Economic Individualism and Job Engagement: Examining the Roles of Work Motivation and Growth Need Strength

Economic individualism—involving a belief that the individual should be in control of his/her own economic decisions and an increased emphasis on competition and achievement—is becoming more prominent in several areas of the world, but little is known about the implications of this characteristic for employee attitudes and behavior. Our study investigated the impact of economic individualism on job engagement. More specifically, the research developed and examined a model involving work motivation as a mediator and growth need strength as a moderator. Employees (N = 235, 58.3% male, 33.6% 20–29 years old, 53.2% with a bachelor’s degree) from several companies in South Korea completed survey measures of economic individualism, job engagement, work motivation, and growth need strength. Findings supported work motivation as a mediator and indicated that the indirect effect through work motivation was significant at high levels of growth need strength although not at low levels. These findings provide new insights regarding the individual-level engagement implications of economic individualism and when and why these implications hold, as prior research on economic individualism has focused on the organizational and societal levels.

Computer-Supported Cooperative Work – Revisited

Due to the COVID-19 lockdowns and the related mandated work for home, we have seen a massive increase of the use of collaboration tools in various work settings in the last 18 months. Whereas this might have been a new terrain for some, IT-supported work and the related research domain Computer-Supported Cooperative Work (CSCW) have been around for decades. In this article we briefly review what CSCW has to offer for the currently increasing demand in setting up remote collaboration – and share our own observations about what happened when collaboration tools have been introduced in the pandemic. As a summary, we present some learnings from the experience – both for the current state of CSCW research and for future work.

Are Participants Good Evaluators?

Managers of workforce training programs are often unable to afford costly, full-fledged experimental or nonexperimental evaluations to determine their programs’ impacts. Therefore, many rely on the survey responses of program participants to gauge program impacts. Smith, Whalley, and Wilcox present the first attempt to assess such measures despite their already widespread use in program evaluations. They develop a multidisciplinary framework for addressing the issue and apply it to three case studies: the National Job Training Partnership Act Study, the U.S. National Supported Work Demonstration, and the Connecticut Jobs First Program. Each of these studies were subjected to experimental evaluations that included a survey-based participant evaluation measure. The authors apply econometric methods specifically developed to obtain estimates of program impacts among individuals in the studies and then compare these estimates with survey-based participant evaluation measures to obtain an assessment of the surveys’ efficacy. The authors also discuss how their findings fit into the broader literatures in economics, psychology, and survey research.

Social enterprises and community wellbeing in regional Australia

This article reflects on a research project that has mapped the ways in which social enterprises in regional Australian cities produce wellbeing for their employees. The majority of enterprises in this study offer supported work opportunities to people with a disability while also running commercially viable enterprises delivering goods and services to regional communities. These enterprises demonstrate the challenges and the potential for organisations in regional settings to contribute in meaningful ways, not only to the wellbeing of the workers they support, but to the wellbeing of the broader community. This article considers how social enterprises are understood to be contributing to regional communities and situates them as key actors in a community economy that contributes to wider community wellbeing as distinct from individual wellbeing.

P156 Myositis flares are associated with reduced work productivity and fewer hours worked: illustration of the future potential of digital healthcare solutions in rheumatic diseases

Background/Aims The digital healthcare revolution provides the opportunity for clinicians and researchers to collect useful data on a frequent and remote basis. Work ability is impacted by many rheumatic diseases, including the idiopathic inflammatory myopathies (IIMs), however, methods to assess the real-time impacts are limited. This study aims to explore the impact of IIM flares and symptoms upon employment using frequently collected data via a smartphone-based app. Methods The Myositis Physical Activity Device Study recruited a UK-based adult IIM cohort who completed weekly employment and flare questions via a specially designed smartphone-based app throughout a 91 day period in 2019/20. Employment-related questions were assessed every week (see Table 1 for details). Flares were reported via a weekly question. Employment variables were compared between flare and non-flare weeks using descriptive statistics. The relationship between flares and work productivity was assessed using multi-level mixed effects logistic regression modelling, adjusted for age and sex. Results Data on 13 (69% female) employed participants was analysed. A median of 5 flares were reported per patient during the three month period (IQR 3, 9). Summary employment results are displayed in Table 1. Participants reported greater impact of IIM upon employment, lower productivity and fewer hours worked during a flare week, compared to a non-flare week. There was a significant association between flares and detrimental impact upon work productivity (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.03, 1.12, p < 0.01). Flares were also significantly associated with an increased number of work hours missed due to IIM (OR 1.04, 95% CI 1.01, 1.08, p = 0.02) P156 Table 1:- Summary employment parameters compared between flare and non-flare weeksEmployment parameterAnswer formatWhole study period (159 weeks)Flare weeks (60 weeks)Non-flare weeks (99 weeks)p-value*Number of weeks’ work productivity affected by IIM (%)Dichotomous - “yes”, “no”54 (34.0)33 (55.0)21 (21.2)<0.01Mean effect of IIM upon work productivity (SD)Visual analogue scale - “Myositis had no effect on work" (0);“Myositis completely prevented me from working” (100)29.8 (28.8)46.2 (33.3)19.9 (20.0)<0.01Mean number of scheduled work hours per week per participant (SD)Numerical33.2 (15.8)36.9 (19.33)31.0 (13.0)<0.01Mean number of hours worked per week per participant (SD)Numerical23.5 (15.1)18.86 (17.1)26.23 (13.2)<0.01Proportion of hours worked per week per participant / % (SD)Calculated by research team73.7 (38.7)55.9 (43.2)84.7 (31.1)<0.01Mean number of hours of weekly work missed due to IIM per participant (SD)Numerical6.6 (16.6)14.9 (23.4)1.5 (6.6)<0.01Proportion of hours of weekly work missed due to IIM per participant / % (SD)Calculated by research team12.7% (29.5)29.2 (41.1)2.6 (10.0)<0.01SD = standard deviation *Categorical variables were compared using the Chi-squared test and continuous variables compared using the student t-test. Conclusion Our study has demonstrated that IIM flares are significantly associated with detrimental impact upon employment ability. On average, patients lost 15 hours of work a week during a flare compared to less than 2 hours outside a flare. The economic and personal impact of flares highlights the need for research in this area, with the aim of allowing early identification and instigation of treatment and possible need for supported work. Smartphone based remote monitoring of flares and other pertinent variables could enhance digital consultations, which may become more common in the post COVID-19 setting. Disclosure J.C. Williams: None. A.G.S. Oldroyd: None. W.G. Dixon: None. H. Chinoy: None.

Using Multi-Arm Designs to Test Operating Welfare-to-Work Programs

Background: In the early 1970s, most researchers thought that randomized controlled trials (RCTs) could not be used to measure the effectiveness of large-scale operating welfare reform and employment programs. By the mid-1970s, the Supported Work Demonstration showed that, under certain conditions, this was both feasible and valuable. However, the experimental design was simple; a multi-arm test had been rejected as unrealistic. Within 10 years, a three-arm design was implemented in San Diego to assess both a welfare-to-work program’s overall impact and the contribution of a specific component. Less than 10 years later, the Job Opportunities and Basic Skills Training (JOBS)/National Evaluation of Welfare-to-Work Strategies (NEWWS) study used a more complex design to determine the relative effectiveness of two strategies operated in the same locations: one emphasizing getting a job quickly and the other requiring basic education. In San Diego and JOBS/NEWWS, the tested reforms emerged from political processes and were funded through regular program budgets. In both cases, researchers inserted multi-arm RCTs into operating welfare offices, trading control over the treatment for scale (thousands of people) and real-world conditions. Both RCTs were successfully implemented. Objectives and Results: This article examines why multi-arm designs were attempted, how they were structured, why public administrators cooperated, what various actors sought to learn, and how the researchers determined what strategies the different experimental arms ended up to truly represent. The article concludes that these designs provide convincing evidence and can be inserted into operating programs if the studies address questions that are of keen and immediate interest to state or local program administrators and researchers.

Finite‐Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness

We consider estimation and inference on average treatment effects under unconfoundedness conditional on the realizations of the treatment variable and covariates. Given nonparametric smoothness and/or shape restrictions on the conditional mean of the outcome variable, we derive estimators and confidence intervals (CIs) that are optimal in finite samples when the regression errors are normal with known variance. In contrast to conventional CIs, our CIs use a larger critical value that explicitly takes into account the potential bias of the estimator. When the error distribution is unknown, feasible versions of our CIs are valid asymptotically, even when n ‐inference is not possible due to lack of overlap, or low smoothness of the conditional mean. We also derive the minimum smoothness conditions on the conditional mean that are necessary for n ‐inference. When the conditional mean is restricted to be Lipschitz with a large enough bound on the Lipschitz constant, the optimal estimator reduces to a matching estimator with the number of matches set to one. We illustrate our methods in an application to the National Supported Work Demonstration.