• Journal of Applied Reliability
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• v.17 no.2
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• pp.159-167
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• 2017

Purpose: The purpose of this research is to study research trend in the field of warranty and maintenance policy of second-hand products. Methods: To this end, we consider research articles, which deal with warranty and maintenance of the second-hand products, published on journals during the past 20 years and classify them by taxonomy scheme proposed by Shafiee and Chukova (2013). The taxonomy scheme consists of three maintenance models in warranty for second-hand product. In each models, we analyze proposed maintenance and warranty policies with respect to types of upgrade models, types of preventive maintenances, decision variables and decision criteria model. Results: We obtain the scheme of maintenance and warranty of the second-hand products and define cost related to warranty and maintenance of the second-hand item. Also, we summarize the characteristics of maintenance and warranty policies in each classified model. Conclusion: There have been several research reviews on maintenance and warranty polity of new products. This research surveys researches of authors during the past 20 years and classifies, summarizes and compares proposed maintenance and warranty policies of the second-hand products. This research provides useful information to researchers who are interested in maintenance and warranty of the second-hand products.

• Asian Journal of Business Environment
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• v.8 no.3
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• pp.5-12
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• 2018

Purpose - The purpose of current paper is to identify features of advertisements at social media that generate the ad-click and to further identify if these advertisements lead to purchase. If no purchase is made, then reasons for not making purchase are identified. Users' purchase experience after users clicked at advertisements are also studied. Research design, data, and methodology - Research design followed is exploratory research, where various factors leading to ad-clicks and generating purchase at social media platform were explored. Raw data was gathered by means of survey among a sample of 185 respondents in India using online structured questionnaire. GLM model and multinomial regression were used to analyze the data. Results - Several factors including endorsement by friends, advertisement aesthetics, product reviews, and aggressive pricing played major role in generating ad-clicks. Major impediment to purchase on were product misrepresentation in advertisement, false discounts, and site security. Female users clicked more on social media advertisements and made more purchases compared to their male counterpart. Conclusions - Social media advertisements have significant positive effect on buying behavior of online customers. Transactions culminating from social media ad-click generated significant positive experience for social media users. Thus, social media can be effective marketing tool.

• Journal of Distribution Science
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• v.19 no.3
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• pp.61-70
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• 2021

Purpose: This paper reviews the attitudes of consumers related to the consumption crisis response strategy (i.e., defensive vs. receptive) that companies implement during crises. Research design, data, and methodology: We discuss the interaction between the crisis response strategy and the consumption crisis type (i.e., corporate ability vs. corporate social responsibility). We used SAS ver. 9.4 software to analyze the results. We applied a 2 × 2 intergroup experimental design to our sample of subjects, who were undergraduate and graduate students at a university in Seoul, South Korea. The three experimental variables considered were the entity's risk response strategy, the crisis type, and public relations strategy. The experiments were conducted by presenting a hypothetical scenario to eight groups. Prior to this experiment, five preliminary surveys were conducted to determine the three variables just mentioned. Preliminary surveys were conducted on the basis of these criteria. For eight selected product lines, 320 undergraduates were required to enter the product lines that are frequently used in the assessment center up to the fourth priority. Results: Analysis of variance revealed that experiments related to crisis response strategy, type of enterprise crisis, and type of public relations message were successful. Conclusions: Our results verify the impact of different types of social initiatives on distribution marketplaces.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.439-451
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• 2021

The study aims to investigate the process employed by companies to intentionally create market categories through implementation of product strategy. Much of the research on market category formation focuses on the spontaneous emergence of market categories, with a few studies focusing on the intentional creation of market categories. In the course of this study, I therefore sought to understand the logic by which companies intentionally create market categories, by treating the process through which market categories are formed as a sensemaking process, and by treating the behavior of a company intentionally forming a market category as an effort to manage this sensemaking process. In empirical study, we conducted an exploratory case analysis through content analysis of company press releases and consumer reviews. It is possible that market categories can be formed or changed if the way in which they are shared among market participants can be changed. In this study, we identified two sense-giving activities for the creation of market categories by firms as follows: (1) reorganizing market categories that flat-panel TV manufacturers in the North American market have attempted to form into subcategories of smart TVs, and (2) connecting them to surrounding categories through strategic labeling to establish new categories.

• The Journal of Information Systems
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• v.32 no.3
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• pp.1-17
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• 2023

Purpose The proliferation of data on the internet has created a need for innovative methods to analyze user satisfaction data. Traditional survey methods are becoming inadequate in dealing with the increasing volume and diversity of data, and new methods using unstructured internet data are being explored. While numerous comment-based user satisfaction studies have been conducted, only a few have explored user satisfaction through video and audio data. Multimodal sentiment analysis, which integrates multiple modalities, has gained attention due to its high accuracy and broad applicability. Design/methodology/approach This study uses multimodal sentiment analysis to analyze user satisfaction of iPhone and Samsung products through online videos. The research reveals that the combination model integrating multiple data sources showed the most superior performance. Findings The findings also indicate that price is a crucial factor influencing user satisfaction, and users tend to exhibit more positive emotions when content with a product's price. The study highlights the importance of considering multiple factors when evaluating user satisfaction and provides valuable insights into the effectiveness of different data sources for sentiment analysis of product reviews.

• Journal of Fashion Business
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• v.27 no.6
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• pp.110-123
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• 2023

As social distancing strengthened after the COVID-19 incident, people looked for things they could do alone. Additionally, as people have more financial resources, they purchase products they had previously considered purchasing, and the phenomenon of giving gifts to oneself has also appeared. Accordingly, this study analyzed fashion product reviews of KakaoTalk Gift, the service to exchange gift via SNS mobile app, to discover the phenomenon of self-gifting and the differences from interpersonal-gifting. For post-hoc data, in collected 18,354 pieces after excluding unnecessary data using a Python-based web crawling technique. The self-gifting behavior of KakaoTalk Gift different from the previous study for self-gift. Regardless of the gift-giving contexts, it determines that most self-gift products are material items. There are differences in product types and price levels when choosing gifts for others and oneself. As a self-gift, people typically buy luxury jewelry and branded bags/wallets to wear and show off. As interpersonal, among fashion products, people usually buy beauty products that reflect less personal tastes. When gift-giving to others, people buy products to appropriate prices to reduce the burden on both. When gift-giving to oneself, people buy wanted products regardless of the price. This study is significant because it suggests a new direction in self-gift research by limited online places to give gifts.

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• MALSORI
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• no.43
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• pp.25-44
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• 2002

The intelligibility of a spoken message is influenced by a number of factors. Intelligibility is a joint product of a speaker and a listener. In addition, intelligibility varies with the nature of the language context and the context of communication. Thus a single intelligibility score can not be ascribed to a given individual apart from listener and listening situation. But there is a clinical and research need to develop assessment measures of intelligibility that are quantitative and analytic. Before developing the index of intelligibility, the crucial factors need to be examined. Among them, the most significant in intelligibility is the speech factors of speakers. The following section reviews the literature dealing with the contribution of segmental and suprasegmental factors in speech intelligibility regarding the hearing impaired, alaryngeal, and motor disorders.

• Smart Media Journal
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• v.2 no.2
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• pp.20-27
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• 2013

The concept of social media is top of the agenda for many business executives and decision makers, as well as consultants try to identify ways where companies can make profitable use of applications such as Netflix, Flixster. The social media is playing an increasingly important role as the information sources for customers making product choices etc. With the flourish of Web 2.0 technology, customer reviews are becoming more and more useful and important information resources for people to save their time and energy on purchasing products that they want. This paper proposes the Bayesian Probabilistic Classification algorithm to mine the social media review, and evaluates it by different splits and cross validation mechanism from the real data set. The explored study experimental results show the robustness and effectiveness of proposed approach for mining the social media review.