목적 : 본 연구는 시각장애인을 대상으로 디지털 점자 보조기기를 이용한 점자 훈련 프로그램을 적용하여 점자 촉지 수준 의 변화와 점자 관련 일상 활동에 미치는 효과를 알아보고자 하였다. 연구방법 : 본 연구는 단일집단 사전-사후 비교 설계를 사용하였다. 서울과 강원 지역의 시각장애 관련 기관 2곳에서 모집 한 시각장애인 8명에게 8주간 점자 훈련 프로그램을 진행하였다. 점자 훈련 프로그램은 스마트폰에서 구동되는 점자 학습 앱과 블루투스로 연결된 디지털 점자 보조기기를 사용하였다. 점자 훈련은 주 1회 1시간 시행되었고 추가로 훈련일 제외 주 4일 반복 연습을 자율적으로 시행하도록 독려하였다. 점자 촉지 수준의 변화를 알아보기 위하여 본 연구에서 조작적 정의를 통해 계산한 촉지율과 점자 관련 일상 활동에서의 어려움 정도를 알아보기 위한 K-IPPA검사를 훈련 전, 후로 측정하여 결과를 비교하였다. 결과 : 대상자들은 훈련 프로그램 이후 촉지율이 평균 43%에서 98%로 향상되었고 K-IPPA검사로 측정한 점자 관련 일 상 활동의 어려움이 평균 18점에서 15점으로 낮아진 것으로 나타났다. 결론 : 디지털 점자 보조기기를 이용한 점자 훈련 프로그램은 자립적으로 점자 촉지를 향상시키는데 효과적이었으며, 추 후 국내 점자 교육과 관련한 제한점을 해결할 수 있는 적절한 대안이 될 것으로 생각된다.

목적 : 대학생을 대상으로 디지털기기의 사용시간을 조사하고, 비침습적 눈물막파괴시간(NITBUT)을 측정하여 디지털기기가 눈물막에 미치는 영향을 알아보고자 하였다. 방법 : 2022년 8월부터 2개월간 충남 소개 대학에 재학 중인 대학생 55명을 대상으로 분석하였다. 눈물막 평 가는 Cornea 550을 이용하여 NITBUT를 측정하여 안구건조증과 정상안으로 구분하였다. 설문은 대상자의 일반 적 특성, 디지털기기의 사용 환경, 그리고 안구건조증 관련 자각 증상에 관해 총 14문항을 조사하였다. 결과 : 대상자들이 디지털기기를 사용하는 시간은 하루 평균 9.93±4.90시간이었다. 하루에 디지털기기를 8 시간 이상 사용하는 그룹에서 NITBUT가 안구건조증에 속한 경우는 58.5%였고, 8시간 미만의 21.4% 보다 유의 하게 높았다(p=0.029). 또한, 하루에 8시간 이상 디지털기기를 사용한 대상자들은 눈의 건조감과 안정피로를 더 크게 호소하였고, 연령, 성별, 콘택트렌즈, 굴절수술, 흡연, 수면시간을 보정한 후에 8시간 이상의 디지털기기의 사용은 안구건조증에 걸릴 확률을 5.18배 높였다(p=0.023). 디지털기기가 스마트폰인 경우는 NITBUT가 정상인 그룹에서 53.8%였고, 안구건조증 그룹에서는 69.0%였다(p>0.050). 결론 : 디지털기기의 사용시간이 8시간 이상 일 경우에는 비침습적 눈물막파괴시간이 짧아졌고, 안구건조증에 걸릴 확률이 높아짐을 확인할 수 있었다.

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.

본 논문은 이동이 편리한 간이형 어린이 인형극장 장치에 관한 것으로, 조립식 인형극 세트의 입체화와 디지털 구연동화 출력매체의 접목을 통하여 어린이들을 위한 시청각 교재로서의 흥미도 증진과 함께 인형극 내용의 반복 통일성을 원만하게 유지할 수 있으며 나아가 휴대가 간편한 조립식 인형극 세트를 제공하는 것을 목적으로 가정이나 유아원 또는 유치원에서 손쉽게 인형극 세트를 설치할 수 있어 장소에 구애됨이 없이 어린이들의 학습효과를 증진시킬 수 있는 특징적인 효과를 가진 이러한 본 논문의 연구는 한정된 공간과 제한된 수요층을 대상으로 하던 기존의 인형극 무대장치에 대한 문제점을 해소하고 인형극을 대중에게 보급시키는데 필요한 여러 가지 장점들을 만들어 냈다. 향 후, 본 논문의 연구를 토대로 인형극의 무대장치, 인형, 배경, 음성 장치 등에 대한 여러 가지 측면에서의 다각적인 연구들이 이어져 어린이들은 물론 많은 대중들에게 조금 더 다양한 볼거리들을 제공할 수 있는 인형극들이 많이 만들어지길 바란다.