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Artificial intelligence (AI) is increasingly involved in idea generation and production processes. To understand AI’s pivotal roles in the back-of-house operations of restaurants, this study aims to examine the effects of AI involvement in recipe creation and food production on consumers’ willingness to order food.

We conduct three experiments in the context of casual dining restaurants. The authors examine the main effect of AI involvement in recipe creation and food production on the willingness to order food in a hypothetical restaurant (Study 1) and a real restaurant (Study 2). In addition, the authors also investigate the mediating role of uniqueness neglect. The authors explore whether the negative effect of AI involvement in recipe creation is attenuated in the presence of cues of uniqueness consideration (Study 3).

We demonstrate that AI involvement in food production does not elicit negative responses to a menu but that consumers show unfavorable responses when AI is involved in recipe creation. The authors also identify the mediating role of uniqueness neglect. Furthermore, the authors reveal a way to mitigate the negative perceptions of AI involvement in tasks requiring intuition and instinctive decision-making (i.e. recipe creation) by incorporating cues that emphasize uniqueness considerations.

We deliver causal evidence for the significant impacts of AI involvement in recipe creation and food production, using multiple experimental designs involving both hypothetical and real restaurants. The findings, thus, can tackle an ongoing challenge in the tourism and hospitality industry – the deficit of human resources in back-of-house operations.

Data-driven models are increasingly being used to predict the fatigue life of many engineering components exposed to multiaxial loading. However, owing to their high data requirements, they are cost-prohibitive and underperforming for application scenarios with limited data. Therefore, it is essential to develop an advanced model with good applicability to small-sample problems for multiaxial fatigue life assessment.

Drawing inspiration from the modeling strategy of empirical multiaxial fatigue models, a modular neural network-based model is proposed with assembly of three sub-networks in series: the first two sub-networks undergo pretraining using uniaxial fatigue data and are then connected to a third sub-network trained on a few multiaxial fatigue data. Moreover, general material properties and necessary loading parameters are used as inputs in place of explicit damage parameters, ensuring the universality of the proposed model.

Based on extensive experimental evaluations, it is demonstrated that the proposed model outperforms empirical models and conventional data-driven models in terms of prediction accuracy and data demand. It also holds good transferability across various multiaxial loading cases.

The proposed model explores a new avenue to incorporate uniaxial fatigue data into the data-driven modeling of multiaxial fatigue life, which can reduce the data requirement under the promise of maintaining good prediction accuracy.

This study aims to investigate the impact of firms’ digital orientation (FDO) on corporate green innovation (CGI) among Chinese firms, examining the effects of financial constraint as the mediator and exploring heterogeneous effects across different firm contexts.

Using a sample of 28,697 firm-year observations from Chinese A-share listed companies (2008–2021), we employ a novel multidimensional measure of FDO derived from textual analysis of corporate annual reports. CGI is quantified using patent-based metrics. We utilize fixed-effects panel data models as benchmark regression to quantify FDO’s impact on CGI. Later, we utilize two-stage least squares, alternate measure for core explanatory variable, alternate as well as lead measures for explained variable and propensity score matching to tackle concerns for potential endogeneity.

Our results unveil a substantial positive connection between FDO and CGI. This connection is facilitated through the alleviation of financial constraints. Furthermore, heterogeneity analysis shows that the impact of FDO on CGI is more pronounced for state-owned enterprises, firms in areas with lower financial technology development and politically connected firms.

Our findings suggest that managers should view FDO as a strategic posture that can drive sustainable innovation, not just as a technological imperative. Policymakers should consider the role of FDO when designing policies to promote CGI, particularly in less-developed regions.

This study extends current understanding by: (1) Employing a comprehensive multidimensional measure of FDO that goes beyond the existing technologically focused digital transformation matrices. (2) Identifying financial constraints as a key mediating mechanism in the FDO–CGI relationship. (3) Revealing heterogeneous effects across different firm contexts, providing nuanced insights into how institutional and environmental factors moderate this relationship.

Considering that when endorsing the same product, virtual influencers with different identity types (self-created, collaborative) can have different impacts on consumers' purchasing behaviors, this paper aims to explore how to maximize the impact effects of the VIs' respective identities. It provides companies with new perspectives on endorsement strategies.

The interaction between VI identity type and post type (informational, storytelling) on purchase intention was analyzed in four experiments (N = 1,007), considering informational and normative social influence as intermediate mechanisms and consumer self-construal as moderators.

The findings show that self-created VI is suited to informational posts and collaborative VI to storytelling posts. This identity-content match effectively triggers the social influence mechanism: informational posts of self-created VI significantly enhance informational social influence. In contrast, storytelling posts of collaborative VI primarily stimulate normative social influence. Consumer self-construal also moderates the process of influencing mechanisms.

Based on social influence theory and matching theory, this paper confirms the existence of an interaction between VI identity types, which influences consumers' purchase intention through informational and normative social influence. This finding fills the research gap in the field of VI endorsement strategy. It also emphasizes the importance of consumer self-construal and contributes new insights into the related field.

The article focuses on cross-sectoral analysis concerning services, especially ICT services, flowing from China to European manufacturing. The aim of the study is to analyse Sino-European relations in terms of ICT servicification. The article attempts to answer the following questions: does China’s relationship with Europe in terms of the servicification of manufacturing align with global servicification trends? Have global economic shocks, such as decoupling policies, diminished the flows of Chinese ICT services in European advanced manufacturing sectors?

This study employed input–output models to analyse the increasing role of China as a supplier of ICT services to European manufacturing. It also identified the industries that are most dependent on Chinese ICT services.

The analysis highlights the increasing reliance of European manufacturing on Chinese ICT services, with a notable rise across both Western Europe and CEE. This dependency is particularly strong in advanced sectors such as automotive and electronics, and there is no evidence of decoupling from China, even amidst global shocks or geopolitical tensions like the Trump presidency. Additionally, the BRI had limited direct impact, as the servicification trends appear driven more by broader globalization processes.

The study investigates all European countries and their manufacturing sectors’ reliance on Chinese services. It concentrates on services related to high technology, specifically ICT. Moreover, the previous research has focused on servicification of manufacturing, in general, neglecting industry-specific analysis. It contributes to the literature by providing insights into the relationships between developing and developed economies in terms of GVCs in the context of digital servicification and decoupling conditions.

Laser fading, commonly used in the denim industry, is a computer-controlled, dry, ecological finishing method whereas conventional methods include high water, energy and time consumption. Resolution and pixel time are crucial parameters of laser source influencing the effect of laser treatment. The purpose of this study is to determine the optimum laser parameters of CO₂ laser followed by enzyme washing and to compare the tensile strength and color values of laser-treated denim fabric with that of conventional enzyme-faded.

Two different indigo-dyed, sulfur bottom-indigo-dyed and only indigo-dyed organic cotton denim fabrics with different unit weights, were lasetreated with different laser parameters and then subjected to 10 min enzyme washing. Tensile strength, abrasion resistance, and change in fabric unit weight were tested. CIE (L*a*b*, ΔE*, h°, C*) color values, color strength (K/S), yellowness and whiteness indexes were measured to identify the color differences. Color fastness tests including washing, rubbing, light, water and perspiration fastness were investigated.

Most effective laser fading in terms of good mechanical properties and color values was obtained at 40 dpi resolution and 300 µs pixel time.

Conventional enzyme fading of denim fabrics is a wet process and requires a long process time of 40–45 min and high temperatures, leading to high energy and water consumption. Laser fading, on the other hand, is a dry and ecological method, but causes a decrease in mechanical properties of the fabric, and an increase in yellowness. In this study, unlike the similar studies in the literature, denim fading was carried out by a combination of laser treatment followed by only 10 min enzyme washing in order to eliminate or minimize the drawbacks of the denim fading, such as high energy and water consumption for enzyme fading and decrease in mechanical properties of the fabric and increase in yellowness for laser fading. This method was applied to two different dyed denim fabrics, sulfur (bottom) and indigo (top) and laser process conditions were optimized to achieve the desired fading effects compared to conventional enzyme fading.

This study investigates the applications of computer vision (CV) technology in the tourism sector to predict visitors' facial and emotion detection, augmented reality (AR) visitor engagements, destination crowd management and sustainable tourism practices.

This study employed a systematic literature review, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology and bibliometric study on research articles related to the tourism sector. In total, 407 articles from the year, 2013 to 2024, all indexed in Scopus, were screened. However, only 150 relevant ones on CV in Tourism were selected based on the following criteria: academic journal publication, English language, empirical evidence provision and publication up to 2024.

The findings reveal a burgeoning interest in utilizing CV in tourism, highlighting its potential for crowd management and personalized experience. However, ethical concerns surrounding facial recognition and integration challenges need addressing. AR enhances engagement, but ethical and accessibility issues persist. Image processing aids sustainability efforts but requires precision and integration for effectiveness.

The study’s originality lies in its thorough examination of CV’s role in tourism, covering facial recognition, crowd insights, AR and image processing for sustainability. It addresses ethical concerns and proposes advancements for a more responsible and sustainable tourist experience, offering novel insights for industry development.

This study uses a comprehensive theoretical framework that combines social cognitive theory and neighborhood effect to investigate the influence of neighborhood effects on farmers’ outcome expectations, observational learning and self-efficacy. This study aims is to analyze the mechanisms that underlie the adoption of social media by farmers for knowledge exchange in the agricultural context. Specifically, this research explores the role of neighborhood effects, outcome expectations, observational learning and self-efficacy in shaping farmers’ decision-making process regarding the use of social media platforms for exchanging agricultural knowledge.

The study data was collected through a sample survey conducted among 570 agricultural households residing in the provinces of Thai Nguyen, Cao Bang, Bac Kan and Phu Tho, located in the northern region of Vietnam. To analyze the data, structural equation modeling was used as the statistical technique of choice.

The findings of the study indicate a significant influence of neighborhood effects on outcome expectations, observational learning and self-efficacy. These factors, derived from social cognitive theory, also exhibit a positive association with farmers’ adoption of social media for knowledge exchange. Additionally, the study highlights that neighborhood contribute to a favorable adoption of social media among farmers via outcome expectations, observational learning, and self-efficacy.

The study is limited in examining farmers’ social media adoption for agriculture knowledge exchange in Northern mountainous area of Vietnam. This study could be replicated across various regions or nations, providing comparative insights into the adoption of social media among farmers for knowledge exchange.

The study findings suggest practical and innovative means to promote farmers’ social media adoption for agriculture knowledge exchange.

This study presents a pioneering approach by integrating social cognitive theory and neighborhood effect to elucidate the factors influencing farmers’ adoption of social media for the purpose of agriculture knowledge exchange.

The purpose of this study is to assess the effects of fused filament fabrication (FFF) printing parameters on the surface quality and dimensional accuracy of FFF-fabricated copper green parts using the appropriate filaments. The orthogonal experiments were implemented and the errors in length, width and height were measured and analyzed. The results of range analysis and variance analysis indicated the orders of effect factors. Dissolvent debinding combined with thermal debinding was adopted to remove the binders inside the green part by calculating debinding rate. The influence mechanism of sintering temperatures on the microstructure and shrinkage was elaborated.

The extrusion-based FFF in manufacturing copper parts can overcome shortcomings for high reflectivity and heat dissipation in laser powder bed fusion process at cost-saving and materials saving. This study makes an attempt to prepare copper/binder composite filaments through mixing, extrusion and flowability evaluation.

The results showed that the suitable composite filaments applied for FFF should balance rigidity and plasticity. The combination of printing speed and heating temperature impacts on the surface quality significantly, and the major factor in determining the dimensional accuracy is layer thickness. Two-stage debinding procedure was beneficial for binder removal and sintering process. The higher sintering temperature results in less voids, sizes shrinkage and densified microstructure, which is attributed to the occurrence of sintering neck among the fused copper powders.

The self-prepared copper/binder composite filaments were successfully manufactured using the FFF process. This study provides unique approach and print guidance for fabricating complex structures of pure copper components.

The architecture, engineering and construction (AEC) industry is currently undergoing a paradigm shift as it integrates innovations such as digital twins (DT) in its activities. As a result, this study aims to ascertain the barriers affecting the implementation of digital twin (DT) technology in Nigeria’s AEC sector.

The study employed a quantitative approach using a questionnaire distributed via Google Forms, yielding 120 valid responses from built environment professionals in Nigeria. The data were subjected to statistical tests such as the Kolmogorov–Smirnov test, Cronbach’s alpha, descriptive statistics and the Kruskal–Wallis test. Hypotheses were validated through partial least squares structural equation modelling (PLS-SEM).

The study revealed that out of the 43 identified barriers, inadequate system integration, challenges in guaranteeing interoperability, university education on the subject is deficient, and new system compatibility with legacy systems are the main barriers to implementing DT for sustainable construction practices in the AEC Industry of Nigeria.

The study was conducted in Nigeria with a focus on the Federal Capital Territory. The study identified the barriers of DT in the construction sector.

This study developed and assessed a theoretical framework, examining the relationships between variables. The findings have important implications for the construction industry, offering opportunities to improve construction processes. Furthermore, the study will help improve sustainable practices within the built environment.

The study categorised the barriers of DT into the following: system integration; security-related; performance-related; organizational-related; data quality issues and environmental related issues.