The ongoing digitisation of previously analogue systems through the Fourth Industrial Revolution transforms modern societies. Almost every citizen and businesses operating in most parts of the economy are increasingly dependent on the ability of computer systems to accurately execute people's command. This requires efficient data processing capabilities and effective data input methods that can accurately capture and process instructions given by a user. This thesis is concerned with the analysis of state-of-the-art technologies for reliable data input through three case studies. In the first case study, we analyse the UI of Windows 10 and macOS 10.14 for their ability to capture accurate input from users intending to erase data. We find several shortcomings in how both OS support users in identifying and selecting operations that match their intentions and propose several improvements. The second study investigates the use of transaction authentication technology in online banking to preserve the integrity of transaction data in the presence of financial malware. We find a complex interplay of personal and sociotechnical factors that affect whether people successfully secure their transactions, derive representative personas, and propose a novel transaction authentication mechanism that ameliorates some of these factors. In the third study, we analyse the Security Code AutoFill feature in iOS and macOS and its interactions with security processes of remote servers that require users to handle security codes delivered via SMS. We find novel security risks arising from this feature's design and propose amendments, some of which were implemented by Apple. From these case studies, we derive general insights on latent failure as causes for human error that extend the Swiss Cheese model of human error to non-work environments. These findings consequently extend the Human Factors Analysis and Classification System and can be applied to human error incident investigations
