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
We investigated what factors motivate gamers to participate in a guild and why they continue to be engaged as members of the guild. We find that, based on the result of focus group interviews with MMORPG gamers, social support and self-esteem factors play important roles. Considering both prior research and the focus group interviews we have conducted, we define social support and character control as independent variables. Character identity, guild identity, and self-esteem are proposed as mediating variables while guild flow and game loyalty as dependent variables. Accordingly, we develop the research model and hypotheses, and verify them empirically. Based on our experiences of playing the WoW game, we proposed a research model and conducted focus-group interviews (FGIs). FGIs involve formulating a hypothesis and then collecting some relevant data. FGIs were conducted face-to-face with students of C University in Korea. We formulated structured interview schedules, and the questions were based on our research variables and personal experiences. The questions for the interviews encompassed the following areas: (a) the demographic characteristics of the focus group; (b) the number of years for which respondents had played online games; (c) the motive for starting a game; (d) the number of game-characters assumed by each gamer; (e) the type of game played; and (f) other issues such as the reasons for involvement in the play, the willingness to reuse the game in case new versions were released, etc. On average, it took two hours to interview each of three groups. A primary set of FGIs was conducted with three groups on the premise that there would be some differences caused by character race (Horde vs. Alliance) or by playable server (Normal vs. Combat). With respect to the manner of playing, we found that guild members shared information, felt a sense of belonging, and played computer games for quite a long time through the guild; however, they did not undergo these experiences when playing alone. Gamers who belonged to a specific guild helped other players without expecting compensation for that, freely shared information about the game, gave away items for free, and more generous with other members who made mistakes. The guild members were aware of the existence other members and experienced a sense of belonging through interactions with, and evaluations from, other players. It was clear that social support was shown within the guild and that it played an important role as a major research variable. Based on the results of the first FGIs, a second set of in-depth FGIs was carried out with a focus on the psychology of the individual within the guild and the social community of the guild. The second set of FGIs also focused on the guild's offline meetings. Gamers, over all, recognize the necessity of joining a community, not only off-line but also online world of the guild. They admit that the guild is important for them to easily and conveniently enjoy playing online computer games. The active behavior and positive attitudes of existing guild members can motivate new members of the guild to adapt themselves to the guild environment. They then adopt the same behaviors and attitudes of established guild members. In this manner, the new members of the guild strengthen the bonds with other gamers while feeling a sense of belonging, and developing social identity, thereby. It was discovered that the interaction among guild members and the social support encouraged new gamers to quickly develop a sense of social identity and increase their self-esteem. The guild seemed to play the role of socializing gamers. Sometimes, even in the real world, the guild members helped one another; therefore, the features of the guild also spilled over to the offline environment. We intend to use self-esteem, which was found through the second set of FGIs, as an important research variable. To collect data, an online survey was designed with a questionnaire to be completed by WoW gamers, who belong to a guild. The survey was registered on the best three domestic game-sites: 'WoW playforum,' 'WoW gamemeca,' and 'Wow invent.' The selected items to be measured in the questionnaire were decided based on prior research and data from FGIs. To verify the content of the questionnaire, we carried out a pilot test with the same participants to point out ambiguous questions as a way to ensure maximum accuracy of the survey result. A total of 244 responses were analyzed from the 250 completed questionnaires. The SEM analysis was used to test goodness-of-fit of the model. As a result, we found important results as follows: First, according to the statistics, social support had statistically significant impacts on character control, character identity, guild identity and self-esteem. Second, character control had significant effects on character identity, guild identity and self-esteem. Third, character identity shows its clear impact on self-esteem and game loyalty. Fourth, guild identity affected self-esteem, guild flow and game loyalty. Fifth, self-esteem had a positive influence on the guild flow. These days, the number of virtual community is rising along with its significance largely because of the nature of the online games. Accordingly, this study is designed to clarify the psychological relationship between gamers within the guild that has been generally established by gamers to play online games together. This study focuses on the relationships in which social support influences guild flow or game loyalty through character control, character identity, guild identity, and self-esteem, which are present within a guild in the MMORPG game environment. The study results are as follows. First, the effects of social support on character control, character identity, guild identity and self-esteem are proven to be statistically significant. It was found that character control improves character identity, guild identity and self-esteem. Among the seven variables, social support, which is derived from FGIs, plays an important role in this study. With the active support of other guild members, gamers can improve their ability to develop good characters and to control them. Second, character identity has a positive effect on self-esteem and game loyalty, while guild identity has a significant effect on self-esteem, guild flow and game loyalty. Self-esteem affects guild flow. It was found that the higher the character and guild identities become, the greater the self-esteem is established. Contrary to the findings of prior research, our study results indicate that the relationship between character identity and guild flow is not significant. Rather, it was found that character identity directly affects game players' loyalty. Even though the character identity had no direct effect on increasing guild flow, it has indirectly affected guild flow through self-esteem. The significant relationship between self-esteem and guild flow indicates that gamers achieve flow, i.e., a feeling of pleasure and excitement through social support. Several important implications of this study should be noted. First, both qualitative and quantitative methods were used to conduct this study. Through FGIs, it was observed that both social support and self-esteem are important variables. Second, because guilds had been rarely studied, this research is expected to play an important role in the online community. Third, according to the result, six hypotheses (H1, H5, H6, H7, H8, and H11) setup based on FGIs, were statistically significant; thus, we can suggest the corresponding relationships among the variables as a guideline for follow-up research. Our research is significant as it has following implications: first, the social support of the guild members is important when establishing character control, character identity, guildidentity and self-esteem. It is also a major variable that affects guild flow and game loyalty. Second, character control when improved by social support shows notable influence on the development of character identity, guild identity and self-esteem. Third, character identity and guild identity are major factors to help establish gamers' own self-esteem. Fourth, character identity affects guild flow through self-esteem and game loyalty. The gamers usually express themselves through characters; the higher character identity is, the more loyalty a gamer has. Fifth, guild identity, established within the guild, has clear effects on self-esteem, guild flow and game loyalty. Sixth, qualitative and quantitative methods are employed to conduct this study. Based on the results of focus group interviews and SEM analysis, we find that the social support by guild members and psychological factors are significant in strengthening the flow of guild and loyalty to the game. As such, game developers should provide some extra functions for guild community, through which gamers can play online games in collaboration with one another. Also, we suggest that positive self-esteem which is built up through social support can help gamers achieve higher level of flow and satisfaction, which will consequently contribute to minimizing the possibility for the players to develop negative attitude toward the guild they belong to.
본 연구는 점포를 방문하는 동안 노출되는 매장의 물리적 환경 특성이 서비스 브랜드 개성과 재구매의도에 미치는 영향력을 규명하기 위해 시도되었다. 이를 위해 연구모형을 개발하여, 특정 서비스 브랜드의 이용객을 대상으로 설문조사를 실시하고 구조방정식을 이용하여 분석하였다. 연구 결과는 우선, 서비스의 물리적 환경은 주변요인, 디자인요인, 사회요인으로, 그리고 서비스브랜드 개성은 유능함, 성실함, 흥분됨, 세련됨, 강인함 차원으로 분류되었다. 둘째, 물리적 환경의 모든 차원들이 모든 서비스 브랜드 개성차원에 정(+)의 영향을 주었으며, 물리적 환경의 서비스 브랜드 개성에 대한 영향력은 각 차원별로 상이하였다. 셋째, 서비스 브랜드 개성은 모두 재구매의도에 정(+)의 영향을 주었으며, 특히 세련됨 차원에 미치는 영향이 가장 켰다. 넷째, 서비스의 물리적 환경은 재구매의도에 정(+)의 영향을 주었으며, 특히 물리적 환경 중 사회요인이 재구매의도에 가장 큰 영향을 주는 것으로 나타났다. 이와 같은 결과들은 물리적 환경 연출은 브랜드 개성 형성의 결정요인으로 서비스 브랜드 차별화의 핵심요인으로 작용하므로, 호의적인 브랜드 개성 창출을 위해서는 우선적으로 물리적 환경에 대한 효율적 관리 방안이 강구되어야 함을 보여준다.
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.
본 연구에서는 항공권 예약 발권 웹사이트의 서비스 품질을 측정 뿐만 아니라 서비스 회복도 측정하고자 하였다. 또한 서비스 품질과 서비스 회복이 고객만족 및 충성의도에 미치는 영향관계를 실증하고자 하였다. 온라인 서비스 품질과 온라인 서비스 회복의 측정을 위해 Parasuraman, Zeithaml, & Malhotra(2005)가 개발한 E-S-QUAL과 E-RecS-QUAL을 사용했으며, 했다. E-S-QUAL은 온라인 서비스 품질을 측정하는 도구로써, 효율성, 시스템 이용가능성, 이행성, 프라이버시의 4개 차원 22개 항목으로 구성된다. E-RecS-QUAL은 온라인 서비스 회복을 측정하는 도구로써, 반응, 보상, 접촉의 3개 차원 11개 항목으로 구성된다. 실증분석을 위한 설문조사는 항공사나 여행사의 웹사이트를 통해 국내 외 항공권을 구입해 본 경험이 있는 소비자를 대상으로 실시하였는데, 총 400부가 회수되었고, 이 중 342부를 최종분석에 사용하였다. 실증분석을 위해 AMOS 7.0과 SPSS 15.0을 사용하였다. 먼저, SPSS 15.0을 사용하여, 요인점수를 이용한 회귀분석으로 가설검증을 한 결과, <가설 I-1, 2, 3, 4, II-1, 2, 3, III-1, IV-1>이 전부 채택되었다. 온라인 서비스 품질과 온라인 서비스 회복의 각 차원은 모두 전반적인 서비스 품질에 유의한 영향을 보였고, 전반적인 서비스 품질은 고객만족에 유의한 영향을 미쳤다. 마지막으로 고객만족 역시 충성의도에 유의한 영향을 미치는 것으로 확인되었다. 한편 AMOS 7.0을 사용하여 모형 분석을 하였는데, 모형의 적합도는 가설검증을 하기에 합당한 수치가 나왔다. 이를 토대로 가설검증을 한 결과, <가설 I-1, 3, II-1, 3, III-1, IV-1>은 채택되었고, <가설 I-2, 4, II-2>는 기각되었다. 이 결과는 Parasuraman et al.(2005)이 주장한 것처럼 E-S-QUAL을 나타내는 데는 요인점수를 이용한 회귀분석이 더 적합하다는 것을 보여주는 것이라고 판단된다. 이를 토대로 본 연구의 시사점을 정리하였다.