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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 paper is to examine the consumer response to brands offering gendered product differentiation (i.e. products “for her” or “for him”).

Across three experiments, the effect of gendered (vs gender-unrelated) product differentiation on perceived brand sexism and word-of-mouth intention was tested. The moderating effects of feminist identity (Studies 1 and 2), endorsement of sexist beliefs (Study 2) and basis (stereotypical vs biological) for product differentiation (Study 3) were also tested.

Consumers perceive brands that offer gendered product differentiation as sexist, which in turn leads to negative word-of-mouth intention. Moreover, consumers with a strong feminist identity are more likely to perceive brands that offer gendered product differentiation as sexist, whereas consumers who endorse sexist beliefs are less likely to do so. Finally, consumers respond negatively when the gendered product differentiation is based on a gender stereotype, but much less so when it seems based on a biological difference between sexes.

Although multiple brands offering gendered products have been denounced by consumers as sexist, no research has examined this phenomenon. This paper pioneers in examining the consumer response to brands offering gendered product differentiation and in demonstrating that consumers perceive such brands as sexist.

The proliferation of ride-hailing businesses brings significant considerations for improving the driver's operational performance. Informed by the literature on sharing economy, general deterrence theory and protection motivation theory this research investigates the behavioral factors impacting ride-hailing drivers' operational performance.

The authors empirically test the antecedents impacting a ride-hailing driver's operational performance using an online survey dataset comprising 513 ride-hailing drivers working for Uber and Lyft in the United States.

Ride-hailing drivers' intention to comply with the ride-hailing company guidelines results in better operational performance for the driver. Moreover, drivers believe that ride-hailing companies have effective penalties to deter drivers from violating company guidelines. However, drivers also believe that the chances of being caught while ignoring the company guidelines are low.

The results of this research support the decision-making processes of ride-hailing company managers and offer insights on how managers can enhance the operational performance of their drivers.

This study provides unique contributions to emerging research at the intersection of peer-to-peer asset sharing, behavioral studies and technology management. This research is one of the first to explore the role of behavioral factors such as coping mechanisms on the operational performance of sharing economy workers.

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.

Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost.

A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model.

The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited.

The authors confirm that this is an original paper and has not been published or under consideration in any other journal.

Purpose: This study examines the effect of uncertainties on the hospitality industry from different perspectives across the globe. The hospitality industry faces several contemporary issues and challenges that have the potential to impact its growth and development. This study aims to analyse the current problems and uncertainties in the hospitality sector.

Need for the Study: The hospitality industry plays a significant role in the global economy with various services, including accommodation, food and beverage, events, and tourism. However, the sector faces several contemporary issues and challenges that have the potential to impact its growth and development. This study provides an overview of the most significant problems and challenges facing the hospitality industry today.

Methodology: A systematic literature review was conducted to identify and synthesise relevant studies on the effect of uncertainties issues on the hospitality industry. A systematic search of the Web of Science and Scopus databases was conducted to determine relevant studies published between 2010 and 2021. Studies were screened and selected based on pre-defined inclusion and exclusion criteria. A thematic analysis was performed to categorise the uncertainties and issues in the hospitality industry.

Findings: The study identified several uncertainties and issues facing the hospitality industry, including the pandemic uncertainties, financial crisis, whether positive and negative impacts, terrorism attacks on hotels and tourist places, uncertainties in government policies, situational risks like uncertainties, ambiguity, cultural differences, changes in tourist preferences and changing habits of the tourist.

This study aims to explore the challenges faced by the tourism and hospitality industry following the COVID-19 pandemic and to propose effective strategies for recovery and resilience of this sector.

The study analysed the challenges encountered by the tourism and hospitality industry post-pandemic and identified key strategies for overcoming these challenges. The study utilised the modified Delphi method to finalise the challenges and employed the Best-Worst Method (BWM) to rank these challenges. Additionally, solution strategies are ranked using the Criteria Importance Through Intercriteria Correlation (CRITIC) method.

The study identified significant challenges faced by the tourism and hospitality industry, highlighting the lack of health and hygiene facilities as the foremost concern, followed by increased operational costs. Moreover, it revealed that attracting millennial travellers emerged as the top priority strategy to mitigate the impact of COVID-19 on this industry.

This research contributes to understanding the challenges faced by the tourism and hospitality industry in the wake of the COVID-19 pandemic. It offers valuable insights into practical strategies for recovery. The findings provide beneficial recommendations for policymakers aiming to revive and support these industries.

The purpose of this manuscript is to detect heart fault using Electrocardiogram. Mutually low and high frequency noises such as electromyography (EMG) and power line interference (PLI) degrades the performance of ECG signals.

The ECG record depicts the procedural electrical movement of the heart, which is non-invasive foot age obtained by placing surface electrodes on designated locations of the patient’s skin. The main concept of this manuscript is to present a novel filtering method to cancel the unwanted noises in ECG signal. Here, intrinsic time scale decomposition (ITD) is introduced to suppress the effect of PLI from ECG signals.

In the existing ITD, the gain control parameter is a constant value; however, in this paper it is an adaptive feature that varies according to certain constraints. Simulation outcomes show that the proposed method effectively reduces the effect of PLI and quantitatively express the effectiveness with different evaluation metrics.

The results found by the proposed method are compared with Fourier decomposition technique and eigen value decomposition methods (EDM) to validate the effectiveness of the proposed method.