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This study explores central questions related to the connection between social media marketing activities (SMMAs), user engagement and the self-brand connection of restaurant Instagram pages. The study examines the mediating role of user engagement between SMMAs and self–brand connections. Also, this study explores the connection between SMMAs and user engagement through the moderating role of gender and trust.

A convenience sample method was employed to collect data from customers (18–24 years old). A structural equation modeling approach and PROCESS macro were applied based on 298 online questionnaires completed by customers who follow restaurant Instagram pages. The mediating effect for user engagement and the moderating effect for gender and trust were performed.

The findings revealed that SMMAs have a significant positive influence on self–brand connection and user engagement. Further, user engagement acts as a mediator between SMMAs and self–brand connection. The results illustrate the importance of SMMAs in enhancing user engagement in light of gender and trust.

This paper presents significant managerial implications for restaurant businesses about how SMMAs can effectively enhance user engagement behavior and self–brand connection on Instagram pages.

This research developed a theoretical model to understand how SMMAs might enhance user engagement in the restaurant industry by invoking gender and trust as moderating variables in the relationship between SMMAs and user engagement. This paper offers new theoretical and practical contributions that add value to social media marketing (SMM) literature by testing the moderated–mediation model of these constructs in the hospitality sector.

Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

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.

This paper according to the logic of the “digital access divide--digital capability divide--digital outcome divide” aims to systematically discuss the impact of the digital divide on individual happiness in China, accounting for the variations that exist across different groups, as well as the corresponding mechanisms.

This paper presents cross-sectional analyses of the relationship between the digital divide and individual happiness in China. The analyses are based on data from the Chinese General Social Survey 2017, which academic institutions run on the Chinese Mainland. This database contains information on respondents' Internet access, skills and consequences of use, which can measure the digital divide of Chinese individuals at three levels.

First, individual happiness declined when they experienced the digital access divide in China. For the digital capability divide, the lower the usage skills, the more individual happiness declined. When analyzing the digital outcome divide, the greater the negative consequences, the more individual happiness declined. Second, the impacts of digital access, capability and outcome divide vary according to age, gender, education degrees, hukou, region and sub-dimensions. Third, the digital access and capability divide reduce individuals' happiness by lowering their self-rated social and economic status, whereas the digital outcome divide reduce individual happiness by lowering their fairness perception and social trust.

The authors believe that this is the first study to examine the impact and its variations among different groups of the three-level digital divide on individual happiness, as well as its mechanisms.

Reducing carbon emissions in agriculture is vital for fostering sustainable agricultural growth and promoting ecological well-being in rural areas. The adoption of Low-Carbon Agriculture (LCA) by farmers holds great potential to accomplish substantial reductions in carbon emissions. The purpose of this study is to explore the farmers' preference and willingness to engage in LCA.

This study employs the Choice Experiment (CE) method to examine farmers' preferences and willingness to adopt LCA, using field survey data of 544 rural farmers in the Weihe River Basin between June and July 2023. We further investigate differences in willingness to pay (WTP) and personal characteristics among different farmer categories.

The empirical results reveal that farmers prioritize government-led initiatives providing pertinent technical training as a key aspect of the LCA program. Farmers' decisions to participate in LCA are influenced by factors including age, gender, education and the proportion of farm income in household income, with their evaluations further shaped by subjective attitudes and habits. Notably, we discovered that nearly half of the farmers exhibit indifference towards LCA attributes.

To the best of the authors' knowledge, this study is the first to investigate farmers' attitudes toward LCA from their own perspectives and to analyze the factors influencing them from both subjective and objective standpoints. This study presents a fresh perspective for advocating LCA, bolstering rural ecology and nurturing sustainable development in developing nations.

The purpose of this review paper is to investigate the consequences of tourism development and economic growth within the Association of Southeast Asian Nations (ASEAN) countries, focusing specifically on Lao PDR post-pandemic. The adverse effect of COVID-19 on tourism and economic sectors has been pervasive across the ASEAN region, with varying degrees of impact. Some of these difficulties are set to continue, though there are positive signs of recovery and of the resilience of the tourism industry. Utilising case material from Lao PDR in Southeast Asia – an area frequently neglected in tourism studies – the paper sheds light on the post-pandemic landscape to address existing gaps in the current literature.

A case study approach was taken in this review paper, utilising secondary data such as media reports, official reports from Tourism Laos and international governing bodies like United Nations and the World Bank to form a viewpoint discussion in the Lao PDR post-pandemic condition.

This paper reveals that contrary to a long period of recovery post-pandemic, there has been a degree of continuity from the pre-pandemic period. Considerable numbers of backpackers have returned to Vang Vieng, along with Vientiane and Luang Prabang. While the pre-pandemic emphasis on mass tourism persists, there is also an increased focus on regional and domestic markets. Laos, with its strategic location and cross-border connections, aims to take advantage of this shift.

The paper highlights a detailed exploration of the Lao tourism industry post-pandemic. It goes beyond the initial expectations in literature of a complete transformation post-pandemic, highlighting the continuity in visitor sources and traditional tourist attractions. It emphasises the Lao PDR strategic position for market reorientation, providing insight into the nation’s adaptive strategies and a nuanced perspective on the evolving landscape of Lao tourism.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

Three-dimensional (3D) food printing is an innovative technology used to customize food products through the integration of digital technology and food ingredients. The purpose of this study is to assess the current state of research in the field of 3D food printing, identify trending topics and identify promising future research directions.

This bibliometric review systematically evaluates the field of 3D food printing using data from published literature in the Web of Science database. After reference screening, 812 articles were included in the analysis.

The result reveals that research in 3D food printing primarily focuses on the optimization and characterization of mechanical and rheological properties of food inks and that post-printing processing, such as laser treatment, has emerged recently as an important consideration in 3D food printing. However, extant works lack animal and human studies that demonstrate the functionality of 3D-printed food.

This sophisticated bibliometric analysis uncovered the most studied current research topics and the leading figures in the area of 3D food printing, providing promising future research directions.

The objective of this study is to investigate how the implementation of an Emission Trading Scheme (ETS) influences an ETS-regulated firm’s level of earnings smoothness.

Using a staggered difference-in-differences model based on China’s ETS pilots commencing in 2013, this study investigates how the implementation of ETS pilots affects regulated firms’ earnings smoothing relative to non-regulated firms. The sample period spans from 2008 to 2019. This model incorporates time-invariant firm-specific heterogeneity, time-specific heterogeneity, and a series of firm characteristics to establish causality. Robustness tests justify findings.

The results show that after implementing an ETS pilot, regulated firms increase their earnings smoothness relative to non-regulated firms. Regulated firms strategically smooth their earnings to obtain additional financial resources and meet compliance costs arising from an ETS. Further analysis reveals that regulated firms’ earnings smoothing activity is a function of environmental regulations, managerial integrity, and capital market incentives.

This study deviates from past research focusing on the environmental consequences of ETS by indicating that an ETS affects regulated firms’ financial reporting decisions. Specifically, regulated firms resort to earnings smoothing as a short-term exit strategy from financing concerns arising from environmental regulations. This finding expands prior literature primarily focusing on the effect of tax and financial reporting regulations on earnings smoothness. This study also indicates that firms utilize earning smoothing to lower their short-term cost of capital, which enables them to access additional financing at a lower cost and reconfigure their operations to meet stakeholder environmental demands.

The COVID-19 pandemic has profoundly impacted the global economy, disrupting supply chains, causing job losses and altering consumer demand. In Malaysia, the real estate sector has been notably affected, with increased property impairments and overhang due to unprecedented uncertainty. Understanding these effects is crucial for policymakers and investors to prevent real estate and banking crises. This study aims to analyse the relationships between macroeconomic factors during the pandemic on property impairments and overhang, providing insights for maintaining macroeconomic stability. The findings will inform strategies for mitigating economic shocks, identifying opportunities, and guiding real estate policies in Malaysia and potentially globally.

This research article uses a time series ARDL regression analysis to examine pivotal macroeconomic factors including income, housing process, interest rates and unemployment on property loan impairments and property supply overhang in Malaysia. ARDL is effective to measure and analyse time series data, especially to understand the lagged impacts of macroeconomic factors. This can be seen by various economists in analysing macroeconomic factors affecting non-performing loans or the real estate finance using regression analyses both in Malaysia and other regions. The observations are gathered before, during and after the COVID-19 pandemic, spanning a five-year period with monthly frequency from 2018 to 2022.

The study emphasizes the critical importance of effectively managing unemployment and implementing policy interventions, such as moratoriums, to stabilize the economy and reduce the risk of loan impairments during crises like the COVID-19 pandemic. Additionally, this study highlights a significant inverse relationship between income per capita and loan impairments, underscoring the necessity for policies that promote economic growth and income equality. Initiatives targeting job creation, education and skills development can elevate income levels, thereby decreasing loan impairments. Lower lending interest rates during the pandemic also help mitigate the risk of loan impairments by facilitating borrowing, stimulating economic activity and enhancing financial well-being. Furthermore, the study suggests that while lower interest rates incentivize property developers and investors, understanding the intricate interaction between housing prices and supply is crucial for policymakers and stakeholders to effectively manage the housing market and ensure adequate housing supply, especially during crises.

This paper provides insight for policymakers, regulators, investors and property consultants into the dynamic effects of key macroeconomic factors amidst a global recession in how they impact the real estate market with regards specifically to all types of property loan impairments and property supply overhang. The observations are limited to the COVID-19 period, spanning five years with monthly data from 2018 to 2022. This understanding can facilitate the development of targeted strategic monetary policies and investment decisions in case of future recessions.

Policymakers should prioritize initiatives such as moratoriums and job creation programs to mitigate economic downturns. Additionally, financial institutions need to adjust lending practices in response to lower interest rates, while stakeholders in the housing market must understand the complex dynamics between housing prices and supply to ensure a balanced market. Overall, addressing underlying economic factors and implementing targeted policies are essential for building resilience and promoting sustainable economic growth amidst challenging circumstances.

Initiatives aimed at fostering income equality, creating employment opportunities and ensuring housing accessibility contribute to greater social cohesion and well-being. By promoting financial inclusion and building resilience to crises, societies can mitigate the adverse social impacts of economic challenges such as unemployment and housing affordability. Overall, addressing socioeconomic disparities and promoting inclusive growth are essential for fostering a more equitable and resilient society.

The originality and uniqueness of this study lie in its comprehensive analysis of the impact of COVID-19 on loan impairments and housing supply. While previous studies have focused on the pandemic’s effects on specific segments of the real estate market or property prices, this study provides a broad overview of its impact on property loan impairments and housing supply overhang. Finally, this study highlights the social and practical implications. Overall, this study offers a distinctive analysis of COVID-19’s impact on the real estate market and its implications for policymakers, real estate professionals and investors.