Jigish Zaveri

The enabling role of decision support systems in organizational learning

Organizations routinely process information, make decisions, and implement them. Recent advances in computer and communications technologies have changed the way in which organizations perform these functions. Decision support systems (DSSs) are a major category of tools that an organization utilizes to support and enhance its decision-making activities. Traditionally, organizations are considered to have a predefined and static set of goals. However, in order to stay competitive and survive in todays dynamic environment, organizations must be able to quickly respond and adapt to changes in their business settings. Such changes could be due to technological advances, growing and changing customer demands, competitive forces, changes in the labor force, environmental and political concerns, societal impacts, security concerns, and others. In recent years, the field of DSS has become more sophisticated to encompass such paradigms as expert systems (ESs), intelligent DSSs, active DSSs, and adaptive DSSs. Artificial intelligence (AI)-based techniques are being embedded in many DSS applications, thus enhancing the support capabilities of the DSS. Such paradigms have application potential in both individual and organizational learning contexts. However, the degree to which current DSSs can support organizational learning has yet to be investigated in depth. This paper examines the learning strategies employed by organizations and DSSs and provides a framework to demonstrate how a DSS can enhance organizational learning.

Learning by problem processors: adaptive decision support systems

In this paper, we describe the potential advantages of developing Adaptive Decision Support Systems (Adaptive DSSs) for the efficient and/or effective solution of problems in complex domains. The problem processing components of DSSs that subscribe to existing DSS paradigms typically utilize supervised learning strategies to acquire problem processing knowledge (PPK). On the other hand, the problem processor of an Adaptive DSS utilizes unsupervised inductive learning, perhaps in addition to other forms of learning, to acquire some of the necessary PPK. Thus, Adaptive DSSs are, to some extent, self-teaching systems with comparatively less reliance on external agents for PPK acquisition. To illustrate these notions, we examine an application in the domain concerned with the scheduling of jobs in flexible manufacturing systems (FMSs). We provide an architectural description for an Adaptive DSS for supporting static scheduling decisions in FMSs. We illustrate key problem processing features of the system using an example. A prototype system, based on this architecture, is currently under implementation.

How do MIS researchers handle missing data in survey-based research: A content analysis approach

Abstract Missing data is a common occurrence in survey-based research studies. However, the issue of missing data in Management Information Systems (MIS) literature has been overlooked, unlike the case in other disciplines such as Psychology, Marketing, Statistics, and Operations Management. The aim of this paper is to narrow this gap in the MIS field by investigating how MIS researchers address issues of missing data. This paper briefly outlines the causes of missing data in survey-based research as well as the common remedial techniques available to researchers. The paper also reviews how the common statistical software programs namely PASW (SPSS), SAS, LISREL, AMOS, EQS, and PLS handle missing data. It summarizes the common missing data remedial techniques and procedures and outlines how the presence of missing data affect sample size, statistical power, parameter estimates, ability to cope with different missing data patterns, and ease of implementation. Following that is a review of 749 survey-based research articles published between 1990 and 2010 in nine mainstream MIS Journals. The results reveal that researchers rarely report, explicitly, the presence or treatment of missing data and that when they do – they tend to use the least accurate techniques of listwise and pairwise deletion. The research concludes with recommendations that include a call for editorial policies that encourage the reporting of missing data, the reporting of the chosen missing data treatment techniques as well as the justifications for the techniques adopted by the researchers. The authors assert, based on the research, that following these recommendations will affect the rigor and quality of MIS survey-based research.

A Risk-Assessment Model for Cyber Attacks on Information Systems

Industrial process-plants are an integral part of a nations economy and critical infrastructure. The information systems used by automated industrial plants are enticing targets of cyber attacks. However, the financial damages resulting from these cyber attacks are difficult to estimate since the resultant losses are not as tangible as physical losses. In this paper, we propose a mathematical model for determining the financial losses resulting from cyber attacks on a computer-based information system used in industrial plants. Limited work has been published to systematically explore the types of possible cyber attacks and their financial impact on the process. The primary objective of this research is to propose a risk-assessment model to assess the impact of cyber attacks on a plant that runs fully or partially by control systems such as supervisory control and data acquisition (SCADA). Managers could use the model for cost/benefit analysis of security software and hardware acquisition. We also illustrate this models use on a SCADA system using a case. The proposed model could be applied to different industries and organizations with minor modifications to reflect the specifics of that industry or organization. Index Terms-Cyber attacks, computer security, risk assessment, control systems, information systems.

Ramifications of the Sarbanes Oxley (SOX) Act on IT governance

Purpose - – In most firms, accounting and financial information and reporting systems are either incorporated or embedded in computer-based information systems (IS). Despite the important roles that these computer-based IS play in facilitating the SOX Act compliance initiatives, the act is silent on the roles of the CIOs, although it does stipulate specific functions for the CEOs, CFOs, and the auditors. Based on a detailed analysis of the extant literature, this article argues that IT units, under the leadership of the CIOs, contribute significantly in the procurement, design, implementation, and the governance of these computer-based IS. The paper aims to discuss these issues. Design/methodology/approach - – The researchers generate and empirically test hypotheses using a panel data set obtained from press releases issued by firms following the hiring of CIOs between 1999 and 2005. Findings - – The results reveal that, after the enactment of the SOX Act in 2002, many firms hired new CIOs in the post-SOX Act period. Also, many of these executives were hired to fill newly created Chief information officer (CIO) positions. The results support the argument that the SOX Act has influenced the roles of senior IT executives and IT governance. Research limitations/implications - – Although this study focused on hiring trends, there are other characteristics associated with CIOs that might have an impact on corporate IT governance. Future studies could investigate whether or not, for instance, firms reported fewer IT material weaknesses before or after the hire of the CIOs. Originality/value - – This research presents the argument and detailed discussion that while the SOX Act does not explicitly require the CIOs to sign off on the accounting/financial statements and reports, their role is fundamental in making the firm meet the SOX Act compliance standards.

Adaptive Decision Support Systems via Problem Processor Learning

In this chapter, we describe the potential advantages of developing adaptive decision support systems (adaptive DSSs) for the efficient and/or effective solution of problems in complex domains. The problem processing components of DSSs that subscribe to existing DSS paradigms typically utilize supervised learning strategies to acquire problem processing knowledge (PPK). On the other hand, the problem processor of an adaptive DSS utilizes unsupervised inductive learning, perhaps in addition to other forms of learning, to acquire some of the necessary PPK. Thus, adaptive DSSs are, to some extent, self-teaching systems with less reliance on external agents for PPK acquisition. To illustrate these notions, we examine an application in the domain concerned with the scheduling of jobs in flexible manufacturing systems (FMSs). We provide an architectural description for an adaptive DSS for supporting static scheduling decisions in FMSs and illustrate key problem processing features of the system using an example.

A comprehensive review of survey‐based research in MIS

Purpose – MIS researchers have consistently adopted survey‐based research method while investigating MIS and related phenomenon, making survey‐based research method one of the widely used research method in MIS research. This study seeks to revisit some of the inherent characteristics of survey‐based research method with the aim of improving the quality, replication, and validation of results in MIS survey‐based studies. Additionally, this study provides information on the most prevalent analytical and statistical tools used in MIS survey research studies.Design/methodology/approach – In this research, the authors adopt the content analysis technique. The choice of content analysis is premised on the desire to investigate the sources of survey data, units of analysis, research methods, and statistical tools used in MIS research with the aim of improving empirical research in the MIS discipline.Findings – The results show the prevalent sources of data, the dominant units of analysis, the most commonly used...