Publications Database

Welcome to the new Schulich Peer-Reviewed Publication Database!

The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:

  • Faculty Member’s Name;
  • Area of Expertise;
  • Whether the Publication is Open-Access (free for public download);
  • Journal Name; and
  • Date Range.

At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.

If you have any questions or input, please don’t hesitate to get in touch.

 

Search Results

Belk, R., Ko, E. and Megehee, C. (2016). "Leaving Pleasantville: Macro/Micro, Public/Private, Conscious/Non-conscious, Volitional/Imposed, and Permanent/Ephemeral Transformations Beyond Everyday Life", Journal of Business Research, 69(1), 1-5.

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Keywords
Abstract Your first family pet! Your first kiss! Your first real job! Your first day of college! Your induction into whatever! Your first sale of a big idea! Certain transformations are with us forevermore while others are rather micro happenings that we soon are unable to recall. This special issue includes research into 32 different categories of transformations. The articles here are valuable for marketers and consumers. Understanding transformation processes contributes to marketers' ability to design and deliver offerings that are beneficial to customers and that consumers seek to experience. The introductory essay in the special issue proposes a five-dimensional framework for classifying transformation research, places each article in the special issue within the framework, and briefly introduces something unique and interesting about each article. Authors and reviewers participating in this special issue represent a diverse international group of scholars. Get ready! Reading this issue is going to transform you.

Gunalay, Y. and Yeomans, J.S. (2012). "Addressing Unmodelled Objectives and Generating Alternatives in Municipal Solid Waste Management Planning Using a Simulation-Optimization Approach Combined with Niching", Journal of Applied Operational Research, 4(2), 52-68.

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Keywords
Abstract Public sector decision-making typically involves complex problems that are riddled with incompatible performance objectives and possess competing design requirements which are very difficult-if not impossible-to quantify and capture when supporting decision models need to be constructed. There are invariably unmodelled design issues, not apparent at the time of model creation, which can greatly impact the acceptability of the solutions proposed by the model. Consequently, it is generally preferable to create several quantifiably good alternatives that provide multiple, disparate perspectives and very different approaches to the particular problem. These alternatives should possess near-optimal objective measures with respect to the known modelled objectives, but be fundamentally different from each other in terms of the system structures characterized by their decision variables. By generating a set of very different solutions, it is hoped that some of the dissimilar alternatives can provide very different perspectives that may serve to satisfy the unmodelled objectives. This study shows how simulation-optimization SO modelling can be combined with niching operators to efficiently generate multiple policy alternatives that satisfy required system performance criteria in stochastically uncertain environments and yet are maximally different in the decision space. This new stochastic approach is very computationally efficient, since it permits the simultaneous generation of good solution alternatives in a single computational run of the SO algorithm. The efficacy and efficiency of this modelling-to-generate-alternatives MGA method is specifically demonstrated on a municipal solid waste management facility expansion case.