본 논문은 다국적 기업 해외 R&D 자회사의 지식 습득 방향에 있어서 자회사의 글로벌 조직 정체성(global organizational identity)이 미칠 수 있는 영향에 대하여 탐색하였다. 사회 정체성 이론에 근거하여, 본 연구에서는 다국적 기업 해외 R&D 자회사가 본국도 현지도 아닌 그 외의 국가에서 지식을 습득하는 글로벌 지식 습득 행위를 하는데 있어서 해외 R&D 자회사의 글로벌 조직 정체성이 미칠 수 있는 영향에 대해 탐구하였다. 자아 존중감을 향상시키고 불확실성을 감소시키기 위한 목적에서 다국적 기업 해외 R&D 자회사는 자신의 조직 정체성을 일치시키는 방향으로 자신의 태도와 행위를 적합시키게 된다. 사회 정체성 이론에 기초하여, 본 연구에서는 해외 R&D 자회사가 글로벌 혁신가로서 명확한 사명을 가질 때, 보다 높은 자율성을 가질 때, 그리고 글로벌 파트너와의 관계에 보다 깊이 배태되어 있을 때 글로벌 지식 습득 행위를 보다 활발하게 할 수 있을 것이라고 예측 하였다. 그리고 해외 R&D 자회사의 기술적인 역량이 글로벌 조직 정체성 관련 요인들과 글로벌 지식 습득 간의 관계를 강화하는 조절 효과에 관한 가설 또한 수립하였다. 가설 검증을 위해 본 연구에서는 일본 다국적 기업의 해외 R&D 연구소들의 설문 자료와 미국 특허청의 특허 인용 자료를 결합하여 음이항 회귀 모형으로 분석하였다. 실증 분석 결과 해외 R&D 자회사의 자율성과 글로벌 관계에의 배태 정도가 글로벌 지식 습득에 긍정적 영향을 미치는 것으로 나타났으며, 해외 R&D 자회사의 자율성과 글로벌 지식 습득 간의 관계를 자회사의 기술적 역량이 강화하는 것으로 나타났다.

본 연구는 다국적 기업의 해외 R&D 자회사가 본사로부터 지식을 소싱하는 정도에 자회사의 기술 역량과 자회 사와 본사간의 관계 배태성이 미치는 영향을 탐색하였다. 이를 위해 글로벌 반도체, 통신장비, 제약 산업에 속한 34개 다국적 기업의 86개의 해외 R&D 자회사가 미국 특허청에 출원한 특허데이터를 분석하였다. 실증분석 결 과, 해외 R&D 자회사는 절대적 수준의 기술 역량이 높을수록 본사로부터 지식을 더 많이 소싱하는 반면, 자회 사의 본사 대비 상대적 수준의 기술 역량이 높을수록 본사로부터 지식을 덜 소싱하는 것으로 나타났다. 또한, 해 외 R&D 자회사와 본사간의 기술적 배태성과 사회적 배태성은 자회사가 본사로부터 지식을 소싱하는 정도에 모 두 긍정적인 영향을 미치는 것으로 나타났다.

Retailers procure private labels from several sources including national brand manufacturers, dedicated private label manufactures (often overseas or regional), and own manufacturing facilities.2 In the first case, the supplier utilizes its expertise and excess capacity to supply PLs. In the other two cases, the suppliers are dedicated to manufacturing PLs for single or multiple retailers. Consumers generally consider PLs as value substitutes of the corresponding national brands. As private labels become proliferated, more retailers are introducing premium PLs that oftentimes replace marginal national brands. It is natural to assume that the PL sourced from the excess capacity of the NB manufacturer is identical to the corresponding NB except for the branding and packaging. In this paper, we examine a retailer’s problem of tiered PL sourcing, in which a premium PL is supplied such a NB manufacturer (dual brander), and an economy PL is supplied by a dedicated PL supplier. We decompose the value of a product into three components: the NB’s brand equity, the retailer’s reputation, and the intrinsic quality of the NB. In this distribution channel, the NB’s wholesale and retail prices are determined by the traditional bilateral Nash game. However, the premium PL’s transfer price is determined through a profit-sharing negotiation between the channel members. Based on this game scenario, we build a model of price competition, given the quality, brand equity, and retailer reputation parameters, in order to examine strategic implications of the parameters to the equilibrium prices. In our bilateral pricing game, the NB manufacture and the retailer play a Nash pricing game, augmented by a profit sharing negotiation for the premium PL. In the negotiation process, the retailer’s negotiation power over the NB manufacture is reflected in the ratio of incremental profits from the premium PL. From an equilibrium-negotiation solution, we derive profit implications of each of the value components as well as the negotiation power of the retailer. Among several findings, the most interesting takeaway is that, even if the retailer holds a strong negotiation power, it is optimal for the retailer to leave some chips on the table for the NB manufacturer during the transfer pricing negotiation.

본 연구는 일본 다국적 기업의 해외 연구소에 관한 실증 연구를 통하여 다국적 기업 해외 R&D 자회사의 조합 역량, 습득 역량, 자율성이 자회사의 혁신 성과에 미치는 영향에 관하여 검증하는 논문이다. 본 논문에서는 혁신 의 성과에 영향을 미칠 수 있는 요인으로 다양한 지식의 원천에서 어떻게 지식을 골고루 습득하고 조합하여 활용 하는지가 중요하다는 점에 주목하였다. 본 연구에서는 해외 자회사의 혁신 역량의 원천을 습득 역량(sourcing capability)과 조합 역량(combinative capability)으로 구분한 후 이 역량들이 혁신 성과에 미치는 영향을 심 층 분석하였다. 특히 조합 역량을 본국과 현지, 그 외의 국가의 지식들을 조합하는 역량과 다국적 기업 내부의 지식과 외부의 지식을 조합하는 역량으로 세분하고 각각의 조합 역량이 혁신 성과에 미치는 영향에 관한 가설을 수립하였다. 또한 조합 역량이 습득 역량과 혁신 성과 간의 관계를 강화할 수 있는지와 자율성이 조합 역량과 혁 신 성과 간의 관계를 강화할 수 있는지를 검증해 보았다. 일본 다국적 기업의 해외 연구소에 관한 설문 자료와 특허 자료를 결합하여 실행한 음이항 회귀 분석 결과 해외 자회사의 본국-현지-그 외의 국가의 지식을 조합하는 역량과 습득 역량, 자율성이 해외 자회사의 혁신 성과에 긍정적인 영향을 미치는 것으로 나타났다. 그리고 본국- 현지-그 외 국가의 지식을 조합하는 역량이 해외 자회사의 습득 역량이 혁신 성과에 미치는 긍정적 영향을 강화 하는 것으로 나타나 습득 역량과 조합 역량은 상호 보완적인 역할을 하는 것으로 나타났다.

Consumers struggle to find clothes that are fit. This is a problem that has been accentuated with the increasing online sales. It is the largest contribute to sales returns and make customer return about every third item they buy (Ratcliff, 2014). If consumers select to buy online, they can no longer try them out in a fitting room. Most of fast fashion brands have opened their online shops, and many designers’ brands have also started to sell their ready-to-wear lines online. While online shopping is seen as a significant new way to reach more consumers in fashion industry, the size problem becomes more visible and is waiting to be solved. It may decrease the fashion industry’s ambition to piggyback on recent advances in e-commerce. Previous studies on clothing consumption have already touched upon the topic of size and fitting. Consumer surveys in the clothing industry indicate that in general between 35% and 50% of female consumers in the USA were not satisfied with the fitting in their clothing already ahead of the surge in online stores (Desmarteau 2000; Goldsberry et al. 1996). Alexander et al. studied the shopping behaviour of young women in south-eastern USA and found that almost 64% of the respondents frequently changed ready-to-wear clothes to achieve the desired fit (Alexander et al. 2005). Ashdown and Loker pointed out the size problem in current clothing shopping experiences and proposed a conceptual framework called “mass-customized target market sizing”, which is a size system “based on and derived from measurement data exclusively from the people who represent an apparel firm’s target market” (Ashdown and Loker 2010, p147). In this paper we study how innovative mobile technology, social media and crowd sourcing can contribute to solving the size problem. We summarize the current digital approaches that deal with the fitting issues in online clothing shopping and present a new concept, called “Figuracy”, which attempts to find consumers new fitted clothing items through matching their own clothes with anonymous persons’ virtual wardrobe. We have built a mobile application to implement the idea and have done two initial feedback studies to see the consumers’ attitudes. This concept provides new perspectives and opportunities of tackling the fitting problem in online shopping. Ready-to-wear and fast fashion brands are selling their clothes all over the world, not just to one local community. This business model of constantly new collections, draws on standardizing size series that come only in a selected number of models. This adds to the continuous size problem where the interpretation of a particular size varies in-between brands, and even within different models from a single manufacturer. Firms in the ready-to-wear apparel industry in the world use different sizing systems, like general sizes as XS, S, M, L, or more specific standards as numbered sizes from UK, US, FR systems, which can make consumers confused. These sizing systems are usually based on the ideal body types of consumers from certain geographic regions (Nordic Council of Ministers, 2009), thus, a size standard from one region may not fit all types of body within this region. What’s worse, there has been vanity sizing in clothing industry in recent several decades, which actually makes the garments of the normal sizes bigger in physical size (Dooley, 2013). This adds to the difficulty of finding well-fitting apparels. Recently, a few companies have provided a number of solutions to the fitting problem. The solutions are based on fine-grained automatic body measurements; self-generated extended body or garment measurement and body matching. First, the approach to use technology for more automatic and detailed measurements has attracted many interests. For example, it has been to generate a visual presentation of the body by using three-dimensional scan technology. Then consumers do not need to type information manually. A UK-based company, called Bodymetrics, provides the services that use three-dimensional body scanner and “On-line Virtual Try-On” technology to help find the perfect pair of jeans. Second, there are a number of services that require users to manually type the measurements of their bodies or garments that they own and fit. It can be done by users input information of their body types by answering simple questions, such as weight, height, body type, or measurements of bust, waist and hip. Some systems like Mipso, SmartFit can add more personal preferences, such as colours, cuts and budgets. Then the system runs some algorithms to determine the users’ full set of body measurements. It can also be done such as Virtusize, Truefit and Clothes Horse by measuring a garment that the user knows it fits in detail (Perez, 2012). When the personal data is added to the service, it can then match individual’s body measurements with specific brands and sizes of garments. The service recommends fitted ready-to-wear clothing from various brands in online stores, like Fashion Metric and Virtusize. The data can be used to see how fitted the clothing item in an online website is on a virtual avatar built upon the input measurements of the users, like in Virtusize and Mipso. Third, it has been suggested to use self-described body descriptions and then match consumers’ new purchases as a way to recommend clothes that are fit. For instance, Fitbay attempts to personalize clothes suggestions based on the selection of other users with similar descriptions of size and body shape (Lomas, 2014). Through the help of anonymous persons in the community, one could find fitted clothing items. We investigate a fourth approach, called “Figuracy”, where people get suggestions on the garments that are fit by crowd sourcing their existing clothing items and then matching wardrobes among members in the community. It draws on utilizing the matching of consumer-generated data, i.e. crowd sourcing as Fitbay also does. But it uses a description of existing fitting garments in people’s wardrobes in terms of brands, models and size, rather than self-description of bodies. The idea depends on that a community of people are willing to share information of their favourite and fitting clothes from their own wardrobes. The system matches the clothes from one wardrobe with that of other members in order to recommend new fitting clothes to the user. In specific, if the system finds that a user shares a single clothing item with another person in the community, it will predict that the two of them have similar body types. Then it recommends other items from the second person’s wardrobe to the first user. Therefore, the system gains data from the fitting clothes in users’ wardrobe and matches the information with the items from someone else’ wardrobe. If the approach is successful, consumers would not need to input any personal body-data but only data on existing fitting clothes. The concept depends on the existence of a massive database of clothing items from individual wardrobes, i.e. a critical mass, which is also a big challenge to build. The Figuracy concept and implementation is at an early state and it has not yet the amount of content to start generating matching suggestions. Still, the concept is intriguing and the size problem is highly relevant. Thus we were interested in acquiring early users’ feedback. We have done two user studies of two variations of the concept, with a focus on one type of clothing item- the bra. Bras have high requirements on fitting (Chena et al. 2010) and it is considered specifically difficult to find garment of this type that is fit. Since the number of test persons was limited to around ten people each, the system did not generate any matching suggestions. The first test was done in November in 2013 on the app that was available in iPhone’s App-store. This version of the system reads the barcodes, which are usually attached to the price tag, to acquire a unique identifier for brand, model and size. We invited the participants to download the app and scan the barcodes of fitting bras when they try new bras in stores. We conducted phone interviews with six participants. The early feedback identified that users hesitated to add garments based on the barcodes. They did not feel comfortable with adding it in a fitting room in a shop, and they did not have any saved prize tags for the bras at home. Thus, although the barcode is theoretically and technically an easy and effective way of uploading garments, it proved to be inadequate in practice. Based on this feedback, we built a new version, which includes a manual and text-based tag function allowing users to add information about a garment, which do not require barcode reading. The second user feedback study was carried out in December in 2014. We recruited female students on the campuses of Stockholm University and Royal Institute of Technology in Stockholm, Sweden. In the end, nine participants tested the application and input data of their bras at home. After the try-out, we contacted the participants for either a phone interview or a face-to-face interview, each of which lasted between twenty and forty minutes. Each interview was recorded and transcribed. We used a method of qualitative content analysis to study the transcripts. “Qualitative”, as opposed to quantitative, indicates that the analysis goes beyond systematic data coding to identify interesting topics and allow us to contextualize the interpretation of the materials, given theoretical preconceptions (Mayring 2004). Through studying the transcripts, we find the following preliminary results. First, our participants all provided concrete examples of size problems they met in real life. Seven participants considered bras as one of the clothing items that are most difficult in this respect. Three participants stated that size should not be independent from other qualities, such as comfort and style. This implies that future design of similar systems may take into account not only sizes, but also other aspects of clothing. Second, in terms of the input function, all of them followed the instructions and managed to input information of their fitted bras from their wardrobes. Seven participants considered it easy to use while two of them thought it was a bit confusing when to add information. They got stuck at whether they should scan the barcode or just type, since the two functions were presented in one place. All of them used “type” to add items and thought the information on what they should input was acceptable. Four participants felt it complicated to take photos by themselves, so they downloaded pictures from the Internet. In addition, the participants tended to add the latest and favourite items from their wardrobes into the system. Thus these items could represent their personal tastes and styles, which will be useful to link fitting and styles in the future improvement of the application. This also points to a general problem. From an individual perspective, a single item would in the long run be enough to get matches from other people’s wardrobes, if they have added more than one item. If they also only add only a single item, the first person would not get any matches. The users must crowd source their wardrobe, and not for example their latest purchase. The user study shows that this concept might be restricted by users’ unwillingness to present data in such a way. Third, as to social interaction, eight participants at least “liked” one item from others’ wardrobe. Although none of the participants commented on other’s items, they all thought it was important to have some kind of social interactions. Four participants expected more interactions, such as reviews and discussions of certain clothing items, or following fashionable people. Most of the existing fitting systems, except Fitbay, lack the opportunities for social interaction, but Figuracy provides such a platform for users to share their interests and exchange their ideas. Two participants asked for increased opportunities to add the information on their own profile pages even though they were sharing images of their underwear. They asked for means similar to those available when building a blog space so that people in the community would get to know each other better. According to the participants, more social interactions could make them more engaged with the application. Social communities are good for those looking to learn, help and/or support like-minded individuals with common interests. Last but not the least, most of the participants were very interested in the “buy” function, which link the item to online shopping sites. But still three participants revealed their concerns and discussed the problems of it, such as how the system can guarantee the fitting of the item if they conduct a purchase through the system. In sum, the use of crowd sourcing and social interaction to fixing the size problem in online shopping gained great interests among participants. However, they also expressed their uncertainties towards how the system would work. They needed the system to make sure that it could provide fitting clothing items. They thought the biggest problem was that it was unclear what real help they could get from the application at this stage. Although the application failed to provide new recommendations based on matching, the user study allows us to see the feedback of the potential users so that we could improve the system, such as clearer interaction flows in adding items and more interesting interactions. Future research will continue the improvement of the system, start collaborations with online clothing stores and a third recruitment of users to test. On a general level, the study provides an example on how to investigate critical topics in online fashion through the so-called design research. Such research needs to be interdisciplinary and span technology and social science.

본 연구에서는 한국 제조부문 현지생산법인의 국제소싱전략을 81개의 기업별 실태조사 자료를 토대로 분석하였다. 실증분석 결과 나타난 소싱전략상 특징은 본국소싱 비중이 선진외국의 다국적기업에 비해 매우 높다는 점인데, 이와 같은 특징은 수출 비중이 높다는 판매구조와 강한 상관관계가 있는 것으로 나타났다. 또한 해외투자기업의 소싱전략은 표적시장뿐만 아니라 업종 및 현지생산법인의 지배구조 등에 따라서도 다르게 전개되고 있는 것으로 분석되었다. 한국 해외투자기업의 국제소싱전략에 가장 영향력이 큰 변수는 투자진출시기이며, 다음으로는 수송비와 상대적 제조비용 등인 것으로 나타났다. 실증분석 결과 나타난 이같은 국제소싱 행태 및 결정요인들은 한국기업 해외직접투자가 생산비 절감을 위한 동기에서 이루어지고 있는 측면이 강함을 시사해 주고 있다.

외국인직접투자가 첨단기술의 이전경로로 중요한 역할을 하고 있다는 점은 널리 알려져 있다. 그러나 국내 외국인투자기업의 기술도입에 관한 실증적이고 체계적인 연구는 매우 부족한 형편이다. 따라서 국내의 제조업종에 진출한 외국인투자기업을 대상으로 그들의 기술도입메커니즘을 실증적으로 살펴보는데 본 연구의 목적이 있다. 본 연구에서는 국내의 정유화학, 제약, 전기전자, 기계 등 4개 업종에 진출한 외국인투자기업의 기술도입건수를 대상으로 모기업으로부터 기술도입을 촉진시키는 요인을 실증적으로 분석한다. 이를 위하여 본 연구에서는 거래비용이론, 자원의존이론, 대리인이론관점에서 영향요인을 도출하고, 이를 다시 외국인투자기업의 1) 기술특성, 2) 자회사 특성 3) 본사 와의 관계특성으로 구분하여 살펴보고자 한다. 이와 같은 연구결과는 국내 외국인투자기업들이 모기업으로부터 기술도입을 하도록 촉진시키는 영향요인이 무엇인지를 파악할 수 있게 해준다. 또한 본 연구는 외국인직접투자를 통해 해외첨단기술의 국내도입을 추진하는데 있어 관련정책자료로 활용될 수 있을 것이다.

본 연구는 한국제조기업을 대상으로 주요부품 국제소싱전략의 실체와 성과를 분석함으로써 정책적 함의를 구하고, 소싱전략의 내부화와 관련된 기존이론의 설명력을 검증하기 위한 시도에서 이루어졌다. 이를 위하여 기존이론을 중심으로 소싱전략의 내부화정도와 제품성과 간의 관계를 조절하는 소싱관련변수를 도입하여 전략적 적합성에 근거한 상황모형을 설정한 후. 상황변수가 국제내부소싱전략과 제품성과간의 관계를 중재하는 중재변수로서의 역할을 하는지를 분석하였다. 분석의 결과, 소싱관련변수인 상황변수 중에서 공급자협상력의 지표인 교체비용요인과 공급자수가 국제내부소싱전략과 기업의 제품성과관계를 중재하는 변수로 나타났으며, 제품혁신성과 공정혁신성요인은 준중재변수로서의 역할을 하는 것으로 나타났다. 결국, 한국제조기업의 경우, 기업의 내부적 환경요인보다는 외부적 환경요인(시장관련요인)이 국제내부소싱전략과 성과관계에 강한 중재역할을 하고 있다고 볼 수 있다.