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Technology-enhanced learning (TEL), undoubtedly, creates a big difference in higher education students' knowledge and growth, which helps them become globally competitive in the job market eventually. The present study aims to investigate the effect of various factors, i.e. informational quality, compatibility, resource availability, subjective norms, subject interest, institutional branding and self-efficacy on students' adoption intention to TEL enrolled in different government and private educational institutes in Chhattisgarh state.

The primary data were collected from 600 students from different universities and colleges using purposive sampling technique with “criterion sampling”. Hierarchal multiple regression (stepwise) analysis was used on the collected data.

Results concluded that factors, i.e. compatibility, resource availability, subjective norms, subject interest and institutional branding are significantly and positively influencing students' adoption intention to TEL in Chhattisgarh, whereas self-efficacy and informational quality of TEL did not contribute significant effect for students' adoption intention.

There is a lack of research in the knowledge domain, especially in the field of TEL, in the state of Chhattisgarh. The different variables taken in the present study, such as informational quality, self-efficacy, institutional branding, subjective norms, resource availability, compatibility and subject interest of TEL, are the first of its kind where these variables are being examined on the students' adoption intention to TEL.

The emotion regulation research has drawn considerable attention from academicians and scholars in the contemporary world. As a result, the publications that are specifically dedicated to emotion regulation research are rapidly escalating. Therefore, this study aims to conduct a bibliometric analysis of research articles that have been published in the field of “emotion regulation.” The study primarily examines the growth and development of scholarly publications, seminal studies, influential authors, productive journals, research production and collaboration among countries, emerging research themes, research hotspots and thematic evolution of emotion regulation research.

The Web of Science Core Collection database was used to gather the study’s data, which was then analysed using VOSviewer and Bibliometrix, Biblioshiney open-source package of the R language environment.

The study’s results reveal that the research on emotion regulation has grown significantly over the last three decades. Notably, Emotion and Frontiers in Psychology are the most dominant and productive journals in the field of emotion regulation research. The most prominent author in the area of emotion regulation is identified as James Gross, followed by Gratz, Wang and Tull. The USA is at the forefront of research on emotion regulation and has collaborated with most of the developed countries like Germany, England and Canada. The keyword analysis revealed that the most potential research areas in the field of emotion regulation are functional magnetic resonance imaging, amygdala, post-traumatic stress disorder, borderline personality disorder, alexithymia, emotion dysregulation, depression, anxiety, functional connectivity, neuroimaging, mindfulness, self-regulation, resilience and coping. The thematic evolution reflects that the research on emotion regulation has recently focused on issues including Covid-19, non-suicidal self-injury, psychological distress, intimate partner violence and mental health.

The results of this study highlighted the current knowledge gaps in emotion regulation research and suggested areas for further investigation. The present study could be useful for researchers, academicians, planners, publishers and universities engaged in emotion regulation research.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

This study aims to address the challenge of encouraging customers to join hotels’ green loyalty programmes (LPs) by examining the impact on customers’ decisions of their trait reactance, anticipated guilt and the physical attractiveness of service employees.

The authors conducted three preliminary studies and one main study using scenario-based online surveys targeting Chinese hotel customers. The first two preliminary studies (N1A = 100, N1B = 158) explored the negative emotions (guilt vs. shame) linked to non-participation in green LPs, while the third study (N1C = 110) examined gender’s role in perceived physical attractiveness. The main study (n = 836) tested the three-way interaction effect.

This analysis confirms that guilt, rather than shame, plays a significant role in the decision-making process for participating in green LPs. The results reveal that trait reactance strongly deters participation intention when customers anticipate low guilt and perceive service employees as less attractive. Notably, higher anticipated guilt renders trait reactance ineffective in influencing intentions, regardless of employees’ attractiveness.

The results reveal that a high level of anticipated guilt is the key to boosting customers’ intention to participate in a hotel’s green LP, which can mitigate the negative impact of customers’ trait reactance.

To the best of the authors’ knowledge, this is the first study to demonstrate how anticipated guilt can lessen the negative effects of customers’ trait reactance on their intention to participate in green LPs. In addition, the findings reveal that guilt not only narrows customers’ attentional focus but also influences how the attractiveness of service employees affects their decision-making processes. the work introduces a new angle on how emotional responses (anticipated guilt) interact with physical cues (employee attractiveness) in shaping customer decisions concerning the hotel’s green initiatives.

The purpose of this paper is to apply what can be learned from the emergence of nature tourism to understand some current and future trends of tourism.

This study adopted the evolutionary paradigm for investigation.

The emergence of nature tourism in early medieval China can be attributed to four major factors, including transformation of value orientations, seeking longevity, interest in suburbs and population migration.

Historical studies help understand the current and future trends. When the contributing factors for nature tourism are linked to the contemporary world, it can be found that these factors are still playing a part in shaping tourism trends or patterns in their original or alternative forms. These trends or patterns are worthy of scholarly investigations.

This paper offers a comprehensive understanding of the origins of nature tourism.