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This study aims to evaluate the service and crime prevention performance of law enforcement agencies in Taiwan’s municipalities. By analyzing these performances, the study seeks to improve the efficiency of police services through appropriate resource allocation, ultimately achieving enhanced crime prevention.

The study utilized a two-stage data envelopment analysis (DEA) to construct performance metrics. In the first stage, the service performance of the police force was measured using the number of police officers and police stations as inputs and the number of patrols and temporary patrol boxes as outputs. In the second stage, crime prevention performance was assessed with the number of patrols and temporary patrol boxes as inputs and cases of violence, injury, intimidation and theft as outputs. Additionally, a truncated regression model was employed to analyze the impact of environmental variables such as per capita income, population density, proportion of the population with higher education and the unemployment rate on crime prevention performance.

The study found significant variations in both service and crime prevention performance across different municipalities. By identifying the most efficient and inefficient units, the analysis revealed critical insights into resource utilization and areas needing improvement. The regression analysis indicated that environmental factors such as higher per capita income, population density, education level and unemployment rate significantly affect crime prevention performance.

This research provides a novel approach to assessing police service efficiency and crime prevention effectiveness using a two-stage DEA model. By incorporating environmental variables through truncated regression, the study offers a comprehensive evaluation framework that can inform policy decisions and resource allocation strategies. This methodology not only highlights the performance metrics of law enforcement agencies but also underscores the influence of socioeconomic factors on crime prevention, contributing to the broader literature on public safety and resource management.

Understanding community resilience and collective efficacy is essential in terrorist-ridden areas. This study aims to investigate the role of communities in fostering collective resilience in response to violent acts of terrorism. It utilizes social capital and collective efficacy theories to explore the complicated relationship between community resilience and self-efficacy in terrorist-ridden areas.

This study employed an interpretive methodology and conducted thematic data analysis. It documented insights and lessons learned from the diverse perspectives of community stakeholders through semi-structured interviews with 21 participants residing in Quetta, Pakistan. Interviews took place in March and April 2023.

The study demonstrates that community resilience contributes significantly to violent acts of terrorism. After carefully going through data exploration, four intriguing themes appear. The first theme pertained to participants experiencing stress due to terrorism incidents, highlighting the frequency of such stressful events. The second theme examined the escalating backdrop of terrorism, which perpetually looms, prompting communities to fortify their resilience against this persistent threat. The third theme, community resilience during terrorist violence and active participation, revealing active participation in activities aimed at enhancing community quality of life. The fourth theme emphasized the challenges associated with community engagement in resilience-building efforts.

This study advances the understanding of community resilience in terrorist-ridden areas. In addition, it furthers discourse and provides ways for the implementation of strategies aimed at strengthening community resilience following terrorist incidents.

The primary objective of this study was to evaluate the influence of greenery on residential mobility within purpose-built student housing facilities in Northern Ghana.

This research employed a structured questionnaire and utilized an experimental block design, encompassing 124 comparative greened and non-greened student housing facilities, with a total of 995 resident participants. The impact of greenery on residential mobility was analyzed using a repeated sales model and t-test analysis.

Results revealed that residential mobility was significantly higher in non-greened student housing facilities than their greened counterparts. The study further indicated that the presence of greenery had a substantial effect on residential mobility, attributed to residents' preferences for the ecological, social and economic benefits associated with greenery, rather than merely infrastructure considerations.

Enhancing the aesthetic appeal, economic viability, safety, security and health benefits of greened student housing facilities while managing the influence of greenery on infrastructure was found to affect residential mobility. The findings suggest that improving occupancy rates in these facilities through the incorporation of greenery could yield higher rental income and better cash flows for investors involved in student housing operations.

This study highlights the ecological, social and economic advantages of greenery for residents. While the benefits of greenery in residential contexts are increasingly recognized, the specific impact of greenery on residential mobility within the Sub-Saharan African context represents a novel contribution. The application of neighborhood effects theory to the examination of greenery benefits and residential mobility in this region adds a new dimension to existing research.

We examine how leaders can become more self-aware in one graduate program that helped students develop their leadership competencies and become more accurate in their self-assessment of those competencies.

The Master of Science in Executive Leadership and Organizational Change program was created in response to the challenge of developing and accessing leaders in emotional and social intelligence competencies.

The analysis of student self-assessments and assessments by others at the beginning and end of the program provided the data for our research, showing empirical evidence of leadership development and improvement.

The study examined students in one program at one university so the generalizability of results is not clear. We have no way to rule out other experiences students had during the two-year period. We chose a single approach to measuring accuracy of self-assessment, future research may compare the results from a variety of approaches to this measurement issue.

We describe the program that helped students develop leadership competencies, develop the self-awareness necessary to take action as a leader and calibrate a more accurate self-assessment of leadership competencies over a two-year period.

We demonstrate how one leadership development program can help all students become more accurate in their self-assessments. We do not need different development programs to help one group of leaders gain confidence and another for leaders who are overly confident. An educational curriculum can foster transformational learning that enables ongoing leader development (Petriglieri, Wood, & Petriglieri, 2011). Academic programs can create experiences that help students develop and increase their self-awareness so that they are confident enough to put their learning into action. Like effective leadership development programs (Van Velsor, McCauley, Ruderman, & Ruderman, 2010), management education programs can incorporate assessment, feedback and support so that students become the effective and outstanding leaders the world needs.

Psychologically Informed Planned Environments (PIPEs) are designed to support individuals to develop positive relationships. This can be achieved through social activities and engagement with committees. Developing understanding of diversity and inclusion (D&I) continues to be an area of need in criminal justice. A custodial PIPE therefore developed a D&I committee, which included the planning and delivery of community events. This study aims to explore the effectiveness of the committee and events in increasing understanding of protected characteristics, enabling individuals to raise D&I-related issues, developing positive relationships and increasing feelings of safety.

Eight participants residing on a PIPE engaged in semi-structured interviews to explore their experience of the D&I committee and events. Thematic analysis was used to explore the responses and to identify common themes within the data.

Four themes were identified: “you’re in a safe environment here”, connectedness, opportunities to learn and grow and “it could be better”. The research indicated that the D&I committee and events support several of the enabling environments standards and provide prisoners with an opportunity to build on their strengths and capabilities, in line with the Good Lives Model (Ward, 2002; Ward and Gannon, 2006), which underpins the ethos of PIPEs (NOMS and DoH, 2012; HMPPS and NHS, 2023).

There is scope for similar committees to be implemented in other psychologically informed environments, which could potentially be extended to other therapeutic environments and “standard wings” within prisons. However, future research should consider the influence of prisoners’ individual characteristics on their experience of such committees and events.

PIPEs provide a unique environment in which various committees can be implemented. A D&I committee has provided the space and opportunity for staff and prisoners to develop their knowledge and understanding and to build positive relationships. This research has evaluated the effectiveness of this and aims to promote the use of such committees in other services.

This research aims to study the impact that the interaction between internal R&D and R&D cooperation has on the technical efficiency of firms, as well as to analyse whether firms belonging to a group achieve any additional level of technical efficiency.

We employ the stochastic frontier version of a knowledge production function, particularly the inefficiency model, and we combine the stochastic frontier analysis (SFA) and the complementarity approach.

The interaction between internal R&D and R&D cooperation has a positive and significant impact on technical efficiency when the complementarity test is applied between companies belonging to a group, and there is no interaction when they do not belong to a group.

Knowing this type of information in advance is critical for managers and policymakers, as it allows them to avoid undesirable combinations of innovation strategies or contexts not favourable for their implementation, as well as the formulation of policies leading to an efficient allocation of public resources.

To the best of the authors’ knowledge, this paper contributes an original approach in evaluating the complementarity of internal R&D and R&D cooperation from the perspective of technical efficiency and group membership, combining the SFA and the complementarity approach.

本研究擬探討內部研發與研發合作之間的相互作用會如何影響公司的技術效率;研究亦擬分析屬於集團的公司會否達致更高一級的技術效率。

研究人員採用一個知識生產函數的隨機前沿方法,尤其是低效模型,研究人員並且把隨機前沿分析和互補法結合起來進行探討。

研究結果顯示,若互補法在屬於集團的公司之間應用,內部研發與研發合作之間的相互作用會對技術效率產生積極和重大的影響;但是,如果公司不屬於任何集團的話,則沒有任何相互作用。

預先了解這方面的資料和信息對管理人員和政策制定者至關重要,這是因為這可讓他們避開不良的創新策略組合,以及那些不利於推行創新策略的環境;而且,能否制定可為公共資源高效分配的政策,也有賴於了解這方面的信息和資料。

據我們所知,本研究從技術效率和組成員身份的角度去評定內部研發與研發合作的互補性,在這方面提供了新穎的研究方法;而且,本研究結合了隨機前沿分析和互補法,這也是創新的做法。

Network configurations have been proposed as an efficient form of organisation and a promising area of research; however, a lack of conceptual clarity can be noted. The purpose of this review is to allow for a broad appreciation of network configurations and provide guidance for future studies of the concept.

A systematic literature review was conducted based on the PRISMA method; Scopus, Web of Science, PubMed and the Cochrane Library were searched for conference proceedings and journal articles describing organisational networks to integrate resources aimed at care delivery. Around 80 articles were included in the final review and analysed thematically and by use of bibliographic coupling.

The last decades have seen an increase in the frequency of articles describing networks for healthcare delivery. The most common contexts are care for multiple and/or long-term conditions. Three clusters of articles were found, corresponding to different conceptualisations of networks in healthcare: efficiency-enhancing cooperation, efficiency-enhancing integration and involvement for cocreation.

To increase conceptual clarity and allow the research on network configurations in healthcare to produce meta-learnings and guidance to practice, scholars are advised to provide ample descriptions of studied networks and relate them to established network classifications.

The current review has only included articles including networks as a key concept, which provides a focused overview of the use of network configurations but limits the insights into similar approaches not described explicitly as networks.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

This paper aims to check the fragmentation of knowledge across multiple sources of evidence, identifying, scrutinizing and outlining suggestions concerning the challenges researchers face when using multiple sources of data to identify studies.

This study produced a comprehensive database of 15,848 items from Scopus, Web of Science and EBSCO on the organizational growth and decline topics. The analyses carried out to check the fragmentation of scientific knowledge and the challenges in identifying studies have made use of the basic data frame functions in R’s language and the Bibliometrix and Corpus R’s packages.

This study confirms the fragmentation of scientific knowledge as well as it identifies the following challenges: missing information in key fields, nonexistence of standards in terminology, limitations on data extraction, duplicates and multiple formats of cited reference. Additionally, it suggests practical coping procedures and advances implications for stakeholders and an agenda for future research.

This study provides valuable and practical examples with empirical confirmation of scientific knowledge fragmentation and offers an integrated view of many challenges in the process of identifying studies. Moreover, by offering suggestions to address these challenges, this study not only offers a practical guide to scientific researchers but also initiates a wider discussion regarding knowledge organizing in social sciences.

This study aims to contribute to the ongoing assessment of executive compensation by investigating the nexus between managerial entrenchment factors, adopting a multifaceted perspective encompassing both economic and non-economic dimensions.

This research employs pooled cross-sectional Ordinary Least Squares (OLS) regression and Least Squares with Dummy Variables (LSDV) models with fixed effects to examine the determinants of Chief Executive Officer (CEO) compensation.

This research identifies firm size, performance (via ROA and Tobin’s Q), and CEO characteristics (age, tenure, stock ownership, MBA degree) as significant determinants of executive compensation at the 0.05 level. In contrast, the prestige of educational institutions, doctoral degrees, and the MBA’s relevance to short-term performance, along with CEO tenure, do not significantly affect pay. Additionally, the study highlights the significance of industry type (manufacturing vs technology) in shaping compensation, emphasizing the role of firm metrics and CEO credentials in designing executive pay packages.

This research introduces an innovative approach to controlling unobserved heterogeneity and adjusting for the dynamic nature of CEO compensation attributes across diverse CEO characteristics. By integrating both pooled Ordinary Least Squares (OLS) and Least Squares Dummy Variable (LSDV) models, the study addresses the challenges posed by time-invariant variables and unobservable heterogeneity. Such issues have historically skewed the accuracy of traditional OLS models in identifying the comprehensive array of factors—both economic and non-economic—that influence CEO compensation. This novel methodological framework significantly advances the examination of unobservable variables that may vary not only across the firms selected for analysis but also over time periods, thereby offering a more detailed understanding of the determinants of CEO pay.