• International Journal of Human Ecology
• /
• v.10 no.1
• /
• pp.23-33
• /
• 2009

This study identified the appropriateness of acceptance models of smart clothing and differences in the hypothesis of the path to clothing acceptance by classifying consumers depending on the level of technology innovation and fashion innovation through the extended TAM (Technology Acceptance Model) presented by Chae (2009). 815 copies of data were collected from adults over twenty living in major South Korean cities and analyzed them using a SPSS 15.0 and AMOS 5.0 package. Based on the average value of technology innovation and fashion innovation, the respondents were classified into: Group 1 with high technology innovation and fashion innovation, Group 2 with high technology innovation but low fashion innovation, Group 3 with low technology innovation but high fashion innovation, and Group 4 with low technology innovation and fashion innovation. The appropriateness of models for the four classified groups was verified. The analysis proved that an extended TAM for each classified group explains the acceptance process of smart clothing; especially the appropriateness of model of Group 1 and Group 4 was comparatively higher than other groups. Perceived usefulness was revealed as the key variable that affects consumer attitudes to accept smart clothing. Perceived ease of use has indirect positive effects on consumer attitudes passing through perceived usefulness and clothing involvement partly exerted impacts on consumer attitudes and the intention of acceptance. The mediating role of attitudes to explain the intention of the acceptance of smart clothing is high and suggests that it is necessary to take a positive role to help the consumer perceive the functional and useful aspects of the clothing.

• Korean Journal of Human Ecology
• /
• v.19 no.2
• /
• pp.375-387
• /
• 2010

The Technology Acceptance Model (TAM) presented by Davis (1989) has been regarded as highly explanatory as well as the clearest model in explaining consumers' adoption of innovative technology or products. Existing studies have expanded the model by adding related external variables to improve the explanation depending on the type of innovative technology. This study expanded TAM by adding two more variables, namely consumers' technology innovation and clothing involvement considering the feature of smart clothing. The objectives of this study are as follows: 1. to suggest the extended TAM in explaining the adoption process of smart clothing, 2. to verify the differences in the path hypotheses according to the type of smart clothing. A total of 815 effective samples were collected from adults over 20 years old, and AMOS 5.0 package was employed for data analysis. As a result, it was proved that the extended TAM was appropriate for explaining the process of adopting smart clothing according to the path hypotheses of smart clothing types. Technology innovation and clothing involvement were confirmed as antecedent variables in affecting TAM. The perceived usefulness appeared to be a more crucial variable than the perceived ease of use and attitude was found to be an important parameter in adopting smart clothing. Considering the path hypotheses of MP3 playing clothes, perceived usefulness had a direct influence on acceptance intention unlike other types of smart clothing. As for photonic clothes, the influence of perceived ease of use on attitude was supported while it was rejected in the case of MP3 playing clothes and sensing sportswear.

• Journal of the Korean Statistical Society
• /
• v.26 no.4
• /
• pp.521-530
• /
• 1997

The new Laplace autoregressive model of order 2-NLAR92) studied by Dewald and Lewis (1985) is extended to the p-th order model-NLAR(p). A necessary and sufficient condition for the existence of an innovation sequence and a stationary ergodic NLAR(p) model is obtained. It is shown that the distribution of the innovation sequence is given by the probabilistic mixture of independent Laplace distributions and a degenrate distribution.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.305-307
• /
• 1992

This paper presents a technique, which identifies both process noise covariance and sensor noise covariance by using innovation correlation test. A correlation test, which checks whether the square root Kalman filter is workingly optimal or not, is given. The system is stochastic autoregressive moving-average model with auxiliary white noise Input. The linear quadratic Gaussian control is used for minimizing stochastic cost function. This paper indentifies Q, R, and estimates parametric matrics $A(q^{-1}),B(q^{-1}),C(q^{-1})$ by means of extended recursive least squares and model reference control. And The proposed technique has been validated in simulation results on the fourth order system.

• Journal of Korea Technology Innovation Society
• /
• v.17 no.1
• /
• pp.146-173
• /
• 2014

Extant studies have explained that firm's innovations including technological product and process innovations contribute to its competitive advantage and growth, thereby supporting competitiveness and growth of industry. These studies, however, have focused mainly on the role and effect of technological change that is primarily measured by the patent numbers and R&D intensity. Aside from these traditional streams, there has been growing interest on the impact by various dimensions of innovation including non-technological innovations. Apart from the discussions on the dimensions and scope of innovation, stages or processes of innovation also have been studied. Extant studies on innovation process model, however, has limited its interests in the structure of the transformation of knowledge. This study have established a comprehensive model embracing operational and financial performance to investigate the causal paths between innovation and firm performance. Using multi-level generalized linear model with path analysis, this study have found results as follows: First, the processes from innovative activities to innovation output and outcomes including operational and financial performance at firm level were verified. Secondly, the influence of innovation decreases gradually as the distance away from the direct outputs of the innovation increase in the direction of financial outcomes. Third, the effect of innovation on the sales growth rate is higher for small businesses than for medium-sized businesses. The effect of innovation on the profit rate, however, is significant only for medium-sized businesses. For large businesses, innovation has no positive significant impact on any financial performance at all. Fourth, Fourth, the appropriability of innovation has positive impacts on innovative performance, patent applications, and operational performance.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.34 no.6
• /
• pp.969-981
• /
• 2010

This study explains the consumer acceptance of smart clothing using the extended Technology Acceptance Model (TAM); in addition, it compares the difference in the path hypotheses of the perceived group and nonperceived group from the aspect of the extended TAM. A total of 815 copies of questionnaire were collected from a web-based survey in March 2009. Structural equation modeling was used to examine the entire pattern of intercorrelations among the constructs and to test related propositions using an AMOS 5.0 package. The fitness of the extended TAM explains the process of the adaptation of smart clothing. Technology Innovation (TI) and Clothing Involvement (CI) were confirmed as antecedent variables to affect TAM. In the perceived group, Technology Innovation (TI) and Clothing Involvement (CI) showed significant impacts on the Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) while Technology Innovation (TI) did not influence the Perceived Ease of Use (PEOU) in the non-perceived group. Perceived Ease of Use (PEOU) influenced the Perceived Usefulness (PU) and indirectly influenced Attitude (A) through the Perceived Usefulness (PU) in both groups. In addition, Perceived Usefulness (PU) did not influence Acceptance Intention (AI) but indirectly affected Acceptance Intention (AI) through Attitude (A). Therefore, Attitude (A) was found to be an important parameter in the adaptation of smart clothing in both groups. This finding implies that consumers first perceive the usefulness of smart clothing, then take favorable attitudes towards the smart clothing, and finally have the intention to adopt it. Strategies for publishing and informing consumers of the functions of smart clothing and usefulness in life are necessary; in addition, understanding what useful values they expect from the clothing is also crucial.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.10
• /
• pp.76-91
• /
• 2019

There have been many studies to examine the patterns in innovations reflecting industry-specific characteristics from an evolutionary economics perspective. The purpose of this study is to identify industry-specific differences in product innovation patterns and determinants of innovation performance. For this, Korean manufacturing is classified into high-tech industries and low-tech industries according to technology intensity. It is also pointed out that existing research does not reflect the decision-making process of firms' R&D implementations. In order to solve this problem, this study presents a Heckman sample selection model and a double-hurdle model as alternatives, and analyzes data from 1,637 firms in the 2014 Survey on Technology of SMEs. As a result, it was confirmed that the determinants and patterns of manufacturing in small and medium-size enterprise (SME) product innovation are significantly different between high-tech and low-tech industries. Also, through an extended empirical model, we found that there exist a sample selection bias and a hurdle-like threshold in the decision-making process. In this study, the industry-specific features and patterns of product innovation are examined from a multi-sided perspective, and it is meaningful that the decision-making process for manufacturing SMEs' R&D performance can be better understood.

• Journal of Industrial Convergence
• /
• v.20 no.1
• /
• pp.23-33
• /
• 2022

The study was conducted to develop an implementation model for digital transformation (DX) of manufacturing companies. To this end, previous studies on the process of management innovation and digital transformation were reviewed. The DX process model was derived based on the NEBIC theory and innovation theory applied in the innovation process of the Internet business. In addition, a research model including the factors of the will of the top management class (TMT) was constructed and confirmed through empirical data. The research hypothesis were verified based on data collected from members of mid-sized manufacturing companies promoting digital transformation. Through regression analysis, the influence relationship of each stage of the research model (technical knowledge, TK → opportunity perception, OR → performace expectation, PE and → Intention to execute, IE) was confirmed. Hierarchical regression analysis was conducted to understand the mediating effect of the members' perception of the top management's willingness to promote DX in the process. As a result of checking the Sobel test, it was confirmed that the management's perception of DX promotion partially mediated the relationship at each stage. This study is meaningful in that it presented a model applicable to the digital transformation of the mid-sized manufacturing industry. It is also valuable in providing an empirical basis for innovative research and NEBIC expansion. Longitudinal studies are required to overcome the limitations of empirical data for process models with dynamic characteristics whereas extended empirical studies are required in various fields other than manufacturing to generalize research results.

• International Journal of Quality Innovation
• /
• v.2 no.2
• /
• pp.136-144
• /
• 2001

The Markov Chain approach is used to develop an economic adjustment model of a process whose quality can be affected by a single special cause, resulting in changes of the process mean by incorrect adjustment of the process when it is operating according to its capability. The $\bar{X}$ control chart is thus used to signal the special cause. It is demonstrated that the expressions for the expected cycle time and the expected cycle cost are easier to obtain by the proposed approach than by adopting that in Collani, Saniga and Weigang (1994). Furthermore, this approach would be easily extended to derive the expected cycle cost and the expected cycle time for the case of multiple special causes or multiple control charts. A numerical example illustrates the proposed method and its application.

• Earthquakes and Structures
• /
• v.10 no.6
• /
• pp.1253-1272
• /
• 2016

For a nonlinear control system, there are many uncertainties, such as the structural model, controlled parameters and external loads. Although the significant progress has been achieved on the robust control of nonlinear systems through some researches on this issue, there are still some limitations, for instance, the complicated solving process, weak conservatism of system, involuted structures and high order of controllers. In this study, the computational structural mechanics and optimal control theory are adopted to address above problems. The induced norm is the eigenvalue problem in structural mechanics, i.e., the elastic stable Euler critical force or eigenfrequency of structural system. The segment mixed energy is introduced with a precise integration and an extended Wittrick-Williams (W-W) induced norm calculation method. This is then incorporated in the market-based control (MBC) theory and combined with the force analogy method (FAM) to solve the MBC robust strategy (R-MBC) of nonlinear systems. Finally, a single-degree-of-freedom (SDOF) system and a 9-stories steel frame structure are analyzed. The results are compared with those calculated by the $H{\infty}$-robust (R-$H{\infty}$) algorithm, and show the induced norm leads to the infinite control output as soon as it reaches the critical value. The R-MBC strategy has a better control effect than the R-$H{\infty}$ algorithm and has the advantage of strong strain capacity and short online computation time. Thus, it can be applied to large complex structures.