Audencia Business School

This paper explores the interplay between female representation in mutual funds’ management teams and their voting behavior. We find that women-led mutual funds are more likely to support environmental and social (ES) proposals, but not governance ones. This result is robust to excluding sustainable funds, alleviating concerns that the findings are driven by the self-selection of female fund managers into sustainable funds. While women-led mutual funds are also more likely to vote with management in firms headed by female CEOs, this does not conflict with their greater support for ES shareholder proposals. Overall, our results suggest that female representation in fund management teams is related to their voting behavior on ES issues.

As Artificial Intelligence reshapes hospitality service delivery, understanding how culturally diverse guests evaluate AI-enabled encounters is critical. This study examines how cultural value configurations shape evaluations of service robots in hotels. Cultural value configurations are coherent combinations of orientations that jointly influence how individuals interpret and assess service encounters. Using more than 3000 robot-related hotel reviews from guests across 77 countries, we integrate Hofstede’s dimensions with Schwartz’s value theory to explain cross-cultural differences in sentiment. Masculinity and uncertainty avoidance predict more negative sentiment, individualism shows a positive association that is sensitive to model specification, and power distance shows no direct effect. One configuration is robust: masculinity combined with uncertainty avoidance amplifies dissatisfaction, and this interaction holds under multilevel and combined estimation and survives correction for multiple testing. Two further configurations remain suggestive: power distance combined with uncertainty avoidance attenuates risk aversion, and individualism combined with masculinity increases tolerance for imperfection. Both are marginal and do not persist under multilevel or joint estimation, so we report them as patterns for replication rather than as established effects. Hofstede’s indices provide the measurement, while Schwartz’s value theory serves as an interpretive lens rather than as a measured individual-level construct. Findings position culture as a configurational system and provide guidance for AI service design and communication.

This study examines how consumers from individualistic and collectivistic consumer markets form recovery expectations and loyalty intentions in response to service recovery promises and bystander comments on social media. Across two experiments, we find that complainants’ origin fundamentally shapes how they interpret recovery efforts. Individualistic consumers exposed to non-accommodative recovery promises respond more favorably when accompanied by positive consumer comments. In contrast, collectivistic consumers report higher recovery expectations and intentions when the same promises are paired with negative comments. Importantly, bystander comments exert greater influence when companies issue non-accommodative rather than accommodative responses across both markets. This research is the first to reveal that complainant origin (culture) and bystander presence jointly moderate the effects of recovery communications. The findings challenge uniform global customer behavior assumptions and offer actionable insights for managing service failures in global markets. The study advances online service recovery theory.

Sentiment 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 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.

This paper explores how the legacy of state-owned enterprises (SOEs) can simultaneously offer advantages and impose constraints, shaping a firm's strategic development over time. Using the transition from Communism in Central and Eastern Europe as a backdrop, we examine the case of Sberbank, a Russian SOE. In particular, we show how legacy artifacts rooted in the firm's historical circumstances and experiences shaped Sberbank's approach to the present and the future, operating as both a positive inheritance and a negative burden. As a positive inheritance, Sberbank's SOE legacy provided significant market dominance inherited from its historical role under state ownership, giving it a competitive edge in the post-Communist era. However, persistent interference and strong entanglements with the state constrained Sberbank's strategic autonomy substantially. Drawing on interviews, archival materials, and a comprehensive database of corporate communications, we show how Sberbank's management engaged with this challenging legacy. Their main strategy was to incorporate new initiatives that allowed them to decouple and address the negative aspects of state involvement while capitalizing on the market advantages inherited from their past. This study highlights how the concept of legacy is central to understanding how non-family businesses, and especially SOEs, navigate the tension between inherited strengths and constraints in their strategic evolution.