Working on the Future of Work: Q&A with Ruth Kanfer

The School of Psychology professor has a book, a highly cited paper, and a new project to study artificial intelligence’s potential for enhancing adult learning.

July 20, 2022 | Atlanta, GA

In 2019, when School of Psychology Professor Ruth Kanfer was working on a book that would feature the latest science regarding an aging and age-diverse workforce, Kanfer and her three co-authors wanted to write a manual of sorts for supervisors, human resources managers, and organizational leaders, not necessarily academics and scholars. 

Then 2020 happened, and science, in the form of the pandemic, had other ideas on how to influence Ageless Talent: Enhancing the Performance and Well-Being of Your Age-Diverse Workforce (Routledge, 2021). 

“As we were writing, we started thinking about what managers would need to know post-pandemic, how it was affecting workers of different age groups,” says Kanfer, a member of the Industrial/Organizational Psychology program and founding director of Georgia Tech’s Work Science Center. “Towards the end, we wrote about possible implications and what issues might come up.”

Motivating workers in a disrupted and transformed workforce is one of those issues, she adds. 

Motivation related to work has long been Kanfer’s primary research interest. Kanfer, who first came to Georgia Tech in 1997, was recently notified that a 2017 paper in which she was the lead author, “Motivation related to work: A century of progress,” remains in the top ten list of downloaded articles from the Journal of Applied Psychology. “It’s one of the leading journals in the broad area of applied psychology,” says Tansu Celikel, professor and chair of the School of Psychology. 

Kanfer will continue to study work motivation in the National Science Foundation’s new National AI Institute for Adult Learning in Online Education (AI-ALOE). Led by Myk Garn of the Georgia Research Alliance, University System of Georgia, and Ashok Goel, professor in the College of Computing at Georgia Tech, the AI-ALOE Institute will study foundational AI issues and develop AI systems to enhance adult learning.

Kanfer recently spoke with the College of Sciences about AI-ALOE, where the future of the workforce is heading post-pandemic, and whether older workers will return to the workforce.

What are the biggest lessons for you on how the pandemic changed the workplace and workforce, particularly the aging workforce?

There are four lessons. First, the pandemic caused a real upsetting of the apple cart in terms of labor shortages. That does have to do with the aging workforce. The 55 and older group was the fastest-growing segment of the workforce prior to the pandemic, so you can imagine that their sudden departure would have an outsized impact. I had a manager once say to me that 40 percent of the workplace on the front lines of their company was over the age of 55, but if they managed retirements carefully, it would be fine.

Well, during the pandemic no one could manage workforce exits well, and the pandemic caused a lot of early retirements. Whether those folks will come back is unknown, but the loss was substantial. Almost a third of the workforce shortage comes from older workers who did not want to be exposed to the virus or chose to retire a bit earlier than planned. 

Second is the issue of worker well-being. There wasn’t really a lot of interest in this prior to the pandemic. It was more about productivity and new technologies. The pandemic changed that. If you want to preserve your workforce, not just the older workforce, you must pay attention to well-being, and that has stuck. I think we are much more focused on worker well-being than we were pre-pandemic.

The third lesson comes from the impact of technology. The pandemic caused a massive shift to remote work for many people and has accelerated the development and implementation of new technologies. But it is very clear that technological developments can not fully replace human workers. Technology didn’t obliterate jobs, it changed jobs, and it’s still doing that. What technology can’t do well, yet, in implementation, is make complex decisions about things that are not black and white. Not yet.

Technology also isn’t very good with factoring in emotions. Tech is a double-edged sword. It has helped people, and it has provided tools. It has made some jobs more interesting, some less interesting. It’s also pushed humans into new learning, and usually with the workforce, most of the learning you do is on the job after graduation. It used to be that on the job, someone older would train you, but that is often not the case when it comes to implementing new technologies. Now it’s continuous learning, and new skill-learning as part of your job is front and center.

The fourth lesson has to do with work arrangements. Sending everybody home to do remote work has upended assumptions that organizations have long had — that you need to have your employees at the workplace, that you need to be continuously supervising them, or you’re not going to achieve your goals. Well, during the pandemic workers were still productive. And this has left a lot of organizational leaders asking, what are we going to do with all this real estate if workers want to be remote? And if I let my workers be remote, how am I going to bind them psychologically to the organization? This has not been a temporary disruption. It has changed fundamental motives about work, and what binds people to organizations. I think organizations aren’t used to thinking that what binds their employees to them are human relationships. It’s much easier to think in terms of compensation and perks and the more material goods. Prior to the pandemic, the question was should we be in cubicles or open space in offices. Post-pandemic, I don’t think that’s as relevant. Many people won’t do most of their work in the office. They will go to the office to see other people and connect themselves to the organization. Jobs are no longer eight-to-five; they can and often do work at home.

Ageless Talent was published in 2021. What have you heard from organizations and managers who have read the book?

We have received very positive comments and the book has been popular with a wider audience, so it’s sold well on Amazon.

The advantage of the book is it doesn’t just tell you what we know, but how to use what we know in the workplace. And realistically what some of the challenges are that you’re going to face when you’re trying to manage and support balance.

What are those challenges? What’s the one big takeaway from Ageless Talent that would help organization leaders manage their age-diverse workforces?

Age diversity is here to stay. First, people are living longer and working longer, often for financial reasons (insufficient financial resources for retirement), but often also for non-financial reasons such as to structure time, maintain social relationships, and sustain professional identity. Second, as jobs require less physical labor due to automation, and organizations increase flexibility in employment options — flexible scheduling, contract work — it is no longer unusual to see work teams made up of three or more generations/cohorts.  

Knowing the facts about aging and using PIERA (Planning, Implementation, Evaluation, Reflection, Adjustment, a key strategy from the book) to manage an age-diverse workforce helps create a stronger, more collaborative workplace culture. The book provides important information about how and why age differences manifest in the workplace, and a clear set of evidence-based tools to use when managing an age-diverse workforce. 

What is it about the aging workforce that makes it ripe for research, particularly post-pandemic?

One of the things about studying the aging workforce is that when young people first enter the workforce, they typically focus on doing well, learning a lot, and advancing their careers. They’re on a trajectory.

In contrast, in an older workforce — let’s take pilots for example — people have different levels of expertise, different patterns of age-related decline in cognitive abilities, and very different non-work lives. A lot of motives can be satisfied by spending time at home. Others don’t have that option. It’s a much more complicated environment for being able to predict and understand things like retirement, and how people want to retire.

One of the things we have learned is that people are motivated. They generally don’t lose motivation for jobs that allow them to have some autonomy, control, and to make a meaningful contribution.They remain motivated, and there’s a lot that organizations can do to reinforce that with support and training and reducing age stereotypic norms. That will keep older people interested in continuing in the workforce. That’s why I think some of them will come back.

Speaking of motivation, it was the topic of a paper you co-authored five years ago that is still on the Top Ten Most Cited List from the Journal of Applied Psychology. Motivation also gets its own chapter in Ageless Talent. What are the challenges in motivating an age-diverse workforce?

When I started my career I focused on understanding the role of motivation in complex skill learning for jobs like air traffic control. I was really interested in the processes by which motivation impacts performance, irrespective of adult development. Drawing from motivation theory and cognitive psychology we examined when and for whom motivation during training might wane. Over the years there has been a gradual shift away from understanding motivation processes and toward understanding the why of motivation — what are the reasons? How do reasons for action affect what people do and how hard they try to accomplish a goal? For example, some of this has to do with mindset. If you approach a task with the idea of learning, then when you make errors early on, it doesn’t cause you to drop out.  On the other hand, if you have different expectations — you want to look good to your supervisor — and you make errors early, you’re much more likely to back away from further learning. Holding a learning mindset is really important when you’re training working adult learners to use new technologies. Adults in the workplace always want to look competent. We know a lot more now about older workers, and we know that self-paced training is much preferred to instructor-based pacing. Mature individuals work at different speeds. You want to take advantage of that, which can really change the nature of training design.

I think we’ve learned a lot about adult development that we can use to help people. Particularly about “why” people exert effort. It’s usually not a single reason, but what motive is dominant. Am I doing this to get a promotion, because I like to learn, to help others, or maybe to teach younger people? That last generativity motive is typically stronger in middle to late adulthood.

You’re part of the NSF’s cross-disciplinary, collaborative National AI Institute for Adult Learning and Online Education (AI-ALOE) at Georgia Tech. Tell us about that research and what you hope to accomplish there.

The broad goal of AI-ALOE is to develop new AI technologies to improve adult learning. That’s why I’m involved. I’m interested in adult learning for reskilling, upskilling, and lifelong learning. I am using my expertise in age-related changes in cognitive and motivational/affective processes to help in the development of  agents and tools that will help us with theory of mind and benefit adult learners and teachers. The project is less than a year old, and it’s a five-year project. It’s an ambitious project to develop these technologies, and what the Institute learns and develops is expected to be useful to public and private sectors who are concerned with building the 21st century workforce.

The Institute is not just about older people, but adults of all ages who will need or want to update or retrain. The Institute focuses on adult learning, which is not the same as K-12 learning. Adults have different goals and issues. Adults are typically very practically oriented with specific work goals. Adults are impatient. They have other things to do in their life. You want learning to be efficient. AI offers the potential for personalized learning at scale. Personalization is critical for inclusivity and for helping people with different levels of knowledge and learning styles; at scale is important given the rapidity of changes in the workplace that demand new skills. Online learning has taken hold in part because it is asynchronous. That makes the learning experience flexible.  For adult learning to be successful, it must also be relevant, affordable, and enjoyable. 

Kanfer is an elected Fellow of the Academy of Management (AoM), the American Psychological Association (APA), the Association for Psychological Sciences (APS), and the Society for Industrial and Organizational Psychology (SIOP). She has received scientific awards for her work from SIOP (William R. Owens Scholarly Achievement Award; Distinguished Scientific Contributions Award) and the AoM (Outstanding Publication of the Year in Organizational Behavior Award, 2004; 2008). Kanfer’s research has been supported by federal agencies, national foundations, and private organizations, and she has served on journal editorial boards, scientific advisory boards, as the AoM Organizational Behavior Division Chair, and as representative on the AoM Board of Governors. She is a member of the Sloan Research Network on Aging and Work Steering Committee, and recently served on the National Academy of Sciences Science and Practice of Learning Committee that produced How People Learn II (2019).

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