Vend. 14 mai, de 12h00 à 13h30 – Présentation virtuelle. Pour plus d’infos :

Conférencière invitée : Natalia Levina, Professor at the New York University Stern School of Business, and Director of the Fubon Center for Technology, Business and Innovation.

To incorporate or not to incorporate AI for critical judgments: How professionals deal with opacity using AI for medical diagnosis. Artificial intelligence (AI) technologies are promising to transform how professionals are conducting knowledge work, yet the opacity of AI tools is of growing concern, as it is difficult to understand or explain the results they produce. Organizational researchers are only starting to understand whether and how this transformation unfolds in practice. We conducted an in-depth field study in a major US hospital where AI tools were being used within three different radiology departments for forming critical judgments: breast cancer, lung cancer, and bone age. In all three departments, professionals experienced a surge in uncertainty due to the opacity of the AI tools’ results, which often conflicted with their initial diagnosis, yet provided no insight into its underlying reasoning or logic. We found that how professionals dealt with this opacity and its impact on their overall uncertainty were critical to whether and how they incorporated the AI results. Only in one department (of the three we studied), did professionals meaningfully and consistently incorporate AI results into their final judgments. This study reveals how only in this department did the AI tool’s results directly relate to professionals’ locus of uncertainty and led to developing rich interrogation practices of the opaque AI results; this way, using and incorporating the AI results reduced the overall uncertainty of forming their final judgments. Our study unpacks the challenges involved in “augmenting” professional judgment with powerful, yet opaque, technologies and contributes to literatures on opacity in AI, the adoption of new technologies, and the production of knowledge.

Événement conjoint Gresi/Tech3Lab.

Conférenciers : Hillol Bala (Indiana Univ.), Akshatet Lakhiwal et Pierre-Majorique Léger (HEC Montréal).

Love Me or Love Me Not: Behavioral and Neurophysiological Assessment of Ambivalence to Information. The proliferation of digital platforms has made information widely available to individuals who rely on it to make day‐today decisions. This paper focuses on why and how information that is presented on digital platforms and its associated valence (e.g., positivity and negativity) may cumulatively elicit mixed feelings among individuals and influence their decisions. It is theorized that as individuals process information, they could experience coexisting positive and negative dispositions (i.e., ambivalence), which ultimately influences their attention and elicits distinct behavioral outcomes. Yet, summarization of these feelings using existing visual representations often results in a simplified positive‐negative distinction, where more nuanced feelings and attitudes such as ambivalence and indifference are practically indistinguishable. Four randomized controlled experiments, including an electroencephalography (EEG) study are conducted to examine how ambivalence elicited due to information presented in various ways on digital platforms may draw varying degrees of attention and influence decisions. The ability of current information representation structures on digital platforms in capturing or representing mixed feelings (such as ambivalence) is examined and compared to a bivariate intervention that correctly elicits attitudes like ambivalence. Results not only emphasize that mixed feelings such as ambivalence elicit distinct behavioral and neurophysiological outcomes, but also that the inability of digital platforms to accurately recognize, interpret, and present these outcomes could potentially limit individuals’ ability to make fully informed decisions.

Présentation virtuelle d’une “réflexion scientifique en cours”. Conférenciers : Guy Paré, Membre du GReSI, Titulaire de la Chaire de recherche en santé connectée (Dép. TI) et Directeur du programme de doctorat à HEC, & Gerit Wagner, Chercheur postdoctoral de la Chaire de recherche en santé connectée (Dép. TI).

Theory elaboration in information systems: opportunities, tactics and guidelines. Abstract: In this paper, we explain the notion of theory elaboration arguing that this mode of theorizing is essential to improve the explanatory power of existing information systems (IS) theories. This way of advancing knowledge has arguably received limited attention in debates on theorizing despite its substantial promises for IS research. Complementing approaches of theory generation and theory testing, elaboration offers a range of tactics for within-theory improvement aimed at cumulative progression of explanatory power, and, ultimately, a stronger core of theories in a given discipline. We believe theory elaboration offers truly promising opportunities for knowledge development in our field. Our work, presented as a commentary paper, is intended to clarify the process of theory elaboration, to expose researchers and students to the different elaboration tactics, and to provide a set of guidelines for prospective authors of theory elaboration papers.

Présentation virtuelle de Likoebe Maruping, Professor of Computer Information Systems and Member of the Centre for Digital Innovation (CDI), at the J. Mack Robinson College of Business, Georgia State University.

Open Source Collaboration in New Ventures. Open source collaboration (OSC) platforms, such as GitHub, have emerged over the past decade to become a salient venue for organizing innovation efforts and output. As a result, an increasing number of entrepreneurial firms are collaborating with open communities on such platforms to develop and scale their new ventures. From the lens of open innovation, we examine value creation and value capture in high-tech startups’ external collaboration on OSC platforms. We develop a theoretical framework to explicate how the engagement in OSC may affect the value of startup firms and how the effect is contingent on the stage of venture maturity (conception, commercialization, or growth) and the mode of OSC engagement (inbound or outbound). In analyses that pool 22,896 matched startups with monthly panel observations between 2008 and 2017, we find a positive and significant value-added effect of OSC to startups, but the effect is sensitive to the stage of startup maturity and the mode of OSC engagement. In particular, startups in the conception and commercialization stages benefit more from inbound OSC whereas startups in the growth stage benefit more from outbound OSC. As startups increasingly rely on OSC platforms for organizing innovation, our contribution is to show whether, when, and how knowledge flows through startups’ OSC might affect the value of startup firms