Domain-Independent Decision Aids for Managerial Decision Making

An examination of the literature on managerial decision making provides insights for improving the design of Declslon Support Systems. Frequently, these systems are designed using one dominant decision making model; some ignore them altogether. This paper incorporates conflicting decision making constructs into an overall framework for designing Decision Support System and discusses the evolution of Decision Support Systems within this framework. This framework is then used to examine advances in decision support research. Perceived useful ness and appl icabillty of decision support tool s demonstrate the trend toward domai n- independent General Decision Support Systems. Domain-independent systems are those which can be adapted to many different problem areas, usually by the addition or del etion of pertinent data and models. We conclude with an evaluation of the advances that artificial intelligence techniques can bring to decision support system research. The major purpose of this paper is to identify aspects of managerial decision support where techniques of artificial intelligence may provide useful contributions. In addition, a framework is devel oped for positioning and eval uating current research efforts on AI-based Decision Support Systems (DSS) vis-a-vis other approaches identified in the literature. The paper is organized as follows: The first section presents a brief review of the organizational and individual decision making literature relevant to the design and evaluation of DSS. The next section outlines the evolution of DSS design phil osophy over the last two decades with a view toward identifying major contributions made to managerial decision making. Finally, the third section examines recent advances made in AI-based DSS

Digital leisure for development: Reframing new media practice in the global south.

Photoshopping of newlyweds, downloading the latest movies, teens flirting on social network sites and virtual gaming may seem like typical behavior in the West; yet in the context of a village in Mali or a slum in Mumbai, it is seen as unusual and perhaps an anomaly in their new media practice. In recent years, some studies (Ganesh, 2010; Mitra, 2005; Arora, 2010; 2012; Rangaswamy & Nair, 2012; Kavoori, Chadha & Arceneaux, 2006) have documented these leisure-oriented behaviors in the global south and argued for the need to emphasize and reposition these user practices within larger and contemporary discourses on new media consumption. Yet, for the most part, studies in the field of Information and Communication Technologies for Deve

How and Why Decision Models Influence Marketing Resource Allocations

We study how and why model-based Decision Support Systems (DSSs) influence managerial decision making, in the context of marketing budgeting and resource allocation. We consider several questions: (1) What does it mean for a DSS to be "good?"; (2) What is the relationship between an anchor or reference condition, DSS-supported recommendation and decision quality? (3) How does a DSS influence the decision process, and how does the process influence outcomes? (4) Is the effect of the DSS on the decision process and outcome robust, or context specific? We test hypotheses about the effects of DSSs in a controlled experiment with two award winning DSSs and find that, (1) DSSs improve users' objective decision outcomes (an index of likely realized revenue or profit); (2) DSS users often do not report enhanced subjective perceptions of outcomes; (3) DSSs, that provide feedback in the form of specific recommendations and their associated projected benefits had a stronger effect both on the decision making process and on the outcomes. Our results suggest that although managers actually achieve improved outcomes from DSS use, they may not perceive that the DSS has improved the outcomes. Therefore, there may be limited interest in managerial uses of DSSs, unless they are designed to: (1) encourage discussion (e.g., by providing explanations and support for the recommendations), (2) provide feedback to users on likely marketplace results, and (3) help reduce the perceived complexity of the problem so that managers will consider more alternatives and invest more cognitive effort in searching for improved outcomes

How Feedback Can Improve Managerial Evaluations of Model-based Marketing Decision Support Systems

Marketing managers often provide much poorer evaluations of model-based marketing decision support systems (MDSSs) than are warranted by the objective performance of those systems. We show that a reason for this discrepant evaluation may be that MDSSs are often not designed to help users understand and internalize the underlying factors driving the MDSS results and related recommendations. Thus, there is likely to be a gap between a marketing manager’s mental model and the decision model embedded in the MDSS. We suggest that this gap is an important reason for the poor subjective evaluations of MDSSs, even when the MDSSs are of high objective quality, ultimately resulting in unreasonably low levels of MDSS adoption and use. We propose that to have impact, an MDSS should not only be of high objective quality, but should also help reduce any mental model-MDSS model gap. We evaluate two design characteristics that together lead model-users to update their mental models and reduce the mental model-MDSS gap, resulting in better MDSS evaluations: providing feedback on the upside potential for performance improvement and providing specific suggestions for corrective actions to better align the user's mental model with the MDSS. We hypothesize that, in tandem, these two types of MDSS feedback induce marketing managers to update their mental models, a process we call deep learning, whereas individually, these two types of feedback will have much smaller effects on deep learning. We validate our framework in an experimental setting, using a realistic MDSS in the context of a direct marketing decision problem. We then discuss how our findings can lead to design improvements and better returns on investments in MDSSs such as CRM systems, Revenue Management systems, pricing decision support systems, and the like

Choice in Computer-Mediated Environments

In the last several years, the increased diffusion of computer andtelecommunications technologies in businesses and homes has produced newways for organizations to connect with their customers. These computermediated environments (CMEs) such as the World Wide Web raise new researchquestions. In this paper, we examine the potential research issuesassociated with CMEs in five areas: (1) decision processes, (2) advertisingand communications, (3) brand choice, (4) brand communities, and (5)pricing.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47229/1/11002_2004_Article_138117.pd

A simulated annealing methodology for clusterwise linear regression

In many regression applications, users are often faced with difficulties due to nonlinear relationships, heterogeneous subjects, or time series which are best represented by splines. In such applications, two or more regression functions are often necessary to best summarize the underlying structure of the data. Unfortunately, in most cases, it is not known a priori which subset of observations should be approximated with which specific regression function. This paper presents a methodology which simultaneously clusters observations into a preset number of groups and estimates the corresponding regression functions' coefficients, all to optimize a common objective function. We describe the problem and discuss related procedures. A new simulated annealing-based methodology is described as well as program options to accommodate overlapping or nonoverlapping clustering, replications per subject, univariate or multivariate dependent variables, and constraints imposed on cluster membership. Extensive Monte Carlo analyses are reported which investigate the overall performance of the methodology. A consumer psychology application is provided concerning a conjoint analysis investigation of consumer satisfaction determinants. Finally, other applications and extensions of the methodology are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45745/1/11336_2005_Article_BF02296405.pd

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

Modeled to Bits: Decision Models for the Digital, Networked Economy

Leeflang and Wittink (2000) sketch a future for marketing modeling that differs primarily in scale and scope from today’s environment. We have a different vision: the digital networked economy will induce significant structural changes in (a) how models are developed and deployed, (b) who uses marketing models, and (c) what types of models are developed. To be successful, marketing modelers must adapt by gaining a better understanding of the role of marketing modeling in the new environment and by learning how to use emerging IT technologies for developing, deploying, and validating marketing models