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Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The study examines how human resource management practices (HRMPs) – including ability practice, motivation practice and opportunity practice – affect employee well-being (EWB) – including life well-being, job well-being and psychological well-being – in the Chinese cultural context.

A sample of 529 employees from various industries in China participated in the survey for this study. Data were analyzed using structural equation modeling.

The findings indicate that HRMPs have a significant positive effect on EWB. Specifically, practices based on ability, motivation and opportunity have a significant positive effect on job well-being, life well-being and psychological well-being, respectively. Integrity leadership moderates the impact of HRMPs on EWB. Organizational justice has a partial mediating effect on the relationship between HRMPs and EWB. Integrity leadership moderates the mediation effect of organizational justice in the relationship between HRMPs and EWB.

Human resource policies and practices need to create a fair organizational atmosphere, and managers implementing them must have integrity leadership. When selecting and promoting managers, organizations should pay attention to not only a candidate's ability but also his or her integrity.

This study uncovers how the important roles of organizational justice and integrity leadership act on the relationship between HRMPs and EWB, thus advancing our understanding of how HRMPs can effectively increase EWB.

The purpose of this paper is twofold: to identify, evaluate and structure the research that focusses on omni-channel retailing from the perspective of logistics and supply chain management; and to reveal the intellectual foundation of omni-channel retailing research.

The paper applies a multi-method approach by conducting a content-analysis-based literature review of 70 academic papers. Based on the reference lists of these papers, the authors performed a citation and co-citation analysis based on the 34 most frequently cited papers. This analysis included multidimensional scaling, a cluster analysis and factor analysis.

The study reveals the limited consideration of logistics and supply chain management literature in the foundation of the omni-channel retailing research. Further, the authors see a dominance of empirical research as compared to conceptual and analytical research. Overall, there is a focus on the Western retail context in this research field. The intellectual foundation is embedded in the marketing discipline and can be characterised as lacking a robust theoretical foundation.

The contribution of this research is identifying, evaluating and structuring the literature of omni-channel research and providing an overview of the state of the art of this research area considering its interdisciplinary nature. This paper thus supports researchers looking to holistically comprehend, prioritise and use the underpinning literature central to the phenomena of omni-channel retailing. For practitioners and academics alike, the findings can trigger and support future research and an evolving understanding of omni-channel retailing.

This study aims to uncover and develop explicit knowledge of existing smart communities (SCs) to guide services and business solutions for enterprises and serve community users in a well-thought-out manner. These sagacious frameworks will assist in analyzing trends and reaching out to pre-existing setups with different degrees of expertise.

A systematic overview is provided in this paper to unify insights and competencies toward building SCs; a hybrid analytical approach is used consisting of machine learning and bibliometric analysis. Scopus and Web of Science (WoS) are the primary databases for this purpose.

SCs implement cutting-edge technologies to enhance mobility, elevating information and communication technology (ICT) skills and data awareness while improving business processes and efficiency. This system of SC is an evolution of the conventional method. It provides a foundation for intelligent community services based on individual users and technologies such as the Internet of Things (IoT), artificial intelligence, cloud computing and big data. Manufacturing-based, service-based, retail-based, resource management and infrastructure-based SCs exist in the literature.

The paper summarizes a conceptual framework of SCs based on existing works around SCs. To the best of the authors’ knowledge, this is the first systematic literature review that uses a hybrid approach of topic modeling and bibliometric analysis to understand SCs better.