Background: The transcutaneous electrical nerve stimulation (TNES) is the most used non-invasive treatment method in physical therapy. As the mobile TENS (MTENS) has become popular, patients with pain have started using MTENS to reduce pain. Objectives: To evaluate pain, range of motion, and muscle strength before and after using MTNES in patients with wrist pain. Design: Quasi-experimental research. Methods: We conducted an experiment with 80 patients; 15 patients were dropped out, and 35 and 30 patients were evaluated in the experimental group (EG) and control group (CG), respectively. Before and after using MTENS for 4 weeks, patients were evaluated using visual analogue scale (VAS), grip power, range of motion (ROM), and digital infrared thermography imaging (DITI). In the EG, electricity was applied for the MTENS device, while electricity was not applied in the CG. Results: A significant difference in pain reduction was observed between the EG and CG. In the EG, a significant difference in grip strength was also noticed before and after using the MTENS; patients showed significantly increased power grip and tip pinch. A significant difference was observed in pre-rest and post-test wrist ROM and DITI values. Conclusion: MTENS is an appropriate procedure for patients with wrist pain.

NDF (No Defect Found) is a phenomenon in which defects have been found in the manufacturing, operation and use of a product or facility, but phenomenon of defects is not reproduced in the subsequent investigation system or the cause of the defects cannot be identified. Recently, with the development of the fourth industrial revolution, convergence of hardware and software technologies in various fields is spreading to products such as aircraft, home appliances, and mobile devices, and the number of parts is increasing due to functional convergence. The application of such convergence technologies and the increase in the number of parts are major factors that lead to an increase in NDF phenomena. NDF phenomena have a significant negative impact on cost, reliability, and reliability for both manufacturers, service providers and operators. On the other hand, due to the nature of NDF phenomena such as difficult and intermittent cause identification and ambiguity in judgment, it is common to underestimate the cost of NDF or fail to take appropriate countermeasures in corporate management. Therefore, in this paper, we propose a methodology for estimating NDF costs by the PAF model which is a quality cost analysis model and ABC (Activity Based Costing) technique. The methodology of this study suggests a detailed procedure and the concept to accurately estimate the NDF costs, using ABC analysis, accounting system information, and IT system data. In addition case studies have validated the methodology. We think this could be a valid methodology to refer to when estimating the cost of other parts. And, it is meaningful to provide important judgment information in the decision-making process based on quality management and ultimately reduce NDF costs by visualizing them separately by major variable factors.

Recently, with the development of technologies for the fourth industrial revolution, convergence and complex technology are being applied to aircraft, electronic home appliances and mobile devices, and the number of parts used is increasing. Increasing the number of parts and the application of convergence technologies such as HW (hardware) and SW (software) are increasing the No Defect Found (NDF) phenomenon in which the defect is not reproduced or the cause of the defect cannot be identified in the subsequent investigation systems after the discovery of the defect in the product. The NDF phenomenon is a major problem when dealing with complex technical systems, and its consequences may be manifested in decreased safety and dependability and increased life cycle costs. Until now, NDF-related prior studies have been mainly focused on the NDF cost estimation, the cause and impact analysis of NDF in qualitative terms. And there have been no specific methodologies or examples of a working-level perspective to reduce NDF. The purpose of this study is to present a practical methodology for reducing NDF phenomena through data mining methods using quantitative data accumulated in the enterprise. In this study, we performed a cluster analysis using market defects and design-related variables of mobile devices. And then, by analyzing the characteristics of groups with high NDF ratios, we presented improvement directions in terms of design and after service policies. This is significant in solving NDF problems from a practical perspective in the company.

Metal bodies have generally been produced through machining process, even the smallest parts that are assembled and mounted on the metal body. In this study, we will study the process of manufacturing parts called SIM Tray through compound forging process instead of cutting. The process of replacing a series of SIM Tray production process with a composite forging process by simulating the forming process using DEFORM-3D and making process design, mold design, mold fabricating.

By increasing awareness of product offers and availability in the consumer’s proximity, Location Based Marketing (LBM) increases relevance of placed advertisements. However, depending on how it is executed, such advertising can also be perceived as intrusive, irritating, or even violating consumer’s privacy. Existing knowledge does not offer clear directions for retailers, who are keen to know of LBM’s effectiveness on sales. In this paper, authors investigate the effects of LBM on application (app) driven revenues of 116 major mobile retailers from around the globe. In particular, we examine the contingency effects of the roles of device as well as privacy needs of the brand audience. Findings reveal that effects of LBM on app-based revenues vary by tactic (inbound vs. outbound), type of device (Tablet vs. Phone), and user type based on brand of app (Android vs. Apple). Overall, this research identifies critical factors for retailers to consider, in order to best monetize their location based efforts. Contributions of the analysis and managerial implications are discussed.

본 연구는 오존수를 이용한 인체공학적 샴푸대가 분리 가능하고 미세분사 형식의 샤워헤드 와 높낮이 조절이 가능한 샴푸대를 개발할 수 있는 장치기술 및 그 방법을 제시하고자 한다. 개발결과 기존의 장치보다 피시술자 머리가 세발대 안으로 들어오는 인체공학적 디자인과 목받이 부분을 높게 제작하여 물튀김 방지를 완성하였다. 실험결과 물을 공급하는 통에 부착되어 있는 온수 유지 히터를 통 해 피시술자의 기호에 맞는 온도를 설정하여 시술 시 지연 시간 없이 온수 세발 가능하도록 온도센서를 통하여 확인한 결과 수온유지(38℃)가 일정하게 유지되었다. 그리고 오존수 변환장치 설치로 자체 살균 (1PPM) 및 정화 기능까지 가능한 장치의 효용성을 알 수 있었다. 오존수를 20분 정도 측정한 결과 오 존 농도가 1PPM 이하로 측정되어 안정성을 확보하였다. 최종적으로 오존수를 20분 정도 측정한 결과 오존 농도가 1PPM 이하로 측정되어 안정성을 확보하였고, 온수장치와 오존수 변환장치와 함께 이동 샴 푸대의 모든 부분이 사용자가 사용하기에 불편함이 없도록 설계 하였다.

This research was conducted in order to examine the effects of user socio-demographics and recently introduced streamlined technology readiness index TRI 2.0 (Parasuraman & Colby, 2015) on mobile device use in B2B digital services. Mobile adoption has been studied from a consumer perspective, but to the best of the authors’ knowledge, very few studies explore mobile use in B2B markets. Mobile marketing is becoming a strategic effort in companies, as digital services not only in B2C but also in B2B sector are getting increasingly mobile (Leeflang, Verhoef, Dahlström & Freundt 2014). This raises an interest to better understand the characteristics of those mobile enthusiasts who primarily use B2B services via a mobile device rather than via a personal computer. The study tests hypotheses with a large data set of 2,306 business customers of which around 10 percent represent these innovative mobile enthusiasts. Technology readiness is an individual’s propensity to embrace and use new technologies for accomplishing goals in home life and at work (Parasuraman & Colby, 2015; Parasuraman, 2000). Parasuraman and Colby (2015) recently introduced an updated version of the original Technology Readiness Index (TRI 1.0) scale called TRI 2.0 to better match with the recent changes in the technology environment. At the same time they streamlined the scale to a compact 16-item version so that it is easier for researchers to adopt it as a part of research questionnaires. Likewise the original scale, TRI 2.0 consists of four dimensions: optimism, innovativeness, discomfort, and insecurity. Optimism and innovativeness are motivators of technology adoption while discomfort and insecurity are inhibitors of technology readiness, and these motivator and inhibitor feelings can exist simultaneously (Parasuraman & Colby, 2015). Optimism is a general positive view of technology containing a belief that technology offers individuals with increased control, flexibility and efficiency in their lives. Innovativeness refers to a tendency to be a pioneer and thought leader in adopting new technologies. Discomfort reflects a perception of being overwhelmed by technology and lacking control over it. Moreover, insecurity reflects distrust and general skepticism towards technology, and includes concerns about the potential harmful consequences of it. As individuals differ in their propensity to adopt new technologies (Rogers, 1995), the authors propose that technology readiness influences mobile device use of B2B customers: H1: Optimism has a positive effect on mobile device use of B2B digital services. H2: Innovativeness has a positive effect on mobile device use of B2B digital services. H3: Insecurity has a negative effect on mobile device use of B2B digital services. H4: Discomfort has a negative effect on mobile device use of B2B digital services. The earlier literature argues that socio-demographic factors such as gender (Venkatesh & Morris, 2000; Chong, Chan & Ooi, 2012), age (Venkatesh, Thong & Xu, 2012; Chong et al., 2012; Kongaut & Bohlin 2016), education (Agarwal & Prasad, 1999; Chong et al., 2012; Puspitasari & Ishii 2016) and occupation (Okazaki, 2006) influence technology adoption behavior in general, and mobile adoption in particular. For example, men are nearly twice as likely as women to adopt mobile banking, and age is a negative determinant (Laukkanen, 2016). Higher educated use mobile devices more for utilitarian purposes, while lower educated use mobile devices more for entertainment (Chong et al., 2012). Moreover, research suggests that occupational factors influence mobile use (Okazaki, 2006). The authors hypothesize: H5: Males are more likely than females to use mobile device for B2B digital services. H6: Age has a negative effect on the use mobile device for B2B digital services. H7: Customers with higher education level have a higher likelihood for using mobile device for B2B digital services than customers with lower education level. H8: Occupation has an effect on the use mobile device for B2B digital services. The study tests hypotheses with a data collected among B2B customers of four large Finnish companies, all representing different industry fields. The large sample (n=2306) consists of procurement decision-makers all experienced with using B2B digital services. The sample shows that over 90 percent of the B2B customers are still using a computer (laptop or desktop computer) as their primary access device for digital services in their work. The sample divides between females and males in proportion to 46 and 54 percent respectively. University degree represents a majority with 42 percent, while only 2,7 percent of the respondents have a comprehensive or elementary school education. Over half of the sample represent top management or middle management with 24,6 and 28,4 percent respectively, while 9 percent are entrepreneurs, 21,2 percent represent experts, and 16,7 percent are officials or employess. Mean age of the respondents is 51,6 years, ranging from 18 to 81 years. The study uses logistic regression analysis with backward stepwise method in which the dependent variable is a dichotomous binary variable indicating the respondent’s primary access device for B2B digital services with 0=computer and 1=mobile device. As for the independent variables, the study measures individual’s technology propensity with recently introduced 16-item TRI 2.0 scale from Parasuraman and Colby (2015) using a five-point Likert scale ranging from Strongly disagree=1 to Strongly agree=5. The authors used confirmatory factor analysis to verify the theory-driven factor structure of the TRI 2.0 scale, i.e. optimism, innovativeness, discomfort, and insecurity. The analysis show that the measurement model for the TRI 2.0 scale provides an adequate fit and standardized regression estimates for all measure items exceed 0.60 (p<0.001) except for one item in discomfort (β=0.516) and one item in insecurity (β=0.480). After removing these two items the model shows an excellent fit with χ2=478.033 (df=71; p<0.001), CFI=0.965, RMSEA=0.050. Moreover, discriminant validity is supported, as the square root of the average variance extracted (AVE) value of each construct is greater than the correlations between the constructs (Fornell & Larcker, 1981). In addition, composite reliability values vary from 0.726 to 0.852 supporting convergent validity of the TRI 2.0 factors (Table 1). Thereafter, the factor scores of the latent factors showing sufficient internal consistency were imputed to create composite measures. These composite measures were used as independent variables in the logistic regression model. With regards to socio-demographic variables, age is measured as a continuous variable, while gender, education, and occupation are categorical independent variables in the model. The results of the logistic regression analysis show that innovativeness, insecurity, age, and occupation are statistically significant predictors of mobile device use in B2B services, supporting hypotheses H2, H3, H6, H8. The stepwise analysis procedure removed optimism (p=0.860), education (p=0.789), gender (p=0.339), and discomfort (p=0.159) from the model as they proved to be non-significant predictors of mobile device use. The results indicate that occupation is the strongest predictor for mobile device use in B2B digital services so that the top management has the greatest likelihood as the odds ratios of middle management, experts, and officials/employees are 0.610, 0.282, and 0.178 respectively. This means that, for example, the odds of the top management using mobile device as their primary channel for B2B digital services are 1.64 (1/0.610) times greater than the odds of the middle management, and 5.62 (1/0.178) times greater than the odds of the officials/employees. Interestingly the β-value for the entrepreneurs is positive indicating that their likelihood for mobile device use is even greater than the likelihood of the top management. However, the p-value (0.913) indicates that the difference is not statistically significant. With regards to age of the B2B customer, the results indicate a negative relationship with mobile device use. The odds ratio [Exp(β)=0.979] claims that the odds of a B2B customer to use mobile device as the primary channel for digital services decrease by 2 percent for each additional year of age. Regarding the TRI 2.0 constructs, the results show that innovativeness is a highly significant positive predictor for mobile device use, while perceived insecurity has a negative effect (Table 2). Literature suggests that B2B customers increasingly use mobile devices but yet little is known about those individuals most enthusiastic in using B2B digital services via a mobile device. Thus, the current study attempts to better understand those mobile enthusiasts who among the first have adopted mobile devices as their primary method to access B2B digital services. The results suggest that occupation is the most significant predictor of mobile use among B2B customers, implying that top managers are among the most likely to adopt and use mobile device for business services. Moreover, younger B2B customers use mobile devices more eagerly as the results suggest the likelihood for mobile device use degreases by 2 percent with every added year of age. The results further imply that out of the four TRI 2.0 dimensions innovativeness and insecurity influence in the mobile device use of B2B customers, innovativeness positively and insecurity negatively as the theory proposes. Innovativeness represents individual’s tendency to be a pioneer and thought leader in terms of technology adoption, while insecurity stems from the general skepticism and distrust of technology. These results imply that B2B customers who mainly access B2B digital services via a mobile device are open minded towards the possibilities new technologies can provide for them. Moreover, it appears that those B2B customers still accessing digital services primarily via a computer are more skeptical than mobile users towards technology in general. Compared to the use of mobile devices for individual purposes, business related use is more functional in nature, and thus, mobile devices and technologies must be convenient to use, offer real benefits for example in forms of mobility and portability, and be reliable in order for B2B customers to use them. Interestingly, our results do not support the effects of generally positive attitudes towards technology reflecting optimism, or discomfort of using technologies to influence mobile use among B2B customers. In addition, there are organizational factors (e.g. voluntariness of use) that the authors omit in the current study. These may limit the findings. Mobility will be a key driver in the ongoing digital revolution of marketing and sales. Understanding online behavior of mobile enthusiasts assists B2B marketing and sales leaders to plan and implement more effective mobile marketing strategies. Rogers (1995) has shown that the majority will follow the early adopters, and the adaptation cycle has even shortened during the last years (Downes & Nunes, 2014). Thus, mobile devices are evidently becoming the primary method in accessing B2B digital services.

스마트폰을 비롯한 모바일 기기의 디스플레이 해상도는 급격히 증가하고 있다. 최근 출시된 제품들은 대부분 이백만 화소 이상인 디스플레이를 사용하고 있다. 하지만 모바일용으로 널리 사용되는 멀티미디어 영상의 해상도는 디스플레이 해상도에 비해 상대적으로 낮다. 예를 들어 T-DMB(Terrestrial Digital Multimedia Broadcasting)의 화소수는 320x240이며, 인터넷 스트리밍 동영상은 대부분 720x480이하이다. 저해상도의 영상을 고해상도 디스플레이에서 재생하기 위해서는 보간을 수행해야 하는데, 이 과정에 의해 화질이 열화 된다. 특히 영상 확대에 따른 블로킹잡음 (blocking noise)의 가시성 증가는 모바일 디스플레이의 화질 저하에 중요한 영향을 미치는 요소 중에 하나이다. 본 논문에서는 보간된 영상을 대상으로 한 블로킹 잡음 저감 방법을 제안한다. 제안하는 방법은 에지를 보존하며 블로킹 잡음을 효과적으로 저감시킨다. 또한, 간단한 사칙연산, 비트연산과 비교연산만을 사용하여 모바일 기기에서의 하드웨어 구현이 용이하다는 장점을 갖는다.

최근 U-Tour 프로젝트들이 활발하게 진행되면서 모바일 디바이스를 이용하여 개인화된 콘텐츠를 제공하기 위한 시스템들이 개발되고 있다. 하지만 대부분의 시스템들이 모바일 디바이스에 저장되어 있는 콘텐츠만을 단방향으로 서비스하기 때문에 실시간으로 인터렉티브한 콘텐츠를 제공하기가 어렵다. 또한 개인화 서비스는 이메일 발송이나, 관람한 콘텐츠를 개인의 메모리에 저장하는 정도의 제한적인 기능만을 제공하는데 그치고 있다. 본 연구에서는 이러한 문제점들을 해결하기 위하여 스마트 RFID 기술을 이용하여 개인화 콘텐츠를 제공할 수 있는 PCPS(Personalized Contents Providing System)을 개발하였다. 본 시스템은 무선 네트워크를 통해 서버에 저장된 다양한 콘텐츠를 실시간으로 모바일 디바이스를 통하여 인터렉티브하게 제공할 수 있고, 사용자 컨텍스트를 기반으로 선호도를 분석하여 개인화된 콘텐츠를 제공할 수 있다.

본 연구는 소비자가 모바일 기기를 구매함에 있어서 어떤 멘탈 모델을 가지고 있는지를 파악하는데 중점을 두고 멘탈 모델의 추출과 분석을 시도하였다. 본 연구는 모바일 기기 구매 의사결정 과정에서 IT 친숙도에 따라 서로 다른 멘탈 모델을 가지고 있을 것이라는 가설을 가지고 크게 두 부분으로 나누어 연구를 진행하여 인지 과제 분석 방법의 하나인 Critical Decision Method를 이용하여 멘탈 모델을 이루는 27가지 구성요소들을 추출하였고, 이렇게 추출된 구성요소들을 바탕으로 IT 친숙도에 따라 소비자의 멘탈 모델을 두 그룹으로 구분하여 Pathfinder 알고리즘과Social Network Analysis를 이용하여 각각의 멘탈 모델을 분석하고 있다. 분석결과 IT 친숙도가 높은 그룹은 멘탈 모델을 구성하는 요소들이 각각의 독자적 특성에 따라 구매 의사결정 과정에서 비교적 조직적이고 분명하게 구분된 역할을 수행하는 것으로 나타난 반면, IT 친숙도가 낮은 그룹은 멘탈 모델 구성 요소들 간의 관계나 역할이 불분명하고 혼재된 경향을 보였으며 구매 의사결정 과정에서 외부 의견이나 사회적 통념을 중시하는 것으로 나타났다.