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To test the applicability of the user-generated content (UGC) derived from social travel network sites for online reputation management, the purpose of this study is to analyze the spatial clustering of the reputable hotels (based on the TripAdvisor Best-Value indicator) and reputable outdoor seating restaurants (based on ranking indicator).

This study used data mining techniques to obtain the UGC from TripAdvisor. The Hierarchical Density-Based Spatial Clustering method based on algorithm (HDBSCAN) was used for robust cluster analysis.

The findings of this study revealed that best value (BV) hotels and reputable outdoor seating restaurants are most likely to be located in and around the central districts of the urban tourist destinations where population and economic activities are denser. BV hotels' spatiotemporal cluster analysis formed clusters of different sizes, densities and shape patterns.

This study showed that reputable hotels and restaurants (H&Rs) are concentrated within districts near historic city centers. This should be an impetus for applied research on urban investment environments.

The findings would be rational guidance for entrepreneurs and potential investors on the most attractive tourism investment environments.

There has been a lack of studies focusing on analyzing the spatial clustering of the H&Rs using UGC. Therefore, to the best of the authors’ knowledge, this study is the first to map and analyze the spatiotemporal clustering patterns of reputable hotels (TripAdvisor BV indicator) and restaurants (ranking indicator). As such, this study makes a significant methodological contribution to urban tourism research by showing pattern change in H&Rs clustering using data mining and the HDBSCAN algorithm.

为了测试社交旅游网站 (STNS) 的用户生成内容 (UGC) 对在线声誉管理 (ORM) 的适用性, 本研究分析了知名酒店的空间聚类（基于 TripAdvisor 最佳价值指标) 和信誉良好的户外座位 (ODS) 餐厅(基于排名指标)。

该研究使用数据挖掘技术从 TripAdvisor 获取 UGC。 基于（HDBSCAN）算法的分层基于密度的空间聚类方法用于鲁棒聚类分析。

调查结果显示, 最具价值 (BV) 酒店和信誉良好的 ODS 餐厅最有可能位于人口和经济活动较为密集的城市旅游目的地的中心区及其周边地区。 BV 酒店的时空聚类分析形成了不同大小、密度和形状模式的聚类。

目前的文献扔缺乏专注于分析利用 UGC 的酒店和餐厅 (H&R) 空间聚类的研究。 因此, 本研究首次绘制并分析了知名酒店（TripAdvisor BV 指标）和餐厅（排名指标）的时空聚类模式。 因此, 本研究通过利用数据挖掘和 HDBSCAN 算法显示 H&Rs 聚类的模式变化, 为城市旅游研究做出了重要的方法论贡献。

这项研究表明, 著名的 H&R 集中在历史悠久的市中心附近的地区。 这应该是对城市投资环境的应用研究的推动力。

研究结果将为企业家和潜在投资者提供最具吸引力的旅游投资环境的理性指导。

This study aims to unfurl the mediating role of facets of voice behavior. The study also unearths the relationship between servant leadership and voice behavior.

The data were collected from follower manager dyads in two waves of data collection initiated after the completion of the preceding wave. The final sample size obtained was 312.

The study found servant leadership to be related to innovative work behavior and facets of voice behavior. The study also found promotive voice behavior and preventive voice behavior to be related to the innovative work behavior of employees. The study found promotive voice behavior and prohibitive voice behavior work as parallel mediators linking servant leadership to the innovative work behavior of employees.

To the best of the authors’ knowledge, this study is the first to unearth mediation linking servant leadership to innovative work behavior through both facets of voice behavior.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

The study aims to investigate the mediating roles of leadership identification and organizational identification linking abusive supervision to employees' turnover intention.

Using a self-administer survey design, data were collected from 229 nursing workforce employed in hospitals located in Karachi.

The research findings show that abusive supervision has a considerably positive influence on turnover intention. The findings also show that abusive supervision negatively affects nurses' leadership identification and organizational identification. Leadership identification and organizational identification were found to be negatively related to nurses' turnover intention. Finally, leadership identification and organizational identification were found to parallelly mediate the relationship between abusive supervision and turnover intention.

This study helped uncover the previously unknown parallel mediating mechanism of organizational identification and leadership identification. Additionally, abusive supervision was found to negatively affect employees' leadership identification.

Based on social exchange and social learning theories, this study explicates the mediating role of individual-level human capital, structural capital and relational capital in linking servant leadership with the innovative work behavior (IWB) of employees.

Data were collected from 256 manager–employee dyads from the IT sector of Pakistan in three phases through a survey conducted two months apart.

Results showed that two dimensions of individual-level intellectual capital, namely, individual-level human capital and individual-level relational capital, mediated the relationship between servant leadership and IWB, whereas individual-level structural capital did not mediate the relationship between the two variables.

This study confirms the relationship between servant leadership and IWB and tests the mediating role of the three facets of individual-level intellectual capital in linking servant leadership with the IWB of employees.

This study aims to analyze the connection between servant leadership and the promotive and prohibitive voice behavior of employees. In addition, this study explained how servant leadership affects promotive and prohibitive voice behavior through meaning.

For this study, data were collected from employee–manager dyads employed in the service sector. Each of the waves was initiated two months after the first wave. Finally, 286 useful responses were obtained. The collected data was analyzed through covariance-based structural equation modeling.

Servant leadership is related with meaning, promotive voice behavior and prohibitive voice behavior. Meaning is found to mediate the relationship between servant leadership and the two facets of voice behavior.

To the best of the authors’ knowledge, this study is the first to explore the mediating role of meaning relating servant leadership to promotive and prohibitive voice behavior.

The study was conducted to understand how the moral identity of employees mediates the relationship between servant leadership and the moral disengagement of employees. Additionally, the study explores whether servant leadership's ability to build the moral identity of employees is contingent upon employees' perception of organizational politics.

The data were collected from 500 service sector-employed knowledge workers. Data analysis was done through structural equation modeling.

The study found servant leadership to be related to the moral identity of employees. Additionally, moral identity and moral disengagement were found to be negatively related. Moral identity was found to mediate the relationship between servant leadership and moral disengagement. Finally, the study found that the relationship between servant leadership and employees' moral identity was contingent upon their perception of organizational politics.

The study explored the previously unexplored mediating role of moral identity linking servant leadership to the moral disengagement of employees. The study also explained how the relationship between servant leadership and the moral identity of employees was contingent upon employees' perception of organizational politics.

The aim of this research is to examine the relationship between spiritual leadership and employee resilience. Specifically, this research explores how meaning mediates the relationship between spiritual leadership and employee resilience.

This quantitative study collected data from the 439 respondents using a seven-point Likert scale. Structural equation modeling was employed to test the relationship among spiritual leadership, meaning and employee resilience.

The results shows that spiritual leadership, directly and indirectly, influences employee resilience. Spiritual leadership also instills meaning among employees through exhibiting genuine concern and communicating a shared vision. This research also demonstrates that meaning cultivates employee resilience to survive and thrive in a challenging business environment.

First, organizations should assess, incorporate and promote altruistic values and shared vision in their leadership program and staffing process. Second, this study urges leaders and managers to create meaning in the workplace. Lastly, the COVID-19 pandemic has disrupted the daily routines and order that humans are accustomed to, causing distressing circumstances for many people. This research calls for spiritual leadership to respond proactively by providing employees with support, appreciation and direction in those times.

This research moves forward the extant academic discussion on spiritual leadership, meaning and employee resilience in two ways. First, this study adds empirical evidence to the relationship between spiritual leadership and employee resilience, which has drawn scant attention from scholars. Second, this research buttresses the proposed framework from the perspective of positive psychology and broaden-and-build theory.

This research aimed to maximize the value of date exports for the Kingdom of Saudi Arabia.

To achieve its objective, this study relied on secondary data and quantitative economic analysis represented by the Linear programming model.

This study showed that Saudi Arabia exports dates to the United Arab Emirates, Yemen, Kuwait, Turkey, Somalia, Jordan, Oman, India, Indonesia, Bangladesh Morocco, Lebanon, and others. The geographical concentration coefficient for the quantity and value of date exports was 35.05% and 34.74%, respectively, during the study period. Saudi Arabia exported a quantity of dates amounting to 83.08 thousand tons, representing 40.57% of the average total amount of Saudi dates exports during the study period, to Yemen, Somalia, India, Indonesia, Bangladesh, Egypt, China, Djibouti, Bahrain, and Ethiopia, at prices lower than the average export price of 1200.31 dollars/ton, and therefore the export policy needs to restructure the geographical distribution of date exports. Based on the models of geographical distribution, Saudi date exports value can be increased by 32.76–127.12 million dollars, meaning can be increased by 13.77% – 53.44%. In light of the results of the proposed models, this study recommends the need to restructure the geographical distribution of Saudi date exports so that the value of Saudi date exports can be increased by 127.12 million dollars from the current situation for the period 2017–2021.

The paper’s original contribution lies in its proposal to restructure the geographical distribution of Saudi date exports to increase the value of exports.