Charalambos L. Iacovou

A risk profile of offshore-outsourced development projects

Even the best project management skills will not guarantee success in the complex world of offshore outsourcing.

Selective status reporting in information systems projects: a dyadic-level investigation

This study investigates selective reporting behaviors that are pursued by project managers when communicating the status of their information system initiatives to their executives. To understand the types, motivations, impacts, and antecedents of such behaviors, a message-exchange perspective is adopted and the prior literature on IS project status reporting is reviewed. This study incorporates an empirical investigation that examined the influence of five dyadic factors on selective reporting using a survey of 561 project managers. The findings of the study reveal a positive effect of reporting quality on project performance and indicate that a specific type of selective reporting behavior (optimistic biasing) has a degrading effect on reporting quality. Moreover, the findings show that all five antecedents have a significant influence on the propensity of project managers to report selectively. Specifically, the project executives power, the project managers trust in the executive, and the executives quality of communication impact selective reporting directly; the executives familiarity with the IS development process and the executives organizational affiliation vis-a-vis that of the project manager have an indirect influence (it is mediated through other factors). The effects of each of these factors on the two types of selective reporting (optimistic and pessimistic biasing) are examined, and the implications of these findings for both researchers and managers are discussed in this article.

Turning around runaway information technology projects

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

A taxonomy of crowdsourcing based on task complexity

Although a great many different crowdsourcing approaches are available to those seeking to accomplish individual or organizational tasks, little research attention has yet been given to characterizing how those approaches might be based on task characteristics. To that end, we conducted an extensive review of the crowdsourcing landscape, including a look at what types of taxonomies are currently available. Our review found that no taxonomy explored the multidimensional nature of task complexity. This paper develops a taxonomy whose specific intent is the classification of approaches in terms of the types of tasks for which they are best suited. To develop this task-based taxonomy, we followed an iterative approach that considered over 100 well-known examples of crowdsourcing. The taxonomy considers three dimensions of task complexity: (a) task structure – is the task well-defined, or does it require a more open-ended solution; (2) task interdependence – can the task be solved by an individual, or does it require a community of problem solvers; and (3) task commitment – what level of commitment is expected from crowd members? Based on this taxonomy, we identify seven categories of crowdsourcing and discuss prototypical examples of each approach. Furnished with such an understanding, one should be able to determine which crowdsourcing approach is most suitable for a particular task situation.

An Investigation of User Interface Features of Crowdsourcing Applications

We investigated the user interface features of seven different types of crowdsourcing applications, which were classified along three dimensions of task type: (1) Task Structure: does the task have a well-defined solution? (2) Task Interdependence: can the task be solved by an individual or does it require a community of problem solvers? (3) Task Commitment: what level of resources is required to perform the task? Our initial investigation revealed a number of differences in the seven categories including: site searchability, online credentialing, community building features, gamification, mobility, and the use of wiki software.

A Comparison of Pairs, Triads and Quads in Multi-Attribute Decision Making

The pair-wise comparison technique is a common approach for completing multi-attribute evaluations. However, this approach has limitations, especially for larger attribute sets, where the use of the technique is time-consuming because it requires a relatively large number of comparisons. The authors conducted an experiment to test the efficacy of three alternative approaches for eliciting preferences, specifically pairs, triads and quads. Ninety-three subjects used one of the three approaches to rank the importance of fifteen items. The results indicate that those employing the pair-wise approach took significantly longer than those using the triad or quad approach. In addition, the triad technique yielded more accurate results (compared to the pair and quad methods). Finally, the quad approach generated fewer intransitivities than the pair-wise or triad approaches. No differences were observed across the three techniques with respect to reliability or perceived ease of use. Implications are provided for both practitioners and researchers.