Search arXivSearch

arXiv · 2410.20919

Co-produced decentralised surveys as a trustworthy vector to put employees' well-being at the core of companies' performance

Abstract

Assessing employees' well-being has become central to fostering an environment where employees can thrive and contribute to companies' adaptability and competitiveness in the market. Traditional methods for assessing well-being often face significant challenges, with a major issue being the lack of trust and confidence employees may have in these processes. Employees may hesitate to provide honest feedback due to concerns not only about data integrity and confidentiality, but also about power imbalances among stakeholders. In this context, blockchain-based decentralised surveys, leveraging the immutability, transparency, and pseudo-anonymity of blockchain technology, offer significant improvements in aligning responsive actions with employees' feedback securely and transparently. Nevertheless, their implementation raises complex issues regarding the balance between trust and confidence. While blockchain can function as a confidence machine for data processing and management, it does not inherently address the equally important cultural element of trust. To effectively integrate blockchain technology into well-being assessments, decentralised well-being surveys must be supported by cultural practices that build and sustain trust. Drawing on blockchain technology management and relational cultural theory, we explain how trust-building can be achieved through the co-production of decentralised well-being surveys, which helps address power imbalances between the implementation team and stakeholders. Our goal is to provide a dual cultural-technological framework along with conceptual clarity on how the technological implementation of confidence can connect with the cultural development of trust, ensuring that blockchain-based decentralised well-being surveys are not only secure and reliable but also perceived as trustworthy vector to improve workplace conditions.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Adèle Bréart De Boisanger, Wendy Sims-Schouten, Francois Sicard. 2024-10-28. Co-produced decentralised surveys as a trustworthy vector to put employees' well-being at the core of companies' performance. https://arxiv.org/abs/2410.20919

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A Brief AI Literacy Intervention Does Not Significantly Reduce Over-Reliance and Increases Under-Reliance on ChatGPT: A Randomized Study

In this study, we examined whether a brief AI literacy intervention influences high school students' reliance on recommendations from large language models (LLMs). In a randomized experiment, students were assigned to either a control group receiving a brief introduction to LLMs or an intervention group receiving additional information about how LLMs work, their limitations, and effective usage strategies. Participants then solved eight math puzzles with ChatGPT's advice, which was incorrect in half of the trials. Results indicated widespread over-reliance, with incorrect recommendations adopted in 52.1% of the trials. The intervention did not significantly reduce over-reliance. Instead, it led to an increase in under-reliance, as students were more likely to reject correct recommendations. These findings provide preliminary evidence that brief text-based interventions may be ineffective in fostering appropriate reliance. More comprehensive and interactive approaches may be required to meaningfully influence students' real-world reliance on LLMs.

cs.CY

Examining Community-Requested Fact-Checking: Request Alerts Are Associated with Greater Diversity and Visibility of Community Notes

Crowdsourced fact-checking systems such as Community Notes are increasingly used on social media platforms, yet concerns remain about which content receives scrutiny and how visible that scrutiny is. X allows users to request notes for specific posts. When sufficient requests accumulate, an alert is displayed, creating an interface cue that may guide contributor behavior. We present a quantitative, non-causal analysis comparing the diversity and visibility of community notes written for X posts with and without request alerts. We infer alert presence at note submission and analyze 10,432 alerted and 44,442 non-alerted English notes from 318 top writers. We find that, alerted notes are associated with greater individual-level topical diversity, but also with stronger collective concentration in the Politics category. Mixed-effects models estimate that alerted notes are 8.4-20.2 percentage points more likely to be modeled helpful and visible, though this visibility gain diminishes as topics diverge from writers' prior interests.

cs.CY

Scarcity and Predictive Uncertainty: Implications for Societal Resource Allocation

An emerging literature examines the critical question of when and how prediction can be useful in allocating scarce societal resources. We examine a novel variant of this question: What happens when predictive uncertainty differs systematically across the population? This can occur in several situations; for example, when machine learning models have significantly different accuracies across different demographics. We show that this uncertainty has serious implications for resource allocation when coupled with commonly used binary measures of societal benefit from allocation. We formulate a novel mathematical model of scarce resource allocation that accounts for heterogeneous predictive uncertainties and analyze implications for both the allocation mechanism and the realized population-level benefits. We find that when resources are very scarce, maximum marginal benefit (MMB) prioritization favors individuals with lower predictive uncertainty even at the identical underlying initial state. However, we observe a flip in prioritization when resources are abundant, targeting higher-uncertainty individuals. We illustrate the implications of our results on the PISA educational testing dataset. Our findings have meaningful ramifications for the distributional outcomes of prioritization policies in many domains touched by the theory of local justice, including the allocation of public education resources, medical triage, and homelessness services. They also reveal a new moral dilemma in the ethics of scarce resource allocation - is it just to allocate a resource to one person over another solely based on predictive uncertainty about their futures? We also assess efficiency losses under both MMB and the vulnerability-first (VF) prioritization. Our model predicts efficiency losses across all resource levels, but particularly in low-resource settings.

cs.CY