A representative problem in domestic chestnut industry is the high loss of flesh due to excessive knife peeling in order to increase the peeling rate, resulting in a decrease in production efficiency. In this study, a prediction model for weight loss rate of chestnut by stage of knife peeling process was developed as undergarment study to optimize conditions of the machine. 51 control conditions of the two-stage blade peeler used in the experiment were derived and repeated three times to obtain a total of 153 data. Machine learning(ML) models including artificial neural network (ANN) and random forest (RF) were implemented to predict the weight loss rate by chestnut peel stage (after 1st peeling, 2nd peeling, and after final discharge). The performance of the models were evaluated by calculating the values of coefficient of determination (R), normalized root mean square error (nRMSE), and mean absolute error (MAE). After all peeling stages, RF model have better prediction accuracy with higher R values and low prediction error with lower nRMSE and MAE values, compared to ANN model. The final selected RF prediction model showed excellent performance with insignificant error between the experimental and predicted values. As a result, the proposed model can be useful to set optimum condition of knife peeling for the purpose of minimizing the weight loss of domestic chestnut flesh with maximizing peeling rate.
The aim of this study is to identify the competencies required for fashion-related majors that meet contemporary demands, align with the objectives of university education, and reflect the qualities desired in graduates. To achieve this goal, we conducted content analysis of relevant data and in-depth interviews with experts. First, the content analysis involved coding key information from the introductions, educational goals, desired qualities of graduates, and curricula published on the websites of both South Korea and international fashion-related universities. Additionally, we analyzed the National Competency Standards (NCS) and the Meta-goals of higher education programs set by the International Textile Apparel Association (ITAA), extracting six core competencies. Second, in-depth interviews were conducted with six experts, each with 23 to 31 years of experience in Korean and international apparel industry and academia. The interviews were recorded, transcribed, and keywords were extracted. To ensure the validity of the coding results, cross-checks were performed among the researchers. The analysis identified the following competencies: empathic communication, social responsibility, professional thinking, creative and integrative thinking, global perspective, and challenging leadership. Based on these findings, establishing competencies that meet contemporary demands and developing corresponding curricula are essential steps towards creating a feedback system. Future research should focus on developing and implementing curricula that foster a virtuous cycle, ultimately enhancing students' competency levels.
Since the implementation of economic reforms in 1978, the Chinese economy grows rapidly at an average annul growth rate of 9% over the post two decades. Franchising has been widely recognized as an important source of entrepreneurial activity. Trust is important in that it facilitates relational exchanges by permits partners to transcend short-run inequities or risks to concentrate on long-term profits or gains. In the relationship between the franchisors and franchisees, trust has been described as an important source of competitive advantage. However, little research has been done on the factors affecting trust in Chinese franchisor-franchisee relationships. The purpose of this study is to investigate what factors affect the trust in the franchise system in China, and to provide guidelines and insights to franchisors which enter Chinese market. In this study, according to Morgan and Hunt (1994), trust is defined as the extending when one party has confidence in an exchange partner's reliability and integrity. We offered a conceptual model of the empirical study. The model shows that the factors affecting the trust include franchisor's supports, communication, satisfaction with previous outcome and conflict. We also suggested the franchisor's supports and communication like to enhance the franchisee's satisfaction with previous outcome, and the franchisor's supports, communication and he franchisee's satisfaction with previous outcome tend to decrease conflict. Before the formal study, a pretest involving exploratory interviews with owners from three franchisees was conducted to make sure the questionnaire was relevant and clear to the respondents. The data were collected using trained interviewers to carry out personal interviews with the aid of an unidentified, muti-page, structured questionnaire. The respondents comprised of owners, managers, and owner managers of franchisee-owned food service franchises located in Beijing, China. Even though a total of 256 potential franchises were initially contacted, the finally usable sample consisted of 125 respondents. As expected, the sampling method was successful in soliciting respondents with waried personal and firm characteristics. Self-administrated questionnaires were used for all measures. And established scales were used to measure the latent constructs in this study. The measures tapped the franchisees' perceptions of the relationship with the referent franchisor. Five-point Likert-type scales ranging from "strongly disagree" (=1) to "strongly agree" (=7) were used throughout the constructs (trust, eight items; support, five items; communication, four items; satisfaction, six items; conflict, three items). The reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than.80. The proposed measurement model was estimated using SPSS 12.0 and AMOS 5.0 analysis package. We conducted A series of exploratory factor analyses and confirmatory factor analyses to assess the convergent validity, discriminant validity, and reliability. The results indicate reasonable overall fits between the model and the observed data. The overall fit of measurement model were
In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.
It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.
Trust has been studied extensively in psychology, economics, and sociology, and its importance has been emphasized not only in marketing, but also in business disciplines in general. Unlike past relationships between suppliers and buyers, which take considerable advantage of private networks and may involve unethical business practices, partnerships between suppliers and buyers are at the core of success for industrial marketing amid intense global competition in the 21st century. A high level of mutual cooperation occurs through an exchange relationship based on trust, which brings long-term benefits, competitive enhancements, and transaction cost reductions, among other benefits, for both buyers and suppliers. In spite of the important role of trust, existing studies in buy-supply situations overlook the role of trust and do not systematically analyze the effect of trust on relational performance. Consequently, an in-depth study that determines the relation of trust to the relational performance between buyers and suppliers of business services is absolutely needed. Business services in this study, which include those supporting the manufacturing industry, are drawing attention as the economic growth engine for the next generation. The Korean government has selected business services as a strategic area for the development of manufacturing sectors. Since the demands for opening business services markets are becoming fiercer, the competitiveness of the business service industry must be promoted now more than ever. The purpose of this study is to investigate the effect of the mutual trust between buyers and suppliers on relational performance. Specifically, this study proposed a theoretical model of trust-relational performance in the transactions of business services and empirically tested the hypotheses delineated from the framework. The study suggests strategic implications based on research findings. Empirical data were collected via multiple methods, including via telephone, mail, and in-person interviews. Sample companies were knowledge-based companies supplying and purchasing business services in Korea. The present study collected data on a dyadic basis. Each pair of sample companies includes a buying company and its corresponding supplying company. Mutual trust was traced for each pair of companies. This study proposes a model of trust-relational performance of buying-supplying for business services. The model consists of trust and its antecedents and consequences. The trust of buyers is classified into trust toward the supplying company and trust toward salespersons. Viewing trust both at the individual level and the organizational level is based on the research of Doney and Cannon (1997). Normally, buyers are the subject of trust, but this study supposes that suppliers are the subjects. Hence, it uniquely focused on the bilateral perspective of perceived risk. In other words, suppliers, like buyers, are the subject of trust since transactions are normally bilateral. From this point of view, suppliers' trust in buyers is as important as buyers' trust in suppliers. The suppliers' trust is influenced by the extent to which it trusts the buying companies and the buyers. This classification of trust using an individual level and an organization level is based on the suggestion of Doney and Cannon (1997). Trust affects the process of supplier selection, which works in a bilateral manner. Suppliers are actively involved in the supplier selection process, working very closely with buyers. In addition, the process is affected by the extent to which each party trusts its partners. The selection process consists of certain steps: recognition, information search, supplier selection, and performance evaluation. As a result of the process, both buyers and suppliers evaluate the performance and take corrective actions on the basis of such outcomes as tangible, intangible, and/or side effects. The measurement of trust used for the present study was developed on the basis of the studies of Mayer, Davis and Schoorman (1995) and Mayer and Davis (1999). Based on their recommendations, the three dimensions of trust used for the study include ability, benevolence, and integrity. The original questions were adjusted to the context of the transactions of business services. For example, a question such as "He/she has professional capabilities" has been changed to "The salesperson showed professional capabilities while we talked about our products." The measurement used for this study differs from those used in previous studies (Rotter 1967; Sullivan and Peterson 1982; Dwyer and Oh 1987). The measurements of the antecedents and consequences of trust used for this study were developed on the basis of Doney and Cannon (1997). The original questions were adjusted to the context of transactions in business services. In particular, questions were developed for both buyers and suppliers to address the following factors: reputation (integrity, customer care, good-will), market standing (company size, market share, positioning in the industry), willingness to customize (product, process, delivery), information sharing (proprietary information, private information), willingness to maintain relationships, perceived professionalism, authority empowerment, buyer-seller similarity, and contact frequency. As a consequential variable of trust, relational performance was measured. Relational performance is classified into tangible effects, intangible effects, and side effects. Tangible effects include financial performance; intangible effects include improvements in relations, network developing, and internal employee satisfaction; side effects include those not included either in the tangible or intangible effects. Three hundred fifty pairs of companies were contacted, and one hundred five pairs of companies responded. After deleting five company pairs because of incomplete responses, one hundred five pairs of companies were used for data analysis. The response ratio of the companies used for data analysis is 30% (105/350), which is above the average response ratio in industrial marketing research. As for the characteristics of the respondent companies, the majority of the companies operate service businesses for both buyers (85.4%) and suppliers (81.8%). The majority of buyers (76%) deal with consumer goods, while the majority of suppliers (70%) deal with industrial goods. This may imply that buyers process the incoming material, parts, and components to produce the finished consumer goods. As indicated by their report of the length of acquaintance with their partners, suppliers appear to have longer business relationships than do buyers. Hypothesis 1 tested the effects of buyer-supplier characteristics on trust. The salesperson's professionalism (t=2.070, p<0.05) and authority empowerment (t=2.328, p<0.05) positively affected buyers' trust toward suppliers. On the other hand, authority empowerment (t=2.192, p<0.05) positively affected supplier trust toward buyers. For both buyers and suppliers, the degree of authority empowerment plays a crucial role in the maintenance of their trust in each other. Hypothesis 2 tested the effects of buyerseller relational characteristics on trust. Buyers tend to trust suppliers, as suppliers make every effort to contact buyers (t=2.212, p<0.05). This tendency has also been shown to be much stronger for suppliers (t=2.591, p<0.01). On the other hand suppliers trust buyers because suppliers perceive buyers as being similar to themselves (t=2.702, p<0.01). This finding confirmed the results of Crosby, Evans, and Cowles (1990), which reported that suppliers and buyers build relationships through regular meetings, either for business or personal matters. Hypothesis 3 tested the effects of trust on perceived risk. It has been found that for both suppliers and buyers the lower is the trust, the higher is the perceived risk (t=-6.621, p<0.01 for buyers; t=-2.437, p<0.05). Interestingly, this tendency has been shown to be much stronger for buyers than for suppliers. One possible explanation for this higher level of perceived risk is that buyers normally perceive higher risks than do suppliers in transactions involving business services. For this reason, it is necessary for suppliers to implement risk reduction strategies for buyers. Hypothesis 4 tested the effects of trust on information searching. It has been found that for both suppliers and buyers, contrary to expectation, trust depends on their partner's reputation (t=2.929, p<0.01 for buyers; t=2.711, p<0.05 for suppliers). This finding shows that suppliers with good reputations tend to be trusted. Prior experience did not show any significant relationship with trust for either buyers or suppliers. Hypothesis 5 tested the effects of trust on supplier/buyer selection. Unlike buyers, suppliers tend to trust buyers when they think that previous transactions with buyers were important (t=2.913 p<0.01). However, this study did not show any significant relationship between source loyalty and the trust of buyers in suppliers. Hypothesis 6 tested the effects of trust on relational performances. For buyers and suppliers, financial performance reportedly improved when they trusted their partners (t=2.301, p<0.05 for buyers; t=3.692, p<0.01 for suppliers). It is interesting that this tendency was much stronger for suppliers than it was for buyers. Similarly, competitiveness was reported to improve when buyers and suppliers trusted their partners (t=3.563, p<0.01 for buyers; t=3.042, p<0.01 for suppliers). For suppliers, efficiency and productivity were reportedly improved when they trusted buyers (t=2.673, p<0.01). Other performance indices showed insignificant relationships with trust. The findings of this study have some strategic implications. First and most importantly, trust-based transactions are beneficial for both suppliers and buyers. As verified in the study, financial performance can be improved through efforts to build and maintain mutual trust. Similarly, competitiveness can be increased through the same kinds of effort. Second, trust-based transactions can facilitate the reduction of perceived risks inherent in the purchasing situation. This finding has implications for both suppliers and buyers. It is generally believed that buyers perceive higher risks in a highly involved purchasing situation. To reduce risks, previous studies have recommended that suppliers devise risk-reducing tactics. Moving beyond these recommendations, the present study uniquely focused on the bilateral perspective of perceived risk. In other words, suppliers are also susceptible to perceived risks, especially when they supply services that require very technical and sophisticated manipulations and maintenance. Consequently, buyers and suppliers must solve problems together in close collaboration. Hence, mutual trust plays a crucial role in the problem-solving process. Third, as found in this study, the more authority a salesperson has, the more he or she can be trusted. This finding is very important with regard to tactics. Building trust is a long-term assignment; however, when mutual trust has not been developed, suppliers can overcome the problems they encounter by empowering a salesperson with the authority to make certain decisions. This finding applies to suppliers as well.
Simon and Sullivan(l993) estimated that clothing and textile related brand equity had the highest magnitude comparing any other industry category. It reflects that fashion brands reinforce the symbolic, social values and emotional characteristics being different from generic brands. Recently, Kim and Lim(2002) developed a fashion brand equity scale to measure a brand's psychometric properties. However, they suggested that additional psychometric tests were needed to compare the relative magnitude of each brand's equity. The purpose of this study was to recognize the psychometric constructs of fashion brand equity and validate Kim and Lim's fashion brand equity scale using the measurement invariance test of cross-group comparison. First, we identified the constructs of fashion brand equity using confirmatory factor analysis through structural equation modeling. Second, we compared the relative magnitude of two brands' equity using the measurement invariance test of multi-group simultaneous factor analysis. Data were collected at six major universities in Seoul, Korea. There were 696 usable surveys for data analysis. The results showed that fashion brand equity was comprised of 16 items representing six dimensions: customer-brand resonance, customer feeling, customer judgment, brand imagery, brand performance and brand awareness. Also, we could support the measurement invariance of two brands' equities by configural and metric invariance tests. There were significant differences in five constructs' mean values. The greatest difference was in customer feeling; the smallest, in customer judgment.
Knowledge residing in the heads of employees has always been regarded as one of the most critical resources within a firm. However, many tries to facilitate knowledge transfer among employees has been unsuccessful because of the motivational and cognitive problems between the knowledge source and the recipient. Social capital, which is defined as "the sum of the actual and potential resources embedded within, available through, derived from the network of relationships possessed by an individual or social unit [Nahapiet and Ghoshal, 1998]," is suggested to resolve these motivational and cognitive problems of knowledge transfer. In Social capital theory, there are two research streams. One insists that social capital strengthens group solidarity and brings up cooperative behaviors among group members, such as voluntary help to colleagues. Therefore, social capital can motivate an expert to transfer his/her knowledge to a colleague in need without any direct reward. The other stream insists that social capital provides an access to various resources that the owner of social capital doesn't possess directly. In knowledge transfer context, an employee with social capital can access and learn much knowledge from his/her colleagues. Therefore, social capital provides benefits to both the knowledge source and the recipient in different ways. However, prior research on knowledge transfer and social capital is mostly limited to either of the research stream of social capital and covered only the knowledge source's or the knowledge recipient's perspective. Social network theory which focuses on the structural dimension of social capital provides clear explanation about the in-depth mechanisms of social capital's two different benefits. 'Strong tie' builds up identification, trust, and emotional attachment between the knowledge source and the recipient; therefore, it motivates the knowledge source to transfer his/her knowledge to the recipient. On the other hand, 'weak tie' easily expands to 'diverse' knowledge sources because it does not take much effort to manage. Therefore, the real value of 'weak tie' comes from the 'diverse network structure,' not the 'weak tie' itself. It implies that the two different perspectives on strength of ties can co-exist. For example, an extroverted employee can manage many 'strong' ties with 'various' colleagues. In this regards, the individual-level structure of one's relationships as well as the dyadic-level relationship should be considered together to provide a holistic view of social capital. In addition, interaction effect between individual-level characteristics and dyadic-level characteristics can be examined, too. Based on these arguments, this study has following research questions. (1) How does the social capital of the knowledge source and the recipient influence knowledge transfer respectively? (2) How does the strength of ties between the knowledge source and the recipient influence knowledge transfer? (3) How does the social capital of the knowledge source and the recipient influence the effect of the strength of ties between the knowledge source and the recipient on knowledge transfer? Based on Social capital theory and Social network theory, a multi-level research model is developed to consider both the individual-level social capital of the knowledge source and the recipient and the dyadic-level strength of relationship between the knowledge source and the recipient. 'Cross-classified random effect model,' one of the multi-level analysis methods, is adopted to analyze the survey responses from 337 R&D employees. The results of analysis provide several findings. First, among three dimensions of the knowledge source's social capital, network centrality (i.e., structural dimension) shows the significant direct effect on knowledge transfer. On the other hand, the knowledge recipient's network centrality is not influential. Instead, it strengthens the influence of the strength of ties between the knowledge source and the recipient on knowledge transfer. It means that the knowledge source's network centrality does not directly increase knowledge transfer. Instead, by providing access to various knowledge sources, the network centrality provides only the context where the strong tie between the knowledge source and the recipient leads to effective knowledge transfer. In short, network centrality has indirect effect on knowledge transfer from the knowledge recipient's perspective, while it has direct effect from the knowledge source's perspective. This is the most important contribution of this research. In addition, contrary to the research hypothesis, company tenure of the knowledge recipient negatively influences knowledge transfer. It means that experienced employees do not look for new knowledge and stick to their own knowledge. This is also an interesting result. One of the possible reasons is the hierarchical culture of Korea, such as a fear of losing face in front of subordinates. In a research methodology perspective, multi-level analysis adopted in this study seems to be very promising in management research area which has a multi-level data structure, such as employee-team-department-company. In addition, social network analysis is also a promising research approach with an exploding availability of online social network data.
In this study, genotyping was executed by using 27 microsatellite markers for genetic diversity of 469 Korean Native Chickens [20 population, each population is 24 samples but Hanhyup A line is 13 samples). in total 469 samples were collected from National Institute of Animal Science (Korean Native Chicken (NR, NY, NG, NL and NW), Ogye (NO), Leghorn F,K (NF and NK), Black and Brown cormish (NH and NS), Rhode Island Red C, D (NC and ND), Total is 12 populations] and Hanhyup [H line (HH), F line (HF), G line (HG), V line (HV), S line (HS), W line (HW), Y line (HY), A line (HA), total is 8 populations]. [The allele number were observed 5 (ADL0268) to 20 (MCW0127) each markers. Observed heterozygostiy (