Search results

Cincinnati manufacturers before World War I displayed substantial unity in pursuing the open shop. San Francisco employers were divided, in both their attitudes and their actions, on how to deal with unions. I treat these differences in terms of business class formation. My explanation emphasizes how racial dynamics, class relations, and citizenship practices, acting in cumulative historical sequences, shaped employer solidarity and ideology.

We investigate product innovation by a cohort of entrants who use technology that eventually suffers disruption. We concentrate on two types of entrants – those with and those without relevant prior experience in the disrupted technology. Using the industrial robotics industry as the context of our study, we explore product innovation using disrupted technology during two time periods: the first prior to sales takeoff of the disruptive products and the second subsequent to takeoff. We find that the two types of entrants did not differ in product innovation prior to takeoff, but firms with prior experience in the disrupted technology manufactured more innovative products subsequent to the sales takeoff of disruptive products. Our research underscores that the boundary conditions of the utility of prior experience is more nuanced than that which literature suggests – it affects product innovation only in the post-sales takeoff period when the demand uncertainties are relatively low. Our findings also suggest that the boundary conditions of Christensen’s thesis are narrower than predicted by prior literature.

This paper presents evidence to suggest that despite obstacles that made predatory pricing essentially impossible, the National Cash Register Co. (N.C.R.) managed successfully to deploy an arsenal of non-price predatory strategies that permitted it to consolidate and maintain a nearly complete monopoly of the cash-register trade. N.C.R. took actions to raise the costs and reduce the revenues of its rivals, actions that made sense only to the extent that N.C.R. could recoup their costs through the maintenance of monopoly rents. Our analysis suggests that antitrust prosecution was a significant threat to N.C.R., and ultimately forced the company to agree to abandon its most objectionable practices.

Police technology fundamentally shapes the police role, and the adoption of technology is even linked to the success of police reforms. Police adoption of emerging technological tools changes the way police interact with citizens. The change in police citizen interactions can then have serious implications for the social control that police have over citizens, the civil liberties citizens enjoy, police accountability, and the legitimacy that the police hold in contemporary American society.

While technology impacts these critical issues in policing, not all technology adopted by the police is likely to influence their relationship with the public. As such, this chapter closely examines the ways that several emerging technologies adopted by the police (i.e., body-worn cameras (BWC), aerial surveillance, visual surveillance, social media, mapping and crime prediction, and less lethal force technology) impact issues related to social control, accountability, and legitimacy. The current literature seems to indicate that some innovations such as BWCs enhance police accountability and legitimacy, and also expand social control. Other technologies such as aerial surveillance and conducted energy devices increase social control, and display a complicated or unclear influence over police legitimacy.

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.