• Asia pacific journal of information systems
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• v.24 no.2
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• pp.211-232
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• 2014

Recently, there is a growing interest toward social commerce using SNS(Social Networking Service), and the size of its market is also expanding due to popularization of smart phones, tablet PCs and other smart devices. Accordingly, various studies have been attempted but it is shown that most of the previous studies have been conducted from perspectives of the users. The purpose of this study is to derive user-centered CSF(Critical Success Factor) of social commerce from the previous studies and analyze the CSF perception gap between social commerce service providers and users. The CSF perception gap between two groups shows that there is a difference between ideal images the service providers hope for and the actual image the service users have on social commerce companies. This study provides effective improvement directions for social commerce companies by presenting current business problems and its solution plans. For this, This study selected Korea's representative social commerce business Ticket Monster, which is dominant in sales and staff size together with its excellent funding power through M&A by stock exchange with the US social commerce business Living Social with Amazon.com as a shareholder in August, 2011, as a target group of social commerce service provider. we have gathered questionnaires from both service providers and the users from October 22, 2012 until October 31, 2012 to conduct an empirical analysis. We surveyed 160 service providers of Ticket Monster We also surveyed 160 social commerce users who have experienced in using Ticket Monster service. Out of 320 surveys, 20 questionaries which were unfit or undependable were discarded. Consequently the remaining 300(service provider 150, user 150)were used for this empirical study. The statistics were analyzed using SPSS 12.0. Implications of the empirical analysis result of this study are as follows: First of all, There are order differences in the importance of social commerce CSF between two groups. While service providers regard Price Economic as the most important CSF influencing purchasing intention, the users regard 'Trust' as the most important CSF influencing purchasing intention. This means that the service providers have to utilize the unique strong point of social commerce which make the customers be trusted rathe than just focusing on selling product at a discounted price. It means that service Providers need to enhance effective communication skills by using SNS and play a vital role as a trusted adviser who provides curation services and explains the value of products through information filtering. Also, they need to pay attention to preventing consumer damages from deceptive and false advertising. service providers have to create the detailed reward system in case of a consumer damages caused by above problems. It can make strong ties with customers. Second, both service providers and users tend to consider that social commerce CSF influencing purchasing intention are Price Economic, Utility, Trust, and Word of Mouth Effect. Accordingly, it can be learned that users are expecting the benefit from the aspect of prices and economy when using social commerce, and service providers should be able to suggest the individualized discount benefit through diverse methods using social network service. Looking into it from the aspect of usefulness, service providers are required to get users to be cognizant of time-saving, efficiency, and convenience when they are using social commerce. Therefore, it is necessary to increase the usefulness of social commerce through the introduction of a new management strategy, such as intensification of search engine of the Website, facilitation in payment through shopping basket, and package distribution. Trust, as mentioned before, is the most important variable in consumers' mind, so it should definitely be managed for sustainable management. If the trust in social commerce should fall due to consumers' damage case due to false and puffery advertising forgeries, it could have a negative influence on the image of the social commerce industry in general. Instead of advertising with famous celebrities and using a bombastic amount of money on marketing expenses, the social commerce industry should be able to use the word of mouth effect between users by making use of the social network service, the major marketing method of initial social commerce. The word of mouth effect occurring from consumers' spontaneous self-marketer's duty performance can bring not only reduction effect in advertising cost to a service provider but it can also prepare the basis of discounted price suggestion to consumers; in this context, the word of mouth effect should be managed as the CSF of social commerce. Third, Trade safety was not derived as one of the CSF. Recently, with e-commerce like social commerce and Internet shopping increasing in a variety of methods, the importance of trade safety on the Internet also increases, but in this study result, trade safety wasn't evaluated as CSF of social commerce by both groups. This study judges that it's because both service provider groups and user group are perceiving that there is a reliable PG(Payment Gateway) which acts for e-payment of Internet transaction. Accordingly, it is understood that both two groups feel that social commerce can have a corporate identity by website and differentiation in products and services in sales, but don't feel a big difference by business in case of e-payment system. In other words, trade safety should be perceived as natural, basic universal service. Fourth, it's necessary that service providers should intensify the communication with users by making use of social network service which is the major marketing method of social commerce and should be able to use the word of mouth effect between users. The word of mouth effect occurring from consumers' spontaneous self- marketer's duty performance can bring not only reduction effect in advertising cost to a service provider but it can also prepare the basis of discounted price suggestion to consumers. in this context, it is judged that the word of mouth effect should be managed as CSF of social commerce. In this paper, the characteristics of social commerce are limited as five independent variables, however, if an additional study is proceeded with more various independent variables, more in-depth study results will be derived. In addition, this research targets social commerce service providers and the users, however, in the consideration of the fact that social commerce is a two-sided market, drawing CSF through an analysis of perception gap between social commerce service providers and its advertisement clients would be worth to be dealt with in a follow-up study.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.1-25
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• 2008

Ⅰ. Introduction Retailers in the 21st century are being told that future retailers are those who can execute seamless multi-channel access. The reason is that retailers should be where shoppers want them, when they want them anytime, anywhere and in multiple formats. Multi-channel access is considered one of the top 10 trends of all business in the next decade (Patricia T. Warrington, et al., 2007) And most firms use both direct and indirect channels in their markets. Given this trend, we need to evaluate a channel equity more systematically than before as this issue is expected to get more attention to consumers as well as to brand managers. Consumers are becoming very much confused concerning the choice of place where they shop for durable goods as there are at least 6-7 retail options. On the other hand, manufacturers have to deal with category killers, their dealers network, Internet shopping malls, and other avenue of distribution channels and they hope their retail channel behave like extensions of their own companies. They would like their products to be foremost in the retailer's mind-the first to be proposed and effectively communicated to potential customers. To enable this hope to come reality, they should know each channel's advantages and disadvantages from consumer perspectives. In addition, customer satisfaction is the key determinant of retail customer loyalty. However, there are only a few researches regarding the effects of shopping satisfaction and perceptions on consumers' channel choices and channels. The purpose of this study was to assess Korean consumers' channel choice and satisfaction towards channels they prefer to use in the case of electronic goods shopping. Korean electronic goods retail market is one of good example of multi-channel shopping environments. As the Korea retail market has been undergoing significant structural changes since it had opened to global retailers in 1996, new formats such as hypermarkets, Internet shopping malls and category killers have arrived for the last decade. Korean electronic goods shoppers have seven major channels : (1)category killers (2) hypermarket (3) manufacturer dealer shop (4) Internet shopping malls (5) department store (6) TV home-shopping (7) speciality shopping arcade. Korean retail sector has been modernized with amazing speed for the last decade. Overall summary of major retail channels is as follows: Hypermarket has been number 1 retailer type in sales volume from 2003 ; non-store retailing has been number 2 from 2007 ; department store is now number 3 ; small scale category killers are growing rapidly in the area of electronics and office products in particular. We try to evaluate each channel's equity using a consumer survey. The survey was done by telephone interview with 1000 housewife who lives nationwide. Sampling was done according to 2005 national census and average interview time was 10 to 15 minutes. Ⅱ. Research Summary We have found that seven major retail channels compete with each other within Korean consumers' minds in terms of price and service. Each channel seem to have its unique selling points. Department stores were perceived as the best electronic goods shopping destinations due to after service. Internet shopping malls were perceived as the convenient channel owing to price checking. Category killers and hypermarkets were more attractive in both price merits and location conveniences. On the other hand, manufacturers dealer networks were pulling customers mainly by location and after service. Category killers and hypermarkets were most beloved retail channel for Korean consumers. However category killers compete mainly with department stores and shopping arcades while hypermarkets tend to compete with Internet and TV home shopping channels. Regarding channel satisfaction, the top 3 channels were service-driven retailers: department stores (4.27); dealer shop (4.21); and Internet shopping malls (4.21). Speciality shopping arcade(3.98) were the least satisfied channels among Korean consumers. Ⅲ. Implications We try to identify the whole picture of multi-channel retail shopping environments and its implications in the context of Korean electronic goods. From manufacturers' perspectives, multi-channel may cause channel conflicts. Furthermore, inter-channel competition draws much more attention as hypermarkets and category killers have grown rapidly in recent years. At the same time, from consumers' perspectives, 'buy where' is becoming an important buying decision as it would decide the level of shopping satisfaction. We need to develop the concept of 'channel equity' to manage multi-channel distribution effectively. Firms should measure and monitor their prime channel equity in regular basis to maximize their channel potentials. Prototype channel equity positioning map has been developed as follows. We expect more studies to develop the concept of 'channel equity' in the future.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.17-23
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• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Korean Security Journal
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• no.50
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• pp.175-206
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• 2017

The current study described the history of Islamic extremism and the recent expansion of international Islamic extremism in Indonesia. For doing so, both content analysis of the existing written documents and qualitative interviews were conducted. For the content analysis, media reports and research articles were collected and utilized. For qualitative interviews, Indonesian students and workers in Korea, Korean spouses married to Indonesian, and Korean missionaries in Indonesia were contacted and interviewed. Qualitative interview was conducted between 30 minutes and 2 hours. On the spot, interviews were recorded and later transcribed into written documents. Due to the difficulty of identification of population and the uneasiness of accessability to th study subjects, convenient sampling and snowball sampling were used. According to the results, Islamic extremism in Indonesia had a deep historical root and generally shared similar historical experience with other muslim countries such as Afghanistan, Pakistan, Egypt, and Saudi Arabia where Islamic extremism was deeply rooted in. That is, Islamic extremism began as a reaction to the western imperialism, after independence, Islamic extremism elements were marginalized in the process of construction of the modern nation-state, and Islamic extremist movement was radicalized and became violent during the Soviet-Afghan War. In addition, after 9.11, Islamic extremism in Indonesia was connected to international Islamic extremism network and integrated into such global movement. Such a historical development of Indonesian Islamic extremism was quite organized and robust. Meanwhile, the eastward infiltration and expansion of international Islamic extremism such as IS and Al Qaeda was observed in Indonesia. Particularly, such a worrisome expansion was more clearly visible in the marginalized and underdeveloped countrysides in Indonesia. Such expansion in Indonesia could negatively affect on the security of South Korea. Geographically, Indonesia is proximate to South Korea. This geographical proximity could be a direct security threat to the Korean society, as if Islamic extremism in North Africa and Middle East becomes a direct security threat to Europe. Considering the presence of a large size of Indonesian immigrant workers and communities in South Korea, such a concern is very realistic. The arrest of an Indonesian Islamic extremism supporter in November, 2016, could be a harbinger of the coming trend of Islamic extremism expansion inside South Korea. The Indonesian Islamic community in South Korea could be a passage of Indonesian Islamic extremism into the South Korean society. In this context, it is timely and necessary to pay an attention to the recent trend of Islamic extremism expansion in Indonesia.

• Archives of design research
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• v.18 no.2 s.60
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• pp.37-46
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• 2005

Today's advertisement is dominated by visual images, which deliver messages in the most efficient and engaging way. Vivid and live images, through multimedia, attract the viewer to consume the products being advertised. In this course, the effect of 'Verfremdung' or alienation is often detected. This terminology was at first used in a epic play of Bertolt Brecht to signify a specific effect to block the empathy of the audience by reminding them of their position as the audience separate from the play. 'Verfremdung' helps them to have a sense of objectivity and critical attitude toward the performance. Multimedia commercials nowadays exhibit such a quality together with 'viewer participation:' criticism on reality: and 'speed.' In an advertisement for Shinsegi Communications' mobile system titled <017-I-Touch series, the hand transformed into a web>, the copy, 'my blood type is 'i', ' is accompanied by six unrelated fictitious scenes in which the same ocean appears as a common denominator. Because there is no connection between the scenes, free imagination of the viewer has to be involved and thus plays a significant role in making them into a context. This fact dearly exhibit some characteristics of post- modern advertisement. Momentary 'Gap' and 'Difference' between scenes contribute to 'Verfremdung' or alienation' that makes it hard for the consumers to comprehend the content on the spot. Such an uneasy situation, however, keeps the viewers thinking about the advertisement itself. While repeatedly exposed to the alienated images, the viewers come to get involved in the advertisement, trying to make the fragmented images into a coherent context. In addition, the leaps between the scenes produce a sense of 'speed' in a context, which adds more impact to the way of delivering messages using multimedia. With the help of multimedia, 'Verfremdung: which was originally intended to bring about objective and critical altitude of the audience in a play, plays a crucial role in attracting the viewer's attention and conveying a specific message in a moment in contemporary advertisement.

• Journal of Korea Technology Innovation Society
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• v.20 no.1
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• pp.1-33
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• 2017

In the last decades, technology-oriented small firms, i.e. venture businesses, have been increasingly engaged in R&D collaborations with external parties as strategic means for technological innovation. Despite ample evidence on the benefit of such collaborations for the firms, there has been less attention to examining whether and how the firms' social interactions with cooperating partners and their managerial characteristics contribute to that benefit. Drawing on the theories of social capital and entrepreneurial orientation, this study is to remedy this gap. The theory of social capital, referring to a sum of the value and potential resources embedded in social relationships of collectives, provides an integrated view of social factors among cooperating partners, e.g. strong ties, network stability, trust, reciprocity, shared vision and value. It categorizes these factors into structural, relational, and cognitive dimensions of social capital. Entrepreneurial orientation theory captures firms' managerial characteristics as a combination of innovativeness, proactiveness, and risk-taking. This addresses firms' managerial process to utilize and combine internal and external resources for wealth creation and opportunity realization. Against this background, this study investigates what roles social capital among cooperating R&D partners and entrepreneurial orientation of the collaborating firms play for collective performance improvement in R&D collaborations. In terms of the collective performance, this study adopts two indicators: technological competitiveness and business performance. Technological competitiveness refers to the contribution of a technology developed by a cooperative R&D project to competitive advantage of a firm while business performance is defined as the financial and economic outcome of a collaboration. Using a sample of 218 Korean ventures engaging in R&D collaboration with external parties, the author finds the significant effects of social capital (i.e. structural, relational, and cognitive dimensions) and entrepreneurial orientation (i.e. innovativeness, proactiveness, and risk-taking) on both of the technological competitiveness and the business performance. Further, the higher the social capital among R&D partners, the more likely it is to foster the entrepreneurial orientation at firm-level. Most importantly, the entrepreneurial orientation at firm-level is an significant mediator of the relationship between social capital and collective performance. Beyond these novel empirical findings, this study contributes to the literature on R&D collaboration. The findings' implications for management and policy are deeply discussed in the conclusion.