• Journal of Korea Technology Innovation Society
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• v.10 no.4
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• pp.755-788
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• 2007

This study investigates the effects of individual and group level factors on the use of GSS (Group Support System) and task performance from GSS use. GSS facilitates the group work, so that GSS adoption is not necessarily influenced only by individual perceptions on information systems. Adaptive Structuration Theory (AST) in our study to explain the adoption and success from GSS use. AST contends that the success of IS is not necessarily the technical fit between tasks and technology, instead the political outcome among user socializations. We have the following two research inquires: Are the IS use and performance maximized when information technologies are provided properly?; and, Does TTF always influence positively on IS use (or performance)? To research these issues, we investigate the influence of TTF (Task-Technology Fit) on use and performance of GSS, which is introduced to foster collaboration among organizational members. Drawing insights from the AST, we examine if COA (Consensus on Appropriation) among group members and FOA (Faithfulness of Appropriation) between those who use technology and who design it show any moderating effect. A questionnaire survey was conducted on firms using the GSS for one month from June 2 to June 27 2005 and a sample of 303 responses was used for a statistical analysis. The result demonstrates that TTF exerts a positive influence on use and performance of GSS. We find that the stronger the COA, the greater the effect of W on use of GSS and performance. FOA likewise has a positive effect on both use of GSS and performance. The TTF model has been widely applied to studies on individual performance of information system, whereas the AST theory specifically explains members' adaptation process to information system. By integrating the AST theory with the TTF model, the study contributes to heightening our understanding on if and how individual performance varies with the use of GSS.

• The Journal of Society for e-Business Studies
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• v.16 no.1
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• pp.71-100
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• 2011

There are expectations about future internet technology with IT development by end-users. Cloud computing is attracted to satisfy those demands. However, adoption of cloud computing is not active that much. Therefore, this study verified how cloud computing environment affects performance of team project. We conducted empirical study on performance of team project with cloud computing as technology tool focusing on Task-Technology Fit Model. We collected samples that were undergraduate and graduate school students and had experience on initial cloud computing such as Google-Docs and Webhard when they conducted team project for assignment. We focused on accessibility and reliability as task-technology fit and those variables treated as first order factor. Result showed that cloud computing is suitable technology tool for team project. This study suggests positive effects of cloud computing for collaboration by proving perceived fit and performance in initial cloud computing.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.37-64
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• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.1025-1032
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• 2021

The study investigates factors affecting the loyalty of Food Delivery Application (FDA) service in Thailand. This study employs quantitative research methodology with a non-probability sampling method to draw 510 FDA samples from the FDA users in Thailand. The online questionnaires with a Cronbach's alpha coefficient of 0.886 were used as a research tool to collect data from samples. By using the Structural Equation Modeling (SEM) to analyze data, the results show that trustworthiness, social influence, system design, and task-technology fit affect the user's technology acceptance, which also show the significant relationship with the loyalty of FDA users in Thailand. The study checks the harmony with the statistics; χ2 = 258.686, df. =160, χ2/df. = 1.616, p-value = 0.050, CMIN/DF = 1.616, GFI = 0.960, AGFI = 0.969, TLI = 0.953, CFI = 0.965, RMSEA = 0.047, significant level at 0.05, along with testing the weight factor. In conclusion, the research model was harmonious with the empirical data at the significant level 0.05. The finding of this study suggested that the FDA service provider might apply this research finding to develop a greater understanding of the FDA's customer loyalty, as well as determine marketing strategies, identify opportunities, and create a competitive advantage in the future.

• Journal of Information Technology Applications and Management
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• v.22 no.1
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• pp.37-60
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• 2015

As the dependence on information systems in enterprises has grown dramatically, the importance of implementing information systems in audit has been increased as well. However, there is a lact of about utilization of information system for audit process. Thus, this study is to investigate the factors that effect auditor's adopting Data Analytics to audit work. Through literature research and focus group interview, we added two factors that affect the behavioral intention to UTAUT model. We have selected performance expectancy, effort expectancy, social influence, facilitating conditions, anxiety, task fit, behavioral intention as variables and verified hypotheses based on survey questionnaires from auditors. As a result, it was found that performance expectations, social influence, task fit influenced the behavior intention. In Addition, we analyzed adding two variables, IT-related work experience and type of auditor as moderate variable. This study has an implication for companies to motivate implementation as well as activation of Data Analytics technique.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.99-102
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• 2003

While information technology has been advanced impressively, the issue of system underutilization has continued. Although TAM provides a theoretical and empirical model for explaining information technology acceptance, there exist some issues: lack of focusing task and organization. The present study examines the motivational factors influencing the beliefs about the system, in terms or the extended TTF (task-technology fit) model, to address the issues. For this purpose, an exploratory case study was conducted based on the data gathered from a Web-based survey. The present research proposes five propositions, based on the results of the case study and prior study findings, which can be used as a starting point fur future research.

• Journal of Information Technology Applications and Management
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• v.24 no.3
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• pp.1-21
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• 2017

Business Intelligence (BI) system is a strategic tool that presents an analytical perspective about business and external environments. Even though its strategic value was well known, users often avoid using it or adopt it ceremonially. In fact, over 50 per cent of BI projects worldwide are reported to end in failure. Such an unexpectedly lower success rate has been a key issue in BI studies. In order to enhance a proper use of information systems, MIS field provided a number of theoretical constructs. One example is Goodhue & Thompson's Task-Technology Fit (TTF). In addition, internalization, the degree to which people make their own effort to modify behavior, was recently suggested as another important determinant of use. Though in MIS community both TTF and internalization proved to be a key determinant of system use, there has been not much study aiming to discover antecedents influencing these constructs. In this study we assert that user participation should be highlighted in BI projects. Especially, we emphasize user participation at the phase of feasibility study that is mainly conducted to determine whether a BI system is essentially necessary and practicable. Our research model employs participative feasibility study as a major antecedent for TTF and internalization that consequently will lead to user satisfaction and actual use. This model was empirically tested on 121 BI system users. The result shows that user participation in feasibility study is positively associated with TTF and internalization, each being related to user satisfaction and system use. It implies that, if an organization has BI users get involved in strategic feasibility study phase, the BI system would turn out to fit users' tasks and, furthermore, users would put more efforts spontaneously in order to use it properly.

• The Journal of Information Systems
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• v.17 no.2
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• pp.73-94
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• 2008

Knowledge is a fundamental assets, therefore, the ability to create, acquire, integrate, and share knowledge has emerged as a fundamental organizational capability(Sambamurthy and Subramani, 2005). This apaper reports the results of an empirical study investigating the factors of acceptance and the moderating role of resistance in Knowledge Management Systems(KMS). The research model is based on the theory of planned behavior(TPB) and technology acceptance model(TAM). It includes the perceived usefulness instead of attitude, subjective norm, perceived behavior control and intention of acceptance of KMS. Also, three external variables namely task-technology fit, organizational support, and perceived rewards are added. In the research model, all hypothrses of the baseline model and the moderating effects of resistance were found to be significant. The authors also of fred several implications based chi the findings.

• Journal of Information Technology Applications and Management
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• v.16 no.2
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• pp.1-21
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• 2009

The balanced scorecard(BSC) framework is a tool for strategic decision making and task support that aims for efficiency in performance management and strategy execution. BSC evolved from an old performance management practice, which tended to be limited to a finance-only perspective, to a new system of corporate management looking at corporate tasks from a multi-dimensional, future-inclined value perspective. This form of BSC amounts to a framework capable of driving management innovation and renewing the ways in which companies conceive their strategy and perform their operations. Since BSC draws integrally on user participation, it can be expected that users' satisfaction with BSC systems is an important factor in systems' success or failure. However, previous studies of the BSC system have not yet considered it as a theoretical model, specifically examining BSC system and task characteristics. To date, only a few studies have put forward plans for the implementation and use of BSC systems, and these studies have the common limitation of failing to consider the circumstances or theoretical structure of the companies for which a BSC system is being proposed. This paper then begins to fill some of this gap by characterizing the BSC system from the perspective of contingency theories. Contingency theories can be particularly useful in the Korean context in exploring how different companies use the BSC system in ways determined by their unique environmental characteristics, which may also determine the performance factors behind the application of a company's particular BSC system. In order to provide concrete suggestions for implanting and using the BSC system from a contingency theory perspective, this study sets out to determine the relationships between the contingency variables affecting BSC system performance and BSC system property variables(in given cases) through an empirical analysis. The study takes into account the perspective from which contingency theory is to be applied in individual cases, sets contingency and BSC property variables with reference to the BSC system user's environment and BSC system's character, and frames initial hypotheses concerning corporate structure and environmental variables and BSC system performance variables with reference to previous studies. A survey was then conducted on users in Korean companies that have implemented the BSC system in order to verify the research model and understand results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2952-2971
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• 2016

Outsourcing jobs to a public cloud is a cost-effective way to address the problem of satisfying the peak resource demand when the local cloud has insufficient resources. In this paper, we studied the management of deadline-constrained bag-of-tasks jobs on hybrid clouds. We presented a binary nonlinear programming (BNP) problem to model the hybrid cloud management which minimizes rent cost from the public cloud while completes the jobs within their respective deadlines. To solve this BNP problem in polynomial time, we proposed a heuristic algorithm. The main idea is assigning the task closest to its deadline to current core until the core cannot finish any task within its deadline. When there is no available core, the algorithm adds an available physical machine (PM) with most capacity or rents a new virtual machine (VM) with highest cost-performance ratio. As there may be a workload imbalance between/among cores on a PM/VM after task assigning, we propose a task reassigning algorithm to balance them. Extensive experimental results show that our heuristic algorithm saves 16.2%-76% rent cost and improves 47.3%-182.8% resource utilizations satisfying deadline constraints, compared with first fit decreasing algorithm, and that our task reassigning algorithm improves the makespan of tasks up to 47.6%.