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This paper aims to propose an instrument for measuring social commerce among entrepreneurs from the USA, Mexico and Colombia.

The methodological design was quantitative, explanatory, observational and transversal, where a sample of 1,644 entrepreneurs from the USA (n = 525), Mexico (n = 608) and Colombia (n = 510) was obtained. For the validation and reliability of the instruments, a structural equation model (SEM) was developed for the three studies carried out. Regarding the goodness and adjustment indices of the SEM in the three countries, they turned out to be acceptable.

Through the results obtained in the three studies carried out, it has been verified that the instrument of social commerce in its two factors: Marketing and Market Participation, has sufficient validity and statistical reliability. Likewise, it has been verified that both the manifest variables and the latent variables of the construct show a significant relationship between the three studies carried out in different populations.

The findings obtained in the presented study contribute to entrepreneurs, leaders and managers of the business sector to improve the entrepreneurial project through marketing and market participation of the product and service, as well as the business unit that seeks to position itself in the market. Likewise, it helps entrepreneurs to understand analytically and systematically the constructs that social commerce is made up of, which will help entrepreneurial leaders reduce or control their risk when considering social commerce in their entrepreneurship, achieving the success of the entrepreneurial project through its positioning in the market considering marketing and market participation as main factors of social commerce.

The findings are relevant and of great value to the literature because at present there is not enough research that is focused on the variables analysed related to social commerce in the contexts of the USA, Mexico and Colombia. The relevance of the present scale in comparison to others proposed by the literature is that the proposed scale is focused on entrepreneurs who seek to be more successful through the positioning of their business unit, product or service in the market through the market participation. It is achieved through marketing strategies. Another contribution provided by the present study lies in the methodological robustness of the scale and its analysis, comparing it with studies in three leading entrepreneurship countries in Latin America, comparing the validity and reliability as well as the goodness of fit indices of the proposed model in each of the studies. They were significant and very similar, so the proposed scale is of great value and usefulness in the literature.

This study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.

The research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.

Cognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.

This research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

Lead users are essential participants in crowdsourcing innovation events; their continuance intention significantly affects the success of the crowdsourcing innovation community (CIC). Although researchers have acknowledged the influences of network externalities on users' sustained participation in general information systems, limited work has been conducted to probe these relationships in the CIC context; particularly, the predictors of lead users' continued usage intention in such context are still unclear. Hence, this paper aims to explore the precursors of lead users' continuance intention from a network externalities perspective in CIC.

This work ranked users' leading-edge status to recognize lead users in the CIC. And then, the authors proposed a research model based on the network externalities theory, which was examined utilizing the partial least squares (PLS) technique. The research data were collected from an online survey of lead users (n = 229) of a CIC hosted by a China handset manufacturer.

Results revealed that the number of peers, perceived complementarity and perceived compatibility significantly influence lead users' continuance intention through identification and perceived usefulness.

This work contributes to the crowdsourcing innovation research and provides views regarding how lead users' sustained participation can be developed in the CICs. This work also offers an alternative theoretical framework for further research on users' continued intention in open innovation activities.

This study aims to streamline and enhance the assembly defect inspection process in diesel engine production. Traditional manual inspection methods are labor-intensive and time-consuming because of the complex structures of the engines and the noisy workshop environment. This study’s robotic system aims to alleviate these challenges by automating the inspection process and enabling easy remote inspection, thereby freeing workers from heavy fieldwork.

This study’s system uses a robotic arm to traverse and capture images of key components of the engine. This study uses anomaly detection algorithms to automatically identify defects in the captured images. Additionally, this system is enhanced by digital twin technology, which provides inspectors with various tools to designate components of interest in the engine and assist in defect checking and annotation. This integration facilitates smooth transitions from manual to automatic inspection within a short period.

Through evaluations and user studies conducted over a relatively long period, the authors found that the system accelerates and improves the accuracy of engine inspections. The results indicate that the system significantly enhances the efficiency of production processes for manufacturers.

The system represents a novel approach to engine inspection, leveraging robotic technology and digital twin enhancements to address the limitations of traditional manual inspection methods. By automating and enhancing the inspection process, the system offers manufacturers the opportunity to improve production efficiency and ensure the quality of diesel engines.

Given the potential benefits of gamification in running apps, it is necessary to explore the impact of users’ gameful experience on their intention to continue using running apps. This study aims to empirically investigate this relationship along with the roles of brand attitude as a mediator and negative online reviews as a moderator.

The study surveyed 332 running app users in China. The reliability and validity of measures were confirmed by confirmatory factor analysis (CFA). The proposed hypotheses were verified by Process Macro.

The results show that (1) gameful experience positively impacts intention to continue using running apps, (2) brand attitude mediates the relationship between gameful experience and intention to continue using running apps and (3) negative online reviews moderate the relationship between gameful experience and brand attitude but not the relationship between brand attitude and intention to continue using running apps. Specifically, the effect of gameful experience on brand attitude decreases as users’ perception of negative online reviews increases.

These findings have both theoretical and practical implications for understanding the relationship among users’ gameful experience, brand attitude and intention to continue using running apps, as well as for developing effective gamification strategies to enhance user engagement and retention in running apps.

With the uptake of prefabricated construction (PC) facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation. However, the construction community has paid little attention to PC innovation, especially networked innovation. This study aims to gain deep insights into what impacts the formation and dynamics of a prefabricated construction innovation network (PCIN). With the uptake of PC facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. It first conceptualizes the PCIN based on the quadruple helix model and formulates a corresponding system dynamics model based on causality analysis. After validating the PCIN model using empirical data, simulations are carried out using Vensim PLE software. Finally, this study identifies key factors that promote the formation of PCIN in China through sensitivity analysis.

The results show that PC predicts a continuous increase in practice as of 2030. The tested drivers all positively influence the formation of the PCIN, with market demand and risk sharing having the greatest influence, followed by competitive pressure, profit government support, scientific and technological advancement and collaborative innovation strategy.

The study makes three major contributions. First, it provides a novel angle for a deeper understanding of the PC innovation. Second, it proposes a new approach for probing the formation and dynamics of the PCIN. Finally, it offers a theoretical reference for promoting the formation of innovation networks and the development of PC.

Trust plays a crucial role in overcoming uncertainty and reducing risks. Uncovering the trust mechanism in the sharing economy may enable sharing platforms to design more effective marketing strategies. However, existing studies have inconsistent conclusions on the trust mechanism in the sharing economy. Therefore, this study aims to investigate the antecedents and consequences of different dimensions of trust (trust in platform and trust in peers) in the sharing economy.

First, we conducted a meta-analysis of 57 related articles. We tested 13 antecedents of trust in platform (e.g. economic benefits, enjoyment, and information quality) and eight antecedents of trust in peers (e.g. offline service quality and providers’ reputation), as well as their consequences. Then, we conducted subgroup analyses to test the moderating effects of economic development level (Developed vs Developing), gender (Female-dominant vs Male-dominant), platform type (Accommodation vs Transportation), role type (Obtainers vs Providers), and uncertainty avoidance (Strong vs Weak).

The results confirm that all antecedents and consequences significantly affect trust in platform or peers to varying degrees. Moreover, trust in platform greatly enhances trust in peers. Besides, the results of the moderating effect analyses demonstrate the variability of antecedents and consequences of trust under different subgroups.

This paper provides a clear and holistic view of the trust mechanism in the sharing economy from an object-based trust perspective. The findings may offer insights into trust-building in the sharing economy.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

This paper aims to adopt the dynamic capabilities view to investigate the relationship between managerial ties (i.e. business and political ties), dynamic capabilities and innovation climate on ambidextrous innovation (i.e. balanced and combined ambidextrous innovation), in the renewable and sustainable energy context. It also examines the mediating effects of dynamic capabilities between managerial ties and ambidextrous innovation (i.e. balanced and combined ambidextrous innovation), and moderating effects between dynamic capabilities and ambidextrous innovation relationships.

Multilevel analyses conducted using AMOS 26 on 288 employees working in 47 UAE energy firms.

Results found that business ties influences balanced and combined ambidextrous innovation indirectly, whereas political ties only impact combined ambidextrous innovation indirectly through dynamic capabilities. Dynamic capabilities insignificantly mediated managerial ties–ambidextrous innovation and political ties–balanced ambidextrous innovation relationships, with stronger indirect effect on combined than on the balanced dimension. Findings also indicate that innovation climate is the crucial moderator between dynamic compatibilities and ambidextrous innovation, as well as balanced and combined ambidextrous innovation, with stronger effect on balanced dimension than the combined.

This study addresses recent calls by highlighting the role of dynamic capabilities, an important yet underexplored organizational capabilities in the innovation and ambidexterity literature. Also, this study advances insight into how balanced and combined exploration–exploitation innovation and dynamic capabilities are connected and enhances the understanding into how organizational factors stimulate dynamic capabilities leading to superior innovation.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.