Introduction Many extant studies in the strategic management literature show that a firm’s network influences its innovation outcomes (Ahuja, Lampert, & Tandon, 2008). Networks are characterized by strong and weak ties in terms of the combination of the amount of time, intensity, intimacy, and reciprocal services (Granovetter, 1973). There is, however, a continuing debate about the relative advantages of strong and weak ties. These equivocal findings suggest that the relationship between tie strength and a firm’s innovation outcome is complex, and call for a more detailed examination of this relationship. The implications of networks for a firm’s innovation outcomes are quite significant. Nevertheless, the majority of research studies still examine networks using simple dyadic relationships (e.g., Capaldo, 2007). In reality, a firm’s networks are composed of more than a single dyadic relationship and are much more complex. Thus, dyadic approaches are limited in providing understanding of networks on a firm’s innovation performance. As such, we will take the perspective of a focal firm in a triad network. While still relatively simple, the triad network approach allows us to identify key relationships previously unexplored in network tie configuration, and to shed light on the equivocal results in the extant literature. Specifically, we will examine the position of the strong or weak ties among the firms, and also whether the strong or weak ties are adjacent or non-adjacent to the focal firm. Breakthrough innovation is defined as the basic invention, which leads to the evolution of many subsequent technological developments (Ahuja & Lambert, 2001). This definition suggests that novel and unique knowledge is required to create breakthrough innovation. Indeed, recent research shows that firms need novel knowledge created by network partners to create breakthrough innovation (e.g., Srivastava & Gnyawali, 2011). As such, we investigate how different levels of novel and diverse knowledge arising from the position of network ties impact a focal firm’s breakthrough innovation. Nevertheless, obtaining diverse and novel knowledge from networks does not guarantee the creation of successful breakthrough innovation. A firm needs a capability to learn, absorb and integrate the new knowledge into its works, which is its absorptive capacity. Thus, we examine the moderating role of a firm’s absorptive capacity on the relationship between the impact of the configurations arising from the position of strong/weak ties and a firm’s breakthrough innovation in a triad network relationship. Conceptual Framework We posit six different types of network configurations based on tie strength and position of strong/weak ties that are adjacent or non-adjacent to the focal firm in a triad (Figure 1). For example, Configuration 1 has three strong ties and Configuration 6 has three weak ties. Configuration 2 has two strong ties that are adjacent to a focal firm and one weak tie that is non-adjacent to a focal firm in a triad. Importantly, we use two theories (i.e., network theor and relational theory) to elaborate the impact of six configurations on a firm’s breakthrough innovation, considering the tie strength, the position of strong/weak ties, and whether the strong or weak ties are adjacent or non-adjacent to the focal firm. The first of these is network theory. Network theory and relational theory assert different effects of strong versus weak ties on a firm’s breakthrough innovation. To resolve the ambiguities in the literature on this issue, we combine network theory and relational theory and investigate the implication of the position of the strong or weak ties. We argue that the position of strong/weak ties must be considered to explain the impact of tie strength on firm breakthrough innovation in a triad context. For example, Configuration 2 has two strong ties adjacent to focal firm and one weak tie non-adjacent to focal firm (Figure 1). The non-adjacent weak tie provides potential for diverse and novel knowledge and reduced knowledge redundancy. Also, adjacent two strong ties provide the benefits of commitment and trust, and rich information flow. Additionally, these adjacent strong ties facilitate the transfer of novel knowledge generated from the non-adjacent weak tie. Thus, we argue that Configuration 2 has a potentially positive influence on firm breakthrough innovation. Configuration 5 has two adjacent weak ties and one non-adjacent strong tie. The non-adjacent strong tie has high knowledge redundancy and high trust and commitment between the two actors B and C (Figure 1). The non-adjacent strong tie between B and C induces potential opportunistic tendencies toward focal firm and inhibits information sharing with the focal firm. This indicates that the two firms form an alliance to the detriment of the focal firm. Further, adjacent weak ties provide novel and diverse information while maintaining less commitment and trust with the focal firm. Importantly, diverse information from adjacent weak ties is degraded because of the knowledge redundancy generated by non-adjacent strong tie between B and C. Thus, we argue that Configuration 5 has potentially negative influence on firm breakthrough innovation. The above discussion suggests that one cannot determine which type of network ties will tend create a firm’s breakthrough innovation simply by counting the numbers of weak and strong ties in the configurations. One must also consider the position of the strong and weak ties within the network and the focal firm’s absorptive capacity. For example, though there is a high level of knowledge redundancy created by the non-adjacent strong tie in Configuration 5, if the focal firm has strong ability to learn, the focal firm can still capture and use the limited new knowledge, depending on its absorptive capacity. Next, we explain how firm’s absorptive capacity influences the relationship between six configurations and breakthrough innovation. Methods We will collect a full sample of data from three main data sources: alliance data from the Securities Data Company (SDC) on Joint and Alliances, patent data from the National Bureau of Economic Research (NBER) U.S. Patent Citation 1997-2016, and financial data from COMPUSTAT. The initial sample will consist of all firms that announced a triad of strategic alliance firms across industries from 1997 to 2016 in the United States. Implications Our study makes two key contributions. First, we investigate the impact of various triad strong/weak tie network configurations on a firm’s breakthrough innovation. We test various effects accrued from the position of strong/weak ties that are adjacent or non-adjacent to a focal firm on the focal firm’s breakthrough innovation in the triad network. By examining these relationships, we uncover critical implications of the tie strength previously unexplored in the network literature. We provide conceptual advancement of Granovetter’s notion of the strength of weak ties, showing the importance of the position of strong or weak ties as a critical driver to influence a firm’s breakthrough innovation. Second, we investigate how a focal firm’s absorptive capacity moderates the impact of network configurations on a firm’s breakthrough innovation. This provides a more precise and fine-grained understanding of how a firm’s capability to absorb outside knowledge influences the relationship between network configurations and a focal firm’s breakthrough innovation. Combined, our two contributions help to resolve some of the equivocal results in the extant literature regarding the effects of strong or weak ties on breakthrough innovation.

Introduction How to evaluate quality of advertising? Previous behavioral studies have mainly focused on subjective reports of survey and interview containing social and cognitive bias, or objective data of sell changes suffering huge temporal and monetary cost. Recently, increasing researchers have proposed that techniques of neuro-imaging could provide an objective and effective way to examine cognitive neural mechanisms underlying consumer behavior (referred to as consumer neuroscience) (Karmarkar & Yoon, 2016), and several studies have measured consumer's brain responses to advertising and movie trailer in both single- and two-brain frames (Barnett & Cerf, 2017; Venkatraman et al., 2015). However, still little is known about cognitive neural mechanisms underlying comprehension of advertising. Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique of brain-imaging measuring changes in the hemodynamic properties of human brain. Compared with fMRI and EEG, fNIRS is portable, has few physical constraints on participants with reasonable spatial and temporal resolution, and is tolerant to electromagnetic noise and motion artifact. Therefore, fNIRS is a suitable tool for research of human behavior in daily-life contexts (Liu et al., in press), which is a trends in neuroimaging (Hasson & Honey, 2012) and consumer neuroscience as weill. Methods To examine the neural responses to different quality of advertisings, in the present study we measured 14 undergraduate students' frontal activations while watching 20 advertisings in Study 1 and listening 30 music demos in Study 2 using a portable fNIRS device, and analyzed interpersonal neural network across all participants based on graph theory. Figure 1 shows positions of the fNIRS channels. Positions of the fNIRS channels were measured by a 3D magnetic digitizer. In a pilot study, another group of participants were recruited to score 30 advertisings from three dimensions: degree of liking, degree of willing to pay (WTP), and degree of understanding, and finally top-10 and bottom-10 scored adverting were remained for the final experiment in Study 1. Concerning the music demos used in Study 2, we selected the top-15 and bottow-15 ranking music in the ‘Billboard 2014 hot 100’. During the experiment, participants were asked to score their degree of liking and WTP to the advertising or the music immediately after each stimulus was displayed. After the experiment, they were also instructed to score and report their understanding on each advertising or music. Results and Discussion In Study 1, the intra-brain activations revealed higher medial prefrontal cortex (mPFC) activation when participants watched low-scored advertisings than watched high-scored ones (Fig. 2), and the mPFC activations showed a positive relationship with participants’ understanding on the meaning of the advertisings. This result only suggests that low-scored advertisings were relatively hard for participants to understand the intentions of the adverting, requiring more cognitive resources of mentalizing (Lieberman, 2007). Importantly, when we considered all participants' brains as a network, and then calculated the interpersonal neural connectivity (INS) across the network (defined as the number of participant pairs who showed significant positive inter-brain neural synchronization across them indicating shared understanding) (Hasson et al., 2012), only the network connectivity in the right inferior frontal gyrus (IFG) had significantly positive relationships with participants' scores of attitude towards the advertisings (defined as mean of the scores of liking and WTP) (Fig. 3). Study 2 confirmed the result showing significantly positive relationship between the network connectivity across all participants' brains and their scores of attitude towards the song demos. More importantly, the network connectivity in the right IFG of the small group of participants also significantly predicted the public's attitude towards the songs assessed by the rating scores on Douban website (Fig. 4). Conclusion The right IFG is a core area of mirror neuron system and is closely associated with empathy (Lamm et al., 2007). Thus, the present results suggest that high-scored advertisings may activate consumer's empathic response to simulate and experience their contents and intentions. And the network connectivity across consumers' brains in the right IFG may be a critical index evaluating quality of experiential advertisings. Practically, advertising should invite consumers to experience their products, and then could convey information and emotion more effectively.

As brands’ use of social media to connect with customers becomes increasingly important, there is a need to continually monitor and examine activities in social networks. An important aspect of the social network communications is its unique ways of concentrating and dispersing information among participants (actors) – density and centrality of the network. By looking at both density and centrality, the relationships among actors and their ability to influence others are revealed, allowing deeper understanding into networked behaviors. In this manner, examining whole and ego network patterns, the unique roles of individual actors, can provide brands significant insight in understanding how influencers form and how users connect and spread information. Based on the social network analysis, which represents a combination of theory and analytical methods of networked relationships, this study analyzed Twitter networks of two multi-brand cosmetics and beauty retailers, Sephora (global brand) and Ulta Beauty (U.S. domestic brand). Using NodeXL, daily Twitter data for both brands were gathered to investigate network activities. By examining both ego-networks and the whole networks, the results showed that while ego-networks for brands were quite similar to one another, there was a big difference between the ego-networks and whole networks in regards to the number of actors, type of connectivity, as well as the prominence of brands. Sephora was often not an important part of its hashtag network, and thus was not able to maintain strong control over communications and messages in these networks whereas Ulta maintained its control over its networks. The findings from analyzing these network patterns, the unique roles of individual actors, and the brands within the networks provide significant insights in understanding how influencers form and develop the ability to connect and spread information.

Customers’ opinions on social network platforms are known to influence peer behaviour (Bai, 2011; Eirinaki, Pisal, & Singh, 2012). Customers are also known to be more engaged in sharing their experiences by writing online reviews and recommendations that may be useful to others (Cantallops & Salvi, 2014; Tang & Guo, 2015; Xu & Li, 2016). Actually, user-generated content (UGC) on social network platforms has emerged as an important source for understanding and managing consumers’ expectations, particularly using automated and semi-automated knowledge extraction techniques from text such as text mining and sentiment analysis (Zhang, Zeng, Li, Wang, & Zuo, 2009). This research analyses dimensions of online customer engagement and associated concepts in customers’ reviews through (i) a global sentiment analysis using positive, neutral and negative sentiments and (ii) a topic-sentiment analysis to capture latent topics in online reviews. Furthermore, it examines what influences customers to contribute their online reviews, beyond the features of each focal company or brand. The research methodology is based on a text mining approach, using the MeaningCloud tool. The study focuses on Yelp.com reviews and includes a random sample of 15,000 unique reviews of restaurants, hotels and nightlife entertainment in eleven cities in the USA. An innovative customer engagement dictionary is created, based on previously validated scales using known dimensions of engagement, experience, emotions and brand advocacy, and extended using WordNet 2.1 lexical database. The research findings reveal a high impact of the engagement cognitive processing dimension and hedonic experience on customers’ review endeavour. The study results further indicate that customers seem to be more engaged in positively advocating a company/brand than the contrary. The findings will help social network managers to reinforce their platforms.

Using social network analysis, the current study examines the image of a unique festival in a well-established tourist destination on the East coast of the United State of America, the Myrtle Beach area of South Carolina. Social Network Analysis (SNA) emphasizes the importance of constructing interconnectedness among concepts and knowledge networks in respondents’ minds (Hanneman & Riddle, 2011; Scott & Carrington, 2011). The technique visualizes complicated perception of tourists in a most simple but scientific way. Using concept maps and degree centralities derived from social network analysis as well as dendrograms, this study provides insights on wine and food festival in Myrtle Beach Area. Together with cluster analysis, the SNA suggests unique contributions to the understanding of tourist behaviour in tourism destination research.

본 논문에서는 안테나의 소형화, 이득 및 방사패턴을 개선한 초고속 통신망 시스템을 효과적으로 사용하기 위해 3.3 [GHz], 5 [GHz] 대역에서 동작하는 WLAN/UWB 통신용 안테나를 설계 및 제작을 하였다. 대역폭의 개선을 위해 마이크로스트립 패치 안테나를 선택하였고 각 단계마다 이론적인 근거에 의한 수식을 이용하여 슬롯의 폭, 길이, 전송선로의 폭 등을 계산하였다. 설계된 안테나의 시뮬레이션 결과 반사손실이 3.3 [GHz]에서 -14.053 [dB]이고 5 [GHz]에서 -13.118 [dB]의 값을 보여주었다. 이득은 3.3 [GHz]에서 2.479 [dBi]의 값과 5[GHz]에서 3.317[dBi]의 수치를 보여주었다. 또한 3D 설계가 가능한 CST Microwave Studio 2014 프로그램을 이용하여 최적화 한 후, 이를 바탕으로 제작한 안테나의 특성을 측정하여 성능을 확인하는 방법으로 연구를 진행하였다. 최근 다양하고 발전이 계속 이루어지고 있는 무선 기술인 WLAN과 해당 기술 이용자의 수요 증가에 따른 기술의 주파수 대역도 역시 증가하고 있는 추세의 통신 기술인 UWB을 초고속 무선 통신 시스템으로 사용하는 데 있어 불편함 없도록 해당 이용자를 위한 원활한 통신이 가능할 것으로 보인다.

이제는 모바일 마켓순위에서 많은 게임이 높은 점유율을 차지하지만 점유율을 오랫동안 유지 하는 것은 쉽지 않다. 게이머를 끌어당기는 중요한 요소는 게임 재미(Game Fun)이고, 게임을 재미있게 만드는 가장 중요한 요소는 게임 난이도이다. 하지만 게임 난이도를 디자인하는 것은 매우 어려운 일이다. 본 논문은 두 개의 연속적인 컨볼루셔널 레이어를 사용한 컨볼루셔널 신경망과 SVM 분류기를 이용하여 게임 시 플레이어의 얼굴 표정을 실시간으로 검출하고 판단한다. 실험 결론은 CNN을 이용한 표정 시스템은 게임 play-time 및 score를 늘릴 수 있고, 게임 재미를 증진시키기에 도와 준다고 증명하였다.

픽셀 아트는 낮은 해상도와 제한된 색 팔레트를 가지고 영상을 표현한다. 픽셀 아트는 낮은 연 산 성능과 적은 저장 공간을 가지는 초기 컴퓨터 게임에서 주로 사용되었다. 현대에 이르러, 픽셀 아트는 예술이나 퍼즐, 게임과 같은 다양한 분야에서 찾아볼 수 있게 되었다. 본 논문에서는 게임 캐릭터 영상을 입력으로 받는 픽셀 아트 생성 모델을 제안한다. 기존 방법 과는 달리, 합성곱 신경망(CNN:Convolutional-Neural Network)를 픽셀 아트 생성 목적에 맞게 변형하여 이를 이용하는 방법을 제시한다. 기존의 합성곱 연산 후에 upsampling 과정을 추가하여 픽셀 아트가 생성될 수 있도록 하였다. 네트워크는 ground truth와 생성된 픽셀 아트와의 평균 오차 제곱(MSE:Mean Squared Error)을 최소화해나가며 학습을 수행한다. Ground truth는 실제 아티스트가 생성하도록 하였고, 이미지 회전과 반전 기법을 이용하여 augumentation을 수행하였다. 생성된 데이터 집합은 학습, 검증, 시험 데이터로 나누었다. 이러한 데이터 집합을 기반으로 감독 학습을 실시하여 픽셀 아트 생성 네트워크를 학습하였다. 학습 모델의 학습 과정과 학습 정확도를 제시하고, 시험 데이터 뿐만 아니라 다양한 영상에 대한 픽셀 아트 결과도 함께 제시한다.

본 연구에서는 비용매 유도 상분리와 소결 공정을 혼용하여 기체 및 액체에 대하여 슈퍼플럭스 거동을 보이는 니켈 모세관 지지체를 성공적으로 제조하였다. 니켈 모세관 전구체는 니켈, 폴리술폰, DMAC, PEG를 이용하여 도프용액을 제 조한 후 NIPS 공정에 의하여 제조된 후에, 다양한 소결온도에서 수소 분위기 조건에서 소결하여 니켈 모세관 지지체를 제조 하였다. 최적의 니켈 모세관 지지체는 950°C 소결온도에서 얻어졌는데 외경 722 μm, 내경 550 μm, 두께 94 μm이었다. 니켈 모세관 지지체 기공율은 26%, 평균 기공경은 4 μm이었으며 3차원으로 서로 연결된 기공구조를 갖고 있었다. 그리고 파괴하 중은 2.84 kgf, 파괴 연신율은 13%이었다. 니켈 모세관 지지체의 He, N2, O2, CO2에 대한 단일 기체 투과도는 상온에서 각각 432,327, 281,119, 264,259, 193,143 GPU로 슈퍼플럭스 거동을 보였다. 이는 3차원적으로 서로 연결된 4 μm 크기 마크로기 공을 통하여 viscous flow가 일어났기 때문에 나타나는 현상으로 설명되었다.

이 글은 19세기 중엽 상하이가 개항된 이후 중국을 대표하는 근대 도시로 성장하는 가운데 동북아해역 지식 네트워크의 중심이 되어가는 과정에 대해 살펴본다. 상하이가 근대 동아시아에서 서학을 생산하고 전파하는 중심도시가 된 데에는 서양인 선교사들의 역할이 컸다. 이 서양 선교사들은 여러 단체들을 만들어 중국인들에 대한 선교와 지식 보급을 목적으로 교육 및 출판활동을 전개하였다. 이렇게 생산된 한역서학서는 상하이를 비롯하여 중국의 각지로 전파되었고, 심지어 조선과 일본에도 전해졌다. 이렇게 하여 상하이는 근대 동북아해역 지식네트워크의 중심이 되었다. 그런데 일본의 경우는 막부말부터 메이지 시기에 이르기까지 일찍부터 상하이와의 무역을 통해 상하이에서 생산되는 한역서학서를 많이 수용했지만, 조선의 경우는 쇄국정책으로 인해 단지 베이징과의 육로 교통망을 통해 한역서학서를 수용하였고 그것의 종류 역시 대단히 제한적이었다. 하지만 조선 역시 시기는 일본보다 다소 늦지만 개항이후 관료들이 상하이를 방문하고 또 선교사들에 의해 상하이에서 생산한 서학이 수용되었다. 또 이처럼 상하이에서 생산된 서학이 중국을 비롯해 조선과 일본에 전파되기 위해서는 무엇보다 교통망이 중요하다. 상하이를 중심으로 형성된 동북아해역의 교통망에 대한 연구는 바로 상하이 지식네트워크 형성에 대한 연구의 토대를 이룬다고 할 수 있다.

The proliferation of information technologies made it possible to produce information products of different versions at much lower cost comparing to traditional physical products. Thus it is common for information product manufacturers to consider vertically differentiated product line for more profit through improved market coverage. Another salient characteristic of most information product is network externality. Existing researches dealing with vertical differentiation and network externality usually assumed oligopolistic market where vertically differentiated products are provided by competing companies, respectively. Moreover, they analyzed the essentially dynamic characteristic of network externality statically. In this study, different from the previous researches, the vertical differentiation strategy of a monopolistic company under network externality is dynamically analyzed. We used a two-period model to accommodate the dynamic feature of network externality. Based on the two-period model, the profit maximizing solutions are analyzed. The results showed that a monopolistic company has no incentive to differentiate products vertically when the network externality is absent. On the contrary, when the network externality exists, the monopolistic company can derive more profit by vertically differentiating the product line. It is also shown that, for more profit, the monopolistic company should keep the quality difference between the high quality product and the low quality product as greater as possible.

Using network betweenness centrality we attempt to analyze the characteristics of Seoul metropolitan subway lines. Betweenness centrality highlights the importance of a node as a transfer point between any pairs of nodes. This ‘transfer’ characteristic is obviously of paramount importance in transit systems. For betweenness centrality, both traditional betweenness centrality measure and weighted betweenness centrality measure which uses monthly passenger flow amount between two stations are used. By comparing traditional and weighted betweenness centrality measures of lines characteristics of passenger flow can be identified. We also investigated factors which affect betweenness centrality. It is the number of passenger who get on or get off that significantly affects betweenness centrality measures. Through correlation analysis of the number of passenger and betweenness centrality, it is found out that Seoul metropolitan subway system is well designed in terms of regional distribution of population. Four measures are proposed which represent the passenger flow characteristics. It is shown they do not follow Power-law distribution, which means passenger flow is relatively evenly distributed among stations. It has been shown that the passenger flow characteristics of subway networks in other foreign cities such as Beijing, Boston and San Franciso do follow power-law distribution, that is, pretty much biased passenger flow traffic characteristics. In this study we have also tried to answer why passenger traffic flow of Seoul metropolitan subway network is more homogeneous compared to that of Beijing.

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of gas energy consumption in an air handling unit. To this end, we consider the volatility of the time series and demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the gas consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the gas consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of “context units” in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the gas consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

Three CNN (Convolutional Neural Network) models of GoogLeNet, VGGNet, and Alexnet were evaluated to select the best deep learning based image analysis mothod that can detect pavement distresses of pothole, spalling, and punchout on expressway. Education data was obtained using pavement surface images of 11,056km length taken by Gopro camera equipped with an expressway patrol car. Also, deep learning framework of Caffe developed by Berkeley Vision and Learning Center was evaluated to use the three CNN models with other frameworks of Tensorflow developed by Google, and CNTK developed by Microsoft. After determing the optimal CNN model applicable for the distress detection, the analyzed images and corresponding GPS locations, distress sizes (greater than distress length of 150mm), required repair material quantities are trasmitted to local maintenance office using LTE wireless communication system through ICT center in Korea Expressway Corporation. It was found out that the GoogLeNet, AlexNet, and VGG-16 models coupled with the Caffe framework can detect pavement distresses by accuracy of 93%, 86%, and 72%, respectively. In addition to four distress image groups of cracking, spalling, pothole, and punchout, 22 different image groups of lane marking, grooving, patching area, joint, and so on were finally classified to improve the distress detection rate.

Owing to the development in information and communications technologies have improved the technology for high-speed transmission of massive data, which has changed closed-circuit television (CCTV) video transmission technology. In particular, digitization of the CCTV video format and streaming technology has made it possible to minimize transmission loss and integrate video transmission and camera control(pan/tilt). It has also become possible to provide additional services like remote emergency warning broadcasting with just Internet Protocol (IP). However, because of the structural problems of IP, these changes have also brought about the threat of hacking of CCTV monitoring systems. In this study, we propose a methode to optimize network management by examining cases of enhancement of operational efficiency and security by improving the structure of CCTV monitoring network.

본 연구 목적은 마리나항만법을 대상으로 언어 네트워크 방법을 적용하여 법조문에 대한 내용을 분석하고 법률 특징과 구 조적 모습을 계량적으로 제시하는데 있다. 언어 네트워크 분석은 단어와 단어사이의 네트워크 구조를 파악하여 그 구조와 형태를 파악할 수 있으며, 전통적 내용 분석이 지닌 단점을 보완하는데도 용이하다. 분석 대상은 최근 개정된 마리나항만법 전문(1 4장)이며 부분 적으로는 구분된 장중 분석에 의미가 있는 일부(제2장, 제3장, 제4장)로 설정하였다. 분석 결과 마리나항만법은 마리나항만개발에 집중된 구조적 특징을 지니고 있고 마리나산업의 육성에 관한 종합적 관점보다는 하드웨어적 시설적 설치에 집중된 구조를 지니고 있었다. 또한, 사업체 및 인력 양성 등이 일부 포함되었으나 시설 설치보다는 상대적 중요성이 다소 낮은 것으로 나타났다. 다만, 연구 방 법상 법구조적 시각에 국한된 결과만을 도출하는 한계도 있으나, 현재 법률의 구조와 특징을 파악하고 진단함으로써 후속 연구의 기초 자료로 활용될 수 있다는데 의의가 있다. 향후 연구에서는 본 연구 결과를 토대로 마리나 정책에 대한 정부 성과나 추진 실적과 비교등이 진행되기를 기대한다.

최근 증가되고 있는 모바일 SNS(Social Network Service)를 통한 매개(Mediated) 커뮤니케이션에서 자신의 감성을 표현 가능한 이모티콘을 활용하여 대화하는 감성 커뮤니케이션이 발달하고 있다. 이모티콘 개발이 증가 하고 사용이 대중화가 됨에 따라, 사용자가 원하는 감성 표현이나 의미를 상징하는 아이템에 대한 요구가 확대 되고 있다. 또한, 동일한 이모티콘이라도 사용자에 따라 다르게 해석할 수 있으며, 디자이너의 의도한 감성 표현에 대해 사용자의 의미 해석이 상이할 수 있다. 따라서 본 연구에서는 모바일 SNS 메신저에서 이모티콘을 이용한 커뮤니케이션을 위해 사용되는 감성 어휘를 연구하고 분류하여 감성 커뮤니케이션과 관련된 이모티콘 개발의 초석을 마련하고자 한다. 즉, 사용자 관점에서 이모티콘에 기반하여 표현하고자 하는 감성 어휘에 대한 분류와 모델을 제안한다. 본 연구를 위해 첫째, 문헌조사를 기반으로 감성 형용사를 수집하고 설문, 통계 분석 및 FGI (Focus Group Interview)를 통해 1차 감성 어휘를 추출한다. 둘째, 2차 설문조사 및 통계분석을 통해 최종 적합 어휘를 추출한다. 셋째, 최종 추출된 어휘를 바탕으로 다차원척도 분석을 실행하여 어휘 모델을 도출하고 감성 어휘를 분류한다. 그 결과 ‘미안하다’, ‘바쁘다’, ‘배고프다’, ‘어렵다’, ‘슬프다’, ‘날씨가 춥거나 덥다’, ‘지 루하다’, ‘부끄럽다’, ‘궁금하다’, ‘만족하다’, ‘좋다’, ‘아름답다’, ‘편안하다’ 의 13개 어휘로 분류되었다.