As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2,
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.