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This paper discussed quantifying architect, engineer and contractor (AEC) professionals' cross-work culture productivity by comparing between Malaysian and United Kingdom (UK) projects during industrialized building project delivery. This study addressed the second part of a mixed method research design study.

This study hypothesized that with understanding of cultural work knowledge between professionals during design phase coupled with competent technological support, productivity can be improved. It utilized Cognitive Organizational Theory (COT) protocols to test conceptual models in SimVision®. Organizational structure, project intensity, and statistical validations parameters were performed to obtain the reliability and generalization of the result.

This study found that with Building Information Modeling (BIM) technology intervention, the handling of exception, coordination and decision-making time could be improved, resulting in better project performances. The result also indicated that in choosing organizational fit, national culture factor needed to be considered; otherwise, organizational change would be unacceptable. By changing the operational process from intensive to reciprocal task intensity with BIM technology intervention, the effect on productivity would be similar to changing hierarchical organizational structure to flatter organizational structure.

Project discrepancies issues are limitedly discussed due to companies' confidentiality. The paper only focuses on understanding the effects of human factors during the integrated project delivery phase.

The findings could support developing countries' professionals to collaborate effectively with developed countries' professionals.

The development of the project's cultural knowledge experimentations will provide guidance to teams involved in international projects from developed and developing countries in pursuing joint ventures in project deliveries in either country successfully.

By far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language.

The methodology is divided into corpus development and extraction model development. During corpus development, data were collected and annotated. The development of the extraction model entails feature extraction, the definition of the model architecture, parameter selection and configuration, model training and evaluation, as well as model selection.

The Indonesian corpus of public figure statements attribution achieved 90.06% agreement level between the annotator and experts and could serve as a gold standard corpus. Furthermore, the baseline model predicted most labels and achieved 82.026% F-score.

To the best of the authors’ knowledge, the resulting corpus is the first corpus for attribution of public figures’ statements in the Indonesian language, which makes it a significant step for research on attribution extraction in the language. The resulting corpus and the baseline model can be used as a benchmark for further research. Other researchers could follow the methods presented in this paper to develop a new corpus and baseline model for other languages.

While prior research has established that traumatic brain injury (TBI) is a risk factor for violent offending, there is little understanding of mechanisms that may underpin this relationship. This is problematic, as a better understanding of these mechanisms could facilitate more effective targeting of treatment. This study aims to address these gaps in the extant literature by examining TBI as a predictor of violent offending and test for mediation effects through cognitive constructs of dual systems imbalance and hostility among a sample of justice-involved youth (JIY).

The Pathways to Desistance data were analyzed. The first three waves of this data set comprising the responses of 1,354 JIY were analyzed. Generalized structural equation modeling was used to test for direct and indirect effects of interest. A bootstrap resampling process was used to compute unbiased standard errors for determining the statistical significance of mediation effects.

Lifetime experience of TBI was associated with increased violent offending frequency at follow-up. Hostility significantly mediated this relationship, but dual systems imbalance did not. This indicated that programming focused on reducing hostility among JIY who have experienced TBI could aid in reducing violent recidivism rates.

To the best of the author’s knowledge, this study was the first to identify significant mediation of the relationship between TBI and violent offending through hostility.

The trend toward subscription economy accelerated the rise of access-based consumption models for durable consumer goods, replacing individual ownership with subscription contracts. At the same time, disruptive platform businesses have arisen in several consumer markets, bypassing traditional value chains while growing through network effects. In a conceptual approach, the authors address the future market for durable consumer goods in light of developments toward access-based consumption, subscription models and platform business models.

In a conceptual approach, the authors apply a scenario analysis following the Framework Foresight method and address trends, constants, plans and projections shaping the future market of subscriptions for durable goods. The authors create a baseline scenario and two alternative scenarios for the future of consumer durables and thereby discuss platform growth stages and implications for manufacturer brands.

The rising market power of platform companies leads to a baseline scenario where these platforms enter the market of subscriptions for durable goods. Alternative scenario 1 addresses the successful market entry of new platform businesses. In contrast, alternative scenario 2 describes the rise of manufacturer brand platforms.

This conceptual research enriches the discussion of access-based business models by creating scenarios depicting possible future developments. Moreover, it adds to the increasing focus on platform business models and thereby addresses the role of traditional manufacturer brands in markets for durable consumer goods subscriptions.

This study investigates whether and how place-based industrial relocation policy affects firm innovation.

By exploiting the establishment of China's National Industrial Relocation Demonstration Zones (NIRDZs) as a quasi-natural experiment in a difference-in-differences design, the authors examine the externalities of industrial policies that support sustainable development and growth from the perspectives of firms' patenting activities.

The study consistently finds that the NIRDZs policy significantly boosts local firm innovation, translating into a 60.46% increase in the patent applications of treated firms. The estimation results remain robust to a series of alternative specifications. Moreover, heterogeneity analysis suggests that the firms that benefited most were state-owned enterprises, firms with higher productivity, or firms in non-high-tech industries. Further, the authors find that the NIRDZs policy stimulates firm innovation mainly in the form of utility model patents, followed by designs and invention patents.

The results provide suggestions and implications for policymakers to improve the efficiency of state-led industrial policies and avoid “government failure” in policy implementation.

This study provides suggestions and implications for policymakers to improve the efficiency of state-led industrial policies and avoid “government failure” in the policy implementation.

This study fills the research gap by exploiting quasi-experiments to assess the effectiveness of state-led industrial policies for emerging economies. (2) The analysis sheds empirical light on how corporate innovation is motivated and financed by selective and functional industrial policies. (3) Theoretically, the results rationalize why state-led industrial relocation fuel innovation capabilities of localities from Marshall externalities and competition crowding-out effects.

This research presents the development of a Building Passport for Sustainable Conservation (BPSC) as a questionnaire with a set of 23 core indicators, for a baseline assessment of heritage buildings. The aim of this tool is to identify priorities for future interventions, by recognising the contributions of heritage buildings to sustainability that should be preserved and the fragilities that need to be improved.

The BPSC uses a selection of core indicators for sustainability observable on heritage buildings. It was applied to four different case studies of modern heritage in the Netherlands, to verify its applicability and limitations.

The results suggest that this tool has the potential to contribute to an expedite assessment, reaching consensual evaluations of priorities for sustainable conservation, while reducing the time and cost of the process, contributing to support informed redesign decisions.

Recently, existing building sustainability assessment (BSA) tools have been adapted and new BSA tools developed for heritage buildings. Some tools target existing buildings, but seldom cover cultural significance and heritage values. Others target the after-redesign situations – aiming at assessing how sustainable the redesign is. Often BSA tools are complex and time-consuming, with extensive indicators and data requirements. The BPSC developed in this research covers the main aspects of sustainability and related heritage values, in a simpler tool for a baseline assessment.

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.

This study aims to explore the relationship between chief executive officer (CEO) power and stock price crash risk in India. Furthermore, it seeks to analyse how insider trades may moderate the impact of CEO power on stock price crash risk.

A study of 236 companies from the S&P BSE 500 Index (2014–2023) have been analysed through pooled ordinary least square (OLS) regression in the baseline analysis. To enhance the results' reliability, robustness checks include alternative methodologies, such as panel data regression with fixed-effects, binary logistic regression and Bayesian regression. Additional control variables and alternative crash risk measure have also been utilised. To address potential endogeneity, instrumental variable techniques such as two-stage least squares (IV-2SLS) and difference-in-difference (DiD) methodologies are utilised.

Stakeholder theory is supported by results revealing that CEO power proxies like CEO duality, status and directorship reduce one-year ahead stock price crash risk and vice versa. Insider trades are found to moderate the link between select dimensions of CEO power and stock price crash risk. These findings persist after addressing potential endogeneity concerns, and the results remain consistent across alternative methodologies and variable inclusions.

This study significantly advances research on stock price crash risk, especially in emerging economies like India. The implications of these findings are crucial for investors aiming to mitigate crash risk, for corporations seeking enhanced governance measures and for policymakers considering the economic and welfare consequences associated with this phenomenon.

With the rapid development of short videos in China, the public has become accustomed to using short videos to express their opinions. This paper aims to solve problems such as how to represent the features of different modalities and achieve effective cross-modal feature fusion when analyzing the multi-modal sentiment of Chinese short videos (CSVs).

This paper aims to propose a sentiment analysis model MSCNN-CPL-CAFF using multi-scale convolutional neural network and cross attention fusion mechanism to analyze the CSVs. The audio-visual and textual data of CSVs themed on “COVID-19, catering industry” are collected from CSV platform Douyin first, and then a comparative analysis is conducted with advanced baseline models.

The sample number of the weak negative and neutral sentiment is the largest, and the sample number of the positive and weak positive sentiment is relatively small, accounting for only about 11% of the total samples. The MSCNN-CPL-CAFF model has achieved the Acc-2, Acc-3 and F1 score of 85.01%, 74.16 and 84.84%, respectively, which outperforms the highest value of baseline methods in accuracy and achieves competitive computation speed.

This research offers some implications regarding the impact of COVID-19 on catering industry in China by focusing on multi-modal sentiment of CSVs. The methodology can be utilized to analyze the opinions of the general public on social media platform and to categorize them accordingly.

This paper presents a novel deep-learning multimodal sentiment analysis model, which provides a new perspective for public opinion research on the short video platform.

This study aims to review earned value management (EVM)-relative methods, including the original EVM, earned schedule method (ESM) and earned duration management (EDM(t)). This study then proposes a general implementation procedure and some basic principles for the selection of EVM-relative methods.

After completing an intensive literature review, this study conducts a case study to examine the forecasting performance of project duration using the EVM, ESM and EDM(t) methods.

When the project is expected to finish on time, ESM with a performance factor equal to 1 is the recommended method. EDM(t) would be the most reliable method during a project's entire lifetime if EDM(t) is expected to be delayed based on past experience.

As this research conducts a case study with only one building construction project, the results might not hold true for all types of construction projects.

EVM, ESM and EDM(t) are simple and data-accessible methods. With the help of a general implementation procedure, applying all three methods would be better. The power of the three methods is definitely larger than that of choosing only one for complex construction projects.

Previous studies have discussed the advantages and disadvantages of EVM, ESM and EDM(t). This study amends the available outcomes. Thus, for schedulers or researchers interested in implementing EVM, ESM and EDM(t), this study can provide more constructive instructions.