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This paper aims to find determinants that can predict the helpfulness of online customer reviews (OCRs) with a novel approach.

The approach consists of feature engineering using various text mining techniques including BERT and machine learning models that can classify OCRs according to their potential helpfulness. Moreover, explainable artificial intelligence methodologies are used to identify the determinants for helpfulness.

The important result is that the boosting-based ensemble model showed the highest prediction performance. In addition, it was confirmed that the sentiment features of OCRs and the reputation of reviewers are important determinants that augment the review helpfulness.

Each online community has different purposes, fields and characteristics. Thus, the results of this study cannot be generalized. However, it is expected that this novel approach can be integrated with any platform where online reviews are used.

This paper incorporates feature engineering methodologies for online reviews, including the latest methodology. It also includes novel techniques to contribute to ongoing research on mining the determinants of review helpfulness.

An increasing amount of attention is being paid to three-dimensional (3D) printing technology. The technology itself is based on diverse technologies such as laser beams and materials. Hence, 3D printing technology is a converging technology that produces 3D objects using a 3D printer. To become technologically competitive, many companies and nations are developing technologies for 3D printing. So to know its technological evolution is meaningful for developing 3D printing in the future. The paper aims to discuss these issues.

To get technological competitiveness of 3D printing, the authors should know the most important and essential technology for 3D printing. An understanding of the technological evolution of 3D printing is needed to forecast its future technologies and build the R & D planning needed for 3D printing. In this paper, the authors propose a methodology to analyze the technological evolution of 3D printing. The authors analyze entire patent documents related to 3D printing to construct a technological evolution model. The authors use the statistical methods such as time series regression, association analysis based on graph theory, and principal component analysis for patent analysis of 3D printing technology.

Using the proposed methodology, the authors show the technological analysis results of 3D printing and predict its future aspects. Though many and diverse technologies are developed and involved in 3D printing, the authors know only a few technologies take lead the technological evolution of 3D printing. In this paper, the authors find this evolution of technology management for 3D printing.

If not all, most people would agree that 3D printing technology is one of the leading technologies to improve the quality of life. So, many companies have developed a number of technologies if they were related to 3D printing. But, most of them have not been considered practical. These were not effective research and development for 3D printing technology. In the study, the authors serve a methodology to select the specific technologies for practical used of 3D printing.

Diverse predictions for 3D printing technology have been introduced in many academic and industrial fields. Most of them were made by subjective approaches depended on the knowledge and experience of the experts concerning 3D printing technology. So, they could be fluctuated according to the congregated expert groups, and be unstable for efficient R & D planning. To solve this problem, the authors study on more objective approach to predict the future state of 3D printing by analyzing the patent data of the developed results so far achieved. The contribution of this research is to take a new departure for understanding 3D printing technology using objective and quantitative methods.

This paper presents a new calculating methodology for estimating the quantitative monetary managerial effects as a result of total productive maintenance (TPM) activities. The suggested methodology is to calculate the total saving monetary amount composed of contribution profit and saving costs that are obtained by improving the overall equipment efficiency (OEE) of processing type equipment. The managerial effect that is the total saved monetary effect in keeping the OEE at the 1 percent upraised condition during a given period can be calculated by the sum of additive contribution profit and saved manufacturing cost. The proposed computation methodology is demonstrated by applying to a real industrial processing type of manufacturing equipment. This newly presented model is expected to contribute to raise the maturity of TPM activities by grasping the monetary quantitative managerial effects periodically.