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Prior research into multinational enterprises’ (MNEs) entrepreneurial initiatives has drawn from various theoretical bases, including entrepreneurial cognition, knowledge-based view, and management control theory. Empirical studies and cases have consistently pointed to the temporal dimension, highlighting the dynamic elements of learning, capability development and evolution, and consequences of conflict. By incorporating theory on time use into the analysis, we develop a new theoretical insight regarding the temporal dimension of MNE entrepreneurial initiatives. Our analysis offers a basis for a more explicit focus on time use in studies of entrepreneurial initiatives in MNEs than has been offered to date. Implications for research and practice are discussed.

We are interested in modeling the impact of spatial and interindustry dependence on firm-level innovation of Chinese firms The existence of network ties between cities imply that changes taking place in one city could influence innovation by firms in nearby cities (local spatial spillovers), or set in motion a series of spatial diffusion and feedback impacts across multiple cities (global spatial spillovers). We use the term local spatial spillovers to reflect a scenario where only immediately neighboring cities are impacted, whereas the term global spatial spillovers represent a situation where impacts fall on neighboring cities, as well as higher order neighbors (neighbors to the neighboring cities, neighbors to the neighbors of the neighbors, and so on). Global spatial spillovers also involve feedback impacts from neighboring cities, and imply the existence of a wider diffusion of impacts over space (higher order neighbors).

Similarly, the existence of national interindustry input-output ties implies that changes occurring in one industry could influence innovation by firms operating in directly related industries (local interindustry spillovers), or set in motion a series of in interindustry diffusion and feedback impacts across multiple industries (global interindustry spillovers).

Typical linear models of firm-level innovation based on knowledge production functions would rely on city- and industry-specific fixed effects to allow for differences in the level of innovation by firms located in different cities and operating in different industries. This approach however ignores the fact that, spatial dependence between cities and interindustry dependence arising from input-output relationships, may imply interaction, not simply heterogeneity across cities and industries.

We construct a Bayesian hierarchical model that allows for both city- and industry-level interaction (global spillovers) and subsumes other innovation scenarios such as: (1) heterogeneity that implies level differences (fixed effects) and (2) contextual effects that imply local spillovers as special cases.

We argue that our understanding of how institutions matter has been undermined by a piecemeal approach to temporality in institutional analyses. This paper addresses this shortcoming in the literature. We bring temporality to the fore by conceptualizing practices, which constitute institutions, as understood, situated, and coordinated in time by temporal structures. We elaborate an integrated framework of temporal structures that consist of three types: temporal patterns, temporal conceptions, and temporal orientations – and outline how each type contributes to the reproduction of practices. We discuss the implications of this framework for sustainability initiatives and conclude by suggesting future avenues of research on the temporal foundations of institutions.

This chapter explores how the intersections of race, gender, and class combine to affect Black undergraduate women and their engagement with high impact practices (HIPs). Specifically, this chapter describes the extent to which Black undergraduate women engaged in HIPs of service-learning, research with faculty, and internships; describes factors that contributed to their engagement (or non-engagement) in the HIPs, while attending a PWI; and explores the role race, gender, and class had on engagement (or non-engagement) with these HIPs. This study used a two-phase sequential explanatory mixed methods approach, which consisted of an electronic survey (n = 190) and semi-structured interviews (n = 10). Survey items were taken from the National Survey of Student Engagement, The College Student Report. The study was conceptually grounded by intersectionality, which allowed an exploration of how social inequalities and power relations intersect to potentially influence the college experience of Black women. Findings indicate that Black women are engaging with HIPs. Factors contributing to engagement included positive faculty interactions, desire to make connections on campus and the need to acquire real world work experience. Factors contributing to non-engagement included lack of knowledge regarding HIPs and the impact of the campus climate on students' sense of belonging and occurrences of stereotype threat.

The purpose of this chapter is to explore the Occupational Course of Study (OCS) program through blended learning courses offered through the North Carolina Virtual Public School. In this program, students take classes online with a virtual content area teacher and meet in a face-to-face setting with a certified special education teacher.

This chapter offers a practical exploration of the OCS program. Its intention is to offer insight into the perspectives of virtual teachers and face-to-face teachers and provide an understanding of how this type of blended learning has the potential to deliver high quality academic coursework targeted to meet individual learning needs.

This blended environment format is a viable method for helping highly qualified content area teachers and teachers with disabilities work together to meet the individual learning needs of students with disabilities.

The OCS program is able to support large numbers of students who need transition services. Evaluation on this program reveals that collaboration between various educational professionals supports learning outcomes for students.

Many K12 districts offer alternative diplomas for students with exceptionalities, with a goal of preparing students for their transition to postsecondary employment and independent living. This chapter offers a practical description of this program for the benefit of other systems that may want to consider this model.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.