Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Joel, S., Eastwick, P. W., et al. (2020).

PNAS of the United States of America,

117(32), 19061–19071.

https://doi.org/10.1073/pnas.1917036117

Abstract

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

Significance

What predicts how happy people are with their romantic relationships? Relationship science—an interdisciplinary field spanning psychology, sociology, economics, family studies, and communication—has identified hundreds of variables that purportedly shape romantic relationship quality. The current project used machine learning to directly quantify and compare the predictive power of many such variables among 11,196 romantic couples. People’s own judgments about the relationship itself—such as how satisfied and committed they perceived their partners to be, and how appreciative they felt toward their partners—explained approximately 45% of their current satisfaction. The partner’s judgments did not add information, nor did either person’s personalities or traits. Furthermore, none of these variables could predict whose relationship quality would increase versus decrease over time.

Conclusion

From a public interest standpoint, this study provides provisional answers to the perennial question “What predicts how satisfied and committed I will be with my relationship partner?” Experiencing negative affect, depression, or insecure attachment are surely relationship risk factors. But if people nevertheless manage to establish a relationship characterized by appreciation, sexual satisfaction, and a lack of conflict—and they perceive their partner to be committed and responsive—those individual risk factors may matter little. That is, relationship quality is predictable from a variety of constructs, but some matter more than others, and the most proximal predictors are features that characterize a person’s perception of the relationship itself.

Leadership Takes Self-Control. Here’s What We Know About It

Kai Chi (Sam) Yam, Huiwen Lian, D. Lance Ferris, Douglas Brown
Harvard Business Review
Originally published June 5, 2017

Here is an excerpt:

Our review identified a few consequences that are consistently linked to having lower self-control at work:

1. Increased unethical/deviant behavior: Studies have found that when self-control resources are low, nurses are more likely to be rude to patients, tax accountants are more likely to engage in fraud, and employees in general engage in various forms of unethical behavior, such as lying to their supervisors, stealing office supplies, and so on.
2. Decreased prosocial behavior: Depleted self-control makes employees less likely to speak up if they see problems at work, less likely to help fellow employees, and less likely to engage in corporate volunteerism.
3. Reduced job performance: Lower self-control can lead employees to spend less time on difficult tasks, exert less effort at work, be more distracted (e.g., surfing the internet in working time), and generally perform worse than they would had their self-control been normal.
4. Negative leadership styles: Perhaps what’s most concerning is that leaders with lower self-control often exhibit counter-productive leadership styles. They are more likely to verbally abuse their followers (rather than using positive means to motivate them), more likely to build weak relationships with their followers, and they are less charismatic. Scholars have estimated that the cost to corporations in the United States for such a negative and abusive behavior is at $23.8 billion annually.

Our review makes clear that helping employees maintain self-control is an important task if organizations want to be more effective and ethical. Fortunately, we identified three key factors that can help leaders foster self-control among employees and mitigate the negative effects of losing self-control.

The article is here.

Enhancing responsibility: Nicole Vincent at TEDxSydney 2014

Published on Jun 2, 2014

Performance enhancing has dominated debate in sport the world over. But what about in the rest of our lives? In this thought-provoking talk, Nicole Vincent discusses the fact that, whether we are aware of it or not, people have been actively pursuing ways and means to enhance their performance for years, even decades.

At work, while studying, or on stage, pressure to perform better is also increasing. This is being driven by many factors: competition, consumer demand, societal and even employer expectations. Dr Nicole Vincent proposes that with enhanced abilities comes greater responsibility. And, she says in this fascinating talk, unless we recognise and even regulate this new reality, our ability to choose may be lost.

Working for an Algorithm Might Be an Improvement

By Elaine Ou
Bloomberg
Originally posted January 13, 2017

Here are two excerpts:

Bridgewater isn’t alone. Offices around the country are deploying tools to continuously monitor and assess employee activity. Complaints about the dehumanizing nature of working for algorithmic bosses such as Uber and Amazon have inspired comparisons to “Taylorism,” a scientific management theory remembered primarily for its use of stopwatches and specialized slide rules (like the one pictured below).

(cut)

Scientific management doesn’t have to be a dehumanizing experience. Dalio says that Bridgewater's management systems are designed to improve decision-making, conflict resolution, and personal development. This doesn’t necessarily mean removing managers from the equation; it simply recognizes that humans are unique and fallible, and ideally enables them to develop unbiased solutions. Dalio says that Bridgewater employees have a turnover rate of less than 6 percent after the first two years of attrition. Assuming his employees haven’t been turned into cyborgs, he's probably doing something right.

The article is here.

Do It Well and Do It Right: The Impact of Service Climate and Ethical Climate on Business Performance and theBoundary Conditions

Jiang, K., Hu, J., Hong, Y., Liao, H., & Liu, S.
Journal of Applied Psychology. Advance online publication.

Abstract

Prior research has demonstrated that service climate can enhance unit performance by guiding employees’ service behavior to satisfy customers. Extending this literature, we identified ethical climate toward customers as another indispensable organizational climate in service contexts and examined how and when service climate operates in conjunction with ethical climate to enhance business performance of service units. Based on data collected in 2 phases over 6 months from multiple sources of 196 movie theaters, we found that service climate and ethical climate had disparate impacts on business performance, operationalized as an index of customer attendance rate and operating income per labor hour, by enhancing service behavior and reducing unethical behavior, respectively. Furthermore, we found that service behavior and unethical behavior interacted to affect business performance, in such a way that service behavior was more positively related to business performance when unethical behavior was low than when it was high. This interactive effect between service and unethical behaviors was further strengthened by high market turbulence and competitive intensity. These findings provide new insight into theoretical development of service management and offer practical implications about how to maximize business performance of service units by managing organizational climates and employee behaviors synergistically.

The article is here.

Ethical Leadership: Meta-Analytic Evidence of Criterion-Related and Incremental Validity

By Thomas W. H. Ng and Daniel C. Feldman
J Appl Psychol. 2014 Nov 24

Abstract

This study examines the criterion-related and incremental validity of ethical leadership (EL) with meta-analytic data. Across 101 samples published over the last 15 years (N = 29,620), we observed that EL demonstrated acceptable criterion-related validity with variables that tap followers' job attitudes, job performance, and evaluations of their leaders. Further, followers' trust in the leader mediated the relationships of EL with job attitudes and performance. In terms of incremental validity, we found that EL significantly, albeit weakly in some cases, predicted task performance, citizenship behavior, and counterproductive work behavior-even after controlling for the effects of such variables as transformational leadership, use of contingent rewards, management by exception, interactional fairness, and destructive leadership. The article concludes with a discussion of ways to strengthen the incremental validity of EL.

The entire article is here.