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• W4292348618 abstract "Numerous studies in e-Government have developed numerous models to assess smart cities’ readiness to utilise ICT. However, numerous research notes indicate that e-Government researchers are currently confronted with difficulties developing a reliable scientific model framework and developing readiness models for rural and sub-urban areas, which are highly common in several developing countries, including Indonesia. This study aims to adapt a readiness model based on Chien-(2007) Hsi’ s Technology Preparedness and Acceptance Model framework to assess the readiness of Indonesia’ s Suburban Areas, that’ s called Technology Readiness and Acceptance Model(TRAM). The TRAM framework was developed to evaluate the mobile-based Smart Regency services utilised in Pamekasan and Sumenep Regencies, Madura Island Districts. Data were collected by executing a random purposive and stratified random sampling sample of 186 service users and smart regency stakeholders. AMOS software was used to analyse the data and use structural equation modelling. The findings suggested that the three TRAM model constructs, namely with a critical ratio of 8.038 for Perceived Ease of Use (PEOU), 5.569 for Perceived Usefulness (PU), and 1.785 for Use Intention (UI), all have a significant effect on Smart Regency Readiness. This research contributes to science by adapting the TRAM model for use in Sub Urban Indonesia, an area that is still mostly unstudied. And help effectively by advising that stakeholders in smart regencies pay special attention to important factors affecting the readiness of developing countries, notably Indonesia, to build smart regencies." @default.
• W4292348618 created "2022-08-19" @default.
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• W4292348618 date "2022-07-20" @default.
• W4292348618 modified "2023-09-26" @default.
• W4292348618 title "Understanding Smart Regency Services Readiness in Indonesia with TRAM Model" @default.
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• W4292348618 doi "https://doi.org/10.1109/isitia56226.2022.9855303" @default.
• W4292348618 hasPublicationYear "2022" @default.
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