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)

Little Things That Count: A Call for Organizational Research on Microbusinesses

The purpose of this Incubator is to encourage organizational researchers to attend to the most common type of business in the United States—the microbusiness. After defining and describing these businesses, we propose research questions on defining and managing performance, organizational citizenship, and work–family conflict in this novel business setting

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

On the Conditional and Unconditional Type I Error Rates and Power of Tests in Linear Models with Heteroscedastic Errors

Preliminary tests for homoscedasticity may be unnecessary in general linear models. Based on Monte Carlo simulations, results suggest that when testing for differences between independent slopes, the unconditional use of weighted least squares regression and HC4 regression performed the best across a wide range of conditions

Efficacy of Online Training for Improving Camp Staff Competency

Preparing competent staff is a critical issue within the camp community. This quasi-experimental study examined the effectiveness of an online course for improving staff competency in camp healthcare practices among college-aged camp staff and a comparison group (N = 55). We hypothesized that working in camp would increase competency test scores due to opportunities for staff to experientially apply knowledge learned online. Hierarchical linear modeling was used to analyse the cross-level effects of a between-individuals factor (assignment to experimental or comparison group) and within-individual effects of time (pre-test, post-test #1, and post-test #2) on online course test scores. At post-test #2, the difference in average test scores between groups was ~30 points, with the treatment group scoring lower on average than the comparison group. Factors that may have influenced these findings are explored, including fatigue and the limited durability of online learning. Recommendations for research and practice are discussed

Crowdsourcing in China: Exploring the Work Experience of Solo Crowdworkers and Crowdfarm Workers

Recent research highlights the potential of crowdsourcing in China. Yet very few studies explore the workplace context and experiences of Chinese crowdworkers. Those that do, focus mainly on the work experiences of solo crowdworkers but do not deal with issues pertaining to the substantial amount of people working in ‘crowdfarms’. This article addresses this gap as one of its primary concerns. Drawing on a study that involves 48 participants, our research explores, compares and contrasts the work experiences of solo crowdworkers to those of crowdfarm workers. Our findings illustrate that the work experiences and context of the solo workers and crowdfarm workers are substantially different, with regards to their motivations, the ways they engage with crowdsourcing, the tasks they work on, and the crowdsourcing platforms they utilize. Overall, our study contributes to furthering the understandings on the work experiences of crowdworkers in China

Why, and so what? The work motivations of rideshare gig workers

The growth of digital gigs – such as driving for apps like Uber, and completing brief tasks on websites like MTurk – has challenged much of what organizational psychologists think we know about work. In particular, emerging reports consistently suggest that financial dependence – that is, doing this type of work out of financial need – can define the type of experiences that digital gig workers have. For example, financially dependent gig workers are especially likely to face time‐, location, and customer-based constraints on their work, while workers who are less financially dependent likely experience more true autonomy via gigs. After briefly introducing the gig economy (What is it? How big is it? And, why should we care what the IRS has to say?), I will highlight key, recent findings on work motivations among rideshare drivers. I will also discuss the implications of employment status for designing meaningful interventions and tools for gig workers. This talk will provide a basis for us to consider how we can ensure our science and practice remain relevant as work continually changes