Ali Reza Montazemi

An empirical study of factors affecting software package selection

Evidence suggests that information centers (ICs) have significantly more interest in evaluating software packages and assisting in the selection of software packages than end users have. However, the selection of software packages by the information center product specialists (ICPSs) can compromise their usage. Ease of use and usefulness are believed to be fundamental predictors of usage. The question of whether ICPSs are able to correctly evaluate ease of use and usefulness of software packages for end users is posed. An insight into this issue could enhance end-user computing (EUC) policy and lead to a more effective partnership between end users and information systems (IS) professionals. A search for this insight provided the motivation for our empirical investigation of the perception of ICPSs and end users in assessing the ease of use and usefulness of thirty different software packages. Our investigation was performed in an organization with an IC that had evolved to the formalization stage. The findings show that the perceptions of the end users and ICPSs were similar in terms of assessing the ease of use of software packages. However, end users found the software packages less useful than did IC product specialists. Therefore, in sophisticated environments, end users should be empowered to develop their own user groups and suggest to IC personnel which useful software packages to acquire. Otherwise, selection of software packages without end-user participation could have an adverse effect on their usage.

e-Government: past, present and future

European Journal of Information Systems (2007) 16, 103–105. doi:10.1057/palgrave.ejis.3000678 According to a report by Kable (a leading provider of public sector research), U.K. central government information and communication technology (ICT) spend, which represents one of the largest in the world, will grow by 21% over the next 3 years reaching d3.2 billion by 2007/2008 and is expected to reach d4.2 in 2010/2011 (Kable, 2006). In contrast, U.K. local authorities are expected to deliver a total of d1.2 billion in accumulated efficiency savings by 2007/2008 directly as a result of such e-Government investments (IDABC, 2005). However, the parliamentary office of science and technology (POST) recently reported that the cost of cancelled or over-budget government ICT projects over the last 6 years is greater than d1.5 billion (POST, 2003). Clearly, questioning normative approaches to e-Government provision building and the appropriateness of existing toolsets. Therefore, there is much need as reflected by Irani et al. (2005) to explore e-Government capacity building such that value for the taxpayer is realised. Innovations in ICT offer rich opportunities for governments to significantly improve the delivery of their services and to interact more openly with their constituents. High-quality experiences with responsive, integrated private sector information systems have led citizens to expect the same kinds of experience from public bodies and agencies (Hazlett and Hill, 2003). Consequently, citizens and businesses are demanding more effective and efficient delivery of services as well as an improvement in the quality of information received (Ongaro, 2004). While ICT can be used to transform the way in which governments offer their services, it should be acknowledged that a detailed business case for implementing the technology must be undertaken with emphasis on cultural change, greater degree of commitment, organisational structure and business processes (Liu and Hwang, 2003). However, e-Government is not a simple matter. Although governments have eagerly looked forward to a digital future since the mid-1990s, their efforts to turn vision into reality have come up against a variety of challenges (Ke and Wei, 2004). According to Strejcek and Theil (2002), many ICT projects initiated by governments fail because they are poorly coordinated and because agencies act too independently. Others believe that e-Government is an evolutionary phenomenon and that therefore e-Government initiatives should be derived and implemented accordingly (Layne and Lee, 2001; Lee et al., 2005). Nonetheless, learning from the past and from the experiences of others is essential for improving the way in which governments can deliver better services. Yet, as innovations in technology emerge, governments will be faced with new demands and challenges. This special issue of European Journal of Information Systems presents a series of papers that examine the past, present and future aspects of e-Government. We begin with a paper examining how user perceptions of the characteristics of REVS (Remote Electronic Voting Systems) such as availability, mobility, accuracy, privacy protection and ease of use, affect their intention to use it. Yao and Murphy argue that the intention to use a voting technology can affect a voter’s decision to participate or not in the election process. In doing so, Yurong and Lisa state that the REVS European Journal of Information Systems (2007) 16, 103–105 & 2007 Operational Research Society Ltd. All rights reserved 0960-085X/07

Factors affecting adoption of online banking: A meta-analytic structural equation modeling study

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The effects of modes of information presentation on decision-making: a review and meta-analysis

Abstract Despite the potential benefits that online banking offers consumers, it has low adoption rate. We systematically review online banking adoption literature to propose two research models of factors affecting pre-adoption and post-adoption of the online banking. To test our proposed models, we applied a two-stage random-effects meta-analytic structural equation modeling method to data collected from 25,265 cases from primary empirical studies of online banking adoption. Our findings show that ten factors affect consumers’ adoption of the online banking. Furthermore, we show that the relative importance of these factors differs depending on consumers’ pre-adoption and post-adoption of the online banking.

On the effectiveness of decisional guidance

Abstract:This study investigates the impact of modes of information presentation on information dimensions. Twenty-four published studies were reviewed. The results of sixteen of these were cumulated by application of meta-analysis technique. The ensuing results demonstrate that the bar presentation format is slightly better than the tabular one in terms of information precision; however, the face chart is superior to the tabular format with respect to relevancy, and the multicolor presentation is more relevant than the monocolor. In addition, two factors of influence (moderators) were verified. First, personality, which is classified as field-dependent and field-independent, acts as a moderator between color and information relevancy. Second, when comparing line graphical presentations with tabular, task environment has a moderate effect on both timesaving and precision.

Agent-based buddy-finding methodology for knowledge sharing

Abstract The objective of this study was to determine the impact on decision makers of two forms of computer support — suggestive guidance and informative guidance — on the problem formulation stage of judgmental tasks. Three hypotheses were postulated regarding the effectiveness of these two forms of guidance. The three hypotheses were tested in two experiments comprising two judgmental tasks of differing complexity. The task performance of 282 subjects provided the basis for the assessment of the two forms of computer support. In the first experiment, which had the less complex task, we found that the availability of DSS improved task performance. In this scenario, suggestive guidance was found to enable subjects to perform significantly better than those subjects using informative guidance. However, in the second experiment, which had the more complex task, subjects using informative guidance outperformed those who were either supported with a suggestive guidance or who received no-DSS support at all (i.e., only pencil and paper was used). No significant difference was demonstrated to exist between those who were supported with suggestive guidance and those who were not supported with DSS. The results are analyzed with respect to current findings in decision research.

How they manage IT: SMEs in Canada and the U.S.

The Internet provides opportunity for knowledge sharing among people with similar interests (i.e., buddies). Common methods available for people to identify buddies for knowledge sharing include emails, mailing lists, chat rooms, electronic bulletin boards, and newsgroups. However, these manual buddy finding methods are time consuming and inefficient. In this thesis, we propose an agent-based buddy finding methodology based on a combination of case-based reasoning methodology and fuzzy logic technique. We performed two experiments to assess the effectiveness of our proposed methodology. The first experiment was comprised of a stock market portfolio knowledge sharing environment in which a conventional cluster analysis was used as a benchmark to assess the technical goodness of the proposed methodology in identifying the clusters of buddies. Statistical analysis showed that there was no significant ranking difference between conventional cluster analysis and the proposed buddy-finding methodology in identifying buddies. Cluster analysis requires centralized database to form buddies (clusters) with similar properties. The unique advantage of our proposed agent-based buddy finding methodology is that it can identify similar buddies in distributed as well as centralized database environments. A second experiment, in the context of sharing musical-knowledge among human subjects, was used to find out whether selection of the buddies by the proposed methodology is as good as those done by human subjects. The findings from this latter empirical test showed that the buddies found by agents are as good as the buddies found manually by humans.

An adaptive agent for case description in diagnostic CBR systems

Small and medium-sized companies in the U.S. make better use of IT than their Canadian counterparts.

A connectionist approach for similarity assessment in case-based reasoning systems

Abstract Case-based reasoning (CBR) systems can support diagnosis of complex industrial systems. The success of a diagnostic CBR system depends on its ability to retrieve previous cases that provide information to solve a new case. To this end, the new case must be adequately described. However, to describe a new case in an ill-structured diagnostic decision environment requires considerable domain knowledge and is dependent on the strategies used by a decision maker. In this paper, we develop a framework for the development of an adaptive agent that can assist a decision maker describe a new case to a diagnostic CBR system. The adaptive agent is dynamic and provides its recommendations based on the diagnostic strategy of a decision maker. An empirical evaluation of the proposed framework in the diagnostic of complex industrial machinery supports its effectiveness.

A framework for retrieval in case-based reasoning systems

Abstract Case-Based Reasoning (CBR) systems support ill-structured decision making. In ill-structured decision environments, decision makers (DMs) differ in their problem solving approaches. As a result, CBR systems would be more useful if they were able to adapt to the idiosyncrasies of individual decision makers. Existing implementations of CBR systems have been mainly symbolic, and symbolic CBR systems are unable to adapt to the preferences of decision makers (i.e., they are static). Retrieval of appropriate previous cases is critical to the success of a CBR system. Widely used symbolic retrieval functions, such as nearest-neighbor matching, assume independence of attributes and require specification of their importance for matching. To ameliorate these deficiencies connectionist systems have been proposed. However, these systems are limited in their ability to adapt and grow. To overcome this limitation, we propose a distributed connectionist-symbolic architecture that adapts to the preferences of a decision maker and that, additionally, ameliorates the limitations of symbolic matching. The proposed architecture uses a supervised learning technique to acquire the matching knowledge. The architecture allows the growth of a case base without the involvement of a knowledge engineer. Empirical investigation of the proposed architecture in an ill-structured diagnostic decision environment demonstrated a superior retrieval performance when compared to the nearest-neighbor matching function.