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14. Thrombospondin-1 associated with carotid intima–media thickness among individuals with hypertension.

Author

Lin, Hsiu-Fen, Wu, Meng-Ni, Chen, Chien-Yuan, Lim, Kelly, Juo, Suh-Hang Hank, and Chen, Cheng-Sheng

Abstract

In vivo and in vitro studies have demonstrated that thrombospondin-1 (TSP-1) is involved in atherosclerotic pathogenesis. However, the role of TSP-1 in clinical atherosclerosis remains unknown. This cross-sectional study investigated the relationship between TSP-1 and carotid intima–media thickness (IMT) and examined whether it interacts with conventional cardiovascular risk factors. A total of 587 participants were enrolled from February 2018 to December 2021. TSP-1 was dichotomized based on median value. Carotid IMT was measured bilaterally in each segment, and the average value was taken as the overall IMT variable. Analysis of covariance models were used to ascertain the main and interaction effects of cardiovascular risk factors and circulating TSP-1 levels on carotid IMT. Those with high TSP-1 (n = 294) had significantly higher carotid IMT than did those with low TSP-1 (n = 293; 0.74 ± 0.12 vs 0.72 ± 0.11 mm; p = 0.011). After the combined effects of TSP-1 and vascular risk factors on carotid IMT were evaluated, an interaction effect on IMT was observed between TSP-1 and hypertension (adjusted F = 8.760; p = 0.003). Stratification analysis revealed that individuals with hypertension and high TSP-1 had significantly higher IMT than did those with low TSP-1 (adjusted p = 0.007). However, this difference was not observed in normotensive individuals (adjusted p = 0.636). In conclusion, this is the first study to provide clinical data supporting the correlation between TSP-1 and atherosclerosis. TSP-1 may be a crucial marker of increased susceptibility to atherosclerosis in individuals with hypertension. [ABSTRACT FROM AUTHOR]

Published
2024
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17. The 'Invisible' Group: Acculturation of International Women Partners on United States Campuses

Author

Lin, Hsiu-Fen

Abstract

In a northeastern university, an "invisible" non-funded group has provided cross-cultural programs for international non-matriculated accompanying women partners for 30 years. This study employed interpretative phenomenological analysis to explore the acculturation experiences of group participants based on Berry's acculturation theory. The findings highlight the significance of the type of group that facilitates better acculturation experiences on United States campuses. Recommendations are to create programs for international accompanying partners and sponsor such groups for continuing services.

Published
2018
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18. Perpetrating Economic Abuse across the Globe: Results from the U.N. Multi-Country Study on Men and Violence in Asia and the Pacific.

Author

Postmus, Judy L., Lin, Hsiu-Fen, Nikolova, Kristina, and Johnson, Laura

Subjects
*GENDER role, *CLUSTER sampling, *PSYCHOLOGY of men, *FOOD security, *MULTIVARIATE analysis, *PSYCHOLOGY, *INTERVIEWING, *INTIMATE partner violence, *SOCIOECONOMIC factors, *RISK assessment, *SEX distribution, *FINANCIAL stress, *THEORY, *ALCOHOL drinking, *MENTAL depression, *SCALE analysis (Psychology), *DESCRIPTIVE statistics, *STATISTICAL sampling, *SOCIODEMOGRAPHIC factors, *LOGISTIC regression analysis, *ODDS ratio
Abstract

Using the United Nations (U.N.) Multi-Country Study on Men and Violence in Asia and the Pacific, this study aims to identify the prevalence of men's use of intimate partner violence (IPV) with a focus on economic abuse and its association with food scarcity and other variables. The U.N. Study took place between 2010 and 2013 in Bangladesh, Cambodia, China, Indonesia, Papua New Guinea, and Sri Lanka. Multi-stage cluster sampling was used to obtain a representative sample (n = 8,380) of households. The results found that over 1 in 5 men have perpetrated economic abuse at least once in their lifetime. Men reporting food scarcity, drinking problems, depressive symptoms, and gender inequality beliefs toward their partners were more likely to perpetrate economic abuse. Interventions aimed at reducing food insecurity, addressing men's drinking problems and depression, and changing attitudes toward gender inequality may be effective at decreasing economic abuse. [ABSTRACT FROM AUTHOR]

Published
2023
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19. A Transdisciplinary Analysis of Domestic Violence Homicides in Harris County, Texas (2016-2020).

Author

Lin, Hsiu-Fen, Spearman, Kathryn J., Ross, Jennifer L., Blackburn, Emily, Messing, Jill T., Campbell, Jacquelyn C., AbiNader, Millan A., and Grimm, Kevin J.

Subjects
*DOMESTIC violence, *HOMICIDE, *CRIMINOLOGY, *CRIMINAL justice system
Abstract

A transdisciplinary, community-engaged approach enables researchers to connect knowledge generation with domestic violence homicide prevention. This case study utilizes medical examiner data from Harris County, Texas (2016–2020, n = 2,517) to identify and examine correlates of domestic violence homicides. The research team identified more than double the number of domestic violence homicides than had been flagged in the original data. Geospatial analysis identified unequal distribution of homicide across communities and significant gender-based disparities in domestic violence homicides. Transdisciplinary approaches can be used to accurately identify domestic violence homicides and provide a foundation to develop multi-level interventions. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Renal function is associated with 1-month and 1-year mortality in patients with ischemic stroke

Author

Liu, Chung-Hsiang, Tsai, Chon-Haw, Huang, Wei-Shih, Lu, Chung-Ta, Tsai, Tzung-Chang, Tseng, Chun-Hung, Lin, Kang-Hsu, Shyu, Woei-Cherng, Yang, Yu-Wan, Liu, Yen-Liang, Cho, Der-Yang, Chen, Chun-Chung, Jeng, Jiann-Shing, Tang, Sung-Chun, Tsai, Li-Kai, Yeh, Shin-Joe, Chen, Chih-Hao, Tsai, Hsin-Hsi, Li, Jie-Yuan, Chen, Han-Jung, Lu, Kan, Hsu, Shih-Pin, Kuo, Hung-Chang, Tsou, Jung-Chi, Wang, Yan-Tang, Tai, Yi-Cheng, Hsieh, Meng-Tsang, Liliang, Po-Chao, Liang, Cheng-Loong, Wang, Hao-Kuang, Tsai, Yu-Tun, Wang, Kuo-Wei, Chen, Jui-Sheng, Chen, Po-Yuan, Wang, Yi-Ching, Chen, Chih-Hung, Sung, Pi-Shan, Hsieh, Han-Chieh, Su, Hui-Chen, Chiu, Hou-Chang, Lien, Li-Ming, Chen, Wei-Hung, Bai, Chyi-Huey, Huang, Tzu-Hsuan, Lau, Chi-Ieong, Wu, Ya-Ying, Yeh, Hsu-Ling, Chang, Anna, Lin, Ching-Huang, Yen, Cheng-Chang, Lin, Ruey-Tay, Chen, Chun-Hung, Khor, Gim-Thean, Chao, A-Ching, Lin, Hsiu-Fen, Huang, Poyin, Lin, Huey-Juan, Ke, Der-Shin, Chang, Chia-Yu, Yeh, Poh-Shiow, Lin, Kao-Chang, Cheng, Tain-Junn, Chou, Chih-Ho, Yang, Chun-Ming, Shen, Hsiu-Chu, Chen, An-Chih, Tsai, Shih-Jei, Lu, Tsong-Ming, Kung, Sheng-Ling, Lee, Mei-Ju, Chou, Hsi-Hsien, Chang, Wei-Lun, Chiu, Pai-Yi, Hsu, Min-Hsien, Chan, Po-Chi, Pan, Chau-Hsiung, Shoung, Hai-Ming, Lo, Yi-Chen, Wang, Fu-Hwa, Chang, Wei-Chieh, Lai, Ta-Chang, Yin, Jiu-Haw, Wang, Chung-Jen, Wang, Kai-Chen, Chen, Li-Mei, Denq, Jong-Chyou, Sun, Yu, Lu, Chien-Jung, Lin, Cheng-Huai, Huang, Chieh-Cheng, Liu, Chang-Hsiu, Chan, Hoi-Fong, Lee, Siu-Pak, Sun, Ming-Hui, Ke, Li-Ying, Chen, Po-Lin, Lee, Yu-Shan, Sung, Sheng-Feng, Ong, Cheung-Ter, Wu, Chi-Shun, Hsu, Yung-Chu, Su, Yu-Hsiang, Hung, Ling-Chien, Lee, Jiunn-Tay, Lin, Jiann-Chyun, Hsu, Yaw-Don, Peng, Giia-Sheun, Hsu, Chang-Hung, Lin, Chun-Chieh, Yen, Che-Hung, Cheng, Chun-An, Sung, Yueh-Feng, Chen, Yuan-Liang, Lien, Ming-Tung, Chou, Chung-Hsing, Liu, Chia-Chen, Yang, Fu-Chi, Wu, Yi-Chung, Tso, An-Chen, Lai, Yu- Hua, Chiang, Chun-I., Tsai, Chia-Kuang, Liu, Meng-Ta, Lin, Ying-Che, Hsu, Yu-Chuan, Chiang, Tsuey-Ru, Huang, Pai-Hao, Liao, Pin-Wen, Lee, Mei-Ching, Chen, Jen-Tse, Lie, Sian-King, Sun, Mu-Chien, Hsiao, Pi-Ju, Chen, Wei-Liang, Chen, Ta-Cheng, Chang, Chen-Shu, Lai, Chien-Hsu, Chuang, Chieh-Sen, Chen, Yen-Yu, Lin, Shinn-Kuang, Su, Yu-Chin, Shiao, Jen-Lun, Yang, Fu-Yi, Liu, Chih-Yang, Chiang, Han-Lin, Chen, Guei-Chiuan, Hsu, Po-Jen, Chang, Chun-Yuan, Lin, I.-sheng, Chien, Chung-Hsien, Chang, Yang-Chuang, Chen, Ping-Kun, Hsiao, Yu-Jen, Fang, Chen-Wen, Chen, Yu-Wei, Lee, Kuo-Ying, Lin, Yun-Yu, Li, Chen-Hua, Tsai, Hui-Fen, Hsieh, Chuan-Fa, Yang, Chih-Dong, Liaw, Shiumn-Jen, Liao, How-Chin, Yeh, Shoou-Jeng, Wu, Ling-Li, Hsieh, Liang-Po, Lee, Yong-Hui, Chen, Chung-Wen, Hsu, Chih-Shan, Jhih, Ye-Jian, Zhuang, Hao-Yu, Pan, Yan-Hong, Shih, Shin-An, Chen, Chin-I., Sung, Jia-Ying, Weng, Hsing-Yu, Teng, Hao-Wen, Lee, Jing-Er, Huang, Chih-Shan, Chao, Shu-Ping, Yuan, Rey-Yue, Sheu, Jau-Jiuan, Yu, Jia-Ming, Ho, Chun-Sum, Lin, Ting-Chun, Yu, Shih-Chieh, Chen, Jiunn-Rong, Tsai, Song-Yen, Wei, Cheng-Yu, Hung, Chao-Hsien, Lee, Chia Fang, Yang, Sheng-Kung, Chen, Chih-Lin, Lin, Wei, Tseng, Hung-Pin, Liu, Chin-Hsiung, Lin, Chun-Liang, Lin, Hung-Chih, Chen, Pi-Tzu, Hu, Chaur-Jong, Chan, Lung, Chi, Nai-Fang, Chern, Chang-Ming, Lin, Chun-Jen, Wang, Shuu-Jiun, Hsu, Li-Chi, Wong, Wen-Jang, Lee, I-Hui, Yen, Der-Jen, Tsai, Ching-Piao, Kwan, Shang-Yeong, Soong, Bing-Wen, Chen, Shih-Pin, Liao, Kwong-Kum, Lin, Kung-Ping, Chen, Chien, Shan, Din-E., Fuh, Jong-Ling, Wang, Pei-Ning, Lee, Yi-Chung, Yu, Yu-Hsiang, Huang, Hui-Chi, Tsai, Jui-Yao, Wu, Ming-Hsiu, Chiang, Szu-Yi, Wang, Chiung-Yao, Hsu, Ming-Chin, Chen, Chien-Chung, Yeh, Po-Yen, Tsai, Yu-Tai, Wang, Ko-Yi, Chen, Tsang-Shan, Hsieh, Cheng-Yang, Chen, Wei-Fen, Yip, Ping-Keung, Wang, Vinchi, Wang, Kaw-Chen, Tsai, Chung-Fen, Chen, Chao-Ching, Liu, Yi-Chien, Chen, Shao-Yuan, Zhao, Zi-Hao, Wei, Zhi-Peng, Wu, Shey-Lin, Liu, Ching-Kuan, Lin, Ryh-Huei, Chu, Ching-Hua, Yan, Sui-Hing, Lin, Yi-Chun, Chen, Pei-Yun, Hsiao, Sheng-Huang, Yip, Bak-Sau, Tsai, Pei-Chun, Chou, Ping-Chen, Kuo, Tsam-Ming, Lee, Yi-Chen, Chiu, Yi-Pin, Tsai, Kun-Chang, Liao, Yi-Sheng, Tsai, Ming-Jun, Kao, Hsin-Yi, Wang, I-Kuan, Yen, Tzung-Hai, Tsai, I-Ju, Sung, Fung-Chang, and Hsu, Chung Y.

Published
2018
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24. Cholesterol Levels Are Associated with 30-day Mortality from Ischemic Stroke in Dialysis Patients

Author

Liu, Chung-Hsiang, Tsai, Chon-Haw, Huang, Wei-Shih, Lu, Chung-Ta, Tsai, Tzung-Chang, Tseng, Chun-Hung, Lin, Kang-Hsu, Shyn, Woei-Cherng, Yang, Yu-Wan, Liu, Yen-Liang, Cho, Der-Yang, Chen, Chun-Chung, Jeng, Jiann-Shing, Tang, Sung-Chun, Tsai, Li-Kai, Yeh, Shin-Joe, Hsu, Shih-Pin, Chen, Han-Jung, Chang, Cheng-Sen, Kuo, Hung-Chang, Lee, Lian-Hui, Tsui, Huan-Wen, Tsou, Jung-Chi, Wang, Yan-Tang, Tai, Yi-Cheng, Tsai, Kun-Chang, Chen, Yen-Wen, Lu, Kan, Liliang, Po-Chao, Tsai, Yu-Tun, Liang, Cheng-Loong, Wang, Kuo-Wei, Wang, Hao-Kuang, Chen, Jui-Sheng, Chen, Po-Yuan, Chye, Cien-Leong, Tzeng, Wei-Jie, Wu, Pei-Hua, Chen, Chih-Hung, Sung, Pi-Shan, Hsieh, Han-Chieh, Su, Hui-Chen, Chiu, Hou-Chang, Lien, Li-Ming, Chen, Wei-Hung, Bai, Chyi-Huey, Huang, Tzu-Hsuan, Lau, Chi-Ieong, Wu, Ya-Ying, Yeh, Hsu-Ling, Chang, Anna, Lin, Ching-Huang, Yen, Cheng-Chang, Lin, Ruey-Tay, Chen, Chun-Hung, Khor, Gim-Thean, Chao, A-Ching, Lin, Hsiu-Fen, Huang, Poyin, Lin, Huey-Juan, Ke, Der-Shin, Chang, Chia-Yu, Yeh, Poh-Shiow, Lin, Kao-Chang, Cheng, Tain-Junn, Chou, Chih-Ho, Yang, Chun-Ming, Shen, Hsiu-Chu, Chen, An-Chih, Tsai, Shih-Jei, Lu, Tsong-Ming, Kung, Sheng-Ling, Lee, Mei-Ju, Chou, Hsi-Hsien, Chi, Hsin-Yi, Pan, Chou-Hsiung, Chan, Po-Chi, Hsu, Min-Hsien, Chang, Wei-Lun, Huang, Zhi-Zang, Shoung, Hai-Ming, Lo, Yi-Chen, Wang, Fu-Hwa, Lai, Ta-Chang, Yin, Jiu-Haw, Wang, Chung-Jen, Wang, Kai-Chen, Chen, Li-Mei, Denq, Jong-Chyou, Sun, Yu, Lu, Chien-Jung, Lin, Cheng-Huai, Huang, Chieh-Cheng, Liu, Chang-Hsiu, Chan, Hoi-Fong, Lee, Siu-Pak, Sun, Ming-Hui, Ke, Li-Ying, Chen, Po-Lin, Lee, Yu-Shan, Sung, Sheng-Feng, Ong, Cheung-Ter, Wu, Chi-Shun, Hsu, Yung-Chu, Su, Yu-Hsiang, Hung, Ling-Chien, Lee, Jiunn-Tay, Lin, Jiann-Chyun, Hsu, Yaw-Don, Peng, Giia-Sheun, Hsu, Chang-Hung, Lin, Chun-Chieh, Yen, Che-Hung, Cheng, Chun-An, Sung, Yueh-Feng, Chen, Yuan-Liang, Lien, Ming-Tung, Chou, Chung-Hsing, Liu, Chia-Chen, Yang, Fu-Chi, Wu, Yi-Chung, Tso, An-Chen, Lai, Yu-Hua, Chiang, Chun-I, Tsai, Chia-Kuang, Liu, Meng-Ta, Lin, Ying-Che, Hsu, Yu-Chuan, Chiang, Tsuey-Ru, Lee, Mei-Ching, Huang, Pai-Hao, Lie, Sian-King, Liao, Pin-Wen, Chen, Jen-Tse, Sun, Mu-Chien, Lai, Tien-Pao, Chen, Wei-Liang, Chen, Yen-Chun, Chen, Ta-Cheng, Wang, Wen-Fu, Lee, Kwo-Whei, Chang, Chen-Shu, Lai, Chien-Hsu, Shih, Siao-Ya, Chuang, Chieh-Sen, Chen, Yen-Yu, Chen, Chien-Min, Lin, Shinn-Kuang, Su, Yu-Chin, Hsiao, Cheng-Lun, Yang, Fu-Yi, Liu, Chih-Yang, Chiang, Han-Lin, Chang, Chun-Yuan, Lin, I-sheng, Chien, Chung-Hsien, Chang, Yang-Chuang, Chen, Ping-Kun, Chiu, Pai-Yi, Hsiao, Yu-Jen, Fang, Chen-Wen, Chen, Yu-Wei, Lee, Kuo-Ying, Lin, Yun-Yu, Li, Chen-Hua, Tsai, Hui-Fen, Hsieh, Chuan-Fa, Yang, Chih-Dong, Liaw, Shiumn-Jen, Liao, How-Chin, Yeh, Shoou-Jeng, Wu, Ling-Li, Hsieh, Liang-Po, Lee, Yong-Hui, Chen, Chung-Wen, Hsu, Chih-Shan, Jhih, Ye-Jian, Zhuang, Hao-Yu, Pan, Yan-Hong, Shih, Shin-An, Chen, Chin-I, Sung, Jia-Ying, Weng, Hsing-Yu, Teng, Hao-Wen, Lee, Jing-Er, Huang, Chih-Shan, Chao, Shu-Ping, Yuan, Rey-Yue, Sheu, Jau- Jiuan, Yu, Jia-Ming, Ho, Chun-Sum, Lin, Ting-Chun, Yu, Shih-Chieh, Chen, Jiunn-Rong, Tsai, Song-Yen, Wei, Cheng-Yu, Yang, Chao-Nan, Hung, Chao-Hsien, Shih, Ian, Tseng, Hung-Pin, Liu, Chin-Hsiung, Lin, Chun-Liang, Lin, Hung-Chih, Chen, Pi-Tzu, Hu, Chaur-Jong, Chi, Nai-Fang, Chan, Lung, Chern, Chang-Ming, Lin, Chun-Jen, Wang, Shuu-Jiun, Hsu, Li-Chi, Wong, Wen-Jang, Lee, I-Hui, Yen, Der-Jen, Tsai, Ching-Piao, Kwan, Shang-Yeong, Soong, Bing-Wen, Chen, Shih-Pin, Liao, Kwong-Kum, Lin, Kung-Ping, Chen, Chien, Shan, Din-E, Fuh, Jong-Ling, Wang, Pei-Ning, Lee, Yi-Chung, Yu, Yu-Hsiang, Huang, Hui-Chi, Tsai, Jui-Yao, Wu, Ming-Hsiu, Chen, Shi-Cheng, Chiang, Szu-Yi, Wang, Chiung-Yao, Hsu, Ming-Chin, Chen, Chien-Chung, Yeh, Po-Yen, Tsai, Yu-Tai, Wang, Ko-Yi, Chen, Tsang-Shan, Yip, Ping-Keung, Wang, Vinchi, Wang, Kaw-Chen, Tsai, Chung-Fen, Chen, Chao-Ching, Chen, Chih-Hao, Liu, Yi-Chien, Chen, Shao-Yuan, Zhao, Zi-Hao, Wei, Zhi-Peng, Wu, Shey-Lin, Liu, Ching-Kuan, Lin, Ryh-Huei, Chu, Ching-Hua, Yan, Sui-Hing, Lin, Yi-Chun, Chen, Pei-Yun, Hsiao, Sheng-Huang, Yip, Bak-Sau, Tsai, Pei-Chun, Chou, Ping-Chen, Kuo, Tsam-Ming, Lee, Yi-Chen, Chiu, Yi-Pin, Liao, Yi-Sheng, Tsai, Ming-Jun, Kao, Hsin-Yi, Wang, I.-Kuan, Yen, Tzung-Hai, Muo, Chih-Hsin, Ma, Henry, Wen, Chi-Pang, Sung, Fung-Chang, and Hsu, Chung Y.

Published
2017
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25. Analysis of the XRCCI gene as a modifier of the cerebral response in ischemic stroke

Author

Dutra, Ana V., Lin, Hsiu-Fen, Juo, Suh-Hang H., Mohrenweiser, Harvey, Sen, Souvik, and Grewal, Raji P.

Subjects
dna-repair genes, acid substitution variants, polymorphisms, sensitivity, disease, classification, expression, injury, damage, brain
Abstract

Background: Although there have been studies of the genetic risk factors in the development of stroke, there have been few investigations of role of genes in the cerebral response to ischemia. The brain responds to ischemia in a series of reactions that ultimately influence the volume of a stroke that, in general, correlates with disability. We hypothesize that polymorphisms in genes encoding proteins involved in these reactions could act as modifiers of this response and impact stroke volume. One of the pathways participating in the cerebral ischemic response involves reactive oxygen species which can cause oxidative damage to nucleic acids. DNA repair mechanisms are in place to protect against such damage and imply a role for DNA repair genes in the response of the brain to ischemia and are potential candidate genes for further investigation. Methods: We studied two common polymorphisms in the DNA repair gene, XRCC1, C26304T and G28152A, in 134 well characterized patients with non lacunar ischemic strokes. We also performed a case control association study with 113 control patients to assess whether these variants represent risk factors in the development of ischemic stroke. Results: Independent of etiology, the "T" allele of the C26304T polymorphism is significantly associated with larger stroke volumes (T-test analysis, p < 0.044; multivariate regression analysis, beta = 0.23, p < 0.008). In the case control association study, we found that neither of these polymorphisms represented a risk factor for the development of stroke. Conclusion: Our study suggests a major gene effect of the "T" allele of the C26304T polymorphism modulating the cerebral response to ischemia in non lacunar ischemic stroke.

Published
2006

31. Derivation and Validation of a Discharge Disposition Predicting Model after Acute Stroke

Author

Jeng, Jiann-Shing, Yeh, Shin-Joe, Tsai, Li-Kai, Chiu, Hou-Chang, Lien, Li-Ming, Chen, Wei-Hung, Bai, Chyi-Huey, Huang, Tzu-Hsuan, Lau, Chi-Ieong, Wu, Ya-Ying, Yuan, Rey-Yue, Hu, Chaur-Jong, Sheu, Jau-Jiuan, Yu, Jia-Ming, Ho, Chun-Sum, Chung, Wen-Ting, Sung, Jia-Ying, Chen, Chin-I, Weng, Hsing-Yu, Han, Yu-Hsuan, Huang, Chun-Ping, Lin, Huey-Juan, Ke, Der-Shin, Chang, Chia-Yu, Yeh, Poh-Shiow, Lin, Kao-Chang, Cheng, Tain-Junn, Chou, Chih-Ho, Yang, Chun-Ming, Lo, Yuk-Keung, Lin, Yu-Te, Lin, Ching-Hwung, Lin, Jiann-Chyun, Hsu, Yaw-Don, Denq, Jong-Chyou, Peng, Giia-Sheun, Lee, Jiunn-Tay, Hsu, Chang-Hung, Lin, Chun-Chieh, Yen, Che-Hung, Cheng, Chun-An, Sung, Yueh-Feng, Chen, Yuan-Liang, Lien, Ming-Tung, Chou, Chung-Hsing, Liu, Chia-Chen, Yang, Fu-Chi, Wu, Yi-Chung, Tso, An-Chen, Lai, Yu-Hua, Chiang, Chun-I, Tsai, Chia-Kuang, Liu, Meng-Ta, Lin, Ying-Che, Hsu, Yu-Chuan, Liu, Chung-Hsiang, Tsai, Chon-Haw, Huang, Wei-Shih, Lu, Chung-Ta, Tsai, Tzung-Chang, Tseng, Chun-Hung, Lin, Kang-Hsu, Shyn, Woei-Cherng, Yang, Yu-Wan, Liu, Yen-Liang, Cho, Der-Yang, Chen, Chun-Chung, Sun, Ming-Hui, Yu, Shih-Chieh, Ke, Li-Ying, Lai, Ming-Liang, Chen, Chih-Hung, Chen, Jiunn-Rong, Tsai, Song-Yen, Chiang, Tsuey-Ru, Lee, Mei-Ching, Lin, Shinn-Kuang, Tseng, Yuan-Fu, Lee, Jing-Er, Hsiao, Cheng-Lun, Liang, Jen-Feng, Chen, Chiu-Mei, Wu, Chao-Hsin, Chen, Yu-Wei, Lee, Kuo-Ying, Lin, Yun-Yu, Li, Chen-Hua, Smith, Eric E., Tsai, Hui-Fen, Hsieh, Chuan-Fa, Yang, Chih-Dong, Liaw, Shiumn-Jen, Liao, How-Chin, Hu, Han-Hwa, Wong, Wen-Jang, Luk, Yun-On, Chern, Chang-Ming, Hsu, Li-Chi, Chung, Chih-Ping, Sun, Mu-Chien, Wu, Shey-Lin, Tseng, Hung-Pin, Liu, Chin-Hsiung, Lin, Chun-Liang, Lin, Hung-Chih, Lai, Ta-Chang, Yin, Jiu-Haw, Wang, Chung-Jen, Wang, Kai-Chen, Chen, Li-Mei, Chan, Lung, Lee, Siu-Pak, Sun, Yu, Huang, Chieh-Cheng, Liu, Chang-Hsiu, Lin, Cheng-Huai, Lu, Chien-Jung, Yeh, Shoou-Jeng, Wu, Ling-Li, Hsieh, Liang-Po, Lee, Yong-Hui, Chen, Chung-Wen, Chen, Po-Lin, Lee, Yu-Shan, Chen, Shu-Yi, Chen, Han-Jung, Hsu, Shih-Pin, Chang, Cheng-Sen, Kuo, Hung-Chang, Wu, Shu-Lung, Tsui, Huan-Wen, Tsou, Jung-Chi, Chou, Feng-Hsiung, Lin, Pei-Jung, Tung, Chin-Sung, Lu, Kan, Liliang, Po-Chou, Tsai, Yu-Duan, Liang, Cheng-Loong, Wang, Kuo-Wei, Wang, Hao-Kuang, Chen, Te-Yuan, Hsin, Yue-Loong, Lin, Chih-Yuan, Yip, Bak-Sau, Tsai, Pei-Chun, Chou, Ping-Chen, Kuo, Tsam-Ming, Lee, Yi-Chen, Chiu, Yi-Pin, Tsai, Kun-Chang, Lin, Ruey-Tay, Chen, Chun-Hung, Khor, Gim-Thean, Chao, A-Ching, Lin, Hsiu-Fen, Lin, I-Sheng, Yan, Sui-Hing, Lin, Yi-Chun, Chen, Pei-Yun, Hsiao, Sheng-Huang, Po, Helen L., Lin, Ya-Ju, Tu, Hao-Chieh, Tang, Sung-Chun, Lin, Fu-Yu, Chen, Ping-Kun, Chiu, Pai-Yi, Yip, Ping-Keung, Wang, Vin-Chi, Wang, Kaw-Chen, Tsai, Chung-Fen, Chen, Chao-Ching, Chen, Chih-Hao, Liu, Yi-Chien, Yeh, Chung-Hsin, Pan, Chou-Hsiung, Jih, Shin-Yi, Chan, Po-Chi, Hsu, Min-Hsien, Shoung, Hai-Ming, Lo, Yi-Chen, Wang, Fu-Hwa, Wei, Cheng-Yu, Lee, Jun-Yu, Lin, Ryh-Huei, Chu, Ching-Hua, Hsu, Ming-Chin, Lin, Feng-Jenq, Chen, Pi-Tzu, Mou, Chih-Hsin, Chang, Chun-Yuan, Chen, An-Chih, Hsiao, Yu-Jen, Lin, Ching-Huang, Hsu, Chung-Y., and Sung, Fung-Chang

Published
2015
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38. The Effect of Absorptive Capacity Perceptions on the Context Aware Ubiquitous Learning Acceptance

Author

Lin, Hsiu-Fen

Abstract

Purpose: The purpose of this study is to examine the impact of absorptive capacity (understanding, assimilating and applying u-learning) perceptions on behavioral intention to use u-learning through path analysis and applies the technology acceptance model (TAM) as a theoretical foundation, simultaneously improving the model by adopting prior mobile experience as a meaningful moderator. Design/methodology/approach: Data were collected from 212 students in Taiwan and various relationships in the research model were tested using the partial least squares approach. Findings: The findings show that perceived usefulness and ease of use are determinants of student acceptance of u-learning. Absorptive capacity (understanding, assimilating and applying u-learning) perceptions influence perceived usefulness and ease of use of u-learning. Additionally, assessment of the moderating effect of prior mobile experience on model relationships reveals insights into the determinants of behavioral intention to use u-learning. Practical implications: Personal absorptive capacity to understand, assimilate, and apply u-learning affects student perceptions of usefulness and ease of use of u-learning, which in turn influence usage intentions. Thus, students should be offered demonstrations of how to operate u-learning. More importantly, training courses on various mobile and context-aware services should be provided to students to equip them with comprehensive knowledge of u-learning, improve their real-world observational skills, reduce anxiety associated with u-learning, and increase their confidence in using u-learning. Originality/value: Theoretically, while drawing upon the TAM, this study aims to provide a model that capable of understanding the determinants of behavioral intention to use u-learning. From the practical perspective, the findings should provide further insight into student behaviors resulting, leading to more effective strategies for developing a u-learning environment.

Published
2013
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42. An Application of Fuzzy AHP for Evaluating Course Website Quality

Author

Lin, Hsiu-Fen

Abstract

Although previous studies have identified various influences on course website effectiveness, the evaluation of the relative importance of these factors across different online learning experience groups has not been empirically determined. This study develops an evolution model that integrates triangular fuzzy numbers and analytic hierarchy process to develop a fuzzy evaluation model which prioritized the relative weights of course website quality factors. Firstly, this study conducts a review of the literature on course website quality to generate 16 sub-criteria along with four criteria used to measure course website quality. Secondly, a fuzzy AHP approach is adopted to determine the relative weights linking the above criteria between high and low online learning experience groups. The results indicated that there are some similarities and differences between high- and low-experience groups with regard to the evaluation of course website quality. The evaluation model and results can provide a valuable reference for system designers seeking to enhance course website effectiveness. (Contains 7 tables and 1 figure.)

Published
2010
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