Notice bibliographique
Résumé
What is the state of digital adoption by businesses? Are businesses able to leverage and keep up with the rapid pace of technological change? What policies are needed to ensure robust adoption by start-ups and small businesses? This panel will take stock of business adoption in the enterprise and small business sectors of Canada, and explore the challenges and opportunities to digital technologies. A new body of Canadian research has shown that the majority of businesses are online and using a range of digital tools, from social media to e-commerce to mobile applications. A report by the Canadian Federation of Independent Business that polled over 2000 small and medium sized enterprises concludes that businesses of all sizes and from all sectors are adopting various digital technologies in their operations. Arriving at a similar conclusion a report by Start-up Canada, a trade association representing the Canadian start-up community determined that digital networks have vastly expanded potential market opportunities for small businesses, enabling them to reach customers world-wide. Yet, the research also highlight clear challenges and persistent divides in digital adoption among businesses. The reports broadly agreed that the complexity and time required to adopt digital tools is a significant challenge. The report from Start-up Canada highlighted the pressure of digital onboarding, technology implementation, and maintenance as a significant upfront investment in time and cost for small business owners. An OECD report suggests that training is a barrier to adopting digital technologies in Canada and other G20 nations. Data from the Canadian Chamber’s survey revealed that only 37% of businesses invest in digital skills literacy, 50% invest in software training and only 31% invest in cybersecurity training. Moreover, survey data show that specific groups are particularly unequipped to leverage advanced digital tools. Women entrepreneurs are 20 per cent less likely to leverage digital technologies when operating their business than men. At the same time, digital adoption rates are two times higher amongst immigrant small business owners (SBO) than born-Canadians. Immigrant SBOs are also more likely to both leverage digital technologies in their companies and invest in digital skills building. These findings raise questions on the trends and barriers to online adoption. What are the hurdles inhibiting participation beyond the cost of access? How can we formulate policy that sparks adoption? With the aim of widening and deepening the TPRC community’s knowledge of adoption issues, this panel will frame the challenge and ignite a data-driven, candid conversation using Canada as a case-study. The TPRC community will grapple with the big and broad question on how to ensure that the internet remains a meaningful and transformative technology the digital economy. The panel will be composed of the following voices. Panelist #1 will discuss a Canadian research study that provides insights on internet usage in the small business and start-up scene. Panelist #2 will provide a deeper dive into the challenges felt by enterprise-size businesses. Panelist #3 will consider the trends internationally, including but not necessarily limited to North America.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,009 |
| Études des sciences et des technologies | 0,007 | 0,019 |
| Communication savante | 0,023 | 0,044 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».