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Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet interrelated paths to understand the relative contributions of anatomical and physiological mechanisms to the human brain–behavior relationship. From an organizational neuroscience perspective, the chapter describes over a dozen neuroimaging technologies that are classified under four groupings: morphologic, invasive metabolic, noninvasive metabolic, and electromagnetic. We then discuss neuroimaging variables that may be useful in social science investigations, and we underscore electroencephalography as a particularly useful modality for the study of individuals and groups in organizational settings. The chapter concludes by considering emerging science and novel brain technologies for the organizational researcher as we look to the future.

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.

The simplest part of sound preservation involves technology and its application. The real complexities lie in a mix of social legal, and financial issues. The social issues include how archivists, curators, librarians, historians, or anyone with limited engineering, computing, and other technical training can evaluate competing claims and risks. The legal issues include copyright and the risks that an institution may choose to take about what constitutes fair use and preservation copying. The financial issues include how much of what quality of preservation an institution can afford, and for how many of the items in its collection.

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

Neuroimaging research has substantially enhanced our understanding of the neurobiological mechanisms of typical and atypical learning in children. These developments can advance the design of novel approaches to diagnosis and intervention for learning disabilities. Despite the promise of educational neuroscience, there are still walls between neuroscience and special education researchers such that more collaboration and understanding are needed between these disciplines. This chapter attempts to break down the walls by discussing how neuroimaging techniques can be incorporated into special education research. We also present arguments as to why neuroscience is “the next big thing” in special education research and the obstacles that must be overcome in order for neuroscience to be incorporated into education research. To describe how neurobiology might impact special education, we focus primarily on reading disability. We believe that educational neuroscience can aid in the identification and intervention of other learning disorders as well.

Understanding when entrants might have an advantage over an industry’s incumbent firms in developing and adopting new technologies is a question which several scholars have explained in terms of technological capabilities or organizational dynamics. This paper proposes that the value network—the context within which a firm competes and solves customers’ problems—is an important factor affecting whether incumbent or entrant firms will most successfully innovate. In a study of technology development in the disk drive industry, the authors found that incumbents led the industry in developing and adopting new technologies of every sort identified by earlier scholars—at component and architectural levels; competency-enhancing and competency-destroying; incremental and radical—as long as the technology addressed customers’ needs within the value network in which the incumbents competed. Entrants led in developing and adopting technologies which addressed user needs in different, emerging value networks. It is in these innovations, which disrupted established trajectories of technological progress in established markets, that attackers proved to have an advantage. The rate of improvement in product performance which technologists provide may exceed the rate of improvement demanded in established markets. This mismatch between trajectories enables firms entering emerging value networks subsequently to attack the industry’s established markets as well.

If we want to improve managerial cognition, we need to capture the full spectrum of cognitive functions and the complex processes through which they unfold. I propose two very different methods (one older and low-tech, one newer and high-tech) that allow us to observe cognitive functions and processes directly in real time.