Conjoint Analysis: A Potential Methodology For IS Research

The increasing prevalence of Information Systems (IS) has led to a plethora of methodologies to investigate their impact on organizations and people. In this work, we define a methodology as a combination of one or more data collection methods and one or more analysis methods, in order to answer a research question. The methodologies used in IS research range from case study methodologies (Yin 1994) that use a case method of data collection with varied analysis methods such as trend analysis and regressions to experimental methodologies, with control and experimental groups and statistical hypothesis testing, using analysis methods like Analysis of Variance (ANOVA). In this work, we propose a methodology called a Conjoint Analysis (CA) methodology, which is fairly new to IS research. We first describe the CA methodology, and the structure of a typical CA study. Next, we list some advantages of the CA methodology over other commonly used methodologies in IS research. Finally, we list some types of research questions in IS for which a CA methodology may be useful

A Study of Senior Information Systems Managers\u27 Decision Models in Adopting New Computing Architectures

There has been considerable interest recently in the promise of new computing architectures such as the diskless computing architecture, which runs applications off a network. In previous theoretical work on Information Systems (IS) adoption, the question of whether classical diffusion variables determine the organizational adoption of IS with low knowledge barriers and low user interdependencies is still unresolved. In the practitioner literature, the discussion on new architectures has focused mainly on the costs of ownership of the architectures. This work proposes a novel methodology for IS adoption studies, using conjoint analysis. Issues such as data collection, data analysis, selecting scales and levels of predictor variables, construct validity, formulating testable hypotheses, and selecting an appropriate sample size are all discussed. As an example study, factors important to senior IS managers when deciding to adopt a computing architecture for their organization are identified and operationalized. Using conjoint analysis, the relative importance of these factors is measured as well as whether or not the effect of levels of these factors on decision-making is linear. The findings show that technology factors, which are a subset of classical diffusion variables, are sufficient to explain the adoption of computing architectures, which are a type of IS innovation with low impact on organizational processes and low knowledge barriers for end-users. The software quality associated with an architecture is the most important factor considered by IS managers and its effect is linear. The effect of the cost factor is less important, non-linear, and in some cases, unexpected. The effects of centralization, backward compatibility with the organization, and acceptance by third parties are all linear, but less important than software quality

Large Scale Requirements Modeling: An Industry Analysis, a Model and a Teaching Case

This work provides a fresh perspective on the custom-build versus rent/buy decision that organizations have faced when dealing with large scale information systems. We first describe the historical trends in this area. Next, we present an analysis of what caused the current trend of renting/purchasing off-the-shelf systems and why recent changes may portend an increase in custom building. We make the case for why systems analysis and design will play an increasing role in the building of large custom ISs, and highlight the need for increased teaching emphasis in this area. As a first step in this direction, we present a model called ERA (Entity Relationship Activity) with an accompanying (Computer Aided Software Engineering) CASE tool and present one teaching case that has been used to teach over 250 students at the undergraduate and graduate levels

A Comparison of Expert and Novice Judgments in Selecting Computing Architectures for Organizational Use

In this study, we examine the differences in judgments made by expert and novice information system (IS) managers in selecting computing architectures for their organizations. This problem is interesting because of the wide variety of computing architectures available today, and the current rapid placement of fresh IS graduates in decision-making, managerial roles

The Effect of Abstraction of Constructs in Data Models on Modeling Performance: An Exploratory Empirical Study

Several data models exist at the conceptual level, the most popular being the Extended Entity Relationship Model (EERM). However, the EERM may be difficult to use by end-users, when specifying their data requirements, and is typically used by systems analysts as a precursor to mapping the data to the relational model, in normalized form. A possible reason why the EERM may be difficult to use by end-users or novice data modelers is that the constructs in the EERM may be abstract with regards to the actual usage of the data. In this research in progress, we present an exploratory empirical study that investigates the effects of abstraction of concepts on novice analysts’ data modeling performance

Leveraging Group Cohesiveness to Form Workflow Teams

Past literature shows that workflows will be performed with greater efficiency and/or effectiveness if workflow teams have higher group cohesiveness. The major contribution of this work in progress is the creation and implementation of a formal generalized methodology that incorporates ideas from two diverse fields: social network theory and workflow modeling, and allows optimization of work groups along group cohesiveness. In order to implement this model we present newly created algorithms to structure and represent the problem of workflow load representation, possible team sets and social network metric optimization so that standard integer programming solvers can attempt to solve it