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The contrast of multilevel and comparative research may seem counterintuitive at first. After all, one might argue that comparative research on organizations by necessity spans several levels of analysis (Rokkan, 1966). Yet, multilevel and comparative research on organizations present rather distinct traditions in organization studies, each with its own epistemological assumptions and associated methods. Accordingly, an approach that aims to incorporate both multilevel and comparative ideas needs to start with taking inventory of these prior literatures to situate itself. In the following, we thus turn to the literatures on multilevel and comparative research as different traditions with surprisingly little overlap.

Despite the growing belief that multilevel research is necessary to advance human resource management (HRM) understanding, there remains a lack of multilevel thinking – the application of principles for multilevel theory building. The purpose of this paper is to propose a systematic approach for multilevel HRM research.

The paper proposes an agenda for multilevel HRM research by addressing three obstacles (concerning questions, theories and methods) that impede advancement in this field.

The paper provides an inclusive definition of multilevel HRM research that serves to advance its use, and maps out a multilevel HRM research landscape that captures the various aspects of this concept. The paper identifies unanswered multilevel questions within 16 research domains and develops a systematic approach to tackle these research questions by invoking three relevant theories and methodologies.

Some of the identified research questions could not be answered due to limitations in mainstream multilevel theories and methodologies. In response, this paper proposes theories and methodologies that can address some of the multilevel HRM research questions identified in this paper.

The findings can help HRM academics working on cross-disciplinary and cross-level research problems with a clearly structured approach to what multilevel HRM research is and what steps should be taken in conducting such research.

The originality lies in the systematic approach that precisely describes multilevel HRM research, and addressing obstacles that inhibit rigorous and relevant multilevel HRM research by highlighting relevant research questions, theories and methodologies.

The purpose of this paper is to develop the concept of a high performance alliance macro-culture as a multilevel construct reflective of resilient collaborative systems of exchange within strategic alliances and explores the distinct capabilities of this multilevel approach in predicting alliance outcomes.

The hypotheses developed in this study are tested using primary data collected from 650 members of 15 non-profit organizations in two multi-organizational collaborative networks. Considering the multilevel nature of the study the structural hypotheses are tested using a multilevel confirmatory factor analysis and the predictive hypotheses are tested using multilevel structural equation modeling.

All but one structural hypothesis are supported and all predictive hypotheses are supported suggesting that a multilevel macro-cultural conceptualization is effective in exploring the relationship between collaborative exchange systems and their outcomes.

Limitations stem from the generalizability of the data collected as the alliances formed by non-profit firms may not be wholly reflective of the alliance structures and goals of other firm types.

This study primarily contributes to multilevel study of strategic alliances and the study of collaborative norms and structures of allied groupings. The results of this study lend support to the importance of taking a network governance perspective and illustrate the limitations of traditional single-level approaches when studying interfirm collaborative networks and structural resilience therein.

This chapter acknowledges the difficulties in the empirical study of social ventures – broadly defined as market-driven ventures that produce social change – that arise from the vast differences among social venture firms in terms of missions, goals, identities, strategies, and structures. In an effort to improve research in this area and advance the field of social entrepreneurship, the authors advocate approaching social ventures from a configurational perspective.

This chapter begins with a discussion of what social ventures are and why organizational configurations – sets of firms that are similar across key characteristics – may be an appropriate perspective to utilize. Then, two methods – cluster analysis and set-theoretic analysis – are discussed in detail as ways to approach the study of social venture configurations. Details include descriptions of the techniques, instructions for use, examples, and limitations for each.

This chapter identifies research opportunities using configurations approaches in social venture research. Substantial possibilities for multilevel and temporally based research are discussed in depth.

A configurational approach can address the incongruence and non-findings in current social venture research and offers new opportunities for future research.

To identify the typical multilevel issues in social science, as well as illustrate the theoretical basis, hierarchical models and empirical exemplars of multilevel paradigm.

Hierarchical and multilevel data are extremely common in social systems, but multilevel analysis is constrained by statistical techniques. With the development of social system theory and empirical methods such as hierarchical structure modeling and latent growth modeling, multilevel paradigm can be used to analyze multilevel data. So it is necessary to identify typical multilevel phenomena in social science and discuss multilevel modeling techniques.

This paper identifies four typical multilevel phenomena in social system study: hierarchical and clustered sampling, collective construct research, longitudinal repeated measures, and event history analysis. Hierarchical structure modeling and latent growth modeling are effective multilevel analysis techniques in social science because of their advantages in the integration of social system research.

The quality and availability of multilevel data are the main limitations regarding which model will be applied.

The paper can aid the provision of effective multilevel models to social workers.

This paper provides information on application of multilevel modeling in social science.

This study examines how transformational leadership on the part of senior attorneys in law firms may affect their subordinate attorneys' performance in an industry experiencing both distinctive leadership challenges and widespread economic upheaval. Specifically, our multilevel theoretical model attempts to capture the moderated mediation relationships between transformational leadership, innovative climate, entrepreneurial orientation, and individual performance.

The study employs of a multilevel path analysis to examine the earlier described conceptual model utilizing primary data collected from 484 attorneys at 31 professional service firms.

The authors used multilevel path analysis to examine the existence and the extent of a multilevel mediation effect. They found that a firm's entrepreneurial orientation mediates the relationship between supervising attorneys' transformational leadership and individual attorneys' performances. The authors also found that the indirect effect of supervising attorneys' transformational leadership on individual attorneys' performances through entrepreneurial orientation is conditional on the degree of firm innovative climate.

The authors draw on theories of social learning to construct a dual-level theoretical model that connects domains within the leadership and entrepreneurship literatures. It does so by examining the relationships between the law firms' supervising attorneys' change-oriented leadership and their subordinate attorneys' billable hours during a period of severe economic disruption.

The purpose of this paper is to study the interdependent boundary-spanning activities that characterise the level of permeability of knowledge, information flow and learning among construction supply chains involved in the delivery of building information modelling (BIM)-compliant construction projects. Construction projects are mobilised through a number of interdependent processes and multi-functional activities by different practitioners with myriad specialised skills. Many of the difficulties that manifest in construction projects can be attributed to the fragmented work activities and inter-disciplinary nature of project teams. This is nevertheless becoming ever more pertinent with the rise of technology deployment in construction organisations.

The study combined experts’ sampling interviews and a case study research method to help offer better insights into the kind of emerging multilevel boundary practices as influenced by the rapidly evolving construction technological solutions. The experts’ sampling helped inform better understanding by unravelling the key changes in contemporary boundary configurations and related boundary-spanning practices within technology-mediated construction project settings. The case study also helped to establish the manifestation of best practices for managing multilevel boundaries in BIM-enabled construction project organisations.

The study has revealed that different generic organisational BIM strategies as developed in specialised boundaries are reconfigured as appropriate at the project level to produce project-specific BIM execution plan (BXP). The outcome of project BXP is dependent on the project organisational teams that cooperate in creating new solutions and on conceding space for negotiations and compromises which conflicting interests at the project level can find to be both desirable and feasible. The implementation effort is therefore contingent on mutual translation in which different actors with different insights instigate their practice through negotiation and persuasion which eventually are reinforced by contractual agreements and obligations.

The paper has presented a novel and well-timed empirical insight into BIM-enabled project delivery and best practices that span multilevel boundaries of construction organisations.

The purpose of this paper is to explore the hypothesis that the university context may partially determine the growth of university spin-offs (USOs), with a cross-national analysis and using an “interactionist” approach.

Two samples of USOs, from Spain and Italy (531 and 952 firms, respectively), were examined over the 2005-2013 period. Multilevel modelling was applied to empirically test the hypotheses.

The results confirmed that the university context is a critical and effective element for explaining USOs’ growth. The university context affected USOs’ growth only for the Spanish firms, while for the Italian spin-offs the evidence does not report a significant determining influence of the university context. This finding may be interpreted as the localization externalities, determined by the Spanish universities, have a more effective impact at firm level compared with those generated by the Italian universities.

The paper provides evidence that the university context has a significant role in supporting USOs’ growth in Spain, but not in Italy. This finding, together with the fact that the Italian USOs showed lower growth rates over the period of analysis, may suggest that greater involvement by the Italian parent universities is needed to foster USOs’ growth. The main point to be underlined to decision makers is that policies aimed at fostering USOs need the active involvement of the parent university in the whole growth process of the nascent firm, rather than just in the USO creation process.

A multilevel approach provides both methodological and theoretical contributions to the study of USOs’ growth, which was adopted as an “interactionist” approach is recommended by literature. In addition, a cross-national approach allows for exploration of the actual effect of the university on the growth of USOs, taking into account international differences.

The purpose of this paper is to introduce the concept of work group diversity dynamics as a novel approach for studying diverse work groups. The authors profile the dynamic processes within diverse work-groups and provide an overview of main objectives.

The paper draws on years of accumulated diversity research to cast a temporal and dynamic lens on the processes taking place within diverse work groups. After outlining the state of the art in work group diversity research, the definition, overall framework and profile of work group diversity dynamics is offered.

The paper argues that by adopting a temporal and dynamic perspective for studying diverse work groups, one can shift focus from the traditional perspective of “what is diversity” to “what happens within diverse work groups”. The paper disentangles the activities taking place within diverse work groups, defines the actual team processes and finally highlights how these processes might be affected by time and dynamism.

After almost 30 years of diversity research the mechanisms and processes through which diversity is translated into individual and organizational outcomes are not yet sufficiently understood and studied. This paper highlights a temporal and dynamic perspective for studying work group diversity, a view that is yet uncharted in diversity literature.

This research developed a model to understand and predict housing market dynamics and evaluate the significance of house permits data in the model’s forecasting capability.

The research uses a multilevel algorithm consisting of a machine-learning regression model to predict the independent variables and another regressor to predict the dependent variable using the forecasted independent variables.

The research establishes a statistically significant relationship between housing permits and house prices. The novel approach discussed in this paper has significantly higher prediction capabilities than a traditional regression model in forecasting monthly average prices (R-squared value: 0.5993), house price index prices (R-squared value: 0.99) and house sales prices (R-squared value: 0.7839).

The impact of supply, demand and socioeconomic factors will differ in various regions. The forecasting capability and significance of the independent variables can vary, but the methodology can still be applicable when provided with the considered variables in the model.

The resulting model is helpful in the decision-making process for investments, house purchases and construction as the housing demand increases across various cities. The methodology can benefit multiple players, including the government, real estate investors, homebuyers and construction companies.

Existing algorithms and models do not consider the number of new house constructions, monthly sales and inventory in the real estate market, especially in the United States. This research aims to address these shortcomings using current socioeconomic indicators, permits, monthly real estate data and population information to predict house prices and inventory.