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Majority customers of cosmetics are female. Would this imply a high proportion of inventors of cosmetics technology is female? Would the inventor's gender be related to the characteristics and quality of corresponding patent? This study tries to identify manifestation of gender equity in cosmetics technology in terms of patent application and grant, technical characteristics, and its performance. We apply topic modeling, zero-inflated Poisson regression, and survival analysis to patents related to cosmetics that were applied to the United States Patent and Trademark Office from 1970 to 2016. The results show that women's participation in cosmetic inventions is becoming active and has experienced many changes in technical characteristics, but in terms of performance, it is still sluggish. This study is expected to contribute to deepening our understanding about gender issues in technology development.

One of the recent developments in safety systems is an external airbag installed on the front bumper of a vehicle and autonomous emergency braking system. In this paper, we propose a framework for a cost-benefit analysis of the external airbag and autonomous emergency braking system in order to validate its commercialization. Road traffic crash data obtained from the National Automotive Sampling System/Crashworthiness Data System (NASS/CDS) was used, and three different crash types related to frontal damage in vehicles were extracted to estimate the safety performance of an external airbag. An ordinal logistic regression model was applied to estimate the safety performance in terms of the reduced maximum abbreviated injury scale (MAIS) based on a reduction in the total delta-v following the installation of an external airbag. Given the estimated safety performance of the external airbag, a cost-benefit analysis is conducted. According to the results, the external airbag system saves 46% of occupants with MAIS 3+ injuries and prevents 40% of fatalities. Moreover, the benefit/cost ratios of the external airbag system range from 0.496 to 0.509 depending on the scenario. Lastly, sensitivity analyses were performed with important parameters, including the initial and maximum market penetration ratio and the price of the system. This study aims to evaluate the technology of safety devices by analyzing the effectiveness of a new safety device using real-world vehicle accident data. We also statistically estimated its effectiveness and analyzed its societal value. We expect that our comprehensive findings will be helpful in evaluating the effectiveness of safety devices.

Abstract Although psychometric features have been considered for alternative credit scoring, they have not yet been applied to peer-to-peer (P2P) lending because such information is not available on platforms. This study proposed an alternative credit scoring model for P2P lending by extracting typical personality types inferred from the borrowers’ job category. We projected a virtual space of borrowers by using the affinity matrix based on the Myers–Briggs type indicator (MBTI) that fits each job category. Applying the distance in this space to Lending Club data, we used locally weighted logistic regression to vary the coefficients of the variables, which affect loan repayments, with each MBTI type for predicting the default probability. We found that each MBTI type’s credit scoring model has different significant variables. This study provides insights into breakthroughs in developing alternative credit scoring for P2P lending.

[This corrects the article DOI: 10.1371/journal.pone.0220782.].

In the rapidly changing high-tech industry, firms that produce multi-generational products struggle to consistently introduce new products that are superior in innovativeness. However, developing innovative products in a short time sequence period is likely to cause quality problems. Therefore, considering time and resource constraints, two kinds of strategies are commonly employed: sequential innovation strategy, sequentially introducing a new generation of technology product at every launch interval, ensuring timely innovativeness but with relatively uncertain quality, or quality strategy, intermittently introducing a new generation of products, together with a derivative model between generations to enhance the quality. In this study, we propose a framework for a cost-benefit analysis that compares these two strategies by considering competition between firms within a generation as well as that within a firm across multiple generations (i.e., cannibalization) throughout the launch cycle of high-tech products. We apply our proposed framework to the smartphone market and conduct a sensitivity analysis. The results are expected to contribute to strategic decision-making related to the introduction of multi-generational technology products.

Subcontractors depend heavily on their prime contractor and thus find it very risky to enter a new business on their own. This study proposes a framework for these subcontractors to develop blue ocean technologies related to their prime contractor. First, the primary technologies predicted to be promising are extracted from the business reports of the prime contractor. Sub-technologies are then selected through a patent-based search using keywords and International Patent Classification codes of the primary technologies. From them, blue ocean technologies are proposed by optimizing the weighted mean of the min-max normalized market value, degree of competition in the technology market, and subcontractors' potential technological capabilities for each sub-technology. This study shows that subcontractors can enhance their technology competitiveness by finding a low-risk blue ocean technology. Our empirical research on the subcontractors of a semiconductor firm identified technological patent fields for them to pursue. From our framework, subcontractors can identify blue ocean technologies by considering their prime contractor's future industrial areas and technologies of interest as well as their own technological capabilities. Furthermore, the prime contractors can gain the synergy effect of technology expansion through cooperation.

Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants’ relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers’ relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.

Solving the supply-demand imbalance is the most crucial issue for stable implementation of a public bike-sharing system. This gap can be reduced by increasing the accuracy of demand prediction by considering spatial and temporal properties of bike demand. However, only a few attempts have been made to account for both features simultaneously. Therefore, we propose a prediction framework based on graph convolutional networks. Our framework reflects not only spatial dependencies among stations, but also various temporal patterns over different periods. Additionally, we consider the influence of global variables, such as weather and weekday/weekend to reflect non-station-level changes. We compare our framework to other baseline models using the data from Seoul's bike-sharing system. Results show that our approach has better performance than existing prediction models.

Governments and also local councils create and enforce their own regional public health care plans for the problem of overweight and obesity in the population. Public sports facilities can help these plans. In this paper, we investigated the contribution of public sports facilities to the reduction of the obesity of local residents. We used the data obtained from the Fifth Korea National Health and Nutrition Examination Surveys; and measured the degree of obesity using body mass index (BMI). We conducted various spatial regression analyses including the global Moran’s I test and local indicators of spatial autocorrelation analysis finding that there exists spatial dependence in the error term of spatial regression model for BMI. However, we also observed that the number of local public sports facilities is not significantly related to local BMI. This result can be caused by the low utilization ratio and an unbalanced spatial distribution of local public sports facilities. Based on our findings, we suggest that local councils need to improve the quality of public sports facilities encouraging the establishment of preferred types of pubic sports facilities.

Accurate demand forecasting for new technology products is a key factor in the success of a business. We propose a way to forecasting a new product's diffusion through technology diffusion and interest diffusion. Technology diffusion and interest diffusion are measured by the volume of patent citations and web search traffic, respectively. We apply the proposed method to forecast the sales of hybrid cars and industrial robots in the US market. The results show that that technology diffusion, as represented by patent citations, can explain long-term sales for hybrid cars and industrial robots. On the other hand, interest diffusion, as represented by web search traffic, can help to improve the predictability of market sales of hybrid cars in the short-term. However, interest diffusion is difficult to explain the sales of industrial robots due to the different market characteristics. Finding indicates our proposed model can relatively well explain the diffusion of consumer goods.

BACKGROUND:Recently, the need for rapid wound-healing has significantly increased because of the increasing number of patients who are diagnosed with diabetes and obesity. These conditions have contributed to a surge in the number of patients with chronic wounds worldwide. Furthermore, many cost-effective wound-healing technologies have been developed in order to keep up with the increased demand. In this paper, we performed a quantitative study of the trends associated with wound-healing technologies using patent data. METHODOLOGY:We analyzed the trends considering four different groups of patent applicants: firms, universities, research institutes, and individuals using a structural topic model. In addition, we analyzed the knowledge flow between patent applicants using citation analysis, and confirmed the role of applicants in the knowledge-flow network using k-means clustering. As a result, the primary wound-healing technology patents applied for by the four groups varied considerably, and we classified the roles of patent applicants were found in the knowledge-flow network. CONCLUSIONS:Our results showed the organizations that are leading each area of wound-healing technology. Furthermore, from the results, we identified specific institutions that are efficient for spreading knowledge related to wound-healing technology based on the patents. This information can contribute to the planning of investment strategies and technology policies related to wound-healing.

Because of the remarkable developments in robotics in recent years, technological convergence has been active in this area. We focused on finding patterns of convergence within robot technology using network analysis of patents in both the USPTO and KIPO. To identify the variables that affect convergence, we used quadratic assignment procedures (QAP). From our analysis, we observed the patent network ecology related to convergence and found technologies that have great potential to converge with other robotics technologies. The results of our study are expected to contribute to setting up convergence based R&D policies for robotics, which can lead new innovation.