How Time Management, Autonomy, and Flexibility Can Shape the Employee Experience
Bibliographic record
Abstract
For many employees, the Covid-19 pandemic and the period following it has become a moment to redefine where and how to work. Following the pandemic, employees across industries in the United States are placing an increased importance on time flexibility and autonomy and leaders are looking for ways to implement flexible work strategies that create equitable opportunities for all employees. This renewed focus on flexibility leads us to question whether policies that existed before the pandemic are still effective today (e.g. flexible work policies) and whether policies that were deemed to be harmful in the past are effective now (e.g., telecommuting). To address these critical questions, this symposium looks at how time and autonomy-related rewards, policies, and norms shape the employee experience. Specifically, across five talks, we explore the psychological factors that influence whether telecommuting has positive or negative consequences for the employee experience, the downstream benefits and costs of time-based rewards like vacation and flexible work, how to offer choice to employees in a way that encourages them to express their true feelings (i.e. consent), and how to manage increased interruptions that arise from hybrid work. By studying the informal (team collaboration norms and consent) and formal (telecommuting and flexible work) policies and rewards (paid vacation) that impact the experiences of workers in today’s economy, the papers in this symposium provide novel and timely insights into when and how certain time and autonomy-related practices are beneficial (or harmful) to employees' organizational identification, commitment, career outcomes, and well-being, with potential implications for how leaders should promote these policies and practices. Legitimizing “Deep-Work”: When Collaboration Norms Promote Employee Wellbeing Author: Ashley Whillans; Harvard Business School Author: Justine Murray; Harvard Business School Giving People the Words to Say No Makes Them Feel Freer to Say Yes Author: Rachel Schlund; Cornell U. Author: Roseanna Sommers; U. of Chicago Law School Author: Vanessa Bohns; Cornell U. Staying in Love from Far Away: How Moral Legitimacy of Telecommuting Sustains Commitment Author: Julia D. Hur; New York U. Author: Rachel Lise Ruttan; U. of Toronto Author: Jun Lin; Stanford Graduate School of Business The Career Consequences of Flexible Work Policies: Considering Gender and Rank Author: Vanessa Conzon; Boston College Author: Duanyi Yang; Massachusetts Institute of Technology Author: Dongwoo Park; ILR at Cornell Author: Erin Kelly; Massachusetts Institute of Technology Vacation (vs. Monetary) Rewards Decrease Objectification and Increase Employee Well-Being Author: Alice Jihyun Lee-Yoon; UCLA Anderson School of Management Author: Sanford Ely DeVoe; UCLA
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".