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The purpose of this study is to explore the appeal of social networking services (SNS) games from the perspectives of game usability and the functionality that is facilitated by the internet. Why players prefer SNS games to other types of games and what specific characteristics causes them to be addicted to continuous game play are the two critical issues to be probed.

The authors interviewed 11 experts and surveyed 321 gamers using a questionnaire to gather the data about their preferences. The authors believe that a preference‐based study can reveal the underlying reasons for the appeal of SNS games. The collected data were analysed using Quantification Theory Type I.

The semantic structure of appeal, determined from the interviews of experts, shows the hierarchy of the relationship between the types of appeal or appeal factors, the reasons for gamers’ preferences, and the specific characteristics of SNS games. According to the statistical analysis, the three most important appeal factors of SNS games are “easy and convenient”, “friendly and lively” and “social interaction”, and these are affected in varying degrees by particular reasons and characteristics.

Based on the findings, the paper hypothesises that the popularity of Facebook SNS games can be ascribed to the design of game, which targets the psychological preferences of players.

SNS games have become very popular due to some of their appealing characteristics. The findings provide important information for designers and researchers of Facebook SNS games. The study also contributes to the field of human‐computer interaction in cyberspace.

This study aims to (1) develop an artificial intelligence (AI)-based model to accurately forecast rebar prices and (2) propose procurement strategies to reduce the subjectivity involved in rebar price trend forecasting and minimize procurement costs for construction project general contractors.

Correlation analysis was used to identify the key factors influencing changes in rebar prices over time. An AI-based inference model, symbiotic bidirectional gated recurrent unit (SBiGRU), was developed for rebar price forecasting. The performance of SBiGRU was compared with other AI techniques, and procurement strategies based on the SBiGRU model were proposed.

The SBiGRU model outperformed the other AI techniques in terms of rebar price forecasting accuracy. The proposed rebar price forecasting model (RPFM) and procurement patterns, which integrate inventory management principles and rebar price forecasts, were demonstrated to effectively optimize procurement costs, realizing a remarkable 6.13% reduction in procurement expenses compared to the conventional monthly procurement approach.

The accuracy of AI models may be impacted by disparities in the data used for model training. Future research should explore approaches incorporating price predictions and order factors.

This study significantly extends the bounds of traditional rebar price prediction by integrating AI-driven forecasting with inventory management principles, highlighting the potential of AI-based models to improve construction industry procurement practices, reduce related risks and costs, optimize project operations and maximize project outcomes.

The aim of this paper is to examine the effects of light controlling system that combined high refractive particles (n-TiO₂ [titanium dioxide – TiO₂]) and tartrazine lake dye (TL dye) on thickness, flexural strength, flexural modulus and surface details of the 3D-printed resin.

Influences of different concentrations of n-TiO₂ and TL dye in light-cured resin formulations for 3D printing (3DP) application were evaluated, including curing thickness, flexural strength and surface details under scanning electron microscopy.

The polymerization thickness of samples containing both n-TiO₂ and TL dye was lower compared to samples with TL dye solely. Samples containing more n-TiO₂ and more TL dye exhibited lower flexural strength and modulus. Ramp models showed that for samples containing 1 per cent TL dye, when their n-TiO₂ content increased from 1 to 5 per cent, surface laminate structures became sharper. However, when the TL dye content doubled to 2 per cent, the surface laminate structures were indefinite compared to 1 per cent TL dye-containing counterparts.

In visible-light 3DP, light controlling system in cooperate dye with high refractive particles provides better energy distribution and scattering control. High refractive particles, dyes and light exposure time had influenced the surface resolution and mechanical properties of the 3DP products.

The purpose of this paper is to focus on understanding the factors which affect the social interaction in the case of Facebook. Many authors point out the great potential of these networks for social interaction and as conduits of information. However, studies show that the topology of the network is disconnected, consisting of small sub-networks that make Facebook unsuitable for disseminating information. This situation has created the need to introduce exogenous factors, aimed at boosting and providing cohesion to the network structure. In this context, the authors test the following question: how exogenous and endogenous factors contribute to encouraging social interaction on Facebook.

For the analysis of social interaction on Facebook, a population consisting of all the followers of the walls of ten corporate social networks was used. From the total 269,424 users analyzed, a stratified sample of 132 followers was obtained and networks were built for each of them. The authors then proceeded to search for each follower’s friends and friends of friends to build the social network up to the fourth level, obtaining a total of 132 subnets with 1,628,074 links between them. To determine the impact of both exogenous and endogenous factors in the interaction of the network the authors performed a causal analysis.

The results obtained from this study provide empirical evidence on the adequacy of companies’ dynamization measures used and how exogenous and endogenous factors influence the social interaction on Facebook. Thus, the results show that exogenous factors, such as the activity of the community manager and the digital marketing investment in the network, do not have a significant effect on the interaction. On the other hand, endogenous factors, such as network density and clustering, have a positive effect on the trigger of social interaction between the followers. Therefore, companies must consider the importance of the structural factors that characterize network followers, such as density or clustering coefficient, to be able to interpret and optimize them to obtain higher levels of social interaction.

This is one of a few papers that examine interactions in social network sites (SNS), particularly in corporate network sites in Facebook. The results expose the importance for organizations to have reliable information on the patterns of interaction to properly manage the resources allocated for this purpose in SNS.