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The paper aims is to see why social conflict caused by class segregation did not occur in Gading Serpong? What factors prevent conflict from occurring? This research seeks to find the causes of the nonoccurrence of social conflict due to class segregation in the Gading Serpong cluster area and explore the factors that restrain conflict there.

This research is qualitative research with data collection techniques through in-depth interviews with several parties identified as brokerages in the research object area. In this context, one of the media and analytical tools is to recognize agents or brokers who connect two groups of people. Brokerage occurs in sectors, patterns or forms of informal, personal relationships; to understand it, one must pay close attention to micro-level relationships and social psychological processes. However, brokerage can have a significant impact on macro-level social relations, as it is generally associated with social integration processes.

The lack of involvement of developers in overcoming social conflicts that occur between Gading Serpong natives and migrants in Gading Serpong housing has given rise to new actors. These new actors are what we can call brokers, where they have a role as brokers who are able to connect between migrants and natives in the Gading Serpong area. The broker phenomenon is actually familiar in academia, where in practice the broker acts as someone who is able to find solutions to problems. The broker is the reason even social segregation is created between migrant citizens and native citizens in Gading Serpong but never becomes a conflict between them.

Even if the brokerage phenomenon is the reason why there is no conflict over social segregation brokerage is not the only factor in this nonconflict segregation. Therefore, to cover the larger area of these suburban segregation problems, there must be further research on this topic.

The practical implication of this research is to encourage the housing developers that create urban housing, such as clusters or other gated communities, to evaluate the social factors, such as potential segregation and conflict management. Also to encourage the developers to get involved and create some social engineering systems, like brokerage, market and other social agents, to create some nonconflict segregation or even more inclusive communities.

This research is uncovering the main reason why social segregation between migrant and native people in Gading Serpong, which could potentially lead to conflict, is never a conflict. The main reason is social actors like brokerage.

This paper aims to explore the topic of adaptive reuse referring to urban open spaces into a more-than-human perspective. It underlines that dealing with heritage means being part of an inherent and ongoing process of transformation and so that reuse is inextricably an adaptive practice, constantly facing mutations, and that adaptation is a coral practice that involves different kinds of users and makers, inclusive of human and not human livings.

This paper looks at the lexicon of abandonment, in search of the more essential and intense meanings of words, and at some pioneering practices in Europe to comprehend the aesthetic and ethical implications of adaptive reuse of neglected landscapes.

Processes of reuse involve many different communities of users who in turn continuously redesign the site, into a comprehensive, coral and conflicting collaboration, whose results are never given once for all and are both uncanny and beautiful, scaring and marvellous, like a monster.

Accepting the idea that humans are not the only users and makers of urban sites can widen the range of tools, methods and values involved in heritage adaptive reuse.

This paper tries to widen the meanings of adaptation into a multispecies perspective. It intends to broaden the range of agents that can be involved as users and makers, assuming a more-than-human point of view that is not yet commonly applied.

Effective translation has become essential for seamless cross-cultural communication in an era of global interconnectedness. Translation management systems (TMS) have redefined the translation landscape, revolutionizing project management and execution. This study examines the attitudes of translation agencies and professional translators towards integrating and utilizing TMS, with a specific focus on Saudi Arabia.

The study's design was based on a thorough mixed-methods strategy that purposefully combined quantitative and qualitative procedures to create an array of findings. Through a survey involving 35 participants (both project managers and professional translators) and a series of interviews, this research explores the adoption of TMS, perceived benefits, influencing factors and future considerations. This integrated approach sought to investigate the nuanced perceptions of Saudi translation companies and expert translators about TMS. By combining the strengths of quantitative data's broad scopes and qualitative insights' depth, this mixed-methods approach sought to overcome the limitations of each method, ultimately resulting in a holistic understanding of the multifaceted factors shaping attitudes within Saudi Arabia's unique translation landscape.

Based on questionnaires and interviews, the study shows that 80% of participants were familiar with TMS, and 57% had adopted it in their work. Benefits included enhanced project efficiency, collaboration and quality assurance. Factors influencing adoption encompassed cost, compatibility and resistance to change. The study further delved into participants' demographic profiles and years of experience, with a notable concentration in the 6–10 years range. TMS adoption was linked to improved translation processes, and participants expressed interest in AI integration and mobile compatibility. Deployment models favored cloud-based solutions, and compliance with industry standards was deemed vital. The findings underscore the evolving nature of TMS adoption in Saudi Arabia, with diverse attitudes shaped by cultural influences, technological compatibility and awareness.

This research provides a holistic and profound perspective on the integration of TMS, fostering a more comprehensive understanding of the opportunities, obstacles and potential pathways to success. As the translation landscape continues to evolve, the findings from this study will serve as a valuable compass guiding practitioners and researchers towards effectively harnessing the power of technology for enhanced translation outcomes.

This article highlights the dynamic and evolving nature of libraries and the role of librarians within the changing landscape. It discusses how libraries have traditionally operated and how they have been impacted by 4IR and external factors such as the COVID-19 pandemic.

The study employed a mixed methods research approach, combining an online questionnaire to derive quantitative data and interviews to provide qualitative data. The follow-up interviews provided a comprehensive understanding of how academic librarians at the DUT library use Continuing Professional Development (CPD) to adapt to the evolving environment.

This paper reports that there was consensus that CPD empowers the librarians to adapt seamlessly to the dynamic library landscape. It equips them with the knowledge and skills to stay updated on the latest trends, technologies and best practices in their field.

The findings carry implications for the planning and executing of ongoing CPD programmes and activities across all academic libraries.

This study provides an insight into the results of the importance of CPD for librarians within a developing country in southern Africa.

In spite of the merits of artificial intelligence (AI) in marketing and social media, harm to consumers has prompted calls for AI auditing/certification. Understanding consumers’ approval of AI certification entities is vital for its effectiveness and companies’ choice of certification. This study aims to generate important insights into the consumer perspective of AI certifications and stimulate future research.

A literature and status-quo-driven search of the AI certification landscape identifies entities and related concepts. This study empirically explores consumer approval of the most discussed entities in four AI decision domains using an online experiment and outline a research agenda for AI certification in marketing/social media.

Trust in AI certification is complex. The empirical findings show that consumers seem to approve more of non-profit entities than for-profit entities, with the government approving the most.

The introduction of AI certification to marketing/social media contributes to work on consumer trust and AI acceptance and structures AI certification research from outside marketing to facilitate future research on AI certification for marketing/social media scholars.

For businesses, the authors provide a first insight into consumer preferences for AI-certifying entities, guiding the choice of which entity to use. For policymakers, this work guides their ongoing discussion on “who should certify AI” from a consumer perspective.

To the best of the authors’ knowledge, this work is the first to introduce the topic of AI certification to the marketing/social media literature, provide a novel guideline to scholars and offer the first set of empirical studies examining consumer approval of AI certifications.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

The disruptive human resource management (HRM) technologies are now considered a significant facilitator to change and benefit the entire HRM landscape. This view needs to be further verified by reviewing the knowledge on the subject in the empirical research landscape. Thus, the study's objectives were to find (1) the current knowledge and (2) the areas where empirical research is lacking in disruptive HRM technologies.

The article is a literature review that was followed by the systematic literature review and the preferred reporting items for systematic reviews and meta-analyses (PRISMA). The review considered 45 articles published during the 2008–2021 period extracted from the Scopus database, and bibliometric analysis was performed to achieve the research objectives.

The results found that scholarly attention has been given to electronic HRM (E-HRM) rather than the disruptive HRM technologies. The areas investigated include the determinants of intention, adoptions and use of E-HRM and the outcomes of E-HRM adoptions and use. These outcomes can be further divided into general outcomes and HRM outcomes.

The findings reveal gaps in E-HRM research and disruptive HRM technologies remain untapped in the empirical research landscape. Hence, the study findings provide some implications for future research and applications.

The study found empirically proven determinants of E-HRM intention, adoptions and use and E-HRM adoptions and use outcomes. These were found in the studies conducted during the 2008–2021 period.

顛覆性人力資源管理技術被認為是一個可改變、並幫助整個人力資源管理格局的重要誘導者; 這個看法、需透過檢核在實證研究範疇裡就有關議題的資料、才能作進一步的核實。因此，本研究擬了解 (1): 我們目前對有關議題的認知，以及 (2): 就顛覆性人力資源管理所進行的實證研究有哪些不足之處。

本文是一個文獻探討。研究人員採用系統文獻綜述研究法，以及系統綜述和 meta分析的首選報告項目。研究涵蓋45篇於2008年至2021年期間發表的學術文章; 這些文章均取自 Scopus資料庫。研究人員進行文獻計量分析，以達到研究的目標。

研究結果顯示、被研究探索的是電子化人力資源管理 (E-HRM) ，而不是顛覆性人力資源管理技術。被研究的範疇包括使用電子化人力資源管理的決定因素，以及使用後的結果。使用後的結果再細分為一般性的結果、和與人力資源管理有關的結果。

研究結果顯示、在實證研究的範疇裡，電子化人力資源管理和顛覆性人力資源管理技術的研究均存在未被探索的研究缺口。因此，研究結果為今後的研究和有關的應用提供了啟示。

本研究發現並確認經實證證明的使用電子化人力資源管理的決定因素，以及使用後的結果。研究結果乃基於檢核於2008年至2021年期間進行的學術研究而得來的。

顛覆性人力資源管理技術、系統文獻綜述、系統綜述和meta分析的首選報告項目 (PRISMA)

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

In this study, we question: how do the social costs of failure interact with gendered institutions to affect the early stage entrepreneurship activity? We address this question by employing the institutional theory and a unique dataset of 286,989 entrepreneurs across 35 countries.

To test our hypotheses, we use a multilevel modeling analysis that nests individual entrepreneurs within the countries. To capture individual and country-level variables, we constructed a unique dataset that combines data from the Global Entrepreneurship Monitor (GEM), European Flash Barometer (EUFB), World Bank Development Indicator (WDI), World Bank Doing Business Report (WBDB) and World Economic Forum (WEF).

Our analysis confirms that higher levels of the country-level gender equality positively correlate with the early-stage entrepreneurship activity of women. Moreover, we find that this positive relationship is amplified in institutional environments with high social costs of failure, suggesting that societal intolerance for failure can exacerbate the negative effect of gender inequality on the participation of women in entrepreneurship.

Our research contributes to academic interest on the role of legitimacy in women entrepreneurship and is of particular interest to international business scholars, seeking a better understanding of multidimensional construction of institutional frameworks across countries. In this study, we set out to address an important research question: how do the social costs of failure interact with gendered institutions to affect entrepreneurship activity? Our study provides a comprehensive portrait of gendered institutions by including the framework conditions of education, healthcare and political power. We found that in societies with gender equality, the likelihood of individuals engaging in the early-stage entrepreneurship activity is higher and that the positive relationship is strengthened in national environments with high social costs of failure.

Our study findings underscore the need for government policies addressing global gender gaps in economic empowerment. In particular, policies assisting women in obtaining education in high-growth industries like information technology or providing funding to women-dominated industries may foster activity for women seeking to do business in such industries. Such policies connect the early-stage entrepreneurship activities with gender equality concerns and initiatives.

Regarding the social costs of failure construct, specifically, prior studies generally focus narrowly on the context of failed entrepreneurs. We cast a wider net on men and women entrepreneurs’ entry decisions (irrespective of prior experience with business failure) and provide new views on the effects of social costs of failure on entrepreneurial ecosystems. We also extend the research on the legitimacy of women as entrepreneurs with the gender equality construct.

Unlike previous studies, which often focus on the “3Ms” of market, money and management, our research adopts a more holistic perspective. We recognize that the opportunities and challenges faced by entrepreneurs are shaped not only by individual skills and resources but also by the broader macroenvironment. By incorporating the framework conditions of education, healthcare and political power, alongside the intricate interplay of social costs and norms, our study paints a comprehensive picture of the landscape of female entrepreneurship.

This study aims to address the noted gap in comprehensive overviews detailing the developmental trajectory of Islamic finance (IF) as an interdisciplinary academic field.

The study introduces a unique approach using the combined methodologies of co-word analysis and main path analysis (MPA) by examining a broad collection of IF research articles.

The investigation identifies dominant themes and foundational works that have influenced the IF discipline. The data reveals prominent areas such as Shariah governance, financial resilience, ethical dimensions and customer-centric frameworks. The MPA offers detailed insights, narrating a journey from the foundational principles of IF to its current challenges and opportunities. This journey covers harmonizing religious beliefs with contemporary financial models, changes in regulatory landscapes and the continuous effort to align with broader socioeconomic aspirations. Emerging areas of interest include using new technologies in IF, standardizing global Islamic banking and assessing its socioeconomic effects on broader populations.

This study represents a pioneering effort to map out and deepen the understanding of the IF field, highlighting its dynamic evolution and suggesting potential avenues for future academic exploration.