Exploring temporal changes in obligated and delivered inducements: a dynamic systems perspective

ABSTRACT

The psychological contract (PC) is a dynamic process, where employees actively and passively adjust their perceptions of obligated and delivered inducements. In this study, we focus on the temporal changes in employee’s perceptions of obligated and delivered inducements and show that these micro-level dynamics take place at every stage through the PC process. We introduce a dynamic systems perspective to understand these temporal fluctuations. We show that employees’ perceptions of obligated and delivered inducements interact with each other and form a dynamic system. We compared daily and weekly time-frames and found that the daily time-frame maximizes the temporal fluctuations of employees’ perceptions of obligated and delivered inducements, whereas the weekly frame captures more complex trajectories. Furthermore, inducements that are explicitly communicated demonstrate more temporal fluctuations than inducements that are not explicitly communicated. We advance PC theory by providing more understanding of the temporal dynamics of employees’ perceptions of obligated and delivered inducements. We provide practical guidelines for the choice of an ideal time-frame to study employees’ perceptions of obligated and delivered inducements, depending on the specific inducements and the aim of the research. We propose that researchers further integrate dynamic systems theory into PC models.     

Textured Foot Orthotics on Dynamic Stability and Turning Performance in Parkinson’s Disease

Abstract

The purpose of this study was to facilitate sensory feedback, with textured foot orthotics, to evaluate dynamic stability and turning behavior in Parkinson’s disease individuals. Seven participants with a diagnosis of idiopathic Parkinson’s disease, aged 55–80?years old, participated in this study. Participants completed three testing sessions; baseline, 4?weeks post-baseline, and 5?weeks post-baseline. Three experimental conditions were tested: footwear only (F), footwear?+?non-textured orthotic (FO), and footwear?+?textured orthotic (FOT). Kinematic, kinetic, and video data were collected during the steps preceding a turn task. Variables of interest included dynamic stability (maximum mediolateral (ML), minimum ML, and ML range of the center of mass (COM)-base of support (BOS) relationship) and turning performance (gait velocity and step count). There was a statistically significant increase in maximum ML COM-BOS distance (week 4 [0.1298 m?±?0.054] compared to week 0 [0.1069 m?±?0.050] p = .0076), and a significant decrease in step count (week 0-F [5.52 steps ± 1.08] to week 0-FO [5.23 steps ± 0.87] p = .0296) and (week 4-FO [5.24 steps ± 1.31] to week 4–FOT [4.67 steps ± 0.76] p = .0004). Textured foot orthotics modified dynamic stability and turning performance in Parkinson’s disease individuals completing a 180° degree turn. These preliminary results support this potential treatment option for rehabilitation professionals treating Parkinson’s disease.     

Team adaptability and task cohesion as resources to the non-linear dynamics of workload and sickness absenteeism in firefighter teams

ABSTRACT

The current study builds on the non-linear Dynamic Systems (NDS) perspective to test the assumption that change in sickness absenteeism is non-linear, and that such change is due to workload, team adaptability and task cohesion. Participants were 37 firefighter teams (n = 250 individuals) from a main European capital city. The research hypotheses were tested using SPSS and the “cusp” package, in the statistical software R. The results suggest that change in sickness absenteeism behaviours over time is non-linear, with the cusp catastrophe model predicting such behaviours better than the linear and logistic models. In our model, task cohesion functions as an asymmetry factor (i.e., the independent variable that determines the strength and discrepancy between the two stable states of the dependent variable) leading to a linear change in sickness absenteeism. Interestingly, both workload and team adaptability function as bifurcation (i.e., the independent variable that determines the change between the two stable states of the order parameter) and asymmetry factors leading to non-linear and linear change in sickness absenteeism over time. This study contributes to the growing evidence that incorporating the NDS perspective enables a better understanding of action teams, namely those working in extreme environments.     

The temporal dimension in the understanding of maternal sensitivity in caregiver-infant interactions: The ‘Early Mother-Child Interaction Coding System’

Caregiver-child interaction is known to play a central role in children’s socioemotional development, yet the diversity of measures commonly used to evaluate it call into question the nature of the construct being assessed. Contingency within caregiver-child interactions has been identified as a key dyadic process that requires taking a temporal perspective to evaluating interactions. The Early Mother-Child Interaction Coding System is a psychometrically sound measure that sequentially code the flow of interactive and non-interactive behaviours within a caregiver-infant system. Its use of the framework of nonlinear dynamical systems (NDS) expands our understanding of contingent caregiver-infant dyadic behaviour.     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.