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\"Discover [a] ... new way to look at our health based on the latest ... discoveries in the science of compassion, kindness, and human connection\"--Provided by publisher.

Emotional support from friends is a crucial source of social support for adolescents, significantly influencing their psychological development. However, previous research has primarily focused on how this support correlates with general levels of socioemotional problems among adolescents, neglecting the significance of daily fluctuations in these problems. The aim of this study was to explore the relationship between friend emotional support and both the average and dynamic indicators of daily emotional and peer problems in adolescents. These dynamic indicators include within-domain dynamics—such as inertia, which reflects the temporal dependence of experiences, and volatility, which indicates within-person variance—and cross-domain dynamics, such as transactional effects, which measure the strength of concurrent or lagged associations between daily emotional and peer problems. Participants were 315 seventh-grade Chinese adolescents (Mage = 13.05 years, SD = 0.77 years; 48.3% girls). Adolescents reported on their friends’ emotional support at baseline and then completed measures of daily emotion and peer problems over a 10-day period. Using dynamic structural equation models, the results revealed that higher levels of friend emotional support were associated with fewer daily socioemotional problems. This was evident both in terms of average levels and dynamic aspects, characterized by lower mean levels of daily emotional and peer problems, reduced inertia and volatility of these problems, and a weaker spillover effect from daily emotional issues to peer problems. These findings highlight the significant role of friend-emotional support in mitigating adolescents’ daily socioemotional challenges.

Digital transformation represents a compelling opportunity for manufacturing companies to enhance their competitiveness. This transformative journey offers myriad possibilities, including improved connectivity between workers and machines, as well as seamless machine-to-machine interactions. However, many manufacturing companies encounter challenges when attempting to implement digital transformation effectively. The process of digital transformation is often slow, and most companies find themselves in the early stages of adoption, grappling with the ambiguity surrounding the associated technologies. A systematic approach for the implementation of digital transformation is still elusive for many manufacturing companies. The number of studies exploring digital transformation is increasingly growing, encompassing various sectors and domains. However, within the manufacturing sector, there remains a need for further research and clarity on systematic implementation approaches. To address these issues, this research undertakes a comprehensive analysis to identify the critical factors that influence digital transformation in the manufacturing sector. The objective of this research is to identify the factors that drive the success of digital transformation in manufacturing companies while also uncovering factors that, when neglected, could lead to failure. Through a systematic literature review, this research identifies 11 critical factors. These factors serve as the basis for developing the ARTO model, a structured framework comprising four distinct categories: \"Awareness-related factors,\" \"Readiness-related factors,\" \"Technology Selection-related factors,\" and \"Operations-related factors.\" Moreover, this research incorporates expert perspectives gathered through a survey to refine the ARTO model. This study offers the ARTO model and digital transformation definition as practical tools for successfully implementing digital transformation in manufacturing companies, while also delineating the intricate relationships among the crucial factors. By shedding light on the factors underpinning digital transformation in the manufacturing sector, this research contributes to the ongoing discourse and facilitates more effective adoption of digital transformation strategies. Highlights Digital transformation enhances competitiveness with connectivity and machine interactions. Challenges in implementing digital transformation necessitate the identification of critical influencing factors. Developed ARTO (Awareness, Readiness, Technology, and Operations) model for enabling successful digital transformation based on critical factors. The ARTO model, derived from 11 critical factors, offers a structured framework for successful implementation. The ARTO model and the proposed DT definition guide manufacturing companies in their digital transformation journey. This research sheds light on essential factors, contributing to a more effective adoption of digital transformation strategies.

To explore the influence of bully victims on the suicidal tendencies of college students, and the moderating role of teacher emotional support and family support in the relationship between bully victims and college students' suicidal tendencies, in order to provide a reference for the effective intervention of college students' suicide behavior. Based on a survey of 15,560 college students. Multiple stepwise regression and Interaction analysis explore the impact of the bully victimization on college students' suicidal tendencies and the moderating role of family support and teacher emotional support in the relationship between the bully victim and college students' suicidal tendencies. This study found that the Suicidal Tendencies score of college students was 19.79 points, indicating that some college students have a risk of suicidal tendencies; secondly, verbal bullying ( = 0.084, <0.001), physical bullying ( = 0.222, <0.001) and social relationship bullying ( = 0.122, <0.001) have a positive and significant impact on the suicidal tendencies of college students; in addition, family support and teacher emotional support have a significant regulatory effect on the bully victim and college students Suicidal Tendencies and family support. The regulating effect was significantly higher than that of teacher emotional support. Chinese college students have the risk of suicidal tendencies; peer bullying victimization is an important reason for affecting college students' suicidal tendencies, teacher emotional support is a protective factor for bully victims to affect college students' suicidal tendencies, and family support has a significant moderating effect on the bully victim and college students' suicidal tendencies. Therefore, it is necessary to actively adopt home-school linkage and home-school communication to reduce campus violence and increase the psychological resilience of college students.

Previous research has indicated that students’ academic engagement is related to their emotional support from teachers. However, there is scarce evidence on how teacher emotional support relates to students’ academic engagement. Given the potential role of positive academic emotions in learning, this study investigated the mediating role of students’ positive academic emotions in the relationship between teacher emotional support and academic engagement among Chinese college students. Additionally, this study examined how mastery-approach goals moderated positive academic emotions. A survey instrument containing teacher emotional support, positive academic emotions, mastery-approach goals, and academic engagement was administered to 464 Chinese college students. The results demonstrated that students’ emotional support from their teachers positively influenced their academic engagement. Positive academic emotions mediated the relationship between teacher emotional support and students’ academic engagement. Furthermore, the mastery-approach goals moderated the mediating role of positive academic emotions. Finally, the implications for teachers in teaching for practice and the application prospects are discussed.

The accelerated advancement of artificial intelligence (AI) has positioned it as a novel colleague. However, as employees collaborate with AI colleagues in daily work, their communication and interaction with human colleagues may decrease. This may result in feelings of loneliness and a potential reduction in emotional resources, potentially leading to counterproductive work behavior (CWB). Drawing from the conservation of resources (COR) theory, we hypothesize that employee–AI collaboration may amplify employees’ CWB due to loneliness and emotional fatigue. The potential mitigating effects of leader emotional support on these outcomes are also considered. To test these hypotheses, a 2 × 2 vignette experiment (N = 167) was conducted. The results demonstrate that employee–AI collaboration exerts a substantial positive influence on loneliness. Loneliness further increases employees’ emotional fatigue, which in turn increases CWB. Leader emotional support—the care and motivation demonstrated by leaders has been identified as a key factor in reducing loneliness. This research contributes to the extant literature on employee–AI collaboration and CWB, and expands the application scope of COR. Practical implications arise for managers, who are encouraged to consider the impact of employee–AI collaboration on interpersonal interaction and to address employees’ emotional needs in a timely manner.

Using the stress process paradigm and data from the first national epidemiological survey of Asian Americans, we investigate whether social support from family and friends buffers the stress of discrimination to protect psychological well-being. Results suggest that perceived emotional support from family for a serious problem buffers the stress of high levels of everyday discrimination, controlling for sociodemographics, acculturation, and community factors. Contrary to our expectations, perceived emotional support from family about worries and received social support from regularly talking on the phone and getting together with family are not statistically significant stress buffers. Moreover, perceived and received social support from friends do not buffer the stress of any level of discrimination. Overall, our study has broader implications for research on the influence of social relationships on mental health and advances our knowledge of the social psychological process of coping with discrimination in the United States.

PurposeDrawing from a resource-based perspective in the work–family interface literature, the current study examines how emotional exhaustion, as a resource depletion mechanism, mediates the relationship between family–work conflict and job satisfaction. The authors also considered the content and nature of coworker support to investigate whether there were differential moderating effects of the two distinct types of coworker support: emotional and instrumental support.Design/methodology/approachThe authors surveyed 321 kindergarten employees across multiple sites located in South Korea. Using this sample, the authors performed random coefficient modeling to test the proposed research model.FindingsThe results showed a significant negative indirect relationship between family–work conflict and job satisfaction through emotional exhaustion. Furthermore, the findings suggested differential effects of the two coworker support types, such that the positive relationship between family–work conflict and emotional exhaustion was stronger when coworker emotional support was low than when it was high; the positive relationship was stronger when coworker instrumental support was high than when it was low. Moderated mediation analyses revealed that the two types of coworker support moderated the indirect relationship.Originality/valueThese results highlight the mixed blessing of distinct types of coworker support for researchers and practitioners. A lack of emotional support and greater instrumental support from coworkers each, respectively, exacerbate the negative impact of family–work conflict on employees' well-being and, subsequently, job satisfaction.

Onset of non-suicidal self-injury (NSSI) is most frequent during adolescence. Etiological models indicate that abnormal affective reactivity and regulation within interpersonal contexts is related to heightened NSSI risk. The current study examined the effects of maternal emotional support on adolescent sympathetic nervous system (SNS) activity and observed anger during a conflict discussion among 56 mother-daughter dyads consisting of healthy adolescents and adolescents with a history of self-injury. During the conflict discussion task, maternal emotional support and adolescent anger were coded from behavior, and cardiovascular pre-ejection period was used to index SNS responding. Results demonstrated that maternal emotional support was negatively associated with adolescent anger and SNS activity during the conflict. However, these associations were not significant among adolescents with heightened NSSI history. Maternal emotional support may serve as an interpersonal mechanism for adolescent physiological and behavioral regulation, yet may function differently among adolescents with more frequent NSSI.

Emotional support dialog systems face computational linguistic challenges as they require a deep understanding of explicit utterances and implicit emotional needs. In particular, existing models have shown limitations in effectively capturing subtle emotional contexts, which are essential for providing meaningful emotional support. To address this, we propose Generative Retrieval-Enhanced Emotional Support Conversations (GREEN), an emotional support dialog model using generative retrieval. Inspired by docID, GREEN introduces a Residual Identifier (ResID), enabling the dynamic identification of emotional context and appropriate support strategies from seeker utterances. By approaching emotional support as a context prediction task, our model works to understand both the explicit meaning of utterances and the underlying emotional needs of seekers. GREEN achieves significant improvements over SOTA models on ESConv with over 25% gains in response diversity metrics, 8.3% in content quality (BLEU-4), and 9.8% in strategy prediction accuracy. Our approach integrates generative retrieval with ResID-based context analysis, advancing emotional support dialog systems. For balanced reporting, we note current limitations—ResID stability under quantization/clustering and ambiguity when misidentification occurs—and plan to improve semantic matching and identifier design with broader real-world validation. Plain Language Summary A New AI Approach (GREEN) to Improve Emotional Support Conversations by Understanding Feelings and Giving More Personalized Responses Many people face stress, loneliness, and emotional difficulties but cannot always reach professional help when they need it. Emotional support chat systems using artificial intelligence (AI) can provide comfort, encouragement, and practical guidance in such moments. However, most existing AI chat systems struggle to fully understand the deeper meaning of a person’s words and emotions, often producing responses that feel repetitive, vague, or mismatched. Our study introduces GREEN, a new AI framework designed to improve the quality of emotional support conversations. GREEN uses an innovative process called “Response ID” (ResID), which helps the AI recognize both what someone is saying and the hidden feelings or needs behind it. By combining this with knowledge graphs—databases that capture connections between feelings, problems, and solutions—GREEN can suggest more meaningful and supportive replies. We tested GREEN on a widely used dataset of emotional support dialogs and found that it provided more diverse, empathetic, and contextually appropriate responses compared to leading AI systems. In evaluations with human judges, GREEN was rated higher for empathy, clarity, and helpfulness. This research shows that AI can be developed to give more human-like, compassionate support, with potential benefits such as reducing barriers to emotional assistance, providing around-the-clock availability, and complementing traditional therapy. Importantly, we emphasize that AI systems should never replace professional mental health care but can serve as an additional layer of support when immediate comfort is needed.