Search results

Building on current debates on innovation, knowledge diffusion, and institutional dynamics, we explore the influence of national innovation systems (NISs) on international innovation collaborations in Latin America, focusing on intellectual property rights (IPR), access to scientific knowledge and regulatory quality.

We analyze data from 17 Latin American countries from 2002–2015 using time-series panel analysis to evaluate how different NIS elements affect regional cooperation for innovation.

Regulatory quality can improve international collaboration by compensating for weaker IPR and scientific knowledge bases. Interestingly, while both IPR and scientific knowledge inherently promote cooperation, stronger regulatory environments may diminish the effectiveness of IPR protections, suggesting a potential substitution effect.

The study offers actionable insights for policymakers in developing regions to help them craft more effective policies for collaboration in innovation that consider the balancing act between regulatory quality and other NIS elements.

This research shifts focus from the conventional analysis of how developing countries attract collaboration from developed nations to how they can foster innovation among themselves, providing a unique perspective on the interaction between institutional factors and innovation capabilities within the Latin American context.

Public health measures implemented early in the COVID-19 pandemic brought the idea of essential work into the public discourse, as the public reflected upon what types of work are essential for society to function, who performs that work, and how the labour of essential workers is rewarded. This chapter focusses on the rewards associated with essential work. The authors develop an intersectional lens on work that was officially deemed essential in 2020 to highlight longstanding patterns of devaluation among essential workers, including those undergirded by systemic racism in employment and labour law. The authors use quantitative data from the CPS-MORG to examine earnings differences between essential and non-essential workers and investigate whether the essential worker wage gap changed from month to month in 2020. The authors find that patterns of valuation among essential workers cannot be explained by human capital or other standard labour market characteristics. Rather, intersectional wage inequalities in 2020 reflect historical patterns that are highly durable and did not abate in the first year of the global pandemic.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Considering the restricted and enclosed nature of prisons and closed environments and the prolonged and close contact between individuals, COVID-19 is more likely to have a higher incidence in these settings. This study aims to assess the prevalence of COVID-19 among prisoners.

Papers published in English from 2019 to July 7, 2023, were identified using relevant keywords such as prevalence, COVID-19 and prisoner in the following databases: PubMed/MEDLINE, Scopus and Google Scholar. For the meta-analysis of the prevalence, Cochrane’s Q statistics were calculated. A random effect model was used due to the heterogeneity in COVID-19 prevalence across included studies in the meta-analysis. All analyses were performed in STATA-13.

The pooled data presented a COVID-19 prevalence of 20% [95%CI: 0.13, 0.26] and 24% [95%CI: 0.07, 0.41], respectively, in studies that used PCR and antibody tests. Furthermore, two study designs, cross-sectional and cohort, were used. The results of the meta-analysis showed studies with cross-sectional and cohort designs reported 20% [95%CI: 0.11, 0.29] and 25% [95%CI: 0.13, 0.38], respectively.

Through more meticulous planning, it is feasible to reduce the number of individuals in prison cells, thereby preventing the further spread of COVID-19.

The purpose of this study is to explore the process of scaling second-hand fashion and how different strategic paths develop over time. This is theoretically grounded in two distinct scaling logics synthesised from the literature: a process that includes strategies for organisational growth (breadth-scaling) and a process that influences change in formal and informal institutions (depth-scaling).

The paper presents a process-based study based on the growth of a second-hand fashion retail organisation over 20 years. Qualitative materials such as interviews, observations and documents were collected and analysed.

The findings illustrate how a circular business model (CBM) can make use of a mix of strategies aiming for both organisational growth and impacting practices and habits within the fashion industry. Strategies building on breadth-scaling logic are found to be a prerequisite for engaging with depth-scaling strategies since they contribute to necessary resources and confidence, while depth-scaling secures future scaling opportunities by changing the conditions for scale.

The study contributes to the relatively scant literature on the process of scaling CBMs by exploring how different strategic paths unfold over time. The process-based approach, in combination with the two scaling logics, gives new insights into how CBMs go from niche to mainstream and thus influence the transition to a circular economy (CE).

There is a knowledge gap regarding the determinants of open innovation processes and outcomes in a joint value creation context, as well as what role artificial intelligence (AI) and data management play in facilitating open innovation processes. One strategy to better understand joint value creation through open innovation, supported by AI and data management, is to conduct studies on the digital business ecosystem (DBE). The purpose of this paper is to improve our current knowledge of this urgent issue in contemporary management through the lens of an ecosystem-based theory by conducting an empirical study on two DBEs (called ecosystem micro-communities (EMCs)), developed by Haier, as well as multiple literature reviews on the key concepts “Haier EMC” and “digital business ecosystem”.

By building on multiple literature reviews and empirical data from a multi-year and ongoing research program driven by Haier, this study examines Haier’s EMC model for AI-driven DBEs. Secondary data were collected through iterative literature reviews on DBEs, the EMC concept and the two selected EMC cases. The empirical data were collected through a qualitative study of two Haier EMCs in China.

Haier's ecosystem micro-community concept represents a radical shift towards a more flexible, responsive and innovative cross-industry organizational structure, offering valuable lessons for business leaders and scholars. Haier’s ecosystem micro-community model, part of their RenDanHeYi philosophy and here viewed as a DBE, is a pioneering management concept that not only redefines the management of the firm and the traditional corporate structure, but also the traditional view on innovation management, business strategy, human resource management and marketing (customer centricity). The concept has therefore an important and big impact on traditional management. For scholars, the gap in understanding innovation processes in open business ecosystems is addressed by the concept. However, the concept also opens new areas for academic research, particularly in innovation management, business strategy, human resource management and marketing. The concepts further encourage more interdisciplinary research.

The DBE is a relatively new research area that will need more research. While the EMC model is promising as an effective version of a DBE, its effectiveness across different industries and organizational cultures needs to be explored further. Future research should investigate its applicability and impact in diverse business environments. To understand the EMC’s long-term impact, longitudinal studies are needed. These should focus on the sustained competitive advantages, potential market disruptions and the evolution of customer value propositions over time. Finally, considering increasing concerns about data privacy and security, future research should also explore how DBEs solve the issue of data protection and IP while promoting open innovation and value sharing.

For managers and practitioners, the EMC concept could inspire leaders to learn how to foster innovation by creating smaller, autonomous teams that can respond quickly to market changes in the form of a DBE. The concepts exemplify how value creation and capture could be enhanced for any company and even could be a new strategy in the company’s digital transformation and repositioning into a more competitive, high-end player on the market. The concept also emphasizes employee empowerment and ownership, which can lead to higher job satisfaction and retention rates. The concept can further improve companies’ adaptability and resilience by decentralizing decision-making. Finally, the micro-communities allow businesses to be more customer-centric, developing products and services that better meet specific customer needs.

The social implications could be positive, as complex social problems commonly need an ecosystem approach to develop and deliver impactful solutions. In addition, Haier’s ecosystem micro-community model seems inherently scalable and culturally adaptable.

Haier’s EMC model is well-known in the research literature and is a novel approach to DBEs, which has been proven successful and replicable in different countries and industries. Providing insights from multiple literature reviews and two unique Haier EMC cases will contribute to a better understanding of highly effective data- and AI-driven business ecosystems, as well as of determinants of open innovation processes and outcomes in a joint value creation context, as well as what role AI and data management play in facilitating open innovation processes.

Large class sizes are becoming the norm in higher education against concerns of dropping learning qualities. To maintain the standard of learning and add value, one of the common strategies is for the course convenor to proactively monitor student engagement with learning activities against their assessment outcomes and intervene timely. Learning analytics has been increasingly adopted to provide these insights into student engagement and their performance. This case study explores how learning analytics can be used to meet the convenor’s requirements and help reduce administrative workload in a large health science class at the University of New South Wales.

This case-based study adopts an “action learning research approach” in assessing ways of using learning analytics for reducing workload in the educator’s own context and critically reflecting on experiences for improvements. This approach emphasises reflexive methodology, where the educator constantly assesses the context, implements an intervention and reflects on the process for in-time adjustments, improvements and future development.

The results highlighted ease for the teacher towards the early “flagging” of students who may not be active within the learning management system or who have performed poorly on assessment tasks. Coupled with the ability to send emails to the “flagged” students, this has led to a more personal approach while reducing the number of steps normally required. An unanticipated outcome was the potential for additional time saving through improving the scaffolding mechanisms if the learning analytics were customisable for individual courses.

The results provide further benefits for learning analytics to assist the educator in a growing blended learning environment. They also reveal the potential for learning analytics to be an effective adjunct towards promoting personal learning design.

Mentalization-based treatment (MBT) is a psychotherapeutic approach that aims to increase mentalizing capacity. The purpose of this meta-ethnography was to synthesize the available qualitative literature on MBT to allow for a more thorough understanding of the lived experience of those who have undergone this therapy.

A systematic search of seven databases was carried out. The Critical Appraisal Skills Programme was used to appraise the papers suitable for inclusion. The data were synthesized using a meta-ethnographic approach in which the second-order constructs from each paper were translated and reinterpreted creating a line of argument synthesis.

A total of 11 studies were included in the meta-ethnography. Three overarching themes were identified within the included papers which outlined that participant experience of MBT consisted of three different developmental stages. Navigating the therapeutic process describes the participant’s experience of the therapeutic process of MBT. The processes of change in MBT outlined participant reports of mechanisms of change experienced in MBT. Mentalizing self and others described the experience of generalizing new knowledge and skills to the world outside of therapy and unveiled the impact that engaging in MBT had on participants.

This meta-ethnography offers new insights into how clients experience MBT as a therapeutic process and offers suggestions for implementation in clinical practice as well as areas of focus for research of this therapeutic approach.

This paper aims to examine the role of bullying victimization in predicting psychopathology, encompassing post-traumatic stress disorder (PTSD), risk of developing prodromal psychosis and emotional and behavioural problems, among in-school Nigerian adolescents.

A total of 351 junior secondary students (n = 173 males, 178 females; age range: 9–17 years) were recruited from five randomly selected public secondary schools in Nigeria. Students completed a variety of self-report measures, including a socio-demographic questionnaire, the prodromal questionnaire – brief version, the strengths and difficulties questionnaire (SDQ) and the multidimensional peer victimization scale. They were also interviewed using the PTSD module of the Mini International Neuropsychiatric Interview-Kid Version.

Although bullying victimization was not found to predict the presence of PTSD, it predicted the risk of developing prodromal psychosis. All SDQ subscales also held significant positive associations with bullying victimization. This indicates that higher levels of victimization are associated with increased behavioural and emotional difficulties among adolescents.

The study findings add support to whole system approaches involving relevant stakeholders in health, education, social and criminal justice sectors via protective policies to address the problems of bullying in schools.

The study contributes to evidence demonstrating a need for improved understanding regarding the role of exposure to bullying victimization in predicting various forms of psychopathology. Furthermore, there is specifically a need for research with this focus in developing countries in sub-Saharan Africa and the Nigerian education system.

In this study, we investigated the positive and negative effects of stress that is driven by responsible artificial intelligence (RAI) principles on employee job outcomes by adapting the challenge–hindrance stressors model.

The study design involved empirically validating the proposed model on 299 respondents who use AI for work-related tasks.

The results revealed several RAI-driven challenge and hindrance stressors related to employees’ positive and negative psychological responses and task performance in a digital workplace. Practitioners could use the RAI characteristics to improve employees’ RAI-driven task performance.

This study contributes to the ongoing discussion on technostress and awareness in the context of RAI in the AI literature. By extending the C-HS model to the RAI context, it complements the context-specific technostress literature by conceptualizing different characteristics of RAI as RAI-driven stressors.

Adoption and use of technologies like RAI are not automatically translated into expected job outcomes. Instead, practitioners and academicians also need to know whether the RAI characteristics actually help employees show positive or negative behavior. Furthermore, relying on the challenge–hindrance stressor (C-HS) model, we try to reveal the beneficial and detrimental effects of different RAI characteristics on employees’ job outcomes.