Embracing the Unexpected: A Quasi-experiment to Explore the Effects of Power and Gender on the Decision to Reciprocate a Hug-Or Not-in the Workplace

In business, the ability to develop rapport with a potential exchange partner can significantly impact the outcome of a negotiation. Although non-verbal communication is a key factor in relationship-building, there is little research on use of touch in business, and even less about hugging, even though hugging is becoming more common in the US. To explore hugging as a nonverbal form of communication in the workplace, the researcher adopted a quasi-experimental design informed by Social Exchange Theory (SET). During the experiment, power and dyadic gender composition were manipulated to study their effects on a “hugee’s” decision to reciprocate a hug, or not, in a business setting. Following a scenario-based encounter between subject and confederate, the subjects answered a series of questions about themselves and their experience. This research shows that female research participants are more likely than male participants to reciprocate a hug offered by a same-gender exchange partner; that the power (status) of a “hugger” does not significantly influence whether or not a research participant will reciprocate a hug offered by an exchange partner; that the gender of the research participant does not moderate the effect of power of the exchange partner such that power will have a greater effect on female participants than male participants and that individual traits of Emotional Sensitivity and Social Flexibility do not predict hugging in the workplace

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

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

Dual role of mitochondria in producing melatonin and driving GPCR signaling to block cytochrome c release

International audienc

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

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