• Journal of Industrial Convergence
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• v.8 no.1
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• pp.1-18
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• 2010

The purpose of this study is to examine the impacts of sports marketing mix factors on brand equities and consumer behaviors. The findings of this study are as follows: first, there are differences in sports marketing mix factors, depending on individual characteristics. Differences are found in gender and living standard with regard to individual characteristics and prices. second, there are differences in brand equities, according to individual characters. third, sports marketing mix factors impact brand equities. fourth, the sports marketing mix factors impact perceive quality, brand recognition, location related to brand image and products. fifth, sports marketing mix factors impact brand equities and consumer behaviors. In regards with brand equities and repeat purchases, sports marketing mix factors impact promotion, products, perceived quality and image. In satisfaction, the sports marketing mix factors impact location, price promotion and product recognition. In the intention of transmission by word of mouth, the factors impact price, products, perceive quality and image.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.99-109
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• 2016

During the last two decades, impact factor has been widely used as a journal evaluation metric that differentiates the influence of a specific journal compared with other journals. However, impact factor does not provide a reliable metric between journals in different subject categories. For example, higher impact factors are given to biology and general sciences than those assigned to other traditional engineering and social sciences. This study initially analyzes the trend of the time series of the impact factors of the journals listed in Journal Citation Reports during the last decade. This study then proposes new journal evaluation metrics that adjust the impact factors across different subject categories. The proposed metrics possibly provides a consistent measure to mitigate the differences in impact factors among subject categories. On the basis of experimental results, we recommend the most reliable and appropriate metric to evaluate journals that are less dependent on the characteristics of subject categories.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.255-260
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• 2003

The regulation for floor impact sound level is expected to be amended to 50㏈(L$\_$i, Fmax, AW/) and below in heavy-weight impact sound and 58㏈(L'$\_$n, AW/) and below in light-weight impact sound in Korea. The purpose of this paper is to analyze the influence factors of floor impact sound levels in apartments. The influence factors were air pressure of bang machine, height of microphones, data acquisition rate, etc. The air pressure range of bang machine were from 2.2 Pa to 2.6 Fa. Five microphones were installed at a height of 0.5m, 0.7m, 0.9m, 1.2m, 1.5m or 1.7m from floor level. The floor impact sound level was varied about 1-3㏈(L$\_$i, Fmax, AW/) in heavy-weight impact sound according to the influence factors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.2
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• pp.217-224
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• 2002

This paper is proposed a reasonable method for determining the dynamic properties, the impact factors caused by traffic loads on highway bridges. In addition, the impact factors obtained in previous inspection reports were classified by the span length of the bridge, kind of bridge and type of bridge and the result of the impact factor was adjusted by a statistical method and presented problems. Also, the method for determining the impact factor using traffic load is proposed and the proposed method is compared with the specification code. The method estimating the impact factors due to the traffic loads can efficiently recognize the response of the structure by providing the impact factors and help to save the investigation cost, and also it can be used for the maintenance of structures using the usual test of bridges.

• The Journal of Information Systems
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• v.30 no.3
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• pp.137-158
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• 2021

Purpose This study aims to investigate which factors have impact on the acceptance intention of new electronic ID. For the empirical analysis, this study utilized PLS-SEM after collecting 385 survey data, and analyzed relations between each factors. Design/methodology/approach This study made a design of the research model by integrating the factors deducted from the Unified Theory of Acceptance and Use of Technology with the factors deducted from the Risk Factors. Findings The results are as follow; First, of the UTAUT factors, Performance Expectancy, Effort Expectancy and Social Influence and has positive impact on Acceptance Intention, but Facilitating Conditions doesn't have meaningful impact on Acceptance Intention. Second, of the Risk factors, Innovation Resistance has negative impact on Acceptance Intention, but Perceived Risk dose not have meaningful impact on Acceptance Intention. Finally, regulatory effect of the Promotion Focus, Prevention Focus and Gender has regulatory impact to Acceptance intention. It is expected that the implications of this study enables government effectively to offer new electronic ID.

• Computational Structural Engineering
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• v.5 no.3
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• pp.139-146
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• 1992

Vibrational behaviors of highway girder bridges due to heavy traffics are discussed, and empirical formulae for impact factors are suggested in this paper. Appropriate vehicle model for vibration analysis is found and impact factors are calculated with different surface roughnesses, vehicle speeds and span lengths. It is shown that the present codes tend to underestimate impact factors.

• Computational Structural Engineering
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• v.5 no.2
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• pp.87-91
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• 1992

Vibrational behaviors of highway girder bridges due to heavy traffics are discussed, and empirical formulae for impact factors are suggested in this paper. Appropriate vehicle model for vibration analysis is found and impact factors are calculated with different surface roughnesses, vehicle speeds and span lengths. It is shown that the present codes tend to underestimate impact factors.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.191-202
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• 2023

This study aims to propose a framework for evaluating the impact of artificial intelligence (AI) services, based on the concept of AI service impact. It also suggests a model for evaluating this impact and identifies relevant factors and measurement approaches for each item of the model. The study classifies the impact of AI services into five categories: ethics, safety and reliability, compliance, user rights, and environmental friendliness. It discusses these five categories from a broad perspective and provides 21 detailed factors for evaluating each category. In terms of ethics, the study introduces three additional factors-accessibility, openness, and fairness-to the ten items initially developed by KISDI. In the safety and reliability category, the study excludes factors such as dependability, policy, compliance, and awareness improvement as they can be better addressed from a technical perspective. The compliance category includes factors such as human rights protection, privacy protection, non-infringement, publicness, accountability, safety, transparency, policy compliance, and explainability.For the user rights category, the study excludes factors such as publicness, data management, policy compliance, awareness improvement, recoverability, openness, and accuracy. The environmental friendliness category encompasses diversity, publicness, dependability, transparency, awareness improvement, recoverability, and openness.This study lays the foundation for further related research and contributes to the establishment of relevant policies by establishing a model for evaluating the impact of AI services. Future research is required to assess the validity of the developed indicators and provide specific evaluation items for practical use, based on expert evaluations.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.241-251
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• 2021

This research explores and develops digital transformation factors influencing the logistics service-provider sector in Thailand while also examining the impact sustainability factors associated with digital transformation. Divided into two parts, Part one of the theoretical study framework covers 21 factors relating to logistics, including drivers, objectives, implications, and success factors. The second part concerns 23 factors associated with logistics sustainability, including economic, environmental, and social aspects. This quantitative empirical research was conducted using an online questionnaire instrument, and a structural equation modeling (SEM) technique was used to test the proposed model. The findings from 545 samples collected between August and November 2020 from respondents working in logistics service-provider companies in Thailand show that digital transformation drivers and objectives seem likely to impact success factors and implications in digital transformation positively. Digital transformation success factors also positively impact logistics sustainability. In comparison, logistics sustainability has a significant impact on Thailand's logistics service-provider sector's economic, environmental, and social aspects. Lastly, this research highlights the significance of digital transformation success factors and extends the current knowledge about digital transformation factors and their potential impact on logistics sustainability.

• Journal of the Korea Institute of Building Construction
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• v.11 no.2
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• pp.100-107
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• 2011

As construction duration significantly influences the performance and the success of construction projects, it is necessary to appropriately manage the impact factors affecting construction duration. Recently, interest in the construction industry has been rising due to the recent change in the construction legal system, and the competition among the construction companies on construction time. However, the impact factors are extremely diverse. The existing productivity data on impact factors is not sufficient to properly identify the impact factor and measure the productivity from various perspectives, such as subcontractor, time, crew, work and so on. In this respect, a multidimensional analysis by a data warehouse is very helpful in order to view the manner in which productivity is affected by impact factors from various perspectives. Therefore, this research proposes a method that effectively takes the diverse productivity data of impact factors, and generates a multidimensional analysis. Decision tree analysis, a data mining technique, is also applied in this research in order to supply construction managers with appropriate productivity data on impact factors during the construction management process.