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Recent Journal Publications by COB Faculty

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Academic Journal
Business Law

“Learning to "Think Like a Lawyer": Developing a Metacognitive Model for Legal Reasoning”

In the area of law, metacognition is an implicit goal of instruction, as legal studies classes often stress learning to “think like a lawyer.” However, the explicit metacognitive model for using legal reasoning to break down complex problems and seek solutions is rarely identified. This article explicitly identifies the metacognitive model for thinking like a lawyer and provides concrete steps for direct instruction in this method of analysis. The method of analysis and the resulting model are useful to beyond the legal studies classroom, as the legal reasoning model is substantially similar to a model for critical thinking.
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Conference
DSGN - Apparel Design

“Lend a hand for 3D scans: Scanning methodology and data collection for tool and glove design”

Methods to conduct large-scale anthropometric studies to capture civilian measurements are inefficient and expensive. Industrial engineering principles were applied to improve the data capture process to build comprehensive datasets. The goal was to transform the raw materials (the participant) into a tangible product (anthropometric data) with minimal waste (time, equipment, and space). Traditional elements of an anthropometric study were evaluated based on how the study was conducted. Developed methods were applied to a study capturing scans of 398 participants over 7 days. Participants continually flowed through the study stations and completed it in 23.09 min on average. The study cost $34.18 per participant, compared to a traditional anthropometric study cost of $46.95 per participant. The results present the value of applying industrial engineering principles to anthropometric study design to improve the quality and accessibility of data used for human factors analyses and product design.
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Academic Journal
Marketing

“Leveraging stakeholder networks with outside-in marketing”

The theory of Outside-in marketing (OIM) emphasizes the importance of internal and external partners of a firm to drive strategies for value creation. OIM is based on four key tenets: market sensing and responses, segmentation and targeting, innovation, and employee's learning effort. With this commentary, we apply the theory of OIM to network analysis. By doing so, we identify key stakeholder networks as part of a firm's business ecosystem and discuss the value that can be extracted from different stakeholder networks. Most prior network research in marketing has mainly used customer or employee network data while neglecting other important stakeholder groups. We provide information about how network analysis of stakeholder data can fill gaps in the marketing literature and provide firms with essential knowledge, economic value, and influence over external partners, and improve the value generation process. We first describe each tenet and give examples of stakeholder networks that can be investigated within the realm of the tenet definitions. We then discuss different challenges that social network research can pose, and end with future research questions that can be explored for empirical research studies.
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BIS

“Linking Ontological Resources Using Aggregatable Substance Identifiers to Organize Extracted Relations”

Systems that extract biological regulatory pathway relations from free-text sources are
intended to help researchers leverage vast and growing collections of research literature.
Several systems to extract such relations have been developed but little work has focused on
how those relations can be usefully organized (aggregated) to support visualization systems or
analysis algorithms. Ontological resources that enumerate name strings for different types of
biomedical objects should play a key role in the organization process. In this paper we
delineate five potentially useful levels of relational granularity and propose the use of
aggregatable substance identifiers to help reduce lexical ambiguity. An aggregatable
substance identifier applies to a gene and its products. We merged 4 extensive lexicons and
compared the extracted strings to the text of five million MEDLINE abstracts. We report on
the ambiguity within and between name strings and common English words. Our results show
an 89% reduction in ambiguity for the extracted human substance name strings when using an
aggregatable substance approach.
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