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

Modeling IoT Solutions: A Lack of IoT Device Security, and User Education

The Internet of Things, more commonly known as IoT devices, is an ever growing topic, both in the marketplace and in cyber security. While new devices are released into the public every year, a lack of standardized security concepts is also growing ever so clear. By having a model or standard for IoT devices and manufacturers to follow, the customer-base of these devices will have an easier time identifying trustworthy devices as well as how to secure their own devices