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Recent research highlights the growing use of anthropomorphizing voice commerce, attributing human-like traits to shopping assistants. However, scant research examines the influence of anthropomorphism on the behavioral intention of shoppers. Therefore, the study examines the mediating role of anthropomorphism and privacy concerns in the relationship between utilitarian and hedonic factors with the behavioral intention of voice-commerce shoppers.

The study employs structural equation modeling (SEM) to analyze responses from 279 voice-commerce shoppers.

Results indicate that anthropomorphizing voice commerce fosters adoption for hedonic factors but not for utilitarian factors. Paradoxically, anthropomorphism decreases shoppers’ behavioral intentions and heightens their privacy concerns.

The cross-sectional survey design serves as a notable limitation of the study. Future researchers can rely on longitudinal designs for additional insights.

Marketers should anthropomorphize voice commerce for hedonic shoppers, not for utilitarian shoppers, and consider implementing customized privacy settings tailored to individual preferences.

The study contributes to academia and management by emphasizing the need to customize anthropomorphic features according to utilitarian and hedonic factors. Furthermore, it highlights the adverse effects of anthropomorphizing voice commerce on shoppers’ behavior, offering policymakers guidance for appropriate regulations.

Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.

To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.

Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.

This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

This study aims to examine the impact of using ChatGPT on the Halal tourism experience. It examines the relationships among Halal-friendly travel motivations and satisfaction, revisit intention and electronic word-of-mouth (e-WoM) while testing the moderating effect of ChatGPT on the relationship between satisfaction and revisit intention.

This study employed a quantitative methodology. Using purposive sampling techniques, it approached about 800 tourists (from November 2023 to January 2024) from several halal tourism destinations in Indonesia. A total of 395 usable surveys were analyzed to test the relationships and moderation effects by SEM.

The study indicates that Halal-friendly travel motivations positively impact Muslim tourist satisfaction, which in turn influences e-WoM and revisit intention. Importantly, ChatGPT significantly moderates the relationship between satisfaction and revisit intention, thereby strengthening tourist loyalty for those using the AI tool.

The study’s findings provide practical guidelines for halal tourism providers to enhance Halal-compliant services and incorporate ChatGPT as an AI tool to boost Muslim travelers’ satisfaction, drive e-WoM and increase revisit intentions. AI technology gives Halal tourism companies an advantage in offering customized, immediate support, which leads to Muslim visitors becoming loyal.

The study fills a significant gap in the Halal tourism literature by examining AI’s impact on the market. It expands the Expectation-Confirmation Theory (ECT), the push-pull theory and word-of-mouth models in Halal tourism. It also contributes to AI adoption in Halal tourism by addressing how modern AI tools can influence tourist behaviors, improve satisfaction and encourage repeat visits.

This study aims to enhance our understanding of customer expectations and experiences in grocery shopping within the metaverse. It investigates factors influencing customer satisfaction and driving continued engagement with metaverse platforms, offering insights into the drivers of customer adoption and barriers to usage.

Adopting a qualitative netnographic approach, this study analysed customer reactions to Walmart’s virtual store demonstration. Data were collected from user comments on YouTube, TikTok, Twitter and Reddit. Thematic analysis was employed to identify key factors contributing to satisfaction and dissatisfaction with metaverse grocery shopping experiences.

The study reveals three major drivers shaping customer satisfaction and subsequent positive intentions toward grocery shopping in the metaverse: social, functional and hedonic stimuli. Eight critical barriers affecting the metaverse shopping experience are identified: functional, hedonic, social, financial, privacy, safety, ownership and store atmospherics concerns, including tactile, acoustic and visual elements.

The findings are derived from a qualitative analysis of customer comments on social media platforms, which may limit generalisability. Future studies could adopt a mixed-methods approach to validate these findings across broader datasets.

This work is the first research to examine customer satisfaction with grocery shopping in the metaverse. It offers valuable insights into customer expectations, adoption drivers and critical barriers, laying the groundwork for further exploration of metaverse applications in retail.