BERT, SHAP, Kano 모델을 기반한 소비자 만족 요소 다이나믹스

학위논문(석사) -- 서울대학교대학원 : 경영대학 경영학과, 2022.2. 오정석 교수.최근 10년 간 온라인 쇼핑 산업의 성장으로 온라인 쇼핑몰 플랫폼에 온라인 리뷰 등 무한한 소비자 반응, 만족도 관련 정보가 생성되고 있다. 이에 많은 기업들과 학계에서 이를 활용하여 VoC (Voice of Customer)를 반영한 소비자 만족도 모델링을 시도하고 있다. 본 논문은 BERT, GBM, SHAP 등을 활용하여 카노 모델 (Kano Model)에 기반한 소비자 만족도 특성 (Customer Satisfaction Dimension)을 분류하고 각 특성의 소비자 요구 충족 여부가 소비자 만족도에 미치는 영향도를 측정한다. 본 논문의 방법론에 활용된 각 빅데이터 모델 성능과 선행 연구들에서 사용된 모델 성능을 직접 구현 및 비교하여, 본 논문에서 활용된 모델들의 정확성과 안정성을 보였다. 또한 해석적 머신러닝 기법인 SHAP를 도입하여, 카노 카테고리를 분류하는 통일된 분류 방식을 제안한다. 본 연구는 제시된 방법론을 통해 스마트폰 및 스마트워치 제품군을 대상으로 실증 연구를 진행하며, 산업계에 제품 개발 및 개선, 고객 세분화 전략 등 기업 의사결정 방향성에 유의미한 제언을 제시함으로써 본 방법론의 실용적 가치를 입증하였다.As a large number of online reviews are loaded on e-commerce platforms in recent days, companies are being able to measure customer satisfaction reflecting VoC (Voice of Customer) with big data analytics. This paper proposes the improved framework for identifying characteristics of customer satisfaction dimensions (CSD) based on Kano model using BERT (Bidirectional Encoder Representations from Transformers), GBM (Gradient Boosting Machine), and SHAP (Shapley Additive eXplanation). We proved each model outperformance by comparing other models which previous studies have used. And this paper suggests the unified rule of Kano model classification using SHAP. Furthermore, we conducted empirical studies regarding smartphone and smartwatch products which suggest the direction of product enhancement/development strategy and multi-product level customer segmentation strategy to product manufacturers. This shows proposed methodology’s effectiveness and usefulness on industrial analysis.1. Introduction 1 2. A framework for modelling customer satisfaction from online review 5 3. Research Method 8 3.1 Mining customer’s sentiments toward CSDs from online reviews 8 3.2 Measuring the effects of customer sentiments toward each CSD on customer satisfaction 11 3.3 Identifying the feature of each CSD from the customer’s perspective 11 3.4 Classifying each CSD into Kano categories 14 4. Empirical Study 17 4.1 Study 1 17 4.2 Study 2 24 5. Conclusion 27 6. Reference 29석

Visual analytics for supply network management: system design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip

When to Signal? The Contextual Conditions for Career-Motivated User Contributions in Online Collaboration Communities

This paper examines the contextual conditions for users’ career concern as a motivational driver of contributions in online collaboration communities. On the data of user-level activities from a computer programming-related online Q&A community (Stack Overflow), merged with job-market data for software-developer, we find robust evidence of a positive association between individual users’ career concern and their contributions. More important, we find that this positive relationship is further strengthened through the contextual conditions: the number of vacancies in the job market, the expected salaries from these jobs, and the transparency in the flow of career-related information within the community. We contribute to the literature on motivation in online collaboration communities. Our study thus offers insight into how career concern can be effectively utilized to motivate contributors in these communities. Our findings also foreshadow a possible paradigm change by characterizing online collaboration communities as institutions of career concern and skill signaling

When to Signal? Contingencies for Career-Motivated Contributions in Online Collaboration Communities

Online collaboration communities are increasingly taking on new roles beyond knowledge creation and exchange, especially the role of a skill-signaling channel for career-motivated community members. This paper examines the contingency effects of job-market conditions for career-motivated knowledge contributions in online collaboration communities. From the data of individual-level activities in a computer programming-related online Q&A community (Stack Overflow), merged with job-market data for software developers, we find robust evidence of a positive association between community members’ career motivations and their knowledge contributions. More importantly, we find that this positive relationship is strengthened by job-market conditions: the number of vacancies in the job market, the expected salaries from these jobs, and the transparency in the flow of career-related information between the community and external recruiters. We contribute to the motivation literature in online collaboration communities by identifying and substantiating the role of contextual factors in mobilizing members’ career motivation. Our study thus offers novel insight into how career motivation can be effectively utilized to motivate contributors in these communities. Our findings also point to a possible paradigm change by characterizing online collaboration communities as emerging institutions for career motivation and skill signaling

METHOD FOR THE PRODUCTION OF HIGH SATURATED, LOW POLYUNSATURATED SOYBEAN OIL

Methods of genetically modifying soybean plants to alter the fatty acid properties of the oil are described

Self-positioning Point-based Transformer for Point Cloud Understanding

Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in the number of points. In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity. Specifically, this architecture consists of local self-attention and self-positioning point-based global cross-attention. The self-positioning points, adaptively located based on the input shape, consider both spatial and semantic information with disentangled attention to improve expressive power. With the self-positioning points, we propose a novel global cross-attention mechanism for point clouds, which improves the scalability of global self-attention by allowing the attention module to compute attention weights with only a small set of self-positioning points. Experiments show the effectiveness of SPoTr on three point cloud tasks such as shape classification, part segmentation, and scene segmentation. In particular, our proposed model achieves an accuracy gain of 2.6% over the previous best models on shape classification with ScanObjectNN. We also provide qualitative analyses to demonstrate the interpretability of self-positioning points. The code of SPoTr is available at https://github.com/mlvlab/SPoTr.Comment: Accepted paper at CVPR 202

Stacking of a stearoyl-ACP thioesterase with a dual-silenced palmitoyl-ACP thioesterase and Δ12 fatty acid desaturase in transgenic soybean

Soybean (Glycine max (L.) Merr) is valued for both its protein and oil, whose seed is composed of 40% and 20% of each component, respectively. Given its high percentage of polyunsaturated fatty acids, linoleic acid and linolenic acid, soybean oil oxidative stability is relatively poor. Historically food processors have employed a partial hydrogenation process to soybean oil as a means to improve both the oxidative stability and functionality in end-use applications. However, the hydrogenation process leads to the formation of trans-fats, which are associated with negative cardiovascular health. As a means to circumvent the need for the hydrogenation process, genetic approaches are being pursued to improve oil quality in oilseeds. In this regard, we report here on the introduction of the mangosteen (Garcinia mangostana) stearoyl-ACP thioesterase into soybean and the subsequent stacking with an event that is dual-silenced in palmitoyl-ACP thioesterase and Δ12 fatty acid desaturase expression in a seed-specific fashion. Phenotypic analyses on transgenic soybean expressing the mangosteen stearoyl-ACP thioesterase revealed increases in seed stearic acid levels up to 17%. The subsequent stacked with a soybean event silenced in both palmitoyl-ACP thioesterase and Δ12 fatty acid desaturase activity, resulted in a seed lipid phenotype of approximately 11%–19% stearate and approximately 70% oleate. The oil profile created by the stack was maintained for four generations under greenhouse conditions and a fifth generation under a field environment. However, in generation six and seven under field conditions, the oleate levels decreased to 30%–40%, while the stearic level remained elevated