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In this paper, we contribute to the understanding of how entrepreneurs can deploy their values to enable joint action of heterogeneous stakeholders. Such an understanding forms a critical endeavor to tackle grand challenges adequately. Building on sensegiving research, we conducted a single-case study of an entrepreneurial initiative that tackles gender inequality in Lebanon which has been successful in mobilizing heterogeneous stakeholders who ordinarily would not collaborate with each other. We find that the values of the founders were pivotal for the initiative’s success as those values activated latent values of stakeholders through processes of contextualization and enactment. We subsume these processes under the label value-driven sensegiving. As a result of value-driven sensegiving, heterogeneous stakeholders could make sense of the founders’ aspirational vision and the role they could play in it, which paved ways for tackling grand challenges collaboratively. Our study provides insights into the centrality of values for mobilizing heterogeneous stakeholders across boundaries. Therefore, it contributes to the body of work on sensegiving, societal grand challenges, and new forms of organizing.

Most traditional research on work groups has studied groups and teams that are homogeneous with respect to culture. To alleviate the dearth of material on culturally heterogeneous teams, this chapter provides an overview of the impact of cultural diversity on groups and teams in today’s workforce. First, we focus on the problems involved in defining the constructs of “teams” and “culture.” Second, we provide a brief review of the cultural factors that have been identified as affecting human performance. This review serves as the basis for the third section of this chapter, which investigates if – and how – cultural heterogeneity affects team performance. Finally, we conclude with how culturally diverse workplaces can be managed and how to improve performance when faced with cultural diversity.

This chapter discusses new developments in nonparametric econometric approaches related to empirical modeling of demand decisions. It shows how diverse recent approaches are, and what new modeling options arise in practice. We review work on nonparametric identification using nonseparable functions, semi- and nonparametric estimation approaches involving inverse problems, and nonparametric testing approaches. We focus on classical consumer demand systems with continuous quantities, and do not consider approaches that involve discrete consumption decisions as are common in empirical industrial organization. Our intention is to give a subjective account on the usefulness of these various methods for applications in the field.

Mixed logit models can represent heterogeneity across individuals, in both observed and unobserved preferences, but require computationally expensive calculations to compute probabilities. A few methods for including error covariance heterogeneity in a closed form models have been proposed, and this paper adds to that collection, introducing a new form of a Network GEV model that sub-parameterizes the allocation values for the assignment of alternatives (and sub-nests) to nests. This change allows the incorporation of systematic (nonrandom) error covariance heterogeneity across individuals, while maintaining a closed form for the calculation of choice probabilities. Also explored is a latent class model of nested models, which can similarly express heterogeneity. The heterogeneous models are compared to a similar model with homogeneous covariance in a realistic scenario, and are shown to significantly outperform the homogeneous model, and the level of improvement is especially large in certain market segments. The results also suggest that the two heterogeneous models introduced herein may be functionally equivalent.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.

Purpose – This study investigates the effect of heterogeneity in an environment with a dynamic public bad.

Methodology/approach – Every period agents decide on own level of production that generates private revenue and emissions. Emissions lead to pollution that acts as a public bad and accumulates over time. Our treatment variable is the emission propensity of agents' production technologies. We characterize the Markov perfect equilibrium and social optimum and employ a laboratory experiment to compare the observed behavior to theoretical predictions.

Findings – We find that the observed production levels are between the Markov perfect equilibrium and social optimum. With experience, the strongest adjustment and lowest level of pollution is achieved in the heterogeneous treatment with high average emission propensity. When the costs of climate change are not severe, institutions are most necessary to create incentives for environmentally friendly behavior.

Research limitations/implications – The results of this study apply to the case when heterogeneity is exogenous and the only way to reduce emissions is by reducing production. Natural extensions include the option to invest in clean technologies, the availability of communication, the group size, and endogenously emerging and exogenous regulatory institutions.

Practical implications – Our results suggest that under relatively favorable conditions heterogeneous countries are less likely to achieve sustainability without external enforcement. Under unfavorable conditions the impending common threat of significant damage leads to higher levels of voluntary cooperation.

Originality/value of the chapter – This is the first study to address the practical problem of coordination among technologically diverse countries, and fundamental questions regarding the effect of heterogeneity in environments with a dynamic public bad.

This study investigated the association between structural conditions and social incentives and their effect on the ethnic composition of mobile social networks. Regarding structural conditions, we examined the role of the ethnic group’s size, socioeconomic status, and heterogeneity of the city in which the business was located. Regarding social incentives, we investigated the social diversification hypothesis, which expects that residentially and socially segregated minority groups will take advantage of mobile communications to diversify their mobile communication ties with outgroup members.

Two data sets were used. The first was the aggregation of the mobile communication patterns of business customers as measured by one of Israel’s mobile phone operators in April 2010. The database included 9,099 call data records. The second was a data set of the social characteristics of 103 Israeli cities from the Israeli Bureau of Statistics. Both data sets were merged according to the place of residence of each customer.

Israeli Arab businesses in homogeneous Jewish and mixed cities operate in an environment with more structural opportunities to create outgroup ethnic ties than Arab businesses in homogeneous Arab cities. Jewish businesses in ethnically mixed cities have more outgroup mobile ties than comparable businesses in homogenous Jewish cities.

We expand previous models and suggest a structural diversification approach in which ethnic mobile social networks vary across homogeneous and ethnically mixed cities. These variations result in different social incentives as the diversification approach assumed, as well as different structural conditions, as the structural approach indicates.

This chapter suggests that moving beyond positivism entails a recognition that the social world is made up of complex phenomena that are heterogeneous, and events are caused by contingent conjunctures of causal mechanisms. To theorize the social world as heterogeneous is to recognize that social causes, categories, and groups combine different kinds of phenomena and processes at various levels and scales across time. To speak of conjunctural causation implies not only that events are caused by concatenations of multiple, intersecting forces but also that these combinations are historically unique and nonrepeatable. Both the historical materialist conception of the “conjuncture” and the poststructuralist theory of “assemblages” take heterogeneity and multicausality seriously. I compare and contrast these formulations across three dimensions: the structure of the apparatus, causation, and temporality. I argue that these theories offer useful tools to social scientists seeking to engage in complex, multicausal explanations. I end the article with an example of how to use these concepts in analyzing a complex historical case.