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Drawn from flow theory, this study aims to explore children’s flow experiences in virtual reality (VR) by examining the relationships between VR experience length, arousal and flow experience (time estimation and enjoyment).

A within-subject laboratory experiment was conducted using 9D VR and iMEC 12. A total of 47 children participated in this study. Both survey data and physiological data were collected.

The results revealed that longer VR videos evoked lower arousal, higher respiratory rates and less enjoyment among child participants than shorter videos. This study also verified the mediating effects of respiratory rates measured arousal on time estimations and self-reported arousal in terms of enjoyment.

This study fills a research gap related to children’s flow experiences in VR, as children’s voices remain limited in tourism research. The study findings offer meaningful insights for destination marketers to leverage the growth of parent-child tours and the increasing effects of VR on the tourism industry.

儿童虚拟现实旅游体验探究: 一项基于沉浸理论的心理-生理研究

基于沉浸理论,本研究旨在通过检验虚拟现实（VR）的体验长度、兴奋度, 以及沉浸体验（时间估计和乐趣）之间的关系来探讨儿童的VR沉浸体验。

运用9D虚拟现实模拟器和iMEC12, 本研究进行了一项被试内实验。一共有47位儿童参与本研究。收集了调研数据和生理学实验数据。

研究结果显示与短VR视频相比, 较长的VR视频导致了较低水平的兴奋度, 较高水平的呼吸率,和较少的乐趣。本研究且还证实了呼吸率测量的兴奋度对时间估计的中介作用 和自我报告的兴奋度对乐趣的中介作用。

由于从儿童角度出发的旅游研究有限, 所以本研究填补了儿童VR沉浸体验领域的研究空白。本研究为目的地营销人员提供了有意义的参考, 从而能有效利用亲子旅游和VR对旅游业日益增长的影响。

The purpose of this paper is to investigate the influence of employees’ trait rumination on the variability of their state rumination and the continuing influence on their negative affect at home.

A time-lagged experience sampling method was used for the data collection from full-time employees in the hotel industry. The hypotheses were tested with multilevel modeling using a random coefficient modeling approach.

Hotel employees who are high in trait rumination generally show high levels of state rumination and greater within-person variability in state rumination over time. Additionally, the negative effects of workplace state rumination can last until employees come home and the next day before going to work. Furthermore, employees who are high in trait rumination are more likely to be influenced by state rumination, as they experience more negative affect after arriving home.

Rumination has been shown to decrease hotel employee overall well-being. The findings of this study provide suggestions for remedial measures that can be taken by hotel organizations to help employees address ruminative thinking.

Drawing on response styles and work/family border theories, this study contributes to the rumination literature by considering both trait rumination and state rumination in a broader context. For a comprehensive understanding of the dynamic temporal characteristics of state rumination, this study considers the net intraindividual variability of state rumination as the outcome of trait rumination.

This paper aims to propose a hybrid method based on polynomial fitting bias self-compensation, grey forward-backward linear prediction (GFBLP) and moving average filter (MAF) for error compensation in micro-electromechanical system gyroscope signal especially under motion state.

The error compensation can be divided into two processes: bias correction and noise reduction. A polynomial drift angle fitting algorithm is used to correct bias before denoising processing. For noise reduction, operation can be taken in two stages: detection and processing. First, sample variances are used to judge motion state. According to the detection results, algorithmic system switches between grey GFBLP and MAF to ensure fast convergence rate and small steady-state error.

Experimental results show that the proposed method can correct bias effectively for practical gyroscope signal, and can eliminate noise effectively for both practical gyroscope signal and synthetic signal, which indicates the effectiveness of the proposed method.

Bias correction and noise reduction are considerations. Noise contained in practical or synthetic signal can be reduced rapidly and effectively, which benefits from the new idea of combination grey GFBLP, MAF and sample variances. And most importantly, it is applicable for signal denoising under arbitrary motion state condition, which is different from other methods where the convergence performance is seldom analyzed.

One of the currently debated changes in scientific practice is the implementation of data sharing requirements for peer-reviewed publication to increase transparency and intersubjective verifiability of results. However, it seems that data sharing is a not fully adopted behavior among researchers. The theory of planned behavior was repeatedly applied to explain drivers of data sharing from the perspective of data donors (researchers). However, data sharing can be viewed from another perspective as well: survey participants. The research questions (RQs) for this study were as follows: 1 Does data sharing increase participant's nonresponse? 2 Does data sharing influence participant's response behavior? The purpose of this paper is to address these issues.

To answer the RQs, a mixed methods approach was applied, consisting of a qualitative prestudy and a quantitative survey including an experimental component. The latter was a two-group setup with an intervention group (A) and a control group (B). A list-based recruiting of members of the Medical Faculty of the University of Freiburg was applied for 15 days. For exploratory data analysis of dropouts and nonresponse, we used Fisher's exact tests and binary logistic regressions.

In sum, we recorded 197 cases for Group A and 198 cases for Group B. We found no systematic group differences regarding response bias or dropout. Furthermore, we gained insights into the experiences our sample made with data sharing: half of our sample already requested data of other researchers or shared data on request of other researchers. Data repositories, however, were used less frequently: 28% of our respondents used data from repositories and 19% stored data in a repository.

To the authors’ knowledge, their study is the first study that includes researchers as survey subjects investigating the effect of data sharing on their response patterns.

The purpose of this paper is to use the theoretical perspective of structural inertia as a unique lens to study foreign sequential entry mode choices of multinational firms.

It adopts quantitative analysis of a sample of 121 Chinese publicly listed firms with 564 foreign entry incidents in the 2001-2012 period to test the hypotheses.

The empirical results show that multinational firms have a tendency to adopt the same mode in the subsequent entry as the number of prior entry mode choice of a given type (joint venture (JV) in this study) increases. The results support the theoretical prediction that organizations repeat their past activities due to structural inertia. Moreover, such an inertia effect in foreign sequential entry mode choices becomes stronger for older multinational firms, larger multinational firms and state-owned multinational firms.

Consistent with existing research, this study focuses on the entry mode choice between JV and wholly owned subsidiaries. However, it is better to examine the relationship identified in the study for different types of entry mode choices to assess result generalizability.

It reminds managers of multinational firms that they should be cautious to the influence of structural inertia that can be a barrier to strategic flexibility when they make entry mode choices.

The main contribution of this study resides in introducing structural inertia perspective to help understand the determinants of foreign sequential entry mode choices of multinational firms.

This study aims to investigate the responses of young Chinese consumers with different cultural characteristics to negative brand information about electric vehicles.

The current study is quantitative research with an experimental method. It shows two different levels of severity for negative publicity and asks participants to self-report through questionnaires.

Chinese young consumers, being collectivist and of high uncertainty avoidance, tend to search for and spread information; consumers with low power distance search and share information more under low information severity. In addition, information search positively affects brand attitude under lower severity; negative word-of-mouth intention negatively affects brand attitudes at both severity levels.

The current study examines the influence of personal cultural values on information searching and negative information dissemination among young consumers, providing insights to complement previous studies. Furthermore, it explores how such exposure influences young consumers’ brand attitude and intention to purchase. Limitations include simple sample scopes and single-product stimuli.

This research highlights the importance of cultural dimensions in shaping young consumers’ responses to negative publicity. Marketers worldwide should consider cultural influence and develop specific strategies to address negative information about different products. Understanding customers’ unique characteristics and preferences can help marketers effectively tailor their approaches to counter negative publicity.

This study originally provides a supplement to prior studies on cultural dimensions and consumer behavior and provides suggestions to marketers on young Chinese consumers.

Grassland in Qinghai as the main type of ecosystem in this region is located in arid and semi-arid areas. The ecosystem is fragile and sensitive to climate change. Grassland ecosystem not only provides essential ecological and life functions for human society but also plays a vital role in mitigating and adapting to climate change. The empirical literature on grassland ecosystem services value (ESV) does not consider the impact of climate change and regional economic development level factors, which prevents policymakers from making appropriate decisions. This paper aims to analyze the influencing factors of grassland ESV assessment, and, based on the meta-prediction model, account the grassland ESV in Qinghai province.

To understand the value of grassland ecosystem services in China under climate change, this paper used 61 research literature on the evaluation of grassland ESV in China, including a total of 564 value observations to establish a value transfer database. Based on the meta-analysis method, this study has constructed a value transfer model, to evaluate the grassland ESV in Qinghai province, and an interpretation model, which can analyze if the independent variables affect the grassland ESV significantly.

The study finds that the evaluation methods, types of ecosystem service functions, climate change and grassland types can affect the grassland ESV significantly. Based on the meta-regression prediction model to evaluate the grassland ESV in Qinghai is US$1,542.67/ha/year. It indicates several targeted approaches to increase the grassland ESV, and climate change also has a specific impact on the value of grassland ecosystem services.

This study provides a scientific basis for grassland management related to the development of grasslands and ecological compensation, as well as promote the sustainable development of grassland ecosystems.

This paper contributes to the field of grassland ESV assessment in at least three aspects; first, it innovatively introduces the meta-analysis to carry out an integrated analysis of previous research results; second, it includes a broader set of influence variables in the analysis, including meteorological and economic factors; and third, it establishes a methodological basis for the field of grassland ESV accounting.

This study aims to introduce an important temporal dimension to the research on institution and entrepreneurship in the transition period. This study develops the concept of pre-reform institutional embeddedness, and explores its impact on entrepreneurial reinvestment of private firms in China’s transition economy.

The authors used secondary data of a nationally representative sample of China’s private firms collected in the early days of the institutional transition period and applied ordinary least squares regressions and the Baron and Kenny approach to test the theoretical model.

Pre-reform institutional embeddedness has a negative impact on entrepreneurial reinvestment of private firms in the transition period. This relationship is mediated by guanxi-induced employment, such that pre-reform institutional embeddedness promotes guanxi-induced employment, which in turn discourages a private firm to reinvest. Additionally, the negative impact of guanxi-induced employment on entrepreneurial reinvestment is reduced when decentralization of decision-making is used.

First, entrepreneurs should be aware of pre-reform institutional embeddedness’ negative influence on firms’ risk-taking abilities and incentives. Private firms already constrained by this connection could alleviate the negative impacts through a widespread delegation of decision-making authority. Second, policymakers should be cautious about improper government-business relationships, which may discourage private firms from fully pursuing entrepreneurial growth opportunities.

This paper makes theoretical contributions to the literature on entrepreneurial reinvestment, embeddedness perspective of entrepreneurship and imprinting theory.

This study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.

The authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.

The research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.

Previous literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.