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Mo Adam Mahmood

Bio: Mo Adam Mahmood is an academic researcher from University of Texas at El Paso. The author has contributed to research in topics: Information technology & Organizational performance. The author has an hindex of 22, co-authored 38 publications receiving 2755 citations. Previous affiliations of Mo Adam Mahmood include College of Business Administration & University of Missouri–St. Louis.

Papers
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Journal ArticleDOI
TL;DR: The present analysis found positive support for the influence of all nine variables on end-user IT satisfaction but to varying degrees and the most significant relationships were found to be user involvement in systems development, perceived usefulness, user experience, organizational support and user attitude toward the IS.
Abstract: The level of end-user satisfaction with information technology (IT) has widely been accepted as an indicator of IT success. The present research synthesizes and validates the construct of IT end-user satisfaction using a meta-analysis. It accomplishes this by analysing the empirical results of 45 end-user satisfaction studies published between 1986 and 1998 and by focusing on relationships between end-user satisfaction and nine variables: perceived usefulness, ease of use, user expectations, user experience, user skills, user involvement in system development, organizational support, perceived attitude of top management toward the project and user attitude toward information systems (IS) in widely divergent settings. The present analysis found positive support for the influence of all nine variables on end-user IT satisfaction but to varying degrees. The most significant relationships were found to be user involvement in systems development, perceived usefulness, user experience, organizational support and user attitude toward the IS. This has implications for IS analysis and design as well as user training and the development of training support packages.

408 citations

Journal ArticleDOI
TL;DR: The exploratory research reported here appears to be the first to relate comprehensive sets of IT investment measures to organizational strategic and economic performance measures and depicts a framework for management evaluation of the relationship between IT investment and organizational performance.
Abstract: Organizations are investing ever-increasing amounts in information technology (IT). However, the existing literature provides little evidence of a relationship between IT investment and organizational strategic and economic performance. The exploratory research reported here appears to be the first to relate comprehensive sets of IT investment measures to organizational strategic and economic performance measures. Although the individual IT investment variables were found to be only weakly related to organizational strategic and economic performance, they were significantly related to performance when grouped and analyzed by means of canonical correlation. More specifically, canonical results suggest that organizational strategic and economic performance measures such as sales by employee, return on sales, sales by total assets, return on investment, and market to book value are affected by IT investment measures such as IT budget as percentage of revenue, the percentage of IT budget spent on training of employees, number of PCs per employee, and IT value as a percentage of revenue. The organizational performance measure growth in revenue and IT investment measure percentage of IT budget spent on staff were not significantly related to other measures and therefore were not indicated to be useful for investigating possible effects of IT investment on organizational strategic and economic performance. Finally, a model based on these results is suggested. The model depicts a framework for management evaluation of the relationship between IT investment and organizational performance.

400 citations

Journal ArticleDOI
TL;DR: The author mentions that the insignificant relationship between rewards and actual compliance with information security policies does not make sense and quite possibly this relationship results from not applying rewards for security compliance.
Abstract: Information security was the main topic in this paper. An investigation of the compliance to information security policies were discussed. The author mentions that the insignificant relationship between rewards and actual compliance with information security policies does not make sense. Quite possibly this relationship results from not applying rewards for security compliance. Also mentions that based on the survey conducted, careless employee behavior places an organization's assets and reputation in serious jeopardy. The major threat to information security arises from careless employees who fail to comply with organizations' information security policies and procedures.

226 citations

Book
01 Jan 2002
TL;DR: A comparative performance analysis of artificial Neural networks, MDA and chance showed that artificial neural networks predict better in both training and testing phases, and are promising as an alternative to traditional analytic tools like MDA.
Abstract: Stimulated by recent high-profile incidents, concerns about business ethics have increased over the last decade. In response, research has focused on developing theoretical and empirical frameworks to understand ethical decision making. So far, empirical studies have used traditional quantitative tools, such as regression or multiple discriminant analysis (MDA), in ethics research. More advanced tools are needed. In this exploratory research, a new approach to classifying, categorizing and analyzing ethical decision situations is presented. A comparative performance analysis of artificial neural networks, MDA and chance showed that artificial neural networks predict better in both training and testing phases. While some limitations of this approach were noted, in the field of business ethics, such networks are promising as an alternative to traditional analytic tools like MDA.

217 citations

Journal ArticleDOI
TL;DR: This research seems to be the first comprehensive investigation towards the development of an empirically validated comprehensive model for understanding the potential impact of IT on organizational strategic variables.
Abstract: The existing literature on the impact of information technology (IT) does not include rigorous theory building or empirical studies. This research seems to be the first comprehensive investigation towards the development of an empirically validated comprehensive model for understanding the potential impact of IT on organizational strategic variables. More specifically, organizational and industrial variables that appeared to be affected by IT are identified, measured, and operationalized in the form of a comprehensive model. This study is based on structured interviews with a carefully selected sample of 31 strategic managers who had experience using IT for strategic decisions. In addition, the variables included in the model are well grounded in the information systems literature. The variables are then empirically validated and their reliabilities critically tested. A comprehensive model is derived from these validated variables. The model is a first step towards measuring the overall potential impact of IT on an organization. The model can also be used to gauge IT's potential impact on individual strategic variables. A set of hypotheses is also presented for future research. The hypotheses primarily relate to the impact of IT on organizational strategic performance. The model provides an empirically validated foundation for testing of such hypotheses.

208 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success, but the dependent variable in these studies-I/S success-has been an elusive one to define.
Abstract: A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies-I/S success-has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I/S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I/S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I/S success-SYSTEM QUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed a total of 180 articles are cited and organized according to the dimensions of the taxonomy. Finally, the many aspects of I/S success are drawn together into a descriptive model and its implications for future I/S research are discussed.

10,023 citations

Journal ArticleDOI
TL;DR: The results indicate that the decomposed Theory of Planned Behavior provides a fuller understanding of behavioral intention by focusing on the factors that are likely to influence systems use through the application of both design and implementation strategies.
Abstract: The Technology Acceptance Model and two variations of the Theory of Planned Behavior were compared to assess which model best helps to understand usage of information technology. The models were compared using student data collected from 786 potential users of a computer resource center. Behavior data was based on monitoring 3,780 visits to the resource center over a 12-week period. Weighted least squares estimation revealed that all three models performed well in terms of fit and were roughly equivalent in terms of their ability to explain behavior. Decomposing the belief structures in the Theory of Planned Behavior provided a moderate increase in the explanation of behavioral intention. Overall, the results indicate that the decomposed Theory of Planned Behavior provides a fuller understanding of behavioral intention by focusing on the factors that are likely to influence systems use through the application of both design and implementation strategies.

8,127 citations

Journal ArticleDOI
TL;DR: The paper concludes that the hedonic nature of an information system is an important boundary condition to the validity of the technology acceptance model and perceived usefulness loses its dominant predictive value in favor of ease of use and enjoyment.
Abstract: This paper studies the differences in user acceptance models for productivity-oriented (or utilitarian) and pleasure-oriented (or hedonic) information systems. Hedonic information systems aim to provide self-fulfilling rather than instrumental value to the user, are strongly connected to home and leisure activities, focus on the fun-aspect of using information systems, and encourage prolonged rather than productive use. The paper reports a cross-sectional survey on the usage intentions for one hedonic information system. Analysis of this sample supports the hypotheses that perceived enjoyment and perceived ease of use are stronger determinants of intentions to use than perceived usefulness. The paper concludes that the hedonic nature of an information system is an important boundary condition to the validity of the technology acceptance model. Specifically, perceived usefulness loses its dominant predictive value in favor of ease of use and enjoyment.

3,308 citations