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Stories of employability are told in employment and educational settings, notably the selection interviews. A popular training approach guiding higher education students to construct employability stories has been the past-behaviour storytelling method. However, insufficient research exists regarding the method's effectiveness and optimisation. This study examines whether the method (1) increases the quantity and quality of interview narratives in story forms and (2) can be enhanced by image stimuli.

In a double-blind randomised control trial with repeated measures, participants submitted four weekly interview narratives. After receiving past-behaviour serious storytelling training in Week 3, they were randomly allocated to an exposure group using images and a control group using keywords as a placebo to continue producing interview narratives. The interview narratives were assessed based on the number of stories and quality ratings of narrative conformity, relevance and conciseness. Results before and after the training, and with and without the image stimuli, were analysed.

Training increased the number of stories. Training and repeated practice also increased narrative quality ratings. However, the image-based intervention was the strongest predictor of improved quality ratings (effect size 2.47 points on the observed scale of 0–10, p < 0.01, 95% CI [1.46, 3.47]).

A pre-existing ability to tell employability stories cannot be assumed. Training is necessary, and intervention is required for enhancement. Multi-sensory narrative interventions may be considered.

This study is the first known double-blind randomised control trial with repeated measures evaluating if storytelling training and image stimuli improve interview narratives.

A handful of models and measures currently exist for the purpose of assessing student capacity for leading social change. Ample research suggests that students of different racial and gender identity groups exhibit various understandings, motivations, and behaviors related to social engagement and transformation, yet few studies take into consideration the potential for diverse students to interpret social-change-related survey scale items differently. Using a critical quantitative approach, this study compares factor loading patterns of the Social Issues Advocacy Scale (SIAS; Nilsson, et al., 2011) across eight race/gender subgroups to test the extent to which the factor structure remains invariant. Findings suggest that intersections of race and gender do influence how scale items cluster together. This study lends support for critical quantitative research designs that examine social phenomena using a specific-group approach, and calls for scholars to consider the cultural validity of scales used to measure capacity for social justice leadership.

Noninvasive brain stimulation (NIBS) such a transcranial magnetic stimulation, intermittent theta burst stimulation, transcranial direct current stimulation and electroconvulsive therapy have emerged as an efficacious and well-tolerated therapy for treatment-resistant psychiatric disorders. While novel NIBS techniques are an exciting addition to the current repertoire of neuropsychiatric therapies, their success is somewhat limited by the wide range of treatment responses seen among treated patients.

In this study, the authors will review the studies on relevant genetic polymorphisms and discuss the role of RNA genotyping in personalizing NIBS.

Genome studies have revealed several genetic polymorphisms that may contribute for the heterogeneity of treatment response to NIBS where the presence of certain single nucleotide polymorphisms (SNPs) are associated with responders versus nonresponders.

Historically, mental illnesses have been arguably some of the most challenging disorders to study and to treat because of the degree of biological variability across affected individuals, the role of genetic and epigenetic modifications, the diversity of clinical symptomatology and presentations and the interplay with environmental factors. In lieu of these challenges, there has been a push for personalized medicine in psychiatry that aims to optimize treatment response based on one’s unique characteristics.

This article aims to clarify the concepts used to understand, analyze and improve a patient’s progress through a health service system. A patient pathway describes plans and intentions. Within it, we distinguish between the clinical pathway of decisions and interventions and the care pathway of supportive activities. As a patient pathway is implemented, it turns into a patient journey of what is done, what happens to a patient’s medical condition and what is experienced and felt. We introduce “patient journey disruption” (PJD) as a concept describing the events that need to be prevented from happening to accomplish integrated, coordinated and seamless care.

The method used in this paper is concept analysis. First, an expert steering group worked to refine the concept of PJDs; second, an analysis of similar concepts from related fields was done to root the concept into existing theories, and third, semi-structured interviews with professionals and patients were done to test the concept of PJDs in the home care context.

PJDs are agency-based harmful events in the execution of the care pathway that deviate the patient journey from what can be reasonably expected. PJDs are management failures, which is why they should be studied by healthcare operations management (HOM) and service science scholars with the intention to find ways to prevent them from happening.

This study has limitations, including presenting conceptual ideas and preliminary results that are only indicative.

We believe that the introduction of the concept of PJDs into the literature provides a new, systematic way of approaching the different shortcomings in our healthcare production systems. Moreover, by systematically identifying different PJDs, interventions can be designed and targeted more appropriately.

Managerial challenges regarding healthcare processes have been studied but have not been well defined. The concept of PJDs is an original, well-thought-out definition.

Accomplishing the United Nations’ Sustainable Development Goals (SDGs) is imperative for societies to meet their grand challenges. Achieving these goals by 2030 requires sustainability change agents with a can-do-attitude. This study aims to show how institutions of higher education can become partners for social marketing in bringing forward such change agents.

Taking a case study approach, this paper examines a master’s programme to identify factors relevant to educating sustainability change agents that can serve as a basis for a social marketing planning primer to foster the SDGs.

This study presents the social marketing discipline with a viable option for supporting the achievement of the SDGs through higher education. Its contributions are twofold. First, it is shown that when interdisciplinarity and a project-based approach are conceptualized and organized to create a motivating and meaningful learning environment with the SDGs as guiding principles, students, as sustainability change agents, can increase awareness and have the potential to generate impacts regarding the SDGs at the individual, organizational and institutional levels. Second, based on this, the paper provides guidance to social marketers regarding the planning of a campaign targeting higher education institutions. The authors argue that the aim of this campaign should be to promote the implementation of the SDGs as guiding principles above all, as this can facilitate the process of students becoming sustainability change agents who help achieve the goals in a timely manner.

Whilst single case studies are usually limited in drawing generalizations, the present study offers a starting point for investigating the role of universities as a target group for social marketing in fostering further sustainable development. Building on its findings, future research could test the proposed social marketing planning primer and evaluate the impact on the SDGs at a larger scale than only one university.

It is proposed to use the findings of the study to model a social marketing campaign aimed at universities to motivate them to help develop sustainability change agents in all disciplines by integrating the SDGs as guiding principles for study programmes.

Students’ impacts range from leading peers to buy sustainable products and consume less to influencing a company to adopt sustainable packaging, thereby contributing to social change.

This study is among the first to examine the possible effect of a study programme on the SDGs at different societal levels by taking the perspectives of multiple stakeholders into account and combining the theory of higher education with sustainability and social marketing.

In response to growing concerns over the negative consequences of Internet addiction on adolescents’ mental health, coupled with conflicting results in this literature stream, this meta-analysis sought to (1) examine the association between Internet addiction and depressive symptoms in adolescents, (2) examine the moderating role of Internet freedom across countries, and (3) examine the mediating role of excessive daytime sleepiness.

In total, 52 studies were analyzed using robust variance estimation and meta-analytic structural equation modeling.

There was a significant and moderate association between Internet addiction and depressive symptoms. Furthermore, Internet freedom did not explain heterogeneity in this literature stream before and after controlling for study quality and the percentage of female participants. In support of the displacement hypothesis, this study found that Internet addiction contributes to depressive symptoms through excessive daytime sleepiness (proportion mediated = 17.48%). As the evidence suggests, excessive daytime sleepiness displaces a host of activities beneficial for maintaining mental health. The results were subjected to a battery of robustness checks and the conclusions remain unchanged.

The results underscore the negative consequences of Internet addiction in adolescents. Addressing this issue would involve interventions that promote sleep hygiene and greater offline engagement with peers to alleviate depressive symptoms.

This study utilizes robust meta-analytic techniques to provide the most comprehensive examination of the association between Internet addiction and depressive symptoms in adolescents. The implications intersect with the shared interests of social scientists, health practitioners, and policy makers.

Despite being one of the most used dependent variables in expatriate management research, no clear-cut understanding exists of what expatriate success means. Thus, this study aims to propose an integrative definition of expatriate success by providing an overview of expatriate success's dimensions, antecedents, and their interplay.

A systematic literature review (SLR) was conducted to achieve the purpose. A total of 249 empirical studies (quantitative 111, qualitative 50, mixed-methods 17), literature reviews (67) and meta-analyses (4) on expatriate success were reviewed from Web of Science and Scopus databases published from 1990 until December 2021. The study selection criteria followed the PRISMA flowchart steps, and then descriptive and network analyses were performed to identify expatriates' success dimensions, antecedents and their interplay.

The findings show the interplay among antecedents and dimensions of expatriate success across three levels (individual, interpersonal and organisational) to clarify the concept of expatriate success. Also, the study offers a comprehensive definition of expatriate success based on the dimensions identified.

The suggested definition of expatriate success elucidates the “atheoretical”, multidimensional and socially constructed nature of the construct and hence, calls for more “theoretical”, multidimensional and subjective considerations of the term to ground human resource management practices addressed to attain expatriates' success.

This paper provides an integrative definition of expatriate success, giving greater insight into the construct, in addition to critically reflecting on it.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

Extending the elaboration likelihood model (ELM), this study investigates the impact of social media influencers (SMI) on consumer well-being (CW) as well as the influence of CW on purchase intention.

A between-subjects experiment (macro- vs mega-influencer) was conducted to assess the proposed hypotheses. A total of 190 consumers participated in the experiment, and SmartPLS 3.3 was used for multigroup analyses.

Overall, argument quality (AQ), source's credibility (SC) and influencer's kindness positively predict CW, and CW predicts purchase intention. It was also found that SC is more important when information comes from a mega-influencer, whilst kindness is essential for a macro-influencer.

The results of this study imply that CW should be an essential component of influencer marketing strategy. Marketing managers should hire credible and kind influencers who can produce quality arguments. Additionally, the selection of SMI (macro- vs mega-influencer) should be aligned with the marketing objective and type of persuasion required.

This is one of the early attempts to extend ELM by introducing influencer kindness as a peripheral cue. Moreover, the study offers novelty by examining the effects of influencer characteristics (AQ, SC and kindness) on CW and comparing these effects across macro- and mega-influencers.

藉著擴展詳儘可能性模型, 本研究擬探討網絡紅人對消費者福祉的影響, 以及消費者福祉對購買意圖的影響。

研究人員進行被試間實驗 (中網紅對大型網紅) , 以對提出的假設進行評價。190名消費者參與實驗, 研究人員使用SmartPLS 3.3 進行多群組分析。

總的來說, 論點品質、來源可信度和網紅的仁慈體貼, 均能積極預測消費者福祉, 而消費者福祉亦可預測購買意圖。研究人員亦發現, 若資訊是來自大型網紅的話, 來源可信度則更形重要, 而對中網紅來說, 仁慈體貼則是不可或缺的。

研究結果暗示, 消費者福祉應是網紅市場營銷戰略的基本要素。市場經理應僱用可靠、仁慈體貼、並能提出優質論點的網紅。而且, 網絡紅人 (中網紅對大型網紅) 的挑選, 必須與營銷目標和說服的種類互相協調。

本研究為早期的嘗試, 利用引進網絡紅人的仁慈體貼作為周邊線索, 來擴展詳儘可能性模型。另外, 本研究探討網絡紅人的特徵 (論點品質、來源可信度和仁慈體貼) 會如何影響消費者福祉; 研究人員亦跨中網紅和大型網紅, 對這些影響進行比較, 就此而言, 本研究提供了創新的研究意念。

This integrated care study seeks to highlight how voluntary sector “wellbeing co-ordinators” co-located in a horizontally and vertically integrated, multidisciplinary community hub within one locality of an Integrated Care Organisation contribute to complex, person-centred, co-ordinated care.

This is a naturalistic, mixed method and mixed data study. It is complementing a before-and-after study with a sub-group analysis of people receiving input from the wider hub (including Wellbeing Co-ordination and Enhanced Intermediate Care), qualitative case studies, interviews, and observations co-produced with embedded researchers-in-residence.

The cross-case analysis uses trajectories and outcome patterns across six client groups to illustrate the bio-psycho-social complexity of each group across the life course, corresponding with the range of inputs offered by the hub.

To consider the effectiveness and mechanisms of complex system-wide interventions operating at horizontal and vertical interfaces and researching this applying co-produced, embedded, naturalistic and mixed methods approaches.

How a bio-psycho-social approach by a wellbeing co-ordinator can contribute to improved person reported outcomes from a range of preventive, rehabilitation, palliative care and bereavement services in the community.

To combine knowledge about individuals held in the community to align the respective inputs, and expectations about outcomes while considering networked pathways based on functional status, above diagnostic pathways, and along a life-continuum.

The hub as a whole seems to (1) Enhance engagement through relationship, trust and activation, (2) Exchanging knowledge to co-create a shared bio-psycho-social understanding of each individual’s situation and goals, (3) Personalising care planning by utilising the range of available resources to ensure needs are met, and (4) Enhancing co-ordination and ongoing care through multi-disciplinary working between practitioners, across teams and sectors.