• Journal of Information Technology Services
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• v.17 no.3
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• pp.157-169
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• 2018

This study aims to examine the factors influencing user attitude toward Smart Home service as the demand of Smart Home service is increasing and it somewhat involves privacy risk. To this end, the research model includes five independent variables, trust in service provider, perceived privacy risk, self efficacy, interpersonal influence, and external influence, influencing the attitude toward Smart Home service. So, this study aims to analyze which variable is the most critical and influential among the five factors and suggest the direction of Smart Home industries. This study first reviews the literature on Smart Home services and describes its Korean situation. Data were collected from residents living in a smart apartment complex. The results show that (1) users have a very positive attitude toward Smart Home service in total, (2) trust in service providers, self efficacy, and interpersonal influence positively impact user attitude toward Smart Home service and interpersonal influence is the most influential variable, however, (3) perceived privacy risk and external influence dose not significantly impact it. These results imply that the role of service providers, self efficacy, and interpersonal influence are important factors on the user attitude toward Smart Home service. Finally, the study's findings and limitations are discussed and potential avenues for future research are suggested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1569-1584
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• 2013

Ubiquitous interaction in a pervasive environment is the main attribute of smart spaces. Pervasive systems are weaving themselves in our daily life, making it possible to collect user information invisibly, in an unobtrusive manner by known and even unknown parties. Huge number of interactions between users and pervasive devices necessitate a comprehensive trust model which unifies different trust factors like context, recommendation, and history to calculate the trust level of each party precisely. Trusted computing enables effective solutions to verify the trustworthiness of computing platforms. In this paper, we elaborate Unified Trust Model (UTM) which calculates entity's trustworthiness based on history, recommendation, context and platform integrity measurement, and formally use these factors in trustworthiness calculation. We evaluate UTM behaviour by simulating in different scenario experiments using a Trust and Reputation Models Simulator for Wireless Sensor Networks. We show that UTM offers responsive behaviour and can be used effectively in the low interaction environments.

• Fashion & Textile Research Journal
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• v.14 no.1
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• pp.24-36
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• 2012

This study examines consumer's acceptance intention of sensor-based smart clothing empolying the extended TAM. Technology innovativeness, information innovativeness and trust were used as external variables and perceived palyfulness was included in the extetended TAM. Data were collected from the adults over 20 years old living in Daegu from March 14 to 18, 2011. 193 useful copies of data were analyzed to investigate a structural model and test research hypotheses using AMOS 7.0. The study results showed that the extended TAM for smart clothing was validated empirically in predicting the individual's acceptance of sensor-based smart clothing and 10 hypotheses among 12 hypotheses were supported. Technology innovation, information innovation, and trust were confirmed as antecedent variables in affecting extended TAM. Perceived usefulness and perceived playfulness directly influenced acceptance intention and indirectly influenced acceptance intention mediating attitude. Perceived usefulness affected perceived playfulness and attitude affected acceptance intention. This study will help marketers and managers of fashion companies devise effective tools in planning marketing strategies related to smart clothing.

• Journal of Information Technology Applications and Management
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• v.28 no.6
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• pp.23-43
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• 2021

This research is a study on smart homes products, which interest and research are being conducted, because of the recent development of the Internet of Things. Consumer's Purchase intention was set as a dependent variable, and technological trust was used as a mediating variable. We used Technology Acceptance Model as background theory. Perceived ease of use, Perceived usefulness, Security, and Brand were set as independent variables. The results of this study are as follows. First, it was found that perceived ease of use, perceived usefulness, and security significantly affected technological trust that consumers feel. Second, technological trust also had a significant effect on purchase intention, and it was found that perceived ease of use, perceived usefulness, and product security, excluding brands, had an indirect mediating effect on consumers' purchase intention through technological trust. This study is meaningful in that by conducting user-centered research, and results that are partially contrasted with existing studies are derived from increasing the interest of factors we used.

• Journal of Smart Tourism
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• v.2 no.3
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• pp.5-15
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• 2022

As the utilization of technology in the tourism field becomes familiar, it greatly impacts people's tourism activities. These changes could also affect the behavior of tourists during the pandemic. To investigate consumers' adaptation to the self-service technology (SST) environment during the coronavirus disease of 2019 (COVID-19) pandemic, we adopted a model of absorptive capacity as the main framework for empirical research. To track the social effects of COVID-19, consumers' behavioral intentions for four different points in time are collected. The analysis was conducted using latent growth and structural equation modeling. We set the organizational and environmental characteristics as the first step of the model, with assimilation and trust as a middle step. Intention to use a kiosk is placed at the final step as an exploit. Findings indicate that organizational characteristics and environmental characteristics positively influenced assimilation and trust, except for environmental characteristics. Consumers' assimilation in SST encourages immediate intention to use a kiosk. Consumers' trust in kiosks positively impacts both immediate and continuance intention to use a kiosk during COVID-19.

• Knowledge Management Research
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• v.12 no.2
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• pp.56-68
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• 2011

The rapid changes in business environments are also applicable to the delivery service field. Numerous companies are using smart phones in order to perform work of the delivery service field that is rapidly developing every year. Smart phones established ubiquitous work environments where access to the server of the head office is possible through telephones, scanners, and the internet anywhere and anytime. However, although smart appliances including smart phones have been diffused, empirical analysis of use of them in actual has not been attempted much. In this regard, current study empirically analyzes how connectivity and context-awareness function of smart phones based on the TAM(Technology Acceptance Model) influence the continuous intention to use through trust, usefulness, and ease of use. The results indicate that the connectivity and context-awareness function, which are natures of smart phones, had impacts on the ease of use and trust, but not on the usefulness. Based on these results, this study suggests implications and future research directions regarding the planning and realization of smart appliances.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.89-100
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• 2021

Smart Grid Network (SGN) is a next generation electrical power network which digitizes the power distribution grid and achieves smart, efficient, safe and secure operations of the electricity. The backbone of the SGN is information communication technology that enables the SGN to get full control of network station monitoring and analysis. In any network where communication is involved security is essential. It has been observed from several recent incidents that an adversary causes an interruption to the operation of the networks which lead to the electricity theft. In order to reduce the number of electricity theft cases, companies need to develop preventive and protective methods to minimize the losses from this issue. In this paper, we have introduced a machine learning based SVM method that detects malicious nodes in a smart grid network. The algorithm collects data (electricity consumption/electric bill) from the nodes and compares it with previously obtained data. Support Vector Machine (SVM) classifies nodes into Normal or malicious nodes giving the statues of 1 for normal nodes and status of -1 for malicious -abnormal-nodes. Once the malicious nodes have been detected, we have done a trust evaluation based on the nodes history and recorded data. In the simulation, we have observed that our detection rate is almost 98% where the false alarm rate is only 2%. Moreover, a Trust value of 50 was achieved. As a future work, countermeasures based on the trust value will be developed to solve the problem remotely.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.565-569
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• 2021

The rapid growth and development of ICT (Information and Communication Technology) and internet services has boosted the adoption of Mobile Government services all around the globe. There is a huge increase in the adoption of government services during COVID-19 pandemic. Existing Mobile Government (MG) solutions are not trustworthy and secure. This paper provides secure and trustworthy solution for mobile government, proposes a centralized smart governance architecture which is based on trust manager. Our proposed work has Wireless Bridge Certifying Authority (WBCA) and Wireless Public Key Infrastructure (WPKI) thereby ensuring security and privacy. Our proposed work ensures trust with WBCA as WBCA acts as a Trust Manager (TM). Proposed protocol has less computational cost and energy cost

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.149-161
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• 2023

Internet of Things (IoT) is a relatively new concept that has gained immense popularity in a short period of time due to its wide applicability in making human life more convenient and automated. As an illustration: the development of smart homes, smart cities, etc. However, it is also accompanied by a substantial number of risks and flaws. IoT makes use of low-powered devices, so secure, less time-consuming and energy-intensive transmission (routing) of messages due to the limited availability of energy is one of the many and most significant concerns for IoT developers. The following paper presents a trust-based routing scenario for the Internet of Things (IoT) that exploits the past transmission record from the cupcarbon simulator's log files. Artificial Neural Network is used to quantify knowledge of trust, calculate the value of trust, and share this information with other network devices. As a human behavioural pattern, trust provides a superior method for making routing decisions. If there is a tie in the trust values and no other path is available, the remaining battery power is used to break the tie and make a forwarding decision; this is also seen as a more efficient use of the available resources. The proposed algorithm is observed to have superior energy consumption and routing decisions compared to conventional routing algorithms, and it improves the communication pattern.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.97-117
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• 2020

Five years have passed since the introduction of the smart factory amid the new opportunities for growth and job creation in relation to domestic manufacturing companies. Nevertheless, there is a lack of analysis on SMEs introduction smart factories. This study empirically analyzed the effects on the introduction of smart factories of domestic metal processing SMEs by distinguishing the characteristics of enterprises In this study, 103 companies which introduced smart factories and another 106 companies which did not introduce them were sampled. The Introduction of the Smart Factory was analyzed by four categories such as the Company characteristics (R&D capability, product production capability, organizational change), entrepreneur characteristics (risk sensitivity), relational characteristics (trust, dependence, cooperation, Influence), and structural characteristics (competition). As a result of the research, we found out product production capacity, risk sensitivity, trust and cooperation, Influence, and competition are statistically significant in the introduction of smart factory. But competition was characterized by a negative (-) sign opposite to the hypothesis. This study is meaningful in that the scope of the analysis has been expanded by analyzing whether smart factory was introduced or not considering the characteristics of the company. And there should be continuous research on its utilization as well as the introduction of smart factory.