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Social networking sites have become a predominant means of communication across the globe. Activities on these sites generate massive amounts of personal information and raise concerns about its potential abuse. Means designed to protect the user’s privacy and prevent exploitation of confidential data often go unused. In this study, we draw on the theory of planned behavior, a reasoned action approach, to explain intentions to adopt privacy behaviors on social networking sites, with a focus on Twitter users. Consistent with the theory, an online survey of Twitter users ( n = 1,060) found that instrumental and experiential attitudes and descriptive and injunctive subjective norms regarding these behaviors were direct predictors of intentions. Perceived behavioral control had a moderating effect, such that subjective norm was a better predictor of intentions for participants high as opposed to low in perceived control. We briefly discuss the implications of these results for developing theory-driven and evidence-based interventions to promote privacy behavior.

This project replicates the following study, which is a meta-analysis: Duran, P., Kammerlander, N., Van Essen, M., & Zellweger, T. (2016). Doing more with less: Innovation input and output in family firms. Academy of Management Journal, 59(4), 1224-1264. We focus our replication on the first two hypotheses of the original study, which are: Hypothesis 1a: Family firms have lower innovation input than non-family firms. Hypothesis 1b: Family firms have higher innovation output than non-family firms. The study that we tried to replicate is published and available in the Internet: https://journals.aom.org/doi/abs/10.5465/amj.2014.0424

The COVID-19 pandemic has induced radical uncertainty into the business environment, threatening entrepreneurs' performance and well-being. Drawing on Conservation of Resources (COR) theory, we examine how startup teams allocate resources to cope with this pandemic. In a convergent parallel mixed method design, we analyze 12 waves of survey data and four waves of interviews collected in 35 startup teams in Germany. Findings from an analysis of temporal development of predictors (quantitative results) and entrepreneurs' views (qualitative analysis) explain the effectiveness of resource allocation mechanisms to reduce business impairment and compassion fatigue. Taken together we develop the concept of entrepreneurial coping to extend COR theory and the body of work on entrepreneurial teams and uncertainty in entrepreneurship.

Background: The increasing use of digital services such as social media platforms has led to the emergence of new data sources for psychology research. In this context the micro-blogging site Twitter has gained prominence, both, as a research tool and object. Since the last decade, Tweets have been analyzed for various psychological research questions, predominantly from the fields of media psychology, clinical psychology, and educational psychology. However, the additional value of utilizing Twitter for getting a bird’s- eye view of psychological science or its potential to differentiate human meaning construction in the wake of recent events has not yet been addressed. Aim: In this talk, we aim at expanding prior application fields by presenting two current projects from our research group that employ Twitter as a scientific tool and object. As an example of big-data-enriched scientometrics, the first application leverages tweets for an early detection of trends in bibliometric data. The second application demonstrates the superiority of word embeddings as measures for differentiating and characterizing semantic meaning in tweets. The overarching goal of this talk is to gauge possibilities of Twitter-based research in the light of current technical restrictions and ethical considerations. Application 1 (Twitter as a research tool): “Detecting Psychological Research Trends using Twitter Mining”. For identifying psychological hot topics, traditional bibliometric analyses suffer from a publication delay. Thus, only topics at the very end of the research process (i.e., publication) can be detected. In contrast, Twitter is used by the research community in all phases of the research cycle. A recent proof-of-concept study gave evidence that text mining of tweets can indicate future publication trends earlier than using literature databases alone. Building on this, we present research-in-progress that examines predictive features of Twitter topics, aiming at closing the gap between the early detection of emerging topics and an implementation in research monitoring tools such as PsychTopics (https://psyndex.de/en/trends/psychtopics/). Application 2 (Twitter as a research object): “Using Word Embeddings to Understand Human Meaning Construction”. We present a conceptual and practical framework for exploiting embedding models (e.g., GloVe algorithm) as measures of semantic meaning. This is based on a recent work-in-progress in which we applied word embeddings to gain insights on the dynamics of user behaviour and psychological content of conspiracy-focused tweets on Twitter. The presentation discusses both the advantages and shortcomings of using traditional approaches like dictionaries or other vector models and proposes word embeddings as an option that overcomes several limitations. Finally, we present the main takeaways of how to use these distributed representations in applied work. Discussion: Against the backdrop of the prevalence of Twitter as data source, we highlight both opportunities and pitfalls of current Twitter-based research in psychology. In particular, we address challenges and biases in collecting and processing of tweets, alongside privacy and data sharing issues.

Governmental measures such as social distancing aim at curbing the spread of the novel coronavirus, SARS-COV-2. In March 2020, a large proportion of the German population supported such measures. In this paper, we analysed whether the support for social distancing would dwindle with economic worries superimposing virus-related health worries in the months to follow. We test seven pre-registered hypotheses using data from the German COVID-19 Snapshot MOnitoring (COSMO) survey, which regularly monitors behavioural and psychological factors related to the pandemic. For the present article, we analysed the German COSMO data for the period ranging from March 24, 2020 to July 7, 2020 (Ntotal = 13,094). Results revealed that health worries decreased over time, whereas economic worries remained largely constant. Furthermore, as expected, the acceptance of social distancing considerably decreased. Health worries were positively associated with acceptance of social distancing, whereas the corresponding relationship was negative with regard to economic worries (albeit smaller and less consistent). Unexpectedly, no interactions between health worries and economic worries were found. We conclude that individual differences in health and economic threat perceptions related to COVID-19 play an important role in the acceptance of social distancing.

The outbreak of the COVID-19 pandemic has prompted the German government and the 16 German federal states to announce a variety of public health measures in order to suppress the spread of the coronavirus. These non-pharmaceutical measures intended to curb transmission rates by increasing social distancing (i.e., diminishing interpersonal contacts) which restricts a range of individual behaviors. These measures span moderate recommendations such as physical distancing, up to the closures of shops and bans of gatherings and demonstrations. The implementation of these measures are not only a research goal for themselves but have implications for behavioral research conducted in this time (e.g., in form of potential confounder biases). Hence, longitudinal data that represent the measures can be a fruitful data source. The presented data set contains data on 14 governmental measures across the 16 German federal states. In comparison to existing datasets, the dataset at hand is a fine-grained daily time series tracking the effective calendar date, introduction, extension, or phase-out of each respective measure. Based on self-regulation theory, measures were coded whether they did not restrict, partially restricted or fully restricted the respective behavioral pattern. The time frame comprises March 08, 2020 until May 15, 2020. The project is an open-source, ongoing project with planned continued updates in regular (approximately monthly) intervals. This release note presents the background, dataset structure and coding rules of the dataset., Beitrag aus der Reihe "ZPID Science Information Online"