B2B 시장에서의 서비스 편의성이 관계성과에 미치는 영향 : 관계적 요인의 매개효과 분석 (Effect of Service Convenience on the Relationship Performance in B2B Markets: Mediating Effect of Relationship Factors)
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- 한국유통학회지:유통연구
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- 제16권4호
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- pp.65-93
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- 2011
B2B 시장에서 구매자와 판매자간의 관계는 매우 밀접하고 장기화되는 것이 특징이므로 결국 단순한 제품을 판매하는 것에 그치는 것이 아닌 지속적인 서비스에 대한 중요성이 날로 커지고 있다. 산업재 연구 전반에 걸쳐서도 서비스에 대한 중요성과 관심이 증대되면서 고객이 서비스를 사용하는데 있어서 그 서비스의 품질과 함께 최근 소비자들은 얼마나 빠르고 쉽게 서비스가 제공되어 투입되는 노력을 최소화시킬 수 있는가를 매우 중요하게 생각하기 때문에 편의성이 중요한 요인으로 고려되어지고 있다. 이에 따라, 본 연구에서는 산업재 시장에서 관계만족과 관계성과를 형성하는데 중요하게 생각할 수 있는 새로운 요인이 어떤 것인가 라는 의문점에서 출발하여, 서비스 편의성과 관계성과 사이의 구조적 관계를 조사하고자 하였다. 이 연구의 가장 큰 학문적 기여점은 산업재 연구에서 주류를 이루고 있는 관계품질과 관계성과의 새로운 선행요인을 검증한 것이다. 또한 소비재 시장에서 주로 연구되었던 서비스 편의성 척도를 산업재 시장에 적용하여 그 활용도를 실험해 보았다는 데 의의가 있다. 본 연구는 서비스 편의성의 구성요소인 서비스 편의성을 결정편의성, 접근편의성, 거래편의 성, 편익편의성, 사후편익편의성 다섯가지 차원으로 구분하고 관계적 요인인 관계만족에 미치는 영향과 이러한 관계만족이 관계몰입과 관계성과에 어떠한 영향을 미치는가를 분석하여 서비스 편의성의 관리와 투자에 대한 마케팅 측면의 중요성을 제시하고 있다. 실증분석을 위해 산업재 서비스를 이용하고 있는 기업의 직원들을 대상으로 설문을 통해 데이터를 수집하였으며 서비스 편의성
스마트디바이스의 보급화 이후 게임 산업은 콘솔과 PC Online에서 모바일로 변화하였다. 특히, 스마트디바이스의 어플리케이션 다운로드 수와 매출을 확인해보면 80% 이상이 모바일 게임에 의해 발생되며 문화콘텐츠로서 게임의 위상이 높아져가고 있다. 스마트디바이스의 보급이후 가장 성공한 모바일 게임으로 일본의 퍼즐 앤 드래곤(Puzzle & Dragons)을 들 수 있다. 퍼즐 앤 드래곤은 2012년 일본 소프트뱅크자회사인 겅호 온라인 엔터테인먼트가 개발한 게임으로 출시 1년만인 2013년에 모바일 게임 최초로 1조원의 매출을 올렸다. 2015년에는 4천만 다운로드를 이루어 내며 세계 모바일 게임 매출순위에서도 2년 동안 1위와 2위를 차지했다. 특히 2013년부터 세계의 여러 모바일 게임콘텐츠에 영향을 주며 일본 게임산업의 부흥기를 다시 열었다. 반면 세계 모바일 게임 매출순위에서 아직까지 국내 게임은 찾을 볼 수 없다. 모바일 게임 시장이 매년 크게 증가하는 지금, 국내 모바일 게임만이 유사 콘테츠를 양산하며 게임 강대국의 위상을 잃고 세계에서 도태되고 있는 것이다. 이러한 국내의 실정에 의해 산업적으로나 학술적으로 성공한 게임의 성공요인 분석이 필요하게 되었다. 본 연구에서는 세계에서 가장 성공한 모바일 게임, 퍼즐 앤 드래곤의 성공요인을 분석하기 위해 비즈니스 모델인 K.Masanao의 수익창출구조 매트릭스를 활용했다. 퍼즐 앤 드래곤을 고객가치, 이익, 프로세스라는 세 가지 영역에 대입하고 누가, 무엇을 어떻게 라는 항목으로 분석한 결과는 다음과 같다. 첫째, 다양한 연령층의 이용자 유입을 위해 퍼즐장르와 RPG장르를 융합한 새로운 형태의 장르를 개발하였으며 경쟁요소를 배제하고 협력위주의 싱글 게임으로 개발하였다. 둘째, 한정기간동안 유치되는 갓 페스티벌과 유명 콘텐츠와의 콜라보레이션을 활용한 캐릭터 뽑기 시스템을 도입해 이익을 창출하였다. 셋째, 이용자와의 지속적인 온 오프라인 소통을 통해 소비자의 니즈를 파악하고 게임에 도입하였다. 본 연구에서 도출된 퍼즐 앤 드래곤의 세 가지 성공요인은 국내 모바일 게임 산업에서 게임콘텐츠 제작 방안을 수립하는 데 기여하는 한편, 관련 연구와 이론적 지평을 확대한다는 점에서 산업적으로 학술적으로 중요한 의미를 가진다.
An important business-field of world-wide steel-industry is the coating of thin metal-sheets with zinc, zinc-aluminum and aluminum based materials. These products mostly go into automotive industry. in particular for the car-body. into building and construction industry as well as household appliances. Due to mass-production, the processing is done in large continuously operating plants where the mostly cold-rolled metal-strip as the substrate is handled in coils up to 40 tons unwind before and rolled up again after passing the processing plant which includes cleaning, annealing, hot-dip galvanizing / aluminizing and chemical treatment. In the liquid Zn, Zn-AI, AI-Zn and AI-Si bathes a combined action of corrosion and wear under high temperature and high stress onto the transfer components (rolls) accounts for major economic losses. Most critical here are the bearing systems of these rolls operating in the liquid system. Rolls in liquid system can not be avoided as they are needed to transfer the steel-strip into and out of the crucible. Since several years, ceramic roller bearings are tested here [1.2], however, in particular due to uncontrollable Slag-impurities within the hot bath [3], slide bearings are still expected to be of a higher potential [4]. The today's state of the art is the application of slide bearings based on Stellite\ulcorneragainst Stellite which is in general a 50-60 wt% Co-matrix with incorporated Cr- and W-carbides and other composites. Indeed Stellite is used as the bearing-material as of it's chemical properties (does not go into solution), the physical properties in particular with poor lubricating properties are not satisfying at all. To increase the Sliding behavior in the bearing system, about 0.15-0.2 wt% of lead has been added into the hot-bath in the past. Due to environmental regulations. this had to be reduced dramatically_ This together with the heavily increasing production rates expressed by increased velocity of the substrate-steel-band up to 200 m/min and increased tractate power up to 10 tons in modern plants. leads to life times of the bearings of a few up to several days only. To improve this situation. the Mechanical Alloying (MA) TeChnique [5.6.7.8] is used to prOduce advanced Stellite-based bearing materials. A lubricating phase is introduced into Stellite-powder-material by MA, the composite-powder-particles are coated by High Energy Milling (HEM) in order to produce bearing-bushes of approximately 12 kg by Sintering, Liquid Phase Sintering (LPS) and Hot Isostatic Pressing (HIP). The chemical and physical behavior of samples as well as the bearing systems in the hot galvanizing / aluminizing plant are discussed. DependenCies like lubricant material and composite, LPS-binder and composite, particle shape and PM-route with respect to achievable density. (temperature--) shock-reSistibility and corrosive-wear behavior will be described. The materials are characterized by particle size analysis (laser diffraction), scanning electron microscopy and X-ray diffraction. corrosive-wear behavior is determined using a special cylinder-in-bush apparatus (CIBA) as well as field-test in real production condition. Part I of this work describes the initial testing phase where different sample materials are produced, characterized, consolidated and tested in the CIBA under a common AI-Zn-system. The results are discussed and the material-system for the large components to be produced for the field test in real production condition is decided. Outlook: Part II of this work will describe the field test in a hot-dip-galvanizing/aluminizing plant of the mechanically alloyed bearing bushes under aluminum-rich liquid metal. Alter testing, the bushes will be characterized and obtained results with respect to wear. expected lifetime, surface roughness and infiltration will be discussed. Part III of this project will describe a second initial testing phase where the won results of part 1+11 will be transferred to the AI-Si system. Part IV of this project will describe the field test in a hot-dip-aluminizing plant of the mechanically alloyed bearing bushes under aluminum liquid metal. After testing. the bushes will be characterized and obtained results with respect to wear. expected lifetime, surface roughness and infiltration will be discussed.
Digital Convergence means integration between industry, technology, and contents, and in marketing, it usually comes with creation of new types of product and service under the base of digital technology as digitalization progress in electro-communication industries including telecommunication, home appliance, and computer industries. One can see digital convergence not only in instruments such as PC, AV appliances, cellular phone, but also in contents, network, service that are required in production, modification, distribution, re-production of information. Convergence in contents started around 1990. Convergence in network and service begins as broadcasting and telecommunication integrates and DMB(digital multimedia broadcasting), born in May, 2005 is the symbolic icon in this trend. There are some positive and negative expectations about DMB. The reason why two opposite expectations exist is that DMB does not come out from customer's need but from technology development. Therefore, customers might have hard time to interpret the real meaning of DMB. Time is quite critical to a high tech product, like DMB because another product with same function from different technology can replace the existing product within short period of time. If DMB does not positioning well to customer's mind quickly, another products like Wibro, IPTV, or HSPDA could replace it before it even spreads out. Therefore, positioning strategy is critical for success of DMB product. To make correct positioning strategy, one needs to understand how consumer interprets DMB and how consumer's interpretation can be changed via communication strategy. In this study, we try to investigate how consumer perceives a new product, like DMB and how AD strategy change consumer's perception. More specifically, the paper segment consumers into sub-groups based on their DMB perceptions and compare their characteristics in order to understand how they perceive DMB. And, expose them different printed ADs that have messages guiding consumer think DMB in specific ways, either cellular phone or personal TV. Research Question 1: Segment consumers according to perceptions about DMB and compare characteristics of segmentations. Research Question 2: Compare perceptions about DMB after AD that induces categorization of DMB in direction for each segment. If one understand and predict a direction in which consumer perceive a new product, firm can select target customers easily. We segment consumers according to their perception and analyze characteristics in order to find some variables that can influence perceptions, like prior experience, usage, or habit. And then, marketing people can use this variables to identify target customers and predict their perceptions. If one knows how customer's perception is changed via AD message, communication strategy could be constructed properly. Specially, information from segmented customers helps to develop efficient AD strategy for segment who has prior perception. Research framework consists of two measurements and one treatment, O1 X O2. First observation is for collecting information about consumer's perception and their characteristics. Based on first observation, the paper segment consumers into two groups, one group perceives DMB similar to Cellular phone and the other group perceives DMB similar to TV. And compare characteristics of two segments in order to find reason why they perceive DMB differently. Next, we expose two kinds of AD to subjects. One AD describes DMB as Cellular phone and the other Ad describes DMB as personal TV. When two ADs are exposed to subjects, consumers don't know their prior perception of DMB, in other words, which subject belongs 'similar-to-Cellular phone' segment or 'similar-to-TV' segment? However, we analyze the AD's effect differently for each segment. In research design, final observation is for investigating AD effect. Perception before AD is compared with perception after AD. Comparisons are made for each segment and for each AD. For the segment who perceives DMB similar to TV, AD that describes DMB as cellular phone could change the prior perception. And AD that describes DMB as personal TV, could enforce the prior perception. For data collection, subjects are selected from undergraduate students because they have basic knowledge about most digital equipments and have open attitude about a new product and media. Total number of subjects is 240. In order to measure perception about DMB, we use indirect measurement, comparison with other similar digital products. To select similar digital products, we pre-survey students and then finally select PDA, Car-TV, Cellular Phone, MP3 player, TV, and PSP. Quasi experiment is done at several classes under instructor's allowance. After brief introduction, prior knowledge, awareness, and usage about DMB as well as other digital instruments is asked and their similarities and perceived characteristics are measured. And then, two kinds of manipulated color-printed AD are distributed and similarities and perceived characteristics for DMB are re-measured. Finally purchase intension, AD attitude, manipulation check, and demographic variables are asked. Subjects are given small gift for participation. Stimuli are color-printed advertising. Their actual size is A4 and made after several pre-test from AD professionals and students. As results, consumers are segmented into two subgroups based on their perceptions of DMB. Similarity measure between DMB and cellular phone and similarity measure between DMB and TV are used to classify consumers. If subject whose first measure is less than the second measure, she is classified into segment A and segment A is characterized as they perceive DMB like TV. Otherwise, they are classified as segment B, who perceives DMB like cellular phone. Discriminant analysis on these groups with their characteristics of usage and attitude shows that Segment A knows much about DMB and uses a lot of digital instrument. Segment B, who thinks DMB as cellular phone doesn't know well about DMB and not familiar with other digital instruments. So, consumers with higher knowledge perceive DMB similar to TV because launching DMB advertising lead consumer think DMB as TV. Consumers with less interest on digital products don't know well about DMB AD and then think DMB as cellular phone. In order to investigate perceptions of DMB as well as other digital instruments, we apply Proxscal analysis, Multidimensional Scaling technique at SPSS statistical package. At first step, subjects are presented 21 pairs of 7 digital instruments and evaluate similarity judgments on 7 point scale. And for each segment, their similarity judgments are averaged and similarity matrix is made. Secondly, Proxscal analysis of segment A and B are done. At third stage, get similarity judgment between DMB and other digital instruments after AD exposure. Lastly, similarity judgments of group A-1, A-2, B-1, and B-2 are named as 'after DMB' and put them into matrix made at the first stage. Then apply Proxscal analysis on these matrixes and check the positional difference of DMB and after DMB. The results show that map of segment A, who perceives DMB similar as TV, shows that DMB position closer to TV than to Cellular phone as expected. Map of segment B, who perceive DMB similar as cellular phone shows that DMB position closer to Cellular phone than to TV as expected. Stress value and R-square is acceptable. And, change results after stimuli, manipulated Advertising show that AD makes DMB perception bent toward Cellular phone when Cellular phone-like AD is exposed, and that DMB positioning move towards Car-TV which is more personalized one when TV-like AD is exposed. It is true for both segment, A and B, consistently. Furthermore, the paper apply correspondence analysis to the same data and find almost the same results. The paper answers two main research questions. The first one is that perception about a new product is made mainly from prior experience. And the second one is that AD is effective in changing and enforcing perception. In addition to above, we extend perception change to purchase intention. Purchase intention is high when AD enforces original perception. AD that shows DMB like TV makes worst intention. This paper has limitations and issues to be pursed in near future. Methodologically, current methodology can't provide statistical test on the perceptual change, since classical MDS models, like Proxscal and correspondence analysis are not probability models. So, a new probability MDS model for testing hypothesis about configuration needs to be developed. Next, advertising message needs to be developed more rigorously from theoretical and managerial perspective. Also experimental procedure could be improved for more realistic data collection. For example, web-based experiment and real product stimuli and multimedia presentation could be employed. Or, one can display products together in simulated shop. In addition, demand and social desirability threats of internal validity could influence on the results. In order to handle the threats, results of the model-intended advertising and other "pseudo" advertising could be compared. Furthermore, one can try various level of innovativeness in order to check whether it make any different results (cf. Moon 2006). In addition, if one can create hypothetical product that is really innovative and new for research, it helps to make a vacant impression status and then to study how to form impression in more rigorous way.
In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.