Use of Emerging Technologies in the Internal Operations of Distributed Organizations
Bibliographic record
Abstract
In a context marked by the continuous acceleration of innovations integrating technological, physical, digital, and biological elements, digital transformation and the integration of emerging technologies have become strategic priorities for many organizations (Omol, 2024). Research in information systems has been abundant in several sectors (Wessel et al., 2025) but less developed in the structures of non-profit organizations, particularly those operating in a distributed and networked mode (Martin et al., 2022). For instance, food banks are organizations that embody these characteristics with a social mission, operating with greatly limited resources and relying on many volunteers, most of whom lack the means to benefit from technological advancements. Moreover, faced with the inadequacy between a sufficient global food supply and inefficient distribution, food banks play a crucial role but encounter logistical, informational, and organizational challenges (Martin et al., 2022; Rivera et al., 2023; Wu & Tai, 2024). Drawing on the theoretical frameworks of Frugal Information Systems (Frugal IS) (Watson et al. 2013) and stakeholder theory guided by the principles of Information Technology (IT) for good, this research aims to address the central question: How can emerging technologies support the coordination and efficiency of operations in food banks as distributed organizations, while aligning stakeholder interests and adhering to the principles of Frugal IS and IT for the common good? This study seeks to explore how emerging technologies can be mobilized, in a frugal and inclusive manner, to improve the internal operations of food banks, a representative form of distributed organization with a strong social mission. The goal is to investigate the adoption of affordable, sustainable, and user-centred digital solutions. It aims to understand how these technologies can enhance coordination, traceability of flows, stakeholder engagement, and ultimately the social impact of food banks. Adopting a qualitative methodology based on multiple case studies within the Quebec Food Bank network, this research relies on data from interviews, observations, and document analyses. The expected results include practical recommendations for community organizations seeking to reconcile operational efficiency and social responsibility, as well as original theoretical contributions for researchers interested in the challenges of digital transformation in constrained and distributed contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".