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In light of the growing interest in neuroscience within the managerial and organizational cognition (MOC) scholarly domain at large, this chapter advances current knowledge on core neuroscience methods. It does so by building on the theoretical analysis put forward by Healey and Hodgkinson (2014, 2015), and by offering a thorough – yet accessible – methodological framework for a better understanding of key cognitive and social neuroscience methods. Classifying neuroscience methods based on their degree of resolution, functionality, and anatomical focus, the chapter outlines their features, practicalities, advantages and disadvantages. Specifically, it focuses on functional magnetic resonance imaging, electroencephalography, magnetoencephalography, heart rate variability, and skin conductance response. Equipped with knowledge of these methods, researchers will be able to further their understanding of the potential synergies between management and neuroscience, to better appreciate and evaluate the value of neuroscience methods, and to look at new ways to frame old and new research questions in MOC. The chapter also builds bridges between researchers and practitioners by rebalancing the hype and hopes surrounding the use of neuroscience in management theory and practice.

Inhibitory control (IC) is a central executive function that shows significant development throughout the preschool years. IC is known as a factor that underlies the ability to self-regulate in daily situations. This ability is challenged when a child faces negative emotions; a challenge that is seen in children’s IC performance and brain activity. This chapter elaborates on the effects that negative emotional experiences have on children’s IC functioning. Moreover, previous studies regarding the way emotional experiences are reflected in brain activity are included. Additionally, this chapter will offer a comprehensive review of the factors affecting individual differences in IC, including the role of children’s temperamental effortful control and negative affectivity. Further, the role of parenting behaviors will be discussed, focusing on the way in which maternal self-regulation influences child inhibitory control, including related educational implications.

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

In this chapter, we utilize the residual concept of productivity measures defined in the context of normal-gamma stochastic frontier production model with heterogeneity to differentiate productivity and inefficiency measures. In particular, three alternative two-way random effects panel estimators of normal-gamma stochastic frontier model are proposed using simulated maximum likelihood estimation techniques. For the three alternative panel estimators, we use a generalized least squares procedure involving the estimation of variance components in the first stage and estimated variance–covariance matrix to transform the data. Empirical estimates indicate difference in the parameter coefficients of gamma distribution, production function, and heterogeneity function variables between pooled and the two alternative panel estimators. The difference between pooled and panel model suggests the need to account for spatial, temporal, and within residual variations as in Swamy–Arora estimator, and within residual variation in Amemiya estimator with panel framework. Finally, results from this study indicate that short- and long-run variations in financial exposure (solvency, liquidity, and efficiency) play an important role in explaining the variance of inefficiency and productivity.

The current paper examines the relationships between watching television for various times of day and reading achievement for a subsample of third grade language minority (LM) students compared to third grade students in general.

The analysis uses ECLS-K 1998–99 data to first test for significant differences between the two samples, then further explores these relationships using separate OLS multiple regression models, while controlling for past reading achievements and socioeconomic variation.

Building on more nuanced versions of displacement theory, this paper finds a positive relationship between reading achievement and watching television after dinner on weekdays specifically for LM students. For the general sample, watching TV on weekends or weekdays at any time period has no relationship with reading achievement.

This research suggests the potential for TV or perhaps other media to act as a lingual- or cultural-learning facilitator for LM students, being positively tied to reading achievement. The paper’s unique focus on multimedia use and LM students makes it particularly applicable to educators and public policy officials tasked with confronting the reading skills gap for a growing LM student population.

The present chapter addresses a topic that is of growing interest – namely, the exploration of alternative item response theory (IRT) models for noncognitive assessment. Previous research in the assessment of trait emotional intelligence (or “trait emotional self-efficacy”) has been limited to traditional psychometric techniques (e.g., classical test theory) under the notion of a dominance response processes describing the relationship between individuals' latent characteristics and individuals' response selection. The present study, presents the first unfolding IRT modeling effort in the general field of emotional intelligence (EI). We applied the Generalized Graded Unfolding Model (GGUM) in order to evaluate the response process and the item properties on the short form of the trait emotional intelligence questionnaire (TEIQue-SF). A sample of 866 participants completed the English version of the TEIQue-SF. Results suggests that the GGUM has an adequate fit to the data. Furthermore, inspection of the test information and standard error functions revealed that the TEIQue-SF is accurate for low and middle scores on the construct; however several items had low discrimination parameters. Implications for the benefits of unfolding models in the assessment of trait EI are discussed.

The R environment for statistical computing and graphics (R Development Core Team, 2008) offers practitioners a rich set of statistical methods ranging from random number generation and optimization methods through regression, panel data, and time series methods, by way of illustration. The standard R distribution (base R) comes preloaded with a rich variety of functionality useful for applied econometricians. This functionality is enhanced by user-supplied packages made available via R servers that are mirrored around the world. Of interest in this chapter are methods for estimating nonparametric and semiparametric models. We summarize many of the facilities in R and consider some tools that might be of interest to those wishing to work with nonparametric methods who want to avoid resorting to programming in C or Fortran but need the speed of compiled code as opposed to interpreted code such as Gauss or Matlab by way of example. We encourage those working in the field to strongly consider implementing their methods in the R environment thereby making their work accessible to the widest possible audience via an open collaborative forum.

The leading frameworks of internationalization have contributed significantly to our knowledge of how firms internationalize, but do not fully explain how firms actually create and capture value from customers when internationalizing their activities. Understanding the value creation and capture activities defining their business model(s) is critical for firms moving into less familiar markets, and is particularly relevant for service firms where variability is an inherent feature of the firm/client experience. To address this gap, we take a business model perspective to analyze 144 internationalization events of 10 professional service firms. We find that the case firms adopted four different business models when internationalizing, and that single firms may utilize portfolios of business models. Our findings contribute to both the services internationalization and business model literatures by showing how variability in the internationalization process substantiates the need for business model portfolios.