Assessment of Consumer Behavior Towards Environmental Responsibility: A Structural Equations Modeling Approach

This paper analyses the factors impacting consumer environmental responsibility using a structural equation modeling approach. Consumer environmental responsibility is the intention of a person to act towards remediation of environmental problems not as an individual user with economic interests but as a responsible citizen having concerns about the social and environmental wellbeing of society. Therefore, awareness of environmental problems and knowledge of remedial alternatives will help individuals to pursue their chosen action and bolster a genuine desire to act and determine the measures for alleviation of environmental challenges. The four dimensions of an environmentally responsible consumer – opinion and beliefs, willingness, awareness, and an ability to act – are analysed. During the analysis, the dimension of ‘ability to move’ is found to be critical, because it has a direct influence on the capacity of the consumer to act. This research is intended to guide policy‐ and decision‐makers of regulatory bodies in understanding consumer behavior towards improving environmental performance index. It also helps organizational managers to make their supply chains green, and competitive, which in turn improves brand image and overall organizational performance. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment     

Examining the heterogeneity of treatment patterns in attention deficit hyperactivity disorder among children and adolescents in the Texas Medicaid population: modeling suboptimal treatment response

Objectives: To examine suboptimal responses (SR) in attention deficit hyperactivity disorder (ADHD) among pediatric patients in the Texas Medicaid program receiving osmotic-release oral system methylphenidate (OROS-MPH) or lisdexamfetamine (LDX) and apply an SR prediction model to identify patients most likely to experience an SR to either OROS-MPH or LDX therapies.

Methods: A retrospective cohort study was conducted using Texas Medicaid claims data of ADHD children and adolescents (6–17?years of age) initiating OROS-MPH or LDX. Primary SR endpoints were drug discontinuation, switching, and augmentation 12-months post-ADHD drug initiation. Logistic regression models were developed to predict SR to OROS-MPH and LDX in 1:1 matched groups of children and adolescent cohorts.

Results: A total of 3,633 children and 1,611 adolescents were matched for each cohort. SR was observed among more children (76.4% vs 72.3%; p?p?=?0.002) initiating OROS-MPH compared to LDX. Patient sub-groups with the highest predicted risk of OROS-MPH SR experienced significantly lower observed SR rates (p?Conclusions: This study demonstrated how a personalized medicine approach using administrative claims data can be used to identify sub-groups of child and adolescent ADHD patients with different risks for suboptimal response with OROS-MPH or LDX in a Medicaid population.