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This study aims to examine the research trends in fashion industry during the coronavirus disease 2019 (COVID-19) pandemic. Besides, it also provides an overview on the new marketing and operational strategies, and reveals the corresponding business challenges of a footwear enterprise in Hong Kong during the COVID-19 pandemic.

A comprehensive literature review is first conducted to identify the research trends in fashion industry during the COVID-19 pandemic. A qualitative exploratory case study is then used to illustrate how a footwear enterprise has coped with the COVID-19 pandemic.

The case study has showed that omni-channel retailing, collaboration with e-tailers, quick response system and mixed production strategy are adopted in the targeted case during the COVID-19 pandemic. Besides, the targeted case has also faced the challenges in the areas of sales, customer relationship management, and demand forecasting and inventory planning during the COVID-19 pandemic.

This study provides managerial insights on the real practices used to deal with the COVID-19 pandemic and proposes various academic future research directions in fashion industry based on the real-world observations.

This study aims to understand the impact of the Covid-19 pandemic disruptive event on delivery of the built environment degree apprentice programme in higher education in the UK and identify the key strategies to minimise the effect.

A qualitative approach was used to collect and analyse data from a sample set of built environment degree apprenticeship stakeholders. Semi-structured interviews were conducted with 17 key stakeholders to collate emerging themes on their perceptions of the impacts of the pandemic and strategies to adopted to minimise it.

The investigation reveals that the core impacts of Covid-19 on the apprentices training programme are lack of access to the site, furlough, limited access to off the job training, limited interaction with tutors and peers, too much time on the screen, limited pastoral care and lack of contact with a mentor. The census from the research participants is that despite the development and gain with the various virtual platform used during pandemic physical meetings with their mentor remain pivotal to the built environment apprentices learning and training.

The results provide relevant stakeholders and actors supporting degree apprentices training programmes (training providers and employers, among others) with the information needed to improve the delivery of built environment degree apprenticeship training programmes during a disruptive event Covid-19. The study identifies various strategies to minimise the impact of disruptive events on the apprentices training, including technology, regular meeting with mentors online, and personal and pastoral care.

The study is the first to document the impact of the Covid-19 pandemic on degree apprenticeship programs in the built environment. This study provides an in-depth understanding of how these programs have been affected and offers potential solutions to reduce or mitigate potential damage. The research will inform future policy decisions related to degree apprenticeship programs in the built environment.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks.

This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge.

Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing.

The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.

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.

This study aims to explore the relationships between knowledge management (KM) enablers, employee innovativeness (EI) and market development performance (MDP) in architecture, engineering and construction (A/E/C) firms.

A questionnaire survey is conducted to collect empirical data from A/E/C practitioners in Vietnam. First, factor analysis is used to identify KM enablers in A/E/C firms. Then, a framework which shows the links between KM enablers, EI and MDP is proposed. Structural equation modeling (SEM) is used to examine the proposed relationships.

This study identifies five constructs which can enable A/E/C firms to achieve effective KM implementation, including mutual trust and collaboration, organizational values and norms, information and communication systems, organizational policies and empowerment. Furthermore, the SEM results show that except for organizational policies, four remaining KM enablers significantly affect EI. It is also found that EI has a significant impact on MDP.

The findings could help A/E/C firms to know which KM enablers are critical to EI and provide a better understanding of the link between EI and MDP. Hence, they could make appropriate investments in KM practices to improve both EI and MDP.

The results of this study fill the gap in knowledge by empirically structuring the relationships between KM enablers, EI and MDP. Such results may provide A/E/C firms with useful information to enhance EI and MDP in today’s intensively competitive construction environments.

The purpose of this study is to explore the relationship between nonmarket strategy and innovation performance, as well as the boundary factors that influence this relationship in the context of the pharmaceutical industry in emerging markets.

This study analyzed matched data of 227 Chinese pharmaceutical firms and two secondary databases with SPSS to examine the hypotheses.

Nonmarket strategy promotes the innovation performance. High level of firm internal knowledge utilization ability and strategic flexibility strengthens the effect of nonmarket strategy in promoting innovation performance, while information technology (IT) environment weakens the effect of nonmarket strategy in promoting innovation performance.

The research studies the positive impact of nonmarket strategy on innovation performance in the specific context of Chinese pharmaceutical industry, and it introduces the internal capabilities and external IT environment of the firm as moderators of the relationship between nonmarket strategy and innovation performance. More importantly, this research echoes the call for research on moderator of nonmarket strategy and identifies important boundary conditions. To the best of the authors’ knowledge, it also explores the impact of the IT environment on the implementation of nonmarket strategy for the first time, which deepens the research on nonmarket strategy’s effect on innovation.

Although cloud storage services can bring users valuable convenience, they can be technically complex and intrinsically insecure. Therefore, this research explores the concerns of academic users regarding cloud security and technical issues and how such problems may influence their continuous use in daily life.

This qualitative study used a semi-structured interview approach comprising six main open-ended questions to explore the information security and technical issues for the continuous use of cloud storage services by 20 undergraduate students in Hong Kong.

The analysis revealed cloud storage service users' major security and technical concerns, particularly synchronization and backup issues, were the most significant technical barrier to the continuing personal use of cloud storage services.

Existing literature has focused on how cloud computing services could bring benefits and security and privacy-related risks to organizations rather than security and technical issues of personal use, especially in the Asian academic context.

The purpose of this study is to analyse and compile the literature on various option pricing models (OPM) or methodologies. The report highlights the gaps in the existing literature review and builds recommendations for potential scholars interested in the subject area.

In this study, the researchers used a systematic literature review procedure to collect data from Scopus. Bibliometric and structured network analyses were used to examine the bibliometric properties of 864 research documents.

As per the findings of the study, publication in the field has been increasing at a rate of 6% on average. This study also includes a list of the most influential and productive researchers, frequently used keywords and primary publications in this subject area. In particular, Thematic map and Sankey’s diagram for conceptual structure and for intellectual structure co-citation analysis and bibliographic coupling were used.

Based on the conclusion presented in this paper, there are several potential implications for research, practice and society.

This study provides useful insights for future research in the area of OPM in financial derivatives. Researchers can focus on impactful authors, significant work and productive countries and identify potential collaborators. The study also highlights the commonly used OPMs and emerging themes like machine learning and deep neural network models, which can inform practitioners about new developments in the field and guide the development of new models to address existing limitations.

The accurate pricing of financial derivatives has significant implications for society, as it can impact the stability of financial markets and the wider economy. The findings of this study, which identify the most commonly used OPMs and emerging themes, can help improve the accuracy of pricing and risk management in the financial derivatives sector, which can ultimately benefit society as a whole.

It is possibly the initial effort to consolidate the literature on calibration on option price by evaluating and analysing alternative OPM applied by researchers to guide future research in the right direction.

Social media have been playing a critical role in seeking and sharing health related information and consequently shaping individuals’ health behaviors. This study investigates how information seeking about Covid-19 vaccine on social media is related to vaccine receiving intentions. The study furthers explores the association of trust in social media and uncertainty about Covid-19 with information seeking and the moderating role of prior social media experience on this association.

We developed a questionnaire and collected data from 525 educated social media users through “Google Forms.” Further, we applied ordinary least squares (OLS) regress to test the study hypothesis.

We noted that trust in social media and uncertainty about Covid-19 vaccine positively influenced information seeking which further positively affected vaccine receiving intentions. However, the moderating effect of prior social media experience was not only noted as weak but also found negatively affecting the associations of trust in social media and uncertainty about Covid-19 vaccine with information seeking.

The findings provide insights into understanding of public perceptions regarding Covid-19 vaccine in the cultural contexts of a developing country. Further, it informs about the public patterns of seeking information related to health issues on social media, an understanding which may likely benefit policymakers, health care providers and researchers to understand the antecedents and behavioral outcomes of seeking information through social media during health crisis. The study also elucidates the leveraging power of social media to motivate the public to accept the Covid-19 vaccines.

The study uniquely combines the antecedents and behavioral outcomes of information seeking through social media in the particular context of Covid-19. It further extends the literature by introducing the conditional role of prior social media experience.