Search results

The executive stock option (ESO) valuation model developed in this research amends the popular exchange traded option pricing models such as Black and Scholes (1973), Whaley (1981), and Cox, Ross, and Rubinstein (1979) to include economic features of the ESO contract that previously have been ignored. One of these features is the non‐transferability of the ESO, which creates a situation where the ESO might be exercised when an otherwise identical exchange traded option would not. Another feature is the hybrid nature of the ESO; it is not solely either an American option or a European option. The results of the comparative statics indicate that the impact of the non‐transferability of the ESO value is significant, whereas the hybrid feature of the ESO results in values that are very similar to American option values. The economic implication is that if an American or European option model is used to value ESO's, the probability is very high that a wealth transfer between management and shareholders will occur.

Innovation management in the healthcare sector has undergone significant evolutions over the last decades. These evolutions have been investigated from a variety of perspectives: clusters, ecosystems of innovation, digital ecosystems and regional ecosystems, but the dynamics of networks have seldom been analyzed under the lenses of entrepreneurial ecosystems (EEs). As identified by Cao and Shi (2020), the literature is silent about the organization of resource allocation systems for network orchestration in EEs. This article investigates these elements in the healthcare sector. It discusses the strategic role played by entrepreneurial support organizations (ESOs) in resource allocation and elaborates on the distinction between sponsored and nonsponsored ESOs in EEs. ESOs are active in network orchestration. The literature explains that ESOs lift organizational, institutional and cultural barriers, and support entrepreneurs' access to cognitive and technological resources. However, allocation models are not yet discussed. Therefore, our research questions are as follows: What is the resource allocation model in healthcare-related EEs? What is the role played by sponsored and nonsponsored ESOs as regards resource allocation to support the emergence and development of EEs in the healthcare sector?

The article offers an explanatory, exploratory, and theory-building investigation. The research design offers an abductive research protocol and multi-level analysis of seven (sponsored and nonsponsored) ESOs active in French healthcare ecosystems. Field research elaborates on semi-structured interviews collected between 2016 and 2022.

This article shows explicit complementarities between top-down and bottom-up resource allocation approaches supported by ESOs in the healthcare sector. Despite explicit originalities in each approach, no network orchestration model prevails. Multi-polar coordination is the rule. Entrepreneurs' access to critical technological and cognitive resources is based on resource allocation modalities that differ for sponsored versus nonsponsored ESOs. Emerging from field research, this research also shows that sponsored and nonsponsored ESOs manage their roles in different ways because they confront original issues about organizational legitimacy.

Beyond the results listed above, the main originalities of the paper relate to the instantiation of multi-level analysis operated during field research and to the confrontation between sponsored versus nonsponsored ESOs in the domain of healthcare-related innovation management. This research shows that ESOs have practical relevance because they build original routes for resource allocation and network orchestration in EEs. Each ESO category (sponsored versus nonsponsored) provides original support for resource allocation. The ESO's legitimacy is inferred either from the sponsor or the services delivered to end-users. This research leads to propositions for future research and recommendations for practitioners: ESO managers, entrepreneurs, and policymakers.

This study aims to investigate how a French multinational enterprise (MNE) is developing employee stock ownership (ESO) in its subsidiaries in Peru and Mexico, both Latin American countries with deep social and economic inequalities.

This is a qualitative case study which conducted interviews with representatives of the French MNE and its subsidiaries in Peru and Mexico.

The employee stock purchase plans offered by the company to its employees support the achievement of the sustainable development goals (SDGs) 1, 8 and 10 in these countries.

The authors argue that MNEs could become flagships in the SDG achievement in emerging economies.

By contributing to better workplace outcomes and enhanced corporate performance, ESO is in line with SDG 8. ESO also fulfills SDGs 1 and 10 by allowing employees to build up savings and wealth, whose lack is the main source of inequality and poverty. Reciprocity and binary economics theories explain these relationships.

Drawing on current development practice and literature on Entrepreneurial support organizations (ESO), such as accelerators, incubators, and labs, this chapter explores how refugees in Uganda are supported through entrepreneurial approaches. Following an exploratory method, interviews were conducted with proprietors and managers of ESO in Kampala, the capital city of Uganda. The findings show that while the majority of these organizations purport to follow an innovation discourse, in reality, they support refugees through a mixture of inclusion, resilience, and innovation-led approaches. Inclusion-led approaches focus on basic language skills, establishing peer relationships, and access to survival essentials. Resilience-led approaches nurture livelihood skills, building community ties, and access to seed-corn grants. Innovation-led approaches develop entrepreneurial skills, establishing extra-local connectivity and access to micro-finance. This chapter highlights the critical importance of inclusion and resilience-led approaches in developing supportive ESO for refugees.

This review addresses how student organizations are conceptually framed in the scholarly literature—organizations the authors referred to as “ethnicized student organizations” or “ESOs,” which include both Black Indigenous and People of Color (BIPOC) student organizations and ethnically white student organizations such as mainstream fraternities/sororities and clubs that are normalized as not having a racial/ethnic affiliation.

Critical race theory informs the analysis of 175 articles that address ESOs from 2002 to 2016.

Analysis revealed that a majority of scholarship conceptualizes ESOs in ways that can minimize the role of institutional whiteness where they are positioned as either serving or hindering both individual students and institutional goals. Findings also reveal a smaller body of literature that emphasized institutionalized power dynamics and honors the transformative work of BIPOC students through ESOs.

Despite widespread public commitments to diversity among institutions, whiteness remains a core institutional presence. This study illustrates the relationships among student organizations, white supremacy and higher education transformation.

Employee Stock Options [ESOs] have been used widely as a component of employees' compensation. To maximise the incentive effect of these options it is very important to understand the exercise decision of the employees. This is an important financial decision that is dependent on both rational and psychological factors. This paper aims to study the mediating role of Herding Bias on Personality Traits and the employees' decision to exercise ESOs.

The data were collected through a self-structured questionnaire from 210 employees of Banks and NBFCs [Non-Banking Financial Companies] who have received and exercised the ESOs. SPSS MACRO version 25 was used to understand the mediational effect of Herding Bias on Personality Traits and Employees' decision to exercise their ESOs.

The results showed that Personality Traits affect the employees' decision to exercise their ESOs. The study also shows a partial negative mediating effect of Herding Bias on Personality Traits and employees' decision to exercise ESOs.

Limited study has been conducted on how the employees make their decision to exercise ESOs. Although extant studies have touched upon the importance of including behavioural biases in ascertaining the exercise decision of the employees, the predictors of the behavioural biases have not been studied under this context. To the best of the author's knowledge, this study is the first in itself to study the inter-linkage between Personality Traits, Herding Bias and employees' decision to exercise ESOs.

To understand how narratives used by entrepreneurial support organizations (ESOs) in Detroit's growing entrepreneurial ecosystem shape transitional entrepreneurs' social reality. We offer theoretical and practical insights to elicit critical support, formulate policies and programs and guide ongoing empirical examination of transitional entrepreneurship.

We adopt a multi-case study approach, looking at two ESOs in Detroit: one focused on promoting high-growth entrepreneurship and securing financial capital for technology entrepreneurs, the other focused on promoting everyday entrepreneurship (especially among underserved communities) and amassing a more diverse array of resources. We conduct a thematic analysis of organizational texts and interview data with ESO leaders.

ESO narratives shape Detroit's transitional entrepreneurs by constructing entrepreneurs' social identity, orienting them to the ecosystem and envisioning a collective future in which transitional entrepreneurs are key.

This study offers insight into the definition of transitional entrepreneurs by extending existing conceptions by highlighting the role of institutional actors, like ESOs, and the narratives they adopt in shaping opportunities and challenges for transitional entrepreneurs. Moreover, we push the boundaries of transitional entrepreneurship, including technology start-up entrepreneurs in the definition and call attention to the role of transitional entrepreneurs in post-industrial cities by showcasing their role in community and urban development.

Introduces a faster and improved structural optimisation method which combines fixed grid finite element analysis (FG FEA) and evolutionary structural optimisation (ESO). ESO optimises a structure by removing a few elements at every iteration. FG methods allow fast mesh generation, fast solution and fast re‐evaluation of the modified meshes. The implementation of FG into the ESO process eliminates the need for regenerating the mesh and a few arithmetic calculations replace the full regeneration of the stiffness matrix every time the structure is modified. This greatly reduces the solution time, and the examples presented in this paper demonstrate and validate the method.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The purpose of this paper is to present a constraint and corresponding algorithm enhancing the evolutionary structural optimization (ESO) method, aiming to circumvent its structure break down problem in some special cases, such as the tie-beam problem.

A virtual soft material introduced to an element will change the stiffness of the element and may consequently change the stress distribution of that element and its neighbors. With this property, the virtual stiffness of the selected element is calculated and the threshold of the stress changes is derived. The stress threshold is used to evaluate the role of an element on the load path and therefore decide the contribution of the element to the structure. Adding this checking operation into the original ESO iterations enables validation of element removal.

The reason for structure break down with the ESO method is that the element removal criterion of ESO only works for certain optimal objectives. It cannot guarantee that the structure does not fail. The proposed operation offers a stronger and stricter constraint condition for ESO’s element removal process, preventing the structure from breaking down in some special cases.

The tests on several examples reported in the literature show that the proposed method has the same ability to achieve an optimum solution as the original ESO methods do, while avoiding incorrect deletion of structurally important elements. The benchmark tie-beam problem is solved successfully with this algorithm. The method can be used in other situations as well.