DIGITAL INCLUSION FOR ALL? A GENDER-DISAGGREGATED ANALYSIS OF E-GOVERNMENT SERVICE USE IN INDONESIA Kasmad Ariansyah, Ahmad Budi Setiawan, Darmanto Primary Author mixed material bibliography ENGLAND EMERALD INSIGHT 2023 monographic en ENGLISH

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BAIK Purpose – This study aims to investigate the factors that influence the use of e-government services among male and female samples, aiming at gaining a deeper understanding of how to promote digital inclusion and maximize the benefits of digitalization. Design/methodology/approach – This study uses binomial logistic regression modeling on two data sets: a nationwide survey at the individual level of 8,854 usable samples conducted in 2019 and village potential statistics. Findings – The survey indicates low adoption and use of e-government services among both males and females. The statistical analysis further reveals that although there might be some variations in the influencing factors between the genders, the overall patterns are similar. These findings suggest that the low adoption and use of e-government services among males and females can be attributed to factors such as misperceptions about internet-based services, resulting in low motivation to use the services,inadequate internet connectivity, affordability, limited digital skills and exposure to harmful online content. Originality/value – This study conducts a gender-disaggregated analysis to determine whether the antecedents of males’ decisions to adopt e-government services differ from those of females. Such a study is underrepresented in the extant literature, particularly in the context of e-government service use. e-Government, Gender, Multidimensional digital div NONE Repository Universitas Langlangbuana Sistem Repository Elektronik Skripsi, Tesis, Desertasi dan Penelitian Dosen Universitas Langlangbuana JC301-TG-23 V17.4.2 My Library DIGITAL INCLUSION FOR ALL? A GENDER-DISAGGREGATED ANALYSIS OF E-GOVERNMENT SERVICE USE IN INDONESIA TRANSFORMING_GOVERNMENT.png.png 2464 2023-12-20 09:18:28 2023-12-20 09:19:32 machine generated