nep-spo 2026-04-27 papers

on Sports and Economics

Issue of 2026–04–27
two papers chosen by
Humberto Barreto, DePauw University

Artificial Intelligence as a Strategic Resource in NBA Management By Raymond Corona
Gender Mix and Team Performance: Evidence from Obstetrics By Ambar La Forgia; Manasvini Singh

Artificial Intelligence as a Strategic Resource in NBA Management

By:	Raymond Corona (Capitol Technology University, Laurel, MD, USA)
Abstract:	Artificial intelligence has rapidly shifted from a peripheral technological tool to a central driver of innovation across industries, and professional sports are no exception. Within the National Basketball Association, AI applications have expanded beyond performance analytics into strategic, operational, and managerial domains. This paper positions AI as a strategic resource by applying the Resource-Based Theory (RBT), which emphasizes the importance of valuable, rare, inimitable, and non-substitutable resources in sustaining competitive advantage. Prior studies have illustrated the transformative role of analytics in player evaluation, injury prevention, tactical adjustments, and fan engagement, yet there is limited research that systematically frames these developments through the lens of RBT. By synthesizing existing literature and NBA-specific studies, this research argues that AI functions as a dynamic asset that enhances talent management, strengthens decision-making, and optimizes organizational efficiency. The methodology relies on a quantitative assessment of secondary data and prior empirical studies that measure AI-driven outcomes in basketball contexts, including performance metrics, coaching decisions, and operational cost savings. Findings indicate that NBA franchises integrating advanced analytics and AI systems, such as micromovement tracking and predictive modeling, achieve measurable advantages in player utilization, game planning, and resource allocation compared to less technologically adaptive organizations. Additionally, the results highlight that investment in AI infrastructure correlates with long-term organizational resilience and sustained success, supporting RBTâ€™s assertion that strategic resources underpin competitive positioning. Discussion focuses on the implications for league-wide equity, as disparities in technological adoption may widen performance gaps, and considers the potential for AI to evolve as both a tangible and intangible asset that redefines how NBA teams conceptualize value creation. Future opportunities include the expansion of AI into fan personalization, virtual and augmented reality experiences, and enhanced global market strategies, all of which further illustrate AIâ€™s role as a foundational resource for modern sports management. This paper contributes to academic discourse by extending RBT into the sports industry while offering practical insights for NBA executives, coaches, and policymakers seeking to leverage AI for strategic advantage.
Keywords:	Artificial Intelligence, National Basketball Association, Resource-Based Theory, Competitive Advantage, Sports Analytics, Talent Management, Strategic DecisionMaking, Organizational Efficiency
Date:	2026–03
URL:	https://d.repec.org/n?u=RePEc:smo:raiswp:0629

Gender Mix and Team Performance: Evidence from Obstetrics

By:	Ambar La Forgia; Manasvini Singh
Abstract:	We investigate how the gender mix of expert teams affects performance in a high-stakes setting: childbirth. Using data on 2.5 million births, we exploit the quasi-exogenous assignment of patients to two-member obstetrician teams (Lead–Assisting), and find that: (i) female-only teams achieve the best maternal outcomes, whereas male-only teams have the worst; and (ii) female-led mixed-gender teams perform worse than male-led ones. Specifically, severe maternal complications are 15.8% higher in male-only teams and 7.1-10.8% higher in mixed-gender teams compared to female-only teams. These patterns cannot be explained by patient risk, endogenous team formation, or physician preferences for discretionary practices like C-sections. Instead, gender mix directly affects team decisions and performance, likely through gender norms — a mechanism supported by two findings. First, gender mix affects how closely team decisions reflect member preferences, with female-only teams being especially skilled at this process, possibly due to more collaborative decision-making. Second, gender mix affects team resilience, with female-led mixed gender teams performing especially poorly under challenging conditions (e.g., limited team familiarity), possibly because female leaders invert traditional gender norms. We also document other notable patterns: female-only teams not only achieve the lowest complication rates for Black women, but are also the only team type to have no racial disparity in maternal outcomes. Overall, this study provides new insights into gender dynamics in expert teams, informing managerial efforts to support effective collaboration in increasingly diverse workplaces.
JEL:	D91 I1 J16 M54
Date:	2026–04
URL:	https://d.repec.org/n?u=RePEc:nbr:nberwo:35084

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