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This study aims to investigate additive manufacturing of nickel-based superalloy IN718 made by powder bed fusion processes: powder bed fusion laser beam (PBF-LB) and powder bed fusion electron beam (PBF-EB).

This work has focused on the influence of building methods and post-fabrication processes on the final part properties, including microstructure, surface quality, residual stresses and mechanical properties.

PBF-LB produced a much smoother surface. Blasting and shot peening (SP) reduced the roughness even more but did not affect the PBF-EB surface finish as much. As-printed PBF-EB parts have low residual stresses in all directions, whereas it was much higher for PBF-LB. However, heat treatment removed the stresses and SP created compressive stresses for samples from both PBF processes. The standard Arcam process parameter for PBF-EB for IN718 is not fully optimized, which leads to porosity and inferior mechanical properties. However, impact toughness after hot isostatic pressing was surprisingly high.

The two processes gave different results and also responses to post-treatments, which could be of advantage or disadvantage for different applications. Suggestions for improving the properties of parts produced by each method are presented.

Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management.

The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge.

The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers.

This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.

Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.

This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.

The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.

This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

With the rapid development of social media in the past few years, some dark aspects of usage have appeared, e.g., Weibo addiction. Therefore, the purpose of this paper is to address the question of how Weibo keeps users hooked.

The study utilizes the netnography method to explore the symptoms of Weibo addiction and how it is formed.

First, some Weibo users have typical symptoms of addictive behavior, such as withdrawal, loss of control and general living problems; second, narcissism plays an important role in the process of Weibo addiction and has a stronger effect on content generation than content consumption; third, some users use Weibo as a means to alleviate anxiety, but they can then become more anxious owing to addiction; and fourth, the effect of comments has two sides – on the one hand, comments are one kind of feedback to posts, satisfying posters’ social needs, while on the other hand, a storytelling system consists of one post and its comments, upon which some Weibo users become more dependent.

This research is only a preliminary, exploratory study. Therefore, care should be taken when interpreting these findings.

This study should help social media service providers and users to pay attention to the risk of social media addiction. Social media service providers should take social responsibility, design more user-oriented guidelines for marketing ethics and launch more responsible marketing activities. Users should enhance self-control and better balance social media use and offline real life.

Netnography has not been widely used as an addictive behavior research technique. This study is further bolstered because it has also noticed the difference of addiction mechanisms between addictive micro-bloggers and their followers.