Understanding motivations | Professor Claudia Nisa’s insight as a behavioral scientist

Claudia Nisa, Assistant Professor of Behavioral Science, Duke Kunshan University

Professor Claudia Nisa is an Assistant Professor of Behavioral Science, Duke Kunshan University. Before joining DKU, she had a diverse background in different academic positions.

Claudia began her education in Portugal, where she studied Social and Organizational Psychology and later switched to Economics and Social Policy for a discipline with a more interventional approach. She then moved to the UK for a M.S. in Decision Sciences at the London School of Economics and Political Science (LSE) and later graduated with a PhD in Social Policy from the same institution. During this period, her research mainly focused on applying behavioral science to the design of interventions to change health behavior, such as investigating how money and other types of incentives can be used to encourage people to take better care of their health.

After spending a few years back in Portugal, she then relocated to Singapore to work for ETH Zurich at Singapore-​​ETH Centre for Global Environmental Sustainability (SEC). Starting from here, Professor Nisa developed her interest in sustainability through a research project on future sustainable cities. After Singapore-ETH, Professor Nisa moved to work in the University of Queensland for the social science component within an engineering project, and later on in New York University Abu Dhabi where she published many impactful scientific papers.

Claudia is a passionate advocate for using behavioral science to solve real-world problems and design effective interventions for health and sustainability. While psychological assumptions are often overlooked in other disciplines, her interdisciplinary perspective has prompted intriguing observations of how us humans react to the world.

What is Behavioral Science?

“Behavioral science is at the same time very easy and very hard to define”, as Professor Nisa introduces. In simple terms, behavioral science should be the science of behavior – the science of human behavior, what motivates it and how one can change it. But Prof. Nisa also pointed out that the reality is a bit more complex because behavioral science is a disciplinary hosting “a cocktail of people”, such as political scientists, economists, psychologists and anthropologists – basically anybody who’s interested in anything that relates to humans.

On the other hand, when discussing “behavioral interventions”, usually it refers to things that do not involve laws or government regulations. Prof. Nisa’s work usually excludes financial incentives, although there is not a consensus among behavioral scientists whether these count as behavioral interventions or not. In this research area, she asks questions such as “how can you shape information?”, “What type of messages should you give (to the public)”, “should you add images to text?”. It’s all about working with psychological and behavioral principles to promote change.

Prof. Nisa’s research projects usually involve surveys. With modern technology, the majority of the surveys go online. However, there are also cases where she conducts studies offline, in the field. Usually this means she will need to print and deliver the intervention messages to research participants, later retrieve them back, waiting for the intervention to take effect, sometimes also conduct follow-up studies. In order to observe the effect, the research period usually ranges from weeks to more than a year.

Climate change actions: (almost) a null result

As the climate crisis haunts the science community, Prof. Nisa contributes her own knowledge from the behavioral science perspective.

In one of her recent publications, she concluded a nearly null effect from most behavioral interventions based on a meta-analysis research. In other words, for most people, the common interventions for encouraging people to react to climate change are not really effective.

In this research, Prof Nisa examined a series of studies focusing on the effect sizes of different intervention measures on behaviors related to climate change. These behaviors fall under six groups, including energy consumption, transportation, animal product consumptions, food waste, water consumption and recycling.

Through Prof Nisa’s analysis, it is revealed that most of the interventions actually have quite low impact on changing climate-related behaviors. The strongest category of mitigation is recycling, whereas the other mitigations are seeing unexpected low impacts. For example, interventions on purchase of energy-efficient appliances and decreasing private car use both have low effect sizes close to zero. After removing studies focusing towels used by hotel guests, interventions attempting to reduce daily water use in households have an even lower near-zero effect size. While mitigations for food waste reduction and animal products consumption reduction have higher effect size estimated, they’re merely marginally significant.

For all studies included in this meta-analysis, Prof. Nisa classified the intervention methods into five categories: information, appeal, engagement, social comparison and choice architecture.

From the result, Prof. Nisa demonstrated that measures which do not seem “noble”, such as providing social references or putting the subject under peer pressure from neighbors, actually have been observed to be more effective than merely providing information.

While appeal and engagement seems to also yield higher effect size, these studies tend to recruit self-selected participants, creating a sampling bias and boost the result higher than the general population.

Another case where the intervention measures end up with a larger impact when the people do not fully understand the implication of the intervention, such as choice architecture. As Prof. Nisa introduces, choice architecture, sometimes also called “nudge”, are “interventions that influence human behaviors by removing external barriers, expediting access or altering the structure of the environment in which people make choices“. For example, one may put recycling bins closer to regular waste or set air conditioning by default to higher temperatures, where the people subject to the intervention may not be consciously aware of such change.

As a side note, when trying to resolve the heterogeneity between studies, Prof Nisa also noticed studies with a smaller sample size, or studies that are temporally closer to the current time also tend to produce a larger effect size. The sample size argument may be related to the reported average effect size, as energy, transportation and water were the only behaviors examined in very large studies, while nudges were a strategy only tested in small sample studies.

While this research revealed the low effect from these categories of interventions, Prof. Nisa calls for future research focusing on the interaction of the interventions examined above in combination with other alternative intervention strategies such as financial incentives and/or policy regulations.

Lives vs. Livelihood

Prof. Nisa has also been working in the frontiers of health interventions, and obviously the past years of COVID-19 provided a great opportunity for health researchers. Prof. Nisa has collaborated with many researchers around the globe.

Out of many publications, Prof. Nisa highlighted one of the studies that she loves the best – research on motivating protective health measures. In this study, she and her team designed online surveys to examine perceived risks about COVID-19, to find out what factor could motivate the public to comply more with protective measures such as wearing masks or washing hands. This is quite intriguing research, as it tries to explore people’s preferences over health precautionary methods under different levels of perceived risks.

While people and media often assume health risk is a factor more thoroughly perceived by the general public, her research draws a counterintuitive conclusion that the perceptions of one losing livelihood made them do the right health prevention behaviors, much more than the risk of getting infected of the COVID-19 virus. “Losing livelihood” refers to suffering from economic consequences such as losing jobs, either temporarily or in the long term, means they might not have a place to live or have no income to feed themselves or the family, and that was a very serious concern among different kinds of perceived risk factors.

Prof Nisa first demonstrated that even with massive media coverage and a high death toll and infected cases released by different institutions, “the global risk perceptions are low to moderate”. On average, the general population expects the probability to get infected with the virus is considered to be low to moderate, whereas the probability to suffer economic losses is nearly 50%.

Furthermore, people generally perceive economic risks as far more concerning than health risks, and this conclusion holds unanimously across different countries and sociodemographic groups, such as age, gender, education, financial and employment status. Her study also demonstrated that preventive health behaviors, such as crowd avoidance, washing hands and social isolation (from family and friends) generally received great acceptance among the public. On the other hand, public acceptance of strict containment policies are varied. Generally, mandatory quarantine of infected or exposed individuals is somewhat accepted, but mandatory vaccination and reporting of suspicious cases are marked with a quite low acceptance rate. This means when people claim to disobey health measures “to protect the economy”, it’s not scientifically supported.

Prof. Nisa also explores the associations between risk perception and mitigation measures. Counterintuitively, the results show that perceived economic risk consistently predicts compliance of the mitigation measures with a linear association. The more people perceive themselves to be at risk of suffering economic losses due to the coronavirus, the more people comply with all preventive health behaviors and support strict compliance policies.

As for perceived health risks, the association with health measures are not unified. No association is observed between perceived health risk with the two most followed preventive health precautions – frequent hand washing and avoiding crowds. However, Positive linear associations for health risk were identified with support for two strict compliance measures: the more people perceived a personal health risk, the more they supported mandatory vaccination and supported mandatory quarantine. As for the remaining two measures social isolation and report of suspicious cases, curvilinear relationship is observed. Taking all these into consideration, the perceived health risk is not a good indicator in predicting people following health compliance measures.

A more interesting part of the study is the stratifying across demographic groups. The survey was conducted over Facebook, and such surveys often recruit a population of netizens leaning towards younger and slightly liberal groups among the political spectrum. While the issue of preventing and curing COVID-19 is highly politicized in the United States, people tend to assume that liberals tend to understand the risks of COVID-19 better. However, Prof. Nisa’s work also refutes such conclusions, as she observed that the magnitude of perceived risk from “Lives” vs. from “Livelihood” are almost the same between conservatives and liberals.

Prof Nisa also emphasized that the positive-to-zero interactions between health and economic risks suggests that health precautions and economic concerns do not exhibit antagonizing effects. More importantly, this should shed a light on the zero-sum thinking, which is often observed around media and political forums around COVID-19.

“Discipline of the future”

Prof. Nisa mentioned her mentor when she made the quote that psychology is the discipline of the future, because everything relies on how people think or feel or behave. This is sometimes overlooked because other disciplines are more powerful and impactful in society, but often they don’t realize how much they actually rely on assumptions about human behaviors. Law and Economics are two good examples where decisions are made largely based on assumptions on human behavior and psychology, which might not always be correct. Therefore, behavioral science and psychology could play a better role here and there.

This exciting perspective has shaped the motivation for Prof. Nisa, as she believed there’s a lot more she can contribute as a behavioral scientist to improve communicating health and policies to the public.

To the students who want to join and work in her group, Prof. Nisa is looking forward to candidates who are competent and resilient to all kinds of challenges, who are truly motivated. As for skills, she is willing to teach them as long as the student has motivation. As she goes, “Ideally, we would like them to know the literature or to look for it. We would like them to know how to conduct surveys, how to analyze data, … (but) being motivated and dedicated is definitely the number one thing that is necessary.

Interviewer & writer: Liansai Dong