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

Daniel Belanche, Russell W. Belk, Luis V. Casaló and Carlos Flavián (2024). "The Dark Side of Artificial Intelligence in Services", Service Industries Journal, 44(3–4), 149–172.

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Keywords
Abstract Artificial intelligence (AI) initiatives, including Generative AI, are being increasingly implemented in service industries, and are having a great impact on service operations and on customers’ reactions and behaviors. Previous literature is overoptimistic about AI implementation, and there is still a need to explore the dark side of this technology; that is, its potential negative impacts on consumers, businesses, and society, as well as the moral concerns associated with AI use in services. To establish some fundamental insights related to this research domain, this paper contributes to previous AI based-services literature by proposing a three-part conceptual model inspired by Belanche et al. (2020a), comprised of AI design, customers, and the service encounter. Specifically, we identify key factors and research gaps within each category that need to be addressed. The final research questions provide a research agenda to guide scholars and help practitioners implement AI-based services while avoiding their potential negative outcomes.

Aaron C. Ahuvia, Russell W. Belk, Philip Kotler, Dawn Lerman and Edward Timke (2023). "The Things We Love: How Our Passions Connect Us and Make Us Who We Are", Advertising & Society Quarterly, 24(1).

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Keywords
Abstract In this Author Meets Critics book discussion, Aaron Ahuvia uncovers the mystery behind brand love with some of the scholars who inspired him to research it. The Things We Love: How Our Passions Connect Us and Make Us Who We Are (Little, Brown Spark, 2022). He and marketing and material culture specialists met to explore the psychological phenomena surrounding love and the implications for marketers, students, and general readers. The book and this discussion around it explain the person-thing-person relationship in terms of identity, relationships, materialism, and consumerism. The book extolls how anthropomorphism and its opposite, objectification, operate in relationships with objects, brands, and products. The group discusses the ways in which technology is creating new ephemera and new things to love in the form of machine learning and artificial intelligence.

Russell W. Belk, Daniel Belanche & Carlos Flavián (2023). "Key Concepts in Artificial Intelligence and Technologies 4.0 in Services", Service Business, 17, 1-9.

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Keywords
Abstract The emerging Industry 4.0 technologies that are impacting the global economy also represent an extraordinary opportunity to increase customer value in the service sector. Indeed, the ongoing Fourth Industrial Revolution differs from previous technologies in three main ways: (1) technological developments overcomes humans’ capabilities such that humans or even companies are no longer controlling technology; (2) customers embrace life in new technology-made environments, and (3) the boundaries between human and technology become to be blurred. This document explains these novel insights and defines the key AI-related concepts linked to each of these three distinctive aspects of Technologies 4.0 in services.

Andrei Tara, Hjalmar K. Turesson, Nicolae Natea, Henry M. Kim (2023). "An Evaluation of Storage Alternatives for Service Interfaces Supporting a Decentralized AI Marketplace", IEEE Access, 11, 116919 - 116931.

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Keywords
Abstract Given the exploding interest in generative AI and the concern that a few companies like Microsoft will monopolize access to such models, we address this centralization risk in the context of a DApp that matches buyers and sellers of various AI services. A key question for a decentralized marketplace is where and how to store the metadata that specifies the services’ properties in human and machine-readable formats. Having one or a few actors controlling access to that data constitutes undesirable centralization. We explore data storage alternatives to ensure decentralization, equitable match-making, and efficiency. Classifying decentralized storage alternatives as simple peer-to-peer replication, replication governed by a permissionless consensus, and replication governed by a private consensus, we select an exemplar for each category: IPFS, Tendermint Cosmos and Hyperledger Fabric. We conduct experiments on performance and find that read and write speeds are fastest for IPFS, about two times slower for Tendermint and slowest for Hyperledger. Writing using IPFS and Tendermint takes significantly longer than reading, and finally, specifically with IPFS, write speeds strongly depend on configuration. Given these results and the properties of the storage technologies, we conclude that simple peer-to-peer storage is the best option for the proposed AI marketplace.

Pashang, S., & Weber, O. (2023). "AI for Sustainable Finance: Governance Mechanisms for Institutional and Societal Approaches", The Ethics of Artificial Intelligence for the Sustainable Development Goals, 203-229.

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Keywords
Abstract Artificial intelligence (AI) for sustainable finance has been increasingly employed over the past several years to address the sustainable development goals (SDGs). Two major approaches have emerged: institutional and societal AI for sustainable finance. Broadly described, institutional AI for sustainable finance is used for activities such as environmental, social and governance (ESG) investing, while societal AI for sustainable finance is used to support underbanked and unbanked individuals through financial inclusion initiatives. Despite the growing reliance on such digital tools, particularly during the coronavirus disease 2019 (COVID-19) pandemic, governance mechanisms and regulatory frameworks remain fragmented and underutilized or inhibit progress toward the 17 UN SDGs. While major proposals and reports were released by standard-setting and regulatory bodies leading up to 2020, the COVID-19 pandemic indeed caused major setbacks to adoption and implementation, which in turn have also resulted in inconclusive data and lessons learned. As the global community begins to navigate out of the pandemic, policy makers, through multilateral and cross-sector agreements, are looking to renew governance mechanisms that mitigate new and pre-existing risks while cultivating sustainability and facilitating innovation.

Lévesque, M., Obschonka, M. and S. Nambisan (2022). "Pursuing Impactful Entrepreneurship Research Using Artificial Intelligence", Entrepreneurship Theory and Practice, 46(4).

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Keywords
Abstract It is time for the entrepreneurship field to come to terms with leading-edge artificial intelligence (AI). AI holds great promise to transform entrepreneurship into a more relevant and impactful field, but it must overcome conflicts between the AI-driven research approach and that of the traditional, theory-based research process. We explore these opportunities and challenges and suggest concrete approaches that entrepreneurship researchers can use to harness the power of AI with rigor and enhance research relevance. We conclude that incorporating the power of AI in entrepreneurship research and managing the associated risks offer a new and “grand challenge” for the field.

Belk, R. (2021). "Ethical Issues in Service Robotics and Artificial Intelligence", Services Industries Journal, 41(13-14), 860-876.

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Keywords
Abstract As we come to increasingly rely on robotic and Artificial Intelligence technologies, there are a growing number of ethical concerns to be considered by both service providers and consumers. This review concentrates on five such issues: (1) ubiquitous surveillance, (2) social engineering, (3) military robots, (4) sex robots, and (5) transhumanism. With the partial exception of transhumanism, all of these areas of AI and robotic service interaction already present ethical issues in practice. But all five areas will raise additional concerns in the future as these technologies develop further. These issues have serious consequences and it is imperative to research and address them now. I outline the relevant literatures that can guide this research. The paper fills a gap in recent work on AI and robotics in services. It expands views of service contexts involving robotics and AI, with important implications for public policy and applications of service technologies.

Darmody, A. and Zwick, D. (2020). "Manipulate to Empower: Hyper-Relevance and the Contradictions of Marketing in the Age of Surveillance Capitalism", Big Data & Society, 7(1).

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Keywords
Abstract In this article, we explore how digital marketers think about marketing in the age of Big Data surveillance, automatic computational analyses, and algorithmic shaping of choice contexts. Our starting point is a contradiction at the heart of digital marketing namely that digital marketing brings about unprecedented levels of consumer empowerment and autonomy and total control over and manipulation of consumer decision-making. We argue that this contradiction of digital marketing is resolved via the notion of relevance, which represents what Fredric Jameson calls a symbolic act. The notion of the symbolic act lets us see the centering of relevance as a creative act of digital marketers who undertake to symbolically resolve a contradiction that cannot otherwise be resolved. Specifically, we suggest that relevance allows marketers to believe that in the age of surveillance capitalism, the manipulation of choice contexts and decision-making is the same as consumer empowerment. Put differently, relevance is the moment when marketing manipulation disappears and all that is left is the empowered consumer. To create relevant manipulations that are experienced as empowering by the consumer requires always-on surveillance, massive analyses of consumer data and hyper-targeted responses, in short, a persistent marketing presence. The vision of digital marketing is therefore a fascinating one: marketing disappears at precisely the moment when it extends throughout the life without limit.

Babier, A., Chan, T., Diamant, A., Mahmood, R. and McNiven, A. (2020). "Knowledge-Based Automated Planning with 3-D Generative Adversarial Neural Networks", Medical Physics Journal , 47(2), 297-306.

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Abstract
To develop a knowledge‐based automated planning pipeline that generates treatment plans without feature engineering, using deep neural network architectures for predicting three‐dimensional (3D) dose.
Our knowledge‐based automated planning (KBAP) pipeline consisted of a knowledge‐based planning (KBP) method that predicts dose for a contoured computed tomography (CT) image followed by two optimization models that learn objective function weights and generate fluence‐based plans, respectively. We developed a novel generative adversarial network (GAN)‐based KBP approach, a 3D GAN model, which predicts dose for the full 3D CT image at once and accounts for correlations between adjacent CT slices. Baseline comparisons were made against two state‐of‐the‐art deep learning–based KBP methods from the literature. We also developed an additional benchmark, a two‐dimensional (2D) GAN model which predicts dose to each axial slice independently. For all models, we investigated the impact of multiplicatively scaling the predictions before optimization, such that the predicted dose distributions achieved all target clinical criteria. Each KBP model was trained on 130 previously delivered oropharyngeal treatment plans. Performance was tested on 87 out‐of‐sample previously delivered treatment plans. All KBAP plans were evaluated using clinical planning criteria and compared to their corresponding clinical plans. KBP prediction quality was assessed using dose‐volume histogram (DVH) differences from the corresponding clinical plans.
The best performing KBAP plans were generated using predictions from the 3D GAN model that were multiplicatively scaled. These plans satisfied 77% of all clinical criteria, compared to the clinical plans, which satisfied 67% of all criteria. In general, multiplicatively scaling predictions prior to optimization increased the fraction of clinical criteria satisfaction by 11% relative to the plans generated with nonscaled predictions. Additionally, these KBAP plans satisfied the same criteria as the clinical plans 84% and 8% more frequently as compared to the two benchmark methods, respectively.
We developed the first knowledge‐based automated planning framework using a 3D generative adversarial network for prediction. Our results, based on 217 oropharyngeal cancer treatment plans, demonstrated superior performance in satisfying clinical criteria and generated more realistic plans as compared to the previous state‐of‐the‐art approaches.

Botti, S., Giesler, M., Stefano, P. and Walker, R. (2020). "Consumers and Artificial Intelligence: An Experiential Perspective", Journal of Marketing.

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Keywords
Abstract Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: (1) data capture, (2) classification, (3) delegation, and (4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations’ investments into AI and to lay out an agenda for future research.