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This paper presents the most up-to-date comprehensive meta-analysis of the effectiveness of interventions to enhance psychological capital (PsyCap). It also reports the first meta-analytic examination of longer-term effects (beyond the immediate post-intervention period).

This meta-analysis followed PRISMA 2020 guidelines and utilised the methodologies of Hunter and Schmidt (2004) and Borenstein et al. (2009). The search period was from 2006 until February 2023. A total of 40 studies (N = 4,207) were included in the meta-analyses. The analyses were performed using the Comprehensive Meta-Analysis (CMA) Version 4 software programme.

The results indicate that the impacts of interventions on PsyCap and each of its component resources are greater than those reported in a previous meta-analysis (Lupșa et al., 2020), with the strongest impacts on hope and optimism. Interventions had sustained positive effects on PsyCap, hope, resilience and optimism, but not efficacy, and the frequency of intervention sessions influenced the effectiveness of interventions on hope, efficacy and optimism. Interestingly, specific PsyCap-focussed interventions did not have the greatest impact on PsyCap or its component resources.

This study contributes to emerging research on wellbeing-oriented HRM and provides valuable insights into more effective design and implementation of interventions to enhance PsyCap and its component resources. These interventions are a promising form of investment in employees which may bring mutual gains for individuals and organisations. The present findings extend those of previous studies and specifically respond to the call for further research on the persistence of PsyCap intervention effects.

The purpose of this study is to investigate how expressing gratitude and forgiveness on social media platforms relates to the overall sentiment of users, aiming to understand the impact of these expressions on social media interactions and individual well-being.

The hypothesis posits that users who frequently express gratitude or forgiveness will exhibit more positive sentiment in all posts during the observed period, compared to those who express these emotions less often. To test the hypothesis, sentiment analysis and statistical inference will be used. Additionally, topic modelling algorithms will be used to identify and assess the correlation between expressing gratitude and forgiveness and various topics.

This research paper explores the relationship between expressing gratitude and forgiveness in X (formerly known as Twitter) posts and the overall sentiment of user posts. The findings suggest correlations between expressing these emotions and the overall tone of social media content. The findings of this study can inform future research on how expressing gratitude and forgiveness can affect online sentiment and communication.

The authors have demonstrated that social media users who frequently express gratitude or forgiveness over an extended period of time exhibit a more positive sentiment compared to those who express these emotions less. Additionally, the authors observed that BERTopic modelling analysis performs better than latent dirichlet allocation and Top2Vec modelling analyses when analysing short messages from social media. This research, through the application of innovative techniques and the confirmation of previous theoretical findings, paves the way for further studies in the fields of positive psychology and machine learning.