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The study is interested in knowing “the role of the organizational structure as a mediating variable of the relationship between the information technology and organizational communication”.

The study was conducted in several service companies, and the study adopted the questionnaire as a basic tool for the data collection on the practical side, as 267 opinions were surveyed, in addition to conducting personal interviews, and the normal distribution of data was tested, analyzing, describing and diagnosing study variables, testing correlations and determining direct effects.

Findings show that there is no direct and significant statistical impact of information technology on organizational communications. Whereas there was a positive, direct and statistically significant impact of information technology on the organizational structure. There was also a positive, direct and statistically significant effect of the organizational structure on organizational communication.

This paper is restricted to the role of the organizational structure as a mediating variable of the relationship between the influence of information technology on organizational communication.

As part of the practical implication, the paper suggests the need to increase support and attention to the importance of information technology in service organizations in order to increase coordination and organizational communication and achieve a high ability to explore and exploit ideas.

Apart from the fact that several companies were engaged, the organizational structures of these companies were engaged too to examine the impacts of Information technology (ICT) on organizational communication.

The purpose of this research is to develop a framework of an ontology-based Asset Information Model (AIM) for a Digital Twin (DT) platform and enhance predictive maintenance practices in building facilities that could enable proactive and data-driven decision-making during the Operation and Maintenance (O&M) process.

A scoping literature review was accomplished to establish the theoretical foundation for the current investigation. A study on developing an ontology-based AIM for predictive maintenance in building facilities was conducted. Semi-structured interviews were conducted with industry professionals to gather qualitative data for ontology-based AIM framework validation and insights.

The research findings indicate that while the development of ontology faced challenges in defining missing entities and relations in the context of predictive maintenance, insights gained from the interviews enabled the establishment of a comprehensive framework for ontology-based AIM adoption in the Facility Management (FM) sector.

The proposed ontology-based AIM has the potential to enable proactive and data-driven decision-making during the process, optimizing predictive maintenance practices and ultimately enhancing energy efficiency and sustainability in the building industry.

The research contributes to a practical guide for ontology development processes and presents a framework of an Ontology-based AIM for a Digital Twin platform.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

Sustainability is increasingly becoming an essential aspect of technological innovations. In addition, the diffusion of sustainable new technology-based products appears to be uneven across the globe. The authors examine the effect of three country-level Hofstede measures of culture and two national-level innovation characteristics on the diffusion of Sustainable New Technology-based Products (SNTP).

Regression and Necessary Conditions Analysis were used to analyze a panel dataset of electric and hybrid vehicles sales from 2008 to 2017 across 89 countries.

Results suggest Long-Term Orientation (LTO) was correlated with SNTP diffusion, Indulgence (IVR) was partially correlated with SNTP diffusion and was also a necessary condition. Surprisingly, Uncertainty Avoidance (UAI) was not correlated with SNTP diffusion. In addition, a national proclivity for developing innovations and a history of utilizing prior generic innovations were both correlated and necessary for SNTP diffusion.

This paper measures the impact of several macro-level variables (culture and other innovation related characteristics of countries) on SNTP diffusion. In addition to regression analyses to measure the average effect size, the authors conduct Necessary Conditions Analysis, which assesses the necessity of a variable for the outcome. These insights may help multinational companies better strategize entry decisions for international markets and aid governments in formulating more effective policies by recognizing and accommodating the influences of national culture and innovation attitudes.

Digital technology's integration into education has transformed learning frameworks, necessitating the exploration of factors influencing students’ engagement in digital informal settings. This study, grounded in self-determination theory (SDT), proposes a model comprising artificial intelligence (AI) competence, chatbot usage, perceived autonomy (PA), digital informal learning (DIL) and students’ engagement.

The study collected survey data from 409 participants at Saudi Arabian universities, ultimately using 387 valid responses for analysis. This dataset was subjected to a thorough examination to confirm the validity of our proposed model. To decipher the complex interactions within our model, we utilized partial least squares structural equation modeling (PLS-SEM). The study adopted a disjoint two-stage method to formulate a reflective-formative higher-order construct (HOC).

The study's findings showed that cognitive learning (CL), metacognitive learning (MCL) and social and motivational learning (SML) are the essential components of DIL. Significantly, the study determined that AI competence, chatbot usage, PA and DIL markedly affect students’ engagement. Moreover, the R² value of 0.592 for student engagement indicates the model's robustness in explaining 59.2% of the variance, highlighting its effectiveness in identifying key drivers of student engagement in DIL contexts.

This research enhances understanding by detailing the intricate relationships among AI competence, chatbot usage, and students’ engagement in informal digital learning. It extends SDT to emphasize intrinsic motivations and AI capabilities, introducing reflective-formative HOCs for comprehending educational intricacies. It provides practical strategies for enhancing AI abilities and chatbot use in education, promoting personalized, engaging and autonomous digital learning spaces, thereby advancing educational theory and practice.