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This paper seeks to investigate how to use a web analytics tool to conduct deep analysis of users' web behaviors. This study aims to focus on examining whether the types of traffic sources and temporal fluctuation influence the web visitors' performance on the web portal of a K‐12 resource inventory.

One year's data were collected via the Advanced Segmentation function of Google Analytics. To compare visitors' behavior from different types of traffic recourses with the intervention of temporal effect, clickstream data of three visitor segments were collected.

Traffic sources and temporal effect have been found to influence web site visitors' performance interactively. Search engines seemed good at bringing a significantly large amount of traffic to the eThemes site, but most visitors are likely “information encounters”. However, visitors from direct traffic (bookmark/typed URLs) seemed to visit the eThemes site purposefully – stay for a long time on the site and view more web pages. Additionally, loyal users of the site seemed to employ the eThemes site as an everyday life information source.

This study introduces a strategic approach to study and analyzes web site visitors' behavior longitudinally. The findings of this study contribute to loyal user behavior identification. Empirical evidence has been found to support the correlational relationship between traffic sources, the temporal factor, and Key Performance Identifiers of a site.

As a response to the COVID-19 pandemic, both educators and students adapted to course delivery modes no longer centered on in-person interactions. Resiliency and self-regulation are key to success in online contexts, but the rapid transition to remote learning left many students without the necessary support to develop these skills. Much of the existing literature on self-regulation and resiliency focuses on cognitive processes and strategies such as goal orientation, time management, and mindset. However, the added stress and trauma of learning in the context of a global pandemic highlighted the many other factors relevant to students’ development of these skills. Drawing from the literature, the authors explore evidence-informed teaching practices to foster self-regulation and resiliency, highlight the power and privilege of being able to be resilient, advocate for the development of pedagogies of kindness, and emphasize the “how” of implementing techniques to best support students. The authors provide evidence-informed suggestions with the goal of assisting instructors and students during times of high stress, while acknowledging their limitations in addressing structural inequalities highlighted by the COVID-19 pandemic. Nonetheless, the authors argue that evidence-informed techniques and compassionate pedagogies adopted during a period of upheaval remain applicable to future in-person and online pedagogies.

Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.