PERCEIVED INFORMATION STRUCTURE: IMPLICATIONS FOR DECISION SUPPORT SYSTEM DESIGN

Top-level decision making in business organizations is characterized by high degrees of uncertainty, incomplete information, and conflicting objectives. To support top-level decision making effectively, decision support systems (DSSs) have been proposed. Information supplied by a DSS must be selective in that not all possible information sets may be feasibly or economically represented in the data base. This study suggests that discovery of perceptual complexity (dimensionality) of information items, and the subsequent categorization of decision makers having the same perceptions of those information items, is a first step in the ultimate design of an effective DSS. Through the use of multidimensional scaling in a field setting, this study shows the feasibility of creating relatively homogeneous groups of decision makers according to the content and number of dimensions associated with various information items. Further results of the research suggest that information can be tailored to classes of users, which has cost-benefit implications as well as the potential to improve the quality of the resultant decisions.     

A structured framework to understand CSR decision-making: A case study of multiple rationales

The organisational justifications made for CSR-related decisions and actions are examined over time using a structured framework premised on instrumental, political, integrative and ethical as well as first and second-order rationales. Using material from semi-structured interviews and drawing on documentary sources, we find that the decision-making processes underlying CSR-related initiatives were complex and multi-layered with varied patterns of motivation and justification that was modified over time. While an instrumental rationale was always apparent, political, integrative and ethical rationales were also important in the context of first and second-order rationales. The paper provides a framework to help understand the justification of CSR initiatives in a structured way and has implications for both theory and practice.     

Innovation communities' contributions throughout firms’ innovation processes: An outdoor sports industry case study

Although it is crucial for firms to select openness patterns and strategies to involve innovation communities more effectively, the literature treats innovation communities as homogeneous. However, organizations may mobilize various innovation communities, including external and/or internal members. Similarly, little is known about how firms may involve those different types of innovation community during the distinct phases of their innovation processes. In this context, our main research questions are: For what contributions do companies solicit innovation communities? Do these contributions vary in content and intensity depending on the community and the phase of the innovation process? We distinguish between three types of innovation community (user, practice, and epistemic) and use a dynamic approach, considering four innovation process phases (opportunity recognition, search for solutions, development, and diffusion). The multicase study design includes three iconic firms from the outdoor sports industry and relies on archival data, observations, and 31 semidirective interviews with members of their innovation teams. Our results first show the important involvement of innovation communities during the innovation process. They also highlight the complementary use of those communities to manage their contributions, alongside their dark sides, better. Finally, we emphasize the crucial contribution of internal communities of practice in the firms’ open innovation strategies.     

On the scientific study of small samples: Challenges confronting quantitative and qualitative methodologies

Often phenomena that are important to understand and predict are very rare. Rare events can prove difficult to analyze systematically because they do not generate many sampling observations. In this article I examine how small sample sizes can be studied scientifically. The article begins with an explanation of the distinction between research and science. I then bring to the fore the importance of counterfactual comparisons and outline the nature of the methodological problems posed by the study of small samples. These problems include challenges related to using a single case, small sample sizes, selecting on the dependent variable, regression toward the mean, explaining a variable with a constant, and using the same data to both generate and test hypotheses. I provide potential resolutions to these problems which are: (a) employing matched controls; (b) shifting or widen the category of inquiry; (c) selecting variables based on variance in the independent variable; (d) including counterfactuals; (e) ensuring that both independent and dependent variables demonstrate variation; and (f) testing potential hypotheses against data sets that are fully independent of those used to generate the hypotheses. I conclude with a discussion of future directions for undertaking a more scientific approach to using small samples.     

Conceptualizing Effects of Office Information Systems: A Methodology and Application for the Study of Alpha,Beta, and Gamma Changes*

This article applies the concepts of alpha, beta, and gamma changes to test whether the implementation of a new office information system with networking capabilities changes the way organizational members conceptualize office work. The traditional approach (t-test) was used to measure alpha change and indicated little change in how effectively the respondents felt they performed eight generic office activities before implementation (T1) and nine months after implementation (T2). However, considerable change was detected between effectiveness reported at T1 and a retrospective assessment of T1 effectiveness reported at T2 (called “then” assessments). Strong change was also detected between “then” assessments and T2 effectiveness reported at T2, indicating beta change. Multiple hierarchical tests showed that most of the change was actually gamma change; the T2 and the “then” factor structures and covariances differed significantly. This study supports propositions that using computers to accomplish organizational work may be associated with different conceptualizations of work, which may create ambiguity and uncertainty if training and management policies do not respond appropriately. Finally, this study provides an expanded version of a prior solution to detecting alpha, beta, and gamma changes.     

Stock Evaluation Using a Preference Disaggregation Methodology

This paper presents a real application of a multicriteria decision aid (MCDA) approach to portfolio selection based on preference disaggregation, using ordinal regression and linear programming (UTADIS method; UTilités Additives DIScriminantes). The additive utility functions that are derived through this approach have the extrapolation ability that any new alternative (share) can be easily evaluated and classified into one of several user-predefined groups. The procedure is illustrated with a case study of 98 stocks from the Athens stock exchange, using 15 criteria. The results are encouraging, indicating that the proposed methodology could be used as a tool for the analysis of the portfolio managers' preferences and choices. Furthermore, the comparison with multiple discriminant analysis (either using a stepwise procedure or not) illustrates the superiority of the proposed methodology over a well-known multivariate statistical technique that has been extensively used to study financial decision-making problems.     

A mixed methods evaluation of Multiple Mini Interviews for entry into the Bachelor of Midwifery

BackgroundMultiple-mini interviews (MMI) are increasingly used as part of the admission process into healthcare degrees. Evaluations have found MMIs to be a fair assessment tool in terms of reliability and validity and viewed positively by those involved in the MMI process. The use of MMIs in midwifery is novel and evaluation is lacking.AimTo evaluate the use of MMIs as part of the admission process for the Bachelor of Midwifery in one Australian university.MethodsA basic convergent mixed methods study design was utilised. Data included linked data sets, Likert scale responses to survey questions, focus groups and open-ended survey questions. Integration took place at the interpretation and reporting stage.FindingsParticipants viewed the MMI experience positively. The study confirmed the reliability of the MMIs as an assessment tool. Most variance in MMI scores was attributed to the candidate at 31.4 % with the interviewer and the interview station having less influence on the MMI score at 11 % and 6.4 % variance. Older applicants on average achieved higher MMI scores, and those who spoke a language other than English at home or were first in family to attend university had lower on average MMI scores. Being born overseas did not impact an applicant’s MMI score. The overall experience was seen as fair, offering further opportunity to gain entry into the Bachelor of Midwifery.ConclusionMMIs were viewed positively and findings support the use of MMIs as part of an admissions process for the Bachelor of Midwifery.     

Education TEAM-SIZE EFFECTS ON BUSINESS GAME PERFORMANCE AND DECISION-MAKING BEHAVIORS

The effects of various decision-making team sizes in a complex business game were studied. Firm size was associated with nonlinear performance results and decision curves resulting in differential learning outcomes, learning sources, attrition rates, and decisionmaking behaviors. A three-member format produced the highest learning levels while two-member firms experienced only marginally significant knowledge increases. Single-member firms experienced the most bankruptcies and dropouts. A contingency view of gaming effectiveness is suggested.     

Increasing the Effectiveness of Student Evaluation of Instructor Data through a Factor Score Comparative Report

Effective student evaluation of instructor instruments must provide norm, or comparative, data to aid interpretation. This note highlights an innovative factor score comparative report. Rather than merely reporting comparative data on individual questions items, the innovative report provides comparative percentile data on the six factors underlying our thirty-three item questionnaire. The faculty indicated overwhelming support for the new report. The note also briefly mentions three other activities that are essential for student evaluation of instructor data to improve teacher effectiveness. Our goal is to stimulate other colleges to adopt the innovative norming practice once they have validated their local evaluation instruments. College-wide improvement of teaching will occur through such do-it-yourself efforts and only decision science faculty are technically qualified to lead such efforts.     

Evaluating and Refining Expert Critiquing Systems: A Methodology*

Expert critiquing systems are a rapidly growing class of intelligent decision support systems that apply artificial intelligence techniques to criticize a user's proposed solution to a problem. Critic programs now exist in the medical, engineering, programming, knowledge acquisition, word processing, and other domains. Critic refinement is a nontrivial activity that, even when done well, consumes a sizable fraction of the complete effort to build and deploy a critiquing system. To ease that effort, it is important to adopt a rigorous approach that allows one to reproducibly measure the degree of success of the current critic version and to predict which refinements will improve the critic further. The current article presents one such approach with actual case studies that illustrate its usage and elaborate selected aspects of the refinement process.