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This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Factors like bid price, submission time, and number of bidders influence the procurement process in public projects. These factors and the award criteria may impact the project’s financial compliance. Predicting budget compliance in construction projects has been traditionally challenging, but Machine Learning (ML) techniques have revolutionised estimations.

In this study, Portuguese Public Procurement Data (PPPData) was utilised as the model’s input. Notably, this dataset exhibited a substantial imbalance in the target feature. To address this issue, the study evaluated three distinct data balancing techniques: oversampling, undersampling, and the SMOTE method. Next, a comprehensive feature selection process was conducted, leading to the testing of five different algorithms for forecasting budget compliance. Finally, a secondary test was conducted, refining the features to include only those elements that procurement technicians can modify while also considering the two most accurate predictors identified in the previous test.

The findings indicate that employing the SMOTE method on the scraped data can achieve a balanced dataset. Furthermore, the results demonstrate that the Adam ANN algorithm outperformed others, boasting a precision rate of 68.1%.

The model can aid procurement technicians during the tendering phase by using historical data and analogous projects to predict performance.

Although the study reveals that ML algorithms cannot accurately predict budget compliance using procurement data, they can still provide project owners with insights into the most suitable criteria, aiding decision-making. Further research should assess the model’s impact and capacity within the procurement workflow.

Previous research predominantly focused on forecasting budgets by leveraging data from the private construction execution phase. While some investigations incorporated procurement data, this study distinguishes itself by using an imbalanced dataset and anticipating compliance rather than predicting budgetary figures. The model predicts budget compliance by analysing qualitative and quantitative characteristics of public project contracts. The research paper explores various model architectures and data treatment techniques to develop a model to assist the Client in tender definition.

In response to growing concerns over the negative consequences of Internet addiction on adolescents’ mental health, coupled with conflicting results in this literature stream, this meta-analysis sought to (1) examine the association between Internet addiction and depressive symptoms in adolescents, (2) examine the moderating role of Internet freedom across countries, and (3) examine the mediating role of excessive daytime sleepiness.

In total, 52 studies were analyzed using robust variance estimation and meta-analytic structural equation modeling.

There was a significant and moderate association between Internet addiction and depressive symptoms. Furthermore, Internet freedom did not explain heterogeneity in this literature stream before and after controlling for study quality and the percentage of female participants. In support of the displacement hypothesis, this study found that Internet addiction contributes to depressive symptoms through excessive daytime sleepiness (proportion mediated = 17.48%). As the evidence suggests, excessive daytime sleepiness displaces a host of activities beneficial for maintaining mental health. The results were subjected to a battery of robustness checks and the conclusions remain unchanged.

The results underscore the negative consequences of Internet addiction in adolescents. Addressing this issue would involve interventions that promote sleep hygiene and greater offline engagement with peers to alleviate depressive symptoms.

This study utilizes robust meta-analytic techniques to provide the most comprehensive examination of the association between Internet addiction and depressive symptoms in adolescents. The implications intersect with the shared interests of social scientists, health practitioners, and policy makers.

Despite the extensive benefits of human resource (HR) analytics, the intention to adopt such technology is still a matter of concern in the engineering and construction sectors. This study aims to examine the slow adoption of HR analytics among HR professionals in the engineering and construction sector.

A cross-sectional online survey including 376 HR executives working in Indian-based engineering and construction firms was conducted. Hierarchal regression, structural equation modeling and artificial neural networks (ANN) were applied to evaluate the relative importance of HR analytics predictors.

The results reveal that hedonic motivation (HM), data availability (DA) and performance expectancy (PE) influence the behavioral intention (BI) to use HR analytics, whereas effort expectancy (EE), quantitative self-efficacy (QSE), habit (HA) and social influence (SI) act as barriers to its adoption. Moreover, PE was the most influential predictor of BI.

Based on the findings of this study, engineering and construction industry managers can formulate strategies for the implementation and promotion of HR analytics to enhance organizational performance.

This study draws attention to evidence-based decision-making, emphasizing barriers to the adoption of HR analytics. This study also emphasizes the concept of DA and QSE to enhance adoption among HR professionals, specifically in the engineering and construction industry.

This paper aims to explore how an academic researcher and a practitioner experience scepticism for their qualitative research.

The study applies Olt and Teman's new conceptual phenomenological polyethnography (2019) methodology, a hybrid of phenomenology and duoethnography.

For the researcher-participants, the essence of living with scepticism means feeling a sense of injustice; struggling with the desire for simplicity and quantification; being in a circle of uneasiness; having a survival mechanism; and embracing healthy scepticism. They experience the essence differently and similarly in varied cultural contexts. Through duoethnographic conversations, they acknowledge that while there can be scepticism of their work, it is important to remain sceptical, persistent and curious by challenging traditional concepts. Theoretical and practical advances in artificial intelligence (AI) continue to highlight the need for clarifying qualitative researcher roles in academia and practice.

This paper contributes to the debate of qualitative versus quantitative research. Its originality is in exploring scepticism as lived experience, from an academic and practitioner perspective and applying a phenomenological polyethnography approach that blends two different traditional research paradigms.

Errors are increasingly recognized as beneficial to the learning process and are more frequently integrated into training curriculums. Despite this growing interest, the work carried out so far offers little evidence highlighting the psychological qualities implicit in learning from error. By focussing on the role of specific trainee’s attributes [i.e. learning goal orientation (LGO) motivation to learn and metacognition], this study aims to better understand the reasons why some trainees benefit more (than others) from being confronted with errors during training.

A total of 142 trainees took part in this study by participating in a training on interviewing techniques that also exposed them to various committable errors, and by completing questionnaires at two different times (i.e. before and after training).

Results of bootstrap regression analysis highlights three main findings: LGO is positively linked to learning from errors; a significant portion of the link between LGO and learning from error is explained by motivation to learn and metacognition; and these effects are presented in the form of a double-mediated model which suggests two different explanatory pathways (i.e. motivational and cognitive).

To the best of the authors’ knowledge, this study is among the first to offer insight on the psychological attributes influencing learning from errors and to bring forward the role of two underlying mechanism that are linked to this specific type of learning. It also invites researchers and practitioners to reflect on the best ways to make use of errors in training and promote the value of personal attributes on trainees’ learning experience.

As human resource development (HRD) seeks to develop organizations and unleash human expertise (Swanson and Holton, 2009), leaders should be encouraged to consider every employee as a complex individual with unique needs and aspirations. The purpose of this paper is to introduce the concept of equity leadership (EL), which identifies individual employees’ personal and professional resource, relationship and opportunity needs in an effort to support employees in reaching their fullest potential in the workforce, therefore increasing positive organizational outcomes.

The theoretical foundations of EL are social exchange theory (SET; Saks and Rotman, 2006) and the ERG theory of motivation (Alderfer, 1969). SET recognizes the give-and-take relationship between leaders and employees, while ERG theory of motivation considers an individual’s personal and professional existence, relatedness and growth needs. The theories provide a foundation for EL’s definition.

EL posits that leaders’ attention to employees’ resource, relationship and opportunity needs in the workplace could result in a positive effect on the social exchange between leaders and employees. EL provides a framework for these exchanges to occur and for employee needs to be considered, thus resulting in increased employee engagement, productivity and retention.

EL can take a significant amount of time, especially when starting with new employees; however, the relationships and positive organizational outcomes provide justification for engaging in the leadership style.

This paper seeks to advance the field of HRD by defining EL, exploring the theoretical underpinnings of EL and providing actionable steps for leaders to put EL into action.

The nuanced theory of EL encourages organizations to evolve from the factory model of expectations to a model that considers the unique needs of individuals in organizations. Grounded partly in SET, EL promotes positive relationships between leaders and employees.

There are many leadership theories; however, EL, unlike any other leadership theory, uniquely considers the individual needs of each employee through consistent one-on-one conversations between the leader and individual employees to discover employee needs and also strives for positive organizational outcomes as a result of the social exchanges.

This is amongst the first works to develop a technographic segmentation of smartphone users attending music festivals based on attitudes, motivations and usage patterns. We also aim to describe festivalgoers’ characteristics.

The data were collected from 522 festivalgoers who attended the Rainforest World Music Festival (RWMF) in Malaysia. A two-stage cluster analysis of Ward’s method and k-means was applied to develop technographic segmentation during the festival. Using discriminant analysis, we confirmed that each festivalgoer’s characteristics differ amongst groups.

Four technographic segments were developed: alarm hitters, technological tickers, plug pullers and fuse blowers. The results confirmed that festivalgoers had distinct characteristics and preferences based on smartphone use.

We extend previous research on the technographic segmentation of smartphones and festivalgoers. We highlighted the limitations of cluster analysis in terms of stability to produce a suitable number of segments and to include other festivals. The generalisability of the results may be constrained by the time gap between data collection and publication.

Our results can help marketing managers understand the needs of segments by selecting appropriate advertisements and promotional tools that appeal directly to the desired target segments.

This study will help local communities increase their revenue and job opportunities. The culture of music festivals for the next generation can be sustained and promoted by local and international festival lovers.

This study is the first to present festivalgoers' use of the technographic segmentation term in music festivals.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The purpose of this study is to investigate the determinants of behavioral intention to use smartphone diet applications within the emerging market. Specifically, it focuses on the Privacy Calculus Model constructs, encompassing perceived risk and perceived benefit, as well as the pivotal elements of trust and self-efficacy. It also explores the moderating influence of experience on the influencing factors and intention to use a diet application.

In a survey with 572 respondents, data analysis was conducted using partial least squares (PLS) structural equation modeling.

The findings reveal that perceived risk exerts a significant negative influence on behavioral intention. Conversely, perceived benefit, trust and self-efficacy exhibit a positive impact on behavioral intention. Moreover, the study delves into the moderating role of users' experience, which is found to significantly influence these relationships, suggesting that user experience plays a pivotal role in shaping the adoption dynamics of diet applications.

The limitations of this study may include the sample size and the specific focus on the emerging market of Saudi Arabia. The implications of the findings are relevant for scholars, developers, marketers, and policymakers seeking to promote the use of smartphone diet applications.

This study adds value by exploring the determinants of behavioral intention in the context of smartphone diet applications, and it is a first attempt to test the moderating role of users' experiences, providing valuable insights for various stakeholders in the field.