This study validates and extends the latest unified theory of acceptance and use of technology (UTAUT2) with the privacy calculus model. To evaluate the adoption of healthcare and e-government applications, researchers have recommended—in previous literature—the application of technology adoption models with privacy, trust, and security-related constructs. However, the current UTAUT2 model lacks privacy, trust, and security-related constructs. Therefore, the proposed UTAUT2 with the privacy calculus model is incorporated into four constructs: privacy concern, perceived risk, trust in the smart national identity card (SNIC), and perceived credibility. Results from a survey data of 720 respondents show that habit, effort expectancy, performance expectancy, social influence, hedonic motivation, and price value are direct determinants that influence behavioral intentions to use. Results also revealed that behavioral intentions, facilitating conditions, habits, perceived risks, and privacy concerns are direct predictors of ‘use behavior’. The authors also analyzed the interrelationships among the research constructs. The extended model may lead toward establishing better innovative e-health services to cover the desires of the citizens through the use of health information applications embedded in an all-in-one card.

Misconfiguration is a known and increasingly serious problem in enterprise systems due to frequent code updates and retuning of the configuration parameters. Diagnosing complex, residual misconfiguration problems that lead to inaccessible services or failed transactions often starts with either a user complaint or observation by administrators, followed by a largely manual process of deciding what tests to run and how to proceed with further testing based on the test results. The goal of this paper is to automate this process and thereby make root-cause analysis of accessibility related misconfigurations much speedier and much more effective. We explore a domain-knowledge-driven methodology, called ConfExp using a network emulator that runs real enterprise networking protocols. Thus, by using commonly used tests, we show that the root-cause can be determined in all cases where discriminative tests exist. The methodology also highlights areas where more discriminative tests are needed to pinpoint the precise configuration variables at fault.