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The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

The purpose of this study is to investigate the influences of the characteristics of procurement strategies, in this instance labour-only, on project performance concerning health and safety (H&S), a project performance indicator.

Using non-probability purposeful and snowballing sampling methods, questionnaires were used to collect data from construction professionals in Nigeria. This was then analysed using descriptive (frequency and mean scores) and inferential statistics (Mann–Whitney-U and Kendall's Tau_b tests).

The findings indicate a statistically significant negative correlation between ‘the level of client involvement and ‘fatalities' and a positive one with ‘conducting of health and safety risk assessment' and ‘conducting employee surveys on health and safety attitude’. Poor hygiene is found to be the worst lagging indicator, while conducting of inspection is the most adopted leading indicator of project health and safety performance. It also emerged that there is no significant difference in the health and safety performance of projects procured through the procurement strategy in urban and rural areas.

The study provides valuable insight into the complexities in H&S management due to the high level of client involvement in labour-only procurement system (LoPS) projects and the level of diversity in their responsibilities therein. It creates a fundamental direction for developing a detailed framework or guidance notes for client involvement in the integration of H&S into LoPS projects.

This is the first study that examines the influence of the characteristics of procurement strategy on project health and safety performance. Evidence in the literature shows that project delivery outcomes significantly improve if procurement is strategically used, including when it is considered early in projects. However, integrating H&S into procurement strategies has received little attention.

The social media communication of luxury service firms remains largely unexplored. This study explores the influence of firm-created social media communication (FCSMC) on predicting brand evangelism (BEM) via perceived values, including functional value (FV), emotional value (EV) and social value (SV), by embedding the direct and moderating influence of customer experience (CX) on brand evangelism in the luxury hotel sector.

This study recruited 405 regular travelers to participate in an online survey. Following meticulous data curation, the empirical analysis was performed on 363 responses using structural equation modeling.

The findings revealed that FCSMC substantially impacted perceived values, including FV, EV and SV, as well as BEM. Likewise, perceived values, including FV and EV, were positively associated with BEM. In addition, this study revealed that CX exhibited significant predictive capability with its direct and moderating effects on BEM in the luxury hotel sector.

This original research advances the uses and gratifications theory and attribution theory. It provides novel theoretical insights and practical recommendations for the luxury hotel sector.

The healthcare sector relies on knowledge management systems to improve knowledge flows and effectively capture, leverage and share knowledge with several organizational stakeholders. However, knowledge as a resource represents a social construct that involves additional managerial complexities and challenges, including undesirable knowledge behaviours. The aim of the current study is to provide insight on how knowledge management systems, knowledge hoarding, knowledge hiding and task conflict shape the quality of care provided by hospitals. We propose and test an original revealing model.

We follow a quantitative approach to address the structural relationship between variables using a combination of factor analysis and multiple regression analysis. The model is tested adopting a structural equation modelling approach and using survey data conducted to 318 healthcare professionals working in Portuguese hospitals.

The main findings suggest that knowledge hiding is positively related to task conflict in hospitals, and task conflict negatively influences quality of care. Knowledge management systems directly and indirectly (via knowledge hoarding) promote quality of care. Moreover, knowledge management systems also mitigate the negative influence of task conflict over quality of care. We propose a final corollary on the relevant role of HRM as the backstage for the model.

Our research offers a novel insight into an overlap of organizational behaviour and healthcare management research. It provides an original framework on knowledge management systems, counterproductive knowledge behaviours and task conflict in hospital settings.

Our research offers a novel insight into an overlap of organizational behaviour and healthcare management research. It provides an original framework on knowledge management systems, counterproductive knowledge behaviours and task conflict in hospital settings.

To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.

The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.

Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.

The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.

The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.

We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.

The scarcity of literature related to the PPP (public-private partnership) barriers in construction projects within war areas, and hence the dearth of information to deliver viable and effective strategies to those barriers, are the primary causes for the failures of PPP schemes in such areas, particularly in Palestine. Financial and non-financial investments are more problematic in war zones than non-war nations and may escalate barrier for projects' success. The investigation purposes to discover proper answers to the barriers of PPP infrastructure schemes and highlight the execution of barrier reactions.

Specialists were asked to deliver approaches to alleviate 21 barriers and recommend the period needed for applying them. Later, the relevance of alleviation events was examined through prioritization according to the results attained from three elements, i.e. the impact of every barrier and the strategy's viability and efficacy.

While the most unfavorable barrier was finalized to be the unfeasibility of delivering physical security, the most valid answer was associated with the lack of government cohesiveness and responsibility to perform its duties. The discovered barriers are typical within warring nations, but the paper concentrated on Palestine.

This study is an initial effort to examine PPP barriers in Palestinian infrastructure projects. The presented strategies can be applied as a novel set for barrier reaction improvement in occupied nations such as Palestine. Moreover, the results can develop the usage of PPP and enhance the barrier sharing in this scheme.

Prior studies have put much emphasis on using the uses and gratification (U&G) theory to find out why people use games, social media, the Internet, e-shopping, etc. Despite past research efforts, the root causes underlying this phenomenon still need to be discovered as to why people use interactive virtual rides (vrides) entertainment services, especially when incorporating the hedonic consumption perspective (i.e. playful-consumption experiences). Considering the knowledge gap in the vrides’ context, this study aims to use the UGT to find out why people use the vrides entertainment service from a hedonic consumption point of view.

With 217 usable responses, the research model was tested using partial least squares-based structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA).

Findings reveal that only perceived enjoyment, arousal and sensory experience derive continuous intention behavior to consume vride entertainment service. Findings using the fsQCA revealed multiple causal configurations for the proposed outcome.

This study contributes to extending the assumption of UGT via incorporating the hedonic consumption perspective to explore the potential motives and intention to consume vrides entertainment services. Our study also discusses the important theoretical/practical implications of our findings. Besides, this study is unique because it shows both symmetrical and asymmetrical connections that help us understand why people keep using vrides entertainment service.

In online purchase for dietary supplements, due to the lack of professional advice from pharmacists, electronic word-of-mouth (eWOM) has become an important source of information for consumers to make purchase decisions. How can firms use eWOM resources to increase sales? The purpose of this paper is to provide practical methods for firms by exploring the effects of eWOM on sales and developing a sales prediction model based on eWOM.

The data came from 120 dietary supplements on Tmall.com. The authors extracted the product sales as dependent variable and 11 eWOM factors as independent variables. The multicollinearity was tested by using variance inflation factor and least absolute shrinkage and selection operator. The multiple linear regression was used to investigate the effects of eWOM on sales. Drawing on white- and black-box approaches, six models were developed. Comparing the root mean square error, the authors selected the optimal one as their target sales prediction model.

Product ratings, total reviews and favorites are positively and strongly associated with sales. Questions and additional reviews have negative effects on sales. The random forest model has the best prediction performance.

The research focuses on eWOM of dietary supplement. First, the authors show that easily accessible eWOM from online platforms can be used to evaluate effects and predict sales. Second, the authors introduce white- and black-box models through machine learning to assess eWOM. Firms could use the described models to foster their marketing initiatives.

Predicting a user’s click-through rate on an advertisement or item often uses deep learning methods to mine hidden information in data features, which can provide users with more accurate personalized recommendations. However, existing works usually ignore the problem that the drift of user interests may lead to the generation of new features when they compute feature interactions. Based on this, this paper aims to design a model to address this issue.

First, the authors use graph neural networks to model users’ interest relationships, using the existing user features as the node features of the graph neural networks. Second, through the squeeze-and-excitation network mechanism, the user features and item features are subjected to squeeze operation and excitation operation, respectively, and the importance of the features is adaptively adjusted by learning the channel weights of the features. Finally, the feature space is divided into multiple subspaces to allocate features to different models, which can improve the performance of the model.

The authors conduct experiments on two real-world data sets, and the results show that the model can effectively improve the prediction accuracy of advertisement or item click events.

In the study, the authors propose graph network and feature squeeze-and-excitation model for click-through rate prediction, which is used to dynamically learn the importance of features. The results indicate the effectiveness of the model.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.