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Whilst there have been growing interest and efforts by governments in developing countries to disburse digital government-to-person (G2P) payments to promote financial inclusion, the role of mobile banking in the receipt of social cash remains under-researched. Through an interpretive case study of the Benazir Income Support Programme (BISP) in Pakistan, this paper applies Orlikowski's Duality of Technology that critically examines mobile banking usage by women beneficiaries and technology's effects on the institutional properties of their households. Qualitative data were collected through semistructured interviews from participants located in Pakistan. The findings highlighted that mobile banking enabled women to receive the full amount of grants, securely and conveniently, from agents. However, mobile banking imposed human, socioeconomic and technological constraints which restricted women's access to and usage of financial services that limited financial inclusion. Women were socially and politically empowered, thereby, social inclusion was transformative. This paper theoretically contributes to the Duality of Technology framework that was deterministic for women beneficiaries. The study accentuates the redesign of mobile banking to match women's capabilities, and imparting financial and digital training to them. Also, the provision of a range of financial resources to beneficiaries may steer micro-entrepreneurial activities to advance the inclusion agenda in Pakistan.

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Background Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.

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