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This study aims to predict customer churn in resort hotels by calculating the churn probability of repeat customers for future stays in the same hotel brand.

Based on the recency, frequency, monetary (RFM) paradigm, random forest and logistic regression supervised machine learning algorithms were used to predict churn behavior. The model with superior performance was used to detect potential churners and generate a priority matrix.

The random forest algorithm showed a higher prediction performance with an 80% accuracy rate. The most important variables were RFM-based, followed by hotel sector-specific variables such as market, season, accompaniers and booker. Some managerial strategies were proposed to retain future churners, clustered as “hesitant,” “economy,” “alternative seeker,” and “opportunity chaser” customer groups.

This study contributes to the theoretical understanding of customer behavior in the hospitality industry and provides valuable insight for hotel practitioners by demonstrating the methods that facilitate the identification of potential churners and their characteristics.

Most customer retention studies in hospitality either concentrate on the antecedents of retention or customers’ revisit intentions using traditional methods. Taking a unique place within the literature, this study conducts churn prediction analysis for repeat hotel customers by opening a new area for inquiry in hospitality studies.

This paper will discuss one university’s efforts to initiate a process to better support PK-12 continuous school improvement goals for all 13 schools in their PDS network as a way to broaden the university’s mission and respond more formally to the individual school communities with which they partner.

The paper is conceptual in that it presents an innovative idea to stimulate discussion, generate new ideas and advance thinking about cross-institutional collaboration between universities and professional development schools.

The paper provides insights and ideas for bringing about change and growth in a seasoned PDS partnership network by connecting PK-12 continuous school improvement efforts to PDS partnership work.

This paper fulfills an identified need to study how seasoned partnerships can participate in simultaneous renewal by offering ideas that school–university partnership leaders can build upon as they make efforts to participate in the process of growth and change.

Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investing and the rise of sustainable investment assets. Nevertheless, challenges in ESG disclosure, such as quantifying unstructured data, lack of guidelines and comparability, rampantly exist. ESG rating agencies play a crucial role in assessing corporate ESG performance, but concerns over their credibility and reliability persist. To address these issues, researchers are increasingly utilizing machine learning (ML) tools to enhance ESG reporting and evaluation. By leveraging ML, accounting practitioners and researchers gain deeper insights into the relationship between ESG practices and financial performance, offering a more data-driven understanding of ESG impacts on business communities.

The authors review the current research on ESG disclosure and ESG performance disagreement, followed by the review of current ESG research with ML tools in three areas: connecting ML with ESG disclosures, integrating ML with ESG rating disagreement and employing ML with ESG in other settings. By comparing different research's ML applications in ESG research, the authors conclude the positive and negative sides of those research studies.

The practice of ESG reporting and assurance is on the rise, but still in its technical infancy. ML methods offer advantages over traditional approaches in accounting, efficiently handling large, unstructured data and capturing complex patterns, contributing to their superiority. ML methods excel in prediction accuracy, making them ideal for tasks like fraud detection and financial forecasting. Their adaptability and feature interaction capabilities make them well-suited for addressing diverse and evolving accounting problems, surpassing traditional methods in accuracy and insight.

The authors broadly review the accounting research with the ML method in ESG-related issues. By emphasizing the advantages of ML compared to traditional methods, the authors offer suggestions for future research in ML applications in ESG-related fields.

Collaboration in agricultural value chains (AVCs) has the potential to increase smallholders’ participation in international value chains and increase their benefits from participation. This scoping review explores existing collaboration models among stakeholders of AVCs in developing countries, examines enablers and constraints of collaboration and identifies policy gaps.

We systematically searched three databases, CAB Abstracts, Econlit (EBSCO) and Agricola, for studies published between 2005 and 2023 and included 59 relevant studies on AVC collaboration.

The primary motivations for collaboration are to enhance market access and improve product quality. Key outcomes of collaboration include improvements in farmers’ welfare, market participation and increased production; only a few studies consider improved risk management as an important outcome. Robust support from government and non-governmental entities is a primary enabler of collaboration. Conversely, conflicts of interest among stakeholders and resource limitations constrain collaboration possibilities. Collaboration involving high-value crops prioritizes income increases, whereas collaboration involving staple crops focuses on improving household food security.

This study may have publication bias as unsuccessful instances of collaboration are less likely to be published.

This study is unique in highlighting collaboration models’ characteristics and identifying AVC policy and programmatic areas where private firms, farmers’ groups, local governments and donor agencies can contribute.

In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management.

This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed.

The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making.

The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

The purpose of this study is to provide a systematic review of the existing literature on social media (SM) use and examine its relationship with various facets of social well-being (SWB).

The study identifies and selects relevant articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, wherein 273 articles were identified using the keyword search criteria from 5 databases namely Web of Science, Emerald, Pubmed, Google Scholar and EBSCOhost, and finally, 20 relevant studies were included for this systematic review. In order to provide directions for future research, a thorough profile with the key findings and knowledge gaps is presented.

The majority of the reviewed studies report an increase in the use of SM, especially amongst adolescents, and this suggests a seriously detrimental impact on their SWB in terms of cyberbullying, lifestyle comparison and impact on self-esteem, substance abuse, declined academic performance, fear of missing out (FoMo) and social overload. However, some of the studies reported life satisfaction, a reduction in loneliness and improved social support and belongingness, particularly those focussing on old age people who experience social isolation. The review also affirmed improved job performance and employees’ well-being. These findings vary across various demographic variables and various SM platforms namely Facebook, Twitter, Instagram, WhatsApp, WeChat, YouTube, etc.

The findings have significant implications for SM researchers, family members and educators concerning promoting appropriate SM use, especially in terms of their SWB. The study also provides various suggestions for future studies and the need to further explore the topic as the field of SM use and SWB is ever-growing.

The authors describe an original unit plan that draws from local and national concerns for truthful history education about the history of racial violence in the United States. The unit plan contextualizes one impetus for truth and reconciliation in a community with a history of anti-Black violence.

The participants partnered with the Equal Justice Initiative to pilot the unit in their district’s new African American History course. The unit drew on historical research and cultural memory to situate local history within a broader context of racism and violence.

The teachers identified eight goals for the unit so that students could understand racialized violence, acknowledge racism as the lived experience of many of their students, and participate in a collaborative learning environment with productive discussions. Speaking from their own experiences with racism, and creating opportunities for students to do the same, the teachers aided the community in voicing long-silenced memories.

Besides bridging some of the gaps between local, regional, and national histories, more research is needed to further examine historical trauma and its implications for both the past and present, in order to amplify and humanize experiences of racism. Additional research is a critical step in developing more thoughtful, empathic and holistic discussions of history and racism at the local level.

In the wake of the recent past, the authors have learned that teaching about the history of racial violence can be enhanced and empowered by reference to relevant current events. The resurgence of racially charged language and violence over the past few years makes this goal more urgent than ever. This unit gives practical guidance to teachers who face this challenge.

The sociopolitical reality of historical trauma and racism must be confronted, and proximity to key events is important in conveying the urgency of racial violence and the need for history education that addresses it. Teachers are making difficult decisions about their options for teaching about race, and they are understandably concerned about any perceived missteps. Nonetheless, inclusive, truthful history education is an appropriate and essential response to narratives of exclusion and silence as the authors help students to develop deliberative skills concerning difficult topics such as racial violence. Teachers and students, together, can do the crucial work of remembering.

The stripping away of narrative agency, identity and history can cover up stories about the stripping away of life and dignity. In the unit plan, the authors recognize truth and reconciliation—especially in the education of people who have relatively little exposure to topics of race and racism—as elemental to a restorative stance against racism.

Investment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.

The current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.

Information availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.

Only active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.

This research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.

To the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

This paper aims to examine how Airbnb has been transformed from an informal form of tourism accommodation into an emerging form of tourism e-micro-entrepreneurship through an interesting triangle consisting of three distinct parts: hosts, platform and guests.

Considering that the peer-to-peer response has sealed the sharing economy's success, research methodology involves primary research that focuses on the adeptness of Airbnb hosts as e-micro-entrepreneurs from the customers' perspective. A quantitative methodology was employed by applying a convenience sampling strategy through a structured questionnaire that was distributed online, resulting in a collection of 150 useable responses. A statistical analysis has been performed to test the research's objectives.

Driven by Airbnb hosts' entrepreneurial behavior in managing their listings and guests' responses, research findings led to the development of a post-conceptual IRMA model, which describes this particular form of hosting as an e-micro-entrepreneurship opportunity, while guests' satisfaction confirms the platform's performance and hosts' efforts in service quality provision.

This study brings valuable insights to the tourism e-entrepreneurship literature through the assessment of the Airbnb platform and the hosts as e-micro-entrepreneurs, providing useful information to researchers and managers involved in the Sharing Economy's disruptive innovation and a more complete understanding of the drivers of Airbnb's consumer adoption.

Research on Airbnb mainly focuses on service quality from the customer perspective, while the existing literature does not highlight how a new type of e-micro-entrepreneurship has emerged by operating in the sharing economy's disruptive innovation ecosystem, which illustrates the factors that motivate hosts and guests to share accommodation services in an equilibrium bond.

Stock markets are considered as the largest and most important units for the development and growth of the economy. The present study attempts to provide a comprehensive view of factors influencing investment decision making process of stock market investors. A multi group analysis of gender is also carried out on the proposed model.

The data of 402 valid responses are collected through structured questionnaires from individual investors of North India. SPSS 23 is used to do the descriptive analysis and AMOS 22 is used to establish the validity of the constructs and for hypotheses testing. For performing multi group analysis, several invariance tests have also been conducted to check the robustness of the model.

The results reveal that all the factors such as firm image, accounting information, neutral information, advocate recommendation and personal financial needs significantly influence investment decision making concluding image of the firm being the most influential factor and advocate recommendation being the least influential factor for investment decisions. No significant differences between males and females were found.

The current study suffers from the limitation of restricted geographical area of North India. Moreover, there is also a scope to incorporate more demographic factors for predicting investment decisions.

This study incorporates a range of factors which covers all the aspects of investment decision making. This study also highlights the notion of signaling theory, thus contributing to the limited literature in Indian context.