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The study aims to calculate the recall ratio of selected MSEs and provide a comprehensive ranking for MSEs using features, precision and recall analysis.

The study was divided into three consecutive sections: Keyword selections and checking demographic searchability; recall calculation among the MSEs and third calculating the Equal Weighted Score by allotting equal weight (0.25) to all MSEs to rank the MSEs based on the re-ranking aggregation approach.

The study clearly shows all the four MSEs considered—Dogpile, Metacrawler, DuckDuckGo and Startpage—Metacrawler (71%) ranked highest for recall, followed by DuckDuckGo (68%), Dogpile (63%) and Startpage (60%). The re-ranking aggregation approach results show DuckDuckGo (2) ranked 1st, followed by Startpage (2.5), Dogpile (2.75) and Metacrawler (2.75); lower scores indicate better performance. The findings indicate that DuckDuckGo is the best MSE regarding user experience (UX) and search quality.

The study used a re-ranking aggregation approach confined to past rankings and limited to four MSEs, limiting its generalizability.

The finding helps users and developers understand the strengths and weaknesses of the different MSEs, enabling more informed decision-making and enhancing UX.

The study selected a novel approach for assessing the MSEs, and no similar study conducted in the past used different performance metrics to rank the MSEs.

Mobile edge computing (MEC) services have long been used by private enterprises in Saudi Arabia with considerable success; however, there has been a stark lack of insight into how these services can be used to improve mobile government (M-Government) services for KSA citizens. This study aims to bridge this gap by integrating MEC with an enhanced version of the technology acceptance model (TAM) and examining its effects on user behavior and acceptance.

A closed-ended survey was administered to 1,500 people, and the responses were analyzed using sophisticated advanced statistical techniques to test an expanded TAM, using a quantitative method that uses structural equation modeling to validate the proposed model and hypotheses.

This study reveals that MEC significantly influences users’ intentions about using M-Government services and their tolerance for new technology adoption. Specifically, service cost and social influence are positively linked with end users’ intention to adopt M-Government services.

The novelty and contribution of this paper to existing literature are in highlighting the pivotal role of MEC in transforming public sector service delivery through technology. This study not only supports the adoption of M-Government services to enhance social welfare but also demonstrates and concludes some practical and theoretical ramifications of MEC service adoption.