European School of Management and Technology

This study explores the impact of regional conflicts and the COVID-19 pandemic (2019–2024) on real estate markets in the Gulf Cooperation Council (GCC) countries and Turkey, highlighting their contrasting responses to overlapping crises. Regional wars, including the Yemen conflict and tensions in Syria, compounded by the global pandemic, disrupted economic activities, investment flows, and market stability in both regions. In the GCC, strong fiscal reserves and timely government stimulus measures played a crucial role in cushioning the impact, stabilizing property values, and sustaining construction activities. The pandemic-induced shift toward remote work and lifestyle changes also spurred demand for residential and mixed-use properties, especially in suburban areas. In contrast, Turkey’s real estate market showed heightened vulnerability, driven by geopolitical tensions, inflationary pressures, and currency fluctuations. Despite a short-term boost from foreign property investors seeking to leverage currency depreciation, Turkey faced prolonged volatility and slower recovery. This analysis identifies key trends, including construction delays, evolving investor sentiment, and changes in demand patterns, providing a comparative perspective on market resilience. The findings emphasize the need for adaptive policies, economic diversification, and robust crisis management strategies to mitigate risks and foster sustainable growth in real estate markets amid global and regional uncertainties.

We generalize the captive-and-shopper model of sales to allow asymmetries in production costs and captive audiences, in oligopoly. Bothkinds of asymmetry determine the firms that compete (via randomized sales) to serve the price-comparing shoppers, while other firms exploit their captive audiences. In contrast to a model with symmetric costs (but asymmetric captive audiences), there are natural situations in which more than two firms use sales by engaging in pairwisebattles across distinct price intervals. We also study the choice of production technologies via innovation and extensions to consider costly acquisitions of captives and shoppers, and captives’ choice of captor. (JEL D11, D21, D43, L14, O31)

Scholars have used network concepts and methods to explain a wide range of organizational phenomena. Despite this broad interest, prevalent approaches tend to neglect a critical constitutive dimension of network ties: their history. We invoke the concept of relational history to signify the temporal path and biography of a network tie. We argue that a network tie’s history shapes what the relationship means to the parties involved, what can be accomplished in and through the relationship, and how the relationship might further evolve. In directing the attention of organizational network researchers to the relational history of network ties, we aim to complement and expand the field’s predominant focus on structuralist, ahistorical and reductionist views of relationships and networks. We highlight how a relational history perspective has the potential to reorient core debates in organizational network research, such as those around network cognition, agency, brokerage and multiplexity. We also illustrate the implications of adopting the proposed relational history perspective for the data and methods used by network researchers. Overall, we argue that accounting for relational history can stimulate new research across a broad spectrum of organizational phenomena.

Despite the growing literature on AI, there are still important questions to answer about its impact on companies. For AI to change our work lives, it can depend on complementary organizational change to translate technological change into a change of daily work routines. Failure to scrutinize the organizational adjustments companies make in response to these technological advances will lead to an incomplete comprehension of the evolution of work dynamics. We lack an understanding of the barriers that prevent companies from integrating AI into their current work processes, the areas where reorganization contributes to overcoming these barriers, and identify the mechanisms whereby organizational change complements AI adoption. We need to better understand how AI is redefining the boundaries of a company, specifically how they are changing the organization of work both inside and outside the firm. This symposium contains new research aimed at filling this research gap on the new frontiers of organizing work within firms. Each project offers fresh perspectives for the successful integration of AI within the existing information processing routines of the firm. The Role of External and Internal Human Capital in Shaping AI Human Capital Contagion Effects Author: Manav Raj; The Wharton School, U. of Pennsylvania Author: Robert Channing Seamans; NYU Stern Artificial Intelligence and Organizational Change Author: Matthias Qian; ESMT Berlin Author: Manuel Hoffmann; Harvard Business School The Crowdless Future? How Generative AI is Shaping the Future of Human Crowdsourcing Author: Miaomiao Zhang; Harvard Business School Author: Léonard Boussioux; U. of Washington, Michael G. Foster School of Business Author: Karim R. Lakhani; Harvard U. Machine Predictions and Causal Explanations: Evidence from a Field Experiment Author: Hyunjin Kim; INSEAD Author: Xi Kang; Vanderbilt U. The Labor Implications of Generative AI: Evidence from GitHub Copilot Author: Manuel Hoffmann; Harvard Business School Author: Frank Nagle; Harvard Business School Author: Sam Boysel; Harvard Business School

Many-analysts studies explore how well an empirical claim withstands plausible alternative analyses of the same dataset by multiple, independent analysis teams. Conclusions from these studies typically rely on a single outcome metric (e.g. effect size) provided by each analysis team. Although informative about the range of plausible effects in a dataset, a single effect size from each team does not provide a complete, nuanced understanding of how analysis choices are related to the outcome. We used the Delphi consensus technique with input from 37 experts to develop an 18-item subjective evidence evaluation survey (SEES) to evaluate how each analysis team views the methodological appropriateness of the research design and the strength of evidence for the hypothesis. We illustrate the usefulness of the SEES in providing richer evidence assessment with pilot data from a previous many-analysts study.