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Modeling the AI-Driven Age of Abundance: The Essential Role of Human to AI Leverage Ratio (HAILR) in Macroeconomic Forecasting
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For employees, work involves taking breaks as well as engaging in specific required duties, and sometimes that break taking is construed as “cyberslacking” by employers. After historical treatments of cyberslacking concepts, this article analyzes ways that artificial intelligence (AI) methodologies and the “bossware” platform genre are aiding management to counter the cyberslacking phenomena directly exhibited by employees or projected from previous activities and profiles. It contrasts straightforward “policing” methods that aim toward the identification of cyberslacking instances for selective punishment through surveillance, with “predictive cyberslacking” approaches that profile certain trends and patterns in employee behavior. Such identified inclinations can be used to engage or nudge workers into specific, individualized patterns of work and approved recreational or developmental activity. A medicalization-style approach is often used in bossware to entice employees toward particular mental health-themed activities (including mindfulness and meditation activities).
Organization research on stigma has mostly focused on the stigmatized, limiting the scope for exploring what is possible and lacking recognition of the structural conditions and unequal power relations that create and sustain stigma. Consequently, it overlooks how actors can organize to resist and potentially overcome stigmatization altogether. Addressing this question empirically, we studied the long-term unemployed in Spain using a longitudinal qualitative research design. We develop a typology of responses to stigmatization—getting stuck, getting by, getting out, getting back at, and getting organized— that advances our understanding of stigma in several ways. First, our typology captures stigma as a multilevel phenomenon. Second, it makes explicit that stigma can only be understood in relation to its socio-historical contexts and unequal relations of power. Third, it captures how resisting stigma needs to be a collective enterprise and advances the importance of organizing to both challenge stigmatization and explore alternatives.
Cambridge Core - Optimisation - Measurement of Productivity and Efficiency - by Robin C. Sickles
By all accounts, 2016 is the year of the chatbot. Some commentators take the view that chatbot technology will be so disruptive that it will eliminate the need for websites and apps. But chatbots have a long history. So what's new, and what's different this time? And is there an opportunity here to improve how our industry does technology transfer?
While the data and information services industry dates back to the fifteenth century AD, the information technology (also called as software services) industry is as young as 70 years. Yet the pace at which both have grown is phenomenal, and this is tied to the evolution of computer technology. Networking of computers and people through the Internet has moved into high gear. The decision (or knowledge) sciences arena enveloping the operations research methodologies came into prominence during the Second World War but has had its growth in parallel to the computing technologies till the 1970s. The last two decades have also witnessed a spurt in their development due to advances in expert systems, machine learning, and artificial intelligence (AI) algorithms, all of which are now embedded within the field of data science. These three sectors have given birth to multiple business segments in the process. Delivery of data and information, data and business process outsourcing, analytics, printing and display solutions, and information flow in supply chain management of goods and services are the five segments identified within the information services, while computer-aided software engineering, independent software testing and quality assurance, package and bespoke software implementation and maintenance, network and security management, and hosting and infrastructure management are covered within software services. The data science arena consists of big data, statistical and mathematical techniques, and business domains but covered as one distinct business segment. The automation path of each segment is reviewed in detail. An impact analysis to identify the changing landscape is described. Finally, current trends are traced, followed by predictions for future developments.KeywordsSoftware servicesInformation technology servicesBig data and analyticsCustomer relationship managementEnterprise resource planningData science
Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.
This paper proposes a typology of knowledge workers and their respective knowledge actions. The extant literature on the definition of knowledge work actions is examined and evaluated. The existing classifications of roles of knowledge workers are evaluated and extended with additional literature and empirical findings on the definition of a typology of knowledge worker roles. The empirical data in this paper comes from two studies. In the Task Execution Study 20, a knowledge worker had to carry out a selection of prepared tasks. The computer system that the participants were using was equipped with sensors, so that the execution steps of the tasks could be traced and analyzed. The data from the second study comes from a questionnaire survey of knowledge workers, which yielded 43 responses. The paper shows that the sampled users take on all identified knowledge worker roles, and that the knowledge work actions can be recognized in the sensor data from the first study. This paper contributes to the literature by proposing a new way of classifying the roles of knowledge workers and the knowledge actions they perform during their daily work. Furthermore, the paper provides a preliminary understanding of the relation between knowledge-intense work tasks, the roles they are executed in, and the tools that are used to accomplish the respective tasks.
The Shape of Workbreaks to Come: Reframing Cyberslacking With Bossware and Artificial Intelligence
- J A Oravec