A multi-level analysis of team climate and interpersonal exchange relationships at work

This paper seeks to advance research on interpersonal exchange relationships between supervisors, subordinates, and coworkers at work by integrating social exchange, workplace friendship, and climate research to develop a multi-level model. We tested the model using hierarchical linear modeling (HLM) with data obtained from a sample of 215 manager–employee dyads working in 36 teams. At the individual level, leader–member exchange (LMX) was found to be related to workplace friendship. Further, workplace friendship was positively related to team–member exchange (TMX) and mediated the LMX–TMX relationship. At the team level, HLM results indicated that the relationship between LMX and workplace friendship was moderated by affective climate. These findings suggest that high-quality LMX relationships are associated with enhanced workplace friendship between employees, especially when the affective climate is strong

Leader emotional expression and leader -member exchange

The study of leadership exchanges is extended by investigating the role of leader emotional expression (LEE) in leader-member exchange (LMX) relationships. A laboratory experiment was conducted to explore the effects of three LEE conditions, positive, neutral and negative, and leader gender, on four dimensions of LMX; affect (LMXA), contribution (LMXC), loyalty (LMXL) and professional respect (LMXPR). Participant members attended two work sessions, engaging in creative tasks at one session and repetitive, manual tasks at the other. Relationships between LEE, leader gender and LMX were explored along with their influence on performance on two task types, members\u27 affective states, satisfaction, self-efficacy and persistence intentions. LEE was not associated with members\u27 reported affective states. LEE was found to have a significant effect on each of the LMX dimensions. However, when controlling for LMXA, LEE\u27s effects on the other dimensions were no longer evident. Leader gender was found to moderate the LEE-LMX relationship; expressive male leaders received higher ratings in the positive LEE condition and lower ratings in the negative LEE condition than did similarly expressive female leaders. While LMX predicted attitudinal outcome variables, LEE did not. However, LEE was associated with member performance while LMX was not. These findings point to the merit of continued investigation of LEE and its effects in formative LMX relationships and on member performance

Predicting crash-relevant violations at stop sign–controlled intersections for the development of an intersection driver assistance system

<p><b>Objective</b>: Intersection crashes resulted in over 5,000 fatalities in the United States in 2014. Intersection Advanced Driver Assistance Systems (I-ADAS) are active safety systems that seek to help drivers safely traverse intersections. I-ADAS uses onboard sensors to detect oncoming vehicles and, in the event of an imminent crash, can either alert the driver or take autonomous evasive action. The objective of this study was to develop and evaluate a predictive model for detecting whether a stop sign violation was imminent.</p> <p><b>Methods</b>: Passenger vehicle intersection approaches were extracted from a data set of typical driver behavior (100-Car Naturalistic Driving Study) and violations (event data recorders downloaded from real-world crashes) and were assigned weighting factors based on real-world frequency. A <i>k</i>-fold cross-validation procedure was then used to develop and evaluate 3 hypothetical stop sign warning algorithms (i.e., early, intermediate, and delayed) for detecting an impending violation during the intersection approach. Violation detection models were developed using logistic regression models that evaluate likelihood of a violation at various locations along the intersection approach. Two potential indicators of driver intent to stop—that is, required deceleration parameter (RDP) and brake application—were used to develop the predictive models. The earliest violation detection opportunity was then evaluated for each detection algorithm in order to (1) evaluate the violation detection accuracy and (2) compare braking demand versus maximum braking capabilities.</p> <p><b>Results</b>: A total of 38 violating and 658 nonviolating approaches were used in the analysis. All 3 algorithms were able to detect a violation at some point during the intersection approach. The early detection algorithm, as designed, was able to detect violations earlier than all other algorithms during the intersection approach but gave false alarms for 22.3% of approaches. In contrast, the delayed detection algorithm sacrificed some time for detecting violations but was able to substantially reduce false alarms to only 3.3% of all nonviolating approaches. Given good surface conditions (maximum braking capabilities = 0.8 <i>g</i>) and maximum effort, most drivers (55.3–71.1%) would be able to stop the vehicle regardless of the detection algorithm. However, given poor surface conditions (maximum braking capabilities = 0.4 <i>g</i>), few drivers (10.5–26.3%) would be able to stop the vehicle. Automatic emergency braking (AEB) would allow for early braking prior to driver reaction. If equipped with an AEB system, the results suggest that, even for the poor surface conditions scenario, over one half (55.3–65.8%) of the vehicles could have been stopped.</p> <p><b>Conclusions</b>: This study demonstrates the potential of I-ADAS to incorporate a stop sign violation detection algorithm. Repeating the analysis on a larger, more extensive data set will allow for the development of a more comprehensive algorithm to further validate the findings.</p