Search results

The purpose of this paper is to have a deeper understanding of the nonlinear relationship between the impact of climate change on tourism development. Current studies on the effects of climate change on tourism development primarily rely on linear correlation assumptions.

Based on the New Institutional Economics theory, the institutional setting inherently motivates and ensures the growth of the tourism industry. For a precise evaluation of the nonlinear consequences of climate change on tourism, this paper concentrates on Chinese cities between 2011 and 2021, methodically analyzing the influence of climate change on tourism.

The study findings suggest that there is an “inverse U”-shaped nonlinear relationship between climate change and tourism development, initially strengthening and subsequently weakening. Based on these findings, the research further delves into how institutional contexts shape the nonlinear association between climate change and tourism growth. It was found that in a higher institutional backdrop, the “inverse U” curve tends to flatten and surpass the curve adjusted for a lesser institutional context. Upon deeper mechanism analysis, it was observed that cities with more advanced marketization, improved industrial restructuring and enhanced educational growth exhibit a more evident “inverse U”-shaped nonlinear connection between climate change and tourism evolution.

First, previous studies on climate change and tourism development largely rely on questionnaire data (Hu et al., 2022). In contrast to these studies, this paper uses dynamic panel data, which to some extent overcomes the subjectivity and difficulty of causality identification in questionnaire data, making our research conclusions more accurate and reliable. Second, this study breaks through the linear relationship hypothesis of previous literature regarding climate change and tourism development. By evaluating the nonlinear relationship of climate change to tourism development from the institutional pressure perspective, it more intricately delineates their interplay mechanism, expanding and supplementing the research literature on the relationship mechanism between climate change and tourism development. Thirdly, the conclusions of this study are beneficial for policymakers to better understand and assess the scope of climate change impacts. It also aids relevant departments in clarifying the direction of institutional environment optimization to elevate the level of tourism development when faced with adverse impacts brought about by climate change.

This paper aims to investigate how information and communication technology (ICT) services and digital finance affect the development of international tourism.

The two-way fixed effect panel regression model, spatial econometric model, panel threshold regression model and panel quantile regression model are used. Data on tourism, economic and social development in 198 Chinese cities from 2011 to 2020 are analyzed.

This study finds that digital economy including ICT services and digital finance has significantly promoted the development of international tourism industry, while there is a negative spatial spillover effect. The promotion effect of international tourism increases significantly after digital innovation reaches the threshold value. International tourism is benefiting more from digital economy with the development of international tourism industry.

The development quality of international tourism industry has not been analyzed due to data limitations, and the mechanism has not been tested.

This study creatively reveals the development of international tourism industry in the digital economy era from ICT services and digital finance perspectives. This study also shows the spatial, nonlinear and asymmetric relationship between digital economy and international tourism.

When the bearing operates under static load, the lubrication state of the bearing can be well distinguished by the absolute value of the thermoelectric potential. However, when the bearing operates under dynamic loads, the thermoelectric potential is in an alternating state, fluctuating within a high range, making it difficult to straightforwardly characterize the lubrication state through thermoelectric potential. The purpose of this paper is to find the relationship between the thermoelectric potential and the lubrication state under dynamic load.

A thermoelectric simulation is executed using COMSOL Multiphysics to calculate the relationship between thermoelectric potential, load, oil film thickness, oil film pressure and contact pressure. Based on the test rig experimental data, the characteristics of thermoelectric potential in journal bearings under different dynamic loads and operation conditions are investigated and analyzed using feature extraction, clustering and indicator construction methods.

The results showed that the low potential percentage and the percentage of bad operation state cluster can effectively characterize the lubrication state of journal bearings under dynamic load conditions. Specifically, in this experiment, when the low potential percentage reaches 15% and the bad operation state cluster dominates most of the time, it indicates that the bearing is in a poor lubrication state. The results of scanning electron microscopy and energy-dispersive X-ray spectroscopy of bearing specimens under different operation states also validated the above analytical results.

The relationships elucidating the thermoelectric and tribological interactions under dynamic load remain unclear and there is still no efficient method to estimate the lubrication state using thermoelectric potential in previous studies. This paper used simulation and experimental analysis methods to find the relationship between the thermoelectric potential and the lubrication state under dynamic load.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0410/

Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.

Sensegiving is inherently a sociopolitical activity through which leaders shape their subordinates’ understanding of organizational change. However, it remains unclear as to why and which leaders actively engage in and effectively perform sensegiving during organizational change. This study employs social/political influence theory and develops a two-stage moderated mediation model to address this question. The study aims to examine the antecedent (leader’s willingness to support change) and outcome (team commitment to change) of sensegiving as well as the moderating role of the leader’s political skill in the relationships between the leader’s willingness to support change and sensegiving, and between sensegiving and team commitment to change.

This study utilized a two-stage, dual-source survey method. In the first stage, data were collected from 56 leaders. In the second stage, data were collected from 271 team members. Regression analysis was used to test the hypotheses.

The results show that a leader’s willingness to support change positively affects sensegiving, and sensegiving positively affects team commitment to change. The leader’s political skill moderates the effect of the leader’s willingness to support change on sensegiving as well as the effect of sensegiving on team commitment to change. Moreover, a two-stage mediated moderation model shows that the relationship between a leader’s willingness to support change and team commitment to change through sensegiving is moderated by the leader’s political skill.

This study, for the first time, investigates why leaders engage in sensegiving and the collective-level effects of sensegiving during organizational change, deepening our understanding of the antecedents and consequences of sensegiving. Furthermore, by revealing the role of a leader’s political skill in both the engagement in sensegiving and its effectiveness, this study extends our understanding of which leaders actively engage in and effectively perform sensegiving. These findings also contribute to the literature on collective-level change response.

Drawing upon affective events theory and regulatory focus theory, the current study examines the relationship between customer gratitude and customer-oriented organizational citizenship behavior (customer-oriented OCB), and the mediating role of positive affect and the moderating role of trait promotion focus.

Three-wave time-lagged data from 674 nurses nested within 125 department supervisors was collected. Multilevel modeling and Monte Carlo simulation were performed using Mplus 7.1 software to test all hypotheses.

Customer gratitude was positively associated with nurses’ customer-oriented OCB, and the relationship was mediated by positive affect. Nurse trait promotion focus strengthened the positive relationship between patient gratitude and positive affect, such that the relationship was stronger for those with a higher level of trait promotion focus.

This paper extends research on the effects of customer gratitude by extending our understanding of customer gratitude as a positive workplace event that can potentially promote employees’ positive behaviors toward customers, especially those with a stronger promotion focus.

This study is one of the first to examine the relationship between customer gratitude and employee customer-oriented OCB and uncover potential mechanisms to understand how this relationship unfolds and to whom this relationship is stronger. These findings can inform practices for service organizations.

With the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning; it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users.

Based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software.

The results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources.

There is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education.

This paper examines the direct and indirect effects of CEO intellectual capital on sustainable growth of small and medium-sized enterprises (SMEs) in China.

It adopts a quantitative approach based on 3,474 unbalanced panel data in 836 Chinese listed SMEs from 2008 to 2017.

The findings reveal a significant positive relationship between CEO intellectual capital and sustainable growth of SMEs. The findings also reveal that dual innovation (exploitative innovation and exploratory innovation) has a partial mediating effect on the relationship.

It further provides an original contribution to identifying and testing the mediating effect of dual innovation on the relationship between CEO intellectual capital and sustainable growth of SMEs.

In contrast to most of the extant literature, which considers intellectual capital as a whole at the organizational level or focuses on employee intellectual capital, this paper highlights the role of CEO intellectual capital for SMEs’ growth. It provides an original contribution to identifying and testing the mediating effect of dual innovation on the relationship between CEO intellectual capital and sustainable growth of SMEs.

The international mentoring literature predominantly features traditional company-assigned expatriates as protégés overlooking other types of global talent, such as immigrants, refugees, and international graduates, who may help organizations gain long-term IHRM competitive advantages. We integrate multidisciplinary research to better understand the role of mentoring as a global talent management tool, identify research gaps, and propose future research directions.

We draw on an integrative review of 71 academic journal articles published between 1999 and 2024 to explore the role of mentoring in managing global talent (i.e. expatriates, immigrants, refugees, and international students and graduates).

We found that research has identified and examined relationships between various antecedents and outcomes of mentoring but mainly treating mentoring as a talent development tool. Less is known about the role of mentoring as a recruitment and selection tool in the pre-employment context. Mentoring is an important HRM tool that contributes to managing a global talent pool and developing existing employees.

The review contributes to a better understanding of the characteristics and processes involved in mentoring in a global context by proposing a framework that incorporates antecedents of mentoring, characteristics of the mentoring process, and mentoring outcomes. It highlights the value of mentoring as a recruitment and selection tool supporting global talent management and identifies avenues for future research.

With the arrival of the big data era, governments have appointed a chief data officer (CDO) to meet the opportunities and challenges brought by big data. The existing research on the CDOs is very limited, and what does exist focuses primarily on what are CDOs do. Little research has explored how CDOs do. To fill this gap, this study employed ambidexterity theory to investigate the ambidexterity of CDOs’ impact on data-driven innovation.

To empirically test the model, a survey study was conducted to empirically test the model. Data were collected from 261 CDOs in government and government employees in big data management centers or bureaus. The collected data were analyzed quantitatively to answer hypotheses using a structural equation model.

The findings suggest that data exploitation and data exploration significantly influence data-driven leadership, culture and value propositions. Data-driven leadership and value propositions significantly impact government performance.

This study is one of the first attempts to investigate how CDOs work, especially when promoting data-driven innovation. In addition, this study extends ambidexterity theory into the issue of the CDO in government.