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A knowledge-centric model for government-orchestrated digital transformation among the microbusiness sector
No full textFNEGE 2, ABS 4International audienceMost prior public sector digital transformation (DT) research has examined the role of digitalization in improving either the internal operational efficiency of the government or the quality of government service delivery to external stakeholders such as citizens and businesses. Although policy-driven digitalization of specific sectors is key for promoting public value, government’s role in orchestrating extra-government digitalization initiatives to create public value has not been sufficiently investigated. To address this perceptible void in the public sector DT literature, we study a government-led DT program designed to promote digitalization among microbusinesses (MB), a sector that has major economic and social implications. Given the significant role of technical and business knowledge in facilitating enterprise DT, we examine and theorize different knowledge mechanisms through which government policy initiatives can help foster MBs’ DT. Drawing on qualitative data from a series of structured interviews and focus groups with government agents, digital champions, and MB owner-managers involved in the implementation of a government-led DT program for MBs in Ireland, among the different DT stakeholders, we identify three knowledge pathways playing different knowledge-related roles and aiming to facilitate this transformation: top-down, bottom-up, and multidirectional. Each pathway comprises distinct practices. Collectively, the identified knowledge mechanisms in the DT program knowledge ecosystem foster social value creation for both MBs and government stakeholders, and therefore for the nation as a whole. Specifically, sustenance of the DT program is achieved through “initiation” and “instantiation” knowledge routes. Our findings offer theoretical contributions to the literatures on government-led digital transformations, effectiveness of government-led digital initiatives, and digital transformation in the MB sector. Our study also has significant implications for policy and practic
Satisfaction as a function of user justice: a social exchange theory perspective
No full textFNEGE 3, ABS 2International audienceThis study investigates complaint behaviour as a function of post-satisfaction behaviour stemming from perceptions of justice and security. Grounded in social exchange theory, we propose an integrated framework in which security, expectation confirmation, and dimensions of justice (distributive, procedural, and interactional) influence satisfaction, affecting complaint behaviour towards the eCommerce Digital Supply Chain (DSC) process. Data were collected through a quantitative online survey involving 316 Amazon eShoppers of tech products from the European region. The confirmatory factor analysis results validated the second-order reflective justice construct, encompassing distributive, procedural, and interactional dimensions. Furthermore, the structural relationship results revealed (i) justice and security have a significant impact on eShoppers’ satisfaction; (ii) a significant relationship exists between word of mouth, satisfaction, and eShoppers’ complaint intention. This framework contributes to the existing knowledge base and offers valuable insights for stakeholders in the eCommerce DSC
L’innovation numérique responsable pour un monde plus durable
No full textInternational audienceChristine Balagué et Ahmad Haidar définissent le concept d’innovation numérique responsable et approfondissent les enjeux de l'impact de l'innovation numérique sur la durabilité. Ils identifient également comment l'innovation numérique responsable peut être source de meilleures pratiques au profit de la durabilité, même si de nombreux obstacles demeurent
Humans versus robots: a confrontation regarding shopping in the metaverse
No full textInternational audienceThe research deals with AI agents in the context of advertising and shopping and their interaction with humans in the Metaverse. The results of a qualitative study with in-depth individual interviews show that the functional effectiveness of AI is challenged by consumers' adverse emotional reactions to the agents
On the convergence criterion in three-period lived overlapping generations models
No full textInternational audienceThis paper offers a novel perspective on Kehoe-Levine’s convergence criterion for equilibrium determinacy in three-period overlapping generations (OLG) models. Departing from their primary focus on gross substitutability, our central contribution is demonstrating that equilibrium determinacy, even in Samuelson economies, can be achieved under market complementarities, provided the aggregate demand sensitivities to adjacent-period prices sum positively. Furthermore, we identify critical conditions where gross substitutability or complementarities between goods spaced two periods apart becomes pivotal for equilibrium determination. By elucidating the role of asymmetric complementarities, we significantly extend the understanding of equilibrium determinacy within the Kehoe-Levine framework, challenging the necessity of strict gross substitutability and offering a more nuanced view of dynamic stability in OLG models
Trait-based approach coupled to metatranscriptomic-derived sequence similarity network: towards the depiction of the functional diversity of freshwater microbial eukaryotes
No full textInternational audienceMicrobial eukaryotes play a crucial role in biochemical cycles and aquatic trophic food webs. The recent advances in sequencing technologies have significantly improved our understanding of their taxonomic and functional diversity. However, the link between the diversity of these microorganisms and their ecological role remains poorly described, particularly in freshwater ecosystems. Moreover, the vast amount of data generated by -omics approaches are still largely underexplored, partly due to limitations in public databases. Data-driven methods are therefore becoming essential for predicting the role of eukaryotic microorganisms. Based on environmental genomic data obtained from a meromictic lake ecosystem (Pavin, France), we propose integrating metatranscriptome-derived sequence similarity network– and traits– based approaches. This combined strategy aims to identify genetic signatures of specific traits and biological pathways that are challenging to study using traditional microbiology methods. Using parasitic organisms as a case study, we investigate the genetic specificity of their associated protein families highlighting their high degree of specialization. While no universal marker for parasitism was found, we identified candidate genes at a fine taxonomic level. We notably present several protein families that could be key to understanding host-parasite interactions and pathogenicity. Identifying these protein families may provide valuable insights for developing antiparasitic treatments, with important implications for both health and economic sectors, while also validating our methodological approach
Entrepreneurial resilience in turbulent times: the role of entrepreneurial orientation and innovation in the Middle East
No full textABS 1International audiencePurpose - This study aims to investigate how entrepreneurial orientation (EO) influences entrepreneurial resilience (ER) in the Middle East. Specifically, it examines the mediating role of innovation and the moderating role of crisis perception (CP) in the relationship between EO traits (risk-taking, autonomy and proactiveness) and ER, offering insights for regional small and medium-sized enterprises (SMEs) during crises. Design/methodology/approach - This study is based on data collected through structured questionnaires from 1,523 respondents across diverse sectors in the United Arab Emirates, Kingdom of Saudi Arabia, Jordan and Lebanon. Covariance-based structural equation modeling was used to test the relationships between EO traits, innovation, crisis perception and entrepreneurial resilience. Findings - The results reveal that EO traits (risk-taking, autonomy and proactiveness) significantly enhance ER. Innovation mediates the EO−ER relationship, strengthening business adaptability, while effective crisis perception moderates this relationship, enabling firms to better leverage EO for resilience during crises. Practical implications - Policymakers and business leaders in the region can use these findings to design programs that promote entrepreneurial activities, foster innovation and support resilience during economic and political crises. Originality/value - This research provides new insights into ER in the Middle East, a region often overlooked in EO and ER studies. The findings contribute to understanding how SMEs in politically and economically unstable environments can enhance their resilience through EO and innovation
Aligning higher education with new data-driven jobs. Is higher education meeting the requirements of the new professions linked to A.I. and DATA?
No full textInternational audienceThe advent of Artificial Intelligence (AI) is bringing about far-reaching changes, particularly in terms of the emergence of new professions. These technologies are creating a need for new, specific skills, particularly in the area of data.The aim of our contribution is to identify the skills gaps between the higher education courses that prepare students in the fields of Artificial Intelligence and Data, and the needs of the job market. To do this, we:- extracts training curricula in the field of A.I. and Data, from the websites of higher education establishments, - extracts data from job boards.We then developed an analysis based on a text mining approach, using Python code for natural language processing. Our in-depth analysis using natural language processing algorithms provides a better understanding of the components needed to train new generations in the new data-driven professions. This study can help to better shape training programs to meet the demands of the market for new professions
Total progeny for spectrally negative branching Lévy processes with absorption
No full textWe consider a spectrally negative branching Lévy process in which particles are killed upon crossing below zero. It is known that such a process becomes extinct almost surely if the drift toward -∞ is sufficiently strong to counterbalance the reproduction rate. In this note, we study the tail asymptotics of the number of particles absorbed at the boundary during the lifetime of the process, in both the subcritical and critical regimes
MCQR : Enhancing the Processing and Analysis of Quantitative Proteomics Data by Incorporating Chromatography and Mass Spectrometry Information
No full textInternational audienceIn the field of proteomics, generating biologically relevant results from mass spectrometry (MS) signals remains a challenging task. This is partly due to the fact that the computational strategies for converting MS signals into biologically interpretable data depend heavily on the MS acquisition method. Additionally, the processing and the analysis of these data vary depending on whether the proteomic experiment was performed with or without labeling, and with or without fractionation. Several R packages have been developed for processing and analyzing MS data, but they only incorporate identification and quantification data; none of them takes into account other invaluable information collected during MS runs. To address this limitation, we introduce MCQR, an alternative R package for the in-depth exploration, processing, and analysis of quantitative proteomics data generated from either data-dependent or data-independent acquisition methods. MCQR leverages experimental retention time measurements for quality control, data filtering, and processing. Its modular architecture offers flexibility to accommodate various types of proteomics experiments, including label-free, label-based, fractionated, or those enriched for specific post-translational modifications. Its functions, designed as simple building blocks, are user-friendly, making it easy to test parameters and methods, and to construct customized analysis scenarios. These unique features position MCQR as a comprehensive toolbox, perfectly suited to the specific needs of MS-based proteomics experiments