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The study explores the potential barriers to data science (DS) implementation in organizations and identifies the key barriers. The identified barriers were explored for their interconnectedness and characteristics. This study aims to help organizations formulate apt DS strategies by providing a close-to-reality DS implementation framework of barriers, in conjunction with extant literature and practitioners' viewpoints.

The authors synthesized 100 distinct barriers through systematic literature review (SLR) under the individual, organizational and governmental taxonomies. In discussions with 48 industry experts through semi-structured interviews, 14 key barriers were identified. The selected barriers were explored for their pair-wise relationships using interpretive structural modeling (ISM) and fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analyses in formulating the hierarchical framework.

The lack of awareness and data-related challenges are identified as the most prominent barriers, followed by non-alignment with organizational strategy, lack of competency with vendors and premature governmental arrangements, and classified as independent variables. The non-commitment of top-management team (TMT), significant investment costs, lack of swiftness in change management and a low tolerance for complexity and initial failures are recognized as the linkage variables. Employee reluctance, mid-level managerial resistance, a dearth of adequate skills and knowledge and working in silos depend on the rest of the identified barriers. The perceived threat to society is classified as the autonomous variable.

The study augments theoretical understanding from the literature with the practical viewpoints of industry experts in enhancing the knowledge of the DS ecosystem. The research offers organizations a generic framework to combat hindrances to DS initiatives strategically.

Hiring employees with criminal records has become a heightened topic of conversation for hospitality practitioners. The labor shortage in the hospitality industry has increased consideration for individuals impacted by the justice system, bolstered by programming such as second-chance vocational training programs. However, hospitality practitioners with second-chance employment practices have had challenges managing the multiple stakeholder relationships to employ and retain justice-impacted employees. The purpose of this paper is to aid practitioners in the hospitality industry with an innovative and inclusive hiring practice with timely implications in the United States.

This paper will emphasize the correctional system actors, community-based partners, and justice-impacted employees as salient stakeholders in such hiring contexts. Employing a stakeholder theoretical perspective, we outline a stakeholder map for hospitality practitioners with fair chance hiring practices to better understand second-chance employees.

Organizations do not sit on the periphery of a community; they are interconnected with the community in many direct and indirect ways. Past research has yet to identify a relationship between stakeholder theory and second-chance employment when the stakeholders involved in this employee population are arguably extended. By providing a stakeholder mapping process second-chance employment context, the interwoven actors’ needs can be more holistically assessed.

In America, individuals with a criminal record are often a forgotten and stigmatized labor pool. With this paper, we aim to break down barriers of bias while encouraging the narrative toward true Diversity, Equity, Inclusion, and Belonging (DEIB) hiring practices. Multiple stakeholder management is often an organizational challenge, and by providing this framework, we provide guidelines to practitioners while highlighting the opportunity for community leadership. To that end, we provide guidelines for hospitality practitioners intending to increase justice-involved employee retention through stakeholder relationship management.

Wine consumer behavior has long been a topic of discussion among scholars and industry professionals aiming to understand the underlying predictors of key behavioral outcomes. To help explain wine consumer behavior, concepts such as involvement, expertise, loyalty, satisfaction and perceived risk are often examined. The overarching objective of this study is to determine the relationship between these predictors and their impact on wine purchase intention utilizing a meta-analytical structural equation modeling (MASEM) technique.

As MASEM provides substantive evidence regarding the relationships between theoretical constructs through the combination of multiple studies, the researchers’ aim is to make definitive statements about the predictors of purchase intention.

Findings revealed several relationships that support previous research but also identified relationships that contradict previous literature. This study contributes valuable insights into consumer behavior that wine brands can utilize to improve their marketing efforts.

Wine marketers with a greater understanding of the stronger predictors of purchase intention should be able to create marketing plans that drive wine sales.

Despite the abundance of research that has utilized these theoretical constructs to demonstrate their propensity for determining behavioral outcomes such as purchase intention, no previous attempts have synthesized this body of literature through the use of meta-analysis.

The purpose of this study is to examine institutional quality’s absorptive capacity in African countries’ remittances-finance nexus.

A balanced panel data set of thirty African countries between 2000 and 2022 is used for the study. The study adopts an augmented mean group (AMG), method of moment quantile regression (MMQR) and two-step system generalized method of moment (2SGMM) as the estimation techniques due to the nature of the data set.

The findings of the direct effect reveal that remittances do not constitute the growth of financial development, while institutional quality promotes the growth of financial development in the long. The moderating effect of institutional quality in the linkages shows that the interactive term of institutional quality and remittances has a significant positive effect on financial development in the region. Hence, institutional quality moderates the impact of remittances. These results are robust to different proxies of financial development and estimates obtained from MMQR and 2SGMM.

This study, therefore, suggests that institutional quality is essential in the linkages between remittances and financial development. Hence, remittances should be seen as one of the instruments that can be used to develop the financial sector rather than survival mechanisms for households.

This study contributes to the literature by unearthing the absorptive capacity of institutional quality in the nexus between remittances and financial development in African countries, which extant studies have neglected.

The present study aims to compare the effect of international paid remittances on financial development in three Gulf Cooperation Council (GCC) countries from 1985 to 2020.

The study applied the bound cointegration technique and the autoregressive distributed lag (ARDL) method for long- and short-run estimations as well as diagnostic tests to increase robustness.

The ARDL long-run results showed that international paid remittances had a significant negative effect on financial development in Oman and Saudi Arabia but an insignificant negative effect in Bahrain. The error correction model for the short run of the ARDL slowdown model showed that international paid remittances had a significant positive effect on financial development in Oman, Bahrain, and Saudi Arabia.

Few studies have examined remittance outflows from GCC countries, which are enriched by oil wealth and located in one of the most stable geographical areas in the world. The findings from this study can help policymakers understand how to enable remittances and investments in order to establish regulations that will preserve remittance inflows and meet target services.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。

Social media marketing (SMM) denotes attaining website traffic or attention through social media platforms. The paper aims to focus on examining the viewpoint of library professionals from different universities along with the user respondents from different disciplines and universities on the role of social media tools to market university libraries.

It is a survey-based study that used the questionnaire as its chief data collection instrument designed to be administered to library professionals (n = 100) and users (n = 1,189) from eight universities. The study also adopted the general observation and interview methods to supplement the data. The data was analyzed using SPSS software, and Chi-square test and ANOVA were applied to ascertain the significant variations in viewpoints of the library professionals and the users.

The research study showed that library professionals (from different universities) and users (from different disciplines and universities) felt that social media could be an appropriate marketing tool for libraries in the future. Observations during data collection highlighted a lack of confidence and unwillingness among library professionals to implement any changes in the present time.

The present study provides some significant insights for improving the current situation of the libraries under study in terms of increasing awareness among the students and adopt social media tools for marketing of library facilities and services in future. It is important to note that a few issues that came into light during data collection were a marked lack of willingness and prevalence of less confidence among the library professionals to execute or implement the social media tools to market library resources and services among the users.

SMM programs focus on creating content that attracts attention and encourages readers to use it. This study attempts to fill the gap in of marketing in libraries through social media. The paper offers insights into the use of marketing tools for promoting library resources and services as per the needs of library users. The research work differs from other studies undertaken on library marketing related to social media as it has included both library professionals and users together to obtain a better picture in this regard.

The construction business is widely recognised for its inherent complexity and dynamic nature, which stems from the nature of the job involved. The industry is often regarded as one of the most challenging industries globally in terms of implementing environmental, health and safety (EHS) practices. However, in the absence of EHS, the construction industry cannot be considered sustainable. Therefore, this study aims to identify the trends, knowledge gaps and implications of EHS research to enhance construction activities and knowledge.

The study adopted a science mapping approach involving bibliometric and scientometric analysis of 407 construction EHS publications from the Scopus database with the VOSviewer software. The study is based on journal articles from the Scopus database without restriction to any time range.

The main focus of construction EHS research identified in the study includes sustainability-related studies, risk-related, environmental issues, EHS management, integrated management systems studies, health and safety related and EHS in the construction process. Some emerging areas also identified include productivity, design, culture, social sustainability and machine learning. The most influential and productive publication sources, countries/regions and EHS publications with the highest impact were also determined.

Documents published in the Scopus database were considered for analysis because of the wider coverage of the database. Journal articles written in English language represent the inclusion criteria, whereas other documents were excluded from the analysis. The study also limited the search to articles with the engineering subject area.

The research findings will enlighten stakeholders and practitioners on the focal knowledge areas in the EHS research domain, which are vital for enhancing EHS in the industry.

To the best of the authors’ knowledge, this review-based study is the first attempt to internationally conduct a science mapping on extant literature in the EHS research domain through bibliometric and scientometric assessments.

The purpose of this study is to prepare a state-of-the-art review on advanced ceramic materials including their fabrication techniques, characteristics, applications and wettability.

This review paper presents the various types of advanced ceramic materials according to their compounding elements, fabrication techniques of advanced ceramic powders as well as their consolidation, their characteristics, applications and wetting properties. Hydrophobic/hydrophilic properties of advanced ceramic materials are described in the paper with their state-of-the-art application areas. Optical properties of fine ceramics with their intrinsic characteristics are also presented within. Special focus is given to the brief description of application-based manipulation of wetting properties of advanced ceramics in the paper.

The study of wetting/hydrophobicity/hydrophilicity of ceramic materials is important by which it can be further modified to achieve the required applications. It also makes some sense that the material should be tested for its wetting properties when it is going to be used in some important applications like biomedical and dental. Also, these advanced ceramics are now often used in the fabrication of filters and membranes to purify liquid/water so the study of wetting characteristics of these materials becomes essential. The optical properties of advanced ceramics are equally making them suitable for many state-of-the-art applications. Dental, medical, imaging and electronics are the few sectors that use advanced ceramics for their optical properties.

This review paper includes various advanced ceramic materials according to their compounding elements, different fabrication techniques of powders and their consolidation, their characteristics, various application area and hydrophobic/hydrophilic properties.

The objective of this study is to investigate the interaction effect between incentive type (financial and compassionate incentives) and the ethicality of merchant strategy on consumer willingness to post positive reviews, while also examining potential variations in consumer responses based on consumption experience, shopping frequency and social class.

Building upon construal level theory, we hypothesized the moderating influence of the ethicality of merchant strategy and examined the three-way interaction among consumers’ demographic characteristics (i.e. consumption experience, shopping frequency and social class), incentive type and the ethicality of merchant strategy. To empirically test our hypotheses, we conducted four experiments and employed ANOVA for data analysis.

The ethicality of merchant strategies moderates the association between incentive type and consumer willingness to post positive reviews, with compassionate incentives eliciting more pronounced moral judgments toward merchant strategies compared to financial incentives. The moderating effect of the ethicality of merchant strategy on the relationship between incentive type and consumer willingness to post positive reviews is particularly strong among consumers who have favorable consumption experiences, engage in frequent shopping and belong to lower social classes.

This study contributes to the existing literature on online reviews by examining the impact of compassionate incentives on consumer review behaviors, analyzing the ethicality of merchant strategies within the realm of online reviews and investigating variations in consumer responses to merchant strategies regarding consumption experience, shopping frequency and social class.