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This study aims to explore the transformative potential of digital twins (DTs) in the construction industry by addressing challenges and bridging the technology adoption gap. Focusing on construction offices, on-site environments and their interface, the research identifies key success factors for DT implementation. Emphasizing practical applications, the study foresees a substantial impact on project management, highlighting the pivotal role of DTs in reshaping the construction sector.

We reviewed 30 variables within the DT literature, classifying investigations into three main domains: on-site environments, office environments and site-office interface in construction projects. We employed a questionnaire to examine variables, with collected data analyzed using SmartPLS version 4 software. Confirmatory analysis and structural equation modeling rigorously assessed the model’s reliability, validity and predictive utility.

The study identifies 11 critical factors crucial for successfully implementing DTs in construction projects, emphasizing contributions from office-related (ORF), site-related (SRF) and office-site-related factors (OSRF). Notably, impactful contributions from ORF affirm its pivotal role. The research sheds light on specific aspects within construction offices, on-site environments and their interface, providing valuable insights for industry stakeholders, policymakers and researchers.

Despite discussions on DT’s potential, a research gap exists in addressing essential factors for its effective application in monitoring and optimizing construction projects throughout their lifecycle. This study fills this gap by identifying and categorizing these factors, enhancing the knowledge base for successful DT assimilation and emphasizing the need for practitioners to understand key factors crucial for its integration into construction processes.

The motivation to participate in crowdsourcing (CS) platforms is an emerging challenge. Although researchers and practitioners have focused on crowd motivation in the past, the results obtained through such practices have not been satisfactory. Researchers have left unexplored research areas related to CS pillars, such as the evolution of the crowd’s primary motivations, seekers applying effective policies and incentives, platform design challenges and addressing task complexity using the synchronicity of the crowd. Researchers are now more inclined to address these issues by focusing on sustaining the crowd’s motivation; however, sustaining the crowd’s motivation has many challenges.

To fill this gap, this study conducted a systematic literature review (SLR) to investigate and map the challenges and factors affecting sustained motivation during CS with the overcoming implications. Studies that satisfied the inclusion criteria were published between 2010 and 2021.

Important sustainable factors are extracted using the grounded theory that has sustained participation and the factors' cohesion leads to the identification of challenges that the pillars of CS face. Crowds being the most vital part of CS contests face the challenge of engagement. The results reported the factors that affect the crowd’s primary and post-intentions, perceived value of incentives and social and communal interaction. Seekers face the challenge of knowledge and understanding; the results identify the reason behind the crowd’s demotivation and the impact of theories and factors on the crowd's psychological needs which helped in sustaining participation. Similarly, the platforms face the challenge of being successful and demanding, the results identify the latest technologies, designs and features that seekers proclaim and need the platforms designer's attention. The identified task challenges are completion and achievement; the authors have identified the impact of trait of task and solving mechanisms that have sustained participation.

The study identifies, explores and summarizes the challenges on CS pillars researchers are facing now to sustain contributions by keeping participants motivated during online campaigns. Similarly, the study highlights the implication to overcome the challenges by identifying and prioritizing the areas concerning sustainability through the adoption of innovative methods or policies that can guarantee sustained participation.

Building defects are becoming recurrent phenomena in most high-rise buildings. However, little research exists on the analysis of defects in high-rise buildings based on data from real-life projects. This study aims to develop dashboards and models for revealing the most common locations of defects, understanding associations among defects and predicting the rectification periods.

In total, 15,484 defect reports comprising qualitative and quantitative data were obtained from a company that provides consulting services for the construction industry in Victoria, Australia. Data mining methods were applied using a wide range of Python libraries including NumPy, Pandas, Natural Language Toolkit, SpaCy and Regular Expression, alongside association rule mining (ARM) and simulations.

Findings reveal that defects in multi-storey buildings often occur on lower levels, rather than on higher levels. Joinery defects were found to be the most recurrent problem on ground floors. The ARM outcomes show that the occurrence of one type of defect can be taken as an indication for the existence of other types of defects. For instance, in laundry, the chance of occurrence of plumbing and joinery defects, where paint defects are observed, is 88%. The stochastic model built for door defects showed that there is a 60% chance that defects on doors can be rectified within 60 days.

The dashboards provide original insight and novel ideas regarding the frequency of defects in various positions in multi-storey buildings. The stochastic models can provide a reliable point of reference for property managers, occupants and sub-contractors for taking measures to avoid reoccurring defects; so too, findings provide estimations of possible rectification periods for various types of defects.

This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.

This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.

The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.

This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.

Despite the numerous benefits of digitalization, many business-to-business (B2B) firms have yet to rely on data-driven decision-making, wavering the decision to adopt digital marketing practices. Topical scholarship is scattered across disciplines, schools of thought and methodological approaches, leading to an inability to suggest better management practices. This study aims to review the extant B2B marketing digitalization literature and addresses these concerns.

This paper conducted a systematic literature review of 96 high-quality articles extracted from the Web of Science database. Thereafter, this paper carried out descriptive statistical and content analyses of these articles.

Six primary research streams have been identified, and 16 research propositions have been formulated to comprehensively overview the B2B marketing digitalization landscape. The study delves into the factors and barriers influencing the pace of B2B marketing digitalization, sales lead generation and sales performance. Additionally, it introduces B2B digital value creation frameworks, emphasizing the crucial role of marketing analytics and decision tools in effective B2B marketing. The research also underscores various digitalization strategies aimed at bridging the digitalization gap in B2B companies at both strategic and tactical levels. Finally, the study presents an agenda to stimulate future research on theoretical and managerial topics critical to enriching the field.

This research outlines 16 research propositions that could be further tested to get more detailed insights into the digitalization of B2B marketing. Additionally, practitioners, authorities and researchers in the field may find this review valuable as it provides a comprehensive overview of current research in the domain.

Tacit and explicit knowledge sharing play crucial roles in today’s rapidly changing business environment, particularly in fostering innovation. However, uncovering tacit knowledge sharing remains complex. The purpose of this study is to analyze the mediating roles of tacit and explicit knowledge in the relationship between a knowledge-sharing culture and organizational creativity.

In this study, the authors developed an extended analytical process to analyze the impact of explicit and tacit knowledge on a knowledge-sharing culture and organizational creativity. This process combines two analytical techniques: necessary condition analysis (NCA) and partial least squares structural equation modeling (PLS-SEM). NCA identifies essential bottlenecks for a specific outcome, while PLS-SEM uncovers strong connections between predictor and outcome variables. The authors applied these analyses to a sample of 155 IT experts from a leading telecom company in the Turkish ICT industry to test the relevant hypotheses.

The findings of this study indicate that tacit knowledge, rather than explicit knowledge, partially mediates the relationship between a knowledge-sharing culture and organizational creativity. This mediating role of tacit knowledge is particularly pronounced in the ICT sector. Additionally, the impact of organizational capabilities on organizational creativity is amplified by higher levels of tacit knowledge sharing.

The effect of organizational capabilities on organizational creativity was found to increase because of tacit knowledge sharing compared to explicit knowledge sharing, depending on the knowledge-sharing climate.

The study aims to investigate innovation capability as a mediator in the relationship between talent management (TM) and entrepreneurial firm performance in Micro, Small and Medium Enterprises (MSMEs) in Thailand based on employer perceptions.

A questionnaire survey was completed by 674 MSMEs in Thailand, both directly and online. Four hypotheses were tested using structural equation modelling (SEM).

The results demonstrated that TM has a significant and positive influence on innovation capability, but no evidence was found for a positive relationship between TM and entrepreneurial firm performance. However, innovation capability positively influences entrepreneurial firm performance, and it fully mediates the positive relationship between TM and entrepreneurial firm performance. These results suggest that TM can enhance entrepreneurial firm performance if it is implemented with effective innovation capability.

The SEM model was employed with a limited number of enterprises, so the sample cannot represent the full population, as there are more than three million MSMEs in Thailand. More countries and factors should be incorporated into the model.

The owner-managers and HR practitioners of MSMEs can utilise the study results to develop talent strategies and innovative interventions that will enhance their firm’s performance.

This study thus aims to strengthen the capacities of MSME entrepreneurs and policymakers to elevate inclusive innovation capability in MSMEs and to also accelerate progress towards the Sustainable Development Goals (SDG); innovation is one of the three important aspects of SDG9.

The study contributes by filling the research gap related to TM in the MSME business and enterprise development literature, and it aids in creating innovation capability to increase effective investments in TM.

This study quantitatively investigates the impacts of digital and learning orientations on supply chain resilience (SCR) and firm performance (FP), aiming to fill the gaps in understanding their specific impacts in the context of Industry 4.0 developments and supply chain disruptions.

This study utilized survey techniques and structural equation modelling (SEM) to gather and analyse data through a questionnaire based on a seven-point Likert scale. Hypotheses were formulated based on an extensive literature review and tested using Amos software.

The study confirms SCR’s significant impact on FP, aligning with existing research on resilience’s role in organizational competitiveness. This study uncovers the nuanced impacts of digital and learning orientations on SCR and FP. Internal digital orientation (DOI) positively impacts SCR, while external digital orientation (DOE) does not. Specific dimensions of learning orientation – shared vision (LOS), open-mindedness (LOO) and intraorganizational knowledge sharing (LOI) – enhance SCR, while commitment to learning (LOC) does not. SCR mediates the relationship between DOI and FP but not between DOE and FP.

This research focuses on digital and learning orientations, recommending that future studies investigate other strategic orientations and examine the specific contributions of various digital technologies to SCR across diverse contexts.

The empirical findings emphasize the significance of developing internal digital capabilities and specific learning orientations to enhance SCR and FP, aligning these initiatives with resilience strategies.

This study advances knowledge by distinguishing the impacts of internal and external digital orientations and specific learning dimensions on SCR and FP, offering nuanced insights and empirical validation.

The purpose of the paper is to analyse the sequence of forces acting as barriers in the usage of drones in the construction industry using interpretive structural modelling (ISM). The usage of drones in the construction industry is brought out phase-wise with the help of literature and live cases. Barriers to the usage of drones in construction and steps to derive the interaction between them are described in detail.

The extraction of barriers to the usage of drones in construction is done through cases and supported by the literature. The identification of the interaction between the barriers is done through multi-criteria decision models, namely, ISM and Matriced Impacts Croises Multiplication Appliquee a un Classement (MICMAC) and the results are presented in the form of a hierarchical structure. The paper highlights the potential for the usage of drones in every phase of construction across three stages of construction and eight different applications.

The findings on the interaction between barriers show that technical and research and development-related barriers have a higher driving power, ultimately influencing negativity among stakeholders in drone usage for construction. By analysing interrelationships between barriers, management can frame suitable strategies to adopt drone usage in projects. Awareness about the strength of certain barriers can help management take steps to mitigate the same.

By analysing interrelationships between barriers, management can frame suitable strategies to adopt drone usage in projects. A major limitation is a restriction of the study area to the Indian subcontinent. However, the authors believe that the results can be applied across countries where drone technology is at the nascent stage.

Awareness about the strength of certain barriers can help stakeholders take steps to mitigate the same.

The results of this research also give some inputs to the government’s drone policy for wider usage of drones in the construction industry.

To the best of the authors’ knowledge, most studies on drones in construction industry bring out a list various challenges to their adoption. In this study, the authors have gone further to perform a hierarchical sequencing of barriers to drone adoption based on challenges faced in an emerging economy like India.

This study aims to examine how specific regulatory indicators – such as regulatory quality, information and communications technology regulatory environment, regulation of emerging technologies, e-commerce legislation and privacy protection by law content – affect the economic outcomes, quality of life and sustainable development goals associated with future technologies, including artificial intelligence, robotics, big data analytics, cloud computing and app- and web-enabled markets.

Using Bayesian Belief Network models and Network Readiness Index 2023 data from 134 countries, this study explores the relationships between regulatory factors and various socioeconomic outcomes.

Regulatory quality and e-commerce legislation emerge as central determinants, directly or indirectly impacting economic development, societal well-being and sustainability objectives. Notably, regulatory quality is identified as a pivotal factor across all outcomes, emphasizing the critical role of effective regulatory frameworks in fostering positive outcomes.

The study relies on cross-sectional data, which restricts causal inference, and focuses on national-level data, potentially overlooking subnational variations. In addition, the use of secondary data sources introduces possible measurement errors and biases. Despite these constraints, the study offers valuable insights into regulatory strategies and their role in advancing economic and social outcomes.

The study highlights the importance of tailoring regulatory interventions to address specific needs and challenges faced by countries at different stages of development. The findings provide valuable insights for policymakers, regulatory authorities and stakeholders seeking to navigate the regulatory challenges and opportunities inherent in the era of rapid technological advancement. The study contributes to advancing the understanding of the complex interplay between regulation, technology and development outcomes in the contemporary global landscape.