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In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.

Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.

By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.

This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.

The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

This study builds on a conceptual model by integrating AI features – Perceived intelligence (PIN) and anthropomorphism (PAN) – while extending expectation confirmation theory (ECT) factors – interaction quality (IQU), confirmation (CON), and customer experience (CSE) – to evaluate the continued intention to use (CIU) of AI-enabled digital banking services.

Data were collected through an online questionnaire administered to 390 digital banking customers in India. The data were further analysed, and the presented hypotheses were evaluated using partial least squares structural equation modelling (PLS-SEM).

The research indicates that perceived intelligence and anthropomorphism predict interaction quality. Interaction quality significantly impacts expectation confirmation, consumer experience, and the continuous intention to use digital banking services powered by AI technology. AI design will become a fundamental factor; thus, all interactions should be user-friendly, efficient, and reliable, and the successful implementation of AI in digital banking will largely depend on AI features.

This study is the first to demonstrate the effectiveness of an AI-ECT model for AI-enabled Indian digital banks. The user continuance intention to use digital banking in the context of AI has not yet been studied. These findings further enrich the literature on AI, digital banking, and information systems by focusing on the AI's Intelligence and Anthropomorphism variables in digital banks.

ChatGPT has been receiving mounting research attention recently. However, its application and challenges to adopt for tourism and hospitality businesses remain relatively unexplored. To address this research gap, this study aims to systematically assess the application of ChatGPT and its challenges within the domain of tourism and hospitality.

This study conducts bibliometric and content analyses of papers retrieved from Web of Science and Scopus. Particularly, it systematically reviewed the tourism and hospitality research to identify critical applications of ChatGPT in the context of tourism and hospitality. In addition, this study identified challenges associated with the application of ChatGPT in this context.

It has been revealed that the use of generative artificial intelligence (AI), such as ChatGPT, in tourism and hospitality research is ascending, with an opportunity to advance the existing knowledge in customer service research. In addition, the results suggest an ongoing interest in assessing the role of AI and language modeling for tourism education and human resource management.

The results are constrained by the used search keywords and electronic databases. Additionally, this study covered only papers published in English. However, the findings shed light on existing knowledge concerning ChatGPT’s transformative potential, identify areas for further exploration and offer guidelines for practice in the tourism and hospitality industry. The findings also revealed various challenges that various stakeholders should keep a closer eye on to ensure the effective and safe use of ChatGPT accordingly.

This study initiates a discussion on ChatGPT’s role in tourism and hospitality and underscores the importance of comprehensive AI integration within the sector.

近年来, ChatGPT受到了越来越多的研究关注。然而, 它在旅游和酒店业中的应用及其面临的挑战仍然相对未被探索。为填补这一研究空白, 本研究系统评估了ChatGPT在旅游和酒店业中的应用及其挑战。

本研究通过对从Web of Science（WoS）和Scopus检索的文献进行文献计量分析和内容分析。特别是, 系统回顾了旅游和酒店业的研究, 以识别ChatGPT在这一背景下的关键应用, 并识别了与其应用相关的挑战。

研究揭示了生成式人工智能（如ChatGPT）在旅游和酒店业研究中的应用日益增多, 为推动客户服务研究的现有知识提供了机会。此外, 研究结果表明, 对人工智能和语言建模在旅游教育和人力资源管理中的作用存在持续的兴趣。

本研究开启了对ChatGPT在旅游和酒店业中作用的讨论, 并强调了在该行业中全面整合人工智能的重要性。

本研究受限于所用的搜索关键词和电子数据库。此外, 本研究仅涵盖了英文论文。然而, 研究结果揭示了关于ChatGPT变革潜力的现有知识, 确定了进一步探索的领域, 并为旅游和酒店业实践提供了指导。研究还揭示了各利益相关者应密切关注的各种挑战, 以确保ChatGPT的有效和安全使用。

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

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

College student’s mental health issues have emerged as a significant public health concern. The urban campus environment, being the primary habitat for college students, plays a crucial role in influencing their mental health.

Based on survey data from 34 Chinese universities and 1173 college students in 2021, this study utilized deep learning and street view images to explore the relationship between various urban campus landscapes, college students' exercise participation, and mental health.

The study revealed substantial variations in campus landscape features, particularly in terms of spatial openness. While green campus landscapes (measured by the Green View Index and Normalized Difference Vegetation Index) showed no significant impact on exercise participation or mental health, the Sky View Factor did. Higher levels of campus openness and exercise frequency were associated with better mental health. The study also underscored that the influence of urban campus landscapes on college students' mental health was mediated by their exercise participation. Notably, spatial openness emerged as the most prominent differentiating factor among urban campus landscape attributes, significantly affecting students' exercise participation and mental health.

Thus, fostering open campus environments and reducing spatial constraints are vital steps in creating a sustainable urban landscape that can help alleviate potential negative effects on college students' mental health issues.

This study aims to identify the key hotel characteristics and facilities that significantly influence customer satisfaction based on Google review scores. By applying decision tree analysis, the research seeks to determine which aspects, such as service quality, hotel facilities and location, play pivotal roles in shaping customer experiences. The objective is to provide professional with practical recommendations to improve service quality and cultivate enduring customer loyalty.

The research used a data set collected from Hotels.com, featuring various characteristics of 802 hotels in Izmir Province. Decision tree analysis was conducted using Classification and Regression Tree algorithm to explore the relationship between hotel characteristics and facilities with customer satisfaction.

The analysis revealed that the number of rooms is the primary factor influencing hotel ratings, with proximity to the airport and hotel classification also being significant. Additional factors such as public transportation distance and laundry services were important, while facilities such as ATMs, beach access and spas showed no significant impact on customer satisfaction. These findings emphasize the importance of core facilities and accessibility.

This study contributes to the literature by offering a novel approach, using decision tree analysis to assess hotel customer satisfaction with structured data. It provides practical implications for hotel managers, enabling them to make data-driven improvements to achieve customer satisfaction. The integration rules created by the decision tree model into hotel management systems can enhance operational efficiency and competitive advantage in the hospitality industry.

This study looked at the potential applications of geographic information systems (GIS) and remote sensing (RS) for inclusive community development and participation, sustainable tourism, and rural community-based natural resource management (CBNRM) in sub-Saharan Africa and other rural areas worldwide.

To evaluate resource management systems for rural tourism and the environment in Africa and abroad. The study makes use of reviews of relevant literature and documents, and while linking applications for sustainable tourism and local community empowerment with CBNRM and GIS, vital content was manually analyzed.

The study shows a potential affinity between agricultural and tourism businesses that GIS in line with the CBNRM conception can strengthen. In many rural and underdeveloped regions of the continent, this highlights the need for a credible and varied tourism strategy to develop and empower the relevant communities.

Most agricultural communities in Africa are located in low-income regions. Such areas are rich in natural wildlife and have popular tourist destinations. A mix of regional community development initiatives can be built using GIS, sustainable tourism, CBNRM, and community-based tourism (CBT).

The survival of companies today hinges on their adaptability and flexibility, with digital transformation (DT) and organizational slack (OS) playing crucial roles. Despite their recognized importance, these factors are often studied separately. This study aims to explore how OS facilitates DT and evaluate their synergies and trade-offs to improve performance.

Using data from the European Manufacturing Survey, structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA), we investigate causal relationships and possible combinations between different dimensions of OS and DT that contribute to business performance.

We confirmed the positive effect of OS and DT on business performance, highlighting the importance of organizational over technological factors. While not definitively establishing OS as a precursor to DT, our findings underscore the need for human and operational slack to improve performance, especially in less technology-intensive contexts.

Our findings evidence that decision-makers should integrate OS with DT initiatives to improve the firm’s competitiveness. However, it is worth noting that while OS seems essential in low-tech shopfloors, its importance is lower in high-tech environments. Furthermore, within the possible combinations, managers should promote operational slack and digitalization, as it seems fundamental to improve business performance.

This article contributes to the management field in three ways. First, it clarifies controversies by providing evidence of the positive roles of DT and OS as drivers of competitiveness for manufacturing firms. Second, we verify that OS is not directly linked to DT, challenging existing assumptions. Third, it investigates the combinations of OS and DT that drive business performance improvement, emphasizing their synergies and trade-offs.