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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.

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.

Background – Reliable and valid hospital nurse staffing measures are a major requirement for health services research. As the use of these measures increases, discussion is growing as to whether current nurse staffing measures adequately meet the needs of health services researchers.

Objective – This study assesses whether the measures, sampling frameworks, and data sources meet the needs of health services research in areas such as staffing assessment; patient, nurse, and financial outcomes; and prediction of staffing.

Methods – We performed a systematic review of articles from 1990 through 2007, which use hospital nurse staffing measures in original research, or which address the validity, reliability, and availability of the measures. Taxonomies of measures, sampling frameworks, and sources were developed. Articles were analyzed to assess what measures, sampling strategies, and sources of data were used and to ascertain whether the measures, samples, and sources meet the needs of researchers.

Results – The review identified 107 articles that use hospital nurse staffing measures for original research. Multiple types of measures, some of which are used more often than others and some of which are more valid than others, exist in each of the following categories: staffing counts, staffing/patient load ratios, and skill mix. Sampling frameworks range from hospital units to all hospitals nationally, with all hospitals in a state being the most common. Data sources range from small-scale surveys to national databases. The American Hospital Association Annual Survey is the most frequently used data source, but there are limitations with its nurse staffing measures. Arguably, the multiplicity of measures and differences in sampling and data sources are due, in part, to data availability. The limitations noted by other researchers and by this review indicate that staffing measures need improvements in conceptualization, content, scope, and availability.

Discussion – Recommendations are made for improvements to research and administrative practice and to data.

Across Europe, on average more than 95% of all passenger cars and half of all light commercial vehicles are permanently available to a household. This includes both privately owned vehicles and company cars. The profiles of vehicle use can be specified as average annual distance driven per vehicle and for the fleet as a total, purpose of travel (trip destination), infrastructure use (urban, interurban or motorway road transport) and also fuel consumption together with data on CO₂ emissions. Indicators on vehicle use can be tracked in various ways:

• self-administered panels of households, which permit their vehicles to be followed for several years;

• national or local household travel surveys (with a seven-day trip diary);

• official vehicle inspection and vehicle registration files;

• ‘vehicle surveys’ based on vehicle registry data;

• traffic counts;

• data collected for road-charging purposes.

self-administered panels of households, which permit their vehicles to be followed for several years;

national or local household travel surveys (with a seven-day trip diary);

official vehicle inspection and vehicle registration files;

‘vehicle surveys’ based on vehicle registry data;

traffic counts;

data collected for road-charging purposes.

The paper will present a review of mainly vehicle-based survey methods used in France, Germany, Finland, the United Kingdom, the United States and Canada, describing existing sampling frames to their scope, advantages and limitations, as well as their costs. Issues addressed in this context will be further examined in terms of their methodological challenges as well as their purpose.

The leading questions underlying this paper as well as the corresponding workshop are: why is it necessary to have data on passenger travel or transportation; and, looking at international experience, how good are vehicle-based surveys in delivering the required information? In discussing problems experienced in the different countries with data collection and evaluation methods, emphasis will be put on potential strategies for methodological and technological improvement and problem solving. One example is the potential use, benefits and constraints of new survey technologies presented by vehicle tracking techniques.

Introduction: Coronavirus-19 (COVID-19) global outbreak poses a danger to millions of people’s health and the uncertainty and financial prudence around the world. Without a doubt, the sickness will place a tremendous strain on healthcare systems, which existing or traditional-based treatments cannot adequately handle. Only intelligence derived from diverse data sources can provide the foundation for rigorous clinical and social responses that optimise the use of constrained healthcare resources, create tailored patient treatment plans, educate policy-makers, and accelerate clinical trials

Purpose: This chapter aims to incorporate innovative practices of artificial intelligence (AI) into local, national, and global healthcare systems that can save lives of people and as well helps in human capital management ways that may be deployed rapidly and effectively with minimal errors.

Methodology: AI technologies and tools play a crucial part in COVID-19 crisis response by assisting with the virus discovery, early detection, and the development of effective medications and therapies. In this chapter, significant issues related to COVID-19 and how they may be addressed by applying HRM practices with recent advances in AI. Also, through a literature review of the recent studies implemented in a similar context, an AI solution is proposed by formulating a conceptual model.

Findings: This chapter offers that the latest AI techniques can assist policy-makers in implementing modern human capital management practices to fight against COVID-19. The goal is to remotely monitor patients utilising gadgets that are embedded with state-of-the-art medical technology. To limit hospital visits, or at least cut them down to a minimum, on the one hand, the health clinic also wants to deliver reliable health information to the doctors before or during virtual consultations.