This study analyzed the degree of influence of measurement and improvement of customer satisfaction level targeting kiosk users. In modern times, due to the development of technology and the improvement of the online environment, the probability that simple labor tasks will disappear after 10 years is close to 90%. Even in domestic research, it is predicted that 'simple labor jobs' will disappear due to the influence of advanced technology with a probability of about 36%. there is. In particular, as the demand for non-face-to-face services increases due to the Corona 19 virus, which is recently spreading globally, the trend of introducing kiosks has accelerated, and the global market will grow to 83.5 billion won in 2021, showing an average annual growth rate of 8.9%. there is. However, due to the unmanned nature of these kiosks, some consumers still have difficulties in using them, and consumers who are not familiar with the use of these technologies have a negative attitude towards service co-producers due to rejection of non-face-to-face services and anxiety about service errors. Lack of understanding leads to role conflicts between sales clerks and consumers, or inequality is being created in terms of service provision and generations accustomed to using technology. In addition, since kiosk is a representative technology-based self-service industry, if the user feels uncomfortable or requires additional labor, the overall service value decreases and the growth of the kiosk industry itself can be suppressed. It is important. Therefore, interviews were conducted on the main points of direct use with actual users centered on display color scheme, text size, device design, device size, internal UI (interface), amount of information, recognition sensor (barcode, NFC, etc.), Display brightness, self-event, and reaction speed items were extracted. Afterwards, using the questionnaire, the Kano model quality attribute classification of each expected evaluation item was carried out, and Timko's customer satisfaction coefficient, which can be calculated with accurate numerical values The PCSI Index analysis was additionally performed to determine the improvement priorities by finally classifying the improvement impact of the kiosk expected evaluation items through research. As a result, the impact of improvement appears in the order of internal UI (interface), text size, recognition sensor (barcode, NFC, etc.), reaction speed, self-event, display brightness, amount of information, device size, device design, and display color scheme. Through this, we intend to contribute to a comprehensive comparison of kiosk-based research in each field and to set the direction for improvement in the venture industry.
At the initial stage of Internet advertising, banner advertising came into fashion. As the Internet developed into a central part of daily lives and the competition in the on-line advertising market was getting fierce, there was not enough space for banner advertising, which rushed to portal sites only. All these factors was responsible for an upsurge in advertising prices. Consequently, the high-cost and low-efficiency problems with banner advertising were raised, which led to an emergence of keyword advertising as a new type of Internet advertising to replace its predecessor. In the beginning of 2000s, when Internet advertising came to be activated, display advertisement including banner advertising dominated the Net. However, display advertising showed signs of gradual decline, and registered minus growth in the year 2009, whereas keyword advertising showed rapid growth and started to outdo display advertising as of the year 2005. Keyword advertising refers to the advertising technique that exposes relevant advertisements on the top of research sites when one searches for a keyword. Instead of exposing advertisements to unspecified individuals like banner advertising, keyword advertising, or targeted advertising technique, shows advertisements only when customers search for a desired keyword so that only highly prospective customers are given a chance to see them. In this context, it is also referred to as search advertising. It is regarded as more aggressive advertising with a high hit rate than previous advertising in that, instead of the seller discovering customers and running an advertisement for them like TV, radios or banner advertising, it exposes advertisements to visiting customers. Keyword advertising makes it possible for a company to seek publicity on line simply by making use of a single word and to achieve a maximum of efficiency at a minimum cost. The strong point of keyword advertising is that customers are allowed to directly contact the products in question through its more efficient advertising when compared to the advertisements of mass media such as TV and radio, etc. The weak point of keyword advertising is that a company should have its advertisement registered on each and every portal site and finds it hard to exercise substantial supervision over its advertisement, there being a possibility of its advertising expenses exceeding its profits. Keyword advertising severs as the most appropriate methods of advertising for the sales and publicity of small and medium enterprises which are in need of a maximum of advertising effect at a low advertising cost. At present, keyword advertising is divided into CPC advertising and CPM advertising. The former is known as the most efficient technique, which is also referred to as advertising based on the meter rate system; A company is supposed to pay for the number of clicks on a searched keyword which users have searched. This is representatively adopted by Overture, Google's Adwords, Naver's Clickchoice, and Daum's Clicks, etc. CPM advertising is dependent upon the flat rate payment system, making a company pay for its advertisement on the basis of the number of exposure, not on the basis of the number of clicks. This method fixes a price for advertisement on the basis of 1,000-time exposure, and is mainly adopted by Naver's Timechoice, Daum's Speciallink, and Nate's Speedup, etc, At present, the CPC method is most frequently adopted. The weak point of the CPC method is that advertising cost can rise through constant clicks from the same IP. If a company makes good use of strategies for maximizing the strong points of keyword advertising and complementing its weak points, it is highly likely to turn its visitors into prospective customers. Accordingly, an advertiser should make an analysis of customers' behavior and approach them in a variety of ways, trying hard to find out what they want. With this in mind, her or she has to put multiple keywords into use when running for ads. When he or she first runs an ad, he or she should first give priority to which keyword to select. The advertiser should consider how many individuals using a search engine will click the keyword in question and how much money he or she has to pay for the advertisement. As the popular keywords that the users of search engines are frequently using are expensive in terms of a unit cost per click, the advertisers without much money for advertising at the initial phrase should pay attention to detailed keywords suitable to their budget. Detailed keywords are also referred to as peripheral keywords or extension keywords, which can be called a combination of major keywords. Most keywords are in the form of texts. The biggest strong point of text-based advertising is that it looks like search results, causing little antipathy to it. But it fails to attract much attention because of the fact that most keyword advertising is in the form of texts. Image-embedded advertising is easy to notice due to images, but it is exposed on the lower part of a web page and regarded as an advertisement, which leads to a low click through rate. However, its strong point is that its prices are lower than those of text-based advertising. If a company owns a logo or a product that is easy enough for people to recognize, the company is well advised to make good use of image-embedded advertising so as to attract Internet users' attention. Advertisers should make an analysis of their logos and examine customers' responses based on the events of sites in question and the composition of products as a vehicle for monitoring their behavior in detail. Besides, keyword advertising allows them to analyze the advertising effects of exposed keywords through the analysis of logos. The logo analysis refers to a close analysis of the current situation of a site by making an analysis of information about visitors on the basis of the analysis of the number of visitors and page view, and that of cookie values. It is in the log files generated through each Web server that a user's IP, used pages, the time when he or she uses it, and cookie values are stored. The log files contain a huge amount of data. As it is almost impossible to make a direct analysis of these log files, one is supposed to make an analysis of them by using solutions for a log analysis. The generic information that can be extracted from tools for each logo analysis includes the number of viewing the total pages, the number of average page view per day, the number of basic page view, the number of page view per visit, the total number of hits, the number of average hits per day, the number of hits per visit, the number of visits, the number of average visits per day, the net number of visitors, average visitors per day, one-time visitors, visitors who have come more than twice, and average using hours, etc. These sites are deemed to be useful for utilizing data for the analysis of the situation and current status of rival companies as well as benchmarking. As keyword advertising exposes advertisements exclusively on search-result pages, competition among advertisers attempting to preoccupy popular keywords is very fierce. Some portal sites keep on giving priority to the existing advertisers, whereas others provide chances to purchase keywords in question to all the advertisers after the advertising contract is over. If an advertiser tries to rely on keywords sensitive to seasons and timeliness in case of sites providing priority to the established advertisers, he or she may as well make a purchase of a vacant place for advertising lest he or she should miss appropriate timing for advertising. However, Naver doesn't provide priority to the existing advertisers as far as all the keyword advertisements are concerned. In this case, one can preoccupy keywords if he or she enters into a contract after confirming the contract period for advertising. This study is designed to take a look at marketing for keyword advertising and to present effective strategies for keyword advertising marketing. At present, the Korean CPC advertising market is virtually monopolized by Overture. Its strong points are that Overture is based on the CPC charging model and that advertisements are registered on the top of the most representative portal sites in Korea. These advantages serve as the most appropriate medium for small and medium enterprises to use. However, the CPC method of Overture has its weak points, too. That is, the CPC method is not the only perfect advertising model among the search advertisements in the on-line market. So it is absolutely necessary that small and medium enterprises including independent shopping malls should complement the weaknesses of the CPC method and make good use of strategies for maximizing its strengths so as to increase their sales and to create a point of contact with customers.
Metal industry is one of the most representative heavy industries and the median sales volume of steel and nonferrous metal companies is over one billion dollars in the case America [Forbes 2006]. As seen in the recent business market situation, an increasing number of industrial manufacturers and suppliers are moving from adversarial to cooperative exchange attitudes that support the long-term relationships with their customers. This article presents the results of an empirical study of the antecedent factors of business relationships in metal industry of the United States. Commitment has been reviewed as a significant and critical variable in research on inter-organizational relationships (Hong et al. 2007, Kim et al. 2007). The future stability of any buyer-seller relationship depends upon the commitment made by the interactants to their relationship. Commitment, according to Dwyer et al. [1987], refers to "an implicit or explicit pledge of relational continuity between exchange partners" and they consider commitment to be the most advanced phase of buyer-seller exchange relationship. Bonds are made because the members need their partners in order to do something and this integration on a task basis can be either symbiotic or cooperative (Svensson 2008). To the extent that members seek the same or mutually supporting ends, there will be strong bonds among them. In other words, the principle that affects the strength of bonds is 'economy of decision making' [Turner 1970]. These bonds provide an important idea to study the causes of business long-term relationships in a sense that organizations can be mutually bonded by a common interest in the economic matters. Recently, the framework of structural bonding has been used to study the buyer-seller relationships in industrial marketing [Han and Sung 2008, Williams et al. 1998, Wilson 1995] in that this structural bonding is a crucial part of the theoretical justification for distinguishing discrete transactions from ongoing long-term relationships. The major antecedent factors of buyer commitment such as technology, CLalt, transaction-specific assets, and importance were identified and explored from the perspective of structural bonding. Research hypotheses were developed and tested by using survey data from the middle managers in the metal industry. H1: Level of technology of the relationship partner is positively related to the level of structural bonding between the buyer and the seller. H2: Comparison level of alternatives is negatively related to the level of structural bonding between the buyer and the seller. H3: Amount of the transaction-specific assets is positively related to the level of structural bonding between the buyer and the seller. H4: Importance of the relationship partner is positively related to the level of structural bonding between the buyer and the seller. H5: Level of structural bonding is positively related to the level of commitment to the relationship. To examine the major antecedent factors of industrial buyer's structural bonding and long-term relationship, questionnaire was prepared, mailed out to the sample of 400 purchasing managers of the US metal industry (SIC codes 33 and 34). After a follow-up request, 139 informants returnedthe questionnaires, resulting in a response rate of 35 percent. 134 responses were used in the final analysis after dropping 5 incomplete questionnaires. All measures were analyzed for reliability and validity following the guidelines offered by Churchill [1979] and Anderson and Gerbing [1988]., the results of fitting the model to the data indicated that the hypothesized model provides a good fit to the data. Goodness-of-fit index (GFI = 0.94) and other indices ( chi-square = 78.02 with p-value = 0.13, Adjusted GFI = 0.90, Normed Fit Index = 0.92) indicated that a major proportion of variances and covariances in the data was accounted for by the model as a whole, and all the parameter estimates showed statistical significance as evidenced by large t-values. All the factor loadings were significantly different from zero. On these grounds we judged the hypothesized model to be a reasonable representation of the data. The results from the present study suggest several implications for buyer-seller relationships. Theoretically, we attempted to conceptualize the antecedent factors of buyer-seller long-term relationships from the perspective of structural bondingin metal industry. The four underlying determinants (i.e. technology, CLalt, transaction-specific assets, and importance) of structural bonding are very critical variables of buyer-seller long-term business relationships. Our model of structural bonding makes an attempt to systematically examine the relationship between the antecedent factors of structural bonding and long-term commitment. Managerially, this research provides industrial purchasing managers with a good framework to assess the interaction processes with their partners and, ability to position their business relationships from the perspective of structural bonding. In other words, based on those underlying variables, industrial purchasing managers can determine the strength of the company's relationships with the key suppliers and its state of preparation to be a successful partner with those suppliers. Both the supplying and customer companies can also benefit by using the concept of 'structural bonding' and evaluating their relationships with key business partners from the structural point of view. In general, the results indicate that structural bonding gives a critical impact on the level of relationship commitment. Managerial implications and limitations of the study are also discussed.
Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.