Explore the vast range of titles available.

Research summary: This article examines the adaptation process of a large manufacturer in the Indian steel industry faced with radical sociopolitical shifts in the external ecosystem. It uses the Bower-Burgelman process model in combination with Bourdieu's praxis theory to explain the emergence of competing managerial initiatives and associated contests in the company's internal ecology of strategy-making in terms of socially acquired dispositions. It illuminates process–practice pathways through which top management's resource allocation supported changes in the efficacy of the different forms of capital of the contesting managerial classes, thereby legitimizing the daily \"doings\" of the rising class and institutionalizing a (re)defined adaptive rule structure. Managerial summary: How do managers' early influences, including family upbringing and schooling, bear upon organization's renewal strategy? Our study finds that during discontinuities imposed by socioeconomic upheavals, when organizational performance flounders, managerial initiatives are driven by deepest dispositions derived from early age socialization. Competing managerial fractions jostle to impose practices favorable to their longstanding preferences by putting their weight behind preferred product-market choices and seeking appropriate changes in the ineffective internal rule structure. Administration's challenge lies in leveraging internal contests to iteratively allocate resources in search of winning dispositions and configurations aligned with evolving social relations in the external environment. Internal availability of managerial groups from diverse social origins is crucial for the administration to reclaim organizational advantage by arbitrating between contesting practices and practitioner fortunes.

In this conceptual paper, we bring forward a new theoretical proposition in the form of an Integrated Policy-Making Process Framework. This tool arose from the discussion and hypothesis that the main models used to analyse public policies have practical and theoretical limitations and/or are due for an update, especially when it comes to the study of phenomena with many actors involved, vast legislation, large timeframes, and high degrees of complexity. Our original model encompasses the three fundamental stages of the public policy cycle (agenda; decision making; implementation). Our approach can have a wide spectrum of applications and contribute to the field of knowledge of political sciences. Our proposal of using three frameworks in an integrated way enables researchers and users to gain a holistic vision concerning policy processes, and it offers the possibility to compare and rank categories. The Integrated Policy-Making Process Framework is thus proposed as a new tool to tackle research and studies on decision making in public policies and the policy-making cycle.

The rise of AI technologies is generating novel opportunities for companies to create additional value for their customers by applying a proactive approach, managing uncertainty, and thus improving cost efficiency and increasing revenue. However, AI technology capabilities are not enough—companies need to understand how the technology can be commercialized through appropriate AI business model innovation. When emerging technologies are introduced, business-model concepts often need to be significantly altered. This is necessary to fully capitalize on disruptive technologies because it is just as important to innovate the business model as it is to build advanced technology solutions. Therefore, the purpose of this study is to explain how AI providers align value-creation and value-capture dimensions in order to develop commercially viable AI business models. To fulfill our stated purpose, this study has adopted an inductive and exploratory single case-study approach centered on a market-leading provider of AI-related services. The findings are consolidated into a process framework that explicitly illustrates the key activities that companies need to perform concerning value creation and value capture for AI business model innovation and commercialization. The framework explains that AI providers need to follow three phases—namely, identifying prerequisites for AI value creation, matching value capture mechanisms, and developing AI business model offer. We also find that AI providers need to test and develop multiple AI business models and operate them simultaneously to ensure commercial success.

Although entrepreneurship is not a new phenomenon, attempts to study it in a systematic manner are fairly recent. The field of entrepreneurship has evolved in a rather disjointed or seemingly random manner, and entrepreneurship has developed as a business discipline by borrowing, building upon, and adapting theoretical and conceptual work from such fields as sociology, psychology, anthropology, marketing, management, finance, organizational behavior, and engineering. And yet, it would appear that the volume of work attempting to describe, explain, and predict aspects of entrepreneurship has grown to a point where we can begin to develop a more complete and integrated picture. The purpose of this paper was to examine the various theoretical perspectives and frameworks of entrepreneurship and to offer an integrative perspective through a proposed \"framework of frameworks\" which ties together other existing frameworks, each of which explores a particular aspect of the overall phenomenon of entrepreneurship. The value of using a framework to explain and therefore better understand entrepreneurship is examined.

There is an increasing number of big data science projects aiming to create value for organizations by improving decision making, streamlining costs or enhancing business processes. However, many of these projects fail to deliver the expected value. It has been observed that a key reason many data science projects don’t succeed is not technical in nature, but rather, the process aspect of the project. The lack of established and mature methodologies for executing data science projects has been frequently noted as a reason for these project failures. To help move the field forward, this study presents a systematic review of research focused on the adoption of big data science process frameworks. The goal of the review was to identify (1) the key themes, with respect to current research on how teams execute data science projects, (2) the most common approaches regarding how data science projects are organized, managed and coordinated, (3) the activities involved in a data science projects life cycle, and (4) the implications for future research in this field. In short, the review identified 68 primary studies thematically classified in six categories. Two of the themes (workflow and agility) accounted for approximately 80% of the identified studies. The findings regarding workflow approaches consist mainly of adaptations to CRISP-DM ( vs entirely new proposed methodologies). With respect to agile approaches, most of the studies only explored the conceptual benefits of using an agile approach in a data science project ( vs actually evaluating an agile framework being used in a data science context). Hence, one finding from this research is that future research should explore how to best achieve the theorized benefits of agility. Another finding is the need to explore how to efficiently combine workflow and agile frameworks within a data science context to achieve a more comprehensive approach for project execution.

Wallas’ (1926) model, , is the most popular model of the creative process, coexisting with other later models. It encompasses only cognitive processes: preparation, incubation, illumination, and verification, within a limited stage of the process, excluding other crucial non-mental activities, such as the materialization of the idea or its communication. The 25 models of the creative process analyzed in this study apply a linear temporal framework to a process that is inherently nonlinear. The most popular tests and programs to evaluate and enhance creativity primarily intervene in the ideation stage, and by ignoring other parts of the creative process, they are not effective. Unlike previous models, The Houses of Creativity identify the phases with a conceptual rather than a temporal criterion, structured in a honeycomb pattern, addressing the non-linearity of the process. Additionally, its phases apply not only to creative production but to all tasks susceptible to the process. It includes: (selecting), (observing, perceiving, questioning), (informing), (ideating), (distracting and socializing), (stockpiling), (enjoying), (developing and verifying), and (communicating and convincing). A person who is only good at ideation in the cabin (according to Torrance tests) will not be able to successfully complete the creative process. This theoretical research could pave the way for developing personalized tools to assess and enhance creativity, or to form interdisciplinary teams that incorporate them.

While there is ample evidence of the decline in mental health among youth during the onset of the COVID-19 pandemic, less is known about the determinants of recovery, which is the focus of this study. Drawing on a stress process framework, this study examines the associations of changes in direct, pandemic-related, and indirect, lockdown-related stressors with life satisfaction. A novel representative, longitudinal sample of British 16–25-year-olds is used, drawing on 6 data collections between February 2021 to May 2022 (N = 6000, 51% female, 24% ethnic minority, 46% in work, 35% with higher education). Using linear fixed-effects regression models, the findings suggest a substantial improvement in life satisfaction among youth. An increasing frequency of social contacts, receding worries about career prospects and job skills learning contributed significantly to increases in life satisfaction, whereas direct, health-related COVID-19 stressors did not affect life satisfaction. Sub-group analysis suggests that women’s, adolescents’, and students’ life satisfaction responded more strongly to the stressors considered in this study. The findings highlight the positive effects of less stringent lockdown restrictions, economic recovery, and opportunities for job skills learning on youth’s happiness.

Having identified the need to conduct research on the intersection between entrepreneurship education (EE) and public policies, we carried out a systematic literature review on decision-making processes regarding the implementation of education for entrepreneurship programs in schools and the introduction of this topic in the policy-making process. This SLR followed every process inherent to its well-established protocol. The research undertaken confirmed that the understanding of decision processes associated with the implementation of EE programs is not only a “missing link” in the discussions about the way in which countries manage situations related to EE, but also a gap in academic knowledge. Indeed, the SLR process included only nine articles in the final review (obtained through a methodology based on an algorithm)—which is a clear sign that further scientific research around this specific topic is needed. The articles included in the final review suggest that: (i) entrepreneurship is fundamental to the progress and evolution of countries and their regions, (ii) there is evidence that EE is central to a more entrepreneurial youth, and (iii) the successful implementation of recommendations from regulatory institutions is based on political commitment and implementation capacities.

The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A's future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and outcomes for each party, these correlations form a polytope whose facets define causal inequalities. The case of quantum correlations in this paradigm is captured by the process matrix formalism. We investigate the link between causality and the closely related notion of causal separability of quantum processes, which we here define rigorously in analogy with the link between Bell locality and separability of quantum states. We show that causality and causal separability are not equivalent in general by giving an example of a physically admissible tripartite quantum process that is causal but not causally separable. We also show that there are causally separable quantum processes that become non-causal if extended by supplying the parties with entangled ancillas. This motivates the concepts of extensibly causal and extensibly causally separable (ECS) processes, for which the respective property remains invariant under extension. We characterize the class of ECS quantum processes in the tripartite case via simple conditions on the form of the process matrix. We show that the processes realizable by classically controlled quantum circuits are ECS and conjecture that the reverse also holds.

Under the random design of longitudinal data, observation times are irregular, and there are mainly two frameworks for analyzing such kind of longitudinal data. One is the clustered data framework and the other is the counting process framework. In this paper, we give a thorough comparison of these two frameworks in terms of data structure, model assumptions and estimation procedures. We find that modeling the observation times in the counting process framework will not gain any efficiency when the observation times are correlated with covariates but independent of the longitudinal response given covariates. Some simulation studies are conducted to compare the finite sample behaviors of the related estimators, and a real data analysis of the Alzheimer's disease study is implemented for further comparison.