E-Government with Big Data Enabled through Smartphone for Public Services: Possibilities and Challenges

Big data shows great promise in public services to personalized e-government service delivery. Big data is a new generation of extracting value from a large volume of data sources with enabling high-velocity capture, analysis, and discovery for forecasting and better decision making. This article discusses the potential of big data application in e-government based on survey findings from smartphone habit as part of big data source. Data produced from smartphones usage is one of the main sources of big data. Online services can easily be accessed using smartphones, which support continuous engagement with citizens using e-government services. This study deployed quantitative survey findings from the smartphone habit of users in Brunei Darussalam. It presents an interpretative model that examines the important factors to consider big data in public services and discusses general challenges for integrating e-government services facing a big data era.     

Police Personnel Affective Profiles: Differences in Perceptions of the Work Climate and Motivation

The affective profile model was used to investigate individual differences in police personnel perceptions about the working climate and its influences on motivation. The Positive Affect, Negative Affect Schedule (PANAS) was used to assign police personnel, sworn and non-sworn (N = 595), to four affective profiles: self-fulfilling, low affective, high affective, and self-destructive. The work climate was assessed using the Learning Climate Questionnaire (Management Relations and Style, Time, Autonomy and Responsibility, Team Style, Opportunities to Develop, Guidelines on How to do the Job, and Contentedness). Motivation was evaluated using a modified version (to refer specifically to the individual’s work situation) of the Situational Motivation Scale (intrinsic motivation, external regulation, identified regulation, and amotivation). Self-fulfilling individuals scored higher on all work climate dimensions compared to the other three groups. Compared to low positive affect profiles, individuals with profiles of high positive affect scored higher in intrinsic motivation and identified regulation. Self-destructive individuals scored higher in amotivation. Different aspects of the work climate were related to each motivation dimension among affective profiles. Police personnel may react to their work environment depending on their affective profile. Moreover, the extent to which the work influences police personnel’s motivation is also related to the affective profile of the individual.     

The relationship between socio-economic characteristics, victimization and CPTED principles: evidence from the MIMIC model

This study seeks to explore how different house type, socio-economic variables in the neighborhood (length of residence and household income) and residents’ victimization experience influence Crime Prevention through Environmental Design (CPTED) and its respective principles: natural surveillance, access control, territoriality and maintenance. The study focuses on a sample of 164 inhabitants from a typical neighborhood in the city of Penang, Malaysia. An observation checklist was used to measure all the four principles of CPTED. A Multiple Indicator-Multiple Cause (MIMIC) analysis using AMOS 16.0 was employed to analyze the data at the level of individual property. Each latent factor and the relationships among them were modeled in a priori MIMIC hypothesized model. Prior to the MIMIC analysis, the study employed first and second-order confirmatory factor analysis on CPTED to determine the best indicators for the CPTED construct. The findings confirmed that CPTED is best measured by four principles. The results further indicate that CPTED is associated with a reduced risk of burglary victimization, while household income is positively associated with CPTED. The model shows that territoriality has a negative direct relationship with victimization. There are direct and positive influences of house type on natural surveillance and territoriality, while the length of residence only affects access control.