NEOMA Business School

Service environments increasingly contain visible cues that communicate political engagement, religious identities, LGBTQ + affiliation, environmental advocacy, and other collective values. Although servicescape research has traditionally emphasized atmospheric, aesthetic, and functional cues, less is known about how customers interpret ideological cues embedded within routine service encounters. Drawing on servicescape research and social identity theory, this research examines how ideological servicescape cues are associated with customer discomfort, avoidance intentions, and stated store-choice decisions. Three preregistered, scenario-based experiments in a coffee shop context examine ideological cues displayed through employees and the physical service environment. Study 1 shows that political cues generate greater discomfort than environmental and lifestyle cues, although they do not differ significantly from religious or LGBTQ + cues. Study 2 shows that matching political cues displayed across employee and environmental touchpoints generate greater discomfort than political cues displayed through a single source, while perceived authenticity does not account for this association. Study 3 shows that political cues are associated with lower stated store choice than environmental cues, particularly when matching political cues appear across employee and environmental touchpoints. Across studies, customer discomfort statistically accounts for part of the association between ideological cue conditions and avoidance intentions or stated store-choice decisions. The findings extend servicescape scholarship by showing that politically charged symbolism may shape customer responses through both cue content and cuesource configuration. Because the studies rely on scenario-based experiments and stated responses, the findings should not be interpreted as evidence of observed marketplace behavior or realized financial outcomes.

When decision makers rely on large sets of if-then decision rules to guide complex choices, information overload quickly becomes a barrier to effective use. The Dominance-based Rough Set Approach (DRSA) is a widely used preference learning method whose primary output is a set of if-then decision rules incorporating preference orders over attributes and decision classes. Although effective for knowledge representation and classification, large rule sets can overwhelm decision makers and hinder interpretation. To address this challenge, this paper proposes a new decision graph model for visualising dominance based if-then decision rules. The construction process begins with a preprocessing phase that extracts a binary decision matrix from rule descriptions. To avoid generating nonexistent rules, this matrix may require transformation through column splitting or a combination of column splitting and ordering-based strategies. The transformed matrix is then used to build a directed acyclic decision graph in which nodes represent elementary conditions and decisions, and edges denote their logical relationships. Each path from the root to a decision node corresponds to a complete decision rule. The paper introduces the core concepts of decision graphs and presents algorithms for their construction, linearisation, and exploitation. Both linearisation and exploitation algorithms are based on the Breadth-First Search graph traversal algorithm. The approach is demonstrated using real-world cultural heritage data from Europe, and expert feedback indicates that decision graphs reduce cognitive effort, improve communication, and provide a clear and accessible complement to traditional textual and tabular representations of decision rules.

Accelerating the energy transition is pivotal to achieving sustainable economic development, yet localized environmental policies can profoundly reshape the spatial allocation of corporate capital. The impact of place-based energy transition policies on firms' cross-regional investment remains largely unexplored. Existing studies primarily examine the environmental and economic consequences of such policies, while paying limited attention to their influence on firms' geographic expansion strategies. Using a sample of Chinese A-share listed firms from 2006 to 2022, we examine whether and how the New Energy Demonstration City (NEDC) pilot policy affects corporate cross-regional investment (CORI). We find that the NEDC policy significantly increases firms' CORI, indicating that firms located in pilot cities expand more actively beyond their home provinces. This finding is consistent with the resource-based view. We further find that this effect is stronger for non-state-owned firms, non-heavy-polluting firms, and firms audited by the Big Four, and remains robust to a series of robustness and endogeneity tests. Mechanism analyses show that the NEDC policy promotes CORI by reducing information asymmetry and lowering corporate risk-taking. These findings suggest that place-based energy transition policies reshape firms’ geographic allocation of resources and provide new evidence on how policy-induced environmental change affects corporate investment decisions.

While existing literature emphasizes the theoretical performance benefits of artificial intelligence (AI) in elite sports, the lived experiences, socio-technical negotiations, and future societal implications of this integration remain underexplored. Addressing this gap, this study employs an abductive, qualitative methodology utilizing the Gioia approach to analyze AI integration within elite sports ecosystems. Relying on the concept of information power, this research draws upon in-depth interviews with ten world-class informants, including Olympic and Paralympic champions as well as highly specialized medical staff. The findings reveal four core theoretical dimensions: (1) the algorithmic mediation of the biological body, (2) human-AI strategic symbiosis, (3) the data-driven reconfiguration of organizational trust, and (4) systemic implementation barriers. By conceptualizing these dimensions, the proposed model shifts the academic focus from isolated performance metrics to the holistic restructuring of high-stakes organizations. This paper forecasts the future trajectory of the algorithmic athlete, critically examining the risks of data surveillance, human intuition erosion, and the potential emergence of a technological divide. Empirically, this research provides actionable foresight for policymakers and sports managers to design robust ethical frameworks, ensuring equitable, transparent, and sustainable human-AI collaboration in future organizational models.

This paper examines the consequences of adopting ASC 606, a new revenue recognition standard, on revenue uncertainty and debt contracting, using a quasi-natural experiment surrounding its adoption. We find that affected firms experience an increase in revenue uncertainty, as indicated by both higher analyst forecast dispersion and absolute analyst forecast error. Consequently, the cost of debt rises for affected firms, as covenants are used less in debt contracts reflecting a decreased effectiveness of earnings-based covenants. The effect is mitigated by relationship lending. We also show that the decreased use of earnings-based covenants as well as the increased cost of debt dissipate over time, while the increase in revenue uncertainty persists. Our analyses document a costly transition toward a more principles-based accounting standard but also suggest that some costs are transient.