• The Journal of Industrial Distribution & Business
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• v.4 no.2
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• pp.31-40
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• 2013

Purpose - This study attempted to identify the value promotion clues that may operate as a consumer's motive, by shedding new light on consumer value and by reconstructing each variable analyzed through the means-end chain (MEC) theory. Research design, data, and methodology - In this study, 202 copies of effective questionnaires using the data of Yang and Ju (2012) were subjected to correlation, regression, and SEM. Results - All store selection attributes were verified as having a positive influence on the relationship quality. Although the store selection attributes were verified as exerting a positive influence on the relationship quality, according to the verification result of the mediating effect, consumer value was verified to be influenced only by the relationship quality instead of by the store selection attributes. Conclusion - As a result of path analysis on the proposed model after modification, it was verified that only product factor had a statistically significant positive influence and that social value was completely mediating between relationship quality and emotional value. It may be highlighted that the MEC theory concept would be applicable to the cause-and-effect relationship model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4081-4098
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• 2022

With the aim of tackling the contradiction between computation intensive industrial applications and resource-weak Edge Devices (EDs) in Industrial Internet of Things (IIoT), a novel computation task offloading scheme in SDIN-enabled MEC based IIoT is proposed in this paper. With the aim of reducing the task accomplished latency and energy consumption of EDs, a joint optimization method is proposed for optimizing the local CPU-cycle frequency, offloading decision, and wireless and computation resources allocation jointly. Based on the optimization, the task offloading problem is formulated into a Mixed Integer Nonlinear Programming (MINLP) problem which is a large-scale NP-hard problem. In order to solve this problem in an accessible time complexity, a sub-optimal algorithm GPCOA, which is based on hybrid evolutionary computation, is proposed. Outcomes of emulation revel that the proposed method outperforms other baseline methods, and the optimization result shows that the latency-related weight is efficient for reducing the task execution delay and improving the energy efficiency.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.961-970
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• 2021

This study aims to examine the impact of service recovery in building customer loyalty through the mediating role of customer satisfaction and corporate image. This study also aims to examine the reciprocal relationship between customer satisfaction and corporate image in building customer loyalty. This study uses data from 126 consumers who have received recovery for a service failure in five logistics companies which operates in Southeast Sulawesi in Indonesia, namely; JNEs; J&T Express; Pandu Logistics; MEC; and TiKi. Data was collected using a questionnaire which was then distributed to all respondents via google form. The distribution of respondents at each logistics company provider was as follows: 19 respondents were customers of JNEs; 17 respondents were customers of J&T Express; 32 respondents were customers of Pandu Logistics; 21 respondents were customers of MEC; and 37 respondents were customers of TiKi's. The structural model developed in this study was tested using Partial Least Squared (PLS) method. The research found that service recovery has a direct effect on customer satisfaction and corporate image as positive and significant. Customer satisfaction and corporate image have a reciprocal relationship which then roles in building customer loyalty. Service recovery has an indirect effect on customer loyalty through the mediating role of customer satisfaction and corporate image.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.41-57
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• 2023

Purpose - The purpose of this study is to research how the different types of experiences affect consumer's recognition in terms of luxury vintage fashion products, and what kinds of value consumer can achieve. Design/methodology/approach - The study is based on the means-end chain (MEC) approach for an in-depth understanding of consumers' recognition systems through conducting the laddering interview technique. Above all, the research conducted a pilot test to gain attributes of consumer experiences about luxury vintage fashion products from Korean and Taiwanese. Findings - It is found that not only by actual purchase, experience without purchasing also can lead to consumers' self-fulfilment and self-accomplishment, which filled the lack of relevant literature in the luxury vintage industry. In addition, the study sorted out the channels that consumers approach LVF products, which provide a classification reference for future research related to the luxury vintage consumer. Research implications or originality - As consumers can gain a lot kind of value through LVF products, luxury brands can attract consumers by using vintage as a market strategy. For luxury marketers, by running LVF shopping mall online or opening LVF stores, not only allow consumers' attach with LVF products but also can further lead to the purchase behaviors. In addition, consumers who are interested in LVF are those who are aware of the authenticity, uniqueness, and rarity of the brand. Due the fact, these consumers may be interested in the topic of sustainability.

• Journal of the korean academy of Pediatric Dentistry
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• v.38 no.2
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• pp.170-178
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• 2011

The purpose of this study was to assess the prevalence of periodontopathic bacteria and resistance determinants from oral biofilm of children. Subgingival dental plaque was isolated from 87 healthy children, and PCR was performed to determine the presence of 5 periodontal pathogens including P. gingivalis, T. forsythia, T. denticola, F. nucleatum, A. actinomycetemcomitans, and nine resistance genes including tet(Q), tet(M), ermF, aacA-aphD, cfxA, $bla_{SHV}$, $bla_{TEM}$, vanA, mecA. 1. The prevalence of F. nucleatum, T. forsythia. and P. gingivalis was 95.4%, 55.2%, and 40.2%, respectively. In addition. the prevalence of A. actinomycetemc omitans was 5.7%, while T. denticola was 3.4%. 2. In analysis of antibiotic resistance determinants. cfxA, $bla_{TEM}$ and tet(M) were detected in all the samples tested. It was also found that the prevalence of tet(Q) showing tetracycline resistance. $bla_{SHV}$ associated with resistance to ${\beta}$-lactams, ermF exhibiting erythromycin resistance, and, vanA resulting vancomycin resistance was 88.5%, 29.9% 87.4%, and 48.5%, respectively. The aacA-aphD gene showing resistance to aminoglycosides and mecA gene harboring methicillin resistance exhibited the lowest prevalence with 9.2%. 3. In a correlation analysis between periodontopathic pathogens and antibiotic resistance determinants, it was found that there was a significant correlation between T. forsythia and $bla_{SHV}$. Also, P. gingivalis and vanA showed a correlation. Finally, tet(Q) and ermF showed a significant correlation (phi: 0.514) while mecA and vanA also showed a correlation(phi: 0.25).

• Proceedings of the SAREK Conference
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• 2005.06a
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• pp.230-230
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• 2005

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.874-890
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• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.383-403
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• 2021

With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.797-819
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• 2019

Internet of Things (IoT) is the paradigm of network of Internet-connected things as objects that constantly sense the physical world and share the data for further processing. At the core of IoT lies the early technology of radio frequency identification (RFID), which provides accurate location tracking of real-world objects. With its small size and convenience, RFID tags can be attached to everyday items such as books, clothes, furniture and the like as well as to animals, plants, and even humans. This phenomenon is the beginning of new applications and services for the industry and consumer market. IoT is regarded as a fourth industrial revolution because of its massive coverage of services around the world from smart homes to artificial intelligence-enabled smart driving cars, Internet-enabled medical equipment, etc. It is estimated that there will be several dozens of billions of IoT devices ready and operating until 2020 around the world. Despite the growing statistics, however, IoT has security vulnerabilities that must be addressed appropriately to avoid causing damage in the future. As such, we mention some fields of study as a future topic at the end of the survey. Consequently, in this comprehensive survey of IoT, we will cover the architecture of IoT with various layered models, security characteristics, potential applications, and related supporting technologies of IoT such as 5G, MEC, cloud, WSN, etc., including the economic perspective of IoT and its future directions.

• ETRI Journal
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• v.44 no.1
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• pp.62-72
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• 2022

In this study, we propose a multi-GPU-based 8KVR stitching system that operates in real time on both local and cloud machine environments. The proposed system first obtains multiple 4 K video inputs, decodes them, and generates a stitched 8KVR video stream in real time. The generated 8KVR video stream can be downloaded and rendered omnidirectionally in player apps on smartphones, tablets, and head-mounted displays. To speed up processing, we adopt group-of-pictures-based distributed decoding/encoding and buffering with the NV12 format, along with multi-GPU-based parallel processing. Furthermore, we develop several algorithms such as equirectangular projection-based color correction, real-time CG overlay, and object motion-based seam estimation and correction, to improve the stitching quality. From experiments in both local and cloud machine environments, we confirm the feasibility of the proposed 8KVR stitching system with stitching speed of up to 83.7 fps for six-channel and 62.7 fps for eight-channel inputs. In addition, in an 8KVR live streaming test on the 5G MEC/cloud, the proposed system achieves stable performances with 8 K@30 fps in both indoor and outdoor environments, even during motion.