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The purpose of this study is to assess the value of altmetrics or other indicators, showcasing the impact of academic output, which is seen too often correlated with the citation count.

This study considered three reputed journals of Library and Information Science (LIS) published by Elsevier. A total of 1,164 articles were found in these journals from 2016 to 2020 and the relationships between altmetric attention scores (AAS) and citations were examined. The analysis was extended to compare the grouped data set based on percentile ranks of AAS like top 50%, top 25%, top 10% and top 1%.

Using Spearman correlation analysis, the findings reveal a positive correlation between AAS and citations with different significant levels for all articles, and articles with AAS, as well as for normalized AAS in the top 50%, top 25%, top 10% and top 1% data set. For the three journals International Journal of Information Management (IJIM), Journal of Informetrics (JIF) and Library and Information Science Research (LISR), a significant positive correlation is observed across all data sets. But an unexpected result was observed: in the case of the top 50% of articles for the IJIM and JIF showed no significant correlation but the LISR journal showed a positive correlation for the whole data set. This journal though has fewer articles in comparison to the other two.

A source item that is highly cited may not be having high social media attention as reflected in the findings. This demarcates AAS with citations implying various factors on which these measurements are dependent. The study distinguishes these metrics lucidly. There is not a single guideline or uniformity in assessing the correlation found. But the problem is that the interpretation of the correlation strength affects the conclusion of the study. Moreover, this study will be a role model as a draft for librarians to select relevant journals for their libraries and will facilitate authors in the choice of the publication outlets for their papers, particularly concerning the journals that have both visibility and research impact.

The study reported devising a comprehensive tool to validate AAS as a measure of scholarly impact to include appropriate social media sources and verify its relationship with other metrics. To the best of the authors’ knowledge, this paper is the first attempt to discover the correlation between AAS and citations for the highly impactful LIS journal published by Elsevier. The empirical evidence lies in the citation and altmetric data extracted from the dimension database.

The purpose of this study is to develop a model that accurately forecasts hotel room cancelations and further determines the key cancelation drivers.

Predictive modeling, specifically the machine learning methods, is used to forecast room cancelations and identify the main cancelation factors.

By using three different classification algorithms, this study demonstrates that hotel room cancelation can be accurately predicted using XGBoost, as well as the ensemble method involving Support Vector Machine, Random Forest and XGBoost.

This study attempted to forecast hotel room cancelations by applying a relatively new method, machine learning. By implementing predictive modeling, one of the most emerging and innovative research methods, this study ultimately provides prediction suggestions in various aspects and levels for hotel management operations.

本研究旨在开发一个能够准确预测酒店客房取消的模型, 并进一步确定主要的取消因素。

采用预测建模, 具体来说是机器学习方法, 来预测客房取消, 并识别主要的取消因素。

通过使用三种不同的分类算法, 本研究表明使用XGBoost以及涉及支持向量机、随机森林和XGBoost的集成方法可以准确预测酒店客房取消。

本研究尝试通过应用相对较新的方法, 即机器学习, 来预测酒店客房取消。通过实施预测建模, 这是目前新兴和创新的研究方法之一, 本研究最终为酒店管理运营在各个方面和层面提供了预测建议。

Mergers and acquisitions (M&As) are important strategic actions undertaken by firms to access resources and markets. However, firms face substantial challenges in M&As during deal completion. While prior literature reviews synthesize the studies on the post-merger consequences of M&As, the literature on deal completion is largely fragmented. In this paper, the authors synthesize prior literature on deal completion into the antecedents and consequences framework and map various studies across the international business and management, finance and accounting literature at the macro-, meso- and micro-levels.

The authors adopt a content analysis-based methodology to conduct the review. First, the authors identify existing literature on deal completion based on keyword searches. Next, the authors propose a framework that integrates the extant literature from a multi-theoretic perspective across four broad themes: concepts, antecedents, implications and moderators. In this study, the authors consider not only empirical but also conceptual papers to strengthen the theoretical foundations of M&A literature. Finally, after synthesizing various studies, the authors highlight a future research agenda on deal completion.

Based on the review, this study provides important avenues for future research on M&A deal completion.

This study theoretically integrates multi-disciplinary and multi-country research on acquisition completion.

The purpose of this study is to identify individuals’ preferences for the information attributes of Airbnb, a representative peer-to-peer (P2P) accommodation platform. In the sharing economy, platforms are considered to be the principal intermediaries in supply and demand, and these platforms have distinctly different attributes from traditional accommodation reservation systems.

The present study used a choice experiment, which is a method for systematically identifying attributes’ preferences in the form of marginal willingness-to-pay (MWTP). Amazon Mechanical Turk, a crowdsourcing marketplace, was used for data collection, and 243 respondents ultimately participated in the survey.

Results showed that respondents’ choices were positively affected by the number of pictures of an accommodation, host experience, cancellation policy and local information but negatively affected by cost. Regarding MWTPs, host experience had the highest value (US$84.25), followed by cancellation policy (US$40), photos (US$26.67) and local information (US$10.92).

These study results could provide strategic guidance for guiding the development of P2P accommodation platforms by providing a prioritized list of preferred attributes for Airbnb.

本研究旨在识别个人对Airbnb这一代表性P2P住宿平台信息属性的偏好。在共享经济中, 平台被视为供需的主要中介, 而这些平台与传统住宿预订系统有明显不同的属性。

本研究采用选择实验法（CE）, 这是一种系统地确定属性偏好的方法, 表现为边际支付意愿（MWTP）。数据采集使用了Amazon Mechanical Turk（MTurk）, 最终有243名受访者参与了调查。

结果显示, 受访者的选择受到住宿图片数量、房东经验、取消政策和本地信息的积极影响, 而受到价格的负面影响。关于边际支付意愿, 房东经验具有最高价值（84.25美元）, 其次依次为取消政策（40美元）、照片（26.67美元）和本地信息（10.92美元）

通过提供优先考虑的 Airbnb 偏好属性列表, 本研究结果可以为引导P2P住宿平台的发展提供战略指导.

The study proposes an evaluation model that allows quantitative characterization of the effects of service quality on consumer’s satisfaction for online food delivery (OFD) services in a nonlinear manner. As such, the authors endeavor to bridge the research-to-practice gaps whereby the effect magnitudes and nonlinear patterns of service quality have been overlooked in the current literature.

The quantitative Kano method is adopted. A Kano questionnaire was first developed by synthesizing and operationalizing existing evidence on OFD service qualities. The questionnaire solicited consumers’ evaluations of 21 OFD service attributes, and it was distributed to an online panel in Singapore. With 580 valid responses, the functions that quantitatively depict effects of each attribute on consumer’s satisfaction were subsequently derived.

The results reveal that among Singaporean consumers, food quality, reliability of delivery, responsiveness of customer support, ease-of-use of digital interfaces and promotions are pivotal attributes contributing to above-average satisfaction improvement across all performance levels. Meanwhile, delivery riders’ attitudes and real-time tracking functions emerge as substantial contributors to satisfaction at high-performance levels.

The findings provide crucial insights for OFD practitioners in Singapore in resource prioritization and service optimization. This study demonstrated the importance of streamlining customer support services and focusing on the utilitarian aspects of OFD services. Moreover, these results can be employed in advanced service improvement procedures, providing a roadmap for future OFD service enhancements.

This study pioneers the development of a quantitative quality evaluation model in the OFD context. With the established quantitative Kano model, the study addresses the omission of effect magnitudes and nonlinear patterns of service quality. It highlights the transition from a binary “does it affect satisfaction” to a more nuanced “how much does it affect satisfaction” approach, offering a robust understanding of consumer’s satisfaction dynamics.

As Generation Z (Gen Z) represents one of the most important segments in the travel and tourism sector, the present work aims to analyse Gen Z's behaviour in a post-pandemic scenario. In particular, the present work deepens the factors that may influence future accommodation choices.

This study uses data from a web-based questionnaire targeting Italian tourists belonging to Generation Z, reaching 221 complete responses. After the statistical analysis of the sample, an exploratory factor analysis (EFA) was then carried out.

Data provided evidence about the main factors affecting Gen Z tourist choices. In particular, these young consumers are positively influenced by the information (especially from institutional and verifiable sources), more flexible purchase terms (e.g. full refund in case of cancellation due to contagion of the coronavirus disease 2019 (COVID-19) and digital payments) and structure features (e.g. cleanliness, sanitisation, digitalisation and sustainability).

This work attempts to contribute to the academic literature by looking at the tourism experience from Gen Z's perspective. From this angle, this work underlines the influence some factors exert on the final accommodation choice, especially in the presence of exogenous events. Moreover, as this study focusses on a post-pandemic scenario, the study seeks to provide valuable indications, both at the theoretical and managerial level, for the tourism sector to boost the sector's recovery and prepare the sector to face similar events.

The study has two objectives, first, to examine the effect of COVID-19 deaths and corruption on the government's policy responses, and second, to investigate the effect of COVID-19, corruption and government response on hotel performance, using the developmental system's framework of resilience theory.

The study utilizes hotel data from ten countries collected from 1st March 2020 to 28th February 2021. The data are analyzed using the panel regression analysis in E-views.

The study confirms that government policies direct impact the hotel performance. Specifically, economic support policies have a positive effect on hotel performance, while COVID-19 deaths and restrictions have a negative impact on hotels. The study also found a strong association between corruption and the level of restrictions that governments choose to implement. Therefore, for effective recovery, governments must be mindful of the context in which businesses operate and the effect of their policies on the hotel industry.

The strong correlation between COVID-19 deaths and RevPAR highlights the significance of understanding and addressing customers' risk perception to enhance the resilience of the hotel industry. The findings emphasize the importance of collaboration between the hotel industry and the government for effective crisis management and policymaking.

This study empirically examines how various policy responses and crisis levels impact hotel performance. It sheds light on why countries respond to crises differently and the effects of different policy responses on the hotel industry. The study has many implications for the industry stakeholders and policymakers.

Severe disruptions to maritime supply chains, including port closures, congestion and shortages in shipping capacity, have occurred during the COVID-19 pandemic. This paper’s purpose is to explore flexibility-based countermeasures that enable actors in maritime supply chains to mitigate the effects of disruptions with different characteristics.

Semi-structured interviews were conducted with shipping lines, shippers, forwarders and ports. Data on the COVID-19 pandemic's effects and countermeasures were collected and compared with data regarding the 2016–2017 Gothenburg port conflict.

Spatial, capacity, service and temporal flexibility emerged as the primary countermeasures, whilst important characteristics of disruptions were geographical spread, duration, uncertainty, criticality, the element of surprise and intensity. Spatial flexibility was exercised in both disruptions by switching to alternative ports. During the COVID-19 pandemic, ensuring capacity flexibility included first removing and then adding vessels. Shipping lines exercising service flexibility prioritised certain cargo, which made the spot market uncertain and reduced flexibility for forwarders, importers and exporters that changed carriers or traffic modes. Experience with disruptions meant less surprise and better preparation for spatial flexibility.

Understanding how actors in maritime supply chains exercise flexibility-based countermeasures amid disruptions with different characteristics can support preparedness for coming disruptions.

Comparing flexibility-based measures in a pandemic versus port conflict provides insights into the important characteristics of disruptions and the relevance of mitigation strategies. The resilience of maritime supply chains, although underexamined compared with manufacturing supply chains, is essential for maintaining global supply chain flows.

COVID-19, like many previous crises, proved once more that some hospitality and tourism organizations are more crises resilient than others. Despite increasing frequency and magnitude of crises, little is known about the features of crises resilient organizations and mitigation strategies they adopt. If the characteristics of such resiliency are identified, those strengths might be targeted. Hence, the purpose of this study is to identify characteristics of crises resilient organizations by analyzing the interface between different organizational characteristics, recovery strategies they adopted and impacts of COVID-19 on individual hospitality and tourism organizations.

A global sample of 202 respondents from 20 countries and four continents, representing different sectors of the hospitality and tourism industry, participated in the survey. Descriptive analysis and cluster analysis were used to rank the items and group hospitality and tourism organizations based on their crises resiliency.

Service quality, loyal customers, branding, high paid in capital, domestic market base, hygiene and safety image, information and communication technology adoption, product and market diversification and restructuring debts emerged as major characteristics and strategies of crises resilient organizations. Using cluster analysis, four different groups of organizations were identified. Based on the impacts of COVID-19 on these organizations, Cluster-1 emerged as significantly more crises resilient, whereas Cluster-4 organizations were significantly more vulnerable to crises. Their characteristics and mitigation strategies they adopted were discussed.

The paper not only identified features of crises resilient organizations and successful mitigation strategies but also measured their impact on various performance indicators. Future studies might use characteristics, mitigation strategies and performance indicators identified in this study.

Based on the findings, tourism organizations would focus on strengthening characteristics and implementing strategies that make crises resilient organizations. Public bodies and destination management would also set their decision criteria based on these findings to create a more resilient tourism industry.

This research not only identifies how hospitality and tourism organizations are affected by COVID-19 but also how these impacts change based on different organizational characteristics and strategies. Understanding which organizational characteristics affect the crises vulnerability of hospitality and tourism organizations might inform risk and crises management literature and structural design elements in tourism businesses, hence offer both theoretical and practical implications.

Drawing on the social identity theory, this paper explores the impact of rider–driver ethnicity match on the driver’s expected ride satisfaction and willingness to perform, and rider’s trust on the driver.

The study relies on scenario-based online experiments with 291 ride-hailing drivers and 282 riders in the USA.

The findings indicate that ethnicity match between ride-hailing drivers and riders positively impact driver’s ride satisfaction and willingness to perform, and rider’s trust in the driver. The study also revealed a significant positive moderation effect of ethnic identity on the relationship of ethnicity match and those constructs.

While it may be challenging to influence an individual’s level of ethnic identity, managers can take steps to educate and train their employees regarding the impact of ethnic identity and discrimination, with a particular focus on those individuals who possess a strong sense of ethnic identity.

The findings of this research provide theoretical contributions to the existing literature on ride-hailing services and adds to the limited stream of logistics research that examines the impact of ethnicity on ride-hailing operations.