Search results

The social media expands the scope of museum marketing. Through the social media marketing, visitors get a rich and colorful visual experience, and the museum can quickly and effectively convey various information to visitors. At present, the research on social media in the museum industry mainly focuses on the level of technology use, while the research on the marketing application of social media is relatively scarce, especially from the empirical perspective. This study constructs a conceptual model to identify the impact of SMMAs on visitor experience in the context of the museum industry through the empirical analysis.

A survey is conducted with a total of 538 visitors who follow the fan page of the Palace Museum Weibo. The collected data are analyzed via structural equation modeling.

The results show that SMMAs have significant effects on social presence and social support, which in turn significantly affect flow state. Moreover, the results demonstrate that social presence and social support partially mediates the relationships between SMMAs and flow state.

The contribution of this study is twofold. First, from a theoretical perspective, it offers new insights into the conceptualization of social media marketing. Second, from a pragmatic perspective, the results are helpful to guide museums how to carry out social media marketing activities.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2020-0564

Travel companies are increasingly hosting online communities to extend their initiatives of customer relationship management and gain additional insight into their business. While the benefits to companies from hosting online communities are clear, another closely related issue has not been given comparative attention: Why do customers engage voluntarily in online travel communities? The purpose of this paper is to answer the question by developing and testing a conceptual model that exploring the influence of socialization interactions on customer engagement with the community.

Hypotheses were tested by applying structural equation modeling based on survey data collected from an online travel community (n=665).

The results supported the research hypotheses and demonstrated that socialization interactions (i.e. information and social exchange) in the online travel community are important catalysts for customer engagement.

The contribution of this study is twofold. First, from a theoretical perspective, it offers new insights into the conceptualization of customer engagement and its antecedents in the context of the online travel community. Second, from a pragmatic perspective, the conceptual model derived from this research aids practitioners in stimulating customer engagement from the perspective of socialization interactions.

The purpose of this paper is to explore the possibility of producing Cu-based shape memory alloys (SMA) by means of direct metal laser fabrication (DMLF).

The fabrication approach consists of the combination of laser melting of a metallic powder with heating treatment in a controlled inert atmosphere. Three prospective Cu-Al-Ni alloy compositions were tested, and the effects of laser power, as well as laser exposure time, were verified.

All the processed materials were found to attain microstructures and phase change transformation temperatures typical of this type of SMA.

Further development of this technique will allow for fabrication of large elements with considerable shape memory effect, which are currently not viable due to high cost of nitinol.

This work showed a proof of concept toward the development of DMLF-based additive manufacturing of near net shape components of Cu-based SMAs from elemental powders.

Purpose: The aim of this chapter is to provide a quantitative literature review on machine learning (ML) and artificial intelligence (AI) in the Insurance Sector.

Need for the Study: The current study maps the literature regarding AI and ML in the insurance sector through bibliometric tools to identify the significant gaps in the available literature, considerable insights that emerged, and a scientific evaluation of AI and ML in the Insurance sector.

Methodology: The VOS viewer method was used to conduct the depth and quantitative analysis of the AI and ML in Insurance. The study of 450 articles has been retrieved through the Scopus database from 2012 to 2021. The implication of performance analysis methods has helped to explore influential journals, authors, countries, Keywords, and affiliations, elevating the literature in AI and the Insurance Sector.

Finding: This study conducts an exploratory analysis and identifies the prominent authors, sources, countries, affiliations, and articles using modern bibliometric analysis (BA) tools. The geographic scattering of the study indicates that the USA and the UK have highly influential publications and contribute to AI and Insurance. East and Southern Asia countries are far behind.

Practical Implication: Furthermore, this chapter can be used as a reference paper to explore the new field of study in the insurance sector using AI. The search criteria were set in the study to limit the sample published papers/articles included in Scopus data based on the AI and ML in Insurance.

Today’s business environment has the nature that challenges the decision-making processes of businesses with its many different factors including technological developments and changing consumer structures. Previous research focused on specific contexts of the business environment regarding marketing and innovation issues, however, an integrative approach can be helpful for understanding and taking actions. This study aims to present a comprehensive framework which employs sensemaking from management literature as an approach to evaluate the market environment. Following a conceptual approach for the methodology, a framework consisting of three stages: discovery, sensemaking, and prediction is included in the study. Proposed framework identifies the stages for the sensemaking of consumers and can guide marketing decision-makers for competitiveness in the digital world.

This paper aims to investigate the relationships between empowering leadership, basic psychological needs satisfaction, work-related well-being, and project citizenship behavior.

Drawing upon the self-determination theory (SDT), a conceptual model was developed and then empirically tested using a cross-sectional survey of 435 project members in Chinese construction projects.

The results fully support the research hypotheses proposed in the study, illustrating the positive impacts of empowering leadership on work-related well-being and project citizenship behavior, the mediating role of basic psychological needs satisfaction, and the positive association between work-related well-being and project citizenship behavior.

This research determines the utility of empowering leadership in the context of construction projects, especially in enhancing individual outcomes (i.e. work-related well-being and project citizenship behavior). Therefore, construction project managers can apply empowering leadership to meet the basic psychological needs of subordinates to increase project members' work-related well-being and project citizenship behavior.

To our knowledge, the present study first explores the micro-level impacts of empowering leadership in the construction context. Additionally, this study enriches the understanding of the mediating mechanism between empowering leadership and individual outcomes from a self-determination perspective.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

The information used for supervision by regulatory departments in public-private partnership (PPP) projects is primarily transmitted and processed by the PPP implementation department, which negatively impacts the information quality, leading to information asymmetry and undermining the overall effectiveness of supervision. This study aims to explore how to use blockchain to anchor the information used for supervision in PPP projects to the original information, to strengthen the oversight.

This paper adopts the principles of design science research (DSR) to design a conceptual framework that systematically organizes information along the information dissemination chain, ensuring the reliable anchoring of original information. Two-stage interviews involving experts from academia and industry are conducted, serving as formative and summative evaluations to guide the design.

The framework establishes a weak-centralized information organizing mode, including the design of governance community and on-chain and off-chain governance mechanisms. Feedback from experts is collected via interviews and the designed framework is thought to improve information used for supervision. Constructive suggestions are also collected and analyzed for further development.

This paper provides a novel example exploring the inspirations blockchain can bring to project governance, like exercising caution regarding the disorderly expansion of public sector authority in addressing information disadvantages and how to leverage blockchain to achieve this. Technical details conveyed by the framework deepen understanding of how blockchain benefits and the challenges faced in successful implementation for practitioners and policymakers. The targeted evaluation serves as rigorous validation, guiding experts to provide reliable feedback and richer insights by offering them a more cognitively convenient scenario.

Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed.

A prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory.

Comparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control.

In the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.