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This study aims to examine the underlying emotional process that explains how context-specific stimuli involved in virtual reality (VR) destinations translate into presence perceptions and behavioral intentions.

In total, 403 potential tourists participated in a self-administered online survey after they watched a randomly assigned VR tour. The Lavaan package in R software was used to conduct structural equation analysis and examine the proposed theoretical framework.

The results reveal that media content consisting of informativeness, aesthetics and novelty was positively related to users’ sense of presence in a VR tour. The effect of media content on presence was partially mediated by emotional arousal.

Managers and VR designers can create an emotive virtual tour that contributes to the user’s sense of presence to promote attraction to the target destination. The VR content needs to be informative, aesthetic and novel, which can excite users during the VR tour, portray virtual destinations clearly and eventually influence potential tourists’ visit intentions.

Research on the emotional mechanism to generate presence is still in its infancy. This study integrates presence theory into a conceptual framework to explore how media content influences presence and decision-making through the emotional mechanism.

This study examines the influence of CEO characteristics on firm innovation within the Australian market, using R&D expenditure as a proxy for innovation. The aim is to analyze how factors such as CEO gender, educational background and dual roles (CEO-chairman) impact firms' R&D investment across various industries.

Panel and Tobit regression models are employed to assess the relationship between CEO characteristics and R&D expenditure. The study controls for endogeneity and applies firm-level control variables to ensure robustness, examining CEO traits like gender, educational qualifications and CEO-chairman duality.

The study reveals that CEO gender and educational level significantly impact firm innovation, particularly R&D expenditure, compared to other characteristics like CEO-chairman duality. Female CEOs and those with PhD degrees are associated with higher R&D spending, with variations across industries such as basic materials and healthcare.

The study is limited by its focus on Australian firms and the time span of 2006–2016. Additionally, mixed results for CEO-chairman duality and CEO location may reduce the generalizability of the findings across all industries on the ASX.

The findings highlight the importance of gender diversity and CEO education in driving firm innovation. Companies aiming to enhance competitiveness and performance through R&D activities, especially in industry-specific contexts, should consider these CEO characteristics.

This study provides novel insights by analyzing the impact of CEO characteristics, such as gender and education level, on firm innovation in the underexplored Australian market. By using R&D expenditure as a proxy for innovation and employing both panel and Tobit regression models, it highlights the significance of CEO traits, particularly in specific industries. The findings emphasize the stronger influence of CEO gender and educational level compared to CEO-chairman duality and location, offering valuable implications for gender diversity and industry-specific innovation strategies in enhancing firm competitiveness.

The purpose of this study is to investigate the impact of big data (BD) analytics capabilities (BDACs) on green supply chain integration (GSCI) and green innovation (GI) in the context of a developing country, Jordan. In addition, the mediating effect of GSCI on the relationship between BDAC and GI is investigated.

Data collection was carried out through a survey with 300 respondents from food and beverages manufacturing firms located in Jordan. Partial least squares-structural equation modeling (PLS-SEM) technique was applied to analyze the collected data. Natural resource-based view (NRBV) theory was the adopted theoretical lens for this study.

The results revealed that BDAC positively and significantly affects both GSCI and GI. In addition, the results demonstrated that GSCI positively and significantly affects GI. Further, it is also found that GSCI positively and significantly mediates the relationship between BDAC and GI.

This study developed a theoretical and empirical model to investigate the relationship between BDAC, GSCI and GI. This study offers new theoretical and managerial contributions that add value to the supply chain (SC) management literature by testing the mediation model in food and beverages manufacturing firms located in Jordan.

International trade transactions, extracted from customs declarations, include several fields, among which the product description and the product category are the most important. The product category, also referred to as the Harmonised System Code (HS code), serves as a pivotal component for determining tax rates and administrative purposes. A predictive tool designed for product categories or HS codes becomes an important resource aiding traders in their decision to choose a suitable code. This tool is instrumental in preventing misclassification arising from the ambiguities present in product nomenclature, thus mitigating the challenges associated with code interpretation. Moreover, deploying this tool would streamline the validation process for government officers dealing with extensive transactions, optimising their workload and enhancing tax revenue collection within this domain.

This study introduces a methodology focused on the generation of sentence embeddings for trade transactions, employing Sentence BERT (SBERT) framework in conjunction with the Multiple Negative Ranking (MNR) Loss function following a contrastive learning paradigm. The procedure involves the construction of pairwise samples, including anchors and positive transactions. The proposed method is evaluated using two publicly available real-world datasets, specifically the India Import 2016 and United States Import 2018 datasets, to fine-tune the SBERT model. Several configurations involving pooling strategies, loss functions, and training parameters are explored within the experimental setup. The acquired representations serve as inputs for traditional machine learning algorithms employed in predicting the product categories within trade transactions.

Encoding trade transactions utilising SBERT with MNR loss facilitates the creation of enhanced embeddings that exhibit improved representational capacity. These fixed-length embeddings serve as adaptable inputs for training machine learning models, including support vector machine (SVM) and random forest, intended for downstream tasks of HS code classification. Empirical evidence supports the superior performance of our proposed approach compared to fine-tuning transformer-based models in the domain of trade transaction classification.

Our approach generates more representative sentence embeddings by creating the network architectures from scratch with the SBERT framework. Instead of exploiting a data augmentation method generally used in contrastive learning for measuring the similarity between the samples, we arranged positive samples following a supervised paradigm and determined loss through distance learning metrics. This process involves continuous updating of the Siamese or bi-encoder network to produce embeddings derived from commodity transactions. This strategy aims to ensure that similar concepts of transactions within the same class converge closer within the feature embedding space, thereby improving the performance of downstream tasks.

The purpose of this study is to empirically explore the impact of government subsidies for the digital economy on corporate innovation. It aims to determine whether these subsidies promote innovation, and to examine the specific ways in which they inspire corporate innovation.

This study selects Chinese A-share listed companies during the period from 2007 to 2019 as the research object. It employs panel data to empirically examine the impact of government subsidies in the digital economy on corporate innovation.

The findings reveal that government subsidies for the digital economy effectively promote corporate innovation. They significantly increase the number and share of invention patents and improve the quality of corporate innovation. Moreover, it is noted that the positive impact is largely confined to non-state-owned enterprises, small firms and those in highly competitive markets.

The contribution of this paper lies in focusing on government subsidies in the digital economy, which is distinct from the general government subsidies in a broad sense.

In today’s competitive business landscape, organizations are increasingly recognizing the strategic advantage of implementing sustainable practices to gain a competitive edge. This study aims to investigate the effect of green artificial intelligence (AI) on achieving a green competitive advantage, examining the mediating roles of green organizational learning, green product innovation and green process innovation. Additionally, the research explores the moderating role of perceived green climate in the relationship between green AI and these mediating factors.

This research examined companies in Isfahan, Iran, that have varying levels of artificial intelligence adoption within their business processes. The target population consisted of 148 senior managers from these companies. This study uses structural equation modeling to examine the proposed model.

Green AI positively impacted green organizational learning and green process innovation but not green product innovation. In addition, the results showed that green organizational learning, green product innovation and green process innovation had positive effects on green competitive advantage. Finally, the results showed that the perceived green climate did not play a moderating role in the relationship between green AI and these mediating factors.

Organizations should prioritize green AI initiatives, foster a culture of green learning and invest in green innovation to achieve sustainable growth and outpace competitors in the environmentally conscious marketplace.

This study positions itself at the forefront of research on green AI and green competitive advantage. It offers a unique framework by examining the combined effects of green AI, green learning and both product and process innovation on achieving a sustainable competitive advantage.

In the context of increasing concerns about health, nutraceutical restaurants that provide health benefits have emerged in the marketplace. However, customer experiences at these restaurants are poorly understood. This study focused on sensory experiences and examined the underlying mechanism by which they contribute to memorable dining experiences. Grounded in cognitive appraisal theory, this study developed a memorable dining experience model that links sensory stimuli, meaningfulness, novelty, emotions, and behavioral intentions.

Data were collected from 880 Chinese customers who dined at traditional Chinese medicine restaurants and were analyzed via partial least squares structural equation modeling.

The results revealed that sensory stimuli contributed to memorable dining experiences through meaningfulness, novelty, and emotions. Furthermore, memorable dining experiences increased behavioral intentions to spread positive word-of-mouth and revisit intentions. Additionally, customers’ gender moderated the effects of sensory stimuli on meaningfulness and novelty.

The findings of this study can be used to identify important sensory stimuli and their roles in delivering memorable dining experiences in traditional Chinese medicine restaurants. Therefore, this study’s findings contribute to an improved understanding of how to efficiently manage sensory stimuli to stimulate memorable experiences for restaurant patrons.

This study tests the influence of sensory stimuli on the memorable dining experiences of customers in China.

Health short videos are serving as a powerful tool for encouraging individuals to actively adopt healthier behaviors. The sensory cues applied in these videos can be useful for engaging peripheral processing and enhancing attitudes. While previous research has examined the effects of various single cues, this study features a pioneering attempt to explore the roles of audiovisual cross-modal correspondence, encompassing multisensory cues perceived through different modalities, in health communication.

A 2 (color: warm/cool) × 2 (music tempo: fast/slow) between-subjects experiment was conducted to observe 120 participants’ responses to a health short video promoting eye health that was created using four different combinations of background color and background music tempo.

It was found that the congruent color–tempo pairings, that is blue & slow and orange & fast, led to more positive attitudes toward the videos than the incongruent pairings, that is blue & fast and orange & slow. The effect of cross-modal correspondence on attitude was fully mediated by processing fluency, with gender acting as a moderator between the two variables. Furthermore, individuals’ attitudes toward a short video positively influenced their health behavioral intentions.

These findings not only lend support to the theoretical framework of “multisensory cues-fluency-attitude-intention” chain for persuasion purposes but also have practical implications for creating effective health short videos.

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

This study aims to explore customers’ second-hand clothing purchases and their engagement on the Facebook marketplace.

A conceptual model is developed, building upon the online second-hand shopping motivation (OSSM) scale. Data gathered from 409 respondents was analysed using covariance-based structural equation modelling.

The results highlight that economic, convenience, ideological motivations and environmental concerns impact customers’ purchase intention. Conversely, status consumption was found to impede customers’ purchase intentions. Notably, the elements of social media engagement, including consumption, contribution and creation, demonstrated significance as a consequence of customers’ second-hand clothing purchase intention.

This study’s findings contribute to the knowledge encompassing sustainable fashion consumption, information systems and second-hand social media shopping. It uniquely explores customer behaviours related to second-hand clothes shopping on the Facebook marketplace by building upon the OSSM scale. These findings offer valuable insights, showcasing the determinants that can increase customer-centric social media engagement. These results inform online retailers on Facebook marketplace about sustainable practices, aligning with UN Sustainable Development Goals 12, 13 and 8, to promote a green global economy.