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Information systems (IS) research usually investigates phenomena at one level of analysis at a time. However, complex IS phenomena may be difficult to address from such a single-level perspective. A multilevel perspective offers an alternative means to examine phenomena by simultaneously accounting for multiple levels of analysis. Although useful guidelines for theory development are widely available, they give little specific attention to developing theory that is conceptualized and analyzed at multiple levels. Multilevel theorizing or developing theory from a multilevel perspective is more complex and involves unique challenges. To promote multilevel theorizing in the IS discipline, we focus on addressing challenges involved in multilevel theorizing and propose a holistic framework for systematically developing theory from a multilevel perspective. Drawing from the organization science and IS literature, the proposed framework harmonizes and synthesizes previous guidelines, providing a practical basis for conceptualizing and studying multilevel phenomena. The online appendix is available at https://doi.org/10.1287/isre.2017.0690 .

A multilevel perspective in information systems (IS) research helps researchers to understand phenomena simultaneously at multiple levels of analysis. In understanding and employing the multilevel perspective, researchers may face challenges in relation to the value contribution, the terminology, and the critical differences between multilevel and single-level research. To address the challenges, we synthesize contemporary thinking on the multilevel perspective. In particular, we clarify the various value contributions of the multilevel perspective, offer a consistent terminology for conducting multilevel research, and holistically overview the guidelines in relation to specifying, operationalizing, and testing theoretical models. This tutorial helps researchers to holistically understand the multilevel perspective to allow them to more deeply appreciate the nuanced assumptions underlying the perspective. Thus, this paper contributes by helping researchers to more effectively and more flexibly engage in multilevel research.

The explosive growth of data raised from social networks, hinders researchers from analysing them in a good way. So, is it possible to rapidly “zoom-out” from this huge network while preserving its whole structure? In fact, this technique is named “graph’s reduction” and represents a significant task in social networks’ analysis. Thus, several methods have been developed to pull a smaller succinct version of the graph. Some of them belong to the category of “graph sampling” and risk losing key characteristics of communities. Others are part of “coarsening strategy” and designed to cope with the problem of community discovering, which is our desired purpose. In this paper, we propose a multi-level coarsening algorithm called MCCA (Multi-level Coarsening Compact Areas). The main strategy of this algorithm is to merge well connected zones in every level by updating edge and vertex weight until a stopping criterion is met. Using real-world social networks, we evaluate the quality and scalability of MCCA. Furthermore, we compared it with eight known proposals. We also show how our method can be used as a preliminary step for community detection without much loss of information.

This article is the first of a series that offers a new paradigm for economics, the \"multilevel paradigm,\" using generalized Darwinism as its theoretical framework. Generalized Darwinism refers to all processes that combine the ingredients of variation, selection, and replication - not just genetic evolution - making it relevant to the cultural evolution of economic systems that are embedded in political, social, and environmental systems. We contrast the multilevel paradigm with the neoclassical paradigm and other schools of economic thought. The multilevel paradigm, like the neoclassical paradigm, provides an integrative framework for micro- and macro-economics. It also incorporates the meso level, comprising groups of various sizes, linking micro and macro. Other schools of economic thought are not fully integrative in this sense, constituting instead a form of diffuse pluralism. In the integrative framework of the multilevel paradigm, many important ideas that are currently on the periphery of economics are brought toward the core.

In \"Probing Theoretically into Central and Eastern Europe: Transactions, Resources, and Institutions,\" we outlined the contributions of research in Central and Eastern Europe (CEE) to theoretical debates in business research. In this retrospective, we reflect upon the evolution of the field over the past decade. With the fading impact of CEE's distinct shared history, we suggest that CEE best be analyzed as emerging economies, rather than as a distinct geographic entity. Emerging economy business research is converging on common themes and shared theoretical ideas, while identifying critical variations that constrain generalizations among and beyond emerging economies. This research thus highlights the need to develop a better understanding of the boundary conditions of scholarly theories of business knowledge. Over the past decade, the institutionbased view has emerged from distinct intellectual traditions in institutional economics, organizational theory, and the analysis of business-government bargaining. Research in these converging lines of theorizing places contextual variations at the center of explanations of business phenomena around the world. We suggest that the institution-based view is evolving toward a paradigm, and offer suggestions on how to advance this research agenda further, in particular by exploring how firms engage with different sets of potentially conflicting institutions at multiple levels and locations.

Despite its potential risks, nuclear power energy offers some economic benefits including cheap electricity. This benefit clarifies part of the reason why people support nuclear energy. Our research examined whether there was a difference in the acceptance of nuclear energy across 27 European countries in 2009, before the Fukushima accident. In particular, we analyzed how each factor at the individual and contextual level influences the acceptance. To answer this question, we set up the acceptance of nuclear energy as a dependent variable, and 5 perception variables at the individual level and 11 structural ones at the contextual level as independent variables. We executed multilevel modeling by using a Eurobarometer survey, which covered 27 European countries. The analysis results showed that at the individual level, the perceived benefit explained the largest variance of the acceptance, followed by perceived risk and trust. At the contextual level, the share of the energy supply by nuclear power, environmentalism and ideology influenced the acceptance of nuclear energy. This study shows that individuals’ acceptance of nuclear energy is based on individual beliefs and perceptions, but it is also influenced by the institutional and socio-cultural context which each country faces.

Humans display marked changes to their perceptual experience of a stimulus following prolonged or repeated exposure to a preceding stimulus. A well-studied example of such perceptual adaptation is the tilt-aftereffect. Here, prolonged exposure to one orientation leads to a shift in the perception of subsequent orientations. Such a capacity to adapt suggests the tuning of the visual system can change over time in response to our current visual environment. However, it remains unclear to what extent adaptation occurs in response to statistical regularities of features present in naturalistic scenes, such as oriented contrast. We therefore investigated orientation adaptation in response to natural viewing of filtered live-action film stimuli. Within a session, participants freely viewed 45 min of a film which had been filtered to include increased contrast energy within a specified orientation band (0°, 45°, 90°, or 135°; i.e., the adaptor). To measure adaptation effects, the film was intermittently interrupted to have participants perform a simple orientation judgement task. Having participants complete behavioural trials throughout the testing session, including 45 min of total adaptation time, allowed investigation of the accumulation of response biases and changes in such biases over the course of the session. We found very little evidence of adaptation across our conditions. Indeed, in the very few conditions where significant adaptation was observed, these effects were much weaker than those observed under typical tilt-aftereffect paradigms. Further, within a single session, we observed inconsistent development of adaptation effects. The current findings therefore suggest very minimal and, where present, inconsistent effects of adaptation in response to naturalistic viewing conditions. The divergence of our results from those predicted by prior studies using minimalistic studies, and suggests consideration of further barriers to understanding perceptual adaptation as experienced in nature are needed.

Background Although frequentist paradigm has been the predominant approach to clinical studies for decades, some limitations associated with the frequentist null hypothesis significance testing have been recognized. Bayesian approaches can provide additional insights into data interpretation and inference by deriving posterior distributions of model parameters reflecting the clinical interest. In this article, we sought to demonstrate how Bayesian approaches can improve the data interpretation by reanalyzing the Rural Engagement in Primary Care for Optimizing Weight Reduction (REPOWER). Methods REPOWER is a cluster randomized clinical trial comparing three care delivery models: in-clinic individual visits, in-clinic group visits, and phone-based group visits. The primary endpoint was weight loss at 24 months and the secondary endpoints included the proportions of achieving 5 and 10% weight loss at 24 months. We reanalyzed the data using a three-level Bayesian hierarchical model. The posterior distributions of weight loss at 24 months for each arm were obtained using Hamiltonian Monte Carlo. We then estimated the probability of having a higher weight loss and the probability of having greater proportion achieving 5 and 10% weight loss between groups. Additionally, a four-level hierarchical model was used to assess the partially nested intervention group effect which was not investigated in the original REPOWER analyses. Results The Bayesian analyses estimated 99.5% probability that in-clinic group visits, compared with in-clinic individual visits, resulted in a higher percent weight loss (posterior mean difference: 1.8%[95% CrI: 0.5,3.2%]), a greater probability of achieving 5% threshold (posterior mean difference: 9.2% [95% CrI: 2.4, 16.0%]) and 10% threshold (posterior mean difference: 6.6% [95% CrI: 1.7, 11.5%]). The phone-based group visits had similar result. We also concluded that including intervention group did not impact model fit significantly. Conclusions We unified the analyses of continuous (the primary endpoint) and categorical measures (the secondary endpoints) of weight loss with one single Bayesian hierarchical model. This approach gained statistical power for the dichotomized endpoints by leveraging the information in the continuous data. Furthermore, the Bayesian analysis enabled additional insights into data interpretation and inference by providing posterior distributions for parameters of interest and posterior probabilities of different hypotheses that were not available with the frequentist approach. Trial registration ClinicalTrials.gov Identifier NCT02456636 ; date of registry: May 28, 2015.

The purpose of this study was to examine the evolution of participants’ situational motivation in physical activity. From a sample of 194 individuals, the 20 most self-determined, the 20 moderately self-determined, and the 20 least self-determined in sports were selected to participate in the research protocols. These 60 subjects performed a putting task with a golf club on a 1.50 m mat. Immediately afterward, they had to self-assess using the mouse paradigm software in order to measure their intrinsic situational motivation throughout the task. We used multilevel growth curve analyses (MGCAs) to explore the trajectories of students’ situational intrinsic motivation during the experimental task. The results revealed a significant positive linear and cubic effect of time and a significant negative quadratic effect of time on situational motivation for highly self-determined students. Our study shows that situational intrinsic motivation is dynamic, and the most self-determined subjects experience a positive evolution in their intrinsic motivation in a specific physical activity.

This paper presents a large-scale, comprehensive test of generalized trust across 31 nations. I pay particular attention to the theory and measurement of voluntary associations in promoting trust, hypothesizing that voluntary associations connected to other voluntary associations are more beneficial for the creation of generalized trust than associations isolated from other associations. The theory is tested with a multi-level, cross-national model, including both individual-level and country-level variables to predict the placement of trust. At the individual level, I find that membership in connected associations creates more trust than membership in isolated associations. At the national level, having more connected voluntary associations increases trust, while having more isolated associations decreases trust.