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The purpose of this paper is to explore the joint effects of different types of IT resources and top management support. Especially, the authors attempt to mainly examine a negative synergy or substitution relationship between IT infrastructure resources and CEO support, and a positive synergy or complementary relationship between IT human resources and CEO support among the large-sized enterprises.

A research model that integrates IT infrastructure resources, IT human resources, CEO support and the degree of usage of IT for business objectives (i.e. IT business spanning capability) is developed. Based on a sample of 112 large-sized enterprises, partial least squares is used to analyze the research model.

Whereas the positive moderating role of CEO support in the effectiveness of IT human resources is insignificant, CEO support and IT infrastructure resources have a substitution relationship in predicting IT business spanning capability. Furthermore, the results can explain under which conditions IT infrastructure resources insignificantly or significantly affect IT business spanning capability in large-sized enterprises. Specially, IT infrastructure resources significantly affect IT business spanning capability only when CEO support is low. Thus, in the presence of high CEO support, IT executives in large-sized enterprises should prioritize developing highly effective IT resources, such as IT human resources.

This paper highlights the joint effects of two critical IT resource types (i.e. IT infrastructure and IT human resources) and CEO support in the IT assimilation process among the large-sized enterprises, ultimately contributing to information systems theories and practices.

This study aims to investigate technology-based health and safety (H&S) management to control the spread of disease on construction sites using a partial least squares structural equation modelling (PLS-SEM) approach.

An extensive literature review is conducted to develop a conceptual framework. The variables identified from the literature review are included in a cross-sectional survey which gathered a total of 203 valid feedback. The variables for challenges are grouped under their relevant construct using exploratory factor analysis. Then, a hypothesized model is developed for PLS-SEM analysis using Smart PLS software. Later, the outcome of the model is further validated by nine construction experts using a semi-structured questionnaire survey.

The results rationalized the relationships between the COVID-19 H&S measures, challenges in implementing COVID-19 H&S measures on construction sites and the innovative technologies in transforming construction H&S management during the COVID-19 pandemic. The possible challenges that obstruct the implementation of H&S measures are highlighted. The potential technologies which can significantly transform H&S management by reducing the impact of challenges are presented.

The findings benefited the industry practitioners who are suffering disruption in construction operations due to the pneumonic plague.

By developing a conceptual model, this study reveals the contribution of technology-based H&S management for construction projects during the COVID-19 pandemic, which remains under-studied, especially in the context of the developing world.

The aim of this study is to predict one of the key performance indicators used to improve continually production systems using machine learning techniques known by the ability to teach the machine to perform complex things as opposed to simple statistical methods by giving this machine the historical dataset, according to the kind of machine learning the authors will use, the machine will be able to predict a new output data from the input data given by the user.

This work is divided into six sections: In the first section, the state of art for OEE, machine learning, and regression models. In the second section, the methodology, followed by an experimental study conducted in an automotive company specialised in the manufacturing of manual transmissions.

The three models show a very high accuracy (higher than 99%), a comparison between these three models was done using three indicators, namely mean absolute error (MAE) mean square error (mean squared error and mean absolute percentage error which shows that the best model is the least angle followed by Bayesian Ridge and automatic relevance determination regression.

As the authors can see many works were done in the different production systems for prediction, the most relevant works were done to predict a parameter in the production system such as The prediction of part thickness in aluminium hot stamping process with partition temperature control the prediction of CO₂ trapping performance the prediction of crop yield the prediction of lean manufacturing in automotive parts industry the contribution of the work will be to use the machine learning techniques to predict the key performance indicator “used to measure manufacturing efficiency” which is the overall equipment effectiveness used in the authors’ case to measure the improvement of the production system.

Revenue management (RM) is a significant technique to improve revenue with limited resources. With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China, it is necessary to involve the theory of RM into the operation and decision of railway passenger transport.

This paper proposes the theory and framework of generalized RM of railway passenger transport (RMRPT), and the thoughts and methods of the main techniques in RMRPT, involving demand forecasting, line planning, inventory control, pricing strategies and information systems, are all studied and elaborated. The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT. The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport.

The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport.

The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing online reviews of medical and healthcare service platforms in combination with a questionnaire survey.

This study adopts a combination of review mining and questionnaire surveys. The latent Dirichlet allocation (LDA) model was used to mine hospital reviews on the medical and healthcare service platform to obtain the medical service quality factors that patients pay attention to, and then the questionnaire was administered to obtain the relative importance of these factors to patients' perception of service quality. Finally, the index system was established.

The medical service quality factors patients care about include medical skills and ethics, registration service, operation effect, consulting communication, drug therapy, diagnosis process and medical equipment.

The identification of medical service quality factors provides a reference for medical institutions to improve their medical service quality.

This study uses online review mining to obtain medical service quality factors from the perspective of patients, which is different from previous methods of obtaining factors from relevant literature or expert judgments; then, based on the mining results, a medical service quality evaluation index system is established by using questionnaire data.

Important reserves of oil and gas, which are left to be discovered and produced, are mainly concentrated in challenging locations and under severe conditions such as high pressure (HP)/high temperature (HT). The presence of aggressive environments including H₂S, CO₂ and chlorides plus HP/HT causes a series of corrosion problems, which cost the oil industry billions of dollars a year. Thus, there is an increasing challenge for tubes (i.e. oil country tubular goods, for short, OCTG) used in producing oil and gas. The purpose of this study is to summarize different kinds of corrosion problems and their mitigation, to more efficiently protect OCTG from corrosion.

To effectively select proper mitigation methods, the mechanism of corrosion must be understood, which can be classified into four categories: sweet corrosion, sour corrosion, galvanic corrosion and microbiologically induced corrosion. Also, the effects of environmental and material factors on the corrosion rate are presented. Subsequently, current technology of mitigating these corrosion problems has been discussed, including the development of materials, application of chemical inhibitors and application of protective layers.

It is stressed that limits exist for each individual mitigation method; therefore, a careful balance between economic life of OCTG and safety in operation is required.

The main purpose of this essay is to give a brief review and detailed introduction and analysis about those technologies.

The study aims at reviewing a synthesis of disclosure, transparency, and International Financial Reporting Standards (IFRS) implementation in an attempt to provide directions for future research. Prior research overwhelmingly supports that the IFRS adoption or effective implementation of IFRS will enhance high-quality financial reporting, transparency, enhance the country’s investment environment, and foreign direct investment (FDI) (Dayanandan, Donker, Ivanof, & Karahan, 2016; Gláserová, 2013; Muniandy & Ali, 2012). However, some researchers provide conflicting evidence that developing countries implementing IFRS are probably not going to encounter higher FDI inflows (Gheorghe, 2009; Lasmin, 2012). It has also been argued that the IFRS adoption decreases the management earnings in countries with high levels of financial disclosure. In general, the study indicates that the adoption of IFRS has improved the financial reporting quality. The common law countries have strong rules to protect investors, strict legal enforcement, and high levels of transparency of financial information. From the extensive structured review of literature using the Scopus database tool, the study reviewed 105 articles, and in particular, the topic-related 94 articles were analysed. All 94 articles were retrieved from a range of 59 journals. Most of the articles (77 of 94) were published 2010–2018. The top five journals based on the citations are Journal of Accounting Research (187 citations), Abacus (125 citations), European Accounting Review (107 citations), Journal of Accounting and Economics (78 citations), and Accounting and Business Research (66 citations). The most-cited authors are Daske, Hail, Leuz, and Verdi (2013); Daske and Gebhardt (2006); and Brüggemann, Hitz, and Sellhorn (2013). Surprisingly, 65 of 94 articles did not utilise the theory. In particular, four theories have been used frequently: agency theory (15), economic theory (5), signalling theory (2), and accounting theory (2). The study calls for future research on the theoretical implications and policy-related research on disclosure and transparency which may inform the local and international standard setters.

This paper aims to systematically demonstrate a methodology to determine the relative and absolute encapsulation efficiencies (α_Re and α_Ab) for thermally- and chemically-robust inorganic pigments, typically like ZrSiO₄-based pigments, thereby enhancing their coloring performance.

The authors designed a route, surplus alkali-decomposition and subsequently strong-acid dissolution (SAD2) to completely decompose three classic zircon pigments (Pr–ZrSiO₄, Fe₂O₃@ZrSiO₄ and CdS@ZrSiO₄) into clear solutions and preferably used inductively coupled plasma-optical emission spectrometry (ICP-OES) to determine the concentrations of host elements and chromophores, thereby deriving the numeric data and interrelation of α_Re and α_Ab.

Zircon pigments can be thoroughly decomposed into some dissoluble zirconate–silicate resultants by SAD2 at a ratio of the fluxing agent to pigment over 6. ICP-OES is proved more suitable than some other quantification techniques in deriving the compositional concentrations, thereby the values of α_Re and α_Ab, and their transformation coefficient K_RA, which maintains stably within 0.8–0.9 in Fe₂O₃@ZrSiO₄ and CdS@ZrSiO₄ and is slightly reduced to 0.67–0.85 in Pr–ZrSiO₄.

The SAD2 method and encapsulation efficiencies are well applicable for both zircon pigments and the other pigmental or non-pigmental inhomogeneous systems in characterizing their accurate composition.

The authors herein first proposed strict definitions for the relative and absolute encapsulation efficiencies for inorganic pigments, developed a relatively stringent methodology to determine their accurate values and interrelation.

This study aims to examine the role of face consciousness, materialism and risk of embarrassment in determining consumer purchase intention toward counterfeit luxury brand. In addition, the authors explore boundary conditions of these relationships.

Study 1, a survey research (N = 321), examines the mediating role of risk of embarrassment between face consciousness on the purchase intention toward luxury counterfeits. Study 2 (N = 345), an experimental study, examines the moderating role of brand prominence of counterfeit (i.e. whether it contains prominent brand signals). Study 3 (N = 315) explores how the above-mentioned relationships are moderated by consumers’ moral rationalization (i.e. whether consumers seek rationalization when behaving unethically).

First, this research shows that risk of embarrassment mediates the negative relationship between face consciousness and Chinese consumers’ purchase intention toward luxury counterfeits, whereas this relationship was not found between materialism and counterfeit purchase intentions. Second, this negative mediating effect holds only when the counterfeit brand is highly prominent. Third, the mediating effect depends on consumers’ moral rationalization, with a positive impact on the purchase intention toward luxury counterfeits, regardless of brand prominence.

This study represents a preliminary inquiry into the dynamics between face consciousness and materialism in influencing Chinese consumers’ purchase intention toward counterfeit luxury products. Unlike their Western counterparts, whose materialistic views of possessions predict their counterfeit luxury consumption (Davidson et al., 2019), Chinese consumers are more likely to be driven by the social implications of counterfeit luxury to communicate a prestigious social image to others on account of genuine luxuries’ high social recognition.

While Chinese consumers are one of the most potent global luxury buyers, they are immersed in the world’s biggest counterfeit luxury market. By digging into the core value of Chinese consumers (i.e. face consciousness), this research provides a number of managerial implications for luxury goods companies to engage in international efforts to educate consumers against counterfeit luxury.

This study makes at least three contributions to the counterfeit consumption literature. First, this study represents a preliminary inquiry into the dynamics between face consciousness and materialism in influencing Chinese consumers’ purchase intention toward counterfeit luxury products. Second, this research identified the complex mechanism of face consciousness as an independent variable on consumers’ purchase intention toward luxury counterfeits. Finally, the authors examined the boundary conditions of brand prominence and consumers’ moral rationalization. The findings may help luxury brand managers identify strategies to discourage counterfeit consumption.

With the massive use of fossil energy polluting the natural environment, clean energy has gradually become the focus of future energy development. The purpose of this article is to propose a new hybrid forecasting model to forecast the production and consumption of clean energy.

Firstly, the memory characteristics of the production and consumption of clean energy were analyzed by the rescaled range analysis (R/S) method. Secondly, the original series was decomposed into several components and residuals with different characteristics by the ensemble empirical mode decomposition (EEMD) algorithm, and the residuals were predicted by the fractional derivative grey Bernoulli model [FDGBM (p, 1)]. The other components were predicted using artificial intelligence (AI) models (least square support vector regression [LSSVR] and artificial neural network [ANN]). Finally, the fitting values of each part were added to get the predicted value of the original series.

This study found that clean energy had memory characteristics. The hybrid models EEMD–FDGBM (p, 1)–LSSVR and EEMD–FDGBM (p, 1)–ANN were significantly higher than other models in the prediction of clean energy production and consumption.

Consider that clean energy has complex nonlinear and memory characteristics. In this paper, the EEMD method combined the FDGBM (P, 1) and AI models to establish hybrid models to predict the consumption and output of clean energy.