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Pandemics have historically shaped the world of work in various ways. With COVID-19 presenting as a global pandemic, there is much speculation about the impact that this crisis will have for the future of work and for people working in organizations. In this article, we discuss 10 of the most relevant research and practice topics in the field of industrial and organizational (IO) psychology that will likely be impacted by COVID-19. For each of these topics, the pandemic crisis is creating new work-related challenges, but also presenting various opportunities. The topics discussed herein include occupational health and safety, work-family issues, telecommuting, virtual teamwork, job insecurity, precarious work, leadership, human resources policy, the aging workforce, and careers. This article sets the stage for further discussion of various ways in which IO psychology research and practice can address the impacts of COVID- 19 for work and organizational processes that are affecting workers now and will shape the future of work and organizations in both the short and long term. This article concludes by inviting IO psychology researchers and practitioners to address the challenges and opportunities of COVID-19 head-on by proactively innovating the work that we do in support of workers, organizations, and society as a whole.

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dc.contributor.author: Gruber-Risak, Martin

This study contributes to the emerging literature on age diversity effects at the organizational level of analysis by comparing the role of chronological‐age diversity versus subjective‐age diversity. We hypothesize a multilevel model in which organizational‐level subjective‐age diversity is negatively related to bonding social capital within organizations, which, in turn, contributes to heightened employee engagement and lowered turnover intentions. The assumed relationships are tested in a multilevel sample of 96 German small and medium‐sized companies with 16,274 employees participating. We gathered data from four different sources to circumvent common source problems and received support for most of the proposed relationships. Given the potentially detrimental effects of high subjective‐age diversity in the workplace, the paper concludes with practical recommendations on how to manage subjective‐age diversity in companies pro‐actively.

In this article, the authors analyze citizens’ reactions to Brexit on social media after the referendum results by performing a content analysis of 5877 posts collected from the social media platform Flickr, written in English, German, French, Spanish or Italian. Their research aims to answer the three following questions: What multimodal practices are adopted by citizens when they react to societal events like Brexit? To what extent do these practices illustrate types of citizenship that are specific to social networks? Can we observe different reactions to Brexit according to the languages used by the citizens? The authors focus on the types of visual content the citizens used to react to Brexit, as well as on what types of social relations this content can particularly create between their authors and the other members of the Flick community. Their article also highlights to what extent these posts shared on Flickr show content that is in favour of, or against, Brexit.

Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.

Analysing disaster recovery, understood as a complex process that spans multiple disaster risk reduction (DRR) activities [1], is an ambitious undertaking, that requires a broad set of instruments to capture all relevant dimensions [2]. This paper proposes that a critical juncture approach, as used in institutional analysis [3, 4], can be a useful addition to existing approaches to analyse the process of disaster recovery. In contrast to concepts like focusing events [5], the concept of critical junctures allows to gauge the impact of institutional legacies through longer timespans [4] and is, thereby, especially useful in the effort to better understand long-term trajectories of recovery policies. To assess this claim, a cursory literature review was conducted to examine the approaches used thus far by social scientists to research disaster recovery and the new critical juncture approach. Subsequently, a short illustrative case study was used to demonstrate some first applications of the critical juncture approach. This process-tracing analysis confirms that examining critical junctures helps to identify long-term trends in disaster recovery policies and how they relate to the institutional dimension of the process.

Existing research on the revolving door examines why employers hire former politicians. I complement this demand-side approach by demonstrating the importance of the supply-side. In particular, I argue that one important institutional factor that shapes politicians' willingness to leave office for a private sector job is campaign finance legislation. Less restrictive rules increase campaign spending for incumbents, which makes revolving door employment less attractive. Empirically, I use novel data from the US states and a difference-in-differences design to show that the exogenous removal of campaign finance legislation through Citizens United reduced the probability that incumbents left office to work as lobbyists. The supply-side approach provides insights into comparative differences in the prevalence of the revolving door.

Age heterogeneity in Western workforces is increasing, generating potential informational benefits as well as harmful age‐based social categorizations. When can firms benefit from age heterogeneity? Building on the categorization‐elaboration model, we propose the average age distance between employees as a fundamental contingency. Using a longitudinal archival sample of 3,336 Belgian firms (2012–2015), we find that firms with a high level of age heterogeneity are less productive when employees' average distance is great (Study 1). Through an online experiment with 260 US participants, we show that employees in age‐heterogeneous workforces are less willing to engage in inter‐age cooperative contact and knowledge exchange under a great level of average age distance (Study 2). Our findings support that great distances foster age‐based social categorizations that undermine the productive information elaborations between employees of different ages. This broadens our knowledge on the implications of workforce age diversity and helps organizations understand when they can(not) reap the productivity benefits of their age‐diverse workforce. Moreover, this study's theory and implications are relevant to other types of diversity for which both heterogeneity and distance are meaningful constructs. We also discuss the practical implications of this study.

The article proposes one of the first quantitative operationalizations of the Multiple Streams Framework’s (MSF) concept of policy community integration, which determines the gestation process of policy ideas in the policy stream. It argues that operationalization by the Gini coefficient of nodal degree and by Freeman’s segregation score enables us to indicate better the type of a policy community (less integrated, or more integrated), and thus, to assume the trajectory of the gestation process. The findings suggest that Policy Network Analysis offers a more progressive operationalization of the integration concept and helps to compare policy communities

What explains geographical representation in the professional staff of intergovernmental organizations (IOs)? We address this question from an organizational perspective by considering IO recruitment processes. In the United Nations (UN) system, recruitment processes are designed to ensure bureaucratic merit, with experience and education being the relevant merit criteria. We develop and test a supply-side theory, postulating that differences in countries’ supply of well-educated and highly experienced candidates can explain geographical representation. Drawing on staff data from 34 IOs and supply data from 174 member states, and controlling for endogeneity and alternative explanations, we find no such relationship for education. However, countries with a high supply of candidates with relevant working and regional experiences have significantly higher representation values. These findings offer a complementary narrative as to why some countries are more strongly represented in the international professional staff than others. Findings also unveil the nature of bureaucratic merit in the UN, which seems to emphasize local knowledge and working experience over formal (Western) education.