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IN THIS article the evolution of formation flight technology is traced including discussion of the primary characteristics of three specific station keeping system concepts developed by Lockheed‐Georgia. One of the most significant aspects of the overall formation flight problem is that of stability of the formation in a dynamic environment. Detailed simulation studies of the dynamic station keeping geometry have been performed, and the results are presented in the form of fundamental formation flight stability criteria. The extension of present‐day technology to development of formation flight systems for short range aircraft such as helicopters and V/STOL transports is considered briefly.

This literature review is on advanced computer analytics, which is a major trend in the field of Human Resource Management (HRM). The authors focus specifically on computer-assisted text analysis (CATA) because text data are a prevalent yet vastly underutilized data source in organizations. The authors gathered 341 articles that use, review, or promote CATA in the management literature. This review complements existing reviews in several ways including an emphasis on CATA in the management literature, a description of the types of software and their advantages, and a unique emphasis on findings in employment. This examination of CATA relative to employment is based on 66 studies (of the 341) that bear on measuring constructs potentially relevant to hiring decisions. The authors also briefly consider the broader machine learning literature using CATA outside management (e.g., data science) to derive relevant insights for management scholars. Finally, the authors discuss the main challenges when using CATA for employment, and provide recommendations on how to manage such challenges. In all, the authors hope to demystify and encourage the use of CATA in HRM scholarship.

The purpose of this chapter is to assess the importance of individual social capital characteristics in determining wages, both directly through their valuation by employers and indirectly through their impact on individual occupational choice. We find that a person’s level of sociability and care for others works through both channels to explain wage differences between social and nonsocial occupations. Additionally, expected wages in each occupation type are found to be at least as important as a person’s level of social capital in choosing a social occupation. We make use of restricted 2000 Decennial Census and 2000 Social Capital Community Benchmark Survey.

The purpose of this study was to measure the bias on a binary option's effect estimate that appeared in the types of questions asked and in the placement changes of public service users.

The author designed Monte Carlo simulations with the analytical strategy of latent trait theory leveraging a probability of care-placement change. The author used difference-in-difference (DID) method to estimate the effects of care settings.

The author explained the extent of discrepancy between the estimates and the true values of care service effects in changes across time. The time trend of in-home care for the combined effect of in-home care, general maturity, and other environmental factors was estimated in a biased manner, while the bias for the estimate of the incremental effect for foster care could be negligible.

This study was designed based on individual child-unit only. Therefore, higher-level units, such as care setting or cluster, county, and state, should be considered for the simulation model.

This study contributed to illuminating an overlooked facet in causal inferences that embrace disproportionate selection biases that appear in categorical data scales in public management research.

To model the nuance of a disproportionate self-selection problem, the author constructed a scenario surrounding a caseworker's judgment of care placement in the child welfare system and investigated potential bias of the caseworker's discretion. The unfolding model has not been widely used in public management research, but it can be usefully leveraged for the estimation of a decision probability.

Recent performance appraisal (PA) literature suggests that alongside cognitive biases, rating distortions may stem from rater disposition and PA context. The study investigated the role of social value orientation (rater disposition), PA purposes and rater accountability (PA context) toward rating distortions at both performance levels, i.e. good and poor.

The authors designed an experimental study and elicited data from N = 110 undergraduate students about two video-taped performances of good and poor performers. In these videos, two managers conducted assessment interviews of two different employees for the job of a sales representative at an information technology organization. To ensure the validity of performance ratings, the authors invited 10 senior managers to provide benchmark ratings of the video-taped performances. While being placed in two separate groups, the study participants gave performance ratings on both the video-taped performances. The authors used repeated-measures analysis to analyze data.

The results revealed that rating distortions took place not because of rater social value orientation, but the PA context. Different rating distortion patterns emerged for different levels of ratees' performance.

This study’s findings furnish new insights for assessing rating distortions for poor as well as good performers. Moreover, the results support previous findings that for good performers, accountable raters are tempted toward accurate ratings and refrained from deflation. Similarly, for poor performers, accountable raters do not inflate ratings. The findings will open research avenues to examine the role of PA purposes in rating distortions for different performance levels.

The purpose of this study was to explore college students’ understanding of sustainability and, specifically, the extent to which students see social justice as being integral to sustainability.

Between fall 2015 and 2017, an online survey study was deployed to students at a Midwestern University in the USA to assess attitudes and concerns about environmental issues and awareness of the university’s activities related to these issues. This analysis included ten assessment items from a larger study, of which 1,929 participants were included in the final sample. A chi-square goodness-of-fit and variable cluster analysis were performed on the included items.

Items such as “recycling,” “economic viability” and “fair treatment of all” were identified as integral to the concept of sustainability, while items such as “growing organic vegetables” and “reducing meat consumption” had high levels of “not applicable” and “don’t know” responses, with differences arising across gender and class standing. Social justice-related items were seen as more distally connected to sustainability.

This study is limited by a non-random sample of students.

College students tend not to recognize the integral nature of social justice or the relevance of food to sustainability, providing an opportunity for universities to better prepare their students for a sustainable future.

Universities might adopt policies and curricula that address these areas of ignorance.

This study is among the first to identify specific areas of college students’ lack of understanding about sustainability.

The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.

Education funding has been a significant concern of both providers and consumers of the educational system in the United States for many generations. This expenditure accounts for the single largest allocation in most state and local government budgets; and scholars, practitioners, and the general public often question whether or not American students are getting the most “bang for their buck”. While school board members are not responsible for the allocation of revenues from federal, state, and local governments, these individuals are directly responsible for the distribution of these funds for the operation of local schools within their districts. The purpose of this study is to determine if school board members in Tennessee understand the role they play in allocating educational finances which ultimately influence student achievement. The results of this analysis suggest that the perceptions of school board members in Tennessee are not always reflective of the actual outcomes when real data is examined regarding school district expenditures.