This study investigates whether configurations of situational determinants impact R&D intensity. Using a qualitative comparative analysis (QCA), the study assesses the consistency and coverage, both cross-sectionally and over time, of these configurations. The global solution presents four alternative combinations conducive to R&D intensity and concludes that situational determinants matter. The way managers use their aspirations, the proximity of bankruptcy, and the availability of slack affects the propensity to engage in R&D. Further, the results show that firms are not all alike and highlight the differences in how these determinants combine to impact R&D intensity

Growing attention has focused on employees’ repeated workplace decision to engage in work-related tasks and responsibilities at a minimum level that will not lead to their dismissal. This pattern of employee work behavior, labeled “Quiet Quitting,” represents a potentially problematic organizational phenomenon, particularly given increasingly complex work demands that can lower organizational performance. However, the literature lacks both a conceptually anchored definition of the Quiet Quitting construct and an associated empirically validated measure. Through four studies (sample of graduate students, Prolific sample, snowball sample from participants around the world, and a field sample of employees), we develop and validate a two-dimensional Quiet Quitting scale. This scale will facilitate measurement of the construct and development of a more nuanced understanding of its nomological network, correlates, and consequences. Implications for theory and research bearing on employee Quiet Quitting and employee work contributions are offered.

New ventures and small and medium-sized enterprises (SMEs) are the engines that drive the development of the economy, productivity, and business. However, they differ with respect to their natures, and that may affect their choices and success. This paper investigates the determinants of failure for SMEs and new ventures in Portugal by employing a logistic regression technique to develop the one-year prediction models individually over the period from 2010 to 2018. The results show that age and size always play significant roles in discriminating the failure risk of both types of firm, but the financial predictors selected in the final default prediction models for SMEs and new ventures vary. Moreover, based on financial, age, and size predictors, the SME model performs much better than that of the new venture in the classification accuracy reported. This indicates that separate treatment should be carried out while predicting the failure likelihood of SMEs and new ventures.