaluation panels with meaningful information. At Glasgow, we ask applicants to describe in 100 words the importance of their output, and their contribution to it. Many organisations have switched to the use of narrative formats, for instance the Royal Society , or the Dutch research council ( NWO ). To show that we value all dimensions of research, we also ask for a commitment to open research and give parity of credit to academic outputs (such as papers) and the societal impact they create (see Box 1 ).

To ensure that changes are felt on the ground, we are embedding these priorities in annual appraisals, promotion and recruitment, so that the same expectations are encountered in every relevant setting. We have also included the importance of responsible metrics in recruitment training, and will be working with our colleagues in human resources to ensure that local conversations with hiring managers are consistent with our metrics policy (see Box 2 ).

Responsible metrics

The policy on the responsible use of metrics means ensuring that the mechanisms we use to evaluate research quality are appropriate and fairly applied. For example, we need to make sure that quantitative indicators are suitably benchmarked and normalised by subject, and that they are used along qualitative ones. This is to avoid the over-reliance on single-point metrics (such as research funding) and over-use of unreliable proxies for quality (such as journal impact factors).

The policy describes our approach to evaluating the quality of our outputs, our supervision and our grant capture. The proof, however, is in the way the policy is implemented in practice. For example, applicants to our strategic recruitment schemes are requested to select their four best outputs, describe the significance of each output to the field (without relying on impact factors), and narrate their contribution to the work. Applicants are also asked to describe their commitment to open research. This approach allows the recruitment panel to obtain a more rounded impression of the candidate and, we hope, reduces the use of unhelpful proxies such as length of publication list or journal impact factors.

Start, even if you cannot see the finish line

Once you have decided on the general direction, start by doing something without worrying about scoping the project from start to finish.

At Glasgow we started by doing a 360-degree review of our provision for research integrity: this was not just about the training but also about raising the visibility of this agenda in the community. We did not call it ‘culture’ then, but we realised that progress would come from communicating the dimensions of good practice (e.g. open research) rather than by sanctioning breaches of conduct. That exercise gave us experience of getting support from senior management, managing a cross-institutional working group, and getting buy-in from the academic body through the establishment of a network of 29 integrity advisers . These individuals champion this agenda to researchers, contribute to training and policy and also participate in research misconduct panels.

From integrity, we moved to open research, and from there, to careers. It started with compliance, and progressed towards culture. Do not wait for the rules to come to you. Make your own. Have confidence that once projects are initiated, they will suggest future courses of action.

Shout about it

If you want to be noticed, it helps to over-communicate. If your project serves more than one agenda, then your colleagues in, say, human resources, the library, the research office, and the equality, diversity, and inclusion team will already be helping you to amplify the message. We have set up a Culture and Careers group that brings together a range of relevant professional groups and colleagues. Focusing on our culture activities and the training that we can provide to staff and students helps us to share knowledge and to highlight where different agendas can reinforce each other.

Make the framework easy to understand: at Glasgow we talk about supporting what we value (e.g. CRediT), recognising what we value (e.g. our promotion criteria), and celebrating those values, for instance with our recently launched research culture awards . These highlight outstanding activities that promote collegial behaviours and contribute to a positive research culture. In 2019, over 30 applications were received from across the institution, reflecting a variety of career stages, coming from academic, technical and professional services roles, and ranging from groups of researchers to individual staff. The awards have changed the conversation as to what culture actually is.

But equally do not fret if colleagues do not know how your various activities fit together under a ‘culture’ agenda. It is far more important that researchers embrace the activities themselves (see “Practice, not policy” above).

Communication takes legwork, so use any channel you have. Present at committees, consult with different disciplines and career stages. Speak to the willing. Welcome the challenge. Bring together different voices in a discussion forum. For example, we recently organised a research culture event involving action-oriented conversations with academics, administrators, funders, societies, and publishers; this helped to build our evidence base, share perspectives and move forward institutional thinking in relation to key areas of culture (see the illustration for a summary of the discussion).

Map of the ideas discussed at the Re-imagining research culture workshop organised at the University of Glasgow in September 2019.

Jacquie Forbes at drawntolearn.co.uk (CC BY 4.0)

A research culture survey allowed us to assess how we were doing. It gathered examples of good practice (for example, that the community appreciated reading groups and the opportunity for internal peer review) and it highlighted the aspects of research our staff were comfortable with (open access, for instance). It also pointed us towards what people wanted to know more about, such as how to increase the visibility of their research. Together, the event and survey have informed our next actions (you can access the question set here ) and our action plan for the next five years.

No such thing as a single culture

If you work in a research organisation, you are probably relaxed about the fact that different parts of the institution have their own priorities, as befits the disciplinary community.

Institution-wide projects should be designed to address the broad ambitions of the university: for example, all areas of the university can participate in the research culture awards or meet the requirement for collegiality in our promotion criteria.

Each discipline can then be invited to implement the culture programme that suits them. Getting this right requires a bit of flexibility, some confidence that things will not unravel, but also clear leadership. Some institutional glue can be provided by sharing case studies between areas, which is helped by collecting feedback on how policies and guidance are being implemented at the university level. For example, our new guidance on embedding equality, diversity and inclusion in conferences and events contains a weblink to a feedback survey. We hope that this will help us to pinpoint where colleagues are struggling to implement best practice, perhaps due to other organisational challenges such as funding, lack of clear guidance or procurement.

What’s next?

We have published an action plan for our 2020 – 2025 university strategy , which covers career development, research evaluation, collegiality, open research and research integrity. The starting point will be to focus on supporting career development, on helping researchers to enhance their visibility, and on developing an informed and committed leadership across the university.

We have also published an institutional statement to highlight the road travelled and our future plans. All the while, we are drawing inspiration from others: the Wellcome Trust and the Royal Society, and the progressive policies introduced by publishers such as PLoS, eLife, Wiley, and F1000. We are excited by the launch of initiatives that will inform better decision-making in the culture space, and online groups for sharing ideas. We want to be a part of organisations, such as the UK Reproducibility Network , that identify priorities and work together in implementing them.

We are also casting our eyes towards broader aspects of culture: how do we define and encourage research creativity, how do we make more time, and how might we extend the scope of our actions beyond research staff to all those that contribute to research?

Culture does not happen at the expense of excellence; an updated culture is what will allow even more of us to excel.

Author details

Tanita Casci (@tanitacasci) is the Head of Research Policy at the University of Glasgow, Glasgow, United Kingdom

For correspondence

Competing interests.

Elizabeth Adams (@researchdreams) is the Researcher Development Manager at the University of Glasgow, Glasgow, United Kingdom

Acknowledgements

We are grateful to the extended network of academics, technicians, students and professional services staff who over a long time have variously driven, supported, and constructively challenged what we are doing.

Publication history

• Received: January 29, 2020
• Accepted: January 29, 2020
• Version of Record published: February 10, 2020 (version 1)

© 2020, Casci and Adams

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Research Culture

Research culture is a broad topic that can encompass the behaviours, values, expectations, attitudes, and norms of research systems ( Royal Society, 2018 ). Science Europe is committed to opening up the discussion on research culture to create the best possible research ecosystem where researchers thrive.

Why is research culture important?

In the European Research Area (ERA), and globally, there are a diversity of approaches to research culture that influence the way that science is governed, funded, performed, and communicated. Research Culture also impacts upon the career pathways of researchers and research-associated positions. Importantly, the culture that pervades any given research system influences the quality of its outputs and outcomes.

At long last, discussions on research culture are gathering speed. This offers an opportunity to reflect upon the links between culture, processes, and policies that govern research systems on one hand, and the practices of those that are involved in the scientific endeavour on the other. A holistic view is needed when considering reforms to areas such as Research Assessment Systems and Open Science, acknowledging the interconnected nature of these key themes.

What are the current aims and objectives of Science Europe?

Research culture touches upon the core business of Science Europe’s Member Organisations as it is linked to the quality of research, the contribution of science to knowledge generation, the role of science in and for society, and the sustainability of research systems as a whole. Science Europe and its member organisations will work towards defining and implementing positive cultures that foster sustainable research ecosystems where research and researchers thrive.

What is Science Europe doing to achieve these aims?

Science Europe’s activities on research culture focus on the core values that we put at the centre of scientific research and ensure that they are reflected in policies and practices. Science Europe aims to:

• Inspire the reappraisal of institutional approaches and values to support the diversity and sustainability of research systems. In doing so, lead and facilitate reflections on the boundaries, degree of self-organisation, and values of the current and future European R&I system and in the ERA.
• Develop recognition frameworks that support quality-driven and healthy research cultures. This is done by promoting policies and practices that recognise, reward, and incentivise actions and behaviours that affirm the core values that underpin research systems.
• Foster research recognition systems that value a broader array of research activities and outputs, as well as supporting a larger variety of career pathways for researchers and research-related staff.
• Promote coherence between policy areas to improve the research ecosystem and conditions for researchers within it, such as: Open Science; access to Research Infrastructures; research integrity; promotion of equality, diversity, and inclusivity (EDI); recognition of methodological rigour; transparency; the reproducibility of scientific results; and research assessment processes.

The Science Europe holistic approach to research culture will help implementing specific actions in the areas above to improve the overall conditions for research activity and for researchers.

Statement on Research Culture - Empowering Researchers with a Thriving Research System

The new statement on Research Culture envisages an ERA that focusses on the quality of research and its processes, supports scientific freedom, and promotes social diversity and inclusion, acknowledging that these conditions will, in turn, foster a productive research system.

7 ways to promote better research culture

Establishing support systems is essential for promoting research culture. Image:  REUTERS/Amir Cohen

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The culture in UK research establishments is one of the reasons the country is an attractive and productive place to undertake research. If you want excellent research, you need a positive research culture that supports all individuals involved. Alongside national policies and programmes, local policies and the attitudes and behaviour of staff at all levels influences this.

An important aspect of research culture is an organization’s approach to research integrity – the formal and informal ethics, standards, protocols and policies researchers follow in their environment. Organizations are increasingly recognizing the importance of the role of research integrity.

Enhancing research culture doesn’t require major effort and resources. Organizations across the UK and globally have made changes linked to integrity that have improved their research culture. These range from simple approaches such as using informal communication channels to nurture a supportive environment, discussing successes and “failures”, to embedding research integrity into the heart of institutional culture, requiring research leaders and senior administrators to lead by example.

Here are seven things that could help you promote good research integrity, and contribute to improving research culture, in your organization:

Facilitating open discussions can help foster a more collaborative environment, by giving researchers the chance to share their experiences of not only their successes, but also their “failures”. This helps to build respect and trust within the research team, by talking openly and giving support when things don’t always go right. The Tobacco and Alcohol Research Group, based at the University of Bristol, have a range of different communication channels to support their work, with one focused specifically on “triumph and disaster”, which dispels the assumption that senior academics have had continuous successes to get to their esteemed positions.

Providing and promoting career counselling, coaching and support services available to staff may help to reduce pressures within a research environment, which is imperative to staff well-being. This can help in limiting stress and time pressures, and connect researchers to other resources available at their institution, such as forms for deadline extensions, assistance programmes, career services and mental health and well-being services. The Fred Hutchinson Cancer Research Center provides a career-counselling service solely to their scientists, providing the opportunity to discuss their career paths and the steps they need to take to progress.

Research teams could openly discuss, amend and build on existing guidelines, to develop a consensus on their collective and individual behaviours and attitudes. This could be used to develop a group standard or pledge, ensuring all team members are aware of what is expected in the research environment. This helps to enhance a positive culture by refining standards and “norms”. The Barcelona Biomedical Research Park developed a code of good scientific practice , which sets out the expectations of individuals and the collective research team.

Encourage researchers and support staff to find time and space to meet to share ideas and experiences. By involving other departments, institutions and sectors, discussions can focus on improving research integrity and culture, to share best practice on what has worked, what hasn’t and its impact. The Barcelona Biomedical Research Park is one example of where this has been put into practice.

Often seen as “role models” to their early-career peers, organization, department and team leaders who are at the forefront of promoting a positive research culture – such as by taking part in training, encouraging discussions to address difficult questions in an open and honest way and by having an open door policy – set a “norm” and redefine standards. Participants at the Royal Society ’s research culture workshops gave examples of leaders initiating small but impactful ways to set culture and improve morale in the workplace. An example of this is setting regular hours, to tackle the perception that only academics working extensive hours are successful.

Career progression is a key factor in retention and enhances not only the quality of research for the institution, but for the research community as a whole. Researchers can feel more valued if skills needs are reviewed individually and as a group, ensuring they all possess the necessary skills for their role, such as statistics, data-handling, proposal-writing and resource management. And following on from this, identifying gaps and offering courses for development. Software Carpentry developed such an initiative, by running training workshops at the University of Florida, to increase the data literacy of university staff.

Highlight the importance of research culture and engage all staff across the organization by hosting a research culture and integrity day. Presentations, workshops and panel discussions could be given from across the organization. Different departments could showcase the ways they have improved research culture and integrity, as well as addressing areas where there is still room for improvement. The University of Nevada organised an “Ignite Integrity week” where all staff were encourage to participate in activities to discuss good practice.

Research culture is pivotal to research excellence; we are at the beginning of a conversation round promoting this idea. In Integrity in Practice, the Royal Society and the UK Research Integrity Office showcase examples of initiatives led by organizations from across the world to inspire and encourage innovation in research culture. The more research culture is talked about, the more progressive these discussions will become. So why not think about research culture within your institution and start the conversation on what positive improvements you can make, both collectively and as an individual.

• The Royal Society’s Integrity in Practice toolkit launched at the World Economic Forum’s Annual Meeting of the New Champions 2018. The Code of Ethics , produced by the World Economic Forum Young Scientists Community, is profiled in Integrity in Practice.

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The Importance of Research About Research on Culture: A Call for Meta-research on Culture

• Social and Family Dynamics, T. Denny Sanford School of (SSFD)

Research output : Contribution to journal › Article › peer-review

Objectives: It is crucial to examine how research on culture is fueled by assumptions, policies, and practices. The goal of this article is to promote meta-research on culture, the critical study of how investigations on culture are performed and interpreted, howscientific knowledge about culture is produced and transmitted, and the importance of scrutinizing assumptions, policies, and practices in a way that challenge views of minoritized groups as deviant and pathological. Method: We define key concepts, such as meta-research, culture, and meta-research on culture. Results: We approach cultural research as a system of people (researchers, participants), places (academic institutions, journals), practices (sampling, comparing groups), and power (legitimizing some groups as normative and others as deviant). We discuss assumptions, policies, and practices, and review landmark studies and methods. Conclusions: Meta-research on culture is an emerging field that can improve scientific understanding of human culture, guide efforts to elevate the perspectives of people who have historically experienced marginalization, inform institutional support and the creation of nurturing academic spaces, and guide the implementation of better research and training practices.

• Meta-research
• Meta-research on culture

ASJC Scopus subject areas

• Social Psychology
• Sociology and Political Science

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• 10.1037/cdp0000516

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• Link to publication in Scopus

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• research practice Social Sciences 100%
• Group Social Sciences 95%
• Research Personnel Medicine & Life Sciences 84%

T1 - The Importance of Research About Research on Culture

T2 - A Call for Meta-research on Culture

AU - Causadias, José M.

AU - Korous, Kevin M.

AU - Cahill, Karina M.

AU - Rea-Sandin, Gianna

N1 - Funding Information: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We declare no conflict of interest. Kevin M. Korous is now at the Department of Family and Preventive Medicine, University of Utah School of Medicine. Publisher Copyright: © 2021. American Psychological Association

N2 - Objectives: It is crucial to examine how research on culture is fueled by assumptions, policies, and practices. The goal of this article is to promote meta-research on culture, the critical study of how investigations on culture are performed and interpreted, howscientific knowledge about culture is produced and transmitted, and the importance of scrutinizing assumptions, policies, and practices in a way that challenge views of minoritized groups as deviant and pathological. Method: We define key concepts, such as meta-research, culture, and meta-research on culture. Results: We approach cultural research as a system of people (researchers, participants), places (academic institutions, journals), practices (sampling, comparing groups), and power (legitimizing some groups as normative and others as deviant). We discuss assumptions, policies, and practices, and review landmark studies and methods. Conclusions: Meta-research on culture is an emerging field that can improve scientific understanding of human culture, guide efforts to elevate the perspectives of people who have historically experienced marginalization, inform institutional support and the creation of nurturing academic spaces, and guide the implementation of better research and training practices.

AB - Objectives: It is crucial to examine how research on culture is fueled by assumptions, policies, and practices. The goal of this article is to promote meta-research on culture, the critical study of how investigations on culture are performed and interpreted, howscientific knowledge about culture is produced and transmitted, and the importance of scrutinizing assumptions, policies, and practices in a way that challenge views of minoritized groups as deviant and pathological. Method: We define key concepts, such as meta-research, culture, and meta-research on culture. Results: We approach cultural research as a system of people (researchers, participants), places (academic institutions, journals), practices (sampling, comparing groups), and power (legitimizing some groups as normative and others as deviant). We discuss assumptions, policies, and practices, and review landmark studies and methods. Conclusions: Meta-research on culture is an emerging field that can improve scientific understanding of human culture, guide efforts to elevate the perspectives of people who have historically experienced marginalization, inform institutional support and the creation of nurturing academic spaces, and guide the implementation of better research and training practices.

KW - Culture

KW - Meta-research

KW - Meta-research on culture

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U2 - 10.1037/cdp0000516

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Research and innovation culture

People and culture are fundamental to research and innovation because they drive the creative and dynamic system that we need to support the community to thrive.

Positive cultures support not just researchers and innovators but also the entire ecosystem which supports the research and innovation endeavour.

We strive to foster a system where everyone is appreciated and valued, that works for everyone, by everyone.

The role of UKRI

UKRI is uniquely positioned to catalyse efforts to improve culture, building on good work already under way, to connect up the whole system, covering multiple disciplines and cross-sector research and innovation.

Our approach in this area is multi-faceted and includes:

• reflecting on our own systems and processes
• how these influence the wider system
• improving our understanding of how we can support the whole research and innovation (R&I) community to catalyse the co-creation of environments that support a positive culture.
• support the ongoing creation and development of positive cultures across the research and innovation sector
• build the evidence base of what contributes to positive culture and the relationship between positive culture and quality research
• raise awareness of the importance of research and innovation culture nationally and internationally, and share our work in this area
• lead by example by embedding research and innovation culture within UKRI.

Main activities

These include:

• exploring a shared approach across the sector to adopting a narrative CV, Résumé for Research and Innovation (R4RI), for use in funding and beyond. The work of both the Joint Funders Group and the Alternative Uses Group will collectively minimise unnecessary bureaucracy through shared approaches. It will accelerate culture change by enabling a more efficient, cohesive and comprehensive approach to assessing what is visible and valued across a range of personal and organisational needs
• rolling out the UKRI Résumé for Research and Innovation narrative CV across UKRI funding opportunities, to support wider recognition of diverse contributions and career paths
• curating a multidisciplinary Good research resource hub  to help individuals, groups and organisations deliver excellent work and create positive environments
• establishing the good practice exchange (GPEx) to create connections, build the evidence, and champion culture change across the R&I sector
• catalysing a joined-up approach to the ethical implications of research by convening individuals and organisations who consider these questions across the sector to think about useful resources that can be developed
• commissioning work with others to gather insights on the adoption of various initiatives currently in place in the research sector and their impact on research culture
• working collaboratively with the Department for Business, Energy and Industrial Strategy and others to deliver the ambitions and commitments outlined in the research and development (R&D) people and culture strategy .

Key resources

Find policies, standards and guidance on research and innovation culture in our good research resource hub .

Open letters on research culture

Read more about UKRI’s response to open letters we have received on research culture .

Get in touch

Contact us to find out more about research and innovation culture.

Email: [email protected]

Last updated: 18 January 2024

This is the website for UKRI: our seven research councils, Research England and Innovate UK. Let us know if you have feedback or would like to help improve our online products and services .

Culture: 4 keys to why it matters

Delivers large-scale performance-improvement programs that foster culture change and counsels senior executives making leadership transitions

March 27, 2018 What separates the highest performing organizations from the rest? Clever strategy? Superior products? Better people?

Perhaps they do – for a while – but any advantage fades if it is not built atop something more fundamental. Something that enables a competitive advantage to sustain and grow over time. That something is culture .

What is culture?

Culture starts with what people do and how they do it. In any industry, what people do may not differ dramatically, but high-performing organizations distinguish themselves in how they do it. This cumulative effect of what is done and how it is done ultimately determines an organization’s performance.

Culture also encompasses why people do what they do. As the Titanic’s captain grasped a little too late on that fateful night in 1912, 90 percent of an iceberg’s mass lies beneath the surface. Culture is similar as it includes observable behaviors (the what and how above the surface) as well as everything underneath – the shared mindsets and beliefs that influence how people in an organization behave. Just like a captain navigating frigid waters, anyone trying to understand an organization’s culture must recognize that most of what matters cannot be readily seen.

Thus, culture is the common set of behaviors and underlying mindsets and beliefs that shape how people work and interact day to day.

Four reasons why culture matters

• Culture correlates with performance. Based on our research of over 1,000 organizations that encompass more than three million individuals, those with top quartile cultures (as measured by our Organizational Health Index ) post a return to shareholders 60 percent higher than median companies and 200 percent higher than those in the bottom quartile.
• Culture is inherently difficult to copy. The quickening pace of innovation means that products and business models face the constant threat of being replicated. In this environment, the ultimate competitive advantage is a healthy culture that adapts automatically to changing conditions to find new ways to succeed.
• Healthy cultures enable organizations to adapt. In a world where the one constant is change, culture becomes even more important because organizations with high-performing cultures thrive on change. The converse also holds true: Unhealthy cultures do not respond well to change. Our research shows that 70 percent of transformations fail, and 70 percent of those failures are due to culture-related issues.
• Unhealthy cultures lead to underperformance…or worse. Over time, not only do unhealthy cultures foster lackluster performance, but they can be your undoing. Daily headlines attest, culture can bring corporate giants to their knees.

This topic – how to create healthier and higher-performing cultures – is one we will explore in more detail in subsequent posts.

Learn more about our People & Organizational Performance Practice

Explaining research performance: investigating the importance of motivation

• Original Paper
• Open access
• Published: 23 May 2024
• Volume 4 , article number  105 , ( 2024 )

Cite this article

You have full access to this open access article

• Silje Marie Svartefoss   ORCID: orcid.org/0000-0001-5072-1293 1   nAff4 ,
• Jens Jungblut 2 ,
• Dag W. Aksnes 1 ,
• Kristoffer Kolltveit 2 &
• Thed van Leeuwen 3  

3 Altmetric

In this article, we study the motivation and performance of researchers. More specifically, we investigate what motivates researchers across different research fields and countries and how this motivation influences their research performance. The basis for our study is a large-N survey of economists, cardiologists, and physicists in Denmark, Norway, Sweden, the Netherlands, and the UK. The analysis shows that researchers are primarily motivated by scientific curiosity and practical application and less so by career considerations. There are limited differences across fields and countries, suggesting that the mix of motivational aspects has a common academic core less influenced by disciplinary standards or different national environments. Linking motivational factors to research performance, through bibliometric data on publication productivity and citation impact, our data show that those driven by practical application aspects of motivation have a higher probability for high productivity. Being driven by career considerations also increases productivity but only to a certain extent before it starts having a detrimental effect.

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Introduction

Motivation and abilities are known to be as important factors in explaining employees’ job performance of employees (Van Iddekinge et al. 2018 ), and in the vast scientific literature on motivation, it is common to differentiate between intrinsic and extrinsic motivation factors (Ryan and Deci 2000 ). In this context, path-breaking individuals are said to often be intrinsically motivated (Jindal-Snape and Snape 2006 ; Thomas and Nedeva 2012 ; Vallerand et al. 1992 ), and it has been found that the importance of these of types of motivations differs across occupations and career stages (Duarte and Lopes 2018 ).

In this article, we address the issue of motivation for one specific occupation, namely: researchers working at universities. Specifically, we investigate what motivates researchers across fields and countries (RQ1) and how this motivation is linked to their research performance (RQ2). The question of why people are motivated to do their jobs is interesting to address in an academic context, where work is usually harder to control, and individuals tend to have a lot of much freedom in structuring their work. Moreover, there have been indications that academics possess an especially high level of motivation for their tasks that is not driven by a search for external rewards but by an intrinsic satisfaction from academic work (Evans and Meyer 2003 ; Leslie 2002 ). At the same time, elements of researchers’ performance are measurable through indicators of their publication activity: their productivity through the number of outputs they produce and the impact of their research through the number of citations their publications receive (Aksnes and Sivertsen 2019 ; Wilsdon et al. 2015 ).

Elevating research performance is high on the agenda of many research organisations (Hazelkorn 2015 ). How such performance may be linked to individuals’ motivational aspects has received little attention. Thus, a better understanding of this interrelation may be relevant for developing institutional strategies to foster environments that promote high-quality research and research productivity.

Previous qualitative research has shown that scientists are mainly intrinsically motivated (Jindal-Snape and Snape 2006 ). Other survey-based contributions suggest that there can be differences in motivations across disciplines (Atta-Owusu and Fitjar 2021 ; Lam 2011 ). Furthermore, the performance of individual scientists has been shown to be highly skewed in terms of publication productivity and citation rates (Larivière et al. 2010 ; Ruiz-Castillo and Costas 2014 ). There is a large body of literature explaining these differences. Some focus on national and institutional funding schemes (Hammarfelt and de Rijcke 2015 ; Melguizo and Strober 2007 ) and others on the research environment, such as the presence of research groups and international collaboration (Jeong et al. 2014 ), while many studies address the role of academic rank, age, and gender (see e.g. Baccini et al. 2014 ; Rørstad and Aksnes 2015 ). Until recently, less emphasis has been placed on the impact of researchers’ motivation. Some studies have found that different types of motivations drive high levels of research performance (see e.g. Horodnic and Zaiţ 2015 ; Ryan and Berbegal-Mirabent 2016 ). However, researchers are only starting to understand how this internal drive relates to research performance.

While some of the prior research on the impact of motivation depends on self-reported research performance evaluations (Ryan 2014 ), the present article combines survey responses with actual bibliometric data. To investigate variation in research motivation across scientific fields and countries, we draw on a large-N survey of economists, cardiologists, and physicists in Denmark, Norway, Sweden, the Netherlands, and the UK. To investigate how this motivation is linked to their research performance, we map the survey respondents’ publication and citation data from the Web of Science (WoS).

This article is organised as follows. First, we present relevant literature on research performance and motivation. Next, the scientific fields and countries are then presented before elaborating on our methodology. In the empirical analysis, we investigate variations in motivation across fields, gender, age, and academic position and then relate motivation to publications and citations as our two measures of research performance. In the concluding section, we discuss our findings and implications for national decision-makers and individual researchers.

Motivation and research performance

As noted above, the concepts of intrinsic and extrinsic motivation play an important role in the literature on motivation and performance. Here, intrinsic motivation refers to doing something for its inherent satisfaction rather than for some separable consequence. Extrinsic motivation refers to doing something because it leads to a separable outcome (Ryan and Deci 2000 ).

Some studies have found that scientists are mainly intrinsically motivated (Jindal-Snape and Snape 2006 ; Lounsbury et al. 2012 ). Research interests, curiosity, and a desire to contribute to new knowledge are examples of such motivational factors. Intrinsic motives have also been shown to be crucial when people select research as a career choice (Roach and Sauermann 2010 ). Nevertheless, scientists are also motivated by extrinsic factors. Several European countries have adopted performance-based research funding systems (Zacharewicz et al. 2019 ). In these systems, researchers do not receive direct financial bonuses when they publish, although such practices may occur at local levels (Stephan et al. 2017 ). Therefore, extrinsic motivation for such researchers may include salary increases, peer recognitions, promotion, or expanded access to research resources (Lam 2011 ). According to Tien and Blackburn ( 1996 ), both types of motivations operate simultaneously, and their importance vary and may depend on the individual’s circumstances, personal situation, and values.

The extent to which different kinds of motivations play a role in scientists’ performance has been investigated in several studies. In these studies, bibliometric indicators based on the number of publications are typically used as outcome measures. Such indicators play a critical role in various contexts in the research system (Wilsdon et al. 2015 ), although it has also been pointed out that individuals can have different motivations to publish (Hangel and Schmidt-Pfister 2017 ).

Based on a survey of Romanian economics and business administration academics combined with bibliometric data, Horodnic and Zait ( 2015 ) found that intrinsic motivation was positively correlated with research productivity, while extrinsic motivation was negatively correlated. Their interpretations of the results are that researchers motivated by scientific interest are more productive, while researchers motivated by extrinsic forces will shift their focus to more financially profitable activities. Similarly, based on the observation that professors continue to publish even after they have been promoted to full professor, Finkelstein ( 1984 ) concluded that intrinsic rather than extrinsic motivational factors have a decisive role regarding the productivity of academics.

Drawing on a survey of 405 research scientists working in biological, chemical, and biomedical research departments in UK universities, Ryan ( 2014 ) found that (self-reported) variations in research performance can be explained by instrumental motivation based on financial incentives and internal motivation based on the individual’s view of themselves (traits, competencies, and values). In the study, instrumental motivation was found to have a negative impact on research performance: As the desire for financial rewards increase, the level of research performance decreases. In other words, researchers mainly motivated by money will be less productive and effective in their research. Contrarily, internal motivation was found to have a positive impact on research performance. This was explained by highlighting that researchers motivated by their self-concept set internal standards that become a reference point that reinforces perceptions of competency in their environments.

Nevertheless, it has also been argued that intrinsic and extrinsic motivations for publishing are intertwined (Ma 2019 ). According to Tien and Blackburn ( 1996 ), research productivity is neither purely intrinsically nor purely extrinsically motivated. Publication activity is often a result of research, which may be intrinsically motivated or motivated by extrinsic factors such as a wish for promotion, where the number of publications is often a part of the assessment (Cruz-Castro and Sanz-Menendez 2021 ; Tien 2000 , 2008 ).

The negative relationship between external/instrumental motivation and performance and the positive relationship between internal/self-concept motivation and performance are underlined by Ryan and Berbegal-Mirabent ( 2016 ). Drawing on a fuzzy set qualitative comparative analysis of a random sampling of 300 of the original respondents from Ryan ( 2014 ), they find that scientists working towards the standards and values they identify with, combined with a lack of concern for instrumental rewards, contribute to higher levels of research performance.

Based on the above, this article will address two research questions concerning different forms of motivation and the relationship between motivation and research performance.

How does the motivation of researchers vary across fields and countries?

How do different types of motivations affect research performance?

In this study, the roles of three different motivational factors are analysed. These are scientific curiosity, practical and societal applications, and career progress. The study aims to assess the role of these specific motivational factors and not the intrinsic-extrinsic distinction more generally. Of the three factors, scientific curiosity most strongly relates to intrinsic motivation; practical and societal applications also entail strong intrinsic aspects. On the other hand, career progress is linked to extrinsic motivation.

In addition to variation in researchers’ motivations by field and country, we consider differences in relation to age, position and gender. Additionally, when investigating how motivation relates to scientific performance we control for the influence of age, gender, country and funding. These are dimensions where differences might be found in motivational factors given that scientific performance, particularly publication productivity, has been shown to differ along these dimensions (Rørstad and Aksnes 2015 ).

Research context: three fields, five countries

To address the research question about potential differences across fields and countries, the study is based on a sample consisting of researchers in three different fields (cardiology, economics, and physics) and five countries (Denmark, Norway, Sweden, the Netherlands, and the UK). Below, we describe this research context in greater detail.

The fields represent three different domains of science: medicine, social sciences, and the natural sciences, where different motivational factors may be at play. This means that the fields cover three main areas of scientific investigations: the understanding of the world, the functioning of the human body, and societies and their functions. The societal role and mission of the fields also differ. While a primary aim of cardiology research and practice is to reduce the burden of cardiovascular disease, physics research may drive technology advancements, which impacts society. Economics research may contribute to more effective use of limited resources and the management of people, businesses, markets, and governments. In addition, the fields also differ in publication patterns (Piro et al. 2013 ). The average number of publications per researcher is generally higher in cardiology and physics than in economics (Piro et al. 2013 ). Moreover, cardiologists and physicists mainly publish in international scientific journals (Moed 2005 ; Van Leeuwen 2013 ). In economics, researchers also tend to publish books, chapters, and articles in national languages, in addition to international journal articles (Aksnes and Sivertsen 2019 ; van Leeuwen et al. 2016 ).

We sampled the countries with a twofold aim. On the one hand, we wanted to have countries that are comparable so that differences in the development of the science systems, working conditions, or funding availability would not be too large. On the other hand, we also wanted to assure variation among the countries regarding these relevant framework conditions to ensure that our findings are not driven by a specific contextual condition.

The five countries in the study are all located in the northwestern part of Europe, with science systems that are foremost funded by block grant funding from the national governments (unlike, for example, the US, where research grants by national funding agencies are the most important funding mechanism) (Lepori et al. 2023 ).

In all five countries, the missions of the universities are composed of a blend of education, research, and outreach. Furthermore, the science systems in Norway, Denmark, Sweden, and the Netherlands have a relatively strong orientation towards the Anglo-Saxon world in the sense that publishing in the national language still exists, but publishing in English in internationally oriented journals in which English is the language of publications is the norm (Kulczycki et al. 2018 ). These framework conditions ensure that those working in the five countries have somewhat similar missions to fulfil in their professions while also belonging to a common mainly Anglophone science system.

However, in Norway, Denmark, Sweden, and the Netherlands, research findings in some social sciences, law, and the humanities are still oriented on publishing in various languages. Hence, we avoided selecting the humanities field for this study due to a potential issue with cross-country comparability (Sivertsen 2019 ; Sivertsen and Van Leeuwen 2014 ; Van Leeuwen 2013 ).

Finally, the chosen countries vary regarding their level of university autonomy. When combining the scores for organisational, financial, staffing, and academic autonomy presented in the latest University Autonomy in Europe Scorecard presented by the European University Association (EUA), the UK, the Netherlands, and Denmark have higher levels of autonomy compared to Norway and Sweden, with Swedish universities having less autonomy than their Norwegian counterparts (Pruvot et al. 2023 ). This variation is relevant for our study, as it ensures that our findings are not driven by response from a higher education system with especially high or low autonomy, which can influence the motivation and satisfaction of academics working in it (Daumiller et al. 2020 ).

Data and methods

The data used in this article are a combination of survey data and bibliometric data retrieved from the WoS. The WoS database was chosen for this study due to its comprehensive coverage of research literature across all disciplines, encompassing the three specific research areas under analysis. Additionally, the WoS database is well-suited for bibliometric analyses, offering citation counts essential for this study.

Two approaches were used to identify the sample for the survey. Initially, a bibliometric analysis of the WoS using journal categories (‘Cardiac & cardiovascular systems’, ‘Economics’, and ‘Physics’) enabled the identification of key institutions with a minimum number of publications within these journal categories. Following this, relevant organisational units and researchers within these units were identified through available information on the units’ webpages. Included were employees in relevant academic positions (tenured academic personnel, post-docs, and researchers, but not PhD students, adjunct positions, guest researchers, or administrative and technical personnel).

Second, based on the WoS data, people were added to this initial sample if they had a minimum number of publications within the field and belonged to any of the selected institutions, regardless of unit affiliation. For economics, the minimum was five publications within the selected period (2011–2016). For cardiology and physics, where the individual publication productivity is higher, the minimum was 10 publications within the same period. The selection of the minimum publication criteria was based on an analysis of publication outputs in these fields between 2011 and 2016. The thresholds were applied to include individuals who are more actively engaged in research while excluding those with more peripheral involvement. The higher thresholds for cardiology and physics reflect the greater frequency of publications (and co-authorship) observed in these fields.

The benefit of this dual-approach strategy to sampling is that we obtain a more comprehensive sample: the full scope of researchers within a unit and the full scope of researchers that publish within the relevant fields. Overall, 59% of the sample were identified through staff lists and 41% through the second step involving WoS data.

The survey data were collected through an online questionnaire first sent out in October 2017 and closed in December 2018. In this period, several reminders were sent to increase the response rate. Overall, the survey had a response rate of 26.1% ( N  = 2,587 replies). There were only minor variations in response rates between scientific fields; the variations were larger between countries. Tables  1 and 2 provide an overview of the response rate by country and field.

Operationalisation of motivation

Motivation was measured by a question in the survey asking respondents what motivates or inspires them to conduct research, of which three dimensions are analysed in the present paper. The two first answer categories were related to intrinsic motivation (‘Curiosity/scientific discovery/understanding the world’ and ‘Application/practical aims/creating a better society’). The third answer category was more related to extrinsic motivation (‘Progress in my career [e.g. tenure/permanent position, higher salary, more interesting/independent work]’). Appendix Table A1 displays the distribution of respondents and the mean value and standard deviation for each item.

These three different aspects of motivation do not measure the same phenomenon but seem to capture different aspects of motivation (see Pearson’s correlation coefficients in Appendix Table A2 ). There is no correlation between curiosity/scientific discovery, career progress, and practical application. However, there is a weak but significant positive correlation between career progress and practical application. These findings indicate that those motivated by career considerations to some degrees also are motivated by practical application.

In addition to investigating how researchers’ motivation varies by field and country, we consider the differences in relation to age, position and gender as well. Field of science differentiates between economics, cardiology, physics, and other fields. The country variables differentiate between the five countries. Age is a nine-category variable. The position variable differentiates between full professors, associate professors, and assistant professors. The gender variable has two categories (male or female). For descriptive statistics on these additional variables, see Appendix Table A3 .

Publication productivity and citation impact

To analyse the respondents’ bibliometric performance, the Centre for Science and Technology Studies (CWTS) in-house WoS database was used. We identified the publication output of each respondent during 2011–2017 (limited to regular articles, reviews, and letters). For 16% of the respondents, no publications were identified in the database. These individuals had apparently not published in international journals covered by the database. However, in some cases, the lack of publications may be due to identification problems (e.g. change of names). Therefore, we decided not to include the latter respondents in the analysis.

Two main performance measures were calculated: publication productivity and citation impact. As an indicator of productivity, we counted the number of publications for each individual (as author or co-author) during the period. To analyse the citation impact, a composite measure using three different indicators was used: total number of citations (total citations counts for all articles they have contributed to during the period, counting citations up to and including 2017), normalised citation score (MNCS), and proportion of publications among the 10% most cited articles in their fields (Waltman and Schreiber 2013 ). Here, the MNCS is an indicator for which the citation count of each article is normalised by subject, article type, and year, where 1.00 corresponds to the world average (Waltman et al. 2011 ). Based on these data, averages for the total publication output of each respondent were calculated. By using three different indicators, we can avoid biases or limitations attached to each of them. For example, using the MNCS, a respondent with only one publication would appear as a high impact researcher if this article was highly cited. However, when considering the additional indicator, total citation counts, this individual would usually perform less well.

The bibliometric scores were skewedly distributed among the respondents. Rather than using the absolute numbers, in this paper, we have classified the respondents into three groups according to their scores on the indicators. Here, we have used percentile rank classes (tertiles). Percentile statistics are increasingly applied in bibliometrics (Bornmann et al. 2013 ; Waltman and Schreiber 2013 ) due to the presence of outliers and long tails, which characterise both productivity and citation distributions.

As the fields analysed have different publication patterns, the respondents within each field were ranked according to their scores on the indicators, and their percentile rank was determined. For the productivity measure, this means that there are three groups that are equal in terms of number of individuals included: 1: Low productivity (the group with the lowest publication numbers, 0–33 percentile), 2: Medium productivity (33–67 percentile), and 3: High productivity (67–100 percentile). For the citation impact measure, we conducted a similar percentile analysis for each of the three composite indicators. Then everyone was assigned to one of the three percentile groups based on their average score: 1: Low citation impact (the group with lowest citation impact, 0–33 percentile), 2: Medium citation impact (33–67 percentile), and 3: High citation impact (67–100 percentile), cf. Table  3 . Although it might be argued that the application of tertile groups rather than absolute numbers leads to a loss of information, the advantage is that the results are not influenced by extreme values and may be easier to interpret.

Via this approach, we can analyse the two important dimensions of the respondents’ performance. However, it should be noted that the WoS database does not cover the publication output of the fields equally. Generally, physics and cardiology are very well covered, while the coverage of economics is somewhat lower due to different publication practices (Aksnes and Sivertsen 2019 ). This problem is accounted for in our study by ranking the respondents in each field separately, as described above. In addition, not all respondents may have been active researchers during the entire 2011–2017 period, which we have not adjusted for. Despite these limitations, the analysis provides interesting information on the bibliometric performance of the respondents at an aggregated level.

Regression analysis

To analyse the relationship between motivation and performance, we apply multinomial logistic regression rather then ordered logistic regression because we assume that the odds for respondents belonging in each category of the dependent variables are not equal (Hilbe 2017 ). The implication of this choice of model is that the model tests the probability of respondents being in one category compared to another (Hilbe 2017 ). This means that a reference or baseline category must be selected for each of the dependent variables (productivity and citation impact). Furthermore, the coefficient estimates show how the probability of being in one of the other categories decreases or increases compared to being in the reference category.

For this analysis, we selected the medium performers as the reference or baseline category for both our dependent variables. This enables us to evaluate how the independent variables affect the probability of being in the low performers group compared to the medium performers and the high performers compared to the medium performers.

To evaluate model fit, we started with a baseline model where only types of motivations were included as independent variables. Subsequently, the additional variables were introduced into the model, and based on measures for model fit (Pseudo R 2 , -2LL, and Akaike Information Criterion (AIC)), we concluded that the model with all additional variables included provides the best fit to the data for both the dependent variables (see Appendix Tables A5 and A6 ). Additional control variables include age, gender, country, and funding. We include these variables as controls to obtain robust effects of motivation and not effects driven by other underlying factors. The type of funding was measured by variables where the respondent answered the following question: ‘How has your research been funded the last five years?’ The funding variable initially consisted of four categories: ‘No source’, ‘Minor source’, ‘Moderate source’, and ‘Major source’. In this analysis, we have combined ‘No source’ and ‘Minor source’ into one category (0) and ‘Moderate source’ and ‘Major source’ into another category (1). Descriptive statistics for the funding variables are available in Appendix Table A4 . We do not control for the influence of field due to how the scientific performance variables are operationalised, the field normalisation implies that there are no variations across fields. We also do not control for position, as this variable is highly correlated with age, and we are therefore unable to include these two variables in the same model.

The motivation of researchers

In the empirical analysis, we first investigate variation in motivation and then relate it to publications and citations as our two measures of research performance.

As Fig.  1 shows, the respondents are mainly driven by curiosity and the wish to make scientific discoveries. This is by far the most important motivation. Practical application is also an important source of motivation, while making career progress is not identified as being very important.

Motivation of researchers– percentage

As Table  4 shows, at the level of fields, there are no large differences, and the motivational profiles are relatively similar. However, physicists tend to view practical application as somewhat less important than cardiologists and economists. Moreover, career progress is emphasised most by economists. Furthermore, as table 5 shows, there are some differences in motivation between countries. For curiosity/scientific discovery and practical application, the variations across countries are minor, but researchers in Denmark tend to view career progress as somewhat more important than researchers in the other countries.

Furthermore, as table 6 shows, women seem to view practical application and career progress as a more important motivation than men; these differences are also significant. Similar gender disparities have also been reported in a previous study (Zhang et al. 2021 ).

There are also some differences in motivation across the additional variables worth mentioning, as Table  7 shows. Unsurprisingly, perhaps, there is a significant moderate negative correlation between age, position, and career progress. This means that the importance of career progress as a motivation seems to decrease with increased age or a move up the position hierarchy.

In the second part of the analysis, we relate motivation to research performance. We first investigate publications and productivity using the percentile groups. Here, we present the results we use using predicted probabilities because they are more easily interpretable than coefficient estimates. For the model with productivity percentile groups as the dependent variable, the estimates for career progress were negative when comparing the medium productivity group to the high productivity group and the medium productivity group to the low productivity group. This result indicates that the probability of being in the high and low productivity groups decreases compared to the medium productivity group as the value of career progress increases, which may point towards a curvilinear relationship between the variables. A similar pattern was also found in the model with the citation impact group as the dependent variable, although it was not as apparent.

As a result of this apparent curvilinear relationship, we included quadric terms for career progress in both models, and these were significant. Likelihood ratio tests also show that the models with quadric terms included have a significant better fit to the data. Furthermore, the AIC was also lower for these models compared to the initial models where quadric terms were not included (see Appendix Tables A5 – A7 ). Consequently, we base our results on these models, which can be found in Appendix Table A7 . Due to a low number of respondents in the low categories of the scientific curiosity/discovery variable, we also combined the first three values into one to include it as a variable in the regression analysis, which results in a reduced three-value variable for scientific curiosity/discovery.

Results– productivity percentile group

Using the productivity percentile group as the dependent variable, we find that the motivational aspects of practical application and career progress have a significant effect on the probability of being in the low, medium, or high productivity group but not curiosity/scientific discovery. In Figs.  2 and 3 , each line represents the probability of being in each group across the scale of each motivational aspect.

Predicted probability for being in each of the productivity groups according to the value on the ‘practical application’ variable

Predicted probability of being in the low and high productivity groups according to the value on the ‘progress in my career’ variable

Figure  2 shows that at low values of application, there are no significant differences between the probability of being in either of the groups. However, from around value 3 of application, the differences between the probability of being in each group increases, and these are also significant. As a result, we concluded that high scores on practical application is related to increased probability of being in the high productivity group.

In Fig.  3 , we excluded the medium productivity group from the figure because there are no significant differences between this group and the high and low productivity group. Nevertheless, we found significant differences between the low productivity and the high productivity group. Since we added a quadric term for career progress, the two lines in Fig.  3 have a curvilinear shape. Figure  3 shows that there are only significant differences between the probability of being in the low or high productivity group at mid and high values of career progress. In addition, the probability of being in the high productivity group is at its highest value at mid values of career progress. This indicates that being motivated by career progress increases the probability of being in the high productivity group but only up to a certain point before it begins to have a negative effect on the probability of being in this group.

We also included age and gender as variables in the model, and Figs.  4 and 5 show the results. Figure  4 shows that age especially impacts the probability of being in the high productivity and low productivity groups. The lowest age category (< 30–34 years) has the highest probability for being in the low productivity group, while from the mid age category (50 years and above), the probability is highest for being in the high productivity group. This means that increased age is related to an increased probability of high productivity. The variable controlling for the effect of funding also showed some significant results (see Appendix Table A7 ). The most relevant finding is that receiving competitive grants from external public sources had a very strong and significant positive effect on being in the high productivity group and a medium-sized significant negative effect on being in the low productivity group. This shows that receiving external funding in the form of competitive grants has a strong effect on productivity.

Predicted probability of being in each of the productivity groups according to age

Figure  5 shows that there is a difference between male and female respondents. For females, there are no differences in the probability of being in either of the groups, while males have a higher probability of being in the high productivity group compared to the medium and low productivity groups.

Results– citation impact group

For the citation impact group as the dependent variable, we found that career progress has a significant effect on the probability of being in the low citation impact group or the high citation group but not curiosity/scientific discovery or practical application. Figure  6 shows how the probability of being in the high citation impact group increases as the value on career progress increases and is higher than that of being in the low citation impact group, but only up to a certain point. This indicates that career progress increases the probability of being in the high citation impact group to some degree but that too high values are not beneficial for high citation impact. However, it should also be noted that the effect of career progress is weak and that it is difficult to conclude on how very low or very high values of career progress affect the probability of being in the two groups.

Predicted probability for being in each of the citation impact groups according to the value on the ‘progress in my career’ variable

We also included age and gender as variables in the model, and we found a similar pattern as in the model with productivity percentile group as the dependent variable. However, the relationship between the variables is weaker in this model with the citation impact group as the dependent variable. Figure  7 shows that the probability of being in the high citation impact group increases with age, but there is no significant difference between the probability of being in the high citation impact group and the medium citation impact group. We only see significant differences when each of these groups is compared to the low citation impact group. In addition, the increase in probability is more moderate in this model.

Predicted probability of being in each of the citation impact groups according to age

Figure  8 shows that there are differences between male and female respondents. Male respondents have a significant higher probability of being in the medium or high citation impact group compared to the low citation impact group, but there is no significant difference in the probability between the high and medium citation impact groups. For female respondents, there are no significant differences. Similarly, for age, the effect also seems to be more moderate in this model compared to the model with productivity percentile groups as the dependent variable. In addition, the effect of funding sources is more moderate on citation impact compared to productivity (see Appendix Table A7 ). Competitive grants from external public sources still have the most relevant effect, but the effect size and level of significance is lower than for the model where productivity groups are the dependent variable. Respondents who received a large amount of external funding through competitive grants are more likely to be highly cited, but the effect size is much smaller, and the result is only significant at p  < 0.1. Those who do not receive much funding from this source are more likely to be in the low impact group. Here, the effect size is large, and the coefficient is highly significant.

Predicted probability for being in each of the citation impact groups according to gender

Concluding discussion

This article aimed to explore researchers’ motivations and investigate the impact of motivation on research performance. By addressing these issues across several fields and countries, we provided new evidence on the motivation and performance of researchers.

Most researchers in our large-N survey found curiosity/scientific discovery to be a crucial motivational factor, with practical application being the second most supported aspect. Only a smaller number of respondents saw career progress as an important inspiration to conduct their research. This supports the notion that researchers are mainly motivated by core aspects of academic work such as curiosity, discoveries, and practical application of their knowledge and less so by personal gains (see Evans and Meyer 2003 ). Therefore, our results align with earlier research on motivation. In their interview study of scientists working at a government research institute in the UK, Jindal-Snape and Snape ( 2006 ) found that the scientists were typically motivated by the ability to conduct high quality, curiosity-driven research and de-motivated by the lack of feedback from management, difficulty in collaborating with colleagues, and constant review and change. Salaries, incentive schemes, and prospects for promotion were not considered a motivator for most scientists. Kivistö and colleagues ( 2017 ) also observed similar patterns in more recent survey data from Finnish academics.

As noted in the introduction, the issue of motivation has often been analysed in the literature using the intrinsic-extrinsic distinction. In our study, we have not applied these concepts directly. However, it is clear that the curiosity/scientific discovery item should be considered a type of intrinsic motivation, as it involves performing the activity for its inherent satisfaction. Moreover, the practical application item should probably be considered mainly intrinsic, as it involves creating a better society (for others) without primarily focusing on gains for oneself. The career progress item explicitly mentions personal gains such as position and higher salary and is, therefore, a type of extrinsic motivation. This means that our results support the notion that there are very strong elements of intrinsic motivation among researchers (Jindal-Snape and Snape 2006 ).

When analysing the three aspects of motivation, we found some differences. Physicists tend to view practical application as less important than researchers in the two other fields, while career progress was most emphasised by economists. Regarding country differences, our data suggest that career progress is most important for researchers in Denmark. Nevertheless, given the limited effect sizes, the overall picture is that motivational factors seem to be relatively similar regarding disciplinary and country dimensions.

Regarding gender aspects of motivation, our data show that women seem to view practical application and career progress as more important than men. One explanation for this could be the continued gender differences in academic careers, which tend to disadvantage women, thus creating a greater incentive for female scholars to focus on and be motivated by career progress aspects (Huang et al. 2020 ; Lerchenmueller and Sorenson 2018 ). Unsurprisingly, respondents’ age and academic position influenced the importance of different aspects of motivation, especially regarding career progress. Here, increased age and moving up the positional hierarchy are linked to a decrease in importance. This highlights that older academics and those in more senior positions drew more motivation from other sources that are not directly linked to their personal career gains. This can probably be explained by the academic career ladder plateauing at a certain point in time, as there are often no additional titles and very limited recognition beyond becoming a full professor. Finally, the type of funding that scholars received also had an influence on their productivity and, to a certain extent, citation impact.

Overall, there is little support that researchers across various fields and countries are very different when it comes to their motivation for conducting research. Rather, there seems to be a strong common core of academic motivation that varies mainly by gender and age/position. Rather than talking about researchers’ motivation per se, our study, therefore, suggests that one should talk about motivation across gender, at different stages of the career, and, to a certain degree, in different fields. Thus, motivation seems to be a multi-faceted construct, and the importance of different aspects of motivation vary between different groups.

In the second step of our analysis, we linked motivation to performance. Here, we focused on both scientific productivity and citation impact. Regarding the former, our data show that both practical application and career progress have a significant effect on productivity. The relationship between practical application aspects and productivity is linear, meaning that those who indicate that this aspect of motivation is very important to them have a higher probability of being in the high productivity group. The relationship between career aspects of motivation and productivity is curve linear, and we found only significant differences between the high and low productivity groups at mid and high values of the motivation scale. This indicates that being more motivated by career progress increases productivity but only to a certain extent before it starts having a detrimental effect. A common assumption has been that intrinsic motivation has a positive and instrumental effect and extrinsic motivation has a negative effect on the performance of scientists (Peng and Gao 2019 ; Ryan and Berbegal-Mirabent 2016 ). Our results do not generally support this, as motives related to career progress are positively linked with productivity only to a certain point. Possibly, this can be explained by the fact that the number of publications is often especially important in the context of recruitment and promotion (Langfeldt et al. 2021 ; Reymert et al. 2021 ). Thus, it will be beneficial from a scientific career perspective to have many publications when trying to get hired or promoted.

Regarding citation impact, our analysis highlights that only the career aspects of motivation have a significant effect. Similar to the results regarding productivity, being more motivated by career progress increases the probability of being in the high citation impact group, but only to a certain value when the difference stops being significant. It needs to be pointed out that the effect strength is weaker than in the analysis that focused on productivity. Thus, these results should be treated with greater caution.

Overall, our results shed light on some important aspects regarding the motivation of academics and how this translates into research performance. Regarding our first research question, it seems to be the case that there is not one type of motivation but rather different contextual mixes of motivational aspects that are strongly driven by gender and the academic position/age. We found only limited effects of research fields and even less pronounced country effects, suggesting that while situational, the mix of motivational aspects also has a common academic core that is less influenced by different national environments or disciplinary standards. Regarding our second research question, our results challenge the common assumption that intrinsic motivation has a positive effect and extrinsic motivation has a negative effect on the performance of scientists. Instead, we show that motives related to career are positively linked to productivity at least to a certain point. Our analysis regarding citation patterns achieved similar results. Combined with the finding regarding the importance of current academic position and age for specific patterns of motivation, it could be argued that the fact that the number of publications is often used as a measurement in recruitment and promotion makes academics that are more driven by career aspects publish more, as this is perceived as a necessary condition for success.

Our study has a clear focus on the research side of academic work. However, most academics do both teaching and research, which raises the question of how far our results can also inform our knowledge regarding the motivation for teaching. On the one hand, previous studies have highlighted that intrinsic motivation is also of high importance for the quality of teaching (see e.g. Wilkesmann and Lauer 2020 ), which fits well with our findings. At the same time, the literature also highlights persistent goal conflicts of academics (see e.g. Daumiller et al. 2020 ), given that extra time devoted to teaching often comes at the costs of publications and research. Given that other findings in the literature show that research performance continues to be of higher importance than teaching in academic hiring processes (Reymert et al. 2021 ), the interplay between research performance, teaching performance, and different types of motivation is most likely more complicated and demands further investigation.

While offering several relevant insights, our study still comes with certain limitations that must be considered. First, motivation is a complex construct. Thus, there are many ways one could operationalise it, and not one specific understanding so far seems to have emerged as best practice. Therefore, our approach to operationalisation and measurement should be seen as an addition to this broader field of measurement approaches, and we do not claim that this is the only sensible way of doing it. Second, we rely on self-reported survey data to measure the different aspects of motivation in our study. This means that aspects such as social desirability could influence how far academics claim to be motivated by certain aspects. For example, claiming to be mainly motivated by personal career gains may be considered a dubious motive among academics.

With respect to the bibliometric analyses, it is important to realise that we have lumped researchers into categories, thereby ‘smoothening’ the individual performances into group performances under the various variables. This has an effect that some extraordinary scores might have become invisible in our study, which might have been interesting to analyse separately, throwing light on the relationships we studied. However, breaking the material down to the lower level of analysis of individual researchers also comes with a limitation, namely that at the level of the individual academic, bibliometrics tend to become quite sensitive for the underlying numbers, which in itself is then hampered by the coverage of the database used, the publishing cultures in various countries and fields, and the age and position of the individuals. Therefore, the level of the individual academic has not been analysed in our study, how interesting and promising outcomes might have been. even though we acknowledge that such a study could yield interesting results.

Finally, our sample is drawn from northwestern European countries and a limited set of disciplines. We would argue that we have sufficient variation in countries and disciplines to make the results relevant for a broader audience context. While our results show rather small country or discipline differences, we are aware that there might be country- or discipline-specific effects that we cannot capture due to the sampling approach we used. Moreover, as we had to balance sufficient variation in framework conditions with the comparability of cases, the geographical generalisation of our results has limitations.

This article investigated what motivates researchers across different research fields and countries and how this motivation influences their research performance. The analysis showed that the researchers are mainly motivated by scientific curiosity and practical application and less so by career considerations. Furthermore, the analysis shows that researchers driven by practical application aspects of motivation have a higher probability of high productivity. Being driven by career considerations also increases productivity but only to a certain extent before it starts having a detrimental effect.

The article is based on a large-N survey of economists, cardiologists, and physicists in Denmark, Norway, Sweden, the Netherlands, and the UK. Building on this study, future research should expand the scope and study the relationship between motivation and productivity as well as citation impact in a broader disciplinary and geographical context. In addition, we encourage studies that develop and validate our measurement and operationalisation of aspects of researchers’ motivation.

Finally, a long-term panel study design that follows respondents throughout their academic careers and investigates how far their motivational patterns shift over time would allow for more fine-grained analysis and thereby a richer understanding of the important relationship between motivation and performance in academia.

Data availability

The data set for this study is available from the corresponding author upon reasonable request.

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We are thankful to the R-QUEST team for input and comments to the paper.

The authors disclosed the receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Research Council Norway (RCN) [grant number 256223] (R-QUEST).

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Silje Marie Svartefoss

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Nordic Institute for Studies in Innovation, Research and Education (NIFU), Økernveien 9, 0608, Oslo, Norway

Silje Marie Svartefoss & Dag W. Aksnes

Department of Political Science, University of Oslo, 0315, Oslo, Norway

Jens Jungblut & Kristoffer Kolltveit

Centre for Science and Technology Studies (CWTS), Leiden University, 2311, Leiden, The Netherlands

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Silje Marie Svartefoss, Jens Jungblut, Dag W. Aksnes, Kristoffer Kolltveit, and Thed van Leeuwen. The first draft of the manuscript was written by all authors in collaboration, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Svartefoss, S.M., Jungblut, J., Aksnes, D.W. et al. Explaining research performance: investigating the importance of motivation. SN Soc Sci 4 , 105 (2024). https://doi.org/10.1007/s43545-024-00895-9

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DOI : https://doi.org/10.1007/s43545-024-00895-9

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