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This study aims to explore the correlation between Management Control Systems, Green Innovation, Social Media Networks, and Company Performance in medium-sized construction and real estate firm in Indonesia.

This research method uses quantitative approach. The sample selection technique uses simple random sampling. The analytical method in this study uses structural equation models based on variance. Statistical test tool used, is Smart PLS 3.0.

The management control systems have a significant and positive impact on social media networks, green innovation, and company performance in the upper-middle-class construction and real estate businesses in Java. Furthermore, social media networks and green innovation were found to mediate the strong relationship between management control systems and firm performance in medium-sized construction and real estate businesses in Java.

This research should provide a detailed, technical, and structured explanation of how companies assess suitability standards for implementing green innovation in Indonesia’s construction and real estate sectors.

The finding emphasize the importance of the management control system in enhancing firm performance. If, the elements of the management control system are met or adequate, it can improve the performance of those in charge, leading to satisfactory performance.

This finding is the first of its kind in Indonesia. It will contribute to shaping future development policies for government and private projects, ensuring they are more advance and environmentally conscious.

The metaverse offers great potential for creating a new educational environment with unique experiences. Currently, it has been integrated into many stages of education, including classroom study aids, clinical skill interaction and image training simulators, thanks to a new generation of Internet applications. This paper aims to provide a comprehensive systematic review using bibliometric analysis on the metaverse in health education and analyze the trends and patterns of research output within the field.

The paper conducts bibliometric analysis and follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous and transparent review process. Specifically, this article identifies research questions, develops a data-collection strategy and establishes a screening approach that includes determining relevant keywords and applying inclusion and exclusion criteria.

A bibliometric analysis is conducted comprising 231 studies from 145 scientific journals to assess the trends, patterns and collaboration networks in research on the use of metaverse technology in health education. This paper provides insights into the research themes, publication trends and countries leading in this field, which can guide future research in this field.

The use of metaverse technology in health education has gained momentum in recent years. Despite this interest, comprehensive studies to review and analyze the existing literature on this topic systematically are lacking. In response, this paper provides a systematic review that explores the potential role of the metaverse in health education. By considering the current research, key trends, research hotspots and opportunities for future investigations are identified. The findings not only shed light on the current state of research but also offer guidance for advancing this exciting field.

The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina.

Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test.

Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables.

To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in this context, and its findings may be generalizable to comparable transitional economies with booming real estate sector like Kosovo.

This study is part of a series aimed at improving the city's environment, as fully restoring the past soundscape is hardly feasible. The initial study aims to uncover the city's sound characteristics, including iconic sounds that have shaped the city's environment for decades, contributing to its status as Indonesia's second most popular tourist destination. This stage is critical for informing policymaking to carefully manage and enhance the urban acoustic environment in alignment with the preserved culture.

The city's sound profile was examined using standard urban sound taxonomies. The study used quantitative methods, including (1) sound pressure level (SPL) measurements and sound recordings, (2) in situ surveys and (3) memory-based surveys. The first set of data were compared to current standards and standard urban sound taxonomies, while the second set was analysed to determine the median rating score for determining the soundscape dimensions. The third data set was used to identify the specific acoustic aspects inherent in Yogyakarta.

Yogyakarta's acoustic environment was bustling, with traffic noise and human activities dominating the soundscape, surpassing the standard levels. Many sounds not classified in standard urban sound taxonomies were present, showing the diverse nature of urban sound classification, particularly in a cultural and traditional city like Yogyakarta. The memory-based survey unveils Yogyakarta's two most remarkable soundmarks, “gamelan” and “andong”, which support the findings of prior studies. The in situ survey rated the city's acoustic environment as eventful, pleasurable and generally appropriate, emphasising the presence of cultural sounds unique to Yogyakarta, even though they are not fully audible in the current environment.

The standard sound taxonomies used in urban areas need to be adjusted to include the unique sounds produced by cultural and traditional activities in developing countries. The ordinates and subordinates of the taxonomies also need to be updated. When cultural and daily activities are massively seen in a particular city, the sounds they produce can be recalled exclusively as the city's signature. It is urgent to implement policies to safeguard the few remaining soundmarks before they disappear entirely.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

Although building information modeling (BIM) has brought competitive advantages and many new jobs, the BIM-related job market is still confusing in China, which will undermine the adoption of BIM. This paper aims to show what kinds of BIM-related jobs are there in China, what employers require and whether all BIM engineers are the same kind.

A text mining approach, structural topic model, was used to process the job descriptions of 1,221 BIM-related online job advertisements in China, followed by a cluster analysis based on it.

First, 10 topics of requirements with the impact of experience and educational background to them were found, namely, rendering software, international project, design, management, personal quality, experience, modeling, relation and certificate. Then, six types were clustered, namely, BIM modeler, BIM application engineer, BIM consultant, BIM manager, BIM developer and BIM designer. Finally, different kinds of BIM engineers proved this title was an expediency leading to confusion.

This paper can provide a clear and insightful look into the confusing and unheeded BIM-related job market in China and might help to cope with the abuse of job titles. It could also benefit both employers and candidates in their recruitment for better matching.

This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs.

The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy.

Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications.

This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

Market evaluation of products is the basis for product innovation, yet traditional expert-based evaluation methods are highly dependent on the specialization of experts. There exist a lot of weak expert-generated texts on the Internet of their own subjective evaluations of products. Analyzing these texts can indirectly extract the opinions of weak experts and transform them into decision-support information that assists product designers in understanding the market.

In social networks, a subset of users, termed “weak experts”, possess specialized knowledge and frequently share their product experiences online. This study introduces a comparative opinion mining framework that leverages the insights of “weak experts” to analyze user opinions.

An automotive product case study demonstrates that evaluations based on weak expert insights offer managerial insights with a 99.4% improvement in timeliness over traditional expert analyses. Furthermore, in the few-shot sentiment analysis module, with only 10% of the sample, the precision loss is just 1.59%. In addition, the quantitative module of specialization weighting balances low-specialization expert opinions and boosts the weight of high-specialization weak expert views. This new framework offers a valuable tool for companies in product innovation and market strategy development.

This study introduces a novel approach to opinion mining by focusing on the underutilized insights of weak experts. It combines few-shot sentiment analysis with specialization weighting and AHP, offering a comprehensive and efficient tool for product evaluation and market analysis.

Grounded in self-congruency theory, this study aims to investigate the impact of different types of congruence in social media influencer marketing on consumers’ online impulse buying intentions under the moderating influence of wishful identification.

This study collects survey responses from an online sample of 232 social media users and analyses them using partial least squares structural equation modelling.

This study delineates two distinct pathways influencing online impulse buying intentions within influencer marketing: direct consumer–product congruence and the conditional role of consumer–influencer congruence. Particularly, the alignment between a consumer’s self-image and the product’s attributes independently drives online impulse buying intentions. Conversely, consumer–influencer congruence, despite high alignment, fails to spur online impulse buying intentions unless amplified by wishful identification – the consumer’s aspirational desire to emulate the influencer. This finding underscores the complexity of impulsive consumer behaviours in the digital marketplace, highlighting the pivotal role of product appeal and the conditional influence of influencer relationships on spontaneous purchasing decisions.

This study pioneers by elucidating the congruence interplay between consumers, influencers and products in online impulse buying, emphasising wishful identification as a critical moderating factor. Theoretically, it expands self-congruency theory by detailing the distinct roles of congruence types on impulsive behaviours, notably underlining the essential role of wishful identification for the effect of consumer–influencer congruence. Practically, the insights equip brands with a deeper understanding of the key drivers behind impulsive purchases in an influencer-centric digital marketplace, offering strategic guidance for optimising influencer collaborations and product presentations to enhance consumer engagement and sales.

This study aims to investigate how work overload, resulting from full-time telecommuting, aggravates telecommuting accounting professionals’ burnout via the mediation of work exhaustion. Further, the study also tests the conditional moderation effect of psychological capital on the association between work exhaustion and burnout, proposing that it becomes least severe for employees who perceive a high level of psychological capital.

The research was conducted using a sample of 322 employees from Big Four accounting firms, and the measurement model was established using confirmatory factor analysis. Hypotheses were tested using structural equation modeling and model-14 in the PROCESS Macro for SPSS.

The results confirmed that work overload directly and indirectly (via the mediation of work exhaustion) aggravates employees’ burnout. However, psychological capital negatively conditions the mediating effect of work exhaustion on burnout such that the aggravating effect of work overload on burnout, via the mediation of work exhaustion, gets least severe (insignificant) for those employees who perceive a high level of psychological capital.

The study contributes to the literature on work overload-induced “work exhaustion burnout” association and offers suggestions for implications.