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This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

The main purposes of this research are: first of all, to re-classify the types of corporate social responsibility (CSR) into primary stakeholder-oriented CSR and secondary stakeholder-oriented CSR from the perspective of stakeholders and, second, to investigate empirically how and which types of CSR can better impact employees' job satisfaction and happiness management.

An online self-administered questionnaire was adopted to test the conceptual model. Questionnaires were sent to Chinese employees and restrict the data to those whose companies had experience implementing CSR. The study employed the partial least squares structural equation modeling (PLS-SEM) technique for data analysis using SmartPLS 4.0 software.

For factors of happiness management, both primary stakeholder-oriented CSR and secondary stakeholder-oriented CSR had significant and positive effects on happiness management. In addition, both primary and secondary stakeholder-oriented CSR positively and significantly affected job satisfaction, with primary stakeholder-oriented CSR way larger than secondary stakeholder-oriented CSR. Job satisfaction, in turn, was positively and significantly associated with happiness management. The results showed that the control variables of gender and education background had significant effects on happiness management.

First, the results provide useful empirical evidence in support of the feasibility that firms could develop competitive and sustainable development strategies by paying more attention to CSR practices. In terms of the primary stakeholder-oriented CSR, managers are recommended to put employees' benefits as a priority and invest in the to offer a healthy and safe working environment or employee support programs. In terms of the secondary stakeholder-oriented CSR, managers are suggested to denote parts of earnings to charity and to people in need. Second, in order to create job satisfaction, firms should put a stronger emphasis on CSR practices. When considering job satisfaction, managers should treat their employees in a socially responsible way and fulfill their demands and rights and place this at the core of their CSR activities.

First, this study makes a contribution to the existing literature by classifying the four important CSR practices into two types from the perspective of stakeholder theory. By incorporating a series of CSR practices and the stakeholder theory, this study provides a comprehensive and reasonable CSR classification, which has not been considered by prior research. Second, this study adds to the literature by defining the construct of happiness management explicitly along with identifying the dimensions of happiness management. Third, to the best of the authors' knowledge, this is one of the first studies exploring the relationship between CSR and happiness management. Finally, this study is among the first to investigate the correlation between job satisfaction and happiness management.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

Outsourcing of logistics has great importance in disaster relief. Aid agencies spend several billion US dollars every year on logistics services. However, the concept of outsourcing has not been established adequately in literature on humanitarian logistics, leading to a fragmented view of the practice. This paper provides a holistic perspective of the concept by constructing a conceptual framework to analyze both practice and research of outsourcing in humanitarian operations. Based on this analysis, we explore future trends and identify research gaps.

The paper is based on a structured review of academic literature, a two-round Delphi study with 31 experts from aid agencies and a complementary full-day focus group with twelve experts from aid agencies and logistics service providers.

The paper systemizes the current practice of outsourcing in humanitarian logistics according to a conceptual framework of five dimensions: subject, object, partner, design and context. In addition, it reveals ten probable developments of the practice over the next years. Finally, it describes eight important research gaps and presents a research agenda for the field.

The literature review considered peer-reviewed academic papers. Practitioner papers could provide additional insights into the practice. Moreover, the Delphi study focused on the perspective of aid agencies. Capturing the views of logistics service providers in more detail would be a valuable addition.

The paper establishes the academic basis for the important practice of outsourcing in humanitarian logistics. It highlights essential research gaps and, thereby, opens up the field for future research.

After the Collective Forest Tenure Reform (CFTR) in China, the enthusiasm of farmers for forestry management is stimulated. However, the forest tenure security varies among farmers, making the research conclusions of its impact on forestry management efficiency inconsistent. Based on the survey data of 1,627 households from the collective forest regions in 6 provinces of China in 2017, this paper not only discusses the differences of farmers' forestry management efficiency after the reform, but also further explores the heterogeneous impact of forest tenure security on forestry management efficiency in combination with different forest management types.

This study employed the stochastic frontier production function model to measure the forestry management efficiency of farmers. Then, Tobit models were used to discuss the influencing factors of farmers' forestry management efficiency.

The results demonstrate that the improvement of farmers' forest tenure security can effectively improve forestry management efficiency, but the effect is affected by forest management types. For farmers who manage economic forests and non-timber forests, safe tenure promotes the forestry management efficiency; while for those who manage ecological public welfare forests, tenure security plays an opposite role.

Therefore, satisfying farmers' differentiated demands for forest tenure according to forest management types to improve forest tenure security and further refining supporting policies of collective forestry reform is of great significance to improve the efficiency of farmers' forestry management in collective forest regions.

Increasing amount of empirical research in operations and supply chain management is using institutional theory as its theoretical lens. Yet, a common scale to measure the three institutional pressures – coercive, mimetic and normative – is lacking. Many studies use proxies or a single, grouped, construct of external pressures which present methodological challenges. This study aims to present the development of multi-item scales to measure institutional pressures (in a purchasing context).

First, items were generated based on the theoretical construct definitions. These items were then tested through academic sorting and an international survey. The first empirical testing failed to produce reliable and valid scales, and further refinement and analysis revealed that coercive pressure splits into two separate constructs. A second q-sorting was then conducted with purchasing practitioners, followed by another survey in Italy to verify the new measurement scale for four institutional pressures.

The multimethod and multistage measurement development reveals that empirically the three institutional pressures actually turn into four pressures. The theoretical construct of coercive pressure splits into two distinct constructs: coercive market pressure and coercive regulatory pressure.

The results of the paper, namely, the measurement scales, are an important theoretical and methodological contribution to future empirical research. They present a much-needed measurement for these theoretical constructs increasingly used in management research.

This scientific article aims to evaluate the efficacy of the drug Doxorubicin for treating hepatocellular carcinoma (HCC) in Egypt. The study analyzes data from patients referred to a multi-disciplinary consultation at the National Cancer Institute, Cairo University. The study includes 40 intermediate-stage HCC patients who underwent treatment with either Doxorubicin-Lipiodol or Doxorubicin-loaded drug-eluting beads-trans-arterial chemoembolization (DEB-TACE).

Patients referred to a multi-disciplinary consultation at the National Cancer Institute, Cairo University with a possible diagnosis of HCC in the intermediate stage were eligible for the study.

The study finds that the plasma peak concentration of Doxorubicin is significantly higher in patients treated with Lipiodol compared to those treated with DEB-TACE. The median plasma peak concentration of patients treated with Lipiodol was significantly higher 424 (202.5–731) than the peak level of patients treated with beads 84.95 (26.6–156.5) with p-value = 0.036. However, there is no significant difference in other pharmacokinetic parameters between the two treatment groups. The research article also investigates the genetic polymorphisms in HCC patients treated with Doxorubicin-Lipiodol and Doxorubicin-loaded DEB-TACE. It identifies a significant association between the ABCB1 gene (C3435T) and the concentration of Doxorubicin in plasma. Patients with the CCand computed tomography (CT) genotypes of ABCB1 have higher concentrations of Doxorubicin compared to those with the TT genotype. Furthermore, the study examines the progression-free survival rates and tumour response in the two treatment groups. It demonstrates that DEB-TACE patients have a higher progression-free survival rate compared to cTACE patients. DEB-TACE also leads to better tumour regression.

The current study helps to increase the understanding of the genetic factors that may contribute to HCC susceptibility in the Egyptian population. However, it is essential to consider that genetic polymorphism is just one aspect of HCC risk, and other factors such as environment, lifestyle and viral infections also play crucial roles. Further research is needed to elucidate the complex interactions between genetic and environmental factors in HCC development among Egyptians.