Strategic Alliance Outcomes: a Transaction‐Cost Economics Perspective

Empirical research on strategic alliances has been limited because previous studies examined alliance outcomes, and the factors associated with them, from a single partner in a manufacturing alliance. Furthermore, many of these studies have been done from a transaction cost perspective and researchers have inferred opportunistic behavior, rather than directly measuring it and observing its actual relationship with alliance performance. Building on previous transaction cost theory and research, this study seeks to address these gaps by analyzing factors associated with both opportunistic behavior and alliance performance within a major service sector, namely the US healthcare industry. After controlling for asset specificity and alliance age, we found that partner trustworthiness and contractual safeguards were negatively related to opportunistic behavior. Furthermore, opportunistic behavior was negatively related to alliance performance, as hypothesized. Interestingly, mutual equity investments were found to be unrelated to opportunistic behavior, counter to transaction‐cost logic. These findings refine and extend the transaction‐cost economics perspective regarding our understanding of strategic alliance behavior and outcomes, and offer executives in service‐based industries some practical ideas for assuring favorable strategic alliance outcomes.     

Context Matters: Differential Effects of Discrimination by Environmental Context on Depressive Symptoms Among College Students of Color

Clinical Social Work Journal - Experiences with more subtle racism – which have been called microaggressions – have a host of negative effects on health, mental health, educational...     

Community care for adults with severe mental disorders in rural China: A case study

In China, family care is the dominant form of care for people with mental disorders. Since 2004, the government has been developing a community‐care model that places more responsibility on community organisations and the local governments at the provincial, municipal and county levels for the provision of formal care. As a large number of people with severe mental disorders live in rural China, this case study was conducted in a rural county in order to examine the development of community care. It was found that, although family care remains dominant, families’ need for formal care is increasing. Community services have improved, but their development is constrained by several contextual and micro factors. In this study, it is argued that the community‐care model introduces a process of reconfiguration of the relative responsibility for care among the family, social organisations and the government, but progress depends on further administrative and fiscal reforms.     

Effects of variations in water quantity and quality in the structure and functions of invertebrates’ community of a Mediterranean urban stream

Urban Ecosystems - Urban streams provide important ecosystem services to cities’ population, from the maintenance of urban biodiversity, temperature, humidity and air quality to improving...     

Quantile regression for large-scale data via sparse exponential transform method

In recent decades, quantile regression has received much more attention from academics and practitioners. However, most of existing computational algorithms are only effective for small or moderate size problems. They cannot solve quantile regression with large-scale data reliably and efficiently. To this end, we propose a new algorithm to implement quantile regression on large-scale data using the sparse exponential transform (SET) method. This algorithm mainly constructs a well-conditioned basis and a sampling matrix to reduce the number of observations. It then solves a quantile regression problem on this reduced matrix and obtains an approximate solution. Through simulation studies and empirical analysis of a 5% sample of the US 2000 Census data, we demonstrate efficiency of the SET-based algorithm. Numerical results indicate that our new algorithm is effective in terms of computation time and performs well for large-scale quantile regression.     

Building a health communication brand for University of Texas System tobacco control

Objective: Despite declining cigarette smoking rates in the US, there is a continued need for tobacco prevention education campaigns to reach young adults. Recognizing the need for improved tobacco control messaging, the University of Texas (UT) System engaged The University of Texas at Austin Center for Health Communication to develop a brand and message that would strengthen tobacco control efforts at its 14 institutions. Methods: This article describes the iterative process involved in creating a brand for tobacco control, including an environmental scan, identifying potential message themes, and creating and refining logos. Results: This article highlights the process of developing a system-wide tobacco control brand. Specifically, the process included coordinating an interdisciplinary team with content and design experts, and presenting ideas to stakeholders for serial feedback and refinement, among others. Conclusions: Ultimately, this project offers a model for other systems of higher education interested in pursuing similar initiatives.     

Second generation Caribbean black male youths discuss obstacles to educational and employment opportunities: a critical race counter-narrative analysis

This study explores obstacles to navigating the high school-to-labor market transition experienced by Second Generation Caribbean Black Male Youth (CBMY) living in Canada's Greater Toronto Area (GTA). Drawing upon interviews with ten CBMY between the ages of 18–27, the article uses a qualitative phenomenological methodology to understand barriers to education and employment from their perspectives. Studies show that 45% of CBMY drop out of high school while 52% are precariously employed in the GTA [Allahar, A. L. 2010. “The Political Economy of ‘Race’ and Class in Canada’s Caribbean Diaspora.” American Review of Political Economy 8 (2): 54; James, C. E. 2012. Students “at Risk” Stereotypes and the Schooling of Black Boys. Urban Education 47 (2), 464–494.; Lewchuk, W., and M. Lafleche. 2014. Precarious Employment and Social Outcomes. Just Labour: A Canadian Journal of Work and Society, 22, 45–50.; Block and Galabuzi 2011. Canada’s Color Coded Labor market: The Gap for Racialized workers. Canadian Centre for Policy Alternatives?=?Centre Canadien de Politiques alternatives]. The ten interview subjects provide retrospective and introspective counter-narratives that expose the race, class and gender-based barriers that frustrate their efforts to secure stable employment. The study utilizes Critical Race Theory (CRT) and the concept of White Supremacy to challenge the view that educational and employment success is based on color-blindness, merit and hard work. The CBMY counter-narratives examined in this study offer profound insight into dominant ideologies and practices that perpetuate racial biases, and present many valuable suggestions, explicit and implied, regarding how to improve the opportunities, inclusion and well-being of CBMY.     

A VARIANCE-REDUCTION STRATEGY FOR RSM SIMULATION STUDIES

The application of optimization techniques in digital simulation experiments is frequently complicated by the presence of large experimental error variances. Two of the more widely accepted design strategies for the resolution of this problem include the assignment of common pseudorandom number streams and the assignment of antithetic pseudorandom number streams to the experimental points. When considered separately, however, each of these variance-reduction procedures has rather restrictive limitations. This paper examines the simultaneous use of these two techniques as a variance-reduction strategy in response surface methodology (RSM) analysis of simulation models. A simulation of an inventory system is used to illustrate the application and benefits of this assignment procedure, as well as the basic components of an RSM analysis.     

An elastic-net penalized expectile regression with applications

To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model.