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Social relationships play an important role in organizational entrepreneurship. They are crucial to entrepreneurs’ decisions because, despite the bleeding-edge technological advancements observed nowadays, entrepreneurs as human beings will always strive to be social. During the COVID-19 pandemic many companies moved activities into the virtual world and as a result offline Social relationships became rarer, but as it turns out, even more valuable, likewise, the inter-organizational cooperation enabling many companies to survive.

This chapter aims to develop knowledge about entrepreneurs’ SR and their links with inter-organizational cooperation. The results of an integrative systematic literature review show that the concept of Social relationships, although often investigated, lacks a clear definition, conceptualization, and operationalization. This chapter revealed a great diversity of definitions for Social relationships, including different scopes of meaning and levels of analysis. The authors identify 10 building blocks and nine sources of entrepreneurs’ Social relationships. The authors offer an original typology of Social relationships using 12 criteria. Interestingly, with regard to building blocks, besides those frequently considered such as trust, reciprocity and commitment, the authors also point to others more rarely and narrowly discussed, such as gratitude, satisfaction and affection. Similarly, the authors discuss the varied scope of sources, including workplace, family/friendship, past relationships, and ethnic or religious bonds. The findings of this study point to a variety of links between Social relationships and inter-organizational cooperation, including their positive and negative influences on one another. These links appear to be extremely dynamic, bi-directional and highly complex.

Entrepreneurs are increasingly relying on artificial intelligence (AI) to assist in creating and scaling new ventures. Research on entrepreneurs’ use of AI algorithms (machine learning, natural language processing, artificial neural networks) has focused on the intra-organizational implications of AI. The purpose of this paper is to explore how entrepreneurs’ adoption of AI influences their inter- and meta-organizational relationships.

To address the limited understanding of the consequences of AI for communities of entrepreneurs, this paper develops a theory to explain how AI algorithms influence the micro (entrepreneur) and macro (system) dynamics of entrepreneurial ecosystems.

The theory’s main insight is that substituting AI for entrepreneurial ecosystem interactions influences not only entrepreneurs’ pursuit of opportunities but also the coordination of their local entrepreneurial ecosystems.

The theory contributes by drawing attention to the inter-organizational implications of AI, explaining how the decision to substitute AI for human interactions is a micro-foundation of ecosystems, and motivating a research agenda at the intersection of AI and entrepreneurial ecosystems.

Catastrophic supply chain disruptions can significantly damage the operational and financial performance of firms. While a growing body of literature on supply network structures has studied what influences supply networks' vulnerability to supply chain disruptions and capability to recover from them, it remains unclear how supply network structures change after major supply chain disruptions. We aim to provide an understanding of how these changes occur.

Using a natural experiment approach and supply network data from Factset, this study investigates how firms' supply network structures change after experiencing the catastrophic supply chain disruptions caused by the 2011 Tohoku earthquake and tsunami in Japan. We capture post-earthquake supply network changes using the measures of degree centrality and ego network density.

The results of the analysis suggest that compared to unaffected firms, the affected firms experience changes in their supply network structures tending toward lower complexity measured by in-degree centrality, out-degree centrality and ego network density.

This study contributes to social network theory and the complex adaptive supply network literature by providing empirical evidence of structural changes in supply networks after catastrophic supply chain disruptions. A managerial contribution is made by providing a reflection on why these changes might be occurring and alert firms to the challenges of managing complexity in their supply networks.

Waiting is associated with pain and stress that leads to frustration. However, consumer narratives may help cope with the stress associated with such waiting. This study aims to understand consumer waiting behaviours within online communities.

Data was gathered following a netnographic approach from a Facebook brand community (FBC) by downloading and archiving the threads related to members’ waiting behaviours. This resulted in 91 pages of data, with 438 individual comments and 179 distinct threads.

The data revealed that members of the sampled FBC exercised waiting behaviour. The authors confirm that waiting for a product is associated with both negative outcomes (frustration, boredom, etc.), but positive ones (create stronger ties with the brand and fellow members, etc.). Members of the FBC exhibited reduced consumer anxiety and stress during the waiting period.

This study found 13 waiting behaviours within the FBC and supports the idea that new value-creating behaviours are noticed within the context of FBCs.

This study focuses on waiting within a goods-based context (waiting to be served has been examined predominantly within the service sector). The study explored the behaviours of consumers who use social media to complain about extended waiting periods to receive the product along with other consumer reactions to these waiting crowds to reduce the emotional pain associated with such delays.

La espera se asocia con el dolor y el estrés, lo que lleva a la frustración. Sin embargo, el relato de otros consumidores puede ayudar a hacer frente al estrés asociado a dicha espera. Este trabajo busca entender el comportamiento de espera de los consumidores dentro de las comunidades online.

Los datos fueron recopilados siguiendo un enfoque netnográfico de una comunidad de marca en Facebook (CMF) mediante la descarga de mensajes; sólo los hilos relacionados con los comportamientos de espera de los miembros fueron descargados y archivados. Esto resultó en 91 páginas de datos con 438 comentarios individuales y 179 hilos distintos.

Los datos revelan que en Facebook se dan comportamientos de espera. Confirmamos que la espera de un producto no sólo está asociada a resultados negativos (frustración, aburrimiento, etc.), sino también a resultados positivos (crear lazos más fuertes con la marca y los compañeros). Además, se comprueba que los miembros de la CMF reducen la ansiedad y el estrés del consumidor durante el período de espera.

Este trabajo encuentra 13 comportamientos de espera dentro de una CMF

Este trabajo se centra en la espera dentro del contexto basado en los bienes (la espera para ser atendido ha sido examinada principalmente dentro del sector de servicios). Además, no sólo nos centramos en los consumidores que utilizan los medios sociales para quejarse de una espera más larga en la recepción del producto, sino también en la reacción de otros consumidores a estas esperas para reducir el dolor emocional asociado a dicho retraso.

There is some evidence to suggest that the historical challenge associated with recruiting and retaining Black and Brown Science, Technology, Engineering and Math (STEM) collegians is tied to early their teaching and learning experiences in Mathematics. This paper describes an National Science Foundation (NSF) funded project (NSF #2151043) whose goal is to attract, prepare and retain math teachers of color in high need school districts ensure that those teachers remain in the field long enough to make a meaningful impact on the minds and hearts of BIPOC students who are often, extrinsically, and intrinsically, discouraged from pursuing careers in STEM professions.

This mixed-methods study, which began in the summer of 2023, seeks to recruit, prepare, support and retain nineteen (19) Black and Brown math teachers for two (2) high need urban school districts. The expectancy value theory will be used to explain the performance, persistence, and choices of the teachers, while grounded theory will be utilized to understand the impact of the intensive mentorship and wellness coaching that applied over the first year of their preservice preparation and subsequent in-service years.

Measures of project efficacy won’t begin until 2025 and as such there are no findings or implications to draw from for the study at this time.

The intention of this paper is to augment the body of knowledge on recruiting and retaining Black and Brown math teachers for urban schools where the need for quality STEM teachers is critical.

This study aims to investigate, for the first time in the literature, the stochastic properties of the US aggregate health-care price inflation rate series, using the data on health-care inflation rates for a panel of 17 major US urban areas for the period 1966-2006.

This goal is undertaken by applying the first- and second-generation panel unit root tests and the panel stationary test developed recently by Carrion-i-Silvestre et al. (2005) that allows for endogenously determined multiple structural breaks and is flexible enough to control for the presence of cross-sectional dependence.

The empirical findings indicate that after controlling for the presence of cross-sectional dependence, finite sample bias, and asymptotic normality, the US aggregate health-care price inflation rate series can be characterized as a non-stationary process and not as a regime-wise stationary innovation process.

The research findings apply to understanding of health-care sector price escalation in US urban areas. These findings have timely implications for the understanding of the data structure and, therefore, constructs of economic models of urban health-care price inflation rates. The results confirming the presence of a unit root indicating a high degree of inflationary persistence in the health sector suggests need for further studies on health-care inflation rate persistence using the alternative measures of persistence. This study’s conclusions do not apply to non-urban areas.

The mean and variance of US urban health-care inflation rate are not constant. Therefore, insurers and policy rate setters need good understanding of the interplay of the various factors driving the explosive health-care insurance rates over the large US metropolitan landscape. The study findings have implications for health-care insurance premium rate setting, health-care inflation econometric modeling and forecasting.

Payers (private and public employers) of health-care insurance rates in US urban areas should evaluate the value of benefits received in relation to the skyrocketing rise of health-care insurance premiums.

This is the first empirical research focusing on the shape of urban health-care inflation rates in the USA.