Yaşlıların Teknoloji Kabul ve Kullanım Davranışlarının Başarılı Yaşlanma Üzerindeki Etkilerinin Analizi: Adana İli Örneği
Ambargo SüresiAcik erisim
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In this study, it was aimed to determine the successful aging levels of the elderly, the acceptance and use of technology behaviour, the effect of technology acceptance and use behaviors on successful aging, the statistical discrepancies of these effects and levels according to demographic variables, and to make recommendations to families, institutions and organizations according to the results of the research. The research was carried out with 912 elderly individuals who were 60 years and older registered to Turkish Retirees Association Adana Branch living in Adana province considering TUIK data. As the aim of the study was to explore the theoretical relationship, the method of Partial Least Squares structural equation model path analysis was used. According to the findings of the research, performance expectancy , hedonic motivation, facilitating conditions and habit of the subcomponents of the technology acceptance model predicted significantly behavior intention. Another sub-component of the technology acceptance model was the habit of predicting technology use behavior significantly. The results of the research showed that “performance expectancy s” and “facilitating conditions” which were the sub-components of the technology acceptance model, predicted “struggle against problems” that was the sub-component of successful aging. According to another finding, performance expectancy and social influence, which were the sub-component of the technology acceptance model, significantly predicted healthy lifestyle, which was the sub-component of successful aging. Significant discrepancies were found between “behavioral intention” of the sub-components of the technology acceptance model and marital status, education level, employment status, lifestyle; between the “habit” sub-dimension and age, marital status, lifestyle; between the “price value” sub-dimension and age, marital status, employment status, life style; between the “hedonic motivation” sub-dimension and age, marital status, education level, employment status, lifestyle, perceived last one month health status; between the “social influence” subscale and marital status, educational level, employment status; between the “facilitating conditions” sub-dimension and educational level, employment status, lifestyle, perceived monthly income status; between the “effort expectancy” sub-dimension and marital status, educational level, employment status, lifestyle, perceived last one month health status; between the “performance expectancy” sub-dimension and marital status, educational level, employment status, perceived last month health status. Significant discrepancies were found between “healthy lifestyle” and “struggling with problems” whic were the sub-dimensions of successful aging and marital status, educational level, employment status, lifestyle, perceived last month health status.
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