• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.154-162
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• 2022

Digital marketing tools are actively used in managing the development of park and recreation complexes to familiarize the population with the objects of natural heritage. This article aims to empirically evaluate digital marketing tools for popularizing the park and recreational complexes. The methodology was based on the concept of ecosystem value of park and recreation complexes as a natural heritage site. These methods included: identifying and selecting websites with information about park and recreation complexes in Slovakia and Ukraine. structural analysis of the main channels of online details about natural parks. Assessing the current state of online identity of the studied sites from the perspective of Internet users. The results indicate that to manage the development of park and recreational complexes developed their driven official websites in the Internet space, on which sections structure the information with the allocation of data on tourism and recreational potential. The article identifies additional digital marketing tools for managing the development of park and recreation complexes, particularly social networks and tourist websites. There is a sufficient amount of information about tourist recreation sites within these natural parks and tourist routes. Among the main problems of the websites: the information on the websites is entirely textual, there is a lack of sufficient data on social networks, despite the created official pages, there is no video content, which was more attracted tourists and visitors, allowing a visual assessment of the tourist potential; there is a problem of many communication channels to present the natural heritage of the countries. The research proves that the website is the primary and most common digital marketing tool for natural heritage, structuring information about tourism potential and recreation.

• The Journal of Industrial Distribution & Business
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• v.9 no.12
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• pp.31-43
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• 2018

Purpose - Smartphone shopping malls provide customers with a variety of tangible and intangible services including web sites, web design, use convenience, information for products and shopping and various after services. Accordingly, it is needed to expand and classify service qualities based on the various services provided by smartphone shopping malls, and then analyze path structures of smartphone shopping malls' qualities → customer satisfaction → behavioral intention. The purpose of this study is to categorize the qualities of smartphone shopping mall users based on the e-SERVQUAL by Lee(2002) and the SERVQUAL by Parasuraman et al.(1988, 2005), the smartphone shopping malls' service qualities based on service quality of smartphone shopping malls used in the previous use studies, and the Website quality factors of service industry and to analyze path structure of smartphone shopping mall's qualities → customer satisfaction → behavioral intention on college students in order to confirm the system of smartphone shopping malls' qualities. Research design, data, and methodology - This study's survey was carried out on the college students of university located in northeastern of Seoul. It was from December 7 - 15, 2017, and a total of 240 questionnaires were distributed, with 228 collected. Of them, effective questionnaires used in the final study were a total of 201 except 27 that couldn't be used. In this study, empirical analysis was done with factor analysis, correlation analysis, multiple regression analysis, simple multiple regression analysis and moderating regression analysis by using Statistics Package SPSS18.0. Results - The study results are as follows: First, smartphone shopping malls' qualities were classified into six categories like customer system quality, Web design quality, convenience quality, information-offering quality, service quality, and product quality. Second, it showed that system quality, Web design quality, and information-offering quality had a positive impact on customer satisfaction, respectively. Third, it suggested that quality factors of smartphone shopping mall users had a positive impact on customer satisfaction in the order of quality, information-offering quality, system quality and Web design quality. Finally, it showed that customer service quality, product quality, and convenience quality did not have a positive impact on customer satisfaction. In addition, it said that customer satisfaction of smartphone shopping mall users had a positive impact on behavioral intention and thereby, the higher the customer satisfaction was, the higher the relations between reuse intention and recommendation intention were. Meanwhile, moderating regression analysis showed that trust did not have moderating effect in the relations between customer satisfaction and behavioral intention. The above study revealed that smartphone shopping malls' qualities were classified into six categories and it was possible to generalize after empirical analysis was made in the path structure. Conclusions - Smartphone shopping mall users consider usefulness of obtaining shopping information and quality on quick and abundant shopping information more important than access environment of smartphone shopping malls and kind services of smartphone shopping mall managers. Thereby, smartphone shopping mall marketers need to take service qualities like system quality and information-offering quality into more consideration.

• Korean journal of communication and information
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• v.32
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• pp.147-168
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• 2006

This study attempts to evaluate the Korean terrestrial television networks‘ internet site effectiveness, specifically in addressing user motivations and user evaluations and in gauging the broadcasters' feedback. The result shows that the primary motive for using a broadcaster's internet homepage was to obtain information. Overall, the users tended to be dissatisfied with the services, especially with regards to its navigational structure and design and with the insufficient number of appropriate content items. The reason cited highest for dissatisfaction was the fee-based services. Among the three networks, Seoul Broadcasting System, the sole commercial network, received the most complaints from the respondents in this respect. The study results reveal that broadcasters' feedback systems to users were deemed responsive and timely, but a true interactivity between the broadcasters and the viewers was lacking. In conclusion, network televisions' internet sites and increased content access through them have provided a platform for increased communication with their viewers, however, these sites are not yet being fully utilized as effective interactive communication feedback channels but more as a unidirectional information source. In addition, the network television services seem to be lacking in providing users with efficient navigational structure, good design, or adequate content level and quality. Thus, to better serve the public, it is recommended that they improve internet homepage usability by assessing and applying qualitative researches regarding the users.

• Clean Technology
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• v.17 no.4
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• pp.285-299
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• 2011

As one of the REACH obligations, the extended safety data sheet (eSDS) should be communicated within the supply chain under the REACH Regulation. Based on technical guidance documents published on the ECHAs website and survey of EU's recent REACH-related informations, this paper includes a study on details of how to develop exposure scenarios (ES) such as structure of ES, process of ES develpoment, standard workflows and key input data to develop ES with an introduction of eSDS concept. This paper also contains an overview on operational conditions (OCs) and risk management measures (RMMs) that are what to consider when building an ES. The structure of Chesar (Chemical Safety Assessment and Report tool) developed by European Chemicals Agency (ECHA) is studied with a review of the available exposure estimation tools for workers, environment and consumers. Case example of generic exposure scenario (GES) for organic solvent is presented. To guide Korean EU-exporting companies, their participating roles in three steps of preparing ES are addressed.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

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.

• Archives of design research
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• v.15 no.1
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• pp.191-202
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• 2002

As family types have been diverse, patterns of living and living space became diverse as much as users are. Therefore, it is needed to provide various remodeled design of living space corresponding to changes of users'living patterns, and to provide these remodeling process to users directly on the web. In this paper, use scenario for the Internet-based Virtual Apartment Remodeling Simulator is researched as an export system to remodel space in accordance with users diverse lifestyle paradigm and the website is developed. The study consists of four parts. First, the general concept of remodeling, including the range and types of remodeling, are defined, and the misleading terms in this field are reviewed and organized by secondary research Second, fixed factors and variable factors are differentiated in the complex building for residence and business that was decided as a basic building type in this study. Third, there needed a database for consulting, final material, pre-estimation real estimation for simulation of remodeling. This database was introduced along with floor plan and elevation. Finally, the remodeling simulator is presented by the case study developed on the web. The system structure and use scenario are also presented. In order to present and inspect design alternatives, prototype was produced. The Final simulator was enhanced by defeating problems regarding interface efficiency and missing information of existing online site.

• Archives of design research
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• v.11 no.1
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• pp.291-302
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• 1998

This paper covers the development process of multimedia evaluation system, especially focused on customer satisfactory factors while customers navigating net-based Interactive multimedia system. Customers usually experience new level of interaction cased by newly developed web-based technology In ordinary multimedia system. However, if it gives customers satisfactory experience is a matter of question. To find out the relationship between customer satisfaction and interactivity factors exposed by multimedia system, a model has been developed which describes the structure of web-based multimedia system and its relation to customer satisfactory factors. Five different experiments, including 'semantic differential', 'focus group interview', and 'expert review', has been conducted and four customer satisfactory factors were identified. Those are 'customery value', 'structural perfectness', 'visual perfectness', and 'contemporaneity'. With these factors and newly delveoped evaluation system, 7 different web-site has been evaluated and analyzed at the end of this report.

• Asia pacific journal of information systems
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• v.21 no.4
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• pp.1-25
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• 2011

Internet-based commerce has undergone an explosive growth over the past decade as consumers today find it more economical as well as more convenient to shop online. Nevertheless, the shift in the common mode of shopping from offline to online commerce has caused consumers to have worries over such issues as private information leakage, online fraud, discrepancy in product quality and grade, unsuccessful delivery, and so forth, Numerous studies have been undertaken to examine the role of perceived risk as a chief barrier to online purchases and to understand the theoretical relationships among perceived risk, trust and purchase intentions, However, most studies focus on empirically investigating the effects of trust on perceived risk, with little attention devoted to the effects of perceived risk on trust, While the influence trust has on perceived risk is worth studying, the influence in the opposite direction is equally important, enabling insights into the potential of perceived risk as a prohibitor of trust, According to Pavlou (2003), the primary source of the perceived risk is either the technological uncertainty of the Internet environment or the behavioral uncertainty of the transaction partner. Due to such types of uncertainty, an increase in the worries over the perceived risk may negatively affect trust, For example, if a consumer who sends sensitive transaction data over Internet is concerned that his or her private information may leak out because of the lack of security, trust may decrease (Olivero and Lunt, 2004), By the same token, if the consumer feels that the online merchant has the potential to profit by behaving in an opportunistic manner taking advantage of the remote, impersonal nature of online commerce, then it is unlikely that the merchant will be trusted, That is, the more the probable danger is likely to occur, the less trust and the greater need to control the transaction (Olivero and Lunt, 2004), In summary, a review of the related studies indicates that while some researchers looked at the influence of overall perceived risk on trust level, not much attention has been given to the effects of different types of perceived risk, In this context the present research aims at addressing the need to study how trust is affected by different types of perceived risk, We classified perceived risk into six different types based on the literature, and empirically analyzed the impact of each type of perceived risk upon consumer trust in an online merchant and further its impact upon purchase intentions. To meet our research objectives, we developed a conceptual model depicting the nomological structure of the relationships among our research variables, and also formulated a total of seven hypotheses. The model and hypotheses were tested using an empirical analysis based on a questionnaire survey of 206 college students. The reliability was evaluated via Cronbach's alphas, the minimum of which was found to be 0.73, and therefore the questionnaire items are all deemed reliable. In addition, the results of confirmatory factor analysis (CFA) designed to check the validity of the measurement model indicate that the convergent, discriminate, and nomological validities of the model are all acceptable. The structural equation modeling analysis to test the hypotheses yielded the following results. Of the first six hypotheses (H1-1 through H1-6) designed to examine the relationships between each risk type and trust, three hypotheses including H1-1 (performance risk ${\rightarrow}$ trust), H1-2 (psychological risk ${\rightarrow}$ trust) and H1-5 (online payment risk ${\rightarrow}$ trust) were supported with path coefficients of -0.30, -0.27 and -0.16 respectively. Finally, H2 (trust ${\rightarrow}$ purchase intentions) was supported with relatively high path coefficients of 0.73. Results of the empirical study offer the following findings and implications. First. it was found that it was performance risk, psychological risk and online payment risk that have a statistically significant influence upon consumer trust in an online merchant. It implies that a consumer may find an online merchant untrustworthy if either the product quality or the product grade does not match his or her expectations. For that reason, online merchants including digital storefronts and e-marketplaces are suggested to pursue a strategy focusing on identifying the target customers and offering products that they feel best meet performance and psychological needs of those customers. Thus, they should do their best to make it widely known that their products are of as good quality and grade as those purchased from offline department stores. In addition, it may be inferred that today's online consumers remain concerned about the security of the online commerce environment due to the repeated occurrences of hacking or private information leakage. Online merchants should take steps to remove potential vulnerabilities and provide online notices to emphasize that their website is secure. Second, consumer's overall trust was found to have a statistically significant influence on purchase intentions. This finding, which is consistent with the results of numerous prior studies, suggests that increased sales will become a reality only with enhanced consumer trust.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.49-65
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• 2014

Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.