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Economists usually shy away from talking about power. They assume an economy comprised of many small and medium-sized firms, each competing for consumer dollars. This circumvents the problem of economic power. John Kenneth Galbraith, however, refused to ignore power. It stood at the center of his economics, and he saw it as a key reason the US economy thrived in the years following World War II (WWII). This chapter examines Galbraith’s changing views regarding economic power. American Capitalism explains how countervailing power, or power on the other side of the market, solves the problem of economic power. In The New Industrial State, scientists and educated managers within the firm (the technostructure) mitigate the negative consequences of economic power wielded by large firms. The Affluent Society and Economics and the Public Purpose look to the government as the main check on corporate power. It does this through labor legislation or programs such as the New Deal and Fair Deal. This chapter then evaluates the different solutions Galbraith proffered to the problem of economic power. It contends that Galbraith got three things right when analyzing economic power. First, we no longer live in a world of scarcity due to oligopolistic firms. Second, capitalism was different in the post-WWII era because the US economy thrived and gains were shared widely. Third, Galbraith understood that power was unequally distributed – both between the public and private sectors and within the private sector itself. On the other hand, Galbraith was overly optimistic in believing the market economy or the public sector could counter corporate power.

The purpose of this longitudinal study is to qualitatively investigate the role of the general managers (GMs) and senior managers (SMs) in strategic talent management (STM) in hotels during COVID-19. Using upper echelon theory and the dynamic attention-based view, this paper explores the role of upper echelon theory cognitive characteristics (orientation towards STM and decision-making approach) and three dynamic attention-based view attention dimensions (communication, resource attention to the HR function and new configurations of STM) in influencing STM.

This study uses semi-structured interviews with hotel GMs and SMs at two time points over the duration of COVID-19 in six hotels (family-owned, boutique and international hotel chain) located in Ireland, the UK, Germany, Singapore and India.

The findings of this study reveal that GMs and SMs across the different hotels differed in their orientation towards STM and their decision-making approaches and this influenced cognitive and resource attention to STM. GMs and SMs remained cognitively attentive to STM through their communications around STM, and they revealed resource attention through resources to the HR function and new configurations of STM practices during COVID-19. The authors identify three distinct configurations of STM practices in operation in hotels during COVID-19.

This study’s findings reveal important practice implications in that GMs and SMs have a key role to play in the implementation of STM and the need to reconfigure how STM is undertaken during the crisis. This contrasts with the more espoused role suggested for these talent actors in the literature.

The authors used a longitudinal qualitative research design to surface the dynamic role of GMs’ and SMs’ cognitive and resource attention to STM in hotels during COVID-19 and the key role that orientation towards STM and decision-making approach affected both cognitive and resource attention dimensions.

Many libraries and archives maintain collections of research documents, such as administrative records, with paper-based formats that limit the documents' access to in-person use. Digitization transforms paper-based collections into more accessible and analyzable formats. As collections are digitized, there is an opportunity to incorporate deep learning techniques, such as Document Image Analysis (DIA), into workflows to increase the usability of information extracted from archival documents. This paper describes the authors' approach using digital scanning, optical character recognition (OCR) and deep learning to create a digital archive of administrative records related to the mortgage guarantee program of the Servicemen's Readjustment Act of 1944, also known as the G.I. Bill.

The authors used a collection of 25,744 semi-structured paper-based records from the administration of G.I. Bill Mortgages from 1946 to 1954 to develop a digitization and processing workflow. These records include the name and city of the mortgagor, the amount of the mortgage, the location of the Reconstruction Finance Corporation agent, one or more identification numbers and the name and location of the bank handling the loan. The authors extracted structured information from these scanned historical records in order to create a tabular data file and link them to other authoritative individual-level data sources.

The authors compared the flexible character accuracy of five OCR methods. The authors then compared the character error rate (CER) of three text extraction approaches (regular expressions, DIA and named entity recognition (NER)). The authors were able to obtain the highest quality structured text output using DIA with the Layout Parser toolkit by post-processing with regular expressions. Through this project, the authors demonstrate how DIA can improve the digitization of administrative records to automatically produce a structured data resource for researchers and the public.

The authors' workflow is readily transferable to other archival digitization projects. Through the use of digital scanning, OCR and DIA processes, the authors created the first digital microdata file of administrative records related to the G.I. Bill mortgage guarantee program available to researchers and the general public. These records offer research insights into the lives of veterans who benefited from loans, the impacts on the communities built by the loans and the institutions that implemented them.