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Construction projects in Malaysia are often delayed and over budget due to heavy reliance on labor. Linear regression (LR) models have been used in most labor cost (LC) studies, which are less accurate than machine learning (ML) tools. Construction management applications have increasingly used ML tools in recent years and have greatly impacted forecasting. The research aims to identify the most influential LC factors using statistical approaches, collect data and forecast LC models for improved forecasts of LC.

A thorough literature review was completed to identify LC factors. Experienced project managers were administered to rank the factors based on importance and relevance. Then, data were collected for the six highest ranked factors, and five ML models were created. Finally, five categorical indices were used to analyze and measure the effectiveness of models in determining the performance category.

Worker age, construction skills, worker origin, worker training/education, type of work and worker experience were identified as the most influencing factors on LC. SVM provided the best in comparison to other models.

The findings support data-driven regulatory and practice improvements aimed at improving labor issues in Malaysia, with the possibility for replication in other countries facing comparable problems.

The purpose of this paper is to examine the effect of mobile customer relationship management (mCRM), service innovation (SI) and word of mouth (WOM) on the investment decisions (IDs) of mutual fund investors.

This paper presents a new model for impacting the IDs of mutual fund investors. To verify the suggested model, Partial Least Squares with Structural Equation Modelling are used. For analysis, a survey questionnaire is designed, and data inputs were invited from more than 400 online mutual fund investors in Delhi NCR.

The outcomes reveal that the ID of mutual fund investors is significantly influenced by WOM. WOM, in turn, is significantly impacted by mCRM applications through SI playing a mediating role.

The limitation with reference to survey respondents was that only online mutual fund investors using mCRM applications were considered. Moreover, this study was conducted in Delhi NRC, and a limited sample was considered.

The result from this research helps the financial organisation to consider SI in their mCRM application as one of the main concerned areas for increased WOM that directly influences the ID of mutual fund investors.

This study highlighted the impact of SI and WOM on the mutual fund investors’ decision, who use mCRM application. The outcome may contribute to the theoretical framework of IDs concerning mCRM applications. The results of this research offer practical implications for financial organisations in strategising their marketing and product development plans in the context of mutual funds. Also, the mutual fund ID through mCRM application is positively influenced by SI and WOM using both constructs as sequential mediating tools. This research offers new insights into mCRM application for mutual fund investors and financial organisations in India.

The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners.

Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses.

The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT.

These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field.

This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.

This research aims to determine the levels of education quality (EQ), technology usage (TU), students' satisfaction (SS) and the impact of EQ on SS. Also, it seeks to find out how TU as a moderator affects EQ and SS in Pakistani private higher education institutions (HEIs).

A total of 440 postgraduate students at eleven private universities in Lahore, Punjab Province, Pakistan, participated in empirical research and data were obtained through the use of an online questionnaire. Simple random sampling was used to choose participants and partial least square structural equation modeling was employed to analyze the data.

The findings revealed that Pakistani private HEIs have a medium level of EQ, TU and SS. Furthermore, the data reveal the existence of a significant positive relationship between EQ and SS, whereas TU as a moderator was found to negatively affect EQ and SS.

Postgraduate students of private HEIs in Pakistan were considered for this investigation and this study was limited to testing only in Punjab province. Another limitation of this study is that it was based on a research framework from previous research and literature. This study employed questionnaire surveys to conduct evaluations of teachers' teaching quality by university students. Furthermore, the questionnaire employed student self-evaluations to assess the quality of teaching. The research data would be more detailed if it were possible to include teachers' self-evaluations of their teaching quality.

The current study provides key insights for policymakers, higher education commission and HEIs. The results suggest leveraging the identified medium levels of EQ, TU and student satisfaction in Pakistani private HEIs. In an effort to boost the medium levels, policymakers are encouraged to enhance the teaching and learning experience by robustly integrating Web 4.0 technologies. Institutions can intervene strategically by investing in infrastructure and innovative tools aligned with students' technological needs. Likewise, policymakers and institutions can optimize learning management systems (LMS) by developing and implementing policies that encourage their adoption and optimization across HEIs. This may contribute to the accomplishment of the United Nations' sustainable development goal of providing quality education. Moreover, with the help of this research HEIs can establish minimum quality standards regarding academic teaching and learning materials. Implementing the above-mentioned practical implications might boost student satisfaction in HEIs which would benefit not only students but also the institutions.

The novelty of the article lies in the fact that it addresses the gap in the existing literature by exploring the levels of EQ, TU and student satisfaction in the context of private higher education in Pakistan. Furthermore, this study investigated whether TU served as a moderator in the relationship between student satisfaction and UNESCO-recommended EQ. This study elaborates on EQ indicators recommended by UNESCO in Pakistan’s private higher education sector.