Perfectionistic Cognitions as Antecedents of Work Engagement : Personal Resources, Personal Demands, or Both?

Whereas personal resources have been established as a counterpart to external job resources in the Job Demands–Resources Theory, personal demands as a counterpart to job demands have been rather neglected. In this study, we propose that multidimensional perfectionism—in the form of daily perfectionistic cognitions—is a relevant personal characteristic for predicting daily work engagement in addition to and in its interplay with daily time pressure as a common job demand. 157 employees participated in a daily diary study for 15 workdays. As hypothesized, multilevel regression analyses yielded a positive unique effect of perfectionistic strivings cognitions and a negative unique effect of perfectionistic concerns cognitions on daily work engagement. Furthermore, we found that both unique perfectionistic strivings cognitions and perfectionistic concerns cognitions moderated a quadratic relationship between daily time pressure and daily work engagement. Building on the Job Demands–Resources Theory, we propose that the dimension of perfectionistic strivings constitutes a personal resource and the dimension of perfectionistic concerns constitutes a personal demand in the prediction of work engagement

Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants' Well-being: Ecological Momentary Assessment

Background: Sensors embedded in smartphones allow for the passive momentary quantification of people’s states in the context of their daily lives in real time. Such data could be useful for alleviating the burden of ecological momentary assessments and increasing utility in clinical assessments. Despite existing research on using passive sensor data to assess participants’ moment-to-moment states and activity levels, only limited research has investigated temporally linking sensor assessment and self-reported assessment to further integrate the 2 methodologies. Objective: We investigated whether sparse movement-related sensor data can be used to train machine learning models that are able to infer states of individuals’ work-related rumination, fatigue, mood, arousal, life engagement, and sleep quality. Sensor data were only collected while the participants filled out the questionnaires on their smartphones. Methods: We trained personalized machine learning models on data from employees (N=158) who participated in a 3-week ecological momentary assessment study. Results: The results suggested that passive smartphone sensor data paired with personalized machine learning models can be used to infer individuals’ self-reported states at later measurement occasions. The mean R 2 was approximately 0.31 (SD 0.29), and more than half of the participants (119/158, 75.3%) had an R 2 of ≥0.18. Accuracy was only slightly attenuated compared with earlier studies and ranged from 38.41% to 51.38%. Conclusions: Personalized machine learning models and temporally linked passive sensing data have the capability to infer a sizable proportion of variance in individuals’ daily self-reported states. Further research is needed to investigate factors that affect the accuracy and reliability of the inference