The Big, Gig Picture: We Can\u27t Assume the Same Constructs Matter

I am concerned about industrial and organizational (I-O) psychology\u27s relevance to the gig economy, defined here as the broad trends toward technology-based platform work. This sort of work happens on apps like Uber (where the app connects drivers and riders) and sites like MTurk (where human intelligence tasks, or HITs, are advertised to workers on behalf of requesters). We carry on with I-O research and practice as if technology comprises only things (e.g., phones, websites, platforms) that we use to assess applicants and complete work. However, technology has much more radically restructured work as we know it, to happen in a much more piecemeal, on-demand fashion, reviving debates about worker classification and changing the reality of work for many workers (Sundararajan, 2016). Instead of studying technology as a thing we use, it\u27s critical that we “zoom out” to see and adapt our field to this bigger picture of trends towards a gig economy. Rather than a phone being used to check work email or complete pre-hire assessments, technology and work are inseparable. For example, working on MTurk requires constant Internet access (Brawley, Pury, Switzer, & Saylors, 2017; Ma, Khansa, & Hou, 2016). Alarmingly, some researchers describe these workers as precarious (Spretizer, Cameron, & Garrett, 2017), dependent on an extremely flexible (a label that is perhaps euphemistic for unreliable) source of work. Although it\u27s unlikely that all workers consider their “gig” a full time job or otherwise necessary income, at least some workers do: An estimated 10–40% of MTurk workers consider themselves serious gig workers (Brawley & Pury, 2016). Total numbers for the broader gig economy are only growing, with recent tax-based estimates including 34% of the US workforce now and up to 43% within 3 years (Gillespie, 2017). It appears we\u27re seeing some trends in work reverse and return to piece work (e.g., a ride on Uber, a HIT on MTurk) as if we\u27ve simply digitized the assembly line (Davis, 2016). Over time, these trends could accelerate, and we could potentially see total elimination of work (Morrison, 2017)

All of the Above?: an Examination of Overlapping Organizational Climates

We examined the largely unexplored issue of strong associations between multiple specific climates (e.g., for safety and for service). Given that workplaces are likely to have more than one specific climate present, it is important to understand how and why these perceptions overlap. Individual ratings (i.e., at the psychological climate level) for seven specific climates and a general positive climate were obtained from 353 MTurk Workers employed in various industries. We first observed strong correlations among a larger set of specific climates than typically studied: climates for collaboration, communication, fair treatment, fear, safety, service, and work-life balance were all strongly correlated. Second, we found that two methodological mechanisms—common method variance (CMV) due to (a) measurement occasion and (b) self-report—and a theoretical mechanism, general climate, each account for covariance among the specific climate measures. General positive climate had a primary (i.e., larger) impact on the relationships between specific climates, but CMV—especially due to measurement occasion—also accounted for significant and non-negligible covariance between climates. We discuss directions for continued research on and practice implementing specific climates in order to accurately model and modify perceptions of multiple climates

Seriously?: Estimates of Gig Work Dependence Vary with Question Wording

In this presentation, Brawley Newlin examines whether gig workers respond differently to questions about their dependence on gig income based on question wording and/or based on objective dependence measures (e.g., number of dependent children, hours worked in the gig). Results show that about half of the variability in responses is due to question wording, and half is due to more objective dependence factors

Textbook Remix: An Introduction to LibreTexts for OER Editing

So, you’ve found an open textbook that you really like, but it’s not quite right for your class? LibreTexts might be the answer! Join us for this informal webinar to learn a little more about this online platform designed for customizing and distributing open textbooks. From Gettysburg College, Scholarly Communications Librarian Mary Elmquist will provide an introduction to the platform, its structure and features, and Dr. Alice Brawley Newlin, Assistant Professor of Management, will speak on her ongoing experiences using LibreTexts to edit and implement an open textbook for a Statistical Methods course. This session should provide insight for both instructors interested in LibreTexts for their own projects and for librarians and other staff on campus who work to support OER adoption. Please bring your questions, as there will be plenty of time for Q&A

The Evolution of a Structured Writing Accountability Group (SWAG)

In this Friday Forum, Professors Chas. Phillips (Political Science), Alice Brawley Newlin (Management), and Patturaja Selvaraj (Management) will cover two key aspects of their ongoing Structured Writing Accountability Group (SWAG). First, we\u27ll talk about we have varied the structure of the SWAG since Summer 2018, including our celebratory end-of-year conference in 2019 which was sponsored through the generosity of the Provost’s Office grants for Faculty Reading/Writing Groups. Second, we\u27ll briefly highlight the projects and products we have accomplished through our SWAG. Though the principles of the SWAG are simple, participating in this group has greatly enhanced the rate and quality of our research outputs. We hope to share with others how the structure has been successful for us in order to inspire others to develop their own SWAGs. We look forward to interdisciplinary discussions of ways to modify and improve SWAGs\u27 success

µ analysis with real parametric uncertainty

The authors give a broad overview, from a LFT (linear fractional transformation) µ perspective, of some of the theoretical and practical issues associated with robustness in the presence of real parametric uncertainty, with a focus on computation. Recent results on the properties of µ in the mixed case are reviewed, including issues of NP completeness, continuity, computation of bounds, the equivalence of µ and its bounds, and some direct comparisons with Kharitonov-type analysis methods. In addition, some advances in the computational aspects of the problem, including a branch-and-bound algorithm, are briefly presented together with the mixed µ problem may have inherently combinatoric worst-case behavior, practical algorithms with modes computational requirements can be developed for problems of medium size (<100 parameters) that are of engineering interest

Practical computation of the mixed μ problem

Upper and lower bounds for the mixed μ problem have recently been developed, and this paper examines the computational aspects of these bounds. In particular a practical algorithm is developed to compute the bounds. This has been implemented as a Matlab function (m-file), and will be available shortly in a test version in conjunction with the μ-Tools toolbox. The algorithm performance is very encouraging, both in terms of accuracy of the resulting bounds, and growth rate in required computation with problem size. In particular it appears that one can handle medium size problems (less than 100 perturbations) with reasonable computational requirements

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 1: Methodology and applications

An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for designs failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented

An improved approach for flight readiness certification: Probabilistic models for flaw propagation and turbine blade failure. Volume 2: Software documentation

An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented