• The Journal of Society for e-Business Studies
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• v.16 no.4
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• pp.283-299
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• 2011

In this paper, we draw upon Task-Technology Fit theory to investigate the fit attributes which impacted on customer preference over three virtual shopping channels which included TV home shopping, Internet shopping, and broadband applications, i.e. IPTV. The fit attributes also reflected the product category contingency, which is classified based on the degree of quality assessing difficulty on the web, such as quasi-commodity, look and feel goods, and look and feel with variable quality goods. Using the collected survey data, we employed stepwise regression analysis to validate the fit attributes in the context of performing virtual shopping task via those three distinctive media technologies. Furthermore, through ANOVA test with Duncan statistics, we reported comparative intensity of the valid fit attributes across the product categories and distinct media technologies. The results validated four critical fit attributes and significant distinctions among product categories and three virtual shopping channels. The findings provide practical insights in distribution channel design exploiting digital convergence technologies.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.577-586
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• 2014

As SNS has increased its influence on the society as a whole, companies also have started to consider how to take advantage of the new service paying specific attention to its characteristics of immediacy, sharability and interactivity. This study aims to circumstantiate the relationship between a task support tool of SNS and task-media fit, user characteristics and performance by focusing on its usage in work field. To address this issue, a Task-Technology Fit model is used to propose a research model considering the characteristics of SNS as a social element, information technology as well as its user characteristics. The outcome shows that job characteristics, virtual competence and media characteristics have a significant influence on task-media fit, whereas virtual competence and SNS characteristics variables have a significant influence on SNS usage. Besides, task-media fit has a significant influence on SNS usage and work performance while SNS usage has a significant influence on work performance. The study suggests that strategic use of SNS helps improve work performance and these individual characteristics should be considered in planning of SNS utilizing strategy.

• Asia pacific journal of information systems
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• v.16 no.2
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• pp.47-67
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• 2006

This study takes a quantitative approach to the influence of TTF (Task-Technology Fit) on the individual's use and performance of GSS (Group Support Systems), while traditional studies on TTF have taken the experimental approach to explore the characteristic fit between diverse tasks and technologies. We have the following two research inquires: Are the IS use and performance maximized when information technologies are provided by the exact amount of demand?; and, Does TTF at the high level between task and IT produce better IS use (or performance) than at the low level? To investigate these issues, we use the polynomial regression analysis and response surface methodology of Edwards (1993) instead of traditional direct measure of TTF. This method measures the degree of desired and actual level of information technologies in conducting tasks, and traces the dynamic changes of dependent variables (IS use and performance) according to the variances of each independent variable. Our results conclude that user's IS use and performance are maximized when information technologies are actually provided by no more or less than the desired level. We also found that TTF at the high level promotes better IS use and performance than TTF at the low level.

• Journal of Information Technology Services
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• v.15 no.2
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• pp.185-201
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• 2016

Recently, due to the rapid development of IT (information technologies), a variety of attempts have been made to incorporate IT into other fields such as finance and manufacturing. Among them, a novel concept in the spotlight is FinTech, which is a combined word of finance and technology. FinTech is a line of business demonstrating an innovation development through IT in the financial service industry. One of the most popular types of FinTech is a third-party mobile payment service (MPS), the examples of which can be easily found in South Korea while the actual use of the service is relatively inactive. Therefore, the main purpose of this paper is to empirically investigate influencing factors of intention to use the third-party MPS. Based on individual characteristics and the task-technology fit model, the research model of the study is developed, with switching cost included as a moderating variable. The results of structural equation model testing with 316 potential users of Kakao Pay, one of the most popular business models of the third-party MPS, show that innate innovativeness, task characteristics, and technology characteristics are positively influencing task-technology fit, which in turn significantly affects intention to use the third-party MPS. The negative moderating role of switching cost is also found. These results could help managers develop better strategies to motivate potential users to participate in their services.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.538-555
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• 2014

Systematic studies have been rarely conducted on the acceptance of big data technology despite the technology drawing much attention from academia, industry and general public. With big data technology still being in the infant stage in Korea, a study model was constructed in this paper by integrating the innovation diffusion theory and the task technology fit theory with this technology acceptance model (TAM) as the central framework to make big data technology more readily acceptable in the country, and the aim of making big data technology readily acceptable was expanded as the moderator variable of the TAM. The results of this study showed that "subjective norm" and "task technology fit" showed the most significant effect as the exogenous variables of the TAM. In addition, the "innovative characteristic of the organization" was the significant exogenous variable affecting the intention to accept big data technology to those "technology utilizers" that try to come up with new services or products that are technology-based; however, "subjective norm" was the rather significant factor affecting those simple "technology users". Finally, a significant difference was seen in the verification of mediation effect.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.13-25
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• 2022

The purpose of this study is to explore the factors that influence the purchase of AI speakers in China. We integrate the Unified Theory of Acceptance and Use of Technology (UTAUT) and Task-technology fit (TTF) model into one model and put forward assumptions. According to the characteristics of AI speakers, we selected 6 independent variables, such as Performance Expectation, Effort Expectation, Social Influence, Facilitating Conditions, Task and Technology-characteristics. The final impact on purchase behavior is evaluated through Task-technology fit and purchase intention. After counting 478 samples, through SPSS22.0 and AMOS analysis, hypotheses have been proved by strong experimental data, except facilitating conditions. These results also imply that improving the technical level of AI speakers and enhancing consumers' purchasing intention are the central line of marketing. Based on this, we put forward several suggestions to marketers, including strengthening the research and development of AI speaker technology, and building a circle of friends of AI speakers.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.931-940
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• 2021

As information security(IS) is recognized as a critical success factor for organizational growth, organizations are increasing their investment in adopting and operating strict IS policies and technologies. However, when strict IS technology is adopted, IS-related techno-stress may occur in the employees who apply IS technology to their tasks. This study proposes the effect of IS-related techno-stress formed in individuals on IS policy resistance through IS strain and proves that task-technology fit mitigates the negative effect of techno-stress. Research models and hypotheses were presented through previous studies, and the secured samples were used, and structural equation modeling was applied to verify hypothesis. As a result of the study, it was confirmed that IS-related techno-stress (overload, complexity) affected IS policy resistance through IS strain (anxiety, fatigue), and that task-technology fit moderated the relationship between techno-stress and strain. This study suggests a strategic direction for improving the level of internal IS from the viewpoint of suggesting ways to mitigate the stress of employees that may occur when IS policies and technologies are adopted.

• Journal of Distribution Science
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• v.20 no.5
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• pp.85-96
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• 2022

Purpose: This study aims to dissemination a new concept based on empirical research on enhancing entrepreneurship intention (EI) in the theory of planned behavior (TPB) through entrepreneurship mindset (EM) and task technology fit (TTF). The TTF is a moderating variable in strengthening the relationship between EM and EI. Research design, data, and methodology: This research design was quantitative research. The respondents were 202 students from Malaysia and Indonesia who had filled out and collected an online questionnaire in Microsoft form. Three hypotheses examined the direct influence and the indirect impact of EM on EI through antecedent variables of TPB, and the effect of TTF as moderating variable to enhance the relationship between EM and EI. The data was analyzed using the WarpPLS version 7.0. Results: The result showed that EM had a significant impact on the students' EI. The interaction of EM and TTF was significantly able to improve EI. Conclusions: The findings contributed new ideas to develop the theoretical framework of the TPB model and were able be utilized by lecturers to consider the integration of EM and TTF in the model. The novelty of this study elaborated the EM and TTF variables as an extended model of the TPB.

• Asia pacific journal of information systems
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• v.25 no.2
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• pp.265-288
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• 2015

IT convergence services, as the main stream of the digital age, are currently on their way to include the concept of Software as a Service (SaaS), where IT products and services are integrated as one. In particular, the recently introduced web-service-based SaaS is expected to be a more developed SaaS model. This new model provides greater influence on clients' job performances than its previous models, such as application service providers and the web-native phase. However, the effects of technology maturity on task performance have been overlooked in adoption and performance studies. Accordingly, this study introduces SaaS technology maturity as the exogenous technological characteristic influencing job performance. This study also examines the relationships among various SaaS-related performances according to the different levels of SaaS maturity. Results suggest that applying innovative technologies (such as SaaS), particularly when the technology reaches a certain level of maturity, is more helpful for managers in improving task-technology fit and job performance. This study makes an academic contribution by establishing and validating a performance model empirically with SaaS technology maturity perspectives.