Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission

Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

Identity and integration: The roles of relationship and retention in nonprofit mergers

Prior research suggests that nonprofits are flexible and possess multiple identities, although we know less about how transformative changes, such as mergers, shape nonprofit identity. This qualitative study draws upon in‐depth interviews from 13 nonprofit merger cases to explore factors that influence postmerger identity and integration. In particular, we focus on the roles of organizational similarity and relationships, program and personnel retention, and rebranding. Ultimately, we derive a typology of postmerger integration in nonprofits and suggest that postmerger identity may be classified in terms of absorption, preservation, or creation. Implications for nonprofit leaders are discussed.     

Feminized Forestry: The Promises and Pitfalls of Change in a Masculine Organization

The gendering of forestry as a distinctly masculine profession has led to a wide range of negative outcomes, including legal actions concerning discrimination, poor public perceptions and poor environmental records. Forestry organizations have addressed these concerns by attempting to increase the number of women in the profession. These efforts have been largely ineffective. Using the case of community‐based forestry, I argue that when we begin to consider not only women but also normatively feminine values as agents of change, our understanding of the profession of forestry may be rejuvenated.     

Responsibilisation,social work and inclusive social security in Finland

ABSTRACT

In this article responsibilisation in social work is studied by analysing two Finnish state-level policy documents (called final report and research report) which concern a current activation initiative called inclusive social security (ISS). It is asked how social workers and clients are constructed as responsible subjects in these documents. Responsibilisation refers to the advanced liberal mode of governmentality, which aims to strengthen citizens’ abilities to self-governance through various techniques that include the intertwined elements of surveillance and empowerment. It is demonstrated that the policy documents construct the social workers’ and the clients’ responsibilities partly in different ways. The final report leads activation to be based on shared responsibility and social work to be more community-based, whereas the research report strengthens more individual-based responsibility of clients and social workers. For the clients, the interpretation of ISS based on shared responsibility would probably be less stigmatising and paternalistic than the one based on individual responsibilities, i.e. approaching long-term unemployed citizens as being personally ‘at risk’ and thus a justified target group of individualised techniques for activation. For social workers and clients, future activation appears to be a wide mix of different techniques, moral expectations and possible ways of being a responsible subject.     

Incumbent capability enhancement in response to radical innovations

Schumpeterian market disequilibrium marked by entrepreneurial entry and incumbent exit has long held an important place in management literature. The extant literature has overwhelmingly championed the newcomer, despite incumbents' obvious advantages in resources, experience and market knowledge. The current research provides evidence for the effectiveness of the incumbent's strategy of capability enhancement (along an established technological trajectory) while responding to radical technological innovations. We develop a cognitive process model that integrates managerial cognition with capability development and deployment views, depicting the dynamics of the incumbent's capability enhancement process. We analyze the cognitive drivers of organizational actions in all stages (rigidity, triggering event, and capability renewal) and elucidate the role of top management cognition in the processes of detecting and correcting errors in a strategic course of action. We ground our model in the case of a cork-stopper industry veteran's decline as corks ceded ground to screw tops and other stoppers in the wine industry. How a major company fought back in response to the emergence of these, in the industry context, radical technological innovations, provides the basis for our narrative. The proposed theoretical model contributes to literature on technology management (with regard to incumbent strategies in response to radical innovation threats) as well as the role of cognition in strategy (providing an explanation of the cognitive underpinnings of capability development).     

More than Face Value: Context and Age Differences in Negative Emotion Discrimination

Journal of Nonverbal Behavior - Age-related deficits are often observed in emotion categorization tasks that include negative emotional expressions like anger, fear, and sadness. Stimulus...     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.