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Controversy and well‐argued differences of opinion are the hallmarks of a successful conference, and these were certainly evident at RMDP's second conference on “Techniques for Shop Location”, held in London in June. Attended by nearly 200 delegates, this one‐day event emphasised the practical application of increasingly sophisticated locational techniques, especially those “friendly” to personal computers. But debating points quickly arose. Ross Davies, from the chair, argued that local authorities have become more supportive to superstore development, but this was questioned by Asda's David Gransby. The superstore developer thinks that retail use should be defined geographically; convenience shopping out of town allows the High Street to thrive on comparison stores. But planning economist Peter Jones thinks that some existing centres must be allowed to decline in favour of innovative developments elsewhere, such as combinations of superstores, warehouses and factories. David Powell, setting his arguments against a wide social and political background, took the view that the government should take positive steps to encourage retailers to locate in depressed regions and inner cities, thus creating change in current locational policies. But one delegate thought that government intervention would prove restrictive. Mike Poynor of the CDT, representing RDM at the conference, outlines the points of agreement and the differences.

How do institutions affect entrepreneurship? Conversely, how do entrepreneurs impact institutions? Institutional theory has long struggled to explain the action and agency inherent in entrepreneurship (DiMaggio, 1988; Barley & Tolbert, 1997). Contemporary institutionalist research in organization studies began with the question of how the institutional environment shapes the structures and behaviors of existing organizations. This research largely focused on how normative, regulative, and cognitive dimensions of the environment (Scott, 2008) constrain large, mature organizations and the circumstances that increase the adoption of new structures by such organizations (Meyer & Rowan, 1977; DiMaggio & Powell, 1983; Tolbert & Zucker, 1983). A subsequent wave of research in the institutional tradition focused on institutional change within mature organizational fields (see Dacin, Goodstein, & Scott, 2002). Some recent research has studied the actors – “institutional entrepreneurs” – that create new or transform existing institutions (e.g., Greenwood, Suddaby, & Hinings, 2002; Maguire, Hardy, & Lawrence, 2004). Much less attention, however, has been paid within the institutional-theory literature to entrepreneurship: the processes of founding and managing new organizations.

During the 20th century, the United States rapidly developed its research capacity by fostering a broad base of institutions of higher education led by a small core of highly productive research universities. By the latter half of the century, scientists in a greatly expanded number of universities across the United States published the largest annual number of scholarly publications in STEM+ fields from one nation. This expansion was not a product of some science and higher education centralized plan, rather it flowed from the rise of mass tertiary education in this nation. Despite this unprecedented productivity, some scholars suggested that universities would cease to lead American scientific research. This chapter investigates the ways that the United States’ system of higher education underpinned American science into the 21st century.

The authors present a historical and sociological case study of the development of the United States’ system of higher education and its associated research capacity. The historical and sociological context informs our analysis of data from the SPHERE team dataset, which was compiled from the Thomson Reuters’ Science Citation Index Expanded (SCIE) database.

We argue that American research capacity is a function of the United States’ broad base of thousands of public and broadly accessible institutions of higher education plus its smaller, elite sector of “super” research universities; and that the former serve to culturally support the later. Unlike previous research, we find that American higher education is not decreasing its contributions to the nation’s production of STEM+ scholarship.

The chapter provides empirical analyses, which support previous sociological theory about mass higher education and super research universities.

For discussion of social entrepreneurship in middle-income economies, emerging markets generally and Africa, specifically, Quali Health presents interesting questions about entrepreneurial funding, scaling and the interplay between social entrepreneurial activities and the informal sector.

South Africa’s primary health outcomes do not correspond to the country’s spending on public health, with South Africa ranking among the worst globally in the incidence of tuberculosis, HIV prevalence, infant mortality and life expectancy. In part, this poor outcome can be explained by high inequality in access to healthcare, which reflects South Africa’s grossly skewed income and wealth distributions, with the bulk of the country’s population reliant upon an underfunded, inefficient and poorly managed public health system. This substandard service for the working poor in South Africa’s townships with high population densities offered a profitable entrepreneurial opportunity to provide affordable and effective primary care with vast gains in quality and outcomes improved dignity for patients. After receiving her MBA, physician and entrepreneur Dr Nthabiseng Legoete self-funded the launch of Quali Health in 2017. The business model set out to disrupt healthcare delivery for South Africa’s poorest citizens. Drawing patients from the working poor in Diepsloot, Quali Health’s inaugural site was cash flow positive within five months when the facility hit only 30% of installed service capacity. With quick success, Dr Legoete faced the strategic question of how fast to scale and finance the expansion. She also considered a new micro-insurance product for her clientele.

For discussion of social entrepreneurship in middle-income economies, emerging markets generally and Africa, specifically, Quali Health presents interesting questions about entrepreneurial funding, scaling and the interplay between social entrepreneurial activities and the informal sector.

Teaching notes are available for educators only.

CSS: 3 Entrepreneurship.

This interview with David Wood of the HCIMA highlights his belief that recruitment and staffing are going to be the big problems of the new millennium. With hospitality perceived as a low‐skilled, low‐paid industry, recruitment presents difficulties now. Education in the industry has changed dramatically emphasising the continued need to match training and learning with the real needs of industry.

This article presents practical guidelines for the design of interactive software for children with autism. Many existing software design techniques rely on social interaction and so are not appropriate for this group, and little practical guidance is available. The guidelines presented are based on research experiences during the development of an interactive software game called TouchStory, which was designed to promote an understanding of narrative structure while adapting to the learning needs of individual children with autism. Our results indicate that some children with autism were actively engaged in self‐directed, curiosity‐driven learning, and found TouchStory enjoyable, even after repeated exposures on as many as 20 occasions. The guidelines are not limited to the appearance and behaviour of the software system, and may be fundamental to the research questions asked and the approach adopted. They provide a useful basis for furthering our understanding of the provision of assistive technology for children with autism.

The story of Tesco's abandonment of trading stamps and launching of Operation Checkout has been told perhaps more than enough over the past year. What is not so well‐known is the effect that rapidly boosted turnover had upon their distribution system. “We must recognise the fact,” says Ian MacLaurin, “that we came very close to breakdown.” How Tesco coped with the problem is described in this exclusive feature.

In this chapter, we examine the development of a technology path in the nanotube (NT) field – one of the most well-developed areas of nanotechnology. Although early developments suggested that there were equally viable pathways related to the development of carbon nanotubes (CNTs) and others made with organic molecules and polymers, carbon-based technologies became valorized. We show how the carbon science path developed and try to unpack how it happened. We argue that it was not due to the inherent efficiency or applications of CNTs, but to sociopolitical dynamics. Even though much intellectual property research focuses on patent-level analysis, we underscore the importance of patent categories as key cognitive elements that organize the different knowledge domains within the world of NT patenting. We show that interlinkages between patent categories are crucial to the formation and development of a particular technology path. In unpacking the selection of the carbon science path, we highlight the key role played by a cadre of star scientists and the political neglect of alternative pathways as the field herded toward the CNT path.

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).

The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).

The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.