This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

This study examines the impact of emotional intelligence on the complaint handling process and outcome in the Chinese hotel setting. The results of the study indicate that the TARP model can be applied to China's hotel environment; “network evaluation” has become an important factor in assessing the severity of complaints. Besides, the negotiation and communication methods need to be adaptive in the context of Chinese consumer culture, and the complaints in the hotel environment should be handled immediately. Compared with the negative cases, the frequency of emotional intelligence application in positive cases is higher in every aspect of the TARP model. For the first time, the qualitative case study method is applied to similar research topics, and the application of various dimensions of emotional intelligence in hotel complaint handling process is thoroughly explored. This study not only has theoretical contributions but also serves as a reference for hotels to formulate a high-quality complaint handling standard operating procedure

본 연구는 자동차 산업 내 소비자들의 고객불평행동과 기업과의 관계유지를 실증적으로 분석한 연구이다. 특히, 기업의 불평관리에 대한 소비자들의 인지된 공정성과 제품 원산지의 조절효과를 검증하고자 하였다. 이를 위해 222명의 국산차 소비자들과 232명의 외제차 소비자들을 대상으로 설문조사를 실시하였다. 실증분석 결과, 모든 세 가지 유형 (직접행동, 사적행동, 제삼자행동)의 소비자 불평행동들은 기업과의 관계유지에 부정적인 영향을 미치는 것으로 나타났다. 조절효과에 있어서는 기업의 불평관리에 대한 소비자들의 인지된 공정성이 높을수록 직접행동과 관계유지의 부정적인 관계를 약화시키는 것으로 나타났다. 또한, 국산차와 외제차를 기준으로 나눈 두 소비자 그룹 간의 원산지 효과 차이는 유의한 것으로 나타났다. 사적행동과 관계유지의 부정적인 관계는 국산차 소비자들보다 외제차 소비자들에게서 더 약한 것으로 나타났다. 본 연구의 실증결과는 서비스 마케팅 분야에서의 이론적 시사점뿐만 아니라 실무자들에게 유용한 시사점을 제공할 수 있을 것으로 기대된다.