Colorado Deer Hunting Experiences

Those responsible for managing environmental resources, like big game, have often posed questions regarding how best to manage and allocate the resource to “provide benefits to people.” One approach to obtaining information for answering these questions is based on consumer behavior concepts and research. Our consumer-oriented approach to deriving management information for environmental resources, particularly game and other recreational resources, rests on ideas conceptualized by Wagar (1966) and having their theoretical base in psychology’s expectancy-value theory (Lawler 1973). The general theoretical orientation we follow is described in Driver and Brown (1975). We also acknowledge a debt to the multiple satisfactions approach to game management articulated by Hendee (1974). The management orientation of this paper suggests that managers should produce opportunities for game-related recreation which recognize the multiple dimensions of the experience. It is the experience that is the important product of recreation, and quality experiences are a function of how well the consumer’s desired satisfactions are fulfilled. Within this orientation, this paper reports characteristics of the Colorado deer hunter population in terms of the kinds of satisfaction that make up deer hunting experiences. In doing so, the usefulness of cluster analytic techniques for social research in wildlife management is illustrated. The information and analytical techniques discussed in this paper have implications for resource valuation, resource allocation, user management, and related aspects of wildlife planning and management

Scientific, Legal, and Ethical Concerns About AI-Based Personnel Selection Tools: A Call to Action

Organizations are increasingly turning toward personnel selection tools that rely on artificial intelligence (AI) technologies and machine learning algorithms that, together, intend to predict the future success of employees better than traditional tools. These new forms of assessment include online games, video-based interviews, and big data pulled from many sources, including test responses, test-taking behavior, applications, resumes, and social media. Speedy processing, lower costs, convenient access, and applicant engagement are often and rightfully cited as the practical advantages for using these selection tools. At the same time, however, these tools raise serious concerns about their effectiveness in terms of their conceptual relevance to the job, their basis in a job analysis to ensure job relevancy, their measurement characteristics (reliability and stability), their validity in predicting employee-relevant outcomes, their evidence and normative information being updated appropriately, and the associated ethical concerns around what information is being represented to employers and told to job candidates. This paper explores these concerns, concluding with an urgent call to industrial and organizational psychologists to extend existing professional standards for employment testing to these new AI and machine learning based forms of testing, including standards and requirements for their documentation

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

The Relationship between Religiousness and Corporate Social Responsibility Orientation: Are there Differences Between Business Managers and Students?

corporate social responsibility, business ethics, managers, religiousness, students,