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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.

This study assesses the relationship between job satisfaction, perceived organizational support and workplace factors on officer turnover intention within an urban, municipal police organization.

Using data from an online survey of New York City Police Officers (n = 1,823), both bivariate analysis and logistic regression models were utilized to assess the salience of police officer job satisfaction, perceived organizational support and perception of six workplace domains, including financial compensation, environmental factors, professional fulfillment, work/quality of life balance, treatment from management and occupational risk, on predicting turnover intention.

The cross-sectional study finds that job satisfaction, financial factors (salary, benefits and retirement benefits) and fulfillment predict lower levels of turnover intention (i.e. higher levels of organizational commitment). Work–life balance and environmental factors (cleanliness of work environment and condition of equipment) predict higher levels of turnover intention. Both perceptions of organizational support and occupational risk, while significant in the bivariate models, were not significantly associated after accounting for other factors. There is no evidence that officer perception of public support or the risk of being injured/killed at work were related to officer turnover intention.

The current study is limited by its focus on only one police department and its use of cross-sectional data, which may limit the generalizability of the results to agencies that differ in size and type and do not allow for assessment of causality.

Officer turnover intention may be reduced by increasing financial compensation, improving the work environment and promoting a healthy work–life balance.

The study contributes to a growing body of research on police officer voluntary turnover by evaluating established predictors along with workplace factors in an urban police department: the setting where officer turnover intention is hypothesized to be the greatest.

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).

The present study aimed to investigate the impact of social networks on the use of academic libraries by university students.

The method used in the present study was a survey. The statistical population included 461 university students. The data collection tool was a questionnaire. The result of the Cronbach test was equal to 0.726 indicating the acceptable reliability of the questionnaire. For data analysis, descriptive statistical methods and inferential statistical methods using SPSS 21 software were employed.

The findings showed that 243 of the participants used social networks for 4–6 h a day, 192 students never used university libraries and 229 used the university library only once in a month. Communication with friends was also reported to be one of the main goals in using social networks. The results of regression analysis also indicated that four predictor variables including information retrieval, social influence, trust and attractiveness of social networking environment were statistically able to explain the variance of reluctance to use university libraries.

The present study is one of the few studies that has examined the negative impact of social networks on visiting university libraries.

Status characteristics theory states that influence in small groups reflects the distribution of group members' status characteristics. This process is mediated by expectations for task performance. Vocal accommodation is an unobtrusive measure that indicates expectations. We test whether vocal accommodation predicts influence and then examine the role of expectations in this process.

We conducted a laboratory experiment in which status-differ-entiated dyads completed a collective problem-solving task. We use a common measure of vocal accommodation to predict influence, and we employ questionnaire data to measure performance expectations. We hypothesize that the actor that exerts more effort in the synchronization process will have less influence over group decisions and that performance expectations will mediate the effect.

Results from GSEM analyses of 65 dyads show that levels of vocal accommodation significantly predict influence. Further analysis shows that performance expectations mediate a significant portion of the relationship between AAR and influence.

Vocal accommodation is useful for predicting both status perceptions and influence. Since this technique is an unobtrusive measure, it presents new possibilities for status research, including opening new lines of theoretical inquiry, providing a tool for conducting replications outside of the standard experimental setting, and for examining status organizing processes in a variety of environments.

We present a novel method for examining status outcomes, including a measure of influence that is analogous to existing measures that status scholars use but which is more suitable for studying status processes in open interaction.

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.