Leadership as a determinant of need fulfillment: implications for meta-theory, methods, and practice

Of all the most prominent business concepts (e.g., DE&I, employee well-being, employee engagement, organizational culture, etc.) none rivals leadership in terms of public interest and annual monetary investment. Despite the obvious importance of leadership as a determinant of many important outcomes, the concept of leadership has been surprisingly hard to pin down, lacking consensus as to its precise meaning. As numerous authors introduce ever more constructs (e.g., servant leadership, toxic leadership, sustainable leadership, transformational leadership, etc.), the leadership concept has become emblematic of the problem of construct proliferation. Like the related fields of employee engagement, subjective well-being, and organizational culture, the leadership field is in desperate need of a clearly articulated meta-theory to house its many constructs, allowing theory and measurement to build up instead of continuing to pile up. This paper argues for grounding the concept of leadership within the psychological literature on human needs. In reviewing the leading definitions of leadership in the literature we find that they are reducible to a core set of follower needs that can be facilitated or inhibited by leaders. We propose that there is substantial value in adopting a comprehensive needs-based taxonomy over current approaches. We consider the impact of setting the concepts of leadership within existing need constructs for each of the following: (a) theory, especially the development of leadership frameworks and particularly how the concept of leadership relates to the concepts of organizational culture, employee well-being, and employee engagement; (b) methods, including the value of applying a comprehensive, structured model; and (c) practice, where we emphasize the practical advantages of clear operational definitions

Structural Integrity, Flexibility, and Timing: Introduction to a Special Issue on Resilience

This introduction to a special issue of Nonlinear Dynamics, Psychology and Life Sciences on the topic of resilience discusses the contributing articles in terms of their flexibility in methods, models, scale, and contexts combined with their integrity in shared theoretical understanding and generative knowledge. The ubiquity of resilience is discussed, a feature of potentially any living or non-living system and substance. This breadth calls for a flexible set of models and methods, along with the quest for integrative theory to make resilience science more resilient. Since resilience involves the ability of a substance or system to persist, to repair or recover, and to evolve, any common theory would consider structural integrity (the ability to hold together), flexibility (the ability to adjust and return), time and timing. Nonlinear dynamical systems theory is proposed as the only scientific perspective capable of building this sort of common knowledge of a ubiquitous process involving these specific features. The synopsis of each article”s contribution to the issue includes an analysis of the flexibility the article adds in terms of models, methods, scale, and applied context, along with the theoretical integrity produced with respect to these common features of resilient processes: flexibility, integrity, time, and timing

Targeting Social Connection in the Context of Trauma: Functional Outcomes and Mechanisms of Change

The current study presents and preliminarily tests a brief, theory driven intervention designed to target social connectivity as a transdiagnostic mechanism of health. We tested four hypotheses to examine whether and how explicitly targeting social behavior engagement (activating values-led behaviors towards specific network members) may improve other downstream aspects of social connectivity (i.e., social cognitions measured as loneliness, interpersonal closeness, perceived social support) and functioning (quality of life [QOL] and posttraumatic stress symptoms [PTS]). Methods. Participants included 15 patients (10 veterans, 5 firefighters) who completed the six-session intervention. Demographics: age (M = 46, SD = 17), 87% male, race (80% Caucasian, 20% Hispanic), 60% married/partnered, 47% living alone. Our multi-analytic approach included parametric and non-parametric tests: (a) significance testing and effect sizes to examine whether variables of interest changed, and (b) Granger causality analysis of repeated measures to examine the mechanistic theory of change (does social behavior engagement lead to improved social cognition and functioning?). Results. Statistically significant, medium-large effect size improvements were shown for QOL (Cohens d = 1.05), PTS (d = 1.05), social behavior engagement (d = 0.78), and several social cognitions (loneliness, d = 0.80, interpersonal closeness, d = 0.53). Models accounted for medium-large variance explained in improved QOL (R2 = 0.47, 95% CI [0.00,0.66]) and PTS (R2 = 0.56, 95% CI[0.07,0.72]). The theory of change was supported, with increase in social behaviors preceding improvement in social cognitions (not vice-versa). Conclusions. Improving social connectivity is a mechanism for improving QOL and mental health. Focus on initiating values-driven social behaviors may be an efficient and effective entry point to stimulate change

Psychotherapy Is Chaotic— (Not Only) in a Computational World

Objective: The aim of this article is to outline the role of chaotic dynamics in psychotherapy. Besides some empirical findings of chaos at different time scales, the focus is on theoretical modeling of change processes explaining and simulating chaotic dynamics. It will be illustrated how some common factors of psychotherapeutic change and psychological hypotheses on motivation, emotion regulation, and information processing of the client’s functioning can be integrated into a comprehensive nonlinear model of human change processes. Methods: The model combines 5 variables (intensity of emotions, problem intensity, motivation to change, insight and new perspectives, therapeutic success) and 4 parameters into a set of 5 coupled nonlinear difference equations. The results of these simulations are presented as time series, as phase space embedding of these time series (i.e., attractors), and as bifurcation diagrams. Results: The model creates chaotic dynamics, phase transition-like phenomena, bi- or multi-stability, and sensibility of the dynamic patterns on parameter drift. These features are predicted by chaos theory and by Synergetics and correspond to empirical findings. The spectrum of these behaviors illustrates the complexity of psychotherapeutic processes. Conclusion: The model contributes to the development of an integrative conceptualization of psychotherapy. It is consistent with the state of scientific knowledge of common factors, as well as other psychological topics, such as: motivation, emotion regulation, and cognitive processing. The role of chaos theory is underpinned, not only in the world of computer simulations, but also in practice. In practice, chaos demands technologies capable of real-time monitoring and reporting on the nonlinear features of the ongoing process (e.g., its stability or instability). Based on this monitoring, a client-centered, continuous, and cooperative process of feedback and control becomes possible. By contrast, restricted predictability and spontaneous changes challenge the usefulness of prescriptive treatment manuals or other predefined programs of psychotherapy

Defining the Essential Function of Yeast Hsf1 Reveals a Compact Transcriptional Program for Maintaining Eukaryotic Proteostasis

Despite its eponymous association with the heat shock response, yeast heat shock factor 1 (Hsf1) is essential even at low temperatures. Here we show that engineered nuclear export of Hsf1 results in cytotoxicity associated with massive protein aggregation. Genome-wide analysis revealed that Hsf1 nuclear export immediately decreased basal transcription and mRNA expression of 18 genes, which predominately encode chaperones. Strikingly, rescuing basal expression of Hsp70 and Hsp90 chaperones enabled robust cell growth in the complete absence of Hsf1. With the exception of chaperone gene induction, the vast majority of the heat shock response was Hsf1 independent. By comparative analysis of mammalian cell lines, we found that only heat shock-induced but not basal expression of chaperones is dependent on the mammalian Hsf1 homolog (HSF1). Our work reveals that yeast chaperone gene expression is an essential housekeeping mechanism and provides a roadmap for defining the function of HSF1 as a driver of oncogenesis

Stability and Flexibility in Psychotherapy Process Predict Outcome

Ten good outcome and ten poor outcome psychotherapy cases were compared to investigate whether or not the temporal stability and flexibility of their process variables can predict their outcomes. Each participant was monitored daily using the Therapy Process Questionnaire (TPQ), which has 43 items and seven sub-scales, and responses over time were analyzed in terms of correlation robustness and correlation variability across the TPQ sub-scales. “Correlation robustness” and “correlation variability” are two basic characteristics of any correlation matrix: the first is calculated as the sum of the absolute values of Pearson correlation coefficients, the second as the standard deviation of Pearson correlation coefficients. The results demonstrated that the patients within the poor outcome group had lower values on both variables, suggesting lower stability and flexibility. Furthermore, a higher number of cycles of increase and decrease in correlation robustness and variability of the TPQ sub-scales was observed within good outcome psychotherapies, suggesting that, these cycles can be considered as process-markers of good-outcomes. These results provide support for the validity of these quantitative process-parameters, correlation robustness and variability, in predicting psychotherapeutic outcomes. Moreover, the results lend support to the common clinical experience of alternating periods of flexibility and integration being beneficial to good psychotherapeutic processes

Predicting response to leuprolide of women with premenstrual dysphoric disorder by daily mood rating dynamics

Approximately 60–70 percent of women with premenstrual dysphoric disorder (PMDD) show symptomatic improvement in response to the GnRH agonist leuprolide acetate, which suppresses ovarian function. However, it has been very difficult to either predict or understand why some women respond, while others do not. We applied several complementary statistical methods to the dynamics of pre-treatment mood rating data to determine possible predictors of response for women with PMDD. We compared responders (n = 33) to nonresponders (n = 12) in clinical trials of leuprolide (three months in duration) as a treatment for PMDD, on the basis of pre-trial daily self-ratings of sadness, anxiety, and irritability. We analyzed both sequential irregularity (approximate entropy, ApEn) and a quantification of spikiness of these series, as well as a composite measure that equally weighted these two statistics. Both ApEn and Spikiness were significantly smaller for responders than nonresponders (P ≤ 0.005); the composite measure was smaller for responders compared with nonresponders (P ≤ 0.002) and discriminated between the subgroups with high sensitivity and specificity. In contrast, mean symptom levels were indistinct between the subgroups. Relatively regular and non-spiky pre-trial dynamics of mood ratings predict a positive response to leuprolide by women with PMDD with high probability, moreover based on typically less than 3 months of daily records. The statistical measures may have broad and direct applicability to behavioral studies for many psychiatric disorders, facilitating both accurate diagnosis and the prediction of response to treatment

Psychotherapy Is Chaotic-(Not Only) in a Computational World

Objective: The aim of this article is to outline the role of chaotic dynamics in psychotherapy. Besides some empirical findings of chaos at different time scales, the focus is on theoretical modeling of change processes explaining and simulating chaotic dynamics. It will be illustrated how some common factors of psychotherapeutic change and psychological hypotheses on motivation, emotion regulation, and information processing of the client's functioning can be integrated into a comprehensive nonlinear model of human change processes. Methods: The model combines 5 variables (intensity of emotions, problem intensity, motivation to change, insight and new perspectives, therapeutic success) and 4 parameters into a set of 5 coupled nonlinear difference equations. The results of these simulations are presented as time series, as phase space embedding of these time series (i.e., attractors), and as bifurcation diagrams. Results: The model creates chaotic dynamics, phase transition-like phenomena, bi-or multi-stability, and sensibility of the dynamic patterns on parameter drift. These features are predicted by chaos theory and by Synergetics and correspond to empirical findings. The spectrum of these behaviors illustrates the complexity of psychotherapeutic processes. Conclusion: The model contributes to the development of an integrative conceptualization of psychotherapy. It is consistent with the state of scientific knowledge of common factors, as well as other psychological topics, such as: motivation, emotion regulation, and cognitive processing. The role of chaos theory is underpinned, not only in the world of computer simulations, but also in practice. In practice, chaos demands technologies capable of real-time monitoring and reporting on the nonlinear features of the ongoing process (e.g., its stability or instability). Based on this monitoring, a client-centered, continuous, and cooperative process of feedback and control becomes possible. By contrast, restricted predictability and spontaneous changes challenge the usefulness of prescriptive treatment manuals or other predefined programs of psychotherapy