Multilevel Framework Research Articles

Empowering local arts organizations and governance: The case of Gordon Square Arts District in Cleveland, Ohio

This study argues for the significance of the network governance model in bottom-up cultural district development aimed at neighborhood revitalization. This study, employing a multi-level policy design framework in a case study of the Gordon Square Arts District in Cleveland, Ohio, found that having a formalized governance structure and ongoing technical assistance empowered local arts organizations to succeed in advocating and bargaining for their organizational interests and needs as well as the broader needs of their community. Both network governance and technical assistance created more opportunities to achieve a relatively balanced power of cultural and non-cultural actors in decision-making on policy objectives, mechanisms, implementation tools, and solutions against the issue of marginalization caused by displacement via market-driven overdevelopment.

Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)

Transformers are the most important elements in the power system. Due to their mass and complexity, they require constant monitoring and maintenance. Maintenance of power transformers increases the availability of the power system. The large number of substations and the specifics of their locations make condition-based maintenance (CBM) useful as part of the system's on-demand response. Unlike other system responses, the transformer contains a large amount of uncertain information, both qualitative and numerical. A large amount of information is necessary to implement CBM, but due to the often incomplete information, an analysis tool is essential. In this paper, a multi-level condition assessment framework based on evidential reasoning is proposed. A model for condition-based maintenance of a power transformer and procedures for the aggregation process based on evidential reasoning are presented. The implementation of the decomposition model with appropriate weights of a baseline and general attributes was made. Based on the decomposition model, the data and ratings of baseline attributes were collected. By carrying out the aggregation process, the ratings of the baseline attributes, as well as the ratings of the condition of the individual elements and the overall rating of the system condition as a whole, for several points in time, were obtained. The scientific contribution of the work is the proposal of an analysis that provides an insight into the condition of a complex technical system based on a single numerical value, thus determining its priority in the maintenance process.

Contact-number-driven virus evolution: A multi-level modeling framework for the evolution of acute or persistent RNA virus infection.

Viruses evolve in infected host populations, and host population dynamics affect viral evolution. RNA viruses with a short duration of infection and a high peak viral load, such as SARS-CoV-2, are maintained in human populations. By contrast, RNA viruses characterized by a long infection duration and a low peak viral load (e.g., borna disease virus) can be maintained in nonhuman populations, and the process of the evolution of persistent viruses has rarely been explored. Here, using a multi-level modeling approach including both individual-level virus infection dynamics and population-scale transmission, we consider virus evolution based on the host environment, specifically, the effect of the contact history of infected hosts. We found that, with a highly dense contact history, viruses with a high virus production rate but low accuracy are likely to be optimal, resulting in a short infectious period with a high peak viral load. In contrast, with a low-density contact history, viral evolution is toward low virus production but high accuracy, resulting in long infection durations with low peak viral load. Our study sheds light on the origin of persistent viruses and why acute viral infections but not persistent virus infection tends to prevail in human society.

Empathy Is Not the Way, Critical and Creative Thinking for Design for Sustainability Transitions

Empathy Is Not the Way, Critical and Creative Thinking for Design for Sustainability Transitions

Understanding the complexities of mathematical cognition: A multi-level framework.

Mathematics skills are associated with future employment, well-being, and quality of life. However, many adults and children fail to learn the mathematics skills they require. To improve this situation, we need to have a better understanding of the processes of learning and performing mathematics. Over the past two decades, there has been a substantial growth in psychological research focusing on mathematics. However, to make further progress, we need to pay greater attention to the nature of, and multiple elements involved in, mathematical cognition. Mathematics is not a single construct; rather, overall mathematics achievement is comprised of proficiency with specific components of mathematics (e.g., number fact knowledge, algebraic thinking), which in turn recruit basic mathematical processes (e.g., magnitude comparison, pattern recognition). General cognitive skills and different learning experiences influence the development of each component of mathematics as well as the links between them. Here, I propose and provide evidence for a framework that structures how these components of mathematics fit together. This framework allows us to make sense of the proliferation of empirical findings concerning influences on mathematical cognition and can guide the questions we ask, identifying where we are missing both research evidence and models of specific mechanisms.

AI-Augmented HRM: Literature review and a proposed multilevel framework for future research

The research using artificial intelligence (AI) applications in HRM functional areas has gained much traction and a steep surge over the last three years. The extant literature observes that contemporary AI applications have augmented HR functionalities. AI-Augmented HRM HRM(AI) has assumed strategic importance for achieving HRM domain-level outcomes and organisational outcomes for a sustainable competitive advantage. Moreover, there is increasing evidence of literature reviews pertaining to the use of AI applications in different management disciplines (i.e., marketing, supply chain, accounting, hospitality, and education). There is a considerable gap in existing studies regarding a focused, systematic literature review on HRM(AI), specifically for a multilevel framework that can offer research scholars a platform to conduct potential future research. To address this gap, the authors present a systematic literature review (SLR) of 56 articles published in 35 peer-reviewed academic journals from October 1990 to December 2021. The purpose is to analyse the context (i.e., chronological distribution, geographic spread, sector-wise distribution, theories, and methods used) and the theoretical content (key themes) of HRM(AI) research and identify gaps to present a robust multilevel framework for future research. Based upon this SLR, the authors identify noticeable research gaps, mainly stemming from - unequal distribution of previous HRM(AI) research in terms of the smaller number of sector/country-specific studies, absence of sound theoretical base/frameworks, more research on routine HR functions(i.e. recruitment and selection) and significantly less empirical research. We also found minimal research evidence that links HRM(AI) and organisational-level outcomes. To overcome this gap, we propose a multilevel framework that offers a platform for future researchers to draw linkage among diverse variables starting from the contextual level to HRM and organisational level outcomes that eventually enhance operational and financial organisational performance.

Examining heterogeneity of education intervention effects using quantile mixed models: a re-analysis of a cluster-randomized controlled trial of a fluency-based mathematics intervention

ABSTRACT Traditionally, cluster randomized controlled trials are analyzed with the average intervention effect of interest. However, in populations that contain higher degrees of heterogeneity or variation may differ across different values of a covariate, which may not be optimal. Within education and social science contexts, exploring the variation in magnitude of treatment effect at different points in the population can indicate where the intervention is most effective rather than assuming an average effect. Data from [Owen, K.L., et al., 2021. Implementation support improves outcomes of a fluency-based mathematics strategy: A cluster-randomized controlled trial. Journal of research on educational effectiveness, 14 (3), 523–542.] were reanalyzed using three modeling approaches: conditional mean-modeling reporting the average treatment effect using linear mixed models, and two quantile regression-based methods. Quantile regressions report the quantile treatment effects at different percentiles: 10th, 25th, 50th, 75th and 90th. For the Quantile approaches, a significant intervention effect in the median to upper quantiles was found and linear quantile mixed model showed improved fit over the other approaches. An improved picture of intervention effects may be apparent using quantile regression methods when analyzing cluster randomized trials that have heterogeneous error variance. In particular, the linear quantile mixed model shows improved model fit allowing a multilevel framework to include random effects. There is considerable scope to extend this framework to incorporate more complex RCT designs.

Bridging Cultural and Individual Factors to Understand Sustainable Consumption The Development of a Multi-level Framework

Bridging Cultural and Individual Factors to Understand Sustainable Consumption The Development of a Multi-level Framework

Free-form multi-level porous model design based on truncated hierarchical B-spline functions

Porous structures have been widely applied in aerospace, paramedical, transportation, and other fields owing to their high interconnectivity, low weight, and extensive internal surface areas. Multi-level porous structures with different hole shapes and distributions at varying scales have shown excellent biological and mechanical performances compared with single-level porous structures. However, designing multi-level porous models with complex topological and geometric structures is a challenge. In this work, we propose a multi-level porous model design framework. Given a model represented by a trivariate B-spline solid and porous structures represented by implicit functions, we design the porous model in the parametric domain. We use threshold distribution fields represented by truncated hierarchical B-spline (THB-spline) functions to control the pore shapes, locations, and number of levels. Furthermore, the proposed THB-spline least squares progressive-iterative approximation algorithm can flexibly adjust the porous model. The model meets the expected porosity through the proposed optimization algorithm. The porous structure in the parametric domain is finally mapped to the trivariate B-spline solid to generate the final model. Moreover, we define a new storage space-saving file format to store multi-level porous models. The experimental results demonstrate the effectiveness and efficiency of the method and the superior performance of the new storage format.

Employee innovative job performance and firms' financial performance: a multilevel insight with big data analytics and entrepreneurial orientation

PurposeThe present research developed and investigated a multilevel theoretical framework by underpinning a resource-based view (RBV) that uncovers how and when academic researchers and organizational practitioners can strategically attain a competitive edge and increase their financial performance.Design/methodology/approachTo consider the dynamic and ever-challenging business environment, this research examined the contingency of big data analytics (BDA) and entrepreneurial orientation (EO). The multi-source and multilevel data used for this research were gathered from 195 manufacturing firms listed on the Pakistan stock exchange by employing a time-lagged design and survey strategy. Additionally, for hypothesis testing, multilevel path analysis was conducted in MPlus.FindingsThe authors uncovered the sequential mediation mechanisms of organizational innovation (OI) and competitive advantage (CA) on the relationship between individual-level employee innovative job performance (EIJP) and firms' financial performance (FFP) based on the resource-based view. The authors noted positive and significant sequential mediation of OI and CA on the effect of EIJP on FFP. Similarly, the relationship between EIJP and OI was found to be stronger when the boundary conditions of BDA and EO were higher than when they were lower.Originality/valueThis research offers new practical insights for managers and organizational practitioners to better value their EIJP, and due diligence is required when employing BDA and EO to attain a competitive edge over competitors and increase financial performance.

Gills, growth and activity across fishes

AbstractLife history theory suggests that maximum size and growth evolve to maximize fitness. In contrast, the Gill Oxygen Limitation Theory (GOLT) suggests that growth and maximum size in fishes and other aquatic, water‐breathing organisms is constrained by the body mass‐scaling of gill surface area. Here, we use new data and a novel phylogenetic Bayesian multilevel modelling framework to test this idea by asking the three questions posed by the GOLT regarding maximum size, growth and gills. Across fishes, we ask whether the body mass‐scaling of gill surface area explains (1) variation in the von Bertalanffy growth coefficient (k) above and beyond that explained by asymptomatic size (W∞), (2) variation in growth performance (a trait that integrates the tradeoff between k and W∞) and (3) more variation in growth performance compared to activity (as approximated by caudal fin aspect ratio). Overall, we find that there is only a weak relationship among maximum size, growth and gill surface area across species. Indeed, the body mass‐scaling of gill surface area does not explain much variation in k (especially for those species that reach the same W∞) or growth performance. Activity explained three to five times more variation in growth performance compared to gill surface area. Our results suggest that in fishes, gill surface area is not the only factor that explains variation in maximum size and growth, and that other covariates (e.g. activity) are likely important in understanding how growth, maximum size and other life history traits vary across species.

Quantifying Evidence for—and against—Granger Causality with Bayes Factors

Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null – we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study.

SDN-based multi-level framework for smart home services

The smart home is a field that uses smart devices and gadgets to automate tasks and operations in the house, and it is expected that this study subject will become a focal point in future civilizations. The adoption of smart home services faces significant obstacles in terms of reliability and stability. The Software Defined Networking (SDN) paradigm offers up novel approaches and trends to embrace smart technologies to tackle the issues of smart homes. To avoid accidents, we developed a vertical SDN-based multi-level structure consisting of two controllers with a parent-child connection. It enables smart homes to make optimal use of current services while also providing new services to smart homes and buildings, opening the path for future smart services. In addition, we created an Application Programming Interface (API) for the smart home to make it easier to use current services and pave the path for future smart services. We designed numerous jobs depending on their categorization to give smart services, which is beneficial for smart homes. Local topologies, cloud topologies, and cloud-local topologies have all been compared in the evaluation section. The performance of POX, NOX, and Flood Light controllers is measured using three performance metrics: mean throughput, Round-Trip Time (RTT), and packet loss across all three topologies. Our findings revealed that the cloud-local topology had the lowest packet loss ratio of 1.4%, while the local and cloud topologies had 1.9% and 2.3%, respectively.

Multi-level predictive maintenance of smart manufacturing systems driven by digital twin: A matheuristics approach

Digital twin technology is gradually being applied to smart manufacturing systems and is providing valuable information for predictive maintenance of swarms of machines, but also raises the need for more accurate and real-time decision making. However, there is still a shortage of research in this area. This paper proposes a general multi-level predictive maintenance decision-making framework driven by digital twin, considering component dependencies, the variable time scale of decisions, and comprehensive maintenance resources, in which an optimal maintenance schedule can be obtained in real time and then fed back to the physical space, so as to realize closed-loop control. A maintenance decision-making optimization model is then formulated based on integer linear programming to minimize total maintenance costs while meeting required production capacity. Further, a novel matheuristics algorithm (i.e., the interoperation of metaheuristics and mathematical programming techniques) is introduced for various maintenance decision scenarios. Finally, a case study of an offshore oil and gas production system consisting of eight subsea Christmas trees is examined, and the effects of changes in production capacity, failure thresholds, and maintenance resources on the multi-level optimization of decision-making solutions are discussed.

Does regionalism increase industrial policy space? An analytical framework applied to the East African textiles and apparel sector

We introduce a multidimensional and multilevel framework for industrial policy space as the set of legally permitted, economically viable and politico-institutionally feasible policy options for industrial development, given constraints at the national, regional and global levels. This is applied to the East African Community (EAC) textiles and apparel (T&A) sector, using data from policy documents and semi-structured interviews. The EAC customs union nominally transfers trade policy sovereignty to the regional level, but we present evidence showing how the duty remission scheme allows governments to provide targeted trade policy rents to domestic T&A firms, maintaining national legal policy space. This comes at a cost, because firms benefiting from national duty remission rents may not sell their goods duty free in other EAC countries, so the expanded economic policy space offered by regional integration is curtailed. In the political-institutional sphere, the EAC allowed a new policy option to emerge at the regional level – import substitution of used clothes – but global-level policy space constraints prevented implementation when US authorities threatened to remove trade preferences underpinning thousands of jobs. Regional integration policies should take into account tensions between different dimensions and levels of industrial policy space to maximise prospects for sustainable development.

From Evidence-Based Corona Medicine to Organismic Systems Corona Medicine

The Covid-19 pandemic has challenged both medicine and governments as they have strived to confront the pandemic and its consequences. One major challenge is that evidence-based medicine has struggled to provide timely and necessary evidence to guide medical practice and public policy formulation. We propose an extension of evidence-based corona medicine to an organismic systems corona medicine as a multilevel conceptual framework to develop a robust concept-oriented medical system. The proposed organismic systems corona medicine could help to prevent or mitigate future pandemics by transitioning to a bifocal medicine that extends an empirical evidence-based medicine to a theory-oriented organismic systems medicine.

Extended producer responsibility: An empirical investigation into municipalities' contributions to and perspectives on e‐waste management

AbstractThe development and implementation of extended producer responsibility (EPR) policies to manage e‐waste provide multilevel governance frameworks for achieving greater material circularity. However, the roles and responsibilities that are allocated to various stakeholders under these policies, which are crucial for program effectiveness, often vary across jurisdictions, and consensus is lacking about the best types of relationships and collaboration that should govern municipalities' contributions to EPR programs. Against this backdrop, and since this issue is poorly researched, we conducted an empirical investigation to identify the main drivers and barriers influencing municipalities' collaboration with an e‐waste EPR program in a Canadian province where municipalities are free to decide whether or not to engage with the program. Based on our study, we explore policy implications for similar programs in other jurisdictions, and propose questions for further research. Our findings identify key motivations for collaboration, including perceived program legitimacy, program funding, and logistical efficiencies. Conversely, a lack of program transparency, failure to support local employment, a focus on recycling instead of reuse, and limited program scope are identified as disincentives to program participation. Policymaking for e‐waste management and circularity need to consider municipalities' interests and contributions to ensure successful implementation.

Multi-level correlation analysis method for abnormal operating indexes of smart distribution network

This article focuses on the interaction between multiple levels of smart distribution network operation, and designs a multi-level correlation analysis framework by considering several related factors, such as network structure, distributed power supply and energy storage access, voltage regulation and reactive power compensation, and three-phase load imbalance. Furthermore, the multi-level correlation analysis method and process of abnormal voltage, load rate and power factor of smart distribution network are proposed, and the multi-level correlation analysis of key operation indicators at each level is realized. It is found that there is coupling influence relationship at different levels of high, medium and low voltage of smart distribution network. This article accurately locates the causes of abnormal problems of key operation indicators and forms a targeted governance decision plan. The practice shows that this method can effectively ensure the safe, reliable and efficient operation of large-scale distributed power and energy storage connected to smart distribution network.

Usability Testing of a Multi-Level Modeling Framework for Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health.

The JITAI is an intervention design to support health behavior change. We designed a multi-level modeling framework for JITAIs and developed a proof-of-concept prototype (POC). This study aimed at investigating the usability of the POC by conducting two usability tests with students. We assessed the usability and the students' workload and success in completing tasks. In the second usability test, however, they faced difficulties in completing the tasks. We will work on hiding the complexity of the framework as well as improving the frontend and the instructions.

Microfoundations of dynamic capabilities: a systematic review and a multilevel framework

PurposeMicrofoundations have become an effective approach for capability scholars to explore the heterogeneity of organizational results. Since the early pioneering work of scholars such as Felin and Foss, the microfoundations of strategic organization had not been extensively studied until 2010. The theoretical and empirical literature associated with the microfoundations of dynamic capabilities has developed rapidly. However, the diversity and fragmentation of micro-elements lead to a lack of a relatively consistent understanding of microfoundations, the study purpose is to aggregate the associated scattered terminology into a cohesive discussion.Design/methodology/approachA systematic literature review was conducted on research papers published between 2000 and 2022 using a hybrid methodology that included bibliometric and content analysis.FindingsThe authors suggest that this line of research can be divided into three stages. The study further develop a framework delineating the main components and mechanism involved in the microfoundations of dynamic capabilities, which in turn help us distill research gaps and opportunities for future work.Originality/valueThe authors construct a framework that can serve as a coherent research platform for further knowledge development. In the framework, the authors highlight that the research of group constructs, culture and leadership, data-driven topics are valuable for our understanding of the microfoundations of dynamics capabilities.