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Qualitative comparative analysis is increasingly applied in strategy and organization research. The main purpose of our essay is to support this growing community of qualitative comparative analysis scholars by identifying best practices that can help guide researchers through the key stages of a qualitative comparative analysis empirical study (model building, sampling, calibration, data analysis, reporting, and interpretation of findings) and by providing examples of such practices drawn from strategy and organization studies. Coupled with this main purpose, we respond to Miller’s essay on configuration research by highlighting our points of agreement regarding his recommendations for configurational research and by addressing some of his concerns regarding qualitative comparative analysis. Our article thus contributes to configurational research by articulating how to leverage qualitative comparative analysis for enriching configurational theories of strategy and organization.

We consider statistical inference for regression when data are grouped into clusters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year differences-in-differences studies with clustering on state. In such settings, default standard errors can greatly overstate estimator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster-specific fixed effects, few clusters, multiway clustering, and estimators other than OLS.

Clusters are geographic concentrations of industries related by knowledge, skills, inputs, demand and/or other linkages. There is an increasing need for cluster-based data to support research, facilitate comparisons of clusters across regions and support policymakers in defining regional strategies. This article develops a novel clustering algorithm that systematically generates and assesses sets of cluster definitions (i.e., groups of closely related industries). We implement the algorithm using 2009 data for U.S. industries (six-digit NAICS), and propose a new set of benchmark cluster definitions that incorporates measures of inter-industry linkages based on co-location patterns, input–output links, and similarities in labor occupations. We also illustrate the algorithm’s ability to compare alternative sets of cluster definitions by evaluating our new set against existing sets in the literature. We find that our proposed set outperforms other methods in capturing a wide range of inter-industry linkages, including the grouping of industries within the same three-digit NAICS.

Purpose The purpose of this paper is to explore the emergence of innovation in smart service systems to conceptualize how actor’s relationships through technology-enabled interactions can give birth to novel technologies, processes, strategies and value. The objectives of the study are: to detect the different enablers that activate innovation in smart service systems; and to explore how these can lead dynamically to the emergence of different innovation patterns. Design/methodology/approach The empirical research adopts an approach based on constructivist grounded theory, performed through observation and semi-structured interviews to investigate the development of innovation in the Italian CTNA (Italian acronym of National Cluster for Aerospace Technology). Findings The identification and re-elaboration of the novelties that emerged from the analysis of the Cluster allow the elaboration of a diagram that classifies five different shades of innovation, introduced through some related theoretical propositions: technological; process; business model and data-driven; social and eco-sustainable; and practice-based. Originality/value The paper embraces a synthesis view that detects the enabling structural and systems dimensions for innovation (the “what”) and the way in which these can be combined to create new technologies, resources, values and social rules (the “how” dimension). The classification of five different kinds of innovation can contribute to enrich extant research on value co-creation and innovation and can shed light on how given technologies and relational strategies can produce varied innovation outcomes according to the diverse stakeholders engaged.

Tech clusters like Silicon Valley play a central role for modern innovation, business competitiveness, and economic performance. This paper reviews what constitutes a tech cluster, how they function internally, and the degree to which policy makers can purposefully foster them. We describe the growing influence of advanced technologies for businesses outside of traditional tech fields, the strains and backlash that tech clusters are experiencing, and emerging research questions for theory and empirical work.

The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced clusters. Rejection frequencies are higher for datasets with 50 clusters proportional to US state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the ‘effective number’ of clusters and simulate placebo laws with dummy variable regressors.

PurposeThis study theoretically investigates the extant literature published about circular economy and sustainable development to identify significant research themes, the most relevant authors, countries and journals.Design/methodology/approachBibliometric analysis is used, followed by cluster formation using co-citation analysis. The clusters are discussed in-depth to identify emerging themes and future research areas.FindingsBy systematically reviewing 596 research articles, significant themes of research in this field were found. These include frameworks and indicators to define and assess the circular economy, circular business models and use cases, global and industrial contexts of application of circular economy and different dimensions of the circular economy.Research limitations/implicationsPublications from only one database have been used. Only articles published in relevant academic journals have been used for the bibliometric analysis. For co-citation analysis and cluster formation, only articles with a high number of citations were selected.Originality/valueThe analysis of the various clusters revealed research areas that can be explored in future research to understand the circular economy better and implement its practices to attain sustainability.

Consumers' preferences for domestic over imported products have been investigated in various isolated studies, but never in a single model incorporating several in-group and out-group consumer orientations at the same time. Building on social identity theory, this study develops and tests—in two countries—a conceptual model that assesses the relative influence of consumer ethnocentrism, national identity, and consumer cosmopolitanism on consumers' product judgments and willingness to buy domestic and foreign products. Furthermore, the study develops an empirically based typology of consumer segments using these sociopsychological traits and subsequently profiles them on consumptionrelevant variables. The findings reveal several undiscovered patterns regarding the interplay of consumer ethnocentrism, national identity, and consumer cosmopolitanism as drivers of consumer behavior and offer managerial guidance on their relevance as segmentation variables.

In today’s knowledge economy, clusters are a key driver of a country’s competitiveness. Yet a cluster’s technological base is now more than ever influenced by constituent firms’ actions to tap into distant knowledge sources. Drawing on a social network perspective, and distinguishing between horizontal versus vertical organization-based linkages, we explore the effects of a cluster’s connectedness to foreign locations on its innovation performance. We show that improvements in horizontal and vertical connectedness both stimulate a cluster’s innovation performance, but that their relative effects vary across cluster types. Innovation in knowledge-intensive clusters disproportionately benefits from enhancements in their constituent firms’ horizontal connectedness to foreign knowledge hotspots. Innovation in labor-intensive clusters mostly gains from stronger vertical connections by their firms to central value chain players abroad. We discuss the implications of our findings for research on global knowledge sourcing and cluster upgrading.

[Display omitted] •Omnichannel deal prone segments use different channels to procure and use promotions.•Motivation, opportunity, and ability factors predict segment membership.•Omnichannel deal prone segments focus either on offline or online, but not on both.•Online and On-and-offline segments respond more to promotions than offline segments.•Managers can use results to target deals to the right segments in the right channels. Today's retail promotional environment is driven by channel proliferation, customer channel preferences, and managers’ efforts to create a unified “omnichannel” customer experience. This paper identifies the omnichannel deal prone segments that emerge in this environment, that is, segments that employ multiple channels to procure and use promotions. We describe these segments, measure the motivations, opportunities, and abilities (MOA) associated with segment membership, and quantify how these segments respond differently to promotions. We apply latent class cluster analysis to a database of over 1,000 respondents in three product categories. We find a rich array of omnichannel deal prone segments. Interestingly, 82% of consumers have bifurcated into online- or offline-focused deal prone segments. That is, most consumers use multiple channels to procure and use promotions, but they focus on either online or offline channels. Only seventeen percent of consumers strongly utilize both online and offline channels. We find that opportunity factors such as access to physical stores, and ability factors such as online shopping experience, explain this finding. We discuss how our results enable managers to target the right promotion designs to the right customers through the appropriate channels.