Conference Agenda

Recent years have seen several calls to take time “seriously” in organization and management studies. This also applies to the field of interorganizational relationships (IORs) and networks (IONs), where attention needs to be devoted to network change, co-evolution of network and actor attributes, and relational events (i.e., sequences of discrete actions between actors, such as economic transactions). While relational (i.e., network) effects such as repetition and reciprocation shape these events, the speeds at which they unfold remain largely underexplored. Addressing this research gap, we argue that relational effect speeds hold substantive meaning. Studying them can provide insights into IOR functioning and dynamics. To better understand the variance in relational effect speeds, we introduce a computational algorithm rooted in relational event modeling methodology that accounts for effect censoring. Applying this algorithm to compute the speeds of relational effects guiding emission allowance exchanges in the EU Emission Trading System (ETS), we find that their variance is non-random and systematically differs within and between pairs of transacting firms. Exploring the possible sources of observed variability using survival analysis, we find that country, industry, ownership similarity, and the number of prior transactions among organizations play a determining role in the speed of transaction repetition, transaction reciprocation, and transactions with partners of partners. With this, we advance the currently limited understanding of relational effects speeds, specifically their antecedents, paving the way for future empirical research while further enriching social network theory with a dimension.

The global transition towards sustainable energy has significantly impacted the European trade landscape for battery and automotive products. This study analyzes official European trade data (Comext) from 2020 to 2024, focusing on the evolution of supply relationships in response to the ecological transition.

Regarding automotive, the findings indicate a significant drop around early 2020, likely due to the impact of the COVID-19 pandemic. This is followed by a strong recovery and fluctuating but generally stable volumes between 2021 and 2023. In late 2024, a sharp increase indicates a surge in trade activity. Meanwhile, the battery market shows an overall steady growth, with cyclic behavior including a strong dip in early 2020 and 2023, followed by a recovery phase in 2024.

These trends suggest a dynamic restructuring of supply chains, likely driven by the accelerated adoption of electric vehicles and related technological advancements. The observed fluctuations may indicate market stabilization or shifts in trade policies and regional production strategies. Using network analysis to assess the centrality and the strategic significance of nations within the trade network, this study provides deeper insights into the evolving structure of European trade. It highlights the ongoing transformation of trade patterns in response to the global ecological transition and underscores the need for continuous monitoring of these supply relationships.

The literature on product space and economic complexity suggests that countries are more likely to begin exporting new products closely related to those already in their export basket. Complex products, in turn, foster economic development by paving the way for new capabilities and a broader range of exports. However, for developing countries, the products closest to their existing export baskets tend to be less complex. Recent research indicates that certain countries have managed to transform their export baskets in ways that reverse this negative correlation between nearness and complexity. In this study, I investigate how these countries overturned the negative correlation, enabling complex products—initially located far from their export baskets—to move closer. To do this, I decompose the positive shift in the correlation between nearness and complexity into three components: complex products moving closer to the export basket, nearby products becoming more complex, and products both moving closer and becoming more complex. My findings show that in past cases of successful export transformation, the dominant factor was the movement of complex products closer to the export basket. I examine how countries such as Ireland and South Korea brought complex products closer to their export baskets. By tracing the network links among products, I identify the connections that enabled these countries to export increasingly complex products.

A key focus of international trade literature is understanding the formation of trade agreements. One major challenge lies in capturing the fact that country pairs form treaties based on bilateral characteristics and in response to the broader web of agreements. This interdependence between agreements violates the assumption of independent observations. Empirical studies have adopted proxies and modelling techniques to mitigate multicollinearity and address endogenous processes, identifying key determinants across economic, institutional, geographical, and political dimensions. Foundational papers have explored political economy theories, such as the domino effect—where signing agreements may lead to new ones—and path dependence, emphasising how past agreements shape future decisions. While these studies provide valuable insights, they fall short of capturing the complexity of today's interconnected trade landscape. They focus primarily on predicting dyadic agreement formation. Recent efforts have employed stochastic actor-oriented models to study trade agreements, demonstrating the importance of network effects. However, these models fall short of incorporating all dimensions previously identified as determinants in tie-based models.

In this paper, I build on insights from trade literature by employing a Dynamic Actor Network Model (DyNAM) to study trade agreement formation. This allows me to test how political economy theories hold under models that fully account for relational dependencies and endogenous processes. I analyse over 600 treaties notified to the World Trade Organisation spanning 1948-2023, complementing this with country- and dyad-level data from well-established trade databases. Finally, I assess the model's predictive power by evaluating whether its calibration aligns with ongoing trade agreement negotiations.

Current models of status competitions in online, networks-supported markets (aka influencer economies) are meritocratic: they emphasize the superior skills of influencers as preceding network initiation. As a consequence of displaying better skills, influencers receive requests for connections and form networks of followers. Alternative to these models, we highlight the role of network initiation as an informal mechanism of competition control and in attaining influencer positions. Using a dataset with over 51,000 ties from an influencer economy, we explore how participants control competition for status by initiating link requests. Participants initiate ties and seek competitions following a change in performance. They have a higher likelihood of accepting ties after improved performance. Status competitors are more likely to accept ties from senders who perform worse and rank lower in their networks. The outcomes of this initiation dynamic are multiple networks in which members maintain ties with those performing worse than them. Members performing less well repeatedly seek new ties and are more likely to receive link requests from others. We argue that status competitions lead to multiple networks (instead of a unique one), within which influencers would dominate, performance-wise, a group of followers. Theoretically, we draw attention to initiation processes as informal mechanisms of competition control. Empirically, we highlight the dynamics of status competitions in social media-supported economies.