The Powerful Triangle of Marketing Data, Managerial Judgment, and Marketing Management Support Systems

In this paper we conceptualize the impact of information technology on marketing decision-making. We argue that developments in information technology affect the performance of marketing decision-makers through different routes. Advances in information technology enhance the possibilities to collect data and to generate information for supporting marketing decision-making. Potentially, this will have a positive impact on decision-making performance. Managerial expertise will favor the transformation of data into market insights. However, as the cognitive capabilities of marketing managers are limited, increasing amounts of data may also increase the complexity of the decision-making context. In turn, increased complexity enhances the probability of biased decision processes (e.g., the inappropriate use of heuristics) thereby negatively affecting decision-making performance. Marketing management support systems, also being the result of advances in information technology, are tools that can help marketers to benefit from the data explosion. These systems are able to increase the value of data and, at the same time, make decision-makers less vulnerable to biased decision processes. Our analysis leads to the expectation that the combination of marketing data, managerial judgment, and marketing management support systems will be a powerful factor for improving marketing management. Implications of our analysis are discussed.decision making;decision biases;information technology;marketing management support systems

Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

Cultivating and Nurturing Undergraduate IS Research

Assurance of student motivation and retention is a central challenge for Information Systems faculty. A promising means of stimulating interest in the Information Systems major and in subsequent graduate degree programs is undergraduate Information Systems research. Undergraduate Information Systems research allows students to engage more deeply with questions pertaining to Information Systems development and use, and it advances students’ cognitive and intellectual growth above and beyond what can be achieved with traditional classroom activities. As such, undergraduate Information Systems research is a high impact learning experience. Yet, this advanced form of student engagement with Information Systems material remains in its infancy; teaching tips are lacking that promote it and provide guidance on how to mentor undergraduate Information Systems researchers. Using Bloom’s taxonomy of cognitive skills and Malachowski’s stages of mentoring framework, the present teaching tip emphasizes the continued need of cultivating and nurturing undergraduate Information Systems research, and it provides guidance for Information Systems faculty on how to mentor undergraduate Information Systems researchers

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

The Powerful Triangle of Marketing Data, Managerial Judgment, and Marketing Management Support Systems

In this paper we conceptualize the impact of information technology on marketing decision-making. We argue that developments in information technology affect the performance of marketing decision-makers through different routes. Advances in information technology enhance the possibilities to collect data and to generate information for supporting marketing decision-making. Potentially, this will have a positive impact on decision-making performance. Managerial expertise will favor the transformation of data into market insights. However, as the cognitive capabilities of marketing managers are limited, increasing amounts of data may also increase the complexity of the decision-making context. In turn, increased complexity enhances the probability of biased decision processes (e.g., the inappropriate use of heuristics) thereby negatively affecting decision-making performance. Marketing management support systems, also being the result of advances in information technology, are tools that can help marketers to benefit from the data explosion. These systems are able to increase the value of data and, at the same time, make decision-makers less vulnerable to biased decision processes. Our analysis leads to the expectation that the combination of marketing data, managerial judgment, and marketing management support systems will be a powerful factor for improving marketing management. Implications of our analysis are discussed

VizCom: A Novel Workflow Model for ICU Clinical Decision-Support

The Intensive Care Unit (ICU) has the highest annual mortality rate (4.4M) of any hospital unit or 25% of all clinical admissions. Studies show a relationship between clinician cognitive load and workflow, and their impact on patient safety and the subsequent occurrence of medical mishaps due to diagnostic error - in spite of advances in health information technology, e.g., bedside and clinical decision support (CDS) systems. The aim of our research is to: 1) investigate the root causes (underlying mechanisms) of ICU error related to the effects of clinical workflow: medical cognition, team communication/collaboration, and the use of diagnostic/CDS systems and 2) construct and validate a novel workflow model that supports improved clinical workflow, with goals to decrease adverse events, increase safety, and reduce intensivist time, effort, and cognitive resources. Lastly, our long-term objective is to apply data from aims one and two to design the next generation of diagnostic visualization-communication (VizCom) system that improves intensive care workflow, communication, and effectiveness in healthcare