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While Big Data offer marketing managers information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn’t necessarily improve their decision-making. Managers would still be vulnerable to confirmation bias, control illusions, communication problems, and confidence issues (the 4Cs). The authors argue that traditional remedies for such biases don’t go far enough and propose a lean start-up approach to data-based learning in marketing management. Specifically, they focus on the marketing analytics component of Big Data and how adaptations of the lean start-up methodology can be used in some combination with such analytics to help marketing managers improve their decision-making and innovation process. Beyond the often discussed technical obstacles and operational costs associated with handling Big Data, this chapter contributes by analyzing the various learning and decision-making problems that can emerge once the 4Vs of Big Data have materialized.

At the outset, though, and before brief summaries of each of these cases are presented, it is important to underscore two key points that guide the organization of the entire volume. First, capital mobility is a complex phenomenon that assumes various forms as different types of capitals move at different velocities. Second, capital mobility is a necessary and irreplaceable component of capitalism. As for the first aspect, we will consider three types of capital: financial capital, productive capital, and labor. Obviously, these three forms of capital are endowed with different features that affect their behaviors and their ability to move through time and space. While all these three forms of capital share the common requirement that they need to be utilized in increasingly accelerated manners if capital accumulation had to expand, they also display tendencies that favor financial and productive capital and subordinate labor. If effect, the subordination of labor to financial and productive capital is one of the primary characteristics of globalization and one item that allowed the rapid expansion of capital accumulation over the last two decades.

The implications and impacts of the educational intelligent economy from the vantage point of digital frontierism is explored using a decolonial framework, with a specific focus on Big Data and data sharing in Comparative and International Education (CIE). Recent debates are reviewed about CIE’s past histories and its current directions to tease out their implications for data sharing. The authors demonstrate how data sharing continues to reinforce imperialism through control, dissemination, and application of data, and how electronic and digital colonialism preserve current intellectual and structural hegemonies. Then, we give an example of how donors and funding agencies, including the National Science Foundation, engage in neoliberal scientism and control of data, and how it affects the future of social sciences, including CIE. Our inquiry is at the intersections of economic intelligence and educational intelligence in a rapidly evolving technocentric, data-dominated, and networked economy. The authors demonstrate how educational intelligence in the global economy may exacerbate the asymmetric access to data between the global North and the South, as educational data are increasingly becoming global commodities to be traded between various public and private actors. Finally, the authors argue that decolonial participatory research designs that aim at positive, sustained transformations, as opposed to the stagnancy of Big Data and data mining, should be used to address the problems inherent to the Educational Intelligent Economy.

Burden transport is a ubiquitous primate behavior. Modern humans, however, take this primate tendency and extend it to a behavioral repertoire that influences many of our daily activities and almost certainly helped shape our physical and behavioral form. I examine the transportation of food in the context of central place foraging, from the perspective of maximizing energy acquisition. A detailed model of the energetic cost of burden transport is presented and its sensitivity to the variables of body mass, burden mass, terrain, incline and velocity discussed.

The increasing prevalence and availability of big data represent a potentially revolutionary development for human resource management (HRM) scholars. Despite this, the current literature provides eclectic and often contradictory guidance for scholars attempting to conceptualize big data and subsequently incorporate it into relevant theoretical frameworks. The authors attempt to bridge this gap by discussing key considerations relevant to understanding and integrating big data into the existing theoretical landscape. Building on a novel, integrative definition of big data, the authors propose a parsimonious theoretical framework utilizing the established dimensions of complexity and dynamism as meta-attributes to bring order to the various attributes that have been proposed as central to defining big data (e.g., volume, variety, velocity, and variability). Throughout, the authors highlight numerous theoretical and empirical opportunities and considerations that this perspective holds for future HRM scholarship.

This chapter aims to explore how social innovation can be achieved through providing employees with information to enhance participation in decision-making.

The chapter takes a largely theoretical and discursive approach. Building on key theories on the links between information and decision-making, it explores the logic behind the potential for empowerment through providing information. Empirical examples are used to demonstrate some elements of the theoretical propositions.

The chapter demonstrates that there is a strong theoretical potential for enhancing employee participation through the provision of more and better information. It also suggests that organisations would benefit economically from this, because of a broader knowledge-base underlying business decisions. However, in many organisations the potential is either not recognised, or, for organisational design and cultural reasons, it is considered undesirable to increase employee participation.

Organisations and employees need to increase their efforts in understanding how they can use IT to provide decision-makers, and in particular employees, with better information.

There is huge potential for social innovation through IS and IT that is currently under-exploited. Because people are exposed to IT in their daily lives so much, they are more likely to want to push for better use of IT and access to information in their organisations.

This chapter provides an unusual angle on social innovation. Drawing on some key theoretical frameworks from the IS domain, it demonstrates a strong link between enhancing information provision and increasing employee participation.

We investigate the effects of technological capabilities on firms’ survival chances during market-fusing technological change. Our context is the matured U.S. machine tool industry. During the period of our study, 1975 through 1995, a drastic shift in demand conditions prompted the buyers of machine tools to demand more versatile products to improve their productivity. The advent of microprocessors enabled manufacturers to meet these demands by combining the functions of previously distinctive products. As a result, market segments fused and machine tool manufacturers in once disparate product categories came into direct competition with one another. We propose that incumbents with broader component and architectural capabilities will be better able to adapt to and hence survive market-fusing technological change. Our results, based on a panel data set of U.S. machine tool incumbents, support the value of broad component capabilities but reveal no adaptive advantage of architectural capabilities.

We use a combination of continuum and car-following models to explore the potential impact of speed-controls on (i) decreasing travel times at times of congested flow; and (ii) increasing the safety of motorway flow approaching the site of an accident.