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The purpose of this paper is to explore the knowledge infrastructure of methodological research on partial least squares structural equation modeling (PLS-SEM) from a network point of view. The analysis involves the structures of authors, institutions, countries and co-citation networks, and discloses trending developments in the field.

Based on bibliometric data downloaded from the Web of Science, the authors apply various social network analysis (SNA) and visualization tools to examine the structure of knowledge networks of the PLS-SEM domain. Specifically, the authors investigate the PLS-SEM knowledge network by analyzing 84 methodological studies published in 39 journals by 145 authors from 106 institutions.

The analysis reveals that specific authors dominate the network, whereas most authors work in isolated groups, loosely connected to the network’s focal authors. Besides presenting the results of a country level analysis, the research also identifies journals that play a key role in disseminating knowledge in the network. Finally, a burst detection analysis indicates that method comparisons and extensions, for example, to estimate common factor model data or to leverage PLS-SEM’s predictive capabilities, feature prominently in recent research.

Addressing the limitations of prior systematic literature reviews on the PLS-SEM method, this is the first study to apply SNA to reveal the interrelated structures and properties of PLS-SEM’s research domain.

The purpose of this paper is to seek reasons for some videos going viral over YouTube (a type of social media platform).

Using YouTube APIs (Application Programming Interface) and Webometrics analyst tool, the authors collected data on about 100 all-time-most-viewed YouTube videos and information about the users associated with the videos. The authors constructed and tested an empirical model to understand the relationship among users’ social and non-social capital (e.g. User Age, Gender, View Count, Subscriber, Join Date, Total Videos Posted), video characteristics (Post Date, Duration, and Video Category), external network capital (in-links and hit counts), and Virality (Likes, Dislikes, Favorite Count, View Count, and Comment Count). Partial least square and Webometric analysis was used to explore the association among the constructs.

Among other findings, the results showed that popularity of the videos was not only the function of YouTube system per se, but that network dynamics (e.g. in-links and hits counts) and offline social capital (e.g. fan base and fame) play crucial roles in the viral phenomenon, particularly view count.

The authors for the first time constructed and tested an empirical model to find out the determinants of viral phenomenon over YouTube.

This study aims to explore Twitter use by Korea's central government by classifying the government's Twitter-based networking strategies into government-to-citizen (G2C) and government-to-government (G2G) strategies.

The study investigates the nature of social media interactions and networking strategies in the Korean government by extracting tweets, follower/following relationships, and hyperlinks for 32 ministries. Network patterns and networking strategies are reviewed through descriptive statistical analysis and social network analysis to map the government's Twitter activity.

The results indicate that the government's direct networking strategy targeting citizens does not necessarily motivate their participation in the government's social media activities but that it plays an instrumental role in reinforcing G2G relationships.

This study investigates the social media use patterns (e.g. network properties and co-link analyses) and strategies (e.g. the reciprocity of relationships and content-push strategies) in the context of G2C and G2G relationships in Korea's public sector.

The purpose of this paper is to: empirically validate a modified unified theory of acceptance and use of technology (UTAUT) model by adding an “e‐learning motivation” construct in the South American context; try to determine the role of e‐learning motivation in the use and adoption of e‐learning systems and conversely the effect of technology on students' e‐learning motivation; and to test region and gender as moderators in the model.

A survey method was used to collect data from 47 schools located at different regions: the coast, Andes, and jungle of Peru. The partial least square technique was used for data analysis.

It was found that “e‐learning motivation” and “social influence” had a positive influence on behavioural intention, while “facilitating condition” had no effect on e‐learning portal use. Furthermore, use behaviour had a positive influence on e‐learning motivation. Also found was the moderating role of “region”.

The analysis is carried out in a single country, thus, caution should be taken in generalisation of the results.

The findings will help policy makers and practitioners in developing countries to better understand students' e‐learning motivation.

By adopting the UTAUT model, a new construct of “e‐learning motivation” is added, and applied to the South American context.

The objective of this research work is to design a data-based solution for administering traffic organization in a smart city by using the machine learning algorithm.

A machine learning framework for managing traffic infrastructure and air pollution in urban centers relies on a predictive analytics model. The model makes use of transportation data to predict traffic patterns based on the information gathered from numerous sources within the city. It can be promoted for strategic planning determination. The data features volume and calendar variables, including hours of the day, week and month. These variables are leveraged to identify time series-based seasonal patterns in the data. To achieve accurate traffic volume forecasting, the long short-term memory (LSTM) method is recommended.

The study has produced a model that is appropriate for the transportation sector in the city and other innovative urban applications. The findings indicate that the implementation of smart transportation systems enhances transportation and has a positive impact on air quality. The study's results are explored and connected to practical applications in the areas of air pollution control and smart transportation.

The present paper has created the machine learning framework for the transportation sector of smart cities that achieves a reasonable level of accuracy. Additionally, the paper examines the effects of smart transportation on both the environment and supply chain.