Management & Information Systems

The growing Internet and mobile technologies create opportunities for efficient communication and coordination among individuals and institutions. However, these technologies also pose security challenges. Although users’ understanding and behavior towards security solutions have been recognized as critical to ensuring effective security solutions, few research articles have examined user security management practices. The literature lacks empirical research that examines users’ everyday behavior and practices to managing security. In an effort to bridge the gap in user security management practices, this paper presents an exploratory study of how Hispanic college students manage the security of their computer systems. Specifically, we examine how ethnicity, gender, and age influence users’ behavior towards updating their operating systems, non-operating system software and antivirus definitions. The results reveal that gender influences the frequency of updating operating systems, antivirus definitions and non- operating system software, whereas ethnicity and age influence only frequency of update of operating systems but not the frequency of update of non-operating system software and antivirus definitions.

Information Security (InfoSec) research is far reaching and includes many approaches to deal with protecting and mitigating threats to the information assets and technical resources available within computer based systems. Although a predominant weakness in properly securing information assets is the individual user within an organization, much of the focus of extant security research is on technical issues. The purpose of this paper is to highlight future directions for Behavioral InfoSec research, which is a newer, growing area of research. The ensuing paper presents information about challenges currently faced and future directions that Behavioral InfoSec researchers should explore. These areas include separating insider deviant behavior from insider misbehavior, approaches to understanding hackers, improving information security compliance, cross-cultural Behavioral InfoSec research, and data collection and measurement issues in Behavioral InfoSec research.

Research examining the information security behavior of individuals with respect to risk has focused primarily on only a handful of constructs; most of which have their roots in Protection Motivation Theory (PMT). However, there is still a lot we do not know about the behavior of individuals. This study examines the information security behavior of home users in the context of one dependent variable: backing up information.

Since the early 1970s, decision support systems (DSS) technology and applications have evolved significantly. Many 12 technological and organizational developments have exerted an impact on this evolution. DSS once utilized more limited 13 database, modeling, and user interface functionality, but technological innovations have enabled far more powerful DSS 14 functionality. DSS once supported individual decision-makers, but later DSS technologies were applied to workgroups or 15 teams, especially virtual teams. The advent of the Web has enabled inter-organizational decision support systems, and has given 16 rise to numerous new applications of existing technology as well as many new decision support technologies themselves. It 17 seems likely that mobile tools, mobile e-services, and wireless Internet protocols will mark the next major set of developments 18 in DSS. This paper discusses the evolution of DSS technologies and issues related to DSS definition, application, and impact. It 19 then presents four powerful decision support tools, including data warehouses, OLAP, data mining, and Web-based DSS. Issues 20 in the field of collaborative support systems and virtual teams are presented. This paper also describes the state of the art of 21 optimization-based decision support and active decision support for the next millennium. Finally, some implications for the 22 future of the field are discussed. D 2002 Published by Elsevier Science B.V. 23 24 Keywords: Decision support technology; DSS development; Collaborative support systems; Virtual teams; Optimization-based decision support 25 26 27 1. Introduction 28 Decision support systems (DSS) are computer tech-29 nology solutions that can be used to support complex 30 decision making and problem solving. DSS have 31 evolved from two main areas of research-the theore-32 tical studies of organizational decision making (Simon, 33 Cyert, March, and others) conducted at the Carnegie 34 Institute of Technology during the late 1950s and early 35 1960s and the technical work (Gerrity, Ness, and 36 others) carried out at MIT in the 1960s [32]. Classic 37 DSS tool design is comprised of components for (i) 38 sophisticated database management capabilities with 39 access to internal and external data, information, and 40 knowledge, (ii) powerful modeling functions accessed 41 by a model management system, and (iii) powerful, 42 yet simple user interface designs that enable interac-0167-9236/02/$ -see front matter D 2002 Published by Elsevier Science B.V. PII: S 0 1 6 7 -9 2 3 6 ( 0 1 ) 0 0 1 3 9 -7 $ Decision Support Systems 931 (2002) xxx -xxx ARTICLE IN PRESS 43 tive queries, reporting, and graphing functions. Much 44 research and practical design effort has been conducted 45 in each of these domains.

The growing interest in e-Government raises the question of how governments can increase citizen adoption and usage of their online government services. e-Government becomes especially important given its potential to reduce costs and improve service compared with alternative traditional modes. Citizen trust is proposed to be an important catalyst of e-Government adoption.