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In the pursuit of competitive advantage, an increasing number of firms are adopting open innovation (OI) strategies. However, previous studies have often overlooked the role of strategic human resource management (SHRM) in promoting OI. This study aims to fill this gap by examining how SHRM impacts OI through the mediating factors of intellectual capital (IC) and supply chain integration (SCI). This research sheds light on the critical interplay between SHRM, IC and SCI in driving OI success. The findings underscore the importance of adopting a comprehensive and integrated approach to OI that encompasses both resources and dynamic capabilities.

By integrating resource-based view with the dynamic capability perspective, the hypotheses were tested with a survey sample of 136 Chinese manufacture firms using hierarchical regression and bootstrap method.

The results show that SHRM has a positive effect on OI, and both IC and SCI are partial mediators of the relationship between SHRM and OI. In addition, the chain mediation effect of “SHRM-IC-SCI-OI” has further been verified.

This study uncovers the “black box” between SHRM and OI, and responds to the call for strengthening research on the relationship between SHRM and OI. The study indicates that firms should implement HR practices, including extensive training, team reward and internal promotion to promote the implementation of OI strategy.

The objective of this research is to propose an immersive framework that integrates virtual reality (VR) technology with directives international safety training certification bodies to enhance construction safety training, which eventually leads to safer construction sites.

The adopted methodology combines expert insights and experimentation to maximize the effectiveness of construction safety training. The first step was identifying key considerations for VR models such as motion sickness prevention and adult learning theories. The second step was developing a game-like VR model for safety training, with multiple hazards and scenarios based on the considerations of the previous step. After that, safety experts evaluated the model and provided valuable feedback on its alignment with international safety training practices. Finally, the developed model is tested by senior students, where the testing format followed the Institution of Occupational Safety and Health (IOSH) working safely exam structure.

An advanced immersive VR safety training model was developed based on extensive lessons learned from the literature, previous work and psychology-informed adult learning theories. Model testing – through focus groups and hands-on experimentation – demonstrated significant benefit of VR in upgrading and complementing traditional training methods.

The findings presented in this paper make a significant contribution to the field of safety training within the construction industry and the broader context of immersive learning experiences. It also fosters further exploration into immersive learning experiences across educational and professional contexts.

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.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

This study aims to synthesize existing findings in the gig worker training literature and identify the training rationales adopted by these studies, using a synthesized framework of organizational training rationales. This study seeks to delineate the rationales behind gig worker training and highlight unaddressed training needs within digital platforms, ultimately proposing a research agenda for future studies in this area.

A systematic review methodology is adopted to synthesize and analyze empirical, peer-reviewed studies on gig worker training.

The systematic review reveals that competency and economic rationales are predominantly adopted in gig worker training studies, with the relationship rationale, common in traditional training, notably absent. This study also outlines seven future research directions to highlight identified challenges and unaddressed training needs.

To the best of the author’s knowledge, this study is the first work that systematically reviews existing findings on gig worker training.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

With the increasing use of small unmanned aircrafts (SUAs), many countries have enacted laws and regulations to ensure the safe use of SUAs. However, there is a lack of industry-specific regulations accounting for the unique features of construction-related SUA operations. Operating SUAs in the construction industry is attributed to specific risks and challenges, which should be regulated to maximize the utility of SUAs in construction. This study, therefore, aims to develop a multi-dimensional regulatory framework for using SUAs in the construction industry.

A combination of quantitative and qualitative methods was used to compare seven selected national/regional SUA regulations to identify the applicability of implementing the existing regulations in construction. The interview surveys were then conducted to diagnose the challenges of construction-related SUA operations and gather interviewees' suggestions on the regulatory framework for SUA uses in construction.

The research found that some challenges of construction-related SUAs operations were not addressed in the current regulations. These challenges included the complex and time-consuming SUA operation permit, lack of regulation for special SUA operations in construction, insufficient regulatory compliance monitoring and a lack of construction-related remote pilots' training. A regulatory framework was then developed based on the findings of comparative analysis and interview surveys.

This study mainly compared seven representative countries/regions' regulations, leading to a small sample size. Further research should be carried out to study the SUA regulations in other places, such as South Africa, South America or Middle East countries. Besides, this study's respondents to the interviews were primarily concentrated in Hong Kong, which may cause the interview results to differ from the construction industry in other countries/regions. A large-scale interview survey should be conducted in other places in the future to validate the current findings.

The proposed regulatory framework provides a reference for the policy-makers to formulate appropriate industry-specific SUA regulations and improve the applicability of SUA regulations in the construction industry. It sheds light upon the future of SUA regulations and the development of regulatory practice in this area.

This study is the first to propose a multi-dimensional regulatory framework for operating SUAs in construction by comprehensive policy comparisons and interviews. The regulatory framework offers a fresh insight into the unexplored research area and points out the direction for subsequent studies on SUA regulations in the construction industry.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Building on social exchange theory (SET), the main aim of this paper is to empirically study the impact of high-commitment work practices (HCWPs) systems on radical innovation. Additionally, the paper examines the mediating roles of employee innovative work behaviour (IWB) and knowledge sharing (KS) in the relationship between HCWPs and radical innovation.

Using a survey questionnaire, data were collected from employees working in pharmaceutical, manufacturing and technological industries in Jordan. A total of 408 employees participated in the study. Structural equation modelling (SEM) using AMOS v28 was employed to test the research hypotheses.

This research found that HCWPs in the form of a bundle of human resource management (HRM) practices are significant for employee IWB and KS. However, similar to previous studies, this paper failed to find a direct significant impact for HCWPs on radical innovation. Rather, the impact was mediated by employee IWB. Additionally, this paper found that HCWPs are significant for KS and that KS is significant for employee IWB.

Distinctively, this paper considered the mediating effect of employee IWB on radical innovation. Extant research treated IWB as a consequence of organisational arrangements such as HRM practices; this paper considered IWB as a foundation and source for other significant organisational outcomes, namely radical innovation. Additionally, the paper considered employees' perspectives in studying the relationship between HRM, KS, IWB and radical innovation.

Burnout has been known to negatively affect volunteers. However, information involving various factors that influence their burnout is severely lacking. This study aims to examine how volunteers displayed adaptability, the ability to change their thoughts, actions and/or behaviors in uncertain situations, to offset the negative relationship with burnout. This study also examined the amount of training a volunteer reported as one factor that may act to moderate this negative relationship between adaptability and burnout.

Using the conservation of resources (COR) theory, the author investigated how volunteers try to maintain their current level of resources, which aids in coping with stress and lowering their risk of burnout.

Using regression, the author discovered that adaptability was negatively related to burnout and this relationship was stronger for volunteers who reported less training. Training was confirmed as a moderator in this relationship. In sum, training acted as a buffer in the negative relationship involving adaptability and burnout.

The current study is one of the few to adopt theories often used to understand employee experiences, and apply them to volunteers. Interestingly, across a variety of volunteer environments, these employment theories and relationships, including adaptability, appear to matter.