HAL - Audencia Group
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From Theorising Othering to Othering Theorising: Reflections from the GWO 2025 Keynote Panel
No full textInternational audienc
The virtue-value spectrum: Managing ethical and pragmatic tensions in virtual reality retail for consumer wellbeing
No full textInternational audienceVirtual reality (VR) retail is transforming consumer experiences, offering personalization while raising ethical and privacy concerns. This study examines how consumers navigate these tensions and their effects on wellbeing. Using Virtue Ethics and integrating psychological dissonance and privacy cynicism theories, we uncover two perspectives: (i) an ethical view centered on moral responsibility and data integrity, and (ii) a pragmatic view emphasizing convenience and experiential value. Study 1 involves 32 interviews with Generation Z VR users in the UK, while Study 2 analyzes Trustpilot reviews and over 35,000 Instagram comments from four global retailers. We propose two models: the virtue-value spectrum, illustrating how consumers balance moral ideals with practical benefits, and the dissonance-cynicism paradox, highlighting the psychological conflict when ethical values clash with data practices in VR. These models expand Virtue Ethics in digital retail, providing insights to foster ethical engagement, rebuild trust, and enhance wellbeing in immersive retail environments.</div
How Nonbinary Individuals Construct and Express Their Gender Identity in Brazil
No full textInternational audienc
Impact of original versus reposted social endorsements on content consumption: The moderating role of Endorsers’ network characteristics
No full textInternational audienc
Invasive rapid innovation: An introduction and exploration of their acceptance
No full textInternational audienceWe introduce the concept of invasive rapid innovations, technologies characterized by both their novelty and the speed of their development, which significantly affect daily routines, personal privacy, or bodily autonomy. The introduction of such innovations is typically accompanied by limited knowledge and heightened uncertainty. Consequently, individuals’ assessments of the benefits, costs, and risks associated with adopting these innovations are often shaped by broader factors, including their trust in government, perceptions of the severity of the threat the innovations are designed to address, and their aversion to ambiguity. To capture these dynamics, we propose an integrative framework that examines these relationships and highlights the social environment asa key factor that can strongly override the influence of such determinants. We validate our framework through an empirical study (n = 916) focusing on vaccine uptake and the adoption of contact-tracing apps. Our findings suggest that policymakers, who often struggle to effectively communicate the benefits and costs of innovations, should leverage the power of social influence to enhance acceptance. For example, an individual’s mistrust in government becomes less consequential when they perceive that their social environment favors the innovation
How practitioners can leverage GenAI to bridge the research-practice gap
No full textInternational audienceDespite the practical relevance of many tourism research studies, organizations and policymakers often struggle to integrate them due to time constraints, language barriers, limited resources, and interaction challenges. Generative artificial intelligence (GenAI) offers new capabilities to overcome these barriers. We propose a GenAI-enabled knowledge translation process with three stages: (i) research curation to identify and translate relevant literature; (ii) content creation to produce materials; and (iii) market research using synthetic guests to pre-test their effectiveness. We examine the capabilities, limitations, and ethical implications of GenAI at each stage, drawing on a systematic review of GenAI and tourism literature. To equip managers with the knowledge and tools needed to harness research-based insights effectively, we offer a toolkit comprising a handbook, a promptbook, and tailored GPT models. The toolkit enables tourism and hospitality practitioners to apply research findings in their decision-making and content strategies without direct stakeholder interaction
Exploring Zero-Shot SLM Ensembles as an Alternative to LLMs for Sentiment Analysis
No full textInternational audienceSentiment analysis has become vital for understanding consumer attitudes, guiding product development, and informing strategic decisions. Although LLMs such as GPT-3.5 and GPT-4 deliver strong zero-shot performance, they can be cost prohibitive and raise privacy concerns. In contrast, Small Language Models (SLMs) provide a lighter and more deployable solution, but their ability to match LLM accuracy, especially in zero-shot scenarios, remains underexplored. In this experimental study , we examine whether ensembles of zero-shot SLMs can serve as a viable alternative to proprietary LLMs in sentiment classification tasks. We investigate five commonly used SLMs (Phi2 Mini, Mistral, Llama, Gemma, Aya) and compare them to GPT-based models (GPT-3.5, GPT-4, GPT-4 omni, GPT-4 omni mini) across seven English-language datasets. By automating prompt generation and filtering responses based on a strict output format, we maintain a purely zero-shot approach. We form SLM ensembles via majority voting and evaluate their performance on accuracy, weighted precision, and weighted F1. We also measure inference time to assess cost and scalability trade-offs. Results show that SLM ensembles as a form of decision fusion, consistently outperform single SLMs, significantly boosting metrics in zero-shot settings. In contrast with GPT models, the ensemble achieves accuracy comparable to GPT-3.5 and even rivals GPT-4 on certain prompts. However, GPT-4 retains a slight edge in both precision and F1 score. Moreover, local SLM ensembles incur higher latency yet offer potential advantages in data privacy and operational control. This experimental study’s findings illuminate the feasibility of employing lightweight, zero-shot SLM ensembles for sentiment analysis, providing organizations with an effective and more flexible alternative to exclusively relying on large proprietary models
Lost in the crowd! Pricing carbon at the age of algorithms
No full textInternational audienceThis work investigates crowdedness in (H)igh (F)requency (T)rading (HFT) in the EU ETS. The empirical findings report a systematic crowdedness-related mispricing, which is observed only in algorithmic trading. While this mispricing is relatively small on a per trade basis, it is persistent and it does not disappear with more capital. Consequently, it can accumulate rapidly, reaching up to 4 % of trading value and potentially leading to market failures such as flash crashes and price spikes. Existing regulatory measures, such as the MiFID II trading rules, mitigate this effect only partially. This suggests that transparency alone is not sufficient in mitigating the risk of market failures and that some kind of speed monitoring is needed
Understanding success factors of Class B enterprises in Chile: a pioneering study on socially and environmentally responsible entrepreneurship
No full textInternational audienceInnovative entrepreneurship drives economic and societal progress by introducing novel products, services, and business models. Class B enterprises stand out for harmonising profitability with exceptional social and environmental performance, yet their success factors remain underexplored. This study examines the success factors of Class B enterprises in Chile across five strategic dimensions. Using a descriptive and empirical approach, in-depth surveys were conducted with 34 of the 74 registered Class B companies in Chile. Statistical analysis revealed critical success factors shaping their performance, including the entrepreneurial ecosystem, access to clients, education quality, and support networks. Notably, 81% of entrepreneurs highlighted the ecosystem as pivotal. These findings provide valuable insights into the operational dynamics of Class B enterprises, underscoring the importance of external influences. This pioneering study enriches the literature by offering a comprehensive analysis of success factors in socially and environmentally responsible entrepreneurship in Chile and Latin America
Field experiments: Overcoming the limitations of survey experiments for actionable behavioural insights
No full textInternational audienceHistorically, one-off cross-sectional survey studies have dominated empirical research in tourism and hospitality. The inability to draw causal conclusions from such data has led to an increased uptake of survey experiments, which are easy and affordable to conduct and can identify causal relationships between constructs under controlled conditions. Survey experiments, however, have a severe limitation: they do not provide insights into real behaviour, restricting researchers’ ability to generate actionable insights and reliable practical recommendations. This article offers a systematic comparison of three approaches (one-off cross-sectional survey studies, survey experiments, and field experiments) and provides step-by-step guidance on the design and implementation of field experiments and quasi-experimental field studies