Andrea Neely

Why does work become so difficult?

What if successful performance tells us less about people than we think, and more about the systems surrounding them? What if the struggle we so often read as a personal failing is really the shape of the conditions people are working inside?

What I keep noticing is that we tend to locate the difficulty in the person. They burned out, they couldn’t cope, they weren’t resilient enough. But burnout, moral injury, and the invisible labor that holds a place together look less like personal shortfalls to me and more like signals about the system people are working inside, and about the support a workplace does or doesn’t build to hold people up.

Threads I’m pulling on

• When we call something “burnout,” are we describing a person or a system?
• How much of the work that keeps an organization running is labor no one can see or count?
• What would it mean to design work that people don’t have to survive heroically?

How do organizations shape the decisions we make?

How do we design organizations where people don’t have to succeed despite the system? Before we ask why someone chose what they chose, it’s worth asking what the system quietly made easy, hard, visible, or invisible.

We like to explain decisions by pointing at the person who made them: their judgment, their values, their nerve. I’m more curious about what the system quietly did first: which option it made effortless, which one it buried in friction, what it rewarded and what it never measured. A lot of what looks like individual choice is really the shape of the environment around it.

Threads I’m pulling on

• Before we judge a choice, what did the system make easy, hard, or invisible?
• Where do incentives quietly overrule the intentions people actually hold?
• Could we design defaults so the good decision is also the easy one?

What should AI actually change?

Not what AI can do, but what it should change about how we work, teach, and evaluate. Where does it give people room to do their best thinking, and where does it just automate the parts that made us human at work?

The question I want to stay honest about isn’t what AI can do; it’s what it should change. Some of what it automates is the tedium that was never the point; some of it is the friction that actually made us think. I’m trying to tell those apart, and to notice where a tool gives people more room for judgment rather than quietly removing the need for it.

Threads I’m pulling on

• Where does AI create room to think, and where does it just remove the thinking?
• Which parts of judgment should stay human on purpose?
• What would “AI that respects agency” actually look like in practice?

How do people really learn?

People need room to wrestle with things in their own time. What would teaching look like if we designed for that, creating the conditions where people think for themselves, rather than repeating someone else’s conclusions?

My own biggest changes never came from someone arguing me into them. They came when I had room and time to question things for myself. That’s shaped how I think about teaching. I’m less interested in students reciting my conclusions than in building the conditions where they can reach their own, and in trusting that real change is usually deeper and slower than winning an argument.

Threads I’m pulling on

• What conditions let someone genuinely change their mind on their own terms?
• How do you teach for questions rather than toward a conclusion?
• What does “room to wrestle” actually look like inside a syllabus?

Research agenda.

Increasingly, my work centers on a single idea: the support an organization does or doesn’t build to sustain people’s motivation, and the invisible labor people quietly take on to make up for it when that support isn’t there. It’s one lens on the conviction that runs through all of this: when good people struggle, the conditions around them usually explain more than the people do.

The questions on this site are the angles I come at that from: why work becomes so hard, how organizations shape the decisions we make, what AI should and shouldn’t change, and how people actually learn. In practice it has meant peer-reviewed work across motivation, social exchange, corporate social responsibility, management education, and, more recently, AI in higher education. You can sit with the open questions themselves on the Ideas page; below is the published record.

Published work.

Journal articles

• Neely, A.R., Karl, K.A., & Peluchette, J.V. Zooming in on messiness and parenthood in virtual work meetings. Journal of Managerial Issues (accepted).
• Davis, W.D., Evans, W.R., & Neely, A.R. (2026). CSR role crafting: Bringing corporate social responsibility into individual work roles. Journal of Business Research, 205, 115901. DOI
• Neely, A.R., & Harrison, T.L. (2026). Taking flight with job design: A paper airplane experiential exercise. Management Teaching Review, 11(2), 209-230. DOI PDF
• Stone, C.B., Neely, A.R., Phillips, W., & Terry, R.P. (2024). Veterans in the workplace: A model of factors affecting veteran transition. Equality, Diversity and Inclusion, 44(7), 886-904. DOI
• Burke-Smalley, L., Neely, A.R., & Bryant, E. (2024). Building professor-student rapport: A model, survey findings, and implications for practicing professors. Business Horizons, 67(2), 137-145. DOI PDF
• Munjal, D., Neely, A.R., Lengnick-Hall, M.L., & Evans, M.D. (2023). Volunteer job design: A fit perspective. Administrative Issues Journal, 13(2), 76-83.
• Bonner, R.L., Neely, A.R., Stone, C.B., Lengnick-Hall, C.A., & Lengnick-Hall, M.L. (2023). Triaging your human capital: An integrative perspective on strategic human capital resource allocation. Management Research Review, 46(3), 467-482. DOI
• Neely, A.R., Lengnick-Hall, M.L., & Evans, M.D. (2022). A process model of volunteer motivation. Human Resource Management Review, 32(4). DOI PDF
• Evans, W.R., Neely, A.R., & Davis, W.D. (2022). A work role perspective of corporate social responsibility. Journal of Managerial Issues, 34(4), 388-408. PDF
• Evans, W.R., Davis, W.D., & Neely, A.R. (2021). The role of organizational cynicism and conscientiousness in the relationship between ethical leadership and deviance. Journal of Managerial Issues, 33(1), 49-68. PDF
• Lengnick-Hall, C.A., Lengnick-Hall, M.L., Neely, A.R., & Bonner, R.L. (2021). Something old, something new: Reframing the integration of social capital into strategic HRM research. Academy of Management Perspectives, 35(3), 535-556. DOI PDF
• Karl, K.A., Peluchette, J.V., & Neely, A.R. (2021). ‘Slip out the back Jack’: Why applicants ghost potential employers. American Journal of Management, 21(1), 49-63. DOI PDF
• Bonner, R.L., Neely, A.R., Neely, A.D., & Mittal, S. (2018). The generationally relevant case method: An alternative approach to case-based undergraduate level management instruction. Journal of Strategic Management Education, 14, 1-18.
• Burke-Smalley, L.A., Rau, B.L., Neely, A.R., & Evans, W.R. (2017). Factors perpetuating the research-teaching gap in management: A review and propositions. The International Journal of Management Education, 15(3), 501-512. Full text PDF
• Neely, A.R., Cotton, J.L., & Neely, A.D. (2017). E-mentoring: A model and review of the literature. AIS Transactions on Human-Computer Interaction, 9(3), 220-242. Full text PDF

Book chapters

• Lengnick-Hall, M.L., & Neely, A.R. (2023). Talent staffing systems for effective knowledge management. In V. Vaiman & C. Vance (Eds.), Smart Talent Management (2nd ed.). Edward Elgar. DOI
• Neely, A.R., & Cotton, J.L. (2020). E-mentoring. In S. Fisher & T. Bondarouk (Eds.), The Encyclopedia of Electronic Human Resource Management. deGruyter. DOI
• Lengnick-Hall, M.L., Neely, A.R., & Stone, C.B. (2018). Human resource management in the digital age: Big data, HR analytics and artificial intelligence. In P. Novo Melo & C. Machado (Eds.), Management and Technological Challenges in the Digital Age (pp. 13-42). CRC Press.
• Lengnick-Hall, M.L., & Neely, A.R. (2015). Outplacement. In C. Cooper (Ed.), Wiley Encyclopedia of Management (3rd ed.).

The full record, including conference papers, awards, and service, lives on my curriculum vitae and Google Scholar.

Teaching for questions, not conclusions.

I don’t believe deep learning can be forced. My own biggest changes came when I had the room and time to question things for myself. So that’s what I try to build for students: the conditions to reach their own conclusions, not repeat mine.

In practice, I teach toward good questions rather than a single answer, and I trust that real understanding is usually slower and deeper than winning an argument. Lately much of that means teaching with AI: building custom bots and scaffolding so the tools support student thinking instead of replacing it.

Courses I primarily teach

• MGT 4140: Managerial Decision-Making A socio-psychological look at how people actually make choices: the judgments, preferences, and mechanisms behind them. Undergraduate
• MGT 5360: Managerial Decision-Making The graduate version, with decision-making theory put to work through cases, experiential exercises, and discussion. Graduate
• MGT 3310: Organizational Motivation & Leadership The leading theories of what motivates people at work, and what leadership does with them. Undergraduate
• MGT 5450: Leading Change How individuals and groups respond to change, and the psychology of what helps a transformation actually take hold. Graduate

Overall course evaluations average 6.35 / 7.0, and my teaching has been recognized with UTC’s Outstanding Tenured/Tenure-Track Faculty Teaching Award. Syllabi available on request.