DSpace@UJKZ

Dépôt institutionnel de l'Université Joseph KI-ZERBO pour les données de recherche numériques

DSpace@UJKZ propose:

  • la publication gratuite des données de la recherche scientifique
  • la mise à disposition gratuite et permanente des données publiées dans le monde entier
  • la possibilité de citer les données publiées (par l'attribution de DOI)
  • une visibilité accrue des données publiées
 

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Recent Submissions

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Social media marketing assimilation in B2B firms: An integrative framework of antecedents and consequences
(2024) Maduku Daniel K. Maduku
Previous studies have addressed social media adoption in business-to-business (B2B) contexts, but limited research has focused on understanding social media marketing assimilation in the B2B context. Using an integrative model, this study examines how top management participation influences the assimilation of social media in the key marketing areas of product development, pricing decision-making, channel management, and promotion. Furthermore, it examines the resulting impact of these assimilations on B2B firms' performance, particularly in respect of sales performance and relationship development. The study also examines the moderating impact of absorptive capacity on top management participation in the assimilation processes. The findings reveal that top management participation strongly influences social media assimilation into marketing functions. However, the impact of social media assimilation on B2B firms' performance is varied. While assimilation for channel management positively impacts both sales performance and relationship development, assimilation for product development is only positively related to relationship development. Conversely, assimilation into the functions of pricing decisions and promotion activities shows no significant impact on either sales performance or relationship development. Finally, absorptive capacity positively moderates top management participation in social media assimilation into all key marketing functions except one. The theoretical and managerial implications of these findings are discussed.
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Judging facts, judging norms: Training machine learning models to judge humans requires a modified approach to labeling data
(2023) Aparna Balagopalan Gillian K. Hadfield 1 *, David Madras 2,3,5,6,7 2,3 , David H. Yang , Marzyeh Ghassemi 1,2,3 2,4 , Dylan Hadfield-Menell 1 ,
Acknowledgments:Wewouldliketo thank D. Simon(Universityof Southern California School of Law), T. Lyon (University of Southern California School of Law), R. Zemel (University of Toronto), E. Creager (University of Toronto), and five anonymous reviewers for their helpful comments and reviews. We also thank the participating annotators for their responses. Funding: We acknowledge the Schwartz Reisman Institute for Technology and Society for funding this research. A.B. was funded in part byan Amazon Science PhD Fellowship at the MIT Science Hub. D.M. was supported by an NSERC Alexander Graham Bell Canada Graduate Scholarship-Doctoral (CGS-D) during a portion of this research. D.H.-M. was funded in part by a gift from the Hirji Wigglesworth Family Foundation and in part by the Bonnie and Marty (1864) Tenenbaum Career Development Chair. G.K.H was funded in part by the Schwartz Reisman Chair in Technology and Society and a CIFAR AI Chair at the Vector Institute. M.G. was funded in part by the Hermann L. F. von Helmholtz Career Development Professorship at MIT, Microsoft Research, a CIFAR AI Chair at the Vector Institute, a CIFAR Azrieli Global Scholar award, and a Canada Research Council Chair. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute. This project was approved by the University of Toronto’s Institutional Research Ethics Board (protocol no. 00037283). Author contributions: The research was conceived by D.H.-M. and G.K.H.; the study was designed by D.H.-M., G.K.H., D.M., andM.G.;datacollection wasdonebyA.B.andD.H.Y.;datainterpretationandanalysis wasdone by A.B., D.M., D.H.Y., D.H.-M., G.K.H., and M.G.; and the manuscript was prepared by A.B., D.M., D.H.-M., G.K.H., and M.G. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Code to reproduce the paper’s main findings can be found at https://doi.org/10.5281/zenodo.7782689.
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Artificial intelligence scribes in primary care
(2024)
1 Artificial intelligence (AI) scribes can mimic human scribes These tools use speech recognition, natural language process ing, and AI technologies to listen during a clinical encounter and generate clinical documentation.1 Clinicians can then review, edit, and sign the generated note in the medical record. 2 Early evidence suggests that AI scribes lessen administrative burden and improve quality of time spent with patients2,3 A 10-week, California-based pilot found that primary care providers spent less time documenting during appointments and using electronic medical records outside office hours.3 Patients felt comfortable with AI scribes and reported clin icians spent less time looking at a computer.3 The use of AI scribes may also improve the quality of medical notes by generating more timely and complete documentation.3,4 3 Clinical documentation generated by AI scribes may contain errors and must always be reviewed3,5 Atrifical Intelligence tools can introduce mistakes, including hallucinations (i.e., documenting things that did not occur) or omission of key information.3,5 They may struggle with differ ent languages and documenting physical examinations.2,3 Clin icians are ultimately responsible for the quality of their documentation and must review all AI-generated notes to ensure accuracy and completeness.4,5 4 Users must ensure software is compliant with local privacy regulations4,5 To date, AI scribes remain unregulated. Clinicians must understand how clinical information captured by the soft ware is stored, retained, accessed, and subsequently used.4,5 Data stored outside of Canada may be subject to foreign laws. The onus is on the clinician or their institution to understand privacy implications and potential for patient harm, and to ensure the software performs as intended. 5 Users must obtain consent before using an AI scribe4,5 Clinicians must explain, receive, and document informed consent from patients, including reasons for use and poten tial risks such as privacy implications and subsequent owner ship, storage, and use of health
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Artificial Intelligence in Science and Society: The Vision of USERN
(2024)
Therecent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact The associate editor coordinating the review of this manuscript and approving it for publication was Derek Abbott
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Multichannel Management
(PETER LANG, 2023) Gottfried Gruber
The thesis deals with pricing strategies for multichannel retailers, especially traditional stores which additionally manage an online shop. The problem of integrating two sales channels and applying a well-suited pricing strategy is still an emergent question. This work develops a stochastic model to represent consumer behavior on pricing. On the one hand the model contains two probability functions which render consumers' reservation prices for each individual channel. On the other hand the stochastic model is based on numerous distributions which represent switching probabilities from and to each separate channel. The various distribution functions will be estimated from the results of a survey. To highlight differences of pricing strategies due to several product categories a cross comparisons of books, clothes and digital cameras will be presented. The results show that there are differences in multichannel pricing of the various products. These inequalities stern from consumers' perceptions of the sales channels. For each product a separate sales channel is preferred by consumers. Therefore, one channel exhibits some advantage versus the alternative channels. This advantage is reflected in different pricing strategies. Further appropriate marketing strategies could help a firm to counter discounting by its competitors. So firms should keep an eye on the reservation price structure of its consumers as well as their demanded marketing activities.